Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : MCTRinIA (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : University of London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1964303.0 €

Chronic inflammation may result from failure of the host response to engage pro-resolving pathways. The current treatment armamentarium for chronic inflammatory conditions may lead to immune suppression. Thus, identification of novel therapeutics that control inflammation without immune suppression will provide an attractive alternative approach. This is especially important since incidence of these conditions increases with an ageing global population. In planaria, mice, human peripheral blood and milk I recently uncovered a new family of endogenous molecules, named Maresin Conjugates in Tissue Regeneration (MCTR). These potently regulate white blood cell responses, promote the resolution of acute inflammation and accelerate tissue regeneration. The aim of this Starting Grant is to identify pathways that lead to failed resolution in inflammatory arthritis, as a prototypical chronic inflammatory condition. The hypothesis is that MCTR biosynthesis is dysregulated in inflammatory arthritis, leading to an unbridled host response, chronic inflammation and tissue destruction. This proposal will employ a multipronged approach to test this hypothesis by 1) Determining MCTR regulation in self-resolving and delayed-resolving arthritis; 2) Investigating the host protective and tissue regenerative actions of MCTRs in inflammatory arthritis; 3) Establishing the MCTR biosynthetic pathway and 4) Determining the regulation if its components during self-limited and delayed-resolving arthritis. Anticipated results will uncover novel pathways that become dysregulated during failed resolution. Results from this Starting Grant will also identify targets and new therapeutic approaches that will engage pro-resolution programs as well as tissue regeneration in conditions characterised by persistent inflammation and hence failed resolution. This will lay the basis for informed structure-activity based studies and the design of therapeutics for treatment of chronic inflammatory conditions.

Mots-clés : innate immunity and inflammation,chemical biology
Projet ERC : CureCKDHeart (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Universitätsklinikum Aachen — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : UKA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1497888.0 €

Chronic kidney disease (CKD) is a growing public health problem with a massively increased cardiovascular mortality. Patients with advanced CKD mostly die from sudden cardiac death and recurrent heart failure due to premature cardiac aging with hypertrophy, fibrosis, and capillary rarefaction. I have recently identified the long sought key cardiac myofibroblast progenitor population, an emerging breakthrough that carries the potential to develop novel targeted therapeutics. Genetic ablation of these Gli1+ perivascular progenitors ameliorates fibrosis, cardiac hypertrophy and rescues left-ventricular function. I propose that Gli1+ cells are critically involved in all major pathophysiologic changes in cardiac aging and uremic cardiomyopathy including fibrosis, hypertrophy and capillary rarefaction. I will perform state of the art genetic fate tracing, ablation and in vivo CRISPR/Cas9 genome editing experiments to untangle their complex mechanism of activation and communication with endothelial cells and cardiomyocytes promoting fibrosis, capillary rarefaction, cardiac hypertrophy and heart failure. To identify novel druggable targets I will utilize new mouse models that allow comparative transcript and proteasome profiling assays of these critical myofibroblast precusors in homeostasis, aging and premature aging in CKD. Novel assays with immortalized cardiac Gli1+ cells will allow high throughput screens to identify uremia associated factors of cell activation and inhibitory compounds to facilitate the development of novel therapeutics. This ambitious interdisciplinary project requires the expertise of chemists, physiologists, biomedical researchers and physician scientists to develop novel targeted therapies in cardiac remodeling during aging and CKD. The passion that drives this project results from a simple emerging hypothesis: It is possible to treat heart failure and sudden cardiac death in aging and CKD by targeting perivascular myofibroblast progenitors.

Mots-clés : chronic kidney disease
Projet ERC : 3DBIOLUNG (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Lund University — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499975.0 €

Chronic lung diseases are increasing in prevalence with over 65 million patients worldwide. Lung transplantation remains the only potential option at end-stage disease. Around 4000 patients receive lung transplants annually with more awaiting transplantation, including 1000 patients in Europe. New options to increase available tissue for lung transplantation are desperately needed. An exciting new research area focuses on generating lung tissue ex vivo using bioengineering approaches. Scaffolds can be generated from synthetic or biologically-derived (acellular) materials, seeded with cells and grown in a bioreactor prior to transplantation. Ideally, scaffolds would be seeded with cells derived from the transplant recipient, thus obviating the need for long-term immunosuppression. However, functional regeneration has yet to be achieved. New advances in 3D printing and 3D bioprinting (when cells are printed) indicate that this once thought of science-fiction concept might finally be mature enough for complex tissues, including lung. 3D bioprinting addresses a number of concerns identified in previous approaches, such as a) patient heterogeneity in acellular human scaffolds, b) anatomical differences in xenogeneic sources, c) lack of biological cues on synthetic materials and d) difficulty in manufacturing the complex lung architecture. 3D bioprinting could be a reproducible, scalable, and controllable approach for generating functional lung tissue. The aim of this proposal is to use custom 3D bioprinters to generate constructs mimicking lung tissue using an innovative approach combining primary cells, the engineering reproducibility of synthetic materials, and the biologically conductive properties of acellular lung (hybrid). We will 3D bioprint hybrid murine and human lung tissue models and test gas exchange, angiogenesis and in vivo immune responses. This proposal will be a critical first step in demonstrating feasibility of 3D bioprinting lung tissue.

Mots-clés : lung tissue engineering,lung bioengineering,ex vivo lung regeneration,3d bioprinting
Projet ERC : ChronicPain (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Weizmann Institute of Science — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Chronic pain is one of the most significant unmet medical needs and health burdens in society today. This is due to limited analgesic efficacy of existing drugs, coupled with adverse side effects, and the development of tolerance over time. Moreover, patients may develop dependence on commonly prescribed opiate-based analgesics, with negative consequences and greatly increased societal costs. Hence there is a pressing need for new targets for drug development and new drug candidates to target chronic pain. During the course of our recently completed ERC grant we screened a number of mouse mutants for sensory neuron phenotypes that might arise from perturbation of neuron growth control mechanisms. We discovered an unexpected phenotype of reduced pain sensitivity in an importin knockout mouse line. Most strikingly, acute knockdown of this specific importin gene had an ameliorating effect in the chronic stage of a neuropathic pain model. Screening for drugs that might mimic this effect lead to the identification of a number of approved drugs that are candidates for repositioning as analgesics. In this PoC we will carry out the following objectives: (1) file for IP protection on the new pain drug target and the initially identified candidate analgesics; (2) validate the new drug target and drug candidates in models of neuropathic and/or chemotherapy-induced chronic pain; (3) devise an HTS-compatible assay for future screening of additional drug leads for chronic pain; and (4) carry out additional IP protection and pre-clinical validation steps to maximize the commercialization potential of this discovery.

Mots-clés : chronic pain,analgesia,target validation,drug discovery
Projet ERC : MHDiscs (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1784300.0 €

Circumstellar discs are the birthplaces of planets. They form around young protostars and dissipate in a few million years. Modern submillimeter and optical telescopes such as ALMA and VLT/SPHERE are now able to resolve thin structures in the bulk of these objects, such as rings, crescents, spirals and winds, probing the very origin of planetary systems similar to our own. Our current understanding of these discs relies on a very crude modelling of a hypothetic magneto-hydrodynamic (MHD) turbulence thought to play an essential role in the evolution and structure of these systems. However, there is now compelling theoretical and observational evidence that these discs are weakly turbulent, if not laminar, because of their low ionisation fraction and thus poor coupling to the magnetic field. This suggests that subtle MHD processes are driving the dynamics of these objects. Moreover, my recent theoretical breakthroughs demonstrate that these gaseous discs are subject to self-organisation and magneto-thermal winds. These processes play a key role for the disc as they can control its radial structure and evolution. I propose that computing global non-ideal MHD models from massively parallel numerical simulations will shed a new light on these processes, connecting the long-term evolution of these discs to the formation of large scale structures seen by ALMA and SPHERE. We expect MHDiscs to provide reliable global evolution models by coupling gas dynamics to dust and irradiation. These models will be used to predict discriminant observables of the processes I propose, setting the stage for a deeper understanding of the formation of planetary systems.

Mots-clés : numerical technics for astrophysics,magnetohydrodynamics
Projet ERC : WAVEMATTERS (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Humboldt-Universität zu Berlin — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1997687.0 €

Cities are critical zones where the intermingling of environmental processes, infrastructural arrangements and human lives is increasingly apparent and disputed. Physical waves, particularly heat radiation, sound waves and radio frequencies, constitute major environmental disturbances that invisibly cross the urban built environment affecting bodies, human and nonhuman, in harmful and uncertain ways. By asking how they come to matter, this project explores how waves become associated to specific bodies and environments, as well as how they become matters of public concern and design intervention. To answer these questions, this project entails extended ethnographic fieldwork at key locations where ur-ban projects aimed at mitigating the urban heat island effect, abating environmental noise and building 5th generation wireless communication networks are currently unfolding. Following techno-scientific researchers, city officials, professional consultants, affected groups and concerned residents, the project will address two major research problems: 1. How bodily exposure is done in practice, paying attention to both knowledge production and controversies concerning wave-related exposure, as well as to how individuals learn to be affected by and bodily attune to physical waves. 2. How waves problematize forms of urban coexistence leading to design interventions that reassemble (and disassemble) urban environments, as well as to practices of imagining other possible urban environments. A unique feature of this project is its emphasis on expanding conventional ethnographic research by means of multimodal collaborations with actors from the field, thus actively engaging in multimedia forms of knowledge pro-duction, prototyping or community building. This is indeed crucial to reassessing the material politics of the Anthropocene as entailing contested practices of materializing abstract or imperceptible environmen-tal disturbances.

Mots-clés : social studies of environmental hazard,affected communities,anthropology of infrastructures,knowledge controversies,ethnographic collaboration
Projet ERC : DUST (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Leeds — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499840.0 €

Civil emergencies such as flooding, terrorist attacks, fire, etc., can have devastating impacts on people, infrastructure, and economies. Knowing how to best respond to an emergency can be extremely difficult because building a clear picture of the emerging situation is challenging with the limited data and modelling capabilities that are available. Agent-based modelling (ABM) is a field that excels in its ability to simulate human systems and has therefore become a popular tool for simulating disasters and for modelling strategies that are aimed at mitigating developing problems. However, the field suffers from a serious drawback: models are not able to incorporate up-to-date data (e.g. social media, mobile telephone use, public transport records, etc.). Instead they are initialised with historical data and therefore their forecasts diverge rapidly from reality. To address this major shortcoming, this research will develop dynamic data assimilation methods for use in ABMs. These techniques have already revolutionised weather forecasts and could offer the same advantages for ABMs of social systems. There are serious methodological barriers that must be overcome, but this research has the potential to produce a step change in the ability of models to create accurate short-term forecasts of social systems. The project is largely methodological, and will evidence the efficacy of the new methods by developing a cutting-edge simulation of a city – entitled the Dynamic Urban Simulation Technique (DUST) – that can be dynamically optimised with streaming ‘big’ data. The model will ultimately be used in three areas of important policy impact: (1) as a tool for understanding and managing cities; (2) as a planning tool for exploring and preparing for potential emergency situations; and (3) as a real-time management tool, drawing on current data as they emerge to create the most reliable picture of the current situation.

Mots-clés : agent based modeling,data assimilation,simulation,smart cities,civil emergency
Projet ERC : GOCART (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : National Oceanography Centre — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : NOC
Type d'entité : Recherche
Financement ERC de l'entité : 1460766.25 €

Climate change driven by CO2 emissions from human activities is a significant challenge facing mankind. An important component of Earth’s carbon (C) cycle is the ocean’s biological C pump; without it atmospheric CO2 would be ~50% higher than it is now. The pump consists of sinking organic matter which is remineralised back into CO2 in the deep ocean. The depth at which remineralisation occurs is the main factor affecting the amount of organic C stored in the ocean. Currently we do not understand how or why remineralisation depth varies in time, which limits our ability to make robust predictions of how the future C cycle, and hence our climate, will change into the future. This is mainly due to the challenges of measuring remineralisation depth using conventional methods– a barrier which autonomous underwater vehicles are poised to overcome by providing high frequency data over long periods. This technological innovation will revolutionise our understanding of this important planetary C flux. I propose an ambitious project to address current uncertainties in remineralisation depth. GOCART encompasses new observations, obtained using cutting-edge technology and novel methodology, through to global climate modelling. Underwater glider deployments will be used to establish the characteristics and significance of temporal variability in organic C flux and remineralisation depth during the most dynamic period of the year. This will enable new insights into the factors driving variability in remineralisation depth, ultimately leading to development of a new model parameterisation incorporating temporal variability. Using an innovative modelling framework, this parameterisation will be tested for its potential to improve predictions of ocean C storage. GOCART represents a significant advance in quantifying temporal variability in remineralisation depth, which is key to reducing uncertainty in model predictions of ocean C storage, and yet currently almost entirely unknown.

Mots-clés : ocean carbon,biological carbon pump,remineralisation length scale,gliders,autonomous underwater vehicles,climate model
Projet ERC : TITANICA (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Heidelberg University — Pays : Allemagne — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 187500.0 €

Climate change is triggering profound changes in the marine environment, especially in the Arctic Ocean (AO) and subpolar North Atlantic (SPNA), two key areas playing a crucial role in feeding the main engine of the global oceans: the Atlantic Meridional Overturning Circulation (AMOC). Despite the utmost importance of AMOC for the climate system, there are still many open fundamental questions regarding its physical mechanisms, water mass pathways, and transport or ventilation timescales. TITANICA aims to improve our understanding of AMOC on the basis of a new set of transient radionuclide tracers (129I, 236U, 39Ar and 14C), a quartet that will allow to study and constrain oceanographic processes on timescales of years to millennia. While the combination of the two nuclear reprocessing-derived 129I and 236U will investigate the shorter transport mechanisms (<100 y) of Atlantic waters in the AO and deep-water convection in the SPNA, the natural 39Ar and 14C will provide answers for the ventilation of intermediate and deep waters (>100 y). Newly available techniques will allow the implementation of 39Ar to unprecedented possibilities, closing the time gap between the artificial transient tracers (CFCs, SF6, 129I, 236U) and natural 14C. TITANICA goes beyond the state-of-the art as it combines cutting-edge analytical techniques (Accelerator Mass Spectrometry and Atom Trap Trace Analysis) with novel data analysis methods pioneered by the PI and supported by binary mixing models, transit time distributions, and regional ocean modeling. During the 5 years journey of TITANICA, two PhD students and two postdocs will join the PI embarking in a suit of ocean expeditions covering the Arctic Ocean, the SPNA and the subtropical Atlantic Ocean. The new data generated and the strong collaboration with European and International partners will combine with the PI unique expertise in this field, triggering a new era of transient tracers in the Arctic and Atlantic oceans.

Mots-clés : transient tracers,atta,arctic,subpolar north atlantic,deep water formation,circulation
Projet ERC : STERCP (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : University of Bergen — Pays : Norvège — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999388.75 €

Climate prediction is the next frontier in climate research. Prediction of climate on timescales from a season to a decade has shown progress, but beyond the ocean skill remains low. And while the historical evolution of climate at global scales can be reasonably simulated, agreement at a regional level is limited and large uncertainties exist in future climate change. These large uncertainties pose a major challenge to those providing climate services and to informing policy makers. This proposal aims to investigate the potential of an innovative technique to reduce model systematic error, and hence to improve climate prediction skill and reduce uncertainties in future climate projections. The current practice to account for model systematic error, as for example adopted by the Intergovernmental Panel on Climate Change, is to perform simulations with ensembles of different models. This leads to more reliable predictions, and to a better representation of climate. Instead of running models independently, we propose to connect the different models in manner that they synchronise and errors compensate, thus leading to a model superior to any of the individual models – a super model. The concept stems from theoretical non-dynamics and relies on advanced machine learning algorithms. Its application to climate modelling has been rudimentary. Nevertheless, our initial results show it holds great promise for improving climate prediction. To achieve even greater gains, we will extend the approach to allow greater connectivity among multiple complex climate models to create a true super climate model. We will assess the approach’s potential to enhance seasonal-to-decadal prediction, focusing on the Tropical Pacific and North Atlantic, and to reduce uncertainties in climate projections. Importantly, this work will improve our understanding of climate, as well as how systematic model errors impact prediction skill and contribute to climate change uncertainties.

Mots-clés : climate prediction,seasonal to decadal prediction,internal climate variability,model systematic error