Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : BiomeRiskFactors (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Life Science
Panel : Integrative Biology: from Genes and Genomes to Systems
Entité participante : Weizmann Institute of Science — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Identifying risk factors for diseases that can be prevented or delayed by early intervention is of major importance, and numerous genetic, lifestyle, anthropometric and clinical risk factors were found for many different diseases. Another source of potentially pertinent disease risk factors is the human microbiome - the collective genome of trillions of bacteria, viruses, fungi, and parasites that reside in the human gut. However, very few microbiome disease markers were found to date. Here, we aim to develop risk prediction tools based on the human microbiome that predict the likelihood of an individual to develop a particular condition or disease within 5-10 years. We will use a cohort of >2200 individuals that my group previously assembled, for whom we have clinical profiles, gut microbiome data, and banked blood and stool samples. We will invite people 5-10 years after their initial recruitment time, profile disease status and blood markers, and develop algorithms for predicting 5-10 year onset of Type 2 diabetes, cardiovascular disease, and obesity, using microbiome data from recruitment time. To increase the likelihood of finding microbiome markers predictive of disease onset, we will develop novel experimental and computational methods for in-depth characterization of microbial gene function, the metabolites produced by the microbiome, the underexplored fungal microbiome members, and the interactions between the gut microbiota and the host adaptive immune system. We will then apply these methods to >2200 banked samples from cohort recruitment time and use the resulting data in devising our microbiome-based risk prediction tools. In themselves, these novel assays and their application to >2200 samples should greatly advance the microbiome field. If successful, our proposal will identify new disease risk factors and risk prediction tools based on the microbiome, paving the way towards using the microbiome in early disease detection and prevention.

Mots-clés : human gut microbiome,metagenomics,metahit,human microbiome project,american gut,human gut bacteria,massively parallel reporter assays
Projet ERC : NEPAL (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
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 : 1954831.0 €

If the Standard Model (SM) of particle physics succeeds in describing the behaviour of fundamental constituents of matter and their interactions observed experimentally, it is unable to solve the most important riddles of our time such as the nature of the dark matter or the origin of the matter-antimatter asymmetry of the Universe. Manifestations of physics beyond the SM are extensively searched for, in particular through heavy flavour decays that are rare or forbidden in the SM. In this domain, final states involving electrons and muons are widely studied while channels involving tau leptons are much less known because of their challenging reconstruction. The interest of decays involving tau leptons is also dramatically reinforced by the recent anomalies reported in tests of lepton flavour universality violation and rare B decays, suggesting a special role of the third family. In particular, in the presence of physics beyond the SM, lepton flavour violating tau decays and rare B decays into tau leptons could be just below the current experimental limits. With the NEPAL project, I propose to build a team of analysts that will exploit the world’s largest B and tau samples recorded in the clean environment of an electron/positron machine by the Belle II experiment. The full detector operation will start end 2018 and aims at recording five times more statistic than the total previous flavour-factory experiments by 2020, and a final dataset of 50 inverse attobarns by 2025. Thanks to the development of a common analysis framework, sophisticated machine learning techniques for signal selections, the use of a full event interpretation and the reconstruction of 95% of tau decays, my team will search for more than thirty lepton flavour violating tau decays and rare B decays into tau leptons. This will allow to set the world’s best limits in the best possible timescale, reshaping the landscape of searches for physics beyond the Standard Model.

Mots-clés : flavour physics,belle ii,tau lepons,lepton flavour violation,data analysis
Projet ERC : IMAGO (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : Universidad de Zaragoza — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UNIZAR
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Image analysis has become a powerful tool in multiple biomedical applications, ranging from clinical cases to cellular experiments. The main purpose of IMAGO is to extract relevant information from image-based analysis to perform comparative studies in 3D cell cultures. Most of the cell-based experiments have been performed in 2D, but 3D conditions are biologically more relevant. IMAGO presents the development of a tool for image analysis focused on, but not limited to, the quantification of cell behavior under different conditions. Indeed, IMAGO goes further by providing, when applicable, an image-based finite element analysis (FEA). There exist different professional software solutions, but either they don’t allow post-processing data analyses or they require specialized formation and/or experience to be used efficiently. Furthermore, to the best of our knowledge, none of them permits the extraction of mechanical clues by means of a linked FEA. IMAGO brings together all these features and offers them through an online service that allows the user to customize his own analysis. During the ERC project INSILICO-CELL, we have developed our own software to quantitatively analyze specific in-vitro tests involving particle transport, cell migration, angiogenic sprouting, actin flow, dendritic protrusions and traction force microscopy among others. But, this software is flexible and can be adapted and extended to a wide variety of in-vitro experiments. Thus, it is worth exploring market opportunities to offer our knowledge and expertise to other laboratories, so they can process images from in-vitro tests and analyze their results, either by using an existing function in our software or by developing a new one on request. Therefore, the expected outcome of IMAGO is to demonstrate the potential feasibility of our software by offering our services to external laboratories, and developing a business strategy plan to eventually make our services available to the market.

Mots-clés : 3d cell culture,quantitative image based analysis,traction force microscopy
Projet ERC : ThermoTex (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Chalmers University of Technology — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Imagine a world, in which countless embedded microelectronic components continuously monitor our health and allow us to seamlessly interact with our digital environment. One particularly promising platform for the realisation of this concept is based on wearable electronic textiles. In order for this technology to become truly pervasive, a myriad of devices will have to operate autonomously over an extended period of time without the need for additional maintenance, repair or battery replacement. The goal of this research programme is to realise textile-based thermoelectric generators that without additional cost can power built-in electronics by harvesting one of the most ubiquitous energy sources available to us: our body heat. Current thermoelectric technologies rely on toxic inorganic materials that are both expensive to produce and fragile by design, which renders them unsuitable especially for wearable applications. Instead, in this programme we will use polymer semiconductors and nanocomposites. Initially, we will focus on the preparation of materials with a thermoelectric performance significantly beyond the state-of-the-art. Then, we will exploit the ease of shaping polymers into light-weight and flexible articles such as fibres, yarns and fabrics. We will explore both, traditional weaving methods as well as emerging 3D-printing techniques, in order to realise low-cost thermoelectric textiles. Finally, within the scope of this programme we will demonstrate the ability of prototype thermoelectric textiles to harvest a small fraction of the wearer’s body heat under realistic conditions. We will achieve this through integration into clothing to power off-the-shelf sensors for health care and security applications. Eventually, it can be anticipated that the here interrogated thermoelectric design paradigms will be of significant benefit to the European textile and health care sector as well as society in general.

Mots-clés : organic electronics,thermoelectrics
Projet ERC : ArtifiCell (Horizon 2020, appel 2014, Advanced grants)
Domaine scientifique : Life Science
Panel : Cellular, Developmental and Regenerative Biology
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 : 2200000.0 €

Imagine being able to design into living cells and organisms de novo vesicle transport mechanisms that do not naturally exist? At one level this is a wild-eyed notion of synthetic biology. But we contend that this vision can be approached even today, focusing first on the process of exocytosis, a fundamental process that impacts almost every area of physiology. Enough has now been learned about the natural core machinery (as recognized by the award of the 2013 Nobel Prize in Physiology or Medicine to the PI and others) to take highly innovative physics/engineering- and DNA-based approaches to design synthetic versions of the secretory apparatus that could someday open new avenues in genetic medicine. The central idea is to introduce DNA-based functional equivalents of the core protein machinery that naturally form (coats), target (tethers), and fuse (SNAREs) vesicles. We have already taken first steps by using DNA origami-based templates to produce synthetic phospholipid vesicles and complementary DNA-based tethers to specifically capture these DNA-templated vesicles on targeted bilayers. Others have linked DNA oligonucleotides to trigger vesicle fusion. The next and much more challenging step is to introduce such processes into living cells. We hope to break this barrier, and in the process start a new field of research into “synthetic exocytosis”, by introducing Peptide-Nucleic Acids (PNAs) of tethers and SNAREs to re-direct naturally-produced secretory vesicles to artificially-programmed targets and provide artificially-programmed regulation. PNAs are chosen mainly because they lack the negatively charged phosphate backbones of DNA, and therefore are more readily delivered into the cell across the plasma membrane. Future steps, would include producing the transport vesicles synthetically within the cell by externally supplied origami-based PNA or similar cages, and - much more speculatively - ultimately using encoded DNA and RNAs to provide these functions.
Projet ERC : DYNACEUTICS (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : University of Birmingham — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499068.0 €

Imagine if doctors could heal patients via remote control. Following simple injections into regions of the body, they could activate internal cells by an external bandage. In this way, they could remotely control the ways tissue heal. This Advanced grant sets out to understand, design and develop the mechano-nano-magnetic platform that will underpin this therapeutic strategy for the future – DYNACEUTICS. Key receptors have been identified such as ion channels, integrins and growth factors which respond to mechanical cues on the membrane and activate downstream pathways. How do we ‘bottle’ an agonist like a drug which can influence or regulate mechano-sensors on the membrane and can be controlled remotely? This project tackles this complex interdisciplinary question through breakthrough nanotechnologies. We aim to expand and develop a platform technology using magnetic particle tagging which will allow us to direct cells for therapeutic purposes. Specifically, we aim • to identify mechano-receptor binding sites on stem and mature cells which will enable remote activation of signalling pathways via magnetic fields, • to design and test magnetic particles with tailored tagging strategies using single cell through 3D human organoid models to in vivo disease models, • to tailor and design external remote control devices • to create clinically relevant treatment modalities for remote control healing. This proposal presents a unique opportunity to launch a new dynamic treatment platform, DYNACEUTICS, which we propose will extend the therapeutic horizon and provide a new form of remote controlled healing.
Projet ERC : NanoChemBioVision (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : University of Southampton — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1916076.0 €

Imagine if one could simply use an optical microscope and see whether a particular virus has infected a biological specimen or not! Or if a single disease causing molecular structure could be detected, 20 years before the disease manifests itself! Conventional microscopy simply does not have such spatial resolution! The challenge is to image endogenous molecules and structures composed of them specifically, in real-time, without tampering and sample destruction. Non-Linear optical techniques such as vibrational sum frequency generation (vSFG) and coherent Raman scattering (CRS), which use the intrinsic properties of molecules for selectively imaging them, provide a solution. They are non-invasive, label-free, chemically selective and non-destructive with capability for video-rate imaging of biomolecules and biochemical structures. However, they need to overcome the frontier of spatial resolution to be able to provide information at <100 nm level, which is much below the limit for these techniques and conventional microscopy. The proposal addresses this challenge by developing and implementing a generic, simple optical ultra-high resolution technology using a novel approach based on super-oscillatory modulation of light coupled with wavelength mixing. We will uniquely apply this approach to the chemically selective vSFG and CRS techniques. Ultra-high spatial resolution with these techniques will allow unprecedented new insight into many biochemical phenomena. To demonstrate the utility of ‘chemical nanoscopy’ developed in this proposal vesicular transport in axons of neurons will be studied, which is highly relevant to cognitive decline observed in ageing and neurodegenerative disorders. The project outcomes have the potential to revolutionize research and biomedical understanding by opening doors to ‘unseen biology’, unravelling disease, viral infection and allergy mechanisms and ultimately, yielding better diagnostics and therapeutics.

Mots-clés : biological imaging,microscopy
Projet ERC : InfoMatter (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Dutch Research Council — Pays : Pays-Bas — Rôle : Partner
Acronyme de l'entité : NWO
Type d'entité : Recherche
Financement ERC de l'entité : 1249686.25 €

Imagine squeezing a crumpled plastic sheet – what is its response? For complex materials – crumpled sheets, granular media, glassy materials – tiny forces already cause a nonlinear response, associated with hopping in the energy landscape, for which we lack a coherent description. The central tenet of this proposal is that this hopping allows complex materials to process information. I propose to understand and unlock the full potential of such complex materials. Central questions are: - How can we describe their nonlinear response? - What are the computational capabilities of complex matter? - Can we create materials for targeted computations? To answer these questions, I will subject macro-scale complex matter to sequentially and spatially patterned driving forces (information input), and observe the sequential response (information output). The responses to multiple forcings form an intricate web of linked pathways, which can be represented in a graph that I interpret as encoding a sequential computation. This is the central link between driving, pathways and information, which allows me to probe the algorithmic capabilities of complex materials. Recent breakthroughs in my group have brought experimental access, modelling and design of pathways within reach. In this proposal, I will investigate these pathways and will develop strategies to create algorithmic matter, using a combination of disordered – crumpling – sheets and carefully designed mechanical metamaterials; I will develop theoretical models for both. Just like a focus on the architecture of complex matter has revolutionized our view of their linear response and gave birth to mechanical metamaterials, a focus on their pathways will revolutionize our understanding of their nonlinear response and opens the door to new forms of information processing matter.

Mots-clés : memory and information,complex matter,mechanical metamaterials
Projet ERC : Immuno-Predictor (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : University of Cyprus — Pays : Chypre — Rôle : PI
Acronyme de l'entité : UCY
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Immunotherapy has revolutionized the treatment of multiple cancers and has already become a standard of care for some tumor types. However, a majority of patients do not benefit from current immunotherapeutics and many develop severe toxicities. Therefore, the identification of biomarkers to classify patients as likely responders or nonresponders to immunotherapy is a timely and of tremendous impact task. My hypothesis is that biomechanical aspects of the tumor microenvironment mediate resistance to immunotherapy. Specifically, many tumors stiffen as they grow and also, tumor growth within the host tissue generates mechanical forces, termed solid stress. Tumor stiffening and solid stress are distinct mechanical abnormalities that compress intratumoral blood vessels, causing hypo-perfusion and hypoxia. Systemic administration of immunotherapeutics requires a well-perfused vasculature, whereas hypo-perfusion and hypoxia promote immunosuppression, helping cancer cells to evade immune responses. The objective of the proposed research is the identification of novel Mechanical Biomarkers related to tumor stiffness, solid stress, perfusion and hypoxia for prediction of immunotherapy. Tumor-bearing mice will be developed and treated with immunotherapeutic drugs and clinically used methods will be combined with computational biomechanical modeling for measuring the Mechanical Biomarkers, making the research transferable to the clinic. The biomarkers will be benchmarked against tumor normalization strategies aiming to restore/normalize mechanical abnormalities and optimize immunotherapy. Finally, the clinical utility of the selected biomarkers will be evaluated in human tumors. Only few tumor-specific biomarkers are used in the clinic - based mainly on genomic analysis. This project is expected to lead to the first biomarkers for immunotherapy prediction exploiting tumor mechanics.

Mots-clés : bioengineering,biomechanics,computational models,mechanical properties,tumor microenvironment,drug delivery,animal tumor models
Projet ERC : SweetAim (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Tel Aviv University — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1479995.0 €

Immunotherapy recently became an important alternative to conventional treatment regimes, yet cancer remains a universal leading cause of death. Thus, novel cancer theranostic approaches are still much desired. Although altered cell surface glycosylation is one of the hallmarks of cancer, targeting this ‘sweet aim’ for cancer therapy has been elusive, largely due to carbohydrates poor immunogenicity and the low affinity of antibodies against them. A red meat-derived carbohydrate antigen is a novel immunogenic moiety providing a key to unlock the theranostic potential of tumor-associated carbohydrate antigens. This foreign non-human sugar can be acquired only through the diet and subsequently appears on diverse cell surface glycoconjugates as ‘self’, accumulating mostly on carcinomas, and resulting in a polyclonal xeno-autoantibodies response. I have shown that such antibodies have both diagnostic and therapeutic potential, although basic understanding of their specificity and potency is scarce. The primary objective of this proposal is to design a novel personalized cancer therapeutic approach based on xeno-autoantibodies against the dietary sugar antigen. We propose an innovative interdisciplinary approach crossing the boundaries of cancer research, glycosciences, immunology and nanotechnology, with cutting-edge technologies, to design, engineer, screen and fully investigate potent targeting of ‘SweetAim’ moieties. Our discovery line is based on a two-arms platform to generate optimized antibodies for passive/active therapy, together with refined tumor cells through glyco-engineering/reprogramming for unveiling novel theranostics, finally evaluated both in vitro and in vivo. I expect our groundbreaking achievements will lead to promising new clinical tools, particularly for cancer, but also for other chronic inflammation-mediated diseases. Importantly, it will establish fundamental new concepts regarding carbohydrate recognition and response by the immune system.

Mots-clés : tumor associated carbohydrate antigen,sialic acid,neu5gc,antibody,xeno autoantibodies,immunotherapy,cancer,cancer vaccine