ERC FUNDED PROJECTS

Cancer is the leading cause of death in the Western world and the second cause of death worldwide. Despite advances in medical research, 30% of cancer patients are prescribed a medication the tumor does not respond to, or, alternatively, drugs that induce adverse side effects patients' cannot tolerate. Nanotechnologies are becoming impactful therapeutic tools, granting tissue-targeting and cellular precision that cannot be attained using systems of larger scale. In this proposal, I plan to expand far beyond the state-of-the-art and develop a conceptually new approach in which diagnostic nanoparticles are designed to retrieve drug-sensitivity information from malignant tissue inside the body. The ultimate goal of this program is to be able to predict, ahead of time, which treatment will be best for each cancer patient – an emerging field called personalized medicine. This interdisciplinary research program will expand our understandings and capabilities in nanotechnology, cancer biology and medicine. To achieve this goal, I will engineer novel nanotechnologies that autonomously maneuver, target and diagnose the various cells that compose the tumor microenvironment and its disseminated metastasis. Each nanometric system will contain a miniscule amount of a biologically-active agent, and will serve as a nano lab for testing the activity of the agents inside the tumor cells. To distinguish between system to system, and to grant single-cell sensitivity in vivo, nanoparticles will be barcoded with unique DNA fragments. We will enable nanoparticle' deep tissue penetration into primary tumors and metastatic microenvironments using enzyme-loaded particles, and study how different agents, including small-molecule drugs, proteins and RNA, interact with the malignant and stromal cells that compose the cancerous microenvironments. Finally, we will demonstrate the ability of barcoded nanoparticles to predict adverse, life-threatening, side effects, in a personalized manner.

1 499 250 €

Start date: 2016-04-01, End date: 2021-03-31

Clinical experience with globally-acting cannabinoid-1 receptor (CB1R) antagonists revealed the benefits of blocking CB1Rs for the treatment of obesity and diabetes. However, their use is hampered by increased CNS-mediated side effects. Recently, I have demonstrated that peripherally-restricted CB1R antagonists have the potential to treat the metabolic syndrome without eliciting these adverse effects. While these results are promising and are currently being developed into the clinic, our ability to rationally design CB1R blockers that would target a diseased organ is limited. The current proposal aims to develop and test cell- and organelle-specific CB1R antagonists. To establish this paradigm, I will focus our interest on the kidney, since chronic kidney disease (CKD) is the leading cause of increased morbidity and mortality of patients with diabetes. Our first goal will be to characterize the obligatory role of the renal proximal tubular CB1R in the pathogenesis of diabetic renal complications. Next, we will attempt to link renal proximal CB1R with diabetic mitochondrial dysfunction. Finally, we will develop proximal tubular (cell-specific) and mitochondrial (organelle-specific) CB1R blockers and test their effectiveness in treating CKD. To that end, we will encapsulate CB1R blockers into biocompatible polymeric nanoparticles that will serve as targeted drug delivery systems, via their conjugation to targeting ligands. The implications of this work are far reaching as they will (i) point to renal proximal tubule CB1R as a novel target for CKD; (ii) identify mitochondrial CB1R as a new player in the regulation of proximal tubular cell function, and (iii) eventually become the drug-of-choice in treating diabetic CKD and its comorbidities. Moreover, this work will lead to the development of a novel organ-specific drug delivery system for CB1R blockers, which could be then exploited in other tissues affected by obesity, diabetes and the metabolic syndrome.

1 500 000 €

Start date: 2016-04-01, End date: 2021-03-31

This proposal aims to investigate the role of cytosolic DNA sensing pathways in CD4 T cell differentiation. Cellular host defense to pathogens relies on the detection of pathogen-associated molecular patterns including deoxyribonucleic acid (DNA), which can be recognized by host myeloid cells through Toll-like receptor (TLR) 9 binding. Recent evidence however suggests that innate immune cells can also perceive cytoplasmic DNA from infectious or autologous origin through cytosolic DNA sensors triggering TLR9-independent signaling. Activation of cytosolic DNA sensor-dependent signaling pathways has been clearly shown to trigger innate immune responses to microbial and host DNA, but the contribution of cytosolic DNA sensors to the differentiation of CD4 T cells, an essential event for shaping adaptive immune responses, has not been documented. This proposal aims to fill this current knowledge gap. We aim to decipher the molecular series of transcriptional events triggered by DNA in CD4 T cells that ultimately result in altered T cell differentiation. This aim will be addressed by combining in vitro and in vivo approaches such as advanced gene expression analysis of CD4 T cells and use of transgenic and gene-deficient mice. Structure activity relationship and biophysical studies will also be performed to unravel novel immunomodulators able to affect CD4 T cell differentiation.

1 500 000 €

Start date: 2016-08-01, End date: 2021-07-31

The immune repertoire of healthy individuals contains a fraction of antibodies (Abs) that are able to bind with high affinity various endogenous or exogenous low molecular weight compounds, including cofactors essential for the aerobic life, such as riboflavin, heme and ATP. Despite identification of cofactor-binding Abs as a constituent of normal immune repertoires, their fundamental characteristics and have not been systematically investigated. Thus, we do not know the origin, prevalence and physiopathological significance of cofactor-binding Abs. Moreover, the molecular mechanisms of interaction of cofactors with Abs are ill defined. Different proteins use cofactors to extend the chemistry intrinsic to the amino acid sequence of their polypeptide chain(s). Thus, one can speculate that the alliance of Abs with low molecular weight compounds results in the emergence of untypical properties of Abs and offers a strategy for designing a new generation of therapeutic Abs. Moreover, cofactor-binding Abs may be used for delivery of cytotoxic compounds to particular sites in the body, or for scavenging pro-inflammatory compounds. The principal goal of the present proposal is to gain a basic understanding on the fraction of cofactor-binding Abs in immune repertoires and to use this knowledge for the rational design of novel classes of therapeutic Abs. In this project, we will address the following questions: 1) understand the origin and prevalence of cofactor-binding Abs in immune repertoires; 2) characterize the molecular mechanisms of interaction of cofactors with Abs; 3) Understand the physiopathological roles of cofactor-binding Abs, and 4) use cofactor binding for the development of novel types of therapeutic Abs. A comprehensive understanding of various aspects of cofactor-binding Abs should lead to advances in fundamental understanding and in the development of innovative therapeutic and diagnostic tools.

1 255 000 €

Start date: 2016-09-01, End date: 2021-08-31

The project focuses on the interface between computational and combinatorial geometry. Geometric problems emerge in a variety of computational fields that interact with the physical world. The performance of geometric algorithms is determined by the description complexity of their underlying combinatorial structures. Hence, most theoretical challenges faced by computational geometry are of a distinctly combinatorial nature. In the past two decades, computational geometry has been revolutionized by the powerful combination of random sampling techniques with the abstract machinery of geometric arrangements. These insights were used, in turn, to establish state-of-the-art results in combinatorial geometry. Nevertheless, a number of fundamental problems remained open and resisted numerous attempts to solve them. Motivated by the recent breakthrough results, in which the PI played a central role, we propose two exciting lines of study with the potential to change the landscape of this field. The first research direction concerns the complexity of Voronoi diagrams -- arguably the most common structures in computational geometry. The second direction concerns combinatorial and algorithmic aspects of geometric intersection structures, including some fundamental open problems in geometric transversal theory. Many of these questions are motivated by geometric variants of general covering and packing problems, and all efficient approximation schemes for them must rely on the intrinsic properties of geometric graphs and hypergraphs. Any progress in responding to these challenges will constitute a major breakthrough in both computational and combinatorial geometry.

1 303 750 €

Start date: 2016-09-01, End date: 2021-08-31

Software and hardware bugs cost hundreds of millions of euros every year to companies and administrations. Formal methods like verification provide automatic means of finding some of these bugs. Certification, using proof assistants like Coq or Isabelle/HOL, make it possible to guarantee the absence of bugs (up to a certain point). These two kinds of tools are crucial in order to design safer programs and machines. Unfortunately, state-of-the art tools are not yet satisfactory. Verification tools often face state-explosion problems and require more efficient algorithms; certification tools need more automation: they currently require too much time and expertise, even for basic tasks that could be handled easily through verification. In recent work with Bonchi, we have shown that an extremely simple idea from concurrency theory could give rise to algorithms that are often exponentially faster than the algorithms currently used in verification tools. My claim is that this idea could scale to richer models, revolutionising existing verification tools and providing algorithms for problems whose decidability is still open. Moreover, the expected simplicity of those algorithms will make it possible to implement them inside certification tools such as Coq, to provide powerful automation techniques based on verification techniques. In the end, we will thus provide efficient and certified verification tools going beyond the state-of-the-art, but also the ability to use such tools inside the Coq proof assistant, to alleviate the cost of certification tasks.

1 407 413 €

Start date: 2016-04-01, End date: 2021-03-31

Optical microscopy, perhaps the most important tool in biomedical investigation and clinical diagnostics, is currently held back by the assumption that it is not possible to noninvasively image microscopic structures more than a fraction of a millimeter deep inside tissue. The governing paradigm is that high-resolution information carried by light is lost due to random scattering in complex samples such as tissue. While non-optical imaging techniques, employing non-ionizing radiation such as ultrasound, allow deeper investigations, they possess drastically inferior resolution and do not permit microscopic studies of cellular structures, crucial for accurate diagnosis of cancer and other diseases. I propose a new kind of microscope, one that can peer deep inside visually opaque samples, combining the sub-micron resolution of light with the penetration depth of ultrasound. My novel approach is based on our discovery that information on microscopic structures is contained in random scattered-light patterns. It breaks current limits by exploiting the randomness of scattered light rather than struggling to fight it. We will transform this concept into a breakthrough imaging platform by combining ultrasonic probing and modulation of light with advanced digital signal processing algorithms, extracting the hidden microscopic structure by two complementary approaches: 1) By exploiting the stochastic dynamics of scattered light using methods developed to surpass the diffraction limit in optical nanoscopy and for compressive sampling, harnessing nonlinear effects. 2) Through the analysis of intrinsic correlations in scattered light that persist deep inside scattering tissue. This proposal is formed by bringing together novel insights on the physics of light in complex media, advanced microscopy techniques, and ultrasound-mediated imaging. It is made possible by the new ability to digitally process vast amounts of scattering data, and has the potential to impact many fields.

1 500 000 €

Start date: 2016-04-01, End date: 2021-03-31

One of the greatest challenges of theoretical physics is to understand the fundamental nature of gravity and how it is reconciled with quantum mechanics. Black holes indicate that gravity is holographic, i.e. it is emergent, together with some of the spacetime dimensions, from a lower-dimensional field theory. The emergence mechanism has just started to be understood in certain special contexts, such as AdS/CFT. However, very little is known about it for the spacetime backgrounds relevant to the real world, due mainly to our lack of knowledge of the underlying field theories. My goal is to uncover the fundamental nature of spacetime and gravity in our universe by: i) formulating and working out the properties of the relevant lower-dimensional field theories and ii) studying the mechanism by which spacetime and gravity emerge from them. I will adress the first problem by concentrating on the near-horizon regions of maximally spinning black holes, for which the dual field theories greatly simplify and can be studied using a combination of conformal field theory and string theory methods. To study the emergence mechanism, I plan to adapt the tools that were succesfully used to understand emergent gravity in anti de-Sitter (AdS) spacetimes - such as holographic quantum entanglement and conformal bootstrap - to non-AdS, more realistic spacetimes.

1 495 476 €

Start date: 2016-09-01, End date: 2021-08-31

Temporal environmental variation in natural systems includes a large component of random fluctuations, the magnitude and predictability of which is modified under current climate change. The need for predicting eco-evolutionary impacts of plastic and evolutionary responses to changing environments is still hampered by lack of strong experimental evidence. FluctEvol aims at shedding a new light on population responses to stochastic environments, and facilitating their prediction, using a unique combination of approaches. First, theoretical models of evolution and demography under a randomly changing optimum phenotype will be designed and analysed, producing new quantitative predictions. Second, statistical methodologies will be developed, and employed in meta-analyses of long-term datasets from natural populations. And third, large-scale and automated experimental evolution in stochastic environments will be carried out with the micro-alga Dunaliella salina, an extremophile that thrives at high and variable salinities. We will manipulate the magnitude and predictability of fluctuations in salinity, and use high-throughput phenotyping and candidate-gene sequencing to analyse the evolution of plasticity for traits involved in salinity adaptation in this species: glycerol and carotene content. We will thus combine the benefits of experimental evolution in microbes (short generations, ample replication) with a priori knowledge of ecologically relevant adaptive traits, allowing for hypothesis-driven experiments. The success of this project in increasing our predictive power about eco-evolutionary dynamics is warranted by the experience of the PI, at the interface between theoretical and empirical approaches. Our experiments will have relevance beyond academia, as we will modify through evolution the plasticity of traits (accumulation of energetic cell metabolites) that are direct targets for bioindustry, thus potentially overcoming current limitations in productivity.

1 499 665 €

Start date: 2016-03-01, End date: 2021-02-28