ERC FUNDED PROJECTS

Today, our daily diet typically contains dozens of food additives (e.g. colours, emulsifiers, sweeteners: ~350 substances allowed on the EU market). Safety assessment is performed by health agencies to protect consumers against potential adverse effects of each additive, yet such an assessment is only based on current available evidence, i.e., for most additives, only in-vitro/in-vivo toxicological studies and exposure simulations. Meanwhile, the long-term health impact of additives intake and any potential ‘cocktail’ effects remain largely unknown and have become a source of serious concern. Growing evidence link the consumption of ultra-processed foods, containing numerous additives, to adverse health outcomes, in particular our recent results on cancer (Fiolet BMJ 2018). While most additives allowed in the EU are likely to be neutral for health and some may even be beneficial, recent animal and cell-based studies have suggested detrimental effects of several such compounds. In humans, data is lacking. No epidemiological study has ever assessed individual-level exposure to a wide range of food additives and its association with health, hampered by unsuited traditional dietary assessment tools facing the high additive content variability across commercial brands. Hence, a major breakthrough will come from the novel and unique tools I developed with my team, notably within the NutriNet-Santé cohort (n=164,000), collecting precise and repeated data on foods and beverages usually consumed, including names and brands of industrial products. With this unique resource, I propose a project at the forefront of international research to provide answers to a question of major importance for public health. Built as a combination of epidemiological studies and in-vitro/in-vivo experiments, this project will shed light on individual exposure to food additive 'cocktails' in relation to obesity, cancer, cardiovascular diseases and mortality, while depicting underlying mechanisms.

2 000 000 €

Start date: 2020-05-01, End date: 2025-04-30

Obesity and its metabolic co-morbidities have given rise to a rapidly expanding ‘metabolic syndrome’ pandemic affecting hundreds of millions of individuals worldwide. The integrative genetic and environmental causes of the obesity pandemic remain elusive. White adipose tissue (WAT)-resident immune cells have recently been highlighted as important factors contributing to metabolic complications. However, a comprehensive understanding of the regulatory circuits governing their function and the cell type-specific mechanisms by which they contribute to the development of metabolic syndrome is lacking. Likewise, the gut microbiome has been suggested as a critical regulator of obesity, but the bacterial species and metabolites that influence WAT inflammation are entirely unknown. We propose to use our recently developed high-throughput genomic and gnotobiotic tools, integrated with CRISPR-mediated interrogation of gene function, microbial culturomics, and in-vivo metabolic analysis in newly generated mouse models, in order to achieve a new level of molecular understanding of how WAT immune cells integrate environmental cues into their crosstalk with organismal metabolism, and to explore the microbial contributions to the molecular etiology of WAT inflammation in the pathogenesis of diet-induced obesity. Specifically, we aim to (a) decipher the global regulatory landscape and interaction networks of WAT hematopoietic cells at the single-cell level, (b) identify new mediators of WAT immune cell contributions to metabolic homeostasis, and (c) decode how host-microbiome communication shapes the development of WAT inflammation and obesity. Unraveling the principles of WAT immune cell regulation and their amenability to change by host-microbiota interactions may lead to a conceptual leap forward in our understanding of metabolic physiology and disease. Concomitantly, it may generate a platform for microbiome-based personalized therapy against obesity and its complications.

2 000 000 €

Start date: 2019-03-01, End date: 2024-02-29

Communication networks have become a critical infrastructure of our digital society. However, with the explosive growth of data-centric applications and the resulting increasing workloads headed for the world’s datacenter networks, today’s static and demand-oblivious network architectures are reaching their capacity limits. The AdjustNet project proposes a radically different perspective, envisioning demand-aware networks which can dynamically adapt their topology to the workload they currently serve. Such self-adjusting networks hence allow to exploit structure in the demand, and thereby reach higher levels of efficiency and performance. The vision of AdjustNet is timely and enabled by recent innovations in optical technologies which allow to flexibly reconfigure the physical network topology. The goal of AdjustNet is to lay the theoretical foundations for self-adjusting networks. We will identify metrics that serve as yardstick of what can and cannot be achieved in a self-adjusting network for a given demand, devise algorithms for online adaption, and validate our framework through case studies. Our novel methodology is motivated by an intriguing connection of self-adjusting networks to known datastructures and to information theory. AdjustNet comes with significant challenges since, similar to self-driving cars, self-adjusting networks require human network operators to give away control, and since more autonomous network operations may lead to instabilities. AdjustNet will overcome these risks and achieve its objectives by pursuing a rigorous approach, devising a theoretical well-founded framework for self-adjusting networks which come with provable guarantees and incorporate self–protection mechanisms. The PI is well-equipped for this project and recently obtained first promising results. As the community is currently re-architecting communication networks, there is a unique opportunity to bridge the gap between theory and practice, and have impact.

1 670 823 €

Start date: 2020-03-01, End date: 2025-02-28

"The proposed research program has three main objectives. The first and second objectives are to seek extreme precisions in optical atomic spectroscopy and optical clocks, and to use this quest as a mean of exploration in atomic physics. The third objective is to explore new possibilities that stem from extreme precision. These goals will be pursued via three complementary activities: #1: Search for extreme precisions with an Hg optical lattice clock. #2: Explore and exploit the rich Hg system, which is essentially unexplored in the cold and ultra-cold regime. #3: Identify new applications of clocks with extreme precision to Earth science. Clocks can measure directly the gravitational potential via Einstein’s gravitational redshift, leading to the idea of “clock-based geodesy”. The 2 first activities are experimental and build on an existing setup, where we demonstrated the feasibility of an Hg optical lattice clock. Hg is chosen for its potential to surpass competing systems. We will investigate the unexplored physics of the Hg clock. This includes interactions between Hg atoms, lattice-induced light shifts, and sensitivity to external fields which are specific to the atomic species. Beyond, we will explore the fundamental limits of the optical lattice scheme. We will exploit other remarkable features of Hg associated to the high atomic number and the diversity of stable isotopes. These features enable tests of fundamental physical laws, ultra-precise measurements of isotope shifts, measurement of collisional properties toward evaporative cooling and quantum gases of Hg, investigation of forbidden transitions promising for measuring the nuclear anapole moment of Hg. The third activity is theoretical and is aimed at initiating collaborations with experts in modelling Earth gravity. With this expertise, we will identify the most promising and realistic approaches for clocks and emerging remote comparison methods to contribute to geodesy, hydrology, oceanography, etc."

1 946 432 €

Start date: 2014-04-01, End date: 2019-03-31