ERC FUNDED PROJECTS

To satisfy our growing energy demand while reducing reliance on fossil fuels, a switch to renewable energy sources is vital. The intermittent nature of the latter means innovations in energy storage technology is a key grand challenge. Cost and sustainability issues currently limit the widespread use of electrochemical energy storage technologies, such as lithium ion and redox flow batteries. As the scale for energy storage is simply enormous, the only option is to look for abundant materials. However, compounds that fulfil the extensive requirements entailed at low cost has yet to be reported. While it is possible that the holy grail of energy storage will be found, for example by advanced computational tools and machine learning to design “perfect” abundant molecules, a more flexible, innovative solution to sustainable and cost-effective large-scale energy storage is required. Bi3BoostFlowBat will develop game changing strategies to widen the choice of compounds utilizable for batteries to simultaneously satisfy the requirements for low cost, optimal redox potentials, high solubility and stability in all conditions. The aim of this project is to develop cost-efficient batteries by using solid boosters and by eliminating cross over. Two approaches will be pursued for cross-over elimination 1) bio-inspired polymer batteries, where cross-over of solubilized polymers is prevented by size-exclusion membranes and 2) biphasic emulsion flow batteries, where redox species are transferred to oil phase droplets upon charge. Third research direction focuses on systems to maintain a pH gradient, to allow operation of differential pH systems to improve the cell voltages. Limits of different approaches will be explored by taking an electrochemical engineering approach to model the performance of different systems and by validating the models experimentally. This work will chart the route towards the future third generation battery technologies for the large-scale energy storage.

1 499 880 €

Start date: 2021-01-01, End date: 2025-12-31

Functional-Magnetic Resonance Imaging (fMRI) has transformed our understanding of brain function due to its ability to noninvasively tag ‘active’ brain regions. Nevertheless, fMRI only detects neural activity indirectly, by relying on slow hemodynamic couplings whose relationships with underlying neural activity are not fully known. We have recently pioneered two unique MR approaches: Non-Uniform Oscillating-Gradient Spin-Echo (NOGSE) MRI and Relaxation Enhanced MR Spectroscopy (RE MRS). NOGSE-MRI is an exquisite microstructural probe, sensing cell sizes (l) with an unprecedented l^6 sensitivity (compared to l^2 in conventional approaches); RE MRS is a new spectral technique capable of recording metabolic signals with extraordinary fidelity at ultrahigh fields. This proposal aims to harness these novel concepts for mapping neural activity directly, without relying on hemodynamics. The specific objectives of this proposal are: (1) Mapping neural activity via sensing cell swellings upon activity (μfMRI): we hypothesize that NOGSE can robustly sense subtle changes in cellular microstructure upon neural firings and hence convey neural activity directly. (2) Probing the nature of elicited activity via detection of neurotransmitter release: we posit that RE MRS is sufficiently sensitive to robustly detect changes in Glutamate and GABA signals upon activation. (3) Network mapping in optogenetically-stimulated, behaving mice: we propose to couple our novel approaches with optogenetics to resolve neural correlates of behavior in awake, behaving mice. Simulations for μfMRI predict >4% signal changes upon subtle cell swellings; further, our in vivo RE MRS experiments have detected metabolites with SNR>50 in only 6 seconds. Hence, these two complementary –and importantly, hemodynamics-independent– approaches will represent a true paradigm shift: from indirect detection of neurovasculature couplings towards direct and noninvasive mapping of neural activity in vivo.

1 787 500 €

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

In recent years, genome-wide association studies (GWAS) have discovered hundreds of single nucleotide polymorphisms (SNPs) which are significantly associated with coronary artery disease (CAD). However, the SNPs identified by GWAS explain typically only small portion of the trait heritability and vast majority of variants do not have known biological roles. This is explained by variants lying within noncoding regions such as in cell type specific enhancers and additionally ‘the lead SNP’ identified in GWAS may not be the ‘the causal SNP’ but only linked with a trait associated SNP. Therefore, a major priority for understanding disease mechanisms is to understand at the molecular level the function of each CAD loci. In this study we aim to bring the functional characterization of SNPs associated with CAD risk to date by focusing our search for causal SNPs to enhancers of disease relevant cell types, namely endothelial cells, macrophages and smooth muscle cells of the vessel wall, hepatocytes and adipocytes. By combination of massively parallel enhancer activity measurements, collection of novel eQTL data throughout cell types under disease relevant stimuli, identification of the target genes in physical interaction with the candidate enhancers and establishment of correlative relationships between enhancer activity and gene expression we hope to identify causal enhancer variants and link them with target genes to obtain a more complete picture of the gene regulatory events driving disease progression and the genetic basis of CAD. Linking these findings with our deep phenotypic data for cardiovascular risk factors, gene expression and metabolomics has the potential to improve risk prediction, biomarker identification and treatment selection in clinical practice. Ultimately, this research strives for fundamental discoveries and breakthrough that advance our knowledge of CAD and provides pioneering steps towards taking the growing array of GWAS for translatable results.

1 498 647 €

Start date: 2019-01-01, End date: 2023-12-31

Known, but mostly novel natural products (NPs) are in high demand – these are used in drugs, cosmetics and agrochemicals and serve also as research tools to probe biological systems. NP structures inspire chemists to develop new syntheses, and NP biosynthetic enzymes add to the metabolic engineer’s toolbox. The advent of next generation DNA-sequencing has revealed a vastly rich pool of NP biosynthetic gene clusters (BGCs) among bacterial genomes, most of which with no corresponding NP. Hence, opportunities abound for the discovery of new chemistry and enzymology that has the potential to push the boundaries of chemical space and enzymatic reactivity. Still, we cannot reliably predict chemistry from BGCs with unusual organization or encoding unknown functionalities, and, for molecules of unorthodox architecture, it is difficult to anticipate how their BGCs are organized. It is the valuable, truly novel chemistry and biochemistry that lies on these unexplored connections, that we aim to reveal with this proposal. To achieve it, we will work with a chemically-talented group of organisms – cyanobacteria, and with a specific structural class – fatty acids (FAs) – that is metabolized in a quite peculiar fashion by these organisms, paving the way for NP and enzyme discovery. On one hand, we will exploit the unique FA metabolism of cyanobacteria to develop a feeding strategy that will quickly reveal unprecedented FA-incorporating NPs. On the other, we will scrutinize the intriguing biosynthesis of three unique classes of metabolites that we have isolated recently and that incorporate and modify FA-moieties. We will find the BGCs for these compounds and dissect the functionality involved in such puzzling modifications to uncover important underlying enzymatic chemistry. This proposal is a blend of discovery- and hypothesis-driven research at the NP chemistry/biosynthesis interface that draws on the experience of the PI’s work on different aspects of cyanobacterial NPs.

1 462 938 €

Start date: 2018-01-01, End date: 2022-12-31