Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : GENESIS (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : Ludwig-Maximilians-Universität München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1970000.0 €

In vertebrates, a receptor-based, innate sensing machinery is used to detect the presence of microbederived molecules or the perturbation microbial infection causes within the host. In the context of viral infection, non-self nucleic acids are sensed by a set of intracellular receptors that upon activation initiate broad antiviral effector responses to eliminate the imminent threat. Over the past years our understanding of these processes has considerably grown, mainly by employing murine knockout models. Recent advances in genome engineering now provide the opportunity to knockout genes or even to perform functional genetic screens in human cells, providing a powerful means to validate and generate hypotheses. We have developed a high-throughput genome targeting and validation platform that allows us to tackle large-scale loss-of-function studies both at a polyclonal as well as an arrayed format. In addition, we have invested considerable efforts to render this technology applicable to study innate immune sensing and signalling pathways in the human system. GENESIS will combine these efforts to tackle pertinent questions in this field that could not have been addressed before: We will systematically dissect known nucleic acid sensing pathways in the human system to explore their unique roles, cooperativity or redundancy in detecting non-self nucleic acids. We will perform polyclonal, genome-wide loss-of-function screens to elucidate signalling events downstream of intracellular DNA and RNA sensing pathways and their roles in orchestrating antiviral effector mechanisms. Moreover, in a large-scale perturbation study, we will specifically address the role of the kinome in antiviral innate immune signalling pathways, exploring the role of its individual members and their epistatic relationships in orchestrating gene expression. Altogether, these studies will allow us to obtain insight into innate immune signalling pathways at unprecedented precision, depth and breadth.

Mots-clés : genome engineering
Projet ERC : ACHILLES-HEEL (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : University of Cambridge — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. Cambridge
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1991054.0 €

Increasing crop yield to feed the world is a grand challenge of the 21st century but it is hampered by diseases caused by filamentous plant pathogens. The arms race between pathogen and plant demands constant adjustment of crop germplasm to tackle emerging pathogen races with new virulence features. To date, most crop disease resistance has relied on specific resistance genes that are effective only against a subset of races. We cannot solely rely on classical resistance genes to keep ahead of the pathogens. There is an urgent need to develop approaches based on knowledge of the pathogen’s Achilles heel: core plant processes that are required for pathogen colonization. Our hypothesis is that disease resistance based on manipulation of host accessibility processes has a higher probability for durability, and is best identified using a broad host-range pathogen. I will employ the filamentous pathogen Phytophthora palmivora to mine plant alleles and unravel host processes providing microbial access in roots and leaves of monocot and dicot plants. In Aim 1 I will utilize plant symbiosis mutants and allelic variation to elucidate general mechanisms of colonization by filamentous microbes. Importantly, allelic variation will be studied in economically relevant barley and wheat to allow immediate translation into breeding programs. In Aim 2 I will perform a comparative study of microbial colonization in monocot and dicot roots and leaves. Transcriptional profiling of pathogen and plant will highlight common and contrasting principles and illustrate the impact of differential plant anatomies. We will challenge our findings by testing beneficial fungi to assess commonalities and differences between mutualist and pathogen colonization. We will use genetics, cell biology and genomics to find suitable resistance alleles highly relevant to crop production and global food security. At the completion of the project, I expect to have a set of genes for resistance breeding.

Mots-clés : non nb lrr resistance,allelic variation,plant pathogen interaction,broad host range pathogen,phytophthora,organ specific mechanisms,crop plants,barley,wheat,medicago truncatula
Projet ERC : BugDrug (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University Medical Center Groningen — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UMCG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999676.0 €

Individual response rates to drugs that are widely prescribed to treat common diseases are typically in the range of 50-75%, yet the slow progress and high costs associated with new drug development do not meet the urgent need for new treatment options, particularly given the rapidly increasing burden of chronic diseases. This ERC project tackles this pressing issue by defining the impacts of the gut microbiome and genetics on drug metabolism, ultimately enabling personalized approaches that enhance efficacy and safety of already marketed drugs via microbiome modulation. I will start with 23 common drugs used to treat chronic cardiometabolic and psychiatric disorders that threaten public health. The project will address important technical and clinical challenges in three innovative parts: 1) a population-based cohort study using the unique LifeLines-10K cohort to build sophisticated models that take genetic, microbial and lifestyle/health factors into account to improve prediction of drug metabolism; 2) pharmacokinetics analyses using innovative, personalized organ-on-a-chip to better understand causality and mechanism; and 3) an intervention trial using probiotics to achieve greater drug efficacy through modulation of the gut microbiome. In particular, the project will deliver microbiome-targeting solutions to improve the efficacy and safety of one or two drugs and a next-generation drug-testing model that uses an innovative organ-on-a-chip system to mimic metabolic flow along the gut-liver axis while taking an individual’s genetics and gut microbiome into account. This project will lay the foundation for major advances in personalized medicine through: (i) better prediction of individual drug metabolism, which will aid therapeutic decision-making; (ii) better understanding of genetics-microbiome interactions in drug metabolism; and (iii) greater drug efficacy through modulation of the gut microbiome.
Projet ERC : GROWTHPATTERN (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Life Science
Panel : Cellular, Developmental and Regenerative Biology
Entité participante : Institute of Science and Technology Austria — Pays : Autriche — Rôle : PI
Acronyme de l'entité : ISTA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499119.0 €

Individuals of the same species vary widely in size, but their organs have reproducible proportions and patterns of cell types. How cell fate specification and tissue growth are coordinated during embryonic development to achieve this reproducibility is a fundamental question in biology. Yet, surprisingly little is known about the underlying mechanisms. A major challenge has been to obtain the quantitative data required to assess the dynamics and variability in growth, pattern and signalling by morphogens – molecules that regulate both cell fate specification and tissue growth. I recently established experimental and theoretical approaches that allowed me to reconstruct with unprecedented resolution the three-dimensional growth and pattern of mouse and chick spinal cord. My data revealed a previously unanticipated role of tissue growth dynamics in controlling pattern reproducibility. This quantitative framework provides an exciting opportunity to elucidate the biophysical and molecular mechanisms of growth and pattern coordination. I will use this unique position to understand: 1) how signalling by multiple morphogens is integrated to control pattern, 2) how morphogens control cell cycle kinetics, 3) how morphogen source and target tissue are coupled to achieve pattern reproducibility. To address these issues, I will build on my experience with quantitative analyses to design novel assays where signalling, cell cycle dynamics and transcriptomes can be precisely measured and manipulated with single cell resolution. I will exploit state-of-the-art genome editing techniques to uncouple the critical feedback links and gain a novel perspective on pattern reproducibility and morphogen function. The project will advance our fundamental understanding of tissue morphogenesis and provide novel insights relevant to understanding information processing by signal transduction cascades, morphogen gradient activity, tissue engineering, and cancer biology.
Projet ERC : DINFO (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
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 : 1705804.0 €

Individuals, firms, and public organizations spend significant time and reIndividuals, firms, and public organizations spend significant time and resources to acquire, collect, and process information that guides and optimizes their decision making. Information is acquired directly by own research efforts, from academic publications that selectively publish scientific research, or from self-interested third parties that control the flow of information to influence decisions, and often information can also be learned through data-collection during day-to-day business. How much and what information should be acquired? How can third parties be incentivised to provide information? What is the role of commitment and verifiability of evidence in communication between a self-interested information provider and a user of that information? How should data collected from day-to-day business be used to optimize decisions, if these decisions also affect and feed back into future data collection? How should results of scientific research be communicated if it affects both decision by practitioners as well as the choice of future research topics? This research programme aims to shed light on these questions. Recognizing that information acquisition takes time, and happens gradually, often in multiple stages, it develops dynamic models of information acquisition and experimentation. The programme has four parts: (1) It develops a novel framework to analyse frictions in dynamic information acquisition and transmission that arise in communication and persuasion. (2) It develops new techniques to analyse robust dynamic information choice. (3) It develops a dynamic model of incentives for information production and communication in scientific research that contributes to the debate on publication standards. (4) It develops a theoretical framework to analyse the optimal use of data in the nascent field of predictive policing and other applications of data-driven resource allocation.

Mots-clés : dynamic information choice,persuasion,experimenation,robustness,data driven resource allocation,communication of research
Projet ERC : SYMCAR (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : TU Wien — Pays : Autriche — Rôle : PI
Acronyme de l'entité : TUW
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Individuals, industries, and nations are depending on software and systems using software. Automated approaches are needed to eliminate tedious aspects of software development, helping software developers in dealing with the increasing software complexity. Automatic program analysis aims to discover program properties preventing programmers from introducing errors while making changes and can drastically cut the time needed for program development. This project addresses the challenge of automating program analysis, by developing rigorous mathematical techniques analyzing the logically complex parts of software. We will carry out novel research in computer theorem proving and symbolic computation, and integrate program analysis techniques with new approaches to program assertion synthesis and reasoning with both theories and quantifiers. The common theme of the project is a creative development of automated reasoning techniques based on our recently introduced symbol elimination method. Symbol elimination makes the project challenging, risky and interdisciplinary, bridging together computer science, mathematics, and logic. Symbol elimination will enhance program analysis, in particular by generating polynomial and quantified first-order program properties that cannot be derived by other methods. As many program properties can best be expressed using quantified formulas with arithmetic, our project will make a significant step in analyzing large systems. Since program analysis requires reasoning in the combination of first-order logic and theories, we will design efficient algorithms for automated reasoning with both theories and quantifiers. Our results will be supported by the development of world-leading tools supporting symbol elimination in program analysis. Our project brings breakthrough approaches to program analysis, which, together with other advances in the area, will reduce the cost of developing safe and reliable computer systems used in our daily life.

Mots-clés : automated reasoning,first order theorem proving,symbolic computation,formal methods,program analysis,program verification
Projet ERC : EQUALSEA (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Santiago de Compostela — Pays : Espagne — Rôle : PI
Acronyme de l'entité : USC
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999478.0 €

Inequality is one of the key major social challenges of our time, with far-reaching ramifications for human well-being. Our oceans, which produce vital food, enable jobs and economic activities, and provide opportunities to shape cultures and identities of people, face unprecedented cumulative pressures from human activities and climate change due to industrialization of the seas. Although some researchers have explored ocean equity, significant gaps remain. Firstly, interdisciplinary approaches combining ecological and social sciences are fundamental to induce transformative changes towards ocean equality, but lacking. Secondly, there is a clear lack of data on different inequalities at seas for both small-scale and commercial fisheries. Thirdly, more than 4.3 billion people globally rely on fish as their major source of protein, but social, cultural and health factors which explain oceans inequalities remain largely unknown. Consequently, there is an urgent need for an interdisciplinary approach that addresses asymmetric social power relationships and concentration of capital assets and ownership of fishing rights focused on the most vulnerable groups. EQUALSEA will (a) develop a new transformative adaptation framework for ocean inequality, (b) identify multiple critical drivers which induce social tipping points dynamics and transformative changes across space and time, and (c) contribute to monitor progress towards ocean equity for local communities and top international organizations. To do this, I will combine modelling and simulation techniques and cross-case comparison to develop a typology of different inequalities tested in 20 MPAs and implemented for 3 in-depth case studies across Africa, Europe and Latin America. Together, the ontological framework and integration of modelling methods will significantly advance research on ocean inequality, developing the necessary tools to deliver sustainable impacts towards achieving equity for economies and societies.

Mots-clés : marine social ecological systems,transformation,equity
Projet ERC : Babylearn (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : Commissariat à l’énergie atomique et aux énergies alternatives — Pays : France — Rôle : PI
Acronyme de l'entité : CEA
Type d'entité : Recherche
Financement ERC de l'entité : 2554923.9 €

Infant is the most powerful learner: He learns in a few months to master language, complex social interactions, etc. Powerful statistical algorithms, simultaneously acting at the different levels of functional hierarchies have been proposed to explain learning. I propose here that two other elements are crucial. The first is the particular human cerebral architecture that constrains statistical computations. The second is the human’s ability to access a rich symbolic system. I have planned 6 work packages using the complementary information offered by non-invasive brain-imaging techniques (EEG, MRI and optical topography) to understand the neural bases of infant statistical computations and symbolic competence from 6 months of gestation up until the end of the first year of life. WP1 studies from which preterm age, statistical inferences can be demonstrated using hierarchical auditory oddball paradigms. WP2 investigates the consequences of a different pre-term environment (in-utero versus ex-utero) on the early statistical computations in the visual and auditory domains and their consequences on the ongoing brain activity along the first year of life. WP3 explores the neural bases of how infants infer word meaning and word category, and in particular the role of the left perisylvian areas and of their particular connectivity. WP4 questions infant symbolic competency. I propose several criteria (generalization, bidirectionality, use of algebraic rules and of logical operations) tested in successive experiments to clarify infant symbolic abilities during the first semester of life. WP5-6 are transversal to WP1-4: WP5 uses MRI to obtain accurate functional localization and maturational markers correlated with functional results. In WP6, we develop new tools to combine and analyse multimodal brain images. With this proposal, I hope to clarify the specificities of a neural functional architecture that are critical for human learning from the onset of cortical circuits.

Mots-clés : brain imaging,brain development,infants,learning,neural mechanisms
Projet ERC : EarlyLife (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
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 : 2450000.0 €

Infections of the gastrointestinal tract and their long-term consequences remain a major cause of childhood mortality and morbidity worldwide. In addition, early life is recognized as a critical and non-redundant time period to prime the mucosal tissue and establish the enteric microbiota that determine the risk to develop prevalent inflammatory, immune-mediated, and metabolic diseases. Three factors: the enteric microbiota, the mucosal immune system and the epithelial barrier cooperate to establish intestinal host-microbial homeostasis after birth. Maturation of the mucosal immune system and establishment of the enteric microbiota have been extensively studied. In contrast, postnatal evolvement of epithelial cell type heterogeneity and functional specialization and the influence of enteric infection on this process have not been explored. With EarlyLife, I propose to further advance innovative, multiscale technical approaches and analytical protocols in combination with novel in vivo models to generate the first comprehensive map of postnatal epithelial cell type and subtype differentiation and analyze the impact of early life infection by important human bacterial, viral and parasitic pathogens. Long-term inflammatory, immune-mediated and metabolic effects will be functionally studied using epigenetic profiling, microbiota-transfer experiments, stem cell organoid culture and co-culture, as well as genetic models. Identified mechanisms will be confirmed using single-cell analysis of human mucosal biopsies, human stem cell organoids and transcriptomic profiling of human fecal samples. As a result, I expect to identify mechanisms of enhanced infection susceptibility of the neonate, decipher the critical and non-redundant influence of the postnatal period for mucosal homeostasis and explain the role of early life imprinting for long-term immune-mediated, inflammatory and metabolic diseases.

Mots-clés : neonate,intestinal epithelium,gastrointestinal infection,enteropathogen,neonatal window of opportunity,microbiota,mucosal immunology,metabolism