ERC FUNDED PROJECTS

This project aims at delineating the regulatory signaling processes that enable cells to overcome DNA damage during DNA replication, a major challenge to the integrity of the genome as the normal replication machinery is unable to replicate past DNA lesions. This may result in collapse of the replication fork, potentially giving rise to gross genomic alterations. To mitigate this threat, all cells have evolved DNA damage bypass strategies such as translesion DNA synthesis (TLS), involving low fidelity DNA polymerases that can replicate damaged DNA, albeit in an error-prone manner, offering a trade-off between limited mutagenesis and gross chromosomal instability. How DNA damage bypass pathways are regulated and integrated with DNA replication and repair remain poorly resolved questions fundamental to understanding genome stability maintenance and disease onset. Regulatory signaling mediated by the small modifier protein ubiquitin has a prominent role in orchestrating the reorganization of the replication fork necessary for overcoming DNA lesions, but this involvement has not been systematically explored. To remedy these gaps in our knowledge, I propose to implement a series of innovative complementary strategies to isolate and identify the regulatory factors and ubiquitin-dependent processes that promote DNA damage responses at the replication fork, allowing for subsequent in-depth characterization of their roles in protecting genome integrity by targeted functional studies. This project will enable an advanced level of mechanistic insight into key regulatory processes underlying replication-associated DNA damage responses that has not been feasible to achieve with exisiting methodologies, providing a realistic outlook for groundbreaking discoveries that will open up many new avenues for further research into mechanisms and biological functions of regulatory signaling processes in the DNA damage response and beyond.

1 996 356 €

Start date: 2014-07-01, End date: 2019-06-30

"Knowing that the lysosomes contain a powerful cocktail of hydrolases capable of digesting cells and entire tissues, it is obvious that the maintenance of lysosomal membrane integrity is of utmost importance for all cells, and especially for cancer cells with dramatically increased lysosomal activity. Yet, the mechanisms that regulate lysosomal membrane stability have remained obscure, largely due to the lack of methods sensitive enough to detect partial lysosomal leakage and suitable for screening purposes. We have finally succeeded in developing such a method, which allows me to propose here a project whose major aim is to reveal how cells maintain the integrity of lysosomal membranes. Based on our emerging data that firmly connect heat shock protein 70 and sphingolipid metabolism to lysosomal membrane stability, we will devote a large part of the project to the molecular details of these connections and to the characterization of the effects of new and already approved (cationic amphiphilic drugs) sphingolipid-regulating drugs on lysosomal membrane stability and cell survival. Additionally, we will screen selected siRNA libraries to identify signaling networks and lysosome-associated proteins essential for lysosomal membrane integrity, and small molecule libraries to identify compounds that induce lysosomal cell death. The next step is to identify lysosome-stabilizing mechanisms that are especially important for cancer cell survival. And the ultimate goal is to validate corresponding drug targets and drugs (old and new) for the induction of lysosomal cell death in therapy resistant cancers. As a “by-product” we expect to identify putative drug targets for the treatment of degenerative diseases and lipid storage disorders, where the stabilization of the lysosomal membranes promotes cell survival."

2 499 960 €

Start date: 2014-02-01, End date: 2019-01-31

More than 100 million people living on the floodplains of the Ganges-Brahmaputra-Meghna, Mekong and Red River, all draining the Himalayas, are consuming arsenic contaminated water. Providing safe drinking water for these people requires a quantitative understanding of the processes regulating the groundwater arsenic content and this knowledge is presently not available. In PREDIAS we propose a revolutionary new approach to study these arsenic contaminated aquifers where sediments and groundwaters are considered as one reacting unit that is changing over time. The key hypothesis is that it is the aquifer sediment burial age that is the overall controlling parameter for the arsenic content. This new approach is explored by studying the groundwater chemistry as a function of sediment burial age, which is equivalent to the geological evolution over time, in part of the Red River floodplain in Vietnam. The investigations comprise delineating the sedimentological development over the last 9000 yrs as well as reconstructing hydrogeological conditions over that period. Process studies will reveal the effect of burial age on the chemical properties of the sediments and the arsenic release mechanisms. They comprise the binding and release mechanisms of arsenic to the aquifer sediment, and the reactivity of sedimentary organic carbon and iron oxides which drive the redox reactions controlling the water chemistry and arsenic mobilization. Information on the sedimentological and hydrogeological development over time as well as a quantification of the geochemical processes will be incorporated in a 3-D reactive transport model which over the last 9000 years, in steps of about 1000 years, can predict the evolution of the arsenic content over space and time in the groundwater of the studied area. The model can be extended in a more conceptual form to larger parts of the Red River delta and Bangladesh using satellite imaging to reveal the geological development in those areas.

1 619 932 €

Start date: 2014-01-01, End date: 2018-03-31

Similarity search is the task of identifying, in a collection of items, the ones that are “similar” to a given query item. This task has a range of important applications (e.g. in information retrieval, pattern recognition, statistics, and machine learning) where data sets are often big, high dimensional, and possibly noisy. State-of-the-art methods for similarity search offer only weak guarantees when faced with big data. Either the space overhead is excessive (1000s of times larger than the space for the data itself), or the work needed to report the similar items may be comparable to the work needed to go through all items (even if just a tiny fraction of the items are similar). As a result, many applications have to resort to the use of ad-hoc solutions with only weak theoretical guarantees. This proposal aims at strengthening the theoretical foundation of scalable similarity search, and developing novel practical similarity search methods backed by theory. In particular we will: - Leverage new types of embeddings that are kernelized, asymmetric, and complex-valued. - Consider statistical models of noise in data, and design similarity search data structures whose performance guarantees are phrased in statistical terms. - Build a new theory of the communication complexity of distributed, dynamic similarity search, emphasizing the communication bottleneck present in modern computing infrastructures. The objective is to produce new methods for similarity search that are: 1) Provably robust, 2) scalable to large and high-dimensional data sets, 3) substantially more resource efficient than current state-ofthe- art solutions, and 4) able to provide statistical guarantees on query answers. The study of similarity search has been an incubator for techniques (e.g. locality-sensitive hashing and random projections) that have wide-ranging applications. The new techniques developed in this project are likely to have significant impacts beyond similarity search.

1 889 712 €

Start date: 2014-05-01, End date: 2019-04-30