Project acronym 2STEPPARKIN
Project A novel two-step model for neurodegeneration in Parkinson’s disease
Researcher (PI) Emi Nagoshi
Host Institution (HI) UNIVERSITE DE GENEVE
Call Details Starting Grant (StG), LS5, ERC-2012-StG_20111109
Summary Parkinson’s disease (PD) is the second most common neurodegenerative disorder primarily caused by the progressive loss of dopaminergic (DA) neurons in the substantia nigra (SN). Despite the advances in gene discovery associated with PD, the knowledge of the PD pathogenesis is largely limited to the involvement of these genes in the generic cell death pathways, and why degeneration is specific to DA neurons and why the degeneration is progressive remain enigmatic. Broad goal of our work is therefore to elucidate the mechanisms underlying specific and progressive DA neuron degeneration in PD. Our new Drosophila model of PD ⎯Fer2 gene loss-of-function mutation⎯ is unusually well suited to address these questions. Fer2 mutants exhibit specific and progressive death of brain DA neurons as well as severe locomotor defects and short life span. Strikingly, the death of DA neuron is initiated in a small cluster of Fer2-expressing DA neurons and subsequently propagates to Fer2-negative DA neurons. We therefore propose a novel two-step model of the neurodegeneration in PD: primary cell death occurs in a specific subset of dopamindegic neurons that are genetically defined, and subsequently the failure of the neuronal connectivity triggers and propagates secondary cell death to remaining DA neurons. In this research, we will test this hypothesis and investigate the underlying molecular mechanisms. This will be the first study to examine circuit-dependency in DA neuron degeneration. Our approach will use a combination of non-biased genomic techniques and candidate-based screening, in addition to the powerful Drosophila genetic toolbox. Furthermore, to test this hypothesis beyond the Drosophila model, we will establish new mouse models of PD that exhibit progressive DA neuron degeneration. Outcome of this research will likely revolutionize the understanding of PD pathogenesis and open an avenue toward the discovery of effective therapy strategies against PD.
Summary
Parkinson’s disease (PD) is the second most common neurodegenerative disorder primarily caused by the progressive loss of dopaminergic (DA) neurons in the substantia nigra (SN). Despite the advances in gene discovery associated with PD, the knowledge of the PD pathogenesis is largely limited to the involvement of these genes in the generic cell death pathways, and why degeneration is specific to DA neurons and why the degeneration is progressive remain enigmatic. Broad goal of our work is therefore to elucidate the mechanisms underlying specific and progressive DA neuron degeneration in PD. Our new Drosophila model of PD ⎯Fer2 gene loss-of-function mutation⎯ is unusually well suited to address these questions. Fer2 mutants exhibit specific and progressive death of brain DA neurons as well as severe locomotor defects and short life span. Strikingly, the death of DA neuron is initiated in a small cluster of Fer2-expressing DA neurons and subsequently propagates to Fer2-negative DA neurons. We therefore propose a novel two-step model of the neurodegeneration in PD: primary cell death occurs in a specific subset of dopamindegic neurons that are genetically defined, and subsequently the failure of the neuronal connectivity triggers and propagates secondary cell death to remaining DA neurons. In this research, we will test this hypothesis and investigate the underlying molecular mechanisms. This will be the first study to examine circuit-dependency in DA neuron degeneration. Our approach will use a combination of non-biased genomic techniques and candidate-based screening, in addition to the powerful Drosophila genetic toolbox. Furthermore, to test this hypothesis beyond the Drosophila model, we will establish new mouse models of PD that exhibit progressive DA neuron degeneration. Outcome of this research will likely revolutionize the understanding of PD pathogenesis and open an avenue toward the discovery of effective therapy strategies against PD.
Max ERC Funding
1 518 960 €
Duration
Start date: 2013-06-01, End date: 2018-05-31
Project acronym ACTIVIA
Project Visual Recognition of Function and Intention
Researcher (PI) Ivan Laptev
Host Institution (HI) INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE
Call Details Starting Grant (StG), PE6, ERC-2012-StG_20111012
Summary "Computer vision is concerned with the automated interpretation of images and video streams. Today's research is (mostly) aimed at answering queries such as ""Is this a picture of a dog?"", (classification) or sometimes ""Find the dog in this photo"" (detection). While categorisation and detection are useful for many tasks, inferring correct class labels is not the final answer to visual recognition. The categories and locations of objects do not provide direct understanding of their function i.e., how things work, what they can be used for, or how they can act and react. Such an understanding, however, would be highly desirable to answer currently unsolvable queries such as ""Am I in danger?"" or ""What can happen in this scene?"". Solving such queries is the aim of this proposal.
My goal is to uncover the functional properties of objects and the purpose of actions by addressing visual recognition from a different and yet unexplored perspective. The main novelty of this proposal is to leverage observations of people, i.e., their actions and interactions to automatically learn the use, the purpose and the function of objects and scenes from visual data. The project is timely as it builds upon the two key recent technological advances: (a) the immense progress in visual recognition of objects, scenes and human actions achieved in the last ten years, as well as (b) the emergence of a massive amount of public image and video data now available to train visual models.
ACTIVIA addresses fundamental research issues in automated interpretation of dynamic visual scenes, but its results are expected to serve as a basis for ground-breaking technological advances in practical applications. The recognition of functional properties and intentions as explored in this project will directly support high-impact applications such as detection of abnormal events, which are likely to revolutionise today's approaches to crime protection, hazard prevention, elderly care, and many others."
Summary
"Computer vision is concerned with the automated interpretation of images and video streams. Today's research is (mostly) aimed at answering queries such as ""Is this a picture of a dog?"", (classification) or sometimes ""Find the dog in this photo"" (detection). While categorisation and detection are useful for many tasks, inferring correct class labels is not the final answer to visual recognition. The categories and locations of objects do not provide direct understanding of their function i.e., how things work, what they can be used for, or how they can act and react. Such an understanding, however, would be highly desirable to answer currently unsolvable queries such as ""Am I in danger?"" or ""What can happen in this scene?"". Solving such queries is the aim of this proposal.
My goal is to uncover the functional properties of objects and the purpose of actions by addressing visual recognition from a different and yet unexplored perspective. The main novelty of this proposal is to leverage observations of people, i.e., their actions and interactions to automatically learn the use, the purpose and the function of objects and scenes from visual data. The project is timely as it builds upon the two key recent technological advances: (a) the immense progress in visual recognition of objects, scenes and human actions achieved in the last ten years, as well as (b) the emergence of a massive amount of public image and video data now available to train visual models.
ACTIVIA addresses fundamental research issues in automated interpretation of dynamic visual scenes, but its results are expected to serve as a basis for ground-breaking technological advances in practical applications. The recognition of functional properties and intentions as explored in this project will directly support high-impact applications such as detection of abnormal events, which are likely to revolutionise today's approaches to crime protection, hazard prevention, elderly care, and many others."
Max ERC Funding
1 497 420 €
Duration
Start date: 2013-01-01, End date: 2018-12-31
Project acronym ADAPT
Project Theory and Algorithms for Adaptive Particle Simulation
Researcher (PI) Stephane Redon
Host Institution (HI) INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE
Call Details Starting Grant (StG), PE6, ERC-2012-StG_20111012
Summary "During the twentieth century, the development of macroscopic engineering has been largely stimulated by progress in digital prototyping: cars, planes, boats, etc. are nowadays designed and tested on computers. Digital prototypes have progressively replaced actual ones, and effective computer-aided engineering tools have helped cut costs and reduce production cycles of these macroscopic systems.
The twenty-first century is most likely to see a similar development at the atomic scale. Indeed, the recent years have seen tremendous progress in nanotechnology - in particular in the ability to control matter at the atomic scale. Similar to what has happened with macroscopic engineering, powerful and generic computational tools will be needed to engineer complex nanosystems, through modeling and simulation. As a result, a major challenge is to develop efficient simulation methods and algorithms.
NANO-D, the INRIA research group I started in January 2008 in Grenoble, France, aims at developing
efficient computational methods for modeling and simulating complex nanosystems, both natural and artificial. In particular, NANO-D develops SAMSON, a software application which gathers all algorithms designed by the group and its collaborators (SAMSON: Software for Adaptive Modeling and Simulation Of Nanosystems).
In this project, I propose to develop a unified theory, and associated algorithms, for adaptive particle simulation. The proposed theory will avoid problems that plague current popular multi-scale or hybrid simulation approaches by simulating a single potential throughout the system, while allowing users to finely trade precision for computational speed.
I believe the full development of the adaptive particle simulation theory will have an important impact on current modeling and simulation practices, and will enable practical design of complex nanosystems on desktop computers, which should significantly boost the emergence of generic nano-engineering."
Summary
"During the twentieth century, the development of macroscopic engineering has been largely stimulated by progress in digital prototyping: cars, planes, boats, etc. are nowadays designed and tested on computers. Digital prototypes have progressively replaced actual ones, and effective computer-aided engineering tools have helped cut costs and reduce production cycles of these macroscopic systems.
The twenty-first century is most likely to see a similar development at the atomic scale. Indeed, the recent years have seen tremendous progress in nanotechnology - in particular in the ability to control matter at the atomic scale. Similar to what has happened with macroscopic engineering, powerful and generic computational tools will be needed to engineer complex nanosystems, through modeling and simulation. As a result, a major challenge is to develop efficient simulation methods and algorithms.
NANO-D, the INRIA research group I started in January 2008 in Grenoble, France, aims at developing
efficient computational methods for modeling and simulating complex nanosystems, both natural and artificial. In particular, NANO-D develops SAMSON, a software application which gathers all algorithms designed by the group and its collaborators (SAMSON: Software for Adaptive Modeling and Simulation Of Nanosystems).
In this project, I propose to develop a unified theory, and associated algorithms, for adaptive particle simulation. The proposed theory will avoid problems that plague current popular multi-scale or hybrid simulation approaches by simulating a single potential throughout the system, while allowing users to finely trade precision for computational speed.
I believe the full development of the adaptive particle simulation theory will have an important impact on current modeling and simulation practices, and will enable practical design of complex nanosystems on desktop computers, which should significantly boost the emergence of generic nano-engineering."
Max ERC Funding
1 476 882 €
Duration
Start date: 2012-09-01, End date: 2017-08-31
Project acronym AGELESS
Project Comparative genomics / ‘wildlife’ transcriptomics uncovers the mechanisms of halted ageing in mammals
Researcher (PI) Emma Teeling
Host Institution (HI) UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Call Details Starting Grant (StG), LS2, ERC-2012-StG_20111109
Summary "Ageing is the gradual and irreversible breakdown of living systems associated with the advancement of time, which leads to an increase in vulnerability and eventual mortality. Despite recent advances in ageing research, the intrinsic complexity of the ageing process has prevented a full understanding of this process, therefore, ageing remains a grand challenge in contemporary biology. In AGELESS, we will tackle this challenge by uncovering the molecular mechanisms of halted ageing in a unique model system, the bats. Bats are the longest-lived mammals relative to their body size, and defy the ‘rate-of-living’ theories as they use twice as much the energy as other species of considerable size, but live far longer. This suggests that bats have some underlying mechanisms that may explain their exceptional longevity. In AGELESS, we will identify the molecular mechanisms that enable mammals to achieve extraordinary longevity, using state-of-the-art comparative genomic methodologies focused on bats. We will identify, using population transcriptomics and telomere/mtDNA genomics, the molecular changes that occur in an ageing wild population of bats to uncover how bats ‘age’ so slowly compared with other mammals. In silico whole genome analyses, field based ageing transcriptomic data, mtDNA and telomeric studies will be integrated and analysed using a networks approach, to ascertain how these systems interact to halt ageing. For the first time, we will be able to utilize the diversity seen within nature to identify key molecular targets and regions that regulate and control ageing in mammals. AGELESS will provide a deeper understanding of the causal mechanisms of ageing, potentially uncovering the crucial molecular pathways that can be modified to halt, alleviate and perhaps even reverse this process in man."
Summary
"Ageing is the gradual and irreversible breakdown of living systems associated with the advancement of time, which leads to an increase in vulnerability and eventual mortality. Despite recent advances in ageing research, the intrinsic complexity of the ageing process has prevented a full understanding of this process, therefore, ageing remains a grand challenge in contemporary biology. In AGELESS, we will tackle this challenge by uncovering the molecular mechanisms of halted ageing in a unique model system, the bats. Bats are the longest-lived mammals relative to their body size, and defy the ‘rate-of-living’ theories as they use twice as much the energy as other species of considerable size, but live far longer. This suggests that bats have some underlying mechanisms that may explain their exceptional longevity. In AGELESS, we will identify the molecular mechanisms that enable mammals to achieve extraordinary longevity, using state-of-the-art comparative genomic methodologies focused on bats. We will identify, using population transcriptomics and telomere/mtDNA genomics, the molecular changes that occur in an ageing wild population of bats to uncover how bats ‘age’ so slowly compared with other mammals. In silico whole genome analyses, field based ageing transcriptomic data, mtDNA and telomeric studies will be integrated and analysed using a networks approach, to ascertain how these systems interact to halt ageing. For the first time, we will be able to utilize the diversity seen within nature to identify key molecular targets and regions that regulate and control ageing in mammals. AGELESS will provide a deeper understanding of the causal mechanisms of ageing, potentially uncovering the crucial molecular pathways that can be modified to halt, alleviate and perhaps even reverse this process in man."
Max ERC Funding
1 499 768 €
Duration
Start date: 2013-01-01, End date: 2017-12-31
Project acronym ALGAME
Project Algorithms, Games, Mechanisms, and the Price of Anarchy
Researcher (PI) Elias Koutsoupias
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Call Details Advanced Grant (AdG), PE6, ERC-2012-ADG_20120216
Summary The objective of this proposal is to bring together a local team of young researchers who will work closely with international collaborators to advance the state of the art of Algorithmic Game Theory and open new venues of research at the interface of Computer Science, Game Theory, and Economics. The proposal consists mainly of three intertwined research strands: algorithmic mechanism design, price of anarchy, and online algorithms.
Specifically, we will attempt to resolve some outstanding open problems in algorithmic mechanism design: characterizing the incentive compatible mechanisms for important domains, such as the domain of combinatorial auctions, and resolving the approximation ratio of mechanisms for scheduling unrelated machines. More generally, we will study centralized and distributed algorithms whose inputs are controlled by selfish agents that are interested in the outcome of the computation. We will investigate new notions of mechanisms with strong truthfulness and limited susceptibility to externalities that can facilitate modular design of mechanisms of complex domains.
We will expand the current research on the price of anarchy to time-dependent games where the players can select not only how to act but also when to act. We also plan to resolve outstanding questions on the price of stability and to build a robust approach to these questions, similar to smooth analysis. For repeated games, we will investigate convergence of simple strategies (e.g., fictitious play), online fairness, and strategic considerations (e.g., metagames). More generally, our aim is to find a productive formulation of playing unknown games by drawing on the fields of online algorithms and machine learning.
Summary
The objective of this proposal is to bring together a local team of young researchers who will work closely with international collaborators to advance the state of the art of Algorithmic Game Theory and open new venues of research at the interface of Computer Science, Game Theory, and Economics. The proposal consists mainly of three intertwined research strands: algorithmic mechanism design, price of anarchy, and online algorithms.
Specifically, we will attempt to resolve some outstanding open problems in algorithmic mechanism design: characterizing the incentive compatible mechanisms for important domains, such as the domain of combinatorial auctions, and resolving the approximation ratio of mechanisms for scheduling unrelated machines. More generally, we will study centralized and distributed algorithms whose inputs are controlled by selfish agents that are interested in the outcome of the computation. We will investigate new notions of mechanisms with strong truthfulness and limited susceptibility to externalities that can facilitate modular design of mechanisms of complex domains.
We will expand the current research on the price of anarchy to time-dependent games where the players can select not only how to act but also when to act. We also plan to resolve outstanding questions on the price of stability and to build a robust approach to these questions, similar to smooth analysis. For repeated games, we will investigate convergence of simple strategies (e.g., fictitious play), online fairness, and strategic considerations (e.g., metagames). More generally, our aim is to find a productive formulation of playing unknown games by drawing on the fields of online algorithms and machine learning.
Max ERC Funding
2 461 000 €
Duration
Start date: 2013-04-01, End date: 2019-03-31
Project acronym ALLEGRO
Project Active large-scale learning for visual recognition
Researcher (PI) Cordelia Schmid
Host Institution (HI) INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE
Call Details Advanced Grant (AdG), PE6, ERC-2012-ADG_20120216
Summary A massive and ever growing amount of digital image and video content
is available today, on sites such as
Flickr and YouTube, in audiovisual archives such as those of BBC and
INA, and in personal collections. In most cases, it comes with
additional information, such as text, audio or other metadata, that forms a
rather sparse and noisy, yet rich and diverse source of annotation,
ideally suited to emerging weakly supervised and active machine
learning technology. The ALLEGRO project will take visual recognition
to the next level by using this largely untapped source of data to
automatically learn visual models. The main research objective of
our project is the development of new algorithms and computer software
capable of autonomously exploring evolving data collections, selecting
the relevant information, and determining the visual models most
appropriate for different object, scene, and activity categories. An
emphasis will be put on learning visual models from video, a
particularly rich source of information, and on the representation of
human activities, one of today's most challenging problems in computer
vision. Although this project addresses fundamental research
issues, it is expected to result in significant advances in
high-impact applications that range from visual mining of the Web and
automated annotation and organization of family photo and video albums
to large-scale information retrieval in television archives.
Summary
A massive and ever growing amount of digital image and video content
is available today, on sites such as
Flickr and YouTube, in audiovisual archives such as those of BBC and
INA, and in personal collections. In most cases, it comes with
additional information, such as text, audio or other metadata, that forms a
rather sparse and noisy, yet rich and diverse source of annotation,
ideally suited to emerging weakly supervised and active machine
learning technology. The ALLEGRO project will take visual recognition
to the next level by using this largely untapped source of data to
automatically learn visual models. The main research objective of
our project is the development of new algorithms and computer software
capable of autonomously exploring evolving data collections, selecting
the relevant information, and determining the visual models most
appropriate for different object, scene, and activity categories. An
emphasis will be put on learning visual models from video, a
particularly rich source of information, and on the representation of
human activities, one of today's most challenging problems in computer
vision. Although this project addresses fundamental research
issues, it is expected to result in significant advances in
high-impact applications that range from visual mining of the Web and
automated annotation and organization of family photo and video albums
to large-scale information retrieval in television archives.
Max ERC Funding
2 493 322 €
Duration
Start date: 2013-04-01, End date: 2019-03-31
Project acronym AMYLOID
Project Identification and modulation of pathogenic Amyloid beta-peptide species
Researcher (PI) Christian Haass
Host Institution (HI) LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN
Call Details Advanced Grant (AdG), LS5, ERC-2012-ADG_20120314
Summary The frequency of Alzheimer's disease (AD) will dramatically increase in the ageing western society during the next decades. Currently, about 18 million people suffer worldwide from AD. Since no cure is available, this devastating disorder represents one of the most challenging socio-economical problems of our future. As onset and progression of AD is triggered by the amyloid cascade, I will put particular attention on amyloid ß-peptide (Aß). The reason for this approach is, that even though 20 years ago the Aß generating processing pathway was identified (Haass et al., Nature 1992a & b), the identity of the Aß species, which initiate the deadly cascade is still unknown. I will first tackle this challenge by investigating if a novel and so far completely overlooked proteolytic processing pathway is involved in the generation of Aß species capable to initiate spreading of pathology and neurotoxicity. I will then search for modulating proteins, which could affect generation of pathological Aß species. This includes a genome-wide screen for modifiers of gamma-secretase, one of the proteases involved in Aß generation as well as a targeted search for RNA binding proteins capable to posttranscriptionally regulate beta- and alpha-secretase. In a disease-crossing approach, RNA binding proteins, which were recently found not only to be deposited in Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis but also in many AD cases, will be investigated for their potential to modulate Aß aggregation and AD pathology. Modifiers and novel antibodies specifically recognizing neurotoxic Aß assemblies will be validated for their potential not only to prevent amyloid plaque formation, but also spreading of pathology as well as neurotoxicity. In vivo validations include studies in innovative zebrafish models, which allow life imaging of neuronal cell death, as well as the establishment of microPET amyloid imaging for longitudinal studies in individual animals.
Summary
The frequency of Alzheimer's disease (AD) will dramatically increase in the ageing western society during the next decades. Currently, about 18 million people suffer worldwide from AD. Since no cure is available, this devastating disorder represents one of the most challenging socio-economical problems of our future. As onset and progression of AD is triggered by the amyloid cascade, I will put particular attention on amyloid ß-peptide (Aß). The reason for this approach is, that even though 20 years ago the Aß generating processing pathway was identified (Haass et al., Nature 1992a & b), the identity of the Aß species, which initiate the deadly cascade is still unknown. I will first tackle this challenge by investigating if a novel and so far completely overlooked proteolytic processing pathway is involved in the generation of Aß species capable to initiate spreading of pathology and neurotoxicity. I will then search for modulating proteins, which could affect generation of pathological Aß species. This includes a genome-wide screen for modifiers of gamma-secretase, one of the proteases involved in Aß generation as well as a targeted search for RNA binding proteins capable to posttranscriptionally regulate beta- and alpha-secretase. In a disease-crossing approach, RNA binding proteins, which were recently found not only to be deposited in Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis but also in many AD cases, will be investigated for their potential to modulate Aß aggregation and AD pathology. Modifiers and novel antibodies specifically recognizing neurotoxic Aß assemblies will be validated for their potential not only to prevent amyloid plaque formation, but also spreading of pathology as well as neurotoxicity. In vivo validations include studies in innovative zebrafish models, which allow life imaging of neuronal cell death, as well as the establishment of microPET amyloid imaging for longitudinal studies in individual animals.
Max ERC Funding
2 497 020 €
Duration
Start date: 2013-03-01, End date: 2018-02-28
Project acronym AnoPath
Project Genetics of mosquito resistance to pathogens
Researcher (PI) Kenneth Du Souchet Vernick
Host Institution (HI) INSTITUT PASTEUR
Call Details Advanced Grant (AdG), LS2, ERC-2012-ADG_20120314
Summary Malaria parasite infection in humans has been called “the strongest known force for evolutionary selection in the recent history of the human genome”, and I hypothesize that a similar statement may apply to the mosquito vector, which is the definitive host of the malaria parasite. We previously discovered efficient malaria-resistance mechanisms in natural populations of the African malaria vector, Anopheles gambiae. Aim 1 of the proposed project will implement a novel genetic mapping design to systematically survey the mosquito population for common and rare genetic variants of strong effect against the human malaria parasite, Plasmodium falciparum. A product of the mapping design will be living mosquito families carrying the resistance loci. Aim 2 will use the segregating families to functionally dissect the underlying molecular mechanisms controlled by the loci, including determination of the pathogen specificity spectra of the host-defense traits. Aim 3 targets arbovirus transmission, where Anopheles mosquitoes transmit human malaria but not arboviruses such as Dengue and Chikungunya, even though the two mosquitoes bite the same people and are exposed to the same pathogens, often in malaria-arbovirus co-infections. We will use deep-sequencing to detect processing of the arbovirus dsRNA intermediates of replication produced by the RNAi pathway of the mosquitoes. The results will reveal important new information about differences in the efficiency and quality of the RNAi response between mosquitoes, which is likely to underlie at least part of the host specificity of arbovirus transmission. The 3 Aims will make significant contributions to understanding malaria and arbovirus transmission, major global public health problems, will aid the development of a next generation of vector surveillance and control tools, and will produce a definitive description of the major genetic factors influencing host-pathogen interactions in mosquito immunity.
Summary
Malaria parasite infection in humans has been called “the strongest known force for evolutionary selection in the recent history of the human genome”, and I hypothesize that a similar statement may apply to the mosquito vector, which is the definitive host of the malaria parasite. We previously discovered efficient malaria-resistance mechanisms in natural populations of the African malaria vector, Anopheles gambiae. Aim 1 of the proposed project will implement a novel genetic mapping design to systematically survey the mosquito population for common and rare genetic variants of strong effect against the human malaria parasite, Plasmodium falciparum. A product of the mapping design will be living mosquito families carrying the resistance loci. Aim 2 will use the segregating families to functionally dissect the underlying molecular mechanisms controlled by the loci, including determination of the pathogen specificity spectra of the host-defense traits. Aim 3 targets arbovirus transmission, where Anopheles mosquitoes transmit human malaria but not arboviruses such as Dengue and Chikungunya, even though the two mosquitoes bite the same people and are exposed to the same pathogens, often in malaria-arbovirus co-infections. We will use deep-sequencing to detect processing of the arbovirus dsRNA intermediates of replication produced by the RNAi pathway of the mosquitoes. The results will reveal important new information about differences in the efficiency and quality of the RNAi response between mosquitoes, which is likely to underlie at least part of the host specificity of arbovirus transmission. The 3 Aims will make significant contributions to understanding malaria and arbovirus transmission, major global public health problems, will aid the development of a next generation of vector surveillance and control tools, and will produce a definitive description of the major genetic factors influencing host-pathogen interactions in mosquito immunity.
Max ERC Funding
2 307 800 €
Duration
Start date: 2013-03-01, End date: 2018-02-28
Project acronym Anti-Virome
Project A combined evolutionary and proteomics approach to the discovery, induction and application of antiviral immunity factors
Researcher (PI) Frank Kirchhoff
Host Institution (HI) UNIVERSITAET ULM
Call Details Advanced Grant (AdG), LS6, ERC-2012-ADG_20120314
Summary "Humans are equipped with a variety of intrinsic immunity or host restriction factors. These evolved under positive selection pressure for diversification and represent a first line of defence against invading viruses. Unfortunately, however, many pathogens have evolved effective antagonists against our defences. For example, the capability of HIV-1 to counteract human restriction factors that interfere with reverse transcription, uncoating and virion release has been a prerequisite for the global spread of AIDS. We are just beginning to understand the diversity and induction of antiretroviral factors and how pandemic HIV-1 group M (major) strains evolved to counteract all of them. Here, I propose to use a genetics, proteomics and evolutionary approach to discover and define as-yet-unknown antiviral effectors and their inducers. To identify novel antiviral factors, we will examine the capability of all primate genes that are under strong positive selection pressure to inhibit HIV and its simian (SIV) precursors. This examination from the evolutionary perspective of the invading pathogen will also reveal which adaptations allowed HIV-1 to cause the AIDS pandemic. Furthermore, complex peptide-protein libraries representing essentially the entire human peptidome, will be utilized to identify novel specific inducers of antiviral restriction factors. My ultimate aim is to unravel the network of inducers and effectors of antiviral immunity - the ""Anti-Virome"" - and to use this knowledge to develop novel effective preventive and therapeutic approaches based on the induction of combinations of antiviral factors targeting different steps of the viral life cycle. The results of this innovative and interdisciplinary program will provide fundamental new insights into intrinsic immunity and may offer alternatives to conventional vaccine and therapeutic approaches because most restriction factors have broad antiviral activity and are thus effective against various pathogens."
Summary
"Humans are equipped with a variety of intrinsic immunity or host restriction factors. These evolved under positive selection pressure for diversification and represent a first line of defence against invading viruses. Unfortunately, however, many pathogens have evolved effective antagonists against our defences. For example, the capability of HIV-1 to counteract human restriction factors that interfere with reverse transcription, uncoating and virion release has been a prerequisite for the global spread of AIDS. We are just beginning to understand the diversity and induction of antiretroviral factors and how pandemic HIV-1 group M (major) strains evolved to counteract all of them. Here, I propose to use a genetics, proteomics and evolutionary approach to discover and define as-yet-unknown antiviral effectors and their inducers. To identify novel antiviral factors, we will examine the capability of all primate genes that are under strong positive selection pressure to inhibit HIV and its simian (SIV) precursors. This examination from the evolutionary perspective of the invading pathogen will also reveal which adaptations allowed HIV-1 to cause the AIDS pandemic. Furthermore, complex peptide-protein libraries representing essentially the entire human peptidome, will be utilized to identify novel specific inducers of antiviral restriction factors. My ultimate aim is to unravel the network of inducers and effectors of antiviral immunity - the ""Anti-Virome"" - and to use this knowledge to develop novel effective preventive and therapeutic approaches based on the induction of combinations of antiviral factors targeting different steps of the viral life cycle. The results of this innovative and interdisciplinary program will provide fundamental new insights into intrinsic immunity and may offer alternatives to conventional vaccine and therapeutic approaches because most restriction factors have broad antiviral activity and are thus effective against various pathogens."
Max ERC Funding
1 915 200 €
Duration
Start date: 2013-04-01, End date: 2018-03-31
Project acronym APOQUANT
Project The quantitative Bcl-2 interactome in apoptosis: decoding how cancer cells escape death
Researcher (PI) Ana Jesús García Sáez
Host Institution (HI) EBERHARD KARLS UNIVERSITAET TUEBINGEN
Call Details Starting Grant (StG), LS3, ERC-2012-StG_20111109
Summary The proteins of the Bcl-2 family function as key regulators of apoptosis by controlling the permeabilization of the mitochondrial outer membrane. They form an intricate, fine-tuned interaction network which is altered in cancer cells to avoid cell death. Currently, we do not understand how signaling within this network, which combines events in cytosol and membranes, is orchestrated to decide the cell fate. The main goal of this proposal is to unravel how apoptosis signaling is integrated by the Bcl-2 network by determining the quantitative Bcl-2 interactome and building with it a mathematical model that identifies which interactions determine the overall outcome. To this aim, we have established a reconstituted system for the quantification of the interactions between Bcl-2 proteins not only in solution but also in membranes at the single molecule level by fluorescence correlation spectroscopy (FCS).
(1) This project aims to quantify the relative affinities between an reconstituted Bcl-2 network by FCS.
(2) This will be combined with quantitative studies in living cells, which include the signaling pathway in its entirety. To this aim, we will develop new FCS methods for mitochondria.
(3) The structural and dynamic aspects of the Bcl-2 network will be studied by super resolution and live cell microscopy.
(4) The acquired knowledge will be used to build a mathematical model that uncovers how the multiple interactions within the Bcl-2 network are integrated and identifies critical steps in apoptosis regulation.
These studies are expected to broaden the general knowledge about the design principles of cellular signaling as well as how cancer cells alter the Bcl-2 network to escape cell death. This systems analysis will allow us to predict which perturbations in the Bcl-2 network of cancer cells can switch signaling towards cell death. Ultimately it could be translated into clinical applications for anticancer therapy.
Summary
The proteins of the Bcl-2 family function as key regulators of apoptosis by controlling the permeabilization of the mitochondrial outer membrane. They form an intricate, fine-tuned interaction network which is altered in cancer cells to avoid cell death. Currently, we do not understand how signaling within this network, which combines events in cytosol and membranes, is orchestrated to decide the cell fate. The main goal of this proposal is to unravel how apoptosis signaling is integrated by the Bcl-2 network by determining the quantitative Bcl-2 interactome and building with it a mathematical model that identifies which interactions determine the overall outcome. To this aim, we have established a reconstituted system for the quantification of the interactions between Bcl-2 proteins not only in solution but also in membranes at the single molecule level by fluorescence correlation spectroscopy (FCS).
(1) This project aims to quantify the relative affinities between an reconstituted Bcl-2 network by FCS.
(2) This will be combined with quantitative studies in living cells, which include the signaling pathway in its entirety. To this aim, we will develop new FCS methods for mitochondria.
(3) The structural and dynamic aspects of the Bcl-2 network will be studied by super resolution and live cell microscopy.
(4) The acquired knowledge will be used to build a mathematical model that uncovers how the multiple interactions within the Bcl-2 network are integrated and identifies critical steps in apoptosis regulation.
These studies are expected to broaden the general knowledge about the design principles of cellular signaling as well as how cancer cells alter the Bcl-2 network to escape cell death. This systems analysis will allow us to predict which perturbations in the Bcl-2 network of cancer cells can switch signaling towards cell death. Ultimately it could be translated into clinical applications for anticancer therapy.
Max ERC Funding
1 462 900 €
Duration
Start date: 2013-04-01, End date: 2019-03-31
