Project acronym Agglomerates
Project Infinite Protein Self-Assembly in Health and Disease
Researcher (PI) Emmanuel Doram LEVY
Host Institution (HI) WEIZMANN INSTITUTE OF SCIENCE
Call Details Consolidator Grant (CoG), LS2, ERC-2018-COG
Summary Understanding how proteins respond to mutations is of paramount importance to biology and disease. While protein stability and misfolding have been instrumental in rationalizing the impact of mutations, we recently discovered that an alternative route is also frequent, where mutations at the surface of symmetric proteins trigger novel self-interactions that lead to infinite self-assembly. This mechanism can be involved in disease, as in sickle-cell anemia, but may also serve in adaptation. Importantly, it differs fundamentally from aggregation, because misfolding does not drive it. Thus, we term it “agglomeration”. The ease with which agglomeration can occur, even by single point mutations, shifts the paradigm of how quickly new protein assemblies can emerge, both in health and disease. This prompts us to determine the basic principles of protein agglomeration and explore its implications in cell physiology and human disease.
We propose an interdisciplinary research program bridging atomic and cellular scales to explore agglomeration in three aims: (i) Map the landscape of protein agglomeration in response to mutation in endogenous yeast proteins; (ii) Characterize how yeast physiology impacts agglomeration by changes in gene expression or cell state, and, conversely, how protein agglomerates impact yeast fitness. (iii) Analyze agglomeration in relation to human disease via two approaches. First, by predicting single nucleotide polymorphisms that trigger agglomeration, prioritizing them using knowledge from Aims 1 & 2, and characterizing them experimentally. Second, by providing a proof-of-concept that agglomeration can be exploited in drug design, whereby drugs induce its formation, like mutations can do.
Overall, through this research, we aim to establish agglomeration as a paradigm for protein assembly, with implications for our understanding of evolution, physiology, and disease.
Summary
Understanding how proteins respond to mutations is of paramount importance to biology and disease. While protein stability and misfolding have been instrumental in rationalizing the impact of mutations, we recently discovered that an alternative route is also frequent, where mutations at the surface of symmetric proteins trigger novel self-interactions that lead to infinite self-assembly. This mechanism can be involved in disease, as in sickle-cell anemia, but may also serve in adaptation. Importantly, it differs fundamentally from aggregation, because misfolding does not drive it. Thus, we term it “agglomeration”. The ease with which agglomeration can occur, even by single point mutations, shifts the paradigm of how quickly new protein assemblies can emerge, both in health and disease. This prompts us to determine the basic principles of protein agglomeration and explore its implications in cell physiology and human disease.
We propose an interdisciplinary research program bridging atomic and cellular scales to explore agglomeration in three aims: (i) Map the landscape of protein agglomeration in response to mutation in endogenous yeast proteins; (ii) Characterize how yeast physiology impacts agglomeration by changes in gene expression or cell state, and, conversely, how protein agglomerates impact yeast fitness. (iii) Analyze agglomeration in relation to human disease via two approaches. First, by predicting single nucleotide polymorphisms that trigger agglomeration, prioritizing them using knowledge from Aims 1 & 2, and characterizing them experimentally. Second, by providing a proof-of-concept that agglomeration can be exploited in drug design, whereby drugs induce its formation, like mutations can do.
Overall, through this research, we aim to establish agglomeration as a paradigm for protein assembly, with implications for our understanding of evolution, physiology, and disease.
Max ERC Funding
2 574 819 €
Duration
Start date: 2019-04-01, End date: 2024-03-31
Project acronym ANTHROPOID
Project Great ape organoids to reconstruct uniquely human development
Researcher (PI) Jarrett CAMP
Host Institution (HI) INSTITUT FUR MOLEKULARE UND KLINISCHE OPHTHALMOLOGIE BASEL
Call Details Starting Grant (StG), LS2, ERC-2018-STG
Summary Humans diverged from our closest living relatives, chimpanzees and other great apes, 6-10 million years ago. Since this divergence, our ancestors acquired genetic changes that enhanced cognition, altered metabolism, and endowed our species with an adaptive capacity to colonize the entire planet and reshape the biosphere. Through genome comparisons between modern humans, Neandertals, chimpanzees and other apes we have identified genetic changes that likely contribute to innovations in human metabolic and cognitive physiology. However, it has been difficult to assess the functional effects of these genetic changes due to the lack of cell culture systems that recapitulate great ape organ complexity. Human and chimpanzee pluripotent stem cells (PSCs) can self-organize into three-dimensional (3D) tissues that recapitulate the morphology, function, and genetic programs controlling organ development. Our vision is to use organoids to study the changes that set modern humans apart from our closest evolutionary relatives as well as all other organisms on the planet. In ANTHROPOID we will generate a great ape developmental cell atlas using cortex, liver, and small intestine organoids. We will use single-cell transcriptomics and chromatin accessibility to identify cell type-specific features of transcriptome divergence at cellular resolution. We will dissect enhancer evolution using single-cell genomic screens and ancestralize human cells to resurrect pre-human cellular phenotypes. ANTHROPOID utilizes quantitative and state-of-the-art methods to explore exciting high-risk questions at multiple branches of the modern human lineage. This project is a ground breaking starting point to replay evolution and tackle the ancient question of what makes us uniquely human?
Summary
Humans diverged from our closest living relatives, chimpanzees and other great apes, 6-10 million years ago. Since this divergence, our ancestors acquired genetic changes that enhanced cognition, altered metabolism, and endowed our species with an adaptive capacity to colonize the entire planet and reshape the biosphere. Through genome comparisons between modern humans, Neandertals, chimpanzees and other apes we have identified genetic changes that likely contribute to innovations in human metabolic and cognitive physiology. However, it has been difficult to assess the functional effects of these genetic changes due to the lack of cell culture systems that recapitulate great ape organ complexity. Human and chimpanzee pluripotent stem cells (PSCs) can self-organize into three-dimensional (3D) tissues that recapitulate the morphology, function, and genetic programs controlling organ development. Our vision is to use organoids to study the changes that set modern humans apart from our closest evolutionary relatives as well as all other organisms on the planet. In ANTHROPOID we will generate a great ape developmental cell atlas using cortex, liver, and small intestine organoids. We will use single-cell transcriptomics and chromatin accessibility to identify cell type-specific features of transcriptome divergence at cellular resolution. We will dissect enhancer evolution using single-cell genomic screens and ancestralize human cells to resurrect pre-human cellular phenotypes. ANTHROPOID utilizes quantitative and state-of-the-art methods to explore exciting high-risk questions at multiple branches of the modern human lineage. This project is a ground breaking starting point to replay evolution and tackle the ancient question of what makes us uniquely human?
Max ERC Funding
1 500 000 €
Duration
Start date: 2019-06-01, End date: 2024-05-31
Project acronym BALANCE
Project Mapping Dispersion Spectroscopically in Large Gas-Phase Molecular Ions
Researcher (PI) Peter CHEN
Host Institution (HI) EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Call Details Advanced Grant (AdG), PE4, ERC-2018-ADG
Summary We use IR spectroscopy of trapped ions in a cryogenic FT-ICR spectrometer to probe non-covalent, “dispersion” interactions in large, gas-phase molecular ions. We will measure conformational equilibria by N-H frequency shifts, and correlate gas-phase IR frequency to the N-H-N bond angle in an ionic H-bond. Substituents on “onium” cations can adopt various conformations, whose energies map interaction potentials. Substituents on their proton-bound dimers interact non-covalently through dispersion forces, whose quantitative evaluation in large molecules has remained difficult despite dispersion becoming increasingly cited as a design principle in the construction of catalysts and materials. The non-covalent interactions bend the N-H-N bond, leading to large shifts in the IR frequency. The proton-bound dimer acts like a molecular balance where the non-covalent interaction, is set against the bending potential in an ionic hydrogen bond. Despite encouragingly accurate calculations for small molecules, experimental benchmarks for large molecules in the gas phase remain scarce, and there is evidence that the good results for small molecules may not extrapolate reliably to large molecules. The present proposal introduces a new experimental probe of non-covalent interactions, providing a sensitive test of the diverging results coming from various computational methods and other experiments. The experiment must be done on isolated molecules in the gas phase, as previous work has shown that solvation substantially cancels out the attractive potential. Accordingly, the proposed experimental design, which involves a custom-built spectrometer, newly available tunable IR sources, chemical synthesis of custom substrates, and quantum calculations up to coupled-cluster levels of theory, showcases how an interdisciplinary approach combining physical and organic chemistry can solve a fundamental problem that impacts how we understand steric effects in organic chemistry.
Summary
We use IR spectroscopy of trapped ions in a cryogenic FT-ICR spectrometer to probe non-covalent, “dispersion” interactions in large, gas-phase molecular ions. We will measure conformational equilibria by N-H frequency shifts, and correlate gas-phase IR frequency to the N-H-N bond angle in an ionic H-bond. Substituents on “onium” cations can adopt various conformations, whose energies map interaction potentials. Substituents on their proton-bound dimers interact non-covalently through dispersion forces, whose quantitative evaluation in large molecules has remained difficult despite dispersion becoming increasingly cited as a design principle in the construction of catalysts and materials. The non-covalent interactions bend the N-H-N bond, leading to large shifts in the IR frequency. The proton-bound dimer acts like a molecular balance where the non-covalent interaction, is set against the bending potential in an ionic hydrogen bond. Despite encouragingly accurate calculations for small molecules, experimental benchmarks for large molecules in the gas phase remain scarce, and there is evidence that the good results for small molecules may not extrapolate reliably to large molecules. The present proposal introduces a new experimental probe of non-covalent interactions, providing a sensitive test of the diverging results coming from various computational methods and other experiments. The experiment must be done on isolated molecules in the gas phase, as previous work has shown that solvation substantially cancels out the attractive potential. Accordingly, the proposed experimental design, which involves a custom-built spectrometer, newly available tunable IR sources, chemical synthesis of custom substrates, and quantum calculations up to coupled-cluster levels of theory, showcases how an interdisciplinary approach combining physical and organic chemistry can solve a fundamental problem that impacts how we understand steric effects in organic chemistry.
Max ERC Funding
2 446 125 €
Duration
Start date: 2019-05-01, End date: 2024-04-30
Project acronym CRISPRsition
Project Developing CRISPR adaptation platforms for basic and applied research
Researcher (PI) Ehud Itzhak Qimron
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Consolidator Grant (CoG), LS2, ERC-2018-COG
Summary The CRISPR-Cas system has been extensively studied for its ability to cleave DNA. In contrast, studies of the ability of the system to acquire and integrate new DNA from invaders as a form of prokaryotic adaptive immunity, have lagged behind. This delay reflects the extreme enthusiasm surrounding the potential of using the system’s cleavage capabilities as a genome editing tool. However, the enormous potential of the adaptation process can and should arouse a similar degree of enthusiasm. My lab has pioneered studies on the CRISPR adaptation process by establishing new methodologies, and applying them to demonstrate the essential role of the proteins and DNA elements, as well as the molecular mechanisms, operating in this process. In this project, I will establish novel platforms for studying adaptation and develop them into biotechnological applications and research tools. These tools will allow me to identify the first natural and synthetic inhibitors of the adaptation process. This, in turn, will provide genetic tools to control adaptation, as well as advance the understanding of the arms race between bacteria and their invaders. I will also harness the adaptation process as a platform for diversifying genetic elements for phage display, and for extending phage recognition of a wide range of hosts. Lastly, I will provide the first evidence for an association between the CRISPR adaptation system and gene repression. This linkage will form the basis of a molecular scanner and recorder platform that I will develop and that can be used to identify crucial genetic elements in phage genomes as well as novel regulatory circuits in the bacterial genome. Together, my findings will represent a considerable leap in the understanding of CRISPR adaptation with respect to the process, potential applications, and the intriguing evolutionary significance.
Summary
The CRISPR-Cas system has been extensively studied for its ability to cleave DNA. In contrast, studies of the ability of the system to acquire and integrate new DNA from invaders as a form of prokaryotic adaptive immunity, have lagged behind. This delay reflects the extreme enthusiasm surrounding the potential of using the system’s cleavage capabilities as a genome editing tool. However, the enormous potential of the adaptation process can and should arouse a similar degree of enthusiasm. My lab has pioneered studies on the CRISPR adaptation process by establishing new methodologies, and applying them to demonstrate the essential role of the proteins and DNA elements, as well as the molecular mechanisms, operating in this process. In this project, I will establish novel platforms for studying adaptation and develop them into biotechnological applications and research tools. These tools will allow me to identify the first natural and synthetic inhibitors of the adaptation process. This, in turn, will provide genetic tools to control adaptation, as well as advance the understanding of the arms race between bacteria and their invaders. I will also harness the adaptation process as a platform for diversifying genetic elements for phage display, and for extending phage recognition of a wide range of hosts. Lastly, I will provide the first evidence for an association between the CRISPR adaptation system and gene repression. This linkage will form the basis of a molecular scanner and recorder platform that I will develop and that can be used to identify crucial genetic elements in phage genomes as well as novel regulatory circuits in the bacterial genome. Together, my findings will represent a considerable leap in the understanding of CRISPR adaptation with respect to the process, potential applications, and the intriguing evolutionary significance.
Max ERC Funding
2 000 000 €
Duration
Start date: 2019-12-01, End date: 2024-11-30
Project acronym DYNAPOL
Project Modeling approaches toward bioinspired dynamic materials
Researcher (PI) Giovanni Maria PAVAN
Host Institution (HI) SCUOLA UNIVERSITARIA PROFESSIONALE DELLA SVIZZERA ITALIANA
Call Details Consolidator Grant (CoG), PE4, ERC-2018-COG
Summary Nature uses self-assembly to build fascinating supramolecular materials, such as microtubules and protein filaments, that can self-heal, reconfigure, adapt or respond to specific stimuli in dynamic way. Building synthetic (polymeric) supramolecular materials possessing similar bioinspired properties via the same self-assembly principles is interesting for many applications. But their rational design requires a detailed comprehension of the molecular determinants controlling the assembly (structure, dynamics and properties) that is typically very difficult to reach experimentally.
The aim of this project is to obtain structure-dynamics-property relationships to learn how to control the dynamic bioinspired properties of supramolecular polymers. I propose to unravel the molecular origin of the bioinspired behavior through massive multiscale modeling, advanced simulations and machine learning. First, we will develop ad hoc molecular models to study monomer assembly and the supramolecular structure of various types of self-assembled materials on multiple scales. Second, using advanced simulation approaches we will characterize the supramolecular dynamics of these materials (dynamic exchange of monomers) at high (submolecular) resolution. We will then study bioinspired properties such as the ability of various supramolecular materials to self-heal, adapt or reconfigure dynamically in response to specific stimuli. Our models will be systematically validated by comparison with the experimental evidence from our collaborators. Finally, we will use machine learning approaches to analyze our high-resolution simulations and to identify the key monomer features that control and determine the structure, dynamics and dynamic properties of a supramolecular material (i.e., structure-dynamics-property relationships). This research will produce unprecedented insight and fundamental models for the rational design of artificial dynamic materials with controllable bioinspired properties.
Summary
Nature uses self-assembly to build fascinating supramolecular materials, such as microtubules and protein filaments, that can self-heal, reconfigure, adapt or respond to specific stimuli in dynamic way. Building synthetic (polymeric) supramolecular materials possessing similar bioinspired properties via the same self-assembly principles is interesting for many applications. But their rational design requires a detailed comprehension of the molecular determinants controlling the assembly (structure, dynamics and properties) that is typically very difficult to reach experimentally.
The aim of this project is to obtain structure-dynamics-property relationships to learn how to control the dynamic bioinspired properties of supramolecular polymers. I propose to unravel the molecular origin of the bioinspired behavior through massive multiscale modeling, advanced simulations and machine learning. First, we will develop ad hoc molecular models to study monomer assembly and the supramolecular structure of various types of self-assembled materials on multiple scales. Second, using advanced simulation approaches we will characterize the supramolecular dynamics of these materials (dynamic exchange of monomers) at high (submolecular) resolution. We will then study bioinspired properties such as the ability of various supramolecular materials to self-heal, adapt or reconfigure dynamically in response to specific stimuli. Our models will be systematically validated by comparison with the experimental evidence from our collaborators. Finally, we will use machine learning approaches to analyze our high-resolution simulations and to identify the key monomer features that control and determine the structure, dynamics and dynamic properties of a supramolecular material (i.e., structure-dynamics-property relationships). This research will produce unprecedented insight and fundamental models for the rational design of artificial dynamic materials with controllable bioinspired properties.
Max ERC Funding
1 999 623 €
Duration
Start date: 2019-06-01, End date: 2024-05-31
Project acronym EPICROP
Project Dissecting epistasis for enhanced crop productivity
Researcher (PI) Sebastian Soyk
Host Institution (HI) UNIVERSITE DE LAUSANNE
Call Details Starting Grant (StG), LS2, ERC-2018-STG
Summary A major goal in plant biology is to understand how naturally occurring genetic variation leads to quantitative differences in economically important traits. Efforts to navigate the genotype-to-phenotype map are often focused on linear genetic interactions. As a result, crop breeding is mainly driven by loci with predictable additive effects. However, it has become clear that quantitative trait variation often results from perturbations of complex genetic networks. Thus, understanding epistasis, or interactions between genes, is key for our ability to predictably improve crops. To meet this challenge, this project will reveal and dissect epistatic interactions in gene regulatory networks that guide stem cell differentiation in the model crop tomato. In the first aim, I will utilize exhaustive allelic series for epistatic MADS-box genes that quantitatively regulate flower and fruit production as an experimental model system to study fundamental principles of epistasis that can be applied to other genetic networks. Genome-wide transcript profiling will be used to reveal molecular signatures of epistasis and potential targets for predictable crop improvement by advanced CRISPR/Cas9 gene editing technology. Further, my preliminary data suggests that epistasis is widespread and important across major productivity traits in tomato. Thus, in a second aim, I will access this untapped resource of cryptic genetic variation by sensitizing a tomato diversity panel for weak epistatic effects from unknown natural modifier loci of stem cell differentiation using trans-acting CRISPR/Cas9 editing cassettes. This screen represents a new approach to mutagenesis in plants with potential to reveal cryptic variation in other system. The outcomes of this project will advance our knowledge in a fundamental area of plant genome biology, help uncover and understand the functional architecture of epistasis, and have potential to bring significant improvements to agriculture.
Summary
A major goal in plant biology is to understand how naturally occurring genetic variation leads to quantitative differences in economically important traits. Efforts to navigate the genotype-to-phenotype map are often focused on linear genetic interactions. As a result, crop breeding is mainly driven by loci with predictable additive effects. However, it has become clear that quantitative trait variation often results from perturbations of complex genetic networks. Thus, understanding epistasis, or interactions between genes, is key for our ability to predictably improve crops. To meet this challenge, this project will reveal and dissect epistatic interactions in gene regulatory networks that guide stem cell differentiation in the model crop tomato. In the first aim, I will utilize exhaustive allelic series for epistatic MADS-box genes that quantitatively regulate flower and fruit production as an experimental model system to study fundamental principles of epistasis that can be applied to other genetic networks. Genome-wide transcript profiling will be used to reveal molecular signatures of epistasis and potential targets for predictable crop improvement by advanced CRISPR/Cas9 gene editing technology. Further, my preliminary data suggests that epistasis is widespread and important across major productivity traits in tomato. Thus, in a second aim, I will access this untapped resource of cryptic genetic variation by sensitizing a tomato diversity panel for weak epistatic effects from unknown natural modifier loci of stem cell differentiation using trans-acting CRISPR/Cas9 editing cassettes. This screen represents a new approach to mutagenesis in plants with potential to reveal cryptic variation in other system. The outcomes of this project will advance our knowledge in a fundamental area of plant genome biology, help uncover and understand the functional architecture of epistasis, and have potential to bring significant improvements to agriculture.
Max ERC Funding
1 499 903 €
Duration
Start date: 2019-08-01, End date: 2024-07-31
Project acronym MapCat
Project High spatial resolution mapping of catalytic reactions on single nanoparticles
Researcher (PI) Elad Gross
Host Institution (HI) THE HEBREW UNIVERSITY OF JERUSALEM
Call Details Starting Grant (StG), PE4, ERC-2018-STG
Summary Catalytic nanoparticles are heterogeneous in their nature - and even within the simplest particles structural and compositional differences exist and affect the overall performances of a catalyst. Thus non-disruptive, detailed chemical information at the nanoscale is essential for understanding how surface properties direct the reactivity of these particles. Infrared spectroscopy offers a low-energy route towards conducting in-situ, high spatial resolution mapping of catalytic reactions on the surface of single nanoparticles, yielding the influence of various physiochemical properties on the catalytic reactivity.
In the project my team will employ recently developed Infrared nanospectroscopy measurements to provide high spatial resolution mapping of catalytic reactions on the surface of metallic nanoparticles, while using chemically active N-heterocyclic carbene molecules as indicators for surface reactivity. With this setup I will address fundamental questions in catalysis research and identify, on a single particle basis and under reaction conditions, the ways by which the size, structure, composition and metal-support interactions direct the reactivity of metallic nanoparticles in hydrogenation, oxidation and functionalization reactions. My research group demonstrated recently the feasibility of this novel approach by which structure-reactivity correlations were identified within single nanoparticles. Knowledge gained in this project will provide in-depth understanding of the basic elements that control the reactivity of heterogeneous catalysts and enable the development of optimized catalysts based on rational design. Moreover, one can foresee wide application potential for this experimental approach in various other research fields like batteries and fuel cells, in which high spatial resolution analysis of reactive surfaces is essential for understanding structure-reactivity correlations.
Summary
Catalytic nanoparticles are heterogeneous in their nature - and even within the simplest particles structural and compositional differences exist and affect the overall performances of a catalyst. Thus non-disruptive, detailed chemical information at the nanoscale is essential for understanding how surface properties direct the reactivity of these particles. Infrared spectroscopy offers a low-energy route towards conducting in-situ, high spatial resolution mapping of catalytic reactions on the surface of single nanoparticles, yielding the influence of various physiochemical properties on the catalytic reactivity.
In the project my team will employ recently developed Infrared nanospectroscopy measurements to provide high spatial resolution mapping of catalytic reactions on the surface of metallic nanoparticles, while using chemically active N-heterocyclic carbene molecules as indicators for surface reactivity. With this setup I will address fundamental questions in catalysis research and identify, on a single particle basis and under reaction conditions, the ways by which the size, structure, composition and metal-support interactions direct the reactivity of metallic nanoparticles in hydrogenation, oxidation and functionalization reactions. My research group demonstrated recently the feasibility of this novel approach by which structure-reactivity correlations were identified within single nanoparticles. Knowledge gained in this project will provide in-depth understanding of the basic elements that control the reactivity of heterogeneous catalysts and enable the development of optimized catalysts based on rational design. Moreover, one can foresee wide application potential for this experimental approach in various other research fields like batteries and fuel cells, in which high spatial resolution analysis of reactive surfaces is essential for understanding structure-reactivity correlations.
Max ERC Funding
1 846 009 €
Duration
Start date: 2019-01-01, End date: 2023-12-31
Project acronym MIDNP
Project Metal Ions Dynamic Nuclear Polarization: Novel Route for Probing Functional Materials with Sensitivity and Selectivity
Researcher (PI) Michal LESKES
Host Institution (HI) WEIZMANN INSTITUTE OF SCIENCE
Call Details Starting Grant (StG), PE4, ERC-2018-STG
Summary Materials with specific electrical, optical or chemical properties often derive their special functions from small perturbations in their composition or structure. Thus, rational design of new functional materials demands sensitive and versatile determination of structural and compositional properties, a very difficult goal not presently available. The overarching goal of this ERC project is to develop a novel route for Magic-Angle Spinning Dynamic Nuclear Polarization (MAS-DNP) as an enabling methodology in materials science, introducing new opportunities for investigating and designing functional materials.
Solid State Nuclear Magnetic Resonance (ssNMR) spectroscopy is an excellent probe for local order/disorder, but unfortunately its sensitivity is limited. DNP, a process whereby the large electron spin polarization is transferred to the nuclear spins, had greatly expanded the range of materials systems and questions that can be probed by ssNMR. However, it commonly relies on the use of exogenous nitroxide radicals, thereby limiting its utilization in materials science to nonreactive surfaces.
We propose to develop Metal Ions DNP (MIDNP) utilizing paramagnetic dopants as endogenous polarization agents in the bulk. To effectively harness the electron spin polarization of the dopants for higher sensitivity, we will (a) address challenges such as the effect of bonding, spin interactions and relaxation on DNP via a mechanistic study of carefully selected dopants in energy materials; (b) Develop new techniques for NMR spectral assignment and explore alternative DNP mechanisms for paramagnetic solids; (c) Expand the approach for sensitizing the detection of surfaces and interfaces and elucidate the critical role of surface chemistry in the efficacy of energy storage materials.
MIDNP will provide a novel, sensitive alternative for probing the structure and composition of new materials and will transform the utilization of ssNMR in the study of functional materials.
Summary
Materials with specific electrical, optical or chemical properties often derive their special functions from small perturbations in their composition or structure. Thus, rational design of new functional materials demands sensitive and versatile determination of structural and compositional properties, a very difficult goal not presently available. The overarching goal of this ERC project is to develop a novel route for Magic-Angle Spinning Dynamic Nuclear Polarization (MAS-DNP) as an enabling methodology in materials science, introducing new opportunities for investigating and designing functional materials.
Solid State Nuclear Magnetic Resonance (ssNMR) spectroscopy is an excellent probe for local order/disorder, but unfortunately its sensitivity is limited. DNP, a process whereby the large electron spin polarization is transferred to the nuclear spins, had greatly expanded the range of materials systems and questions that can be probed by ssNMR. However, it commonly relies on the use of exogenous nitroxide radicals, thereby limiting its utilization in materials science to nonreactive surfaces.
We propose to develop Metal Ions DNP (MIDNP) utilizing paramagnetic dopants as endogenous polarization agents in the bulk. To effectively harness the electron spin polarization of the dopants for higher sensitivity, we will (a) address challenges such as the effect of bonding, spin interactions and relaxation on DNP via a mechanistic study of carefully selected dopants in energy materials; (b) Develop new techniques for NMR spectral assignment and explore alternative DNP mechanisms for paramagnetic solids; (c) Expand the approach for sensitizing the detection of surfaces and interfaces and elucidate the critical role of surface chemistry in the efficacy of energy storage materials.
MIDNP will provide a novel, sensitive alternative for probing the structure and composition of new materials and will transform the utilization of ssNMR in the study of functional materials.
Max ERC Funding
1 700 000 €
Duration
Start date: 2019-01-01, End date: 2023-12-31
Project acronym MultiplexGenomics
Project Exploring the Epigenome by Multiplexed Physical Mapping of Individual Chromosomes
Researcher (PI) Yuval EBENSTEIN
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Consolidator Grant (CoG), PE4, ERC-2018-COG
Summary The genome is composed of the genetic code and a rich repertoire of epigenetic chemical DNA modifications, the Epigenome, with distinct signatures in health and disease. Unmasking the interplay between different genomic features is critical for understanding the operating system of life. Specifically, revealing long-range epigenetic regulation may uncover predisposition to cancer. Nevertheless, due to the short read-length of single-cell next-generation sequencing, there is no method today that can integrate multiple genomic observables, on the same genome and at the same time. The missing picture constitutes a major genomic “blind spot”, obscuring epigenetic regulation of gene expression. This project aims to provide a multiplexed view of the genome never before accessible. I will utilize single-molecule physical and chemical mapping of individual chromosomes to discover long-range epigenetic correlations, focusing on markers for predisposition to breast cancer. I will approach multiplexing by applying optical and electrical sensing concepts to detect chemical tags attached to long genomic DNA molecules. Equipped with a toolbox of biochemical DNA labeling reactions, I will develop a unique spectral imager for simultaneous acquisition of high-content genomic information from DNA stretched in nanochannel arrays. DNA tagging will also be used to enhance electrical contrast for nanopore epigenetic sequencing. Finally, by combining electric sensing inside nanochannels I will develop new integrated devices for electro-optical genomic analysis. Together, these developments cover the full range of genomic length scales and resolution. MultiplexGenomics will establish a groundbreaking experimental framework for genetic/epigenetic profiling of native chromosomal DNA. A successful completion of this project will make possible the discovery of novel control networks and hidden long-range regulation, opening new horizons for basic genomic research and personalized medicine.
Summary
The genome is composed of the genetic code and a rich repertoire of epigenetic chemical DNA modifications, the Epigenome, with distinct signatures in health and disease. Unmasking the interplay between different genomic features is critical for understanding the operating system of life. Specifically, revealing long-range epigenetic regulation may uncover predisposition to cancer. Nevertheless, due to the short read-length of single-cell next-generation sequencing, there is no method today that can integrate multiple genomic observables, on the same genome and at the same time. The missing picture constitutes a major genomic “blind spot”, obscuring epigenetic regulation of gene expression. This project aims to provide a multiplexed view of the genome never before accessible. I will utilize single-molecule physical and chemical mapping of individual chromosomes to discover long-range epigenetic correlations, focusing on markers for predisposition to breast cancer. I will approach multiplexing by applying optical and electrical sensing concepts to detect chemical tags attached to long genomic DNA molecules. Equipped with a toolbox of biochemical DNA labeling reactions, I will develop a unique spectral imager for simultaneous acquisition of high-content genomic information from DNA stretched in nanochannel arrays. DNA tagging will also be used to enhance electrical contrast for nanopore epigenetic sequencing. Finally, by combining electric sensing inside nanochannels I will develop new integrated devices for electro-optical genomic analysis. Together, these developments cover the full range of genomic length scales and resolution. MultiplexGenomics will establish a groundbreaking experimental framework for genetic/epigenetic profiling of native chromosomal DNA. A successful completion of this project will make possible the discovery of novel control networks and hidden long-range regulation, opening new horizons for basic genomic research and personalized medicine.
Max ERC Funding
2 750 000 €
Duration
Start date: 2019-10-01, End date: 2024-09-30
Project acronym NanoProt-ID
Project Proteome profiling using plasmonic nanopore sensors
Researcher (PI) Amit MELLER
Host Institution (HI) TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY
Call Details Advanced Grant (AdG), PE4, ERC-2018-ADG
Summary To date, antibody-free protein identification methods have not reached single-molecule precision. Instead, they rely on averaging from many cells, obscuring the details of important biological processes. The ability to identify each individual protein from within a single cell would transform proteomics research and biomedicine. However, single protein identification (ID) presents a major challenge, necessitating a breakthrough in single-molecule sensing technologies.
We propose to develop a method for proteome-level analysis, with single protein resolution. Bioinformatics studies show that >99% of human proteins can be uniquely identified by the order in which only three amino-acids, Lysine, Cysteine, and Methionine (K, C and M, respectively), appear along the proteins’ chain. By specifically labelling K, C and M residues with three distinct fluorophores, and threading them, one by one, through solid-state nanopores equipped with custom plasmonic amplifiers, we hypothesize that we can obtain multi-color fluorescence time-trace fingerprints uniquely representing most proteins in the human proteome. The feasibility of our method will be established by attaining 4 main aims: i) in vitro K,C,M protein labelling, ii) development of a machine learning classifier to uniquely ID proteins based on their optical fingerprints, iii) fabrication of state-of-the-art plasmonic nanopores for high-resolution optical sensing of proteins, and iv) devising methods for regulating the translocation speed to enhance the signal to noise ratio. Next, we will scale up our platform to enable the analysis of thousands of different proteins in minutes, and apply it to sense blood-secreted proteins, as well as whole proteomes in pre- and post-metastatic cancer cells. NanoProt-ID constitutes the first and most challenging step towards the proteomic analysis of individual cells, opening vast research directions and applications in biomedicine and systems biology.
Summary
To date, antibody-free protein identification methods have not reached single-molecule precision. Instead, they rely on averaging from many cells, obscuring the details of important biological processes. The ability to identify each individual protein from within a single cell would transform proteomics research and biomedicine. However, single protein identification (ID) presents a major challenge, necessitating a breakthrough in single-molecule sensing technologies.
We propose to develop a method for proteome-level analysis, with single protein resolution. Bioinformatics studies show that >99% of human proteins can be uniquely identified by the order in which only three amino-acids, Lysine, Cysteine, and Methionine (K, C and M, respectively), appear along the proteins’ chain. By specifically labelling K, C and M residues with three distinct fluorophores, and threading them, one by one, through solid-state nanopores equipped with custom plasmonic amplifiers, we hypothesize that we can obtain multi-color fluorescence time-trace fingerprints uniquely representing most proteins in the human proteome. The feasibility of our method will be established by attaining 4 main aims: i) in vitro K,C,M protein labelling, ii) development of a machine learning classifier to uniquely ID proteins based on their optical fingerprints, iii) fabrication of state-of-the-art plasmonic nanopores for high-resolution optical sensing of proteins, and iv) devising methods for regulating the translocation speed to enhance the signal to noise ratio. Next, we will scale up our platform to enable the analysis of thousands of different proteins in minutes, and apply it to sense blood-secreted proteins, as well as whole proteomes in pre- and post-metastatic cancer cells. NanoProt-ID constitutes the first and most challenging step towards the proteomic analysis of individual cells, opening vast research directions and applications in biomedicine and systems biology.
Max ERC Funding
2 498 869 €
Duration
Start date: 2019-08-01, End date: 2024-07-31
Project acronym PushQChem
Project Pushing Quantum Chemistry by Advancing Photoswitchable Catalysis
Researcher (PI) Anne-Clémence CORMINBOEUF WODRICH
Host Institution (HI) ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Call Details Consolidator Grant (CoG), PE4, ERC-2018-COG
Summary This project exploits the synergy between the trending area of artificial molecular machines and cutting edge computational chemistry approaches. Specific emphasis is placed on photoswitchable catalysts, which respond to external stimuli with a conformational or configurational change. These controllable motions allow catalytic function to be turned ON/OFF in a switch type fashion by opening/hindering access of a substrate to a catalytic site. On one hand, the rich morphology and chemistry of these smart catalysts calls for computational insights and design principles that complement experiment and push the field forward. On the other hand, the inherent complexity of these highly fluxional molecules makes them perfect subjects for driving modern quantum chemistry out of its comfort zone. To benefit from this synergy, the latest innovations in quantum chemistry-based machine learning techniques will be combined with methods capable of thoroughly mapping the intricate chemistry of molecular actuators. Overall, we aim to bridge the gap between the current state-of-the-art, which has reached reasonable quantum chemical accuracy for rigid medium size organic molecules, and more challenging fluxional architectures. The proposed methodological toolbox will be applied to the field of smart catalysis where general strategies for improving the efficiencies and enhancing enantioselectivity will be formulated. Thus, this project involves exploiting a wide range of modern computational approaches to chemical tasks that are broadly relevant to flexible/switchable catalytic systems. The anticipated output will furnish the computational chemistry community with a comprehensive array of novel next-generation approaches with applicability beyond the field of molecular machines.
Summary
This project exploits the synergy between the trending area of artificial molecular machines and cutting edge computational chemistry approaches. Specific emphasis is placed on photoswitchable catalysts, which respond to external stimuli with a conformational or configurational change. These controllable motions allow catalytic function to be turned ON/OFF in a switch type fashion by opening/hindering access of a substrate to a catalytic site. On one hand, the rich morphology and chemistry of these smart catalysts calls for computational insights and design principles that complement experiment and push the field forward. On the other hand, the inherent complexity of these highly fluxional molecules makes them perfect subjects for driving modern quantum chemistry out of its comfort zone. To benefit from this synergy, the latest innovations in quantum chemistry-based machine learning techniques will be combined with methods capable of thoroughly mapping the intricate chemistry of molecular actuators. Overall, we aim to bridge the gap between the current state-of-the-art, which has reached reasonable quantum chemical accuracy for rigid medium size organic molecules, and more challenging fluxional architectures. The proposed methodological toolbox will be applied to the field of smart catalysis where general strategies for improving the efficiencies and enhancing enantioselectivity will be formulated. Thus, this project involves exploiting a wide range of modern computational approaches to chemical tasks that are broadly relevant to flexible/switchable catalytic systems. The anticipated output will furnish the computational chemistry community with a comprehensive array of novel next-generation approaches with applicability beyond the field of molecular machines.
Max ERC Funding
1 949 385 €
Duration
Start date: 2019-10-01, End date: 2024-09-30
Project acronym RegRNA
Project Mechanistic principles of regulation by small RNAs
Researcher (PI) Hanah Margalit
Host Institution (HI) THE HEBREW UNIVERSITY OF JERUSALEM
Call Details Advanced Grant (AdG), LS2, ERC-2018-ADG
Summary Small RNAs (sRNAs) are major regulators of gene expression in bacteria, exerting their regulation in trans by base pairing with target RNAs. Traditionally, sRNAs were considered post-transcriptional regulators, mainly regulating translation by blocking or exposing the ribosome binding site. However, accumulating evidence suggest that sRNAs can exploit the base pairing to manipulate their targets in different ways, assisting or interfering with various molecular processes involving the target RNA. Currently there are a few examples of these alternative regulation modes, but their extent and implications in the cellular circuitry have not been assessed. Here we propose to take advantage of the power of RNA-seq-based technologies to develop innovative approaches to address these challenges transcriptome-wide. These approaches will enable us to map the regulatory mechanism a sRNA employs per target through its effect on a certain molecular process. For feasibility we propose studying three processes: RNA cleavage by RNase E, pre-mature Rho-dependent transcription termination, and transcription elongation pausing. Finding targets regulated by sRNA manipulation of the two latter processes would be especially intriguing, as it would suggest that sRNAs can function as gene-specific transcription regulators (alluded to by our preliminary results). As a basis of our research we will use the network of ~2400 sRNA-target pairs in Escherichia coli, deciphered by RIL-seq (a method we recently developed for global in vivo detection of sRNA targets). Revealing the regulatory mechanism(s) employed per target will shed light on the principles underlying the integration of distinct sRNA regulation modes in specific regulatory circuits and cellular contexts, with direct implications to synthetic biology and pathogenic bacteria. Our study may change the way sRNAs are perceived, from post-transcriptional to versatile regulators that apply different regulation modes to different targets.
Summary
Small RNAs (sRNAs) are major regulators of gene expression in bacteria, exerting their regulation in trans by base pairing with target RNAs. Traditionally, sRNAs were considered post-transcriptional regulators, mainly regulating translation by blocking or exposing the ribosome binding site. However, accumulating evidence suggest that sRNAs can exploit the base pairing to manipulate their targets in different ways, assisting or interfering with various molecular processes involving the target RNA. Currently there are a few examples of these alternative regulation modes, but their extent and implications in the cellular circuitry have not been assessed. Here we propose to take advantage of the power of RNA-seq-based technologies to develop innovative approaches to address these challenges transcriptome-wide. These approaches will enable us to map the regulatory mechanism a sRNA employs per target through its effect on a certain molecular process. For feasibility we propose studying three processes: RNA cleavage by RNase E, pre-mature Rho-dependent transcription termination, and transcription elongation pausing. Finding targets regulated by sRNA manipulation of the two latter processes would be especially intriguing, as it would suggest that sRNAs can function as gene-specific transcription regulators (alluded to by our preliminary results). As a basis of our research we will use the network of ~2400 sRNA-target pairs in Escherichia coli, deciphered by RIL-seq (a method we recently developed for global in vivo detection of sRNA targets). Revealing the regulatory mechanism(s) employed per target will shed light on the principles underlying the integration of distinct sRNA regulation modes in specific regulatory circuits and cellular contexts, with direct implications to synthetic biology and pathogenic bacteria. Our study may change the way sRNAs are perceived, from post-transcriptional to versatile regulators that apply different regulation modes to different targets.
Max ERC Funding
2 278 125 €
Duration
Start date: 2019-09-01, End date: 2024-08-31
Project acronym RNAflashbacks
Project The Neuronal Code of Inheritance
Researcher (PI) Oded Rechavi
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Consolidator Grant (CoG), LS2, ERC-2018-COG
Summary Little is known about non-DNA mediated transgenerational inheritance of parental responses. If inheritance of non-genetic materials is prevalent, it could challenge our conceptions regarding the rules and limits of heredity. We are in particularly intrigued by the possibility that the nervous system can produce heritable changes. Mechanisms for propagation of responses from the soma to the germline are known. Until very recently, the possibility that any type of environmental response could become heritable, let alone somatic responses, was considered blasphemous. In C. elegans nematodes, exogenously-supplied artificial dsRNA transfers from the soma to the germline, triggering transgenerational small RNA-mediated RNA interference. It is unknown whether endogenous small RNAs can transmit specific information about the environment to the progeny. We will investigate if endogenous siRNAs, which are naturally made in somatic tissues, and in particular in neurons, produce transgenerational responses. Specifically, we will test which RNA molecules act transgenerationally, how do they mediate non-cell autonomous gene regulation, and which responses can be communicated to the progeny. What is the code that transforms particular environmental responses to specific arsenals of heritable RNA molecules? We will answer these questions, and moreover study the implications that this completely new form of hereditary has for the offspring’s survival. Can heritable small RNAs retain adaptive memory? Not only will we elucidate natural transmission of the parents’ activity from generation-to-generation, we will moreover devise means to control these mechanisms. We will engineer tools to diagnose, erase, maintain, and modulate the heritable effects, which would be important for basic research and hopefully also translational in the future.
Summary
Little is known about non-DNA mediated transgenerational inheritance of parental responses. If inheritance of non-genetic materials is prevalent, it could challenge our conceptions regarding the rules and limits of heredity. We are in particularly intrigued by the possibility that the nervous system can produce heritable changes. Mechanisms for propagation of responses from the soma to the germline are known. Until very recently, the possibility that any type of environmental response could become heritable, let alone somatic responses, was considered blasphemous. In C. elegans nematodes, exogenously-supplied artificial dsRNA transfers from the soma to the germline, triggering transgenerational small RNA-mediated RNA interference. It is unknown whether endogenous small RNAs can transmit specific information about the environment to the progeny. We will investigate if endogenous siRNAs, which are naturally made in somatic tissues, and in particular in neurons, produce transgenerational responses. Specifically, we will test which RNA molecules act transgenerationally, how do they mediate non-cell autonomous gene regulation, and which responses can be communicated to the progeny. What is the code that transforms particular environmental responses to specific arsenals of heritable RNA molecules? We will answer these questions, and moreover study the implications that this completely new form of hereditary has for the offspring’s survival. Can heritable small RNAs retain adaptive memory? Not only will we elucidate natural transmission of the parents’ activity from generation-to-generation, we will moreover devise means to control these mechanisms. We will engineer tools to diagnose, erase, maintain, and modulate the heritable effects, which would be important for basic research and hopefully also translational in the future.
Max ERC Funding
2 000 000 €
Duration
Start date: 2019-08-01, End date: 2024-07-31
Project acronym SCIPER
Project Studying Cancer Individuality by Personal and Predictive Drug Screening and Differential OMICs
Researcher (PI) Berend SNIJDER
Host Institution (HI) EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Call Details Starting Grant (StG), LS2, ERC-2018-STG
Summary The cellular and molecular systems that determine drug responses in cancer are complex, highly individual, and incompletely understood. As a result, many cancer patients receive ineffective or even harmful therapies, which endangers lives, burdens healthcare systems, and prevents new therapies from reaching clinical approval.
To address this problem, we are developing a platform that measures hundreds of ex vivo drug responses from small patient biopsies by immunofluorescence, automated confocal microscopy, single-cell image analysis, and machine learning. We preserve cellular memory and maximize physiological relevance by not culturing or sorting cells prior to drug exposure. Sub-cellular, single-cell, and cell population-wide image analysis reveals on-target drug responses and disentangles multicellular ones. In a first interventional clinical trial, this phenotypic information alone led to strongly improved treatment of patients with aggressive hematologic malignancies.
Enabled by this high-throughput, predictive, and phenotypic information, I here propose to identify the molecular and cellular systems that govern treatment response individuality in cancer. (Aim 1) We will combine drug response profiling with RNA sequencing and proteomic measurements of malignant and healthy cells from the same biopsies. Critically, the patient-internal comparisons in both screening and OMICs allow neutralizing complex confounding factors. (Aim 2) New multiplexed immunofluorescence and convolutional neural network-based analyses will identify multiclass cell-types and -states, and quantify non-cell-autonomous responses. (Aim 3) Computational integration and causal inference will identify the molecular determinants and governing principles of drug response individuality in cancer, amenable to further validation. This proposal will thus improve our mechanistic understanding of cancer individuality and develop powerful new tools for OMICs-based precision medicine.
Summary
The cellular and molecular systems that determine drug responses in cancer are complex, highly individual, and incompletely understood. As a result, many cancer patients receive ineffective or even harmful therapies, which endangers lives, burdens healthcare systems, and prevents new therapies from reaching clinical approval.
To address this problem, we are developing a platform that measures hundreds of ex vivo drug responses from small patient biopsies by immunofluorescence, automated confocal microscopy, single-cell image analysis, and machine learning. We preserve cellular memory and maximize physiological relevance by not culturing or sorting cells prior to drug exposure. Sub-cellular, single-cell, and cell population-wide image analysis reveals on-target drug responses and disentangles multicellular ones. In a first interventional clinical trial, this phenotypic information alone led to strongly improved treatment of patients with aggressive hematologic malignancies.
Enabled by this high-throughput, predictive, and phenotypic information, I here propose to identify the molecular and cellular systems that govern treatment response individuality in cancer. (Aim 1) We will combine drug response profiling with RNA sequencing and proteomic measurements of malignant and healthy cells from the same biopsies. Critically, the patient-internal comparisons in both screening and OMICs allow neutralizing complex confounding factors. (Aim 2) New multiplexed immunofluorescence and convolutional neural network-based analyses will identify multiclass cell-types and -states, and quantify non-cell-autonomous responses. (Aim 3) Computational integration and causal inference will identify the molecular determinants and governing principles of drug response individuality in cancer, amenable to further validation. This proposal will thus improve our mechanistic understanding of cancer individuality and develop powerful new tools for OMICs-based precision medicine.
Max ERC Funding
1 500 000 €
Duration
Start date: 2018-11-01, End date: 2023-10-31
