ERC FUNDED PROJECTS

The enormous quantities of frozen carbon in the Arctic, held in shallow soils and sediments, act as “capacitors” of the global carbon system. Thawing permafrost (PF) and collapsing methane hydrates are top candidates to cause a net transfer of carbon from land/ocean to the atmosphere this century, yet uncertainties abound. Our program targets the East Siberian Arctic Ocean (ESAO), the World’s largest shelf sea, as it holds 80% of coastal PF, 80% of subsea PF and 75% of shallow hydrates. Our initial findings (e.g., Science, 2010; Nature, 2012; PNAS; 2013; Nature Geoscience, 2013, 2014) are challenging earlier notions by showing complexities in terrestrial PF-Carbon remobilization and extensive venting of methane from subsea PF/hydrates. The objective of the CC-Top Program is to transform descriptive and data-lean pictures into quantitative understanding of the CC system, to pin down the present and predict future releases from these “Sleeping Giants” of the global carbon system. The CC-Top program combines unique Arctic field capacities with powerful molecular-isotopic characterization of PF-carbon/methane to break through on: The “awakening” of terrestrial PF-C pools: CC-Top will employ great pan-arctic rivers as natural integrators and by probing the δ13C/Δ14C and molecular fingerprints, apportion release fluxes of different PF-C pools. The ESAO subsea cryosphere/methane: CC-Top will use recent spatially-extensive observations, deep sediment cores and gap-filling expeditions to (i) estimate distribution of subsea PF and hydrates; (ii) establish thermal state (thawing rate) of subsea PF-C; (iii) apportion sources of releasing methane btw subsea-PF, shallow hydrates vs seepage from the deep petroleum megapool using source-diagnostic triple-isotope fingerprinting. Arctic Ocean slope hydrates: CC-Top will investigate sites (discovered by us 2008-2014) of collapsed hydrates venting methane, to characterize geospatial distribution and causes of destabilization.

2 499 756 €

Start date: 2016-11-01, End date: 2021-10-31

Inherited retinal degenerations (IRDs) are a major cause of blindness worldwide. IRD patients witness inexorable progressive vision loss as no therapy is currently available. In the last decade my group has significantly contributed to a change of this scenario by developing efficient adeno-associated viral (AAV) vectors for retinal gene therapy that are safe and effective in humans. The objective of EYEGET (EYE GEne Therapy) is to overcome some of the current major limitations in the field of retinal gene therapy to expand initial therapeutic successes to a larger number of IRDs. To achieve this, we propose to use four parallel, highly innovative and complementary approaches: i. expansion of the limited AAV cargo capacity by a novel methodology based on co-administration of multiple AAVs that reassemble in target retinal cells and reconstitute large genes; ii. targeting of frequent dominant gain-of-function mutations that cause RP using state-of-the-art AAV-mediated genome editing technologies; iii. induction of retinal cells clearance of toxic IRD products by AAV-mediated activation of autophagy and lysosomal function; iv. development of methodologies to directly convert fibroblasts to photoreceptors that can be transplanted in retinas from IRD patients with advanced PR loss and for whom in vivo gene therapy is no longer an option. We will use a combination of in vitro and in vivo state-of-the-art technologies including novel AAV vector design, high content screening of drugs that enhance AAV transduction, genome editing, and advanced in vivo retinal phenotyping to obtain proof-of-concept for each of these therapeutic strategies. The results from this study may impact the quality of life of millions of people worldwide by providing a cure based on gene and/or cell therapy for a large group of IRDs.

2 499 564 €

Start date: 2017-01-01, End date: 2022-12-31

Next-generation sequencing technology has dramatically reduced the cost and time of reading the DNA. Huge investments are targeted to sequencing the DNA of large populations, and repositories of well-curated sequence data are being collected. Answers to fundamental biomedical problems are hidden in these data, e.g. how cancer arises, how driving mutations occur, how much cancer is dependent on environment. But genomic computing has not comparatively evolved. Bioinformatics has been driven by specific needs and distracted from a foundational approach; hundreds of methods solve individual problems, but miss the broad perspective. The objective of GeCo is to rethink genomic computing through the lens of basic data management. We will first design the data model, using few general abstractions that guarantee interoperability between existing data formats. Next, we will design a new-generation query language inspired by classic relational algebra and extended with orthogonal, domain-specific abstractions for genomics. Query processing will trace metadata and computation steps, opening doors to the seamless integration of descriptive statistics and high-level data analysis (e.g., DNA region clustering and extraction of regulatory networks). Genomic computing is a “big data” problem, therefore we will also achieve computational efficiency by using parallel computing on both clusters and public clouds; the choice of a suitable data model and of computational abstractions will boost performance in a principled way. The resulting technology will be applicable to individual and federated repositories, and will be exploited for providing integrated access to curated data, made available by large consortia, through user-friendly search services. Our most far-fetching vision is to move towards an Internet of Genomes exploiting data indexing and crawling. The PI’s background in distributed data, data modelling, query processing and search will drive a radical paradigm shift.

2 500 000 €

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

Critically important heterogeneous catalytic reactions for energy conversion and chemicals production have been run for several decades in fixed bed reactors randomly packed with catalyst pellets, whose operation is intrinsically limited by slow heat removal/supply. There is urgent need for a new generation of process equipment and chemical reactors to address the current quest for process intensification. I propose that a game-changing alternative is provided by structured reactors wherein the catalyst is washcoated onto or packed into structured substrates, like honeycomb monoliths, open-cell foams or other cellular materials, fabricated with highly conductive metallic (Al, Cu) materials. The goal of this project is to fully elucidate fundamental and engineering properties of such novel conductive structured catalysts, investigate new concepts for their design, manufacturing, catalytic activation and operation (e.g. 3D printing, packed foams, energy supply by solar irradiation), and demonstrate their potential for a quantum leap in the intensification of three crucial catalytic processes for the production of energy vectors: i) distributed H2 generation via efficient small-size reformers; ii) conversion of syngas to clean synthetic fuels in compact (e.g. skid-mounted) reactors; iii) production of solar H2. To this purpose I will combine advanced CFD modelling with lab-scale experimentation in order to identify the optimal structure-performance relation of existing and novel substrates, use such new knowledge to design optimized prototypes, apply unconventional additive manufacturing technologies for their production, and construct a semi-industrial tubular pilot reactor to test them at a representative scale. The project results will enable novel reactor designs based on tuning geometry, materials and configurations of the conductive internals to match the activity - selectivity demands of specific process applications, while impacting also other research areas.

2 484 648 €

Start date: 2016-11-01, End date: 2022-04-30