ERC FUNDED PROJECTS

Parkinson’s disease (PD) is the second most common neurodegenerative disorder primarily caused by the progressive loss of dopaminergic (DA) neurons in the substantia nigra (SN). Despite the advances in gene discovery associated with PD, the knowledge of the PD pathogenesis is largely limited to the involvement of these genes in the generic cell death pathways, and why degeneration is specific to DA neurons and why the degeneration is progressive remain enigmatic. Broad goal of our work is therefore to elucidate the mechanisms underlying specific and progressive DA neuron degeneration in PD. Our new Drosophila model of PD ⎯Fer2 gene loss-of-function mutation⎯ is unusually well suited to address these questions. Fer2 mutants exhibit specific and progressive death of brain DA neurons as well as severe locomotor defects and short life span. Strikingly, the death of DA neuron is initiated in a small cluster of Fer2-expressing DA neurons and subsequently propagates to Fer2-negative DA neurons. We therefore propose a novel two-step model of the neurodegeneration in PD: primary cell death occurs in a specific subset of dopamindegic neurons that are genetically defined, and subsequently the failure of the neuronal connectivity triggers and propagates secondary cell death to remaining DA neurons. In this research, we will test this hypothesis and investigate the underlying molecular mechanisms. This will be the first study to examine circuit-dependency in DA neuron degeneration. Our approach will use a combination of non-biased genomic techniques and candidate-based screening, in addition to the powerful Drosophila genetic toolbox. Furthermore, to test this hypothesis beyond the Drosophila model, we will establish new mouse models of PD that exhibit progressive DA neuron degeneration. Outcome of this research will likely revolutionize the understanding of PD pathogenesis and open an avenue toward the discovery of effective therapy strategies against PD.

1 518 960 €

Start date: 2013-06-01, End date: 2018-05-31

The focus of this project is the development of algorithms that allow one to capture and analyse dynamic events taking place in the real world. For this, we intend to develop smart camera networks that can perform a multitude of observation tasks, ranging from surveillance and tracking to high-fidelity, immersive reconstructions of important dynamic events (i.e. 4D videos). There are many fundamental questions in computer vision associated with these problems. Can the geometric, topologic and photometric properties of the camera network be obtained from live images? What is changing about the environment in which the network is embedded? How much information can be obtained from dynamic events that are observed by the network? What if the camera network consists of a random collection of sensors that happened to observe a particular event (think hand-held cell phone cameras)? Do we need synchronization? Those questions become even more challenging if one considers active camera networks that can adapt to the vision task at hand. How should resources be prioritized for different tasks? Can we derive optimal strategies to control camera parameters such as pan, tilt and zoom, trade-off resolution, frame-rate and bandwidth? More fundamentally, seeing cameras as points that sample incoming light rays and camera networks as a distributed sensor, how does one decide which rays should be sampled? Many of those issues are particularly interesting when we consider time-varying events. Both spatial and temporal resolution are important and heterogeneous frame-rates and resolution can offer advantages. Prior knowledge or information obtained from earlier samples can be used to restrict the possible range of solutions (e.g. smoothness assumption and motion prediction). My goal is to obtain fundamental answers to many of those question based on thorough theoretical analysis combined with practical algorithms that are proven on real applications.

1 757 422 €

Start date: 2008-08-01, End date: 2013-11-30

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

Despite considerable advances in drug discovery, resistance to anticancer chemotherapy confounds the effective treatment of patients. Cancer cells can acquire broad cross-resistance to mechanistically and structurally unrelated drugs. P-glycoprotein (Pgp) actively extrudes many types of drugs from cancer cells, thereby conferring resistance to those agents. The central tenet of my work is that Pgp, a universally accepted biomarker of drug resistance, should in addition be considered as a molecular target of multidrug-resistant (MDR) cancer cells. Successful targeting of MDR cells would reduce the tumor burden and would also enable the elimination of ABC transporter-overexpressing cancer stem cells that are responsible for the replenishment of tumors. The proposed project is based on the following observations: - First, by using a pharmacogenomic approach, I have revealed the hidden vulnerability of MDRcells (Szakács et al. 2004, Cancer Cell 6, 129-37); - Second, I have identified a series of MDR-selective compounds with increased toxicity toPgp-expressing cells (Turk et al.,Cancer Res, 2009. 69(21)); - Third, I have shown that MDR-selective compounds can be used to prevent theemergence of MDR (Ludwig, Szakács et al. 2006, Cancer Res 66, 4808-15); - Fourth, we have generated initial pharmacophore models for cytotoxicity and MDR-selectivity (Hall et al. 2009, J Med Chem 52, 3191-3204). I propose a comprehensive series of studies that will address thefollowing critical questions: - First, what is the scope of MDR-selective compounds? - Second, what is their mechanism of action? - Third, what is the optimal therapeutic modality? Extensive biological, pharmacological and bioinformatic analyses will be utilized to address four major specific aims. These aims address basic questions concerning the physiology of MDR ABC transporters in determining the mechanism of action of MDR-selective compounds, setting the stage for a fresh therapeutic approach that may eventually translate into improved patient care.

1 499 640 €

Start date: 2012-01-01, End date: 2016-12-31