Project acronym AUTO-CD
Project COELIAC DISEASE: UNDERSTANDING HOW A FOREIGN PROTEIN DRIVES AUTOANTIBODY FORMATION
Researcher (PI) Ludvig Magne Sollid
Host Institution (HI) UNIVERSITETET I OSLO
Call Details Advanced Grant (AdG), LS6, ERC-2010-AdG_20100317
Summary The goal of this project is to understand the mechanism of how highly disease specific autoantibodies are generated in response to the exposure to a foreign antigen. IgA autoantibodies reactive with the enzyme transglutaminase 2 (TG2) are typical of coeliac disease (CD). These antibodies are only present in subjects who are HLA-DQ2 or -DQ8, and their production is dependent on dietary gluten exposure. This suggests that CD4+ gluten reactive T cells, which are found in CD patients and which recognise gluten peptides deamidated by TG2 in context of DQ2 or DQ8, are implicated in the generation of these autoantibodies. Many small intestinal IgA+ plasma cells express membrane Ig hence allowing isolation of antigen specific cells. Whereas control subjects lack anti-TG2 IgA+ plasma cells, on average 10% of the plasma cells of CD patients are specific for TG2. We have sorted single TG2 reactive IgA+ plasma cells, cloned their VH and VL genes and expressed recombinant mAbs. So far we have expressed 26 TG2 specific mAbs. There is a strong bias for VH5-51 usage, and surprisingly the antibodies are modestly mutated. TG2 acts on specific glutamine residues and can either crosslink these to other proteins (transamidation) or hydrolyse the glutamine to a glutamate (deamidation). None of the 18 mAbs tested affected either transamidation or deamidation leading us to hypothesise that retained crosslinking ability of TG2 when bound to membrane Ig of B cells is an integral part of the anti-TG2 response. Four models of how activation of TG2 specific B cells is facilitated by TG2 crosslinking and the help of gluten reactive CD4 T cells are proposed. These four models will be extensively tested including doing in vivo assays with a newly generated transgenic anti-TG2 immunoglobulin knock-in mouse model.
Summary
The goal of this project is to understand the mechanism of how highly disease specific autoantibodies are generated in response to the exposure to a foreign antigen. IgA autoantibodies reactive with the enzyme transglutaminase 2 (TG2) are typical of coeliac disease (CD). These antibodies are only present in subjects who are HLA-DQ2 or -DQ8, and their production is dependent on dietary gluten exposure. This suggests that CD4+ gluten reactive T cells, which are found in CD patients and which recognise gluten peptides deamidated by TG2 in context of DQ2 or DQ8, are implicated in the generation of these autoantibodies. Many small intestinal IgA+ plasma cells express membrane Ig hence allowing isolation of antigen specific cells. Whereas control subjects lack anti-TG2 IgA+ plasma cells, on average 10% of the plasma cells of CD patients are specific for TG2. We have sorted single TG2 reactive IgA+ plasma cells, cloned their VH and VL genes and expressed recombinant mAbs. So far we have expressed 26 TG2 specific mAbs. There is a strong bias for VH5-51 usage, and surprisingly the antibodies are modestly mutated. TG2 acts on specific glutamine residues and can either crosslink these to other proteins (transamidation) or hydrolyse the glutamine to a glutamate (deamidation). None of the 18 mAbs tested affected either transamidation or deamidation leading us to hypothesise that retained crosslinking ability of TG2 when bound to membrane Ig of B cells is an integral part of the anti-TG2 response. Four models of how activation of TG2 specific B cells is facilitated by TG2 crosslinking and the help of gluten reactive CD4 T cells are proposed. These four models will be extensively tested including doing in vivo assays with a newly generated transgenic anti-TG2 immunoglobulin knock-in mouse model.
Max ERC Funding
2 291 045 €
Duration
Start date: 2011-05-01, End date: 2017-04-30
Project acronym ENSEMBLE
Project Neural mechanisms for memory retrieval
Researcher (PI) May-Britt Moser
Host Institution (HI) NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET NTNU
Call Details Advanced Grant (AdG), LS5, ERC-2010-AdG_20100317
Summary Memory is one of the most extraordinary phenomena in biology. The mammalian brain stores billions of bits of information but the most remarkable property of memory is perhaps not its capacity but the speed at which the correct information can be retrieved from a pool of thousands or millions of competing alternatives. Despite more than hundred years of systematic study of the phenomenon, scientists are still largely ignorant about the mechanisms that enable mammalian brains to outperform even the best search engines. One of the greatest challenges has been the dynamic nature of memory. Whereas memories can be retrieved over time periods as short as milliseconds, underlying coding principles are normally inferred from activity time-averaged across many minutes. In the present proposal, I shall introduce a new ¿teleportation procedure¿ developed in my lab to monitor the representation of past and present environments in large ensembles of rat hippocampal neurons at ethologically valid time scales. By monitoring the evolution of hippocampal ensemble representations at millisecond resolution during retrieval of a non-local experience, I shall ask
(i) what is the minimum temporal unit of a hippocampal representation,
(ii) how is one representational unit replaced by the next in a sequence,
(iii) what external signals control switches between alternative representations,
(iv) how are representations synchronized across anatomical space, and
(v) when do adult-like retrieval mechanisms appear during ontogenesis of the nervous system and to what extent can their early absence be linked to infantile amnesia.
The proposed research programme is expected to identify some of the key principles for dynamic representation and retrieval of episodic memory in the mammalian hippocampus.
Summary
Memory is one of the most extraordinary phenomena in biology. The mammalian brain stores billions of bits of information but the most remarkable property of memory is perhaps not its capacity but the speed at which the correct information can be retrieved from a pool of thousands or millions of competing alternatives. Despite more than hundred years of systematic study of the phenomenon, scientists are still largely ignorant about the mechanisms that enable mammalian brains to outperform even the best search engines. One of the greatest challenges has been the dynamic nature of memory. Whereas memories can be retrieved over time periods as short as milliseconds, underlying coding principles are normally inferred from activity time-averaged across many minutes. In the present proposal, I shall introduce a new ¿teleportation procedure¿ developed in my lab to monitor the representation of past and present environments in large ensembles of rat hippocampal neurons at ethologically valid time scales. By monitoring the evolution of hippocampal ensemble representations at millisecond resolution during retrieval of a non-local experience, I shall ask
(i) what is the minimum temporal unit of a hippocampal representation,
(ii) how is one representational unit replaced by the next in a sequence,
(iii) what external signals control switches between alternative representations,
(iv) how are representations synchronized across anatomical space, and
(v) when do adult-like retrieval mechanisms appear during ontogenesis of the nervous system and to what extent can their early absence be linked to infantile amnesia.
The proposed research programme is expected to identify some of the key principles for dynamic representation and retrieval of episodic memory in the mammalian hippocampus.
Max ERC Funding
2 499 074 €
Duration
Start date: 2011-11-01, End date: 2017-10-31
Project acronym RNA+P=123D
Project Breaking the code of RNA sequence-structure-function relationships: New strategies and tools for modelling and engineering of RNA and RNA-protein complexes
Researcher (PI) Janusz Marek Bujnicki
Host Institution (HI) INTERNATIONAL INSTITUTE OF MOLECULAR AND CELL BIOLOGY
Call Details Starting Grant (StG), LS2, ERC-2010-StG_20091118
Summary Ribonucleic acid (RNA) is a large class of macromolecules that plays a key role in the communication of biological information between DNA and proteins. RNAs have been also shown to perform enzymatic catalysis. Recently, numerous new RNAs have been identified and shown to perform essential regulatory roles in cells.
As with proteins, the function of RNA depends on its structure, which in turn is encoded in the linear sequence. The secondary structure of RNA is defined by canonical base pairs, while the tertiary (3D) structure is formed mostly by non-canonical base pairs that form three-dimensional motifs. RNA is similar to proteins in that the development of methods for 3D structure prediction is absolutely essential to functionally interpret the information encoded in the primary sequence of genes. For proteins there are many freely available methods for automated protein 3D structure prediction that produce reasonably accurate and useful models. There are also methods for objective assessment of the protein model quality. However, there are no such methods for automated 3D structure modelling of RNA. There are only methods for RNA secondary structure prediction and a few methods for manual 3D modelling, but no automated methods for comparative modelling, fold-recognition of RNA, and evaluation of models. Only recently a few methods for de novo folding of RNA appeared, but they can provide useful models only for very short molecules.
Recently, inspired by methodology for protein modelling, we have developed prototype tools for both comparative (template-based) and de novo (template-free) modelling of RNA, which allow for building models for very large RNA molecules. These tools will be further optimized and tested. The major goal is to developed tools for RNA modelling to the level of existing protein-modelling methods and to combine RNA and protein-centric methods to allow multiscale modelling of protein-nucleic acid complexes, either with or without the aid of experimental data. This proposal also includes the development of methods for the assessment of model quality and benchmarking of methods. The software tools and the theoretical predictions will be extensively tested (also by experimental verification of models), optimized and applied to biologically and medically relevant RNAs and complexes.
In one sentence: The aim of this project is to use bioinformatics and experimental methods to crack the code of sequence-structure relationships in RNA and RNA-protein complexes and to revolutionise the field of RNA & RNP modelling and structure/function analyses.
Summary
Ribonucleic acid (RNA) is a large class of macromolecules that plays a key role in the communication of biological information between DNA and proteins. RNAs have been also shown to perform enzymatic catalysis. Recently, numerous new RNAs have been identified and shown to perform essential regulatory roles in cells.
As with proteins, the function of RNA depends on its structure, which in turn is encoded in the linear sequence. The secondary structure of RNA is defined by canonical base pairs, while the tertiary (3D) structure is formed mostly by non-canonical base pairs that form three-dimensional motifs. RNA is similar to proteins in that the development of methods for 3D structure prediction is absolutely essential to functionally interpret the information encoded in the primary sequence of genes. For proteins there are many freely available methods for automated protein 3D structure prediction that produce reasonably accurate and useful models. There are also methods for objective assessment of the protein model quality. However, there are no such methods for automated 3D structure modelling of RNA. There are only methods for RNA secondary structure prediction and a few methods for manual 3D modelling, but no automated methods for comparative modelling, fold-recognition of RNA, and evaluation of models. Only recently a few methods for de novo folding of RNA appeared, but they can provide useful models only for very short molecules.
Recently, inspired by methodology for protein modelling, we have developed prototype tools for both comparative (template-based) and de novo (template-free) modelling of RNA, which allow for building models for very large RNA molecules. These tools will be further optimized and tested. The major goal is to developed tools for RNA modelling to the level of existing protein-modelling methods and to combine RNA and protein-centric methods to allow multiscale modelling of protein-nucleic acid complexes, either with or without the aid of experimental data. This proposal also includes the development of methods for the assessment of model quality and benchmarking of methods. The software tools and the theoretical predictions will be extensively tested (also by experimental verification of models), optimized and applied to biologically and medically relevant RNAs and complexes.
In one sentence: The aim of this project is to use bioinformatics and experimental methods to crack the code of sequence-structure relationships in RNA and RNA-protein complexes and to revolutionise the field of RNA & RNP modelling and structure/function analyses.
Max ERC Funding
1 500 000 €
Duration
Start date: 2011-01-01, End date: 2015-12-31
Project acronym STOCHPOP
Project Stochastic Population Biology in a Fluctuating Environment
Researcher (PI) Bernt-Erik Sæther
Host Institution (HI) NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET NTNU
Call Details Advanced Grant (AdG), LS8, ERC-2010-AdG_20100317
Summary The aim of this project is to produce a new synthesis integrating ecological and evolutionary processes. This synthetic approach is based on the fundamental premise that the effects of environmental stochasticity are essential for the understanding of biological processes at every time scale because all natural populations are exposed to a fluctuating environment. Following my recent advances in the development of stochastic population models I will in this proposal address three questions. First, I will examine to whether the ecological effects of a fluctuating environment can be predicted from some basic set of characters distributing species along a slow-fast continuum of life history variation. Secondly, I will using my own long-term study systems partition selection on fitness-related traits into different hierarchical components, which all must be estimated for predicting the rate of evolutionary changes in quantitative characters. Thirdly, I will examine to what extent using a comparative approach how the strength of fluctuating selection caused by environmental change is predictable from basic life history characteristics. I expect that any emerging patterns arising from the evaluation of these hypotheses will represent a major break-through in evolutionary biology because it will enable identification of general principles and processes that affect the rate of change of populations both at ecological and evolutionary time scales and hence provide tools for development of quantitative predictions for the expected rate of Darwinian evolution in fluctuating environments. I further anticipate that the knowledge advanced will have significant implications for research in population biology and its future application in applied conservation biology.
Summary
The aim of this project is to produce a new synthesis integrating ecological and evolutionary processes. This synthetic approach is based on the fundamental premise that the effects of environmental stochasticity are essential for the understanding of biological processes at every time scale because all natural populations are exposed to a fluctuating environment. Following my recent advances in the development of stochastic population models I will in this proposal address three questions. First, I will examine to whether the ecological effects of a fluctuating environment can be predicted from some basic set of characters distributing species along a slow-fast continuum of life history variation. Secondly, I will using my own long-term study systems partition selection on fitness-related traits into different hierarchical components, which all must be estimated for predicting the rate of evolutionary changes in quantitative characters. Thirdly, I will examine to what extent using a comparative approach how the strength of fluctuating selection caused by environmental change is predictable from basic life history characteristics. I expect that any emerging patterns arising from the evaluation of these hypotheses will represent a major break-through in evolutionary biology because it will enable identification of general principles and processes that affect the rate of change of populations both at ecological and evolutionary time scales and hence provide tools for development of quantitative predictions for the expected rate of Darwinian evolution in fluctuating environments. I further anticipate that the knowledge advanced will have significant implications for research in population biology and its future application in applied conservation biology.
Max ERC Funding
2 000 000 €
Duration
Start date: 2011-05-01, End date: 2016-04-30
