Project acronym BIGGER
Project Biophysics in gene regulation - A genome wide approach
Researcher (PI) Johan Elf
Host Institution (HI) UPPSALA UNIVERSITET
Call Details Advanced Grant (AdG), LS2, ERC-2019-ADG
Summary In this project, we will develop and use technology that combines synthetic genomics and live-cell imaging. These methods make it possible to study the intracellular biophysics at single-molecule detail in thousands of genetically different bacterial strains in parallel. Our approach is based on in situ genotyping of a barcoded strain library after phenotyping has been performed by live-cell imaging. Within the scope of the proposed project, the new technology will be used to solve mechanistic and structural questions of the bacterial cell cycle.
To this end, we will explore two parallel but complementary applications. In the first application, we will determine the dynamic 3D structure of the E. coli chromosome at 1kb resolution throughout the cell cycle. The structure determination can be seen as a live-cell version of chromatin conformation capture, where we will follow the 3D distances of 10 000 pairs of chromosomal loci over the cell cycle at high resolution. In the second application, we will make a complete CRISPRi knockdown strain library where we can follow the replication forks of the E. coli chromosome and septum formation over the cell cycle in individual cells. Using this strategy, we will resolve how individual gene products contribute to the cell-to-cell accuracy in replication initiation and cell division. In particular, this approach allows us to address the challenging question of size sensing at replication initiation. How the cell can decide that it is large enough to initiate replication is still an open question despite decades of investigations.
The general principles for high-end imaging of pool-synthesized cell libraries have nearly unlimited applications throughout cell biology. The specific applications explored in this project will take the understanding of the bacterial cell cycle to a new level and answer general questions about the chromosomal organization and cell size sensing.
Summary
In this project, we will develop and use technology that combines synthetic genomics and live-cell imaging. These methods make it possible to study the intracellular biophysics at single-molecule detail in thousands of genetically different bacterial strains in parallel. Our approach is based on in situ genotyping of a barcoded strain library after phenotyping has been performed by live-cell imaging. Within the scope of the proposed project, the new technology will be used to solve mechanistic and structural questions of the bacterial cell cycle.
To this end, we will explore two parallel but complementary applications. In the first application, we will determine the dynamic 3D structure of the E. coli chromosome at 1kb resolution throughout the cell cycle. The structure determination can be seen as a live-cell version of chromatin conformation capture, where we will follow the 3D distances of 10 000 pairs of chromosomal loci over the cell cycle at high resolution. In the second application, we will make a complete CRISPRi knockdown strain library where we can follow the replication forks of the E. coli chromosome and septum formation over the cell cycle in individual cells. Using this strategy, we will resolve how individual gene products contribute to the cell-to-cell accuracy in replication initiation and cell division. In particular, this approach allows us to address the challenging question of size sensing at replication initiation. How the cell can decide that it is large enough to initiate replication is still an open question despite decades of investigations.
The general principles for high-end imaging of pool-synthesized cell libraries have nearly unlimited applications throughout cell biology. The specific applications explored in this project will take the understanding of the bacterial cell cycle to a new level and answer general questions about the chromosomal organization and cell size sensing.
Max ERC Funding
2 411 410 €
Duration
Start date: 2020-09-01, End date: 2025-08-31
Project acronym BiomeRiskFactors
Project Discovering microbiome-based disease risk factors
Researcher (PI) Eran Segal
Host Institution (HI) WEIZMANN INSTITUTE OF SCIENCE
Call Details Advanced Grant (AdG), LS2, ERC-2017-ADG
Summary Identifying risk factors for diseases that can be prevented or delayed by early intervention is of major importance, and numerous genetic, lifestyle, anthropometric and clinical risk factors were found for many different diseases. Another source of potentially pertinent disease risk factors is the human microbiome - the collective genome of trillions of bacteria, viruses, fungi, and parasites that reside in the human gut. However, very few microbiome disease markers were found to date.
Here, we aim to develop risk prediction tools based on the human microbiome that predict the likelihood of an individual to develop a particular condition or disease within 5-10 years. We will use a cohort of >2200 individuals that my group previously assembled, for whom we have clinical profiles, gut microbiome data, and banked blood and stool samples. We will invite people 5-10 years after their initial recruitment time, profile disease status and blood markers, and develop algorithms for predicting 5-10 year onset of Type 2 diabetes, cardiovascular disease, and obesity, using microbiome data from recruitment time.
To increase the likelihood of finding microbiome markers predictive of disease onset, we will develop novel experimental and computational methods for in-depth characterization of microbial gene function, the metabolites produced by the microbiome, the underexplored fungal microbiome members, and the interactions between the gut microbiota and the host adaptive immune system. We will then apply these methods to >2200 banked samples from cohort recruitment time and use the resulting data in devising our microbiome-based risk prediction tools. In themselves, these novel assays and their application to >2200 samples should greatly advance the microbiome field.
If successful, our proposal will identify new disease risk factors and risk prediction tools based on the microbiome, paving the way towards using the microbiome in early disease detection and prevention.
Summary
Identifying risk factors for diseases that can be prevented or delayed by early intervention is of major importance, and numerous genetic, lifestyle, anthropometric and clinical risk factors were found for many different diseases. Another source of potentially pertinent disease risk factors is the human microbiome - the collective genome of trillions of bacteria, viruses, fungi, and parasites that reside in the human gut. However, very few microbiome disease markers were found to date.
Here, we aim to develop risk prediction tools based on the human microbiome that predict the likelihood of an individual to develop a particular condition or disease within 5-10 years. We will use a cohort of >2200 individuals that my group previously assembled, for whom we have clinical profiles, gut microbiome data, and banked blood and stool samples. We will invite people 5-10 years after their initial recruitment time, profile disease status and blood markers, and develop algorithms for predicting 5-10 year onset of Type 2 diabetes, cardiovascular disease, and obesity, using microbiome data from recruitment time.
To increase the likelihood of finding microbiome markers predictive of disease onset, we will develop novel experimental and computational methods for in-depth characterization of microbial gene function, the metabolites produced by the microbiome, the underexplored fungal microbiome members, and the interactions between the gut microbiota and the host adaptive immune system. We will then apply these methods to >2200 banked samples from cohort recruitment time and use the resulting data in devising our microbiome-based risk prediction tools. In themselves, these novel assays and their application to >2200 samples should greatly advance the microbiome field.
If successful, our proposal will identify new disease risk factors and risk prediction tools based on the microbiome, paving the way towards using the microbiome in early disease detection and prevention.
Max ERC Funding
2 500 000 €
Duration
Start date: 2019-03-01, End date: 2024-02-29
Project acronym BioMeTRe
Project Biophysical mechanisms of long-range transcriptional regulation
Researcher (PI) Luca GIORGETTI
Host Institution (HI) FRIEDRICH MIESCHER INSTITUTE FOR BIOMEDICAL RESEARCH FONDATION
Call Details Starting Grant (StG), LS2, ERC-2017-STG
Summary In mammals, transcriptional control of many genes relies on cis-regulatory elements such as enhancers, which are often located tens to hundreds of kilobases away from their cognate promoters. Functional interactions between distal regulatory elements and target promoters require mutual physical proximity, which is linked to the three-dimensional structure of the chromatin fiber. Chromosome conformation capture studies revealed that chromosomes are partitioned into Topologically Associating Domains (TADs), sub-megabase domains of preferential physical interactions of the chromatin fiber. Genetic evidence showed that TAD boundaries restrict the genomic range of enhancer-promoter communication, and that interactions between regulatory sequences within TADs are further fine-tuned by smaller-scale structures. However, the mechanistic details of how physical interactions translate into transcriptional outputs are totally unknown. Here we propose to explore the biophysical mechanisms that link chromosome conformation and long-range transcriptional regulation using molecular biology, genetic engineering, single-cell experiments and physical modeling. We will measure chromosomal interactions in single cells and in time using a novel method that relies on an enzymatic process in vivo. Genetic engineering will be used to establish a cell system that allows quantitative measurement of how enhancer-promoter interactions relate to transcription at the population and single-cell levels, and to test the effects of perturbations without confounding effects. Finally, we will develop physical models of promoter operation in the presence of distal enhancers, which will be used to interpret the experimental data and formulate new testable predictions. With this integrated approach we aim at providing an entirely new layer of description of the general principles underlying transcriptional control, which could establish new paradigms for research in epigenetics and gene regulation.
Summary
In mammals, transcriptional control of many genes relies on cis-regulatory elements such as enhancers, which are often located tens to hundreds of kilobases away from their cognate promoters. Functional interactions between distal regulatory elements and target promoters require mutual physical proximity, which is linked to the three-dimensional structure of the chromatin fiber. Chromosome conformation capture studies revealed that chromosomes are partitioned into Topologically Associating Domains (TADs), sub-megabase domains of preferential physical interactions of the chromatin fiber. Genetic evidence showed that TAD boundaries restrict the genomic range of enhancer-promoter communication, and that interactions between regulatory sequences within TADs are further fine-tuned by smaller-scale structures. However, the mechanistic details of how physical interactions translate into transcriptional outputs are totally unknown. Here we propose to explore the biophysical mechanisms that link chromosome conformation and long-range transcriptional regulation using molecular biology, genetic engineering, single-cell experiments and physical modeling. We will measure chromosomal interactions in single cells and in time using a novel method that relies on an enzymatic process in vivo. Genetic engineering will be used to establish a cell system that allows quantitative measurement of how enhancer-promoter interactions relate to transcription at the population and single-cell levels, and to test the effects of perturbations without confounding effects. Finally, we will develop physical models of promoter operation in the presence of distal enhancers, which will be used to interpret the experimental data and formulate new testable predictions. With this integrated approach we aim at providing an entirely new layer of description of the general principles underlying transcriptional control, which could establish new paradigms for research in epigenetics and gene regulation.
Max ERC Funding
1 500 000 €
Duration
Start date: 2018-01-01, End date: 2022-12-31
Project acronym BIOSYNCEN
Project Dissection of centromeric chromatin and components: A biosynthetic approach
Researcher (PI) Patrick Heun
Host Institution (HI) THE UNIVERSITY OF EDINBURGH
Call Details Starting Grant (StG), LS2, ERC-2012-StG_20111109
Summary The centromere is one of the most important chromosomal elements. It is required for proper chromosome segregation in mitosis and meiosis and readily recognizable as the primary constriction of mitotic chromosomes. Proper centromere function is essential to ensure genome stability; therefore understanding centromere identity is directly relevant to cancer biology and gene therapy. How centromeres are established and maintained is however still an open question in the field. In most organisms this appears to be regulated by an epigenetic mechanism. The key candidate for such an epigenetic mark is CENH3 (CENP-A in mammals, CID in Drosophila), a centromere-specific histone H3 variant that is essential for centromere function and exclusively found in the nucleosomes of centromeric chromatin. Using a biosynthetic approach of force-targeting CENH3 in Drosophila to non-centromeric DNA, we were able to induce centromere function and demonstrate that CENH3 is sufficient to determine centromere identity. Here we propose to move this experimental setup across evolutionary boundaries into human cells to develop improved human artificial chromosomes (HACs). We will make further use of this unique setup to dissect the function of targeted CENH3 both in Drosophila and human cells. Contributing centromeric components and histone modifications of centromeric chromatin will be characterized in detail by mass spectroscopy in Drosophila. Finally we are proposing to develop a technique that allows high-resolution mapping of proteins on repetitive DNA to help further characterizing known and novel centromere components. This will be achieved by combining two independently established techniques: DNA methylation and DNA fiber combing. This ambitious proposal will significantly advance our understanding of how centromeres are determined and help the development of improved HACs for therapeutic applications in the future.
Summary
The centromere is one of the most important chromosomal elements. It is required for proper chromosome segregation in mitosis and meiosis and readily recognizable as the primary constriction of mitotic chromosomes. Proper centromere function is essential to ensure genome stability; therefore understanding centromere identity is directly relevant to cancer biology and gene therapy. How centromeres are established and maintained is however still an open question in the field. In most organisms this appears to be regulated by an epigenetic mechanism. The key candidate for such an epigenetic mark is CENH3 (CENP-A in mammals, CID in Drosophila), a centromere-specific histone H3 variant that is essential for centromere function and exclusively found in the nucleosomes of centromeric chromatin. Using a biosynthetic approach of force-targeting CENH3 in Drosophila to non-centromeric DNA, we were able to induce centromere function and demonstrate that CENH3 is sufficient to determine centromere identity. Here we propose to move this experimental setup across evolutionary boundaries into human cells to develop improved human artificial chromosomes (HACs). We will make further use of this unique setup to dissect the function of targeted CENH3 both in Drosophila and human cells. Contributing centromeric components and histone modifications of centromeric chromatin will be characterized in detail by mass spectroscopy in Drosophila. Finally we are proposing to develop a technique that allows high-resolution mapping of proteins on repetitive DNA to help further characterizing known and novel centromere components. This will be achieved by combining two independently established techniques: DNA methylation and DNA fiber combing. This ambitious proposal will significantly advance our understanding of how centromeres are determined and help the development of improved HACs for therapeutic applications in the future.
Max ERC Funding
1 755 960 €
Duration
Start date: 2013-02-01, End date: 2019-01-31
Project acronym BRAIN-MATCH
Project Matching CNS Lineage Maps with Molecular Brain Tumor Portraits for Translational Exploitation
Researcher (PI) Stefan PFISTER
Host Institution (HI) DEUTSCHES KREBSFORSCHUNGSZENTRUM HEIDELBERG
Call Details Consolidator Grant (CoG), LS2, ERC-2018-COG
Summary Brain tumors represent an extremely heterogeneous group of more than 100 different molecularly distinct diseases, many of which are still almost uniformly lethal despite five decades of clinical trials. In contrast to hematologic malignancies and carcinomas, the cell-of-origin for the vast majority of these entities is unknown. This knowledge gap currently precludes a comprehensive understanding of tumor biology and also limits translational exploitation (e.g., utilizing lineage targets for novel therapies and circulating brain tumor cells for liquid biopsies).
The BRAIN-MATCH project represents an ambitious program to address this challenge and unmet medical need by taking an approach that (i) extensively utilizes existing molecular profiles of more than 30,000 brain tumor samples covering more than 100 different entities, publicly available single-cell sequencing data of normal brain regions, and bulk normal tissue data at different times of development across different species; (ii) generates unprecedented maps of normal human CNS development by using state-of-the art novel technologies; (iii) matches these molecular portraits of normal cell types with tumor datasets in order to identify specific cell-of-origin populations for individual tumor entities; and (iv) validates the most promising cell-of-origin populations and tumor-specific lineage and/or surface markers in vivo.
The expected outputs of BRAIN-MATCH are four-fold: (i) delivery of an unprecedented atlas of human normal CNS development, which will also be of great relevance for diverse fields other than cancer; (ii) functional validation of at least three lineage targets; (iii) isolation and molecular characterization of circulating brain tumor cells from patients´ blood for at least five tumor entities; and (iv) generation of at least three novel mouse models of brain tumor entities for which currently no faithful models exist.
Summary
Brain tumors represent an extremely heterogeneous group of more than 100 different molecularly distinct diseases, many of which are still almost uniformly lethal despite five decades of clinical trials. In contrast to hematologic malignancies and carcinomas, the cell-of-origin for the vast majority of these entities is unknown. This knowledge gap currently precludes a comprehensive understanding of tumor biology and also limits translational exploitation (e.g., utilizing lineage targets for novel therapies and circulating brain tumor cells for liquid biopsies).
The BRAIN-MATCH project represents an ambitious program to address this challenge and unmet medical need by taking an approach that (i) extensively utilizes existing molecular profiles of more than 30,000 brain tumor samples covering more than 100 different entities, publicly available single-cell sequencing data of normal brain regions, and bulk normal tissue data at different times of development across different species; (ii) generates unprecedented maps of normal human CNS development by using state-of-the art novel technologies; (iii) matches these molecular portraits of normal cell types with tumor datasets in order to identify specific cell-of-origin populations for individual tumor entities; and (iv) validates the most promising cell-of-origin populations and tumor-specific lineage and/or surface markers in vivo.
The expected outputs of BRAIN-MATCH are four-fold: (i) delivery of an unprecedented atlas of human normal CNS development, which will also be of great relevance for diverse fields other than cancer; (ii) functional validation of at least three lineage targets; (iii) isolation and molecular characterization of circulating brain tumor cells from patients´ blood for at least five tumor entities; and (iv) generation of at least three novel mouse models of brain tumor entities for which currently no faithful models exist.
Max ERC Funding
1 999 875 €
Duration
Start date: 2019-05-01, End date: 2024-04-30
Project acronym BRAINCELL
Project Charting the landscape of brain development by large-scale single-cell transcriptomics and phylogenetic lineage reconstruction
Researcher (PI) Sten Linnarsson
Host Institution (HI) KAROLINSKA INSTITUTET
Call Details Starting Grant (StG), LS2, ERC-2010-StG_20091118
Summary Embryogenesis is the temporal unfolding of cellular processes: proliferation, migration, differentiation, morphogenesis, apoptosis and functional specialization. These processes are well understood in specific tissues, and for specific cell types. Nevertheless, our systematic knowledge of the types of cells present in the developing and adult animal, and about their functional and lineage relationships, is limited. For example, there is no consensus on the number of cell types, and many important stem cells and progenitors remain to be discovered. Similarly, the lineage relationships between specific cell types are often poorly characterized. This is particularly true for the mammalian nervous system. We have developed (1) a reliable high-throghput method for sequencing all transcripts in 96 single cells at a time; and (2) a system for high-throughput phylogenetic lineage reconstruction. We now propose to characterize embryogenesis using a shotgun approach borrowed from genomics. Tissues will be dissected from multiple stages and dissociated to single cells. A total of 10,000 cells will be analyzed by RNA sequencing, revealing their functional cell type, their lineage relationships, and their current state (e.g. cell cycle phase). The novel approach proposed here will bring the powerful strategies pioneered in genomics into the field of developmental biology, including automation, digitization, and the random shotgun method. The data thus obtained will bring clarity to the concept of ‘cell type’; will provide a first catalog of mouse brain cell types with deep functional annotation; will provide markers for every cell type, including stem cells; and will serve as a basis for future comparative work, especially with human embryos.
Summary
Embryogenesis is the temporal unfolding of cellular processes: proliferation, migration, differentiation, morphogenesis, apoptosis and functional specialization. These processes are well understood in specific tissues, and for specific cell types. Nevertheless, our systematic knowledge of the types of cells present in the developing and adult animal, and about their functional and lineage relationships, is limited. For example, there is no consensus on the number of cell types, and many important stem cells and progenitors remain to be discovered. Similarly, the lineage relationships between specific cell types are often poorly characterized. This is particularly true for the mammalian nervous system. We have developed (1) a reliable high-throghput method for sequencing all transcripts in 96 single cells at a time; and (2) a system for high-throughput phylogenetic lineage reconstruction. We now propose to characterize embryogenesis using a shotgun approach borrowed from genomics. Tissues will be dissected from multiple stages and dissociated to single cells. A total of 10,000 cells will be analyzed by RNA sequencing, revealing their functional cell type, their lineage relationships, and their current state (e.g. cell cycle phase). The novel approach proposed here will bring the powerful strategies pioneered in genomics into the field of developmental biology, including automation, digitization, and the random shotgun method. The data thus obtained will bring clarity to the concept of ‘cell type’; will provide a first catalog of mouse brain cell types with deep functional annotation; will provide markers for every cell type, including stem cells; and will serve as a basis for future comparative work, especially with human embryos.
Max ERC Funding
1 496 032 €
Duration
Start date: 2010-11-01, End date: 2015-10-31
Project acronym CANCERBIOME
Project Cancerbiome: Characterization of the cancer-associated microbiome
Researcher (PI) Peer Bork
Host Institution (HI) EUROPEAN MOLECULAR BIOLOGY LABORATORY
Call Details Advanced Grant (AdG), LS2, ERC-2010-AdG_20100317
Summary Deep environmental sequencing (metagenomics) will be used to characterize microbial communities associated with 3 different cancer types: cervical cancer, oral squamous cell carcinoma and colorectal cancer. For all 3 types, non-invasive molecular diagnostics and prognostics are feasible via utilization of vaginal, oral and faecal samples, respectively. The project consequently aims to identify microbial markers in these ¿readouts¿ that correlate with cancer presence or progression. Microbial markers can be individual species or specific community compositions, but also particular genes or pathways. The microbial communities will be sampled locally at tumor surfaces and in healthy control tissues. After DNA extraction and sequencing, a complex bioinformatics pipeline will be developed to characterise the microbiomes and to identify the cancer-specific functional and phylogenetic markers therein. For colorectal cancer, the project intends to go into more details in that it tries i) to establish a correlation of microbiota with cancer progression and it ii) explores differences between distinct cancer subtypes. For each of the 3 cancer types, at least two samples from 40 individuals will be sequenced (as well as controls) at a depth of at least 5Gb each using Illumina technology. This is expected to be sufficient for the identification of microbial markers and also allows superficial genotyping of the individuals at ca 2-3x coverage as a by-product (the samples will contain considerable amounts of human DNA). Further analyses will be designed to study the potential of certain microbial species or community compositions to enhance or even cause one or more of the 3 cancers. The discovery of such causations will open up research towards directed antimicrobial treatment.
Summary
Deep environmental sequencing (metagenomics) will be used to characterize microbial communities associated with 3 different cancer types: cervical cancer, oral squamous cell carcinoma and colorectal cancer. For all 3 types, non-invasive molecular diagnostics and prognostics are feasible via utilization of vaginal, oral and faecal samples, respectively. The project consequently aims to identify microbial markers in these ¿readouts¿ that correlate with cancer presence or progression. Microbial markers can be individual species or specific community compositions, but also particular genes or pathways. The microbial communities will be sampled locally at tumor surfaces and in healthy control tissues. After DNA extraction and sequencing, a complex bioinformatics pipeline will be developed to characterise the microbiomes and to identify the cancer-specific functional and phylogenetic markers therein. For colorectal cancer, the project intends to go into more details in that it tries i) to establish a correlation of microbiota with cancer progression and it ii) explores differences between distinct cancer subtypes. For each of the 3 cancer types, at least two samples from 40 individuals will be sequenced (as well as controls) at a depth of at least 5Gb each using Illumina technology. This is expected to be sufficient for the identification of microbial markers and also allows superficial genotyping of the individuals at ca 2-3x coverage as a by-product (the samples will contain considerable amounts of human DNA). Further analyses will be designed to study the potential of certain microbial species or community compositions to enhance or even cause one or more of the 3 cancers. The discovery of such causations will open up research towards directed antimicrobial treatment.
Max ERC Funding
2 233 740 €
Duration
Start date: 2011-07-01, End date: 2016-06-30
Project acronym CancerFluxome
Project Cancer Cellular Metabolism across Space and Time
Researcher (PI) Tomer Shlomi
Host Institution (HI) TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY
Call Details Starting Grant (StG), LS2, ERC-2016-STG
Summary The metabolism of cancer cells is altered to meet cellular requirements for growth, providing novel means to selectively target tumorigenesis. While extensively studied, our current view of cancer cellular metabolism is fundamentally limited by lack of information on variability in metabolic activity between distinct subcellular compartments and cells.
We propose to develop a spatio-temporal fluxomics approach for quantifying metabolic fluxes in the cytoplasm vs. mitochondria as well as their cell-cycle dynamics, combining mass-spectrometry based isotope tracing with cell synchronization, rapid cellular fractionation, and computational metabolic network modelling.
Spatio-temporal fluxomics will be used to revisit and challenge our current understanding of central metabolism and its induced adaptation to oncogenic events – an important endeavour considering that mitochondrial bioenergetics and biosynthesis are required for tumorigenesis and accumulating evidences for metabolic alterations throughout the cell-cycle.
Our preliminary results show intriguing oscillations between oxidative and reductive TCA cycle flux throughout the cell-cycle. We will explore the extent to which cells adapt their metabolism to fulfil the changing energetic and anabolic demands throughout the cell-cycle, how metabolic oscillations are regulated, and their benefit to cells in terms of thermodynamic efficiency. Spatial flux analysis will be instrumental for investigating glutaminolysis - a ‘hallmark’ metabolic adaptation in cancer involving shuttling of metabolic intermediates and cofactors between mitochondria and cytoplasm.
On a clinical front, our spatio-temporal fluxomics analysis will enable to disentangle oncogene-induced flux alterations, having an important tumorigenic role, from artefacts originating from population averaging. A comprehensive view of how cells adapt their metabolism due to oncogenic mutations will reveal novel targets for anti-cancer drugs.
Summary
The metabolism of cancer cells is altered to meet cellular requirements for growth, providing novel means to selectively target tumorigenesis. While extensively studied, our current view of cancer cellular metabolism is fundamentally limited by lack of information on variability in metabolic activity between distinct subcellular compartments and cells.
We propose to develop a spatio-temporal fluxomics approach for quantifying metabolic fluxes in the cytoplasm vs. mitochondria as well as their cell-cycle dynamics, combining mass-spectrometry based isotope tracing with cell synchronization, rapid cellular fractionation, and computational metabolic network modelling.
Spatio-temporal fluxomics will be used to revisit and challenge our current understanding of central metabolism and its induced adaptation to oncogenic events – an important endeavour considering that mitochondrial bioenergetics and biosynthesis are required for tumorigenesis and accumulating evidences for metabolic alterations throughout the cell-cycle.
Our preliminary results show intriguing oscillations between oxidative and reductive TCA cycle flux throughout the cell-cycle. We will explore the extent to which cells adapt their metabolism to fulfil the changing energetic and anabolic demands throughout the cell-cycle, how metabolic oscillations are regulated, and their benefit to cells in terms of thermodynamic efficiency. Spatial flux analysis will be instrumental for investigating glutaminolysis - a ‘hallmark’ metabolic adaptation in cancer involving shuttling of metabolic intermediates and cofactors between mitochondria and cytoplasm.
On a clinical front, our spatio-temporal fluxomics analysis will enable to disentangle oncogene-induced flux alterations, having an important tumorigenic role, from artefacts originating from population averaging. A comprehensive view of how cells adapt their metabolism due to oncogenic mutations will reveal novel targets for anti-cancer drugs.
Max ERC Funding
1 481 250 €
Duration
Start date: 2017-02-01, End date: 2022-01-31
Project acronym CancerHetero
Project Dissection of tumor heterogeneity in vivo
Researcher (PI) Haikun Liu
Host Institution (HI) DEUTSCHES KREBSFORSCHUNGSZENTRUM HEIDELBERG
Call Details Consolidator Grant (CoG), LS2, ERC-2014-CoG
Summary It is now widely accepted that tumors are composed of heterogeneous population of cells, which contribute
to many aspects of treatment resistance observed in clinic. Despite the acknowledgment of the tumor cell
heterogeneity, little evidence was shown about complexity and dynamics of this heterogeneity in vivo,
mainly because of lacking flexible genetic tools which allow sophisticated analysis in primary tumors. We
recently developed a very efficient mouse somatic brain tumor model which have a full penetrance of high
grade glioma development. Combination of this model with several transgenic mouse lines allow us to
isolate and track different population of cells in primary tumors, most importantly, we also confirmed that
this can be done on single cell level. Here I propose to use this set of valuable genetic tools to dissect the
cellular heterogeneity in mouse gliomas. First we will perform several single cell lineage tracing experiment
to demonstrate the contribution of brain tumor stem cell, tumor progenitors as well as the relatively
differentiated cells, which will provide a complete data sets of clonal dynamics of different tumor cell types.
Second we will further perform this tracing experiment with the presence of conventional chemotherapy.
Third we will perform single cell RNA sequencing experiment to capture the molecular signature, which
determines the cellular heterogeneity, discovered by single cell tracing. This result will be further validated
by analysis of this molecular signatures in human primary tumors. We will also use our established in vivo
target validation approach to manipulate the candidate molecular regulators to establish the functional
correlation between molecular signature and phenotypic heterogeneity. This project will greatly improve our
understanding of tumor heterogeneity, and possibly provide novel approaches and strategies of targeting
human glioblastomas.
Summary
It is now widely accepted that tumors are composed of heterogeneous population of cells, which contribute
to many aspects of treatment resistance observed in clinic. Despite the acknowledgment of the tumor cell
heterogeneity, little evidence was shown about complexity and dynamics of this heterogeneity in vivo,
mainly because of lacking flexible genetic tools which allow sophisticated analysis in primary tumors. We
recently developed a very efficient mouse somatic brain tumor model which have a full penetrance of high
grade glioma development. Combination of this model with several transgenic mouse lines allow us to
isolate and track different population of cells in primary tumors, most importantly, we also confirmed that
this can be done on single cell level. Here I propose to use this set of valuable genetic tools to dissect the
cellular heterogeneity in mouse gliomas. First we will perform several single cell lineage tracing experiment
to demonstrate the contribution of brain tumor stem cell, tumor progenitors as well as the relatively
differentiated cells, which will provide a complete data sets of clonal dynamics of different tumor cell types.
Second we will further perform this tracing experiment with the presence of conventional chemotherapy.
Third we will perform single cell RNA sequencing experiment to capture the molecular signature, which
determines the cellular heterogeneity, discovered by single cell tracing. This result will be further validated
by analysis of this molecular signatures in human primary tumors. We will also use our established in vivo
target validation approach to manipulate the candidate molecular regulators to establish the functional
correlation between molecular signature and phenotypic heterogeneity. This project will greatly improve our
understanding of tumor heterogeneity, and possibly provide novel approaches and strategies of targeting
human glioblastomas.
Max ERC Funding
2 000 000 €
Duration
Start date: 2015-06-01, End date: 2020-05-31
Project acronym CD-LINK
Project Celiac disease: from lincRNAs to disease mechanism
Researcher (PI) Tjitske Nienke Wijmenga
Host Institution (HI) ACADEMISCH ZIEKENHUIS GRONINGEN
Call Details Advanced Grant (AdG), LS2, ERC-2012-ADG_20120314
Summary Celiac disease affects at least 1% of the world population. Its onset is triggered by gluten, a common dietary protein, however, its etiology is poorly understood. More than 80% of patients are not properly diagnosed and they therefore do not follow a gluten-free diet, thereby increasing their risk for disease-associated complications and early death. A better understanding of the disease biology would improve the diagnosis, prevention, and treatment of celiac disease.
This project investigates the disease mechanisms in celiac disease by using predisposing genes and genetic variants as disease initiating factors. Specifically, it will investigate if long, intergenic non-coding RNAs (lincRNAs) are causally involved in celiac disease pathogenesis by regulating protein-coding genes and pathways associated with the disease.
This project is based on two important observations by my group: (1) Our genetic studies, which led to identifying 39 celiac disease risk loci, suggest that the mechanism underlying the disease is largely governed by dysregulation of gene expression. (2) We uncovered a previously unrecognized role for lincRNAs that provides clues as to exactly how genetic variation causes disease, as this class of biologically important RNA molecules regulate gene expression.
The research will be performed in CD4+ T cells, a severely affected cell type in disease pathology. I will first use celiac disease-associated protein-coding genes to delineate their regulatory pathways and then study the transcriptional programs of lincRNAs present in celiac disease loci. Next I will combine the information and investigate if the expressed lincRNAs modulate the pathways and affect T cell function, thereby discovering if lincRNAs are a missing link between non-coding genetic variation and protein-coding genes. Our findings may well lead to potential therapeutic targets and provide a solid scientific basis for new diagnostic markers, particularly biomarkers, based on genetics.
Summary
Celiac disease affects at least 1% of the world population. Its onset is triggered by gluten, a common dietary protein, however, its etiology is poorly understood. More than 80% of patients are not properly diagnosed and they therefore do not follow a gluten-free diet, thereby increasing their risk for disease-associated complications and early death. A better understanding of the disease biology would improve the diagnosis, prevention, and treatment of celiac disease.
This project investigates the disease mechanisms in celiac disease by using predisposing genes and genetic variants as disease initiating factors. Specifically, it will investigate if long, intergenic non-coding RNAs (lincRNAs) are causally involved in celiac disease pathogenesis by regulating protein-coding genes and pathways associated with the disease.
This project is based on two important observations by my group: (1) Our genetic studies, which led to identifying 39 celiac disease risk loci, suggest that the mechanism underlying the disease is largely governed by dysregulation of gene expression. (2) We uncovered a previously unrecognized role for lincRNAs that provides clues as to exactly how genetic variation causes disease, as this class of biologically important RNA molecules regulate gene expression.
The research will be performed in CD4+ T cells, a severely affected cell type in disease pathology. I will first use celiac disease-associated protein-coding genes to delineate their regulatory pathways and then study the transcriptional programs of lincRNAs present in celiac disease loci. Next I will combine the information and investigate if the expressed lincRNAs modulate the pathways and affect T cell function, thereby discovering if lincRNAs are a missing link between non-coding genetic variation and protein-coding genes. Our findings may well lead to potential therapeutic targets and provide a solid scientific basis for new diagnostic markers, particularly biomarkers, based on genetics.
Max ERC Funding
2 319 914 €
Duration
Start date: 2013-02-01, End date: 2018-11-30
