ERC FUNDED PROJECTS

For many years, the ubiquitin-26S proteasome degradation pathway was considered the primary route for proteasomal degradation. However, it is now becoming clear that proteins can also be targeted for degradation by a ubiquitin-independent mechanism mediated by the core 20S proteasome itself. Although initially believed to be limited to rare exceptions, degradation by the 20S proteasome is now understood to have a wide range of substrates, many of which are key regulatory proteins. Despite its importance, little is known about the mechanisms that control 20S proteasomal degradation, unlike the extensive knowledge acquired over the years concerning degradation by the 26S proteasome. Our overall aim is to reveal the multiple regulatory levels that coordinate the 20S proteasome degradation route. To achieve this goal we will carry out a comprehensive research program characterizing three distinct levels of 20S proteasome regulation: Intra-molecular regulation- Revealing the intrinsic molecular switch that activates the latent 20S proteasome. Inter-molecular regulation- Identifying novel proteins that bind the 20S proteasome to regulate its activity and characterizing their mechanism of function. Cellular regulatory networks- Unraveling the cellular cues and multiple pathways that influence 20S proteasome activity using a novel systematic and unbiased screening approach. Our experimental strategy involves the combination of biochemical approaches with native mass spectrometry, cross-linking and fluorescence measurements, complemented by cell biology analyses and high-throughput screening. Such a multidisciplinary approach, integrating in vitro and in vivo findings, will likely provide the much needed knowledge on the 20S proteasome degradation route. When completed, we anticipate that this work will be part of a new paradigm – no longer perceiving the 20S proteasome mediated degradation as a simple and passive event but rather a tightly regulated and coordinated process.

1 500 000 €

Start date: 2015-04-01, End date: 2020-03-31

Despite their amazing success, we believe that computer vision algorithms have only scratched the surface of what can be done in terms of modeling and understanding our world from images. We believe that novel image analysis techniques will be a major enabler and driving force behind next-generation technologies, enhancing everyday life and opening up radically new possibilities. And we believe that the key to achieving this is to develop algorithms for reconstructing and analyzing the 3D structure of our world. In this project, we will focus on three lines of research: A) We will develop algorithms for 3D reconstruction from standard color cameras and from RGB-D cameras. In particular, we will promote real-time-capable direct and dense methods. In contrast to the classical two-stage approach of sparse feature-point based motion estimation and subsequent dense reconstruction, these methods optimally exploit all color information to jointly estimate dense geometry and camera motion. B) We will develop algorithms for 3D shape analysis, including rigid and non-rigid matching, decomposition and interpretation of 3D shapes. We will focus on algorithms which are optimal or near-optimal. One of the major computational challenges lies in generalizing existing 2D shape analysis techniques to shapes in 3D and 4D (temporal evolutions of 3D shape). C) We will develop shape priors for 3D reconstruction. These can be learned from sample shapes or acquired during the reconstruction process. For example, when reconstructing a larger office algorithms may exploit the geometric self-similarity of the scene, storing a model of a chair and its multiple instances only once rather than multiple times. Advancing the state of the art in geometric reconstruction and geometric analysis will have a profound impact well beyond computer vision. We strongly believe that we have the necessary competence to pursue this project. Preliminary results have been well received by the community.

2 000 000 €

Start date: 2015-09-01, End date: 2020-08-31

How does the inside of the proton look like? What generates its spin?
3DSPIN will deliver essential information to answer these questions at the frontier of subnuclear physics. At present, we have detailed maps of the distribution of quarks and gluons in the nucleon in 1D (as a function of their momentum in a single direction). We also know that quark spins account for only about 1/3 of the spin of the nucleon. 3DSPIN will lead the way into a new stage of nucleon mapping, explore the distribution of quarks in full 3D momentum space and obtain unprecedented information on orbital angular momentum. Goals 1. extract from experimental data the 3D distribution of quarks (in momentum space), as described by Transverse-Momentum Distributions (TMDs); 2. obtain from TMDs information on quark Orbital Angular Momentum (OAM). Methodology 3DSPIN will implement state-of-the-art fitting procedures to analyze relevant experimental data and extract quark TMDs, similarly to global fits of standard parton distribution functions. Information about quark angular momentum will be obtained through assumptions based on theoretical considerations. The next five years represent an ideal time window to accomplish our goals, thanks to the wealth of expected data from deep-inelastic scattering experiments (COMPASS, Jefferson Lab), hadronic colliders (Fermilab, BNL, LHC), and electron-positron colliders (BELLE, BABAR). The PI has a strong reputation in this field. The group will operate in partnership with the Italian National Institute of Nuclear Physics and in close interaction with leading experts and experimental collaborations worldwide. Impact Mapping the 3D structure of chemical compounds has revolutionized chemistry. Similarly, mapping the 3D structure of the nucleon will have a deep impact on our understanding of the fundamental constituents of matter. We will open new perspectives on the dynamics of quarks and gluons and sharpen our view of high-energy processes involving nucleons.

1 509 000 €

Start date: 2015-07-01, End date: 2020-06-30

Serotonin (5-HT) is a central neuromodulator and a major target of therapeutic psychoactive drugs, but relatively little is known about how it modulates information processing in neural circuits. The theory of predictive coding postulates that the brain combines raw bottom-up sensory information with top-down information from internal models to make perceptual inferences about the world. We hypothesize, based on preliminary data and prior literature, that a role of 5-HT in this process is to report prediction errors and promote the suppression and weakening of erroneous internal models. We propose that it does this by inhibiting top-down relative to bottom-up cortical information flow. To test this hypothesis, we propose a set of experiments in mice performing olfactory perceptual tasks. Our specific aims are: (1) We will test whether 5-HT neurons encode sensory prediction errors. (2) We will test their causal role in using predictive cues to guide perceptual decisions. (3) We will characterize how 5-HT influences the encoding of sensory information by neuronal populations in the olfactory cortex and identify the underlying circuitry. (4) Finally, we will map the effects of 5-HT across the whole brain and use this information to target further causal manipulations to specific 5-HT projections. We accomplish these aims using state-of-the-art optogenetic, electrophysiological and imaging techniques (including 9.4T small-animal functional magnetic resonance imaging) as well as psychophysical tasks amenable to quantitative analysis and computational theory. Together, these experiments will tackle multiple facets of an important general computational question, bringing to bear an array of cutting-edge technologies to address with unprecedented mechanistic detail how 5-HT impacts neural coding and perceptual decision-making.

2 486 074 €

Start date: 2016-01-01, End date: 2020-12-31