Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : MHINT (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Manchester Metropolitan University — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : MMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 155331.82 €

Despite decades of research, and the introduction of parenting interventions, children of mentally ill mothers remain substantially more likely to have mental health problems themselves. I propose to shed new light on why mental health problems in a mother are passed on to her child, and help break this reinforcing cycle of mental health risk across generations. In order to harness the potential of modifying parenting for the prevention of child mental health risk, I will study parenting using more detailed, ecologically valid and genetically sensitive designs than have been done before. Objectives: 1: To investigate the respective role of genetic and environmental (chiefly parenting) mechanisms in explaining associations between mother and child mental health. HOW: using a consortium of international cohorts with intergenerational genetic and phenotypic data (n>10,000) and, for the first time, modeling genetic risk which is and is not transmitted from mother to child to test alternative hypotheses. 2: To identify behavioural manifestation of maternal mental health, in observed mother-infant interaction, in an ecologically valid way. HOW: recording 300 mother- child dyads at home, using novel wearable cameras, in the next generation of a key cohort (ALSPAC-G2). 3: To identify cognitive underpinnings of maternal behaviour. HOW: including cognitive tasks (with eye tracking) as new measures in ALSPAC-G2, applying computational models to cognitive and (uniquely) real life data (measured in 2). 4: To establish whether modification of maternal parenting (highlighted in 1-3), changes child mental health. HOW: systematic review of parenting intervention trials and new synthesis methods to extract which intervention components reduce child mental health problems. My study will provide critical new evidence regarding the nature of parenting interventions that have potential to improve child mental health and break intergenerational transmission of mental health problems.

Mots-clés : intergenerational,maternal mental health,parenting
Projet ERC : ChAMPioN (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of Manchester — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149138.75 €

Despite decades of research, developing ways to overcome drug resistance in cancer is the most challenging bottleneck for curative therapies. This is because, in some forms of cancer, the cancer stem cells from which the diseases arise are constantly evolving, particularly under the selective pressures of drug therapies, in order to survive. The events leading to drug resistance can occur within one or more individual cancer stem cell(s) – and the features of each of these cells need to be studied in detail in order to develop drugs or drug combinations that can eradicate all of them. The BCR-ABL+ and BCR-ABL- myeloproliferative neoplasms (MPN) are a group of proliferative blood diseases that can be considered both exemplars of precision medicine and of the drug resistance bottleneck. While significant advances in the management of MPN have been made using life-long and expensive tyrosine kinase inhibitors (TKI), patients are rarely cured of their disease. This is because TKI fail to eradicate the leukaemia stem cells (LSC) from which MPN arise and which persist in patients on treatment, often leading to pervasive drug resistance, loss of response to therapy and progression to fatal forms of acute leukaemia. My goal is to change the way we study the LSC that persist in MPN patients as a means of delivering more effective precision medicine in MPN that is a “game-changer” leading to therapy-free remission (TFR) and cure. Here, I will apply an innovative strategy, ChAMPioN, to study the response of the MPN LSC to TKI in innovative pre-clinical laboratory models and directly in patients with MPN - up to the resolution of individual LSC. This work will reveal, for the first time, the molecular and clonal evolution of LSC during TKI therapies, thus enabling the development of more accurate predictions of TKI efficacy and resistance and rational approaches for curative drug therapies.

Mots-clés : bcr abl,jak2,bioinformatics,modelling,nrg xenograft,tyrosine kinase inhibitors,ezh2,c myc,bar coding,single cell,precision medicine,heterogeneity,clonal evolution
Projet ERC : IN-Fo-trace-DG (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : University of Freiburg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2463693.0 €

Despite intensive study in the past on the problem of how information is processed in the brain to enable individual organisms to adapt to their continuously changing environment, little progress has been made on how new similar but discrete memory traces emerge in neuronal networks during learning. Current theories suggest that experience-dependent modifications in excitation-inhibition balance enable a selected group of neurons to form a new cell association during learning which represent the new memory trace. It was further proposed that particularly GABAergic inhibitory interneurons (INs) have a large impact on population activity in neuronal networks by means of their inhibitory output synapses. However, how cell associations emerge in space and time and how INs may contribute to this process is still largely unknown. This complex topic was so far difficult to address due to technical constraints. IN-Fo-Trace-DG aims to address this fundamental question in the dentate gyrus (DG), a brain structure essential for the acquisition of similar but discrete new memories. Based on our detailed knowledge on DG’s cellular elements, their interconnectivity and our recently established molecular interference tools, we will first, visualize the spatial and temporal activity patterns of cell populations during spatial learning in a virtual-reality using 2-Photon imaging. Second, we will determine the role of IN recruitment and plasticity in assembly formation by optogenetic and molecular interference. Third, we will analyze changes in excitatory and inhibitory signals in granule cells (GCs), the principal cells in this brain area, and INs during learning using whole-cell recordings in vivo. Finally, we will examine whether adult-born GCs contribute differently to learning-associated population activity compared to mature ones in the adult DG. This innovative multi-disciplinary approach will provide new insights on the mechanisms of new memory formation in cortical networks.

Mots-clés : population imaging,gabaergic interneurons,dentate gyrus,synaptic plasticity,synaptic,transmission,in vivo recordings,population activity,structural plasticity,parvalbumin
Projet ERC : ASYMOW (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : University of Pisa — Pays : Italie — Rôle : PI
Acronyme de l'entité : UniPi
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 936250.0 €

Despite its success in describing the sub-nuclear realm, the Standard Model of particle and field interactions cannot account for a number of experimental facts that constitute evidence of new and unknown physics. Sitting at both the energy and intensity frontier, the LHC grants the highest chances for solving the current puzzle. By exploiting the data collected by the CMS experiment at the LHC, ASYMOW aims at attaining an unprecedented experimental accuracy on a fundamental parameter of Nature: the mass of the W boson. As of today, there is a tension between the Standard Model expectation and the measured value of the W boson mass. A new measurement with a 10 MeV uncertainty, i.e. twice as small as the single best measurement, is a breakthrough: it could either rule out the tension or build a convincing case that this anomaly is real, thus implying the existence of new physics. The collider physics community has been pursuing this goal for decades. The quest now seems to have hit the wall of systematic uncertainty. This project proposes a new approach towards the W boson mass measurement, which will circumvent the systematic uncertainties that are currently limiting the precision. The novelty of the proposed method lies in its agnosticism with respect to the microscopic picture of W boson production in hadron collisions. The loss of prior knowledge inherent to this new approach will be asymptotically compensated by the large amount of data available for the measurement, as will be collected at the LHC. ASYMOW will be conducted by a small group of scientists and comes with great experimental and theoretical challenges. The main expected result is the measurement of the W boson mass with a precision better than the state-of-the-art. This may open new scenarios in particle physics.

Mots-clés : w mass,precision electroweak measurements,standard model
Projet ERC : BEC-NETWORKS (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : University of Twente — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Despite large advances in both algorithms and computer technology, even typical instances of computationally hard problems are too difficult to be solved on today’s computers. Unconventional computational devices that break with the usual paradigms of digital electronic computers can help to overcome these limitations. In this project, a network of coupled photon Bose-Einstein condensates will be developed and used as experimental platform to perform ultrafast simulations of classical spin systems. Specifically, the network will be capable of solving the ground-state problem in spin glasses (disordered magnets). The latter constitutes a well-known combinatorial problem that can be mapped mathematically to many other computationally hard problems in machine learning, logistics, computer chip design and DNA sequencing. In a proof-of-principle experiment, I aim to demonstrate that the proposed spin glass simulator performs this computationally hard optimisation problem significantly faster than any other computer today. I have pioneered the Bose-Einstein condensation of photons in optical microcavities, which has enabled us to investigate this genuine quantum-mechanical effect with all-optical methods. In a recent work of my group, we experimentally demonstrate controllable phase relations between photon Bose-Einstein condensates in an optical microcavity. The investigated device realises an optical analogue of a Josephson junction. Similar to a transistor for electronics, a controllable photonic Josephson junction represents the key component for ultrafast optical spin glass simulation and, thus, is the crucial basis for the proposed project. The BEC-NETWORKS project will be the main research project of my research group at the University of Twente.

Mots-clés : bose einstein condensation,quantum simulation
Projet ERC : PREDATORS (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : University of Bremen — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499250.0 €

Despite many advances in earthquake science, the tendency for faults to host earthquake slip, aseismic slip or slow slip events is far from well understood. Earthquakes are not yet predictable in a meaningful way, and laboratory observations do not satisfactorily explain many general observations of fault slip. Existing data has been gathered at slip velocities orders of magnitude faster than plate convergence rates, therefore the fundamental question addressed by the PREDATORS project is how faults slip when driven tectonic rates as they are in nature. I suggest that laboratory friction experiments conducted at these rates may reveal widespread frictional instability that explains the occurrence of (both fast and slow) earthquakes on plate-boundary faults, and that long-term shear loading driven by slow, plate convergence rates is more representative of interseismic real faults and captures processes which intermediate- to high-velocity experiments cannot. The experimental research proposed here utilizes an increasing complexity approach, from existing successful techniques to more innovative measurements using equipment modified to reliably shear at appropriately slow rates and under a wide range of interior Earth conditions. Rock and mineral standards will be used to establish a basic and widely applicable framework for frictional behaviour, while natural fault samples will be used for site-specific problems. This project will provide a comprehensive set of measurements and observations of fault behaviour at realistically slow plate tectonic deformation rates. Combined with existing measurements, this will provide a complete description of rock/sediment friction over the entire possible range of slip velocities. By comparison with geophysical observations on real faults, these results will help explain current seismicity patterns and other slip phenomena, and predict fault behaviour at locations where sampling and geologic characterization is limited.
Projet ERC : PROTEUS (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : The Francis Crick Institute — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 2500000.0 €

Despite progress in cancer drug development, the majority of patients who present with advanced, metastatic, solid tumours have incurable disease due to underlying cancer genomic diversity that provides a substrate for evolution and selection of drug resistance. The aim of this proposal is to describe, synthesise and model the micro- and macroevolutionary patterns of genomic instability underpinning the evolutionary dynamics of tumour life histories, to improve patient stratification, treatment and survival outcomes. Longitudinal clinical studies such as TRACERx are highlighting the complex processes that generate this intra-tumour heterogeneity (ITH). Genome Instability (GIN) describes aberrant changes within the genome, encompassing genome doubling (GD), numerical or structural chromosomal instability (CIN), and elevated DNA sequence mutational diversity. TRACERx has revealed that elevated DNA copy-number ITH rather than DNA sequence diversity is associated with increased risk of recurrence or death in non-small cell lung cancer (NSCLC). Why macroevolutionary CIN rather than somatic mutational diversity is associated with poor outcome remains unclear. Current animal models of NSCLC do not sufficiently model the multiple distinct patterns of GIN operating in patients. We aim to develop mouse lung cancer models that recapitulate the patterns of GIN observed in NSCLC patients. Using tumour barcode sequencing, a sensitive method of quantifying cellular fitness and individual tumour growth, we will investigate the effects of targeted-, chemo- and immuno-therapy on the newly generated GIN models. We will decipher if distinct patterns of GIN increase metastatic potential and treatment failure, and test if high mutational burden or high CIN increases the frequency of GD in cancer. Finally, we aim to investigate the effects of GIN upon immune surveillance, immune evasion, immunotherapy response, and the interactions between tumours and the tumour microenvironment.

Mots-clés : tumour evolution,genomic instability,chromosomal instability,genome doubling,mutation,immune escape,nsclc,lung cancer
Projet ERC : QuantSURG (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Université de Strasbourg — Pays : France — Rôle : Partner
Acronyme de l'entité : UNISTRA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1165422.15 €

Despite significant advances in medical imaging technologies, there currently exist no tools to effectively assist healthcare professionals during colorectal surgery. Surgeons mainly rely on their own senses, vision and touch to identify diseased tissue that should be removed or healthy tissue that should be avoided. In turn, surgery remains subjective and dependent on the experience of the surgeon, resulting in unacceptable failure, recurrence and morbidity rates, as well as in significant quality of care disparities across hospitals. The hypothesis underlying our study is that near-infrared light travels deeply into living tissues and interacts with endogenous molecular constituents, namely oxy- and deoxy-hemoglobin, water and lipids, providing key information regarding tissue perfusion, oxygenation, hydration and metabolism. In turn, such information can be used to differentiate diseased from healthy tissue. We recently introduced a novel concept that enables the quantitative imaging of endogenous molecular information over large fields-of-view. Because this concept can be implemented in real-time, it is amenable to provide video-rate endogenous information during colorectal surgery. In this study, we propose to push the limits of this concept by developing ground-breaking theory & technology, and creating a novel surgical guidance device capable of real-time imaging of key endogenous information for colorectal surgery. Correlation between endogenous contrast measurements and histological tissue status will be investigated onto bowel ischemia and colorectal cancer animal models. Finally, a clinically-compatible imaging device will be fabricated and translated into a first-in-human study in patients undergoing colorectal surgery. If successful, this study has the potential to solve a longstanding clinical problem by providing real-time objective feedback during colorectal surgery.

Mots-clés : image guided surgery,endogenous molecular contrast,diffuse optical imaging,cancer margin,detection,ischemia imaging,colorectal surgery,clinical translation
Projet ERC : LEGO (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université Grenoble Alpes — Pays : France — Rôle : PI
Acronyme de l'entité : UGA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1946150.0 €

Despite significant progress in cancer therapy, current treatments are still controversial due to intolerable side effects. Targeted immunotherapy has recently emerged as an ideal alternative to improve treatment modalities for cancers patients. However, very limited approaches are available today and major issues remain to be addressed. The ERC grant offers a unique opportunity to propose a new paradigm for treating cancer. Through a ground-breaking interdisciplinary program, at the crossroad of supramolecular chemistry, synthetic chemistry, molecular engineering, biophysics, biochemistry, immunochemistry and glycoscience, it is my ambition to design, synthesize and study smart biomolecular structures with unprecedented combinations, complexity and immunological properties against cancers. To achieve this purpose, I will develop a “molecular LEGO” approach to construct synthetic molecules capable of redirecting endogenous antibodies present in the human bloodstream against tumors without preliminary immunization. Efficient tumoral killing by immune effectors will be provided by molecules combining innovative antibody and tumor binding modules that will be selected in vitro beforehand. To be successful, I will address fundamental questions that are still unresolved in chemical and biological sciences. The expected breakthroughs will represent a landmark achievement in these fields and will open promising horizons in cancer immunotherapy. Beyond this, it can be expected that our findings will pave the way to future development of synthetic molecules embedded with recognition, labeling, and/or therapeutic functions. They will thus find wider medicinal, diagnostic and even theranostic applications for which the development of more effective and selective biomolecular systems is of the utmost importance.

Mots-clés : immunochemistry,chemical ligation,bioorganic chemistry,combinatorial chemistry,multivalency,glycoscience
Projet ERC : FRAGMENT2DRUG (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Uppsala University — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1467500.0 €

Despite technological advances, industry struggles to develop new pharmaceuticals and therefore novel strategies for drug discovery are urgently needed. G protein-coupled receptors (GPCRs) play important roles in numerous physiological processes and are important drug targets for neurological diseases. My research focuses on modelling of GPCR-ligand interactions at the atomic level, with the goal to increase knowledge of receptor function and develop new methods for drug discovery. Breakthroughs in GPCR structural biology and access to sensitive screening assays provide opportunities to utilize fragment-based lead discovery (FBLD), a powerful approach for drug design. The objective of the project is to create a computational platform for FBLD, with a vision to transform the early drug discovery process for GPCRs. As structural information for these targets is limited, predictive models of receptor-fragment complexes will be crucial for the successful use of FBLD. In this project, computational structure-based methods for discovery of fragment ligands and further optimization of these to potent leads will be developed. These techniques will be applied to address two difficult problems in drug discovery. The first of these is to design ligands of peptide-binding GPCRs that have been challenging for existing methods. One of the promises of FBLD is to provide access to difficult targets, which will be explored by combining molecular docking and biophysical screening against peptide-GPCRs to identify novel lead candidates. A second challenge is that efficient treatment of neurological disorders often requires modulation of multiple targets, which also will be the focus of the project.

Mots-clés : fragment based drug discovery,molecular docking,virtual screening,g protein coupled receptor