Project acronym Age Asymmetry
Project Age-Selective Segregation of Organelles
Researcher (PI) Pekka Aleksi Katajisto
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Starting Grant (StG), LS3, ERC-2015-STG
Summary Our tissues are constantly renewed by stem cells. Over time, stem cells accumulate cellular damage that will compromise renewal and results in aging. As stem cells can divide asymmetrically, segregation of harmful factors to the differentiating daughter cell could be one possible mechanism for slowing damage accumulation in the stem cell. However, current evidence for such mechanisms comes mainly from analogous findings in yeast, and studies have concentrated only on few types of cellular damage.
I hypothesize that the chronological age of a subcellular component is a proxy for all the damage it has sustained. In order to secure regeneration, mammalian stem cells may therefore specifically sort old cellular material asymmetrically. To study this, I have developed a novel strategy and tools to address the age-selective segregation of any protein in stem cell division. Using this approach, I have already discovered that stem-like cells of the human mammary epithelium indeed apportion chronologically old mitochondria asymmetrically in cell division, and enrich old mitochondria to the differentiating daughter cell. We will investigate the mechanisms underlying this novel phenomenon, and its relevance for mammalian aging.
We will first identify how old and young mitochondria differ, and how stem cells recognize them to facilitate the asymmetric segregation. Next, we will analyze the extent of asymmetric age-selective segregation by targeting several other subcellular compartments in a stem cell division. Finally, we will determine whether the discovered age-selective segregation is a general property of stem cell in vivo, and it's functional relevance for maintenance of stem cells and tissue regeneration. Our discoveries may open new possibilities to target aging associated functional decline by induction of asymmetric age-selective organelle segregation.
Summary
Our tissues are constantly renewed by stem cells. Over time, stem cells accumulate cellular damage that will compromise renewal and results in aging. As stem cells can divide asymmetrically, segregation of harmful factors to the differentiating daughter cell could be one possible mechanism for slowing damage accumulation in the stem cell. However, current evidence for such mechanisms comes mainly from analogous findings in yeast, and studies have concentrated only on few types of cellular damage.
I hypothesize that the chronological age of a subcellular component is a proxy for all the damage it has sustained. In order to secure regeneration, mammalian stem cells may therefore specifically sort old cellular material asymmetrically. To study this, I have developed a novel strategy and tools to address the age-selective segregation of any protein in stem cell division. Using this approach, I have already discovered that stem-like cells of the human mammary epithelium indeed apportion chronologically old mitochondria asymmetrically in cell division, and enrich old mitochondria to the differentiating daughter cell. We will investigate the mechanisms underlying this novel phenomenon, and its relevance for mammalian aging.
We will first identify how old and young mitochondria differ, and how stem cells recognize them to facilitate the asymmetric segregation. Next, we will analyze the extent of asymmetric age-selective segregation by targeting several other subcellular compartments in a stem cell division. Finally, we will determine whether the discovered age-selective segregation is a general property of stem cell in vivo, and it's functional relevance for maintenance of stem cells and tissue regeneration. Our discoveries may open new possibilities to target aging associated functional decline by induction of asymmetric age-selective organelle segregation.
Max ERC Funding
1 500 000 €
Duration
Start date: 2016-05-01, End date: 2021-04-30
Project acronym CGCglasmaQGP
Project The nonlinear high energy regime of Quantum Chromodynamics
Researcher (PI) Tuomas Veli Valtteri Lappi
Host Institution (HI) JYVASKYLAN YLIOPISTO
Call Details Consolidator Grant (CoG), PE2, ERC-2015-CoG
Summary "This proposal concentrates on Quantum Chromodynamics (QCD) in its least well understood "final frontier": the high energy limit. The aim is to treat the formation of quark gluon plasma in relativistic nuclear collisions together with other high energy processes in a consistent QCD framework. This project is topical now in order to fully understand the results from the maturing LHC heavy ion program. The high energy regime is characterized by a high density of gluons, whose nonlinear interactions are beyond the reach of simple perturbative calculations. High energy particles also propagate nearly on the light cone, unaccessible to Euclidean lattice calculations. The nonlinear interactions at high density lead to the phenomenon of gluon saturation. The emergence of the "saturation scale", a semihard typical transverse momentum, enables a weak coupling expansion around a nonperturbatively large color field. This project aims to make progress both in collider phenomenology and in more conceptual aspects of nonabelian gauge field dynamics at high energy density:
1. Significant advances towards higher order accuracy will be made in cross section calculations for processes where a dilute probe collides with the strong color field of a high energy nucleus.
2. The quantum fluctuations around the strong color fields in the initial stages of a relativistic heavy ion collision will be analyzed with a new numerical method based on an explicit linearization of the equations of motion, maintaining a well defined weak coupling limit.
3. Initial conditions for fluid dynamical descriptions of the quark gluon plasma phase in heavy ion collisions will be obtained from a constrained QCD calculation.
We propose to achieve these goals with modern analytical and numerical methods, on which the P.I. is a leading expert. This project would represent a leap in the field towards better quantitative first principles understanding of QCD in a new kinematical domain."
Summary
"This proposal concentrates on Quantum Chromodynamics (QCD) in its least well understood "final frontier": the high energy limit. The aim is to treat the formation of quark gluon plasma in relativistic nuclear collisions together with other high energy processes in a consistent QCD framework. This project is topical now in order to fully understand the results from the maturing LHC heavy ion program. The high energy regime is characterized by a high density of gluons, whose nonlinear interactions are beyond the reach of simple perturbative calculations. High energy particles also propagate nearly on the light cone, unaccessible to Euclidean lattice calculations. The nonlinear interactions at high density lead to the phenomenon of gluon saturation. The emergence of the "saturation scale", a semihard typical transverse momentum, enables a weak coupling expansion around a nonperturbatively large color field. This project aims to make progress both in collider phenomenology and in more conceptual aspects of nonabelian gauge field dynamics at high energy density:
1. Significant advances towards higher order accuracy will be made in cross section calculations for processes where a dilute probe collides with the strong color field of a high energy nucleus.
2. The quantum fluctuations around the strong color fields in the initial stages of a relativistic heavy ion collision will be analyzed with a new numerical method based on an explicit linearization of the equations of motion, maintaining a well defined weak coupling limit.
3. Initial conditions for fluid dynamical descriptions of the quark gluon plasma phase in heavy ion collisions will be obtained from a constrained QCD calculation.
We propose to achieve these goals with modern analytical and numerical methods, on which the P.I. is a leading expert. This project would represent a leap in the field towards better quantitative first principles understanding of QCD in a new kinematical domain."
Max ERC Funding
1 935 000 €
Duration
Start date: 2016-10-01, End date: 2021-09-30
Project acronym DynaOmics
Project From longitudinal proteomics to dynamic individualized diagnostics
Researcher (PI) Laura Linnea Maria Elo-Uhlgren
Host Institution (HI) TURUN YLIOPISTO
Call Details Starting Grant (StG), LS7, ERC-2015-STG
Summary Longitudinal omics data hold great promise to improve biomarker detection and enable dynamic individualized predictions. Recent technological advances have made proteomics an increasingly attractive option but clinical longitudinal proteomic datasets are still rare and computational tools for their analysis underdeveloped. The objective of this proposal is to create a roadmap to detect clinically feasible protein markers using longitudinal data and effective computational tools. A biomedical focus is on early detection of Type 1 diabetes (T1D). Specific objectives are:
1) Novel biomarker detector using longitudinal data. DynaOmics introduces novel types of multi-level dynamic markers that are undetectable in conventional single-time cross-sectional studies (e.g. within-individual changes in abundance or associations), develops optimization methods for their robust and reproducible detection within and across individuals, and validates their utility in well-defined samples.
2) Individualized disease risk prediction dynamically. DynaOmics develops dynamic individualized predictive models using the multi-level longitudinal proteome features and novel statistical and machine learning methods that have previously not been used in this context, including joint models of longitudinal and time-to-event data, and one-class classification type techniques.
3) Dynamic prediction of T1D. DynaOmics builds a predictive model of dynamic T1D risk to assist early detection of the disease, which is crucial for developing future therapeutic and preventive strategies. T1D typically involves a relatively long symptom-free period before clinical diagnosis but current tools to predict early T1D risk have restricted power.
The objectives involve innovative and unconventional approaches and address major unmet challenges in the field, having high potential to open new avenues for diagnosis and treatment of complex diseases and fundamentally novel insights towards precision medicine.
Summary
Longitudinal omics data hold great promise to improve biomarker detection and enable dynamic individualized predictions. Recent technological advances have made proteomics an increasingly attractive option but clinical longitudinal proteomic datasets are still rare and computational tools for their analysis underdeveloped. The objective of this proposal is to create a roadmap to detect clinically feasible protein markers using longitudinal data and effective computational tools. A biomedical focus is on early detection of Type 1 diabetes (T1D). Specific objectives are:
1) Novel biomarker detector using longitudinal data. DynaOmics introduces novel types of multi-level dynamic markers that are undetectable in conventional single-time cross-sectional studies (e.g. within-individual changes in abundance or associations), develops optimization methods for their robust and reproducible detection within and across individuals, and validates their utility in well-defined samples.
2) Individualized disease risk prediction dynamically. DynaOmics develops dynamic individualized predictive models using the multi-level longitudinal proteome features and novel statistical and machine learning methods that have previously not been used in this context, including joint models of longitudinal and time-to-event data, and one-class classification type techniques.
3) Dynamic prediction of T1D. DynaOmics builds a predictive model of dynamic T1D risk to assist early detection of the disease, which is crucial for developing future therapeutic and preventive strategies. T1D typically involves a relatively long symptom-free period before clinical diagnosis but current tools to predict early T1D risk have restricted power.
The objectives involve innovative and unconventional approaches and address major unmet challenges in the field, having high potential to open new avenues for diagnosis and treatment of complex diseases and fundamentally novel insights towards precision medicine.
Max ERC Funding
1 499 869 €
Duration
Start date: 2016-06-01, End date: 2021-05-31
Project acronym HRMEG
Project HRMEG: High-resolution magnetoencephalography: Towards non-invasive corticography
Researcher (PI) Lauri Tapio Parkkonen
Host Institution (HI) AALTO KORKEAKOULUSAATIO SR
Call Details Starting Grant (StG), LS7, ERC-2015-STG
Summary To date, neuroimaging has provided a wealth of information on how the human brain works in health and disease. With functional magnetic resonance imaging (fMRI), we can obtain spatially precise information about long-lasting brain activations whereas electro- and magnetoencephalography (EEG/MEG) can track transient cortical responses at millisecond resolution. However, none of these methods excel in time-resolved detection of sustained cortical activations, which are typically reflected as bursts of gamma-range (30–150 Hz) oscillations, frequently present in invasive recordings in patients. Although we have recently demonstrated that in exceptional situations MEG can detect even single gamma responses, their signal-to-noise ratio is usually prohibitively low, largely due to the substantial distance (4–5 cm) between cortex and sensors. Here, I propose to exploit recent advances in a novel magnetic sensor technology—atomic magnetometry—to construct a new kind of MEG system that allows capturing cerebral magnetic fields within millimetres from the scalp. Our simulations show that this proximity leads up to a 5-fold increase in the signal amplitude and an order-of-magnitude improvement of spatial resolution compared to conventional MEG. Therefore, a high-resolution MEG (HRMEG) system based on atomic magnetometers should enable non-invasive recordings of cortical activity at unprecedented sensitivity and detail level, which I propose to capitalize on by characterizing cortical responses, particularly gamma oscillations, during complex cognitive tasks. Additionally, since atomic magnetometers can recover within milliseconds from fields of several tesla, I also propose to combine transcranial magnetic stimulation (TMS) with MEG, leveraging the reciprocity of TMS and MEG and thus allowing better-than-ever characterization of TMS-evoked responses. This proposal comprises the research towards a HRMEG system and its application to study the working human brain in a new way.
Summary
To date, neuroimaging has provided a wealth of information on how the human brain works in health and disease. With functional magnetic resonance imaging (fMRI), we can obtain spatially precise information about long-lasting brain activations whereas electro- and magnetoencephalography (EEG/MEG) can track transient cortical responses at millisecond resolution. However, none of these methods excel in time-resolved detection of sustained cortical activations, which are typically reflected as bursts of gamma-range (30–150 Hz) oscillations, frequently present in invasive recordings in patients. Although we have recently demonstrated that in exceptional situations MEG can detect even single gamma responses, their signal-to-noise ratio is usually prohibitively low, largely due to the substantial distance (4–5 cm) between cortex and sensors. Here, I propose to exploit recent advances in a novel magnetic sensor technology—atomic magnetometry—to construct a new kind of MEG system that allows capturing cerebral magnetic fields within millimetres from the scalp. Our simulations show that this proximity leads up to a 5-fold increase in the signal amplitude and an order-of-magnitude improvement of spatial resolution compared to conventional MEG. Therefore, a high-resolution MEG (HRMEG) system based on atomic magnetometers should enable non-invasive recordings of cortical activity at unprecedented sensitivity and detail level, which I propose to capitalize on by characterizing cortical responses, particularly gamma oscillations, during complex cognitive tasks. Additionally, since atomic magnetometers can recover within milliseconds from fields of several tesla, I also propose to combine transcranial magnetic stimulation (TMS) with MEG, leveraging the reciprocity of TMS and MEG and thus allowing better-than-ever characterization of TMS-evoked responses. This proposal comprises the research towards a HRMEG system and its application to study the working human brain in a new way.
Max ERC Funding
1 498 806 €
Duration
Start date: 2016-09-01, End date: 2021-08-31
Project acronym MYCLASS
Project Towards prevention, early diagnosis, and noninvasive treatment of uterine leiomyomas through molecular classification
Researcher (PI) Lauri Aaltonen
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Advanced Grant (AdG), LS7, ERC-2015-AdG
Summary Every fourth woman suffers from uterine leiomyomas (ULs) – benign tumors of the uterine smooth muscle wall - at some point in premenopausal life. ULs, also called myomas or fibroids, cause a substantial health burden through symptoms such as excessive uterine bleeding, abdominal pain and infertility. These tumors are the most common cause of hysterectomy. Considering the impact that ULs have to women’s health, they are severely understudied. Our breakthrough work has shed important new light on the biology and genesis of ULs. In this ERC proposal we hypothesize that ULs can emerge through several distinct mechanisms and anticipate that each mechanism contributes to somewhat different tumor biology, clinicopathological features, and response to treatment. Also, we hypothesize that predisposing genetic variants may confer susceptibility to a particular UL subclass. To test these hypotheses, we shall create multiple layers of high-throughput data on clinicopathologically characterized ULs, including copy number variation, whole genome sequence, gene expression, and methylome profiles. Integration of these data should establish the existence and key characteristics of the different UL subclasses. Finally, we shall examine the effect of currently used drugs as well as new lead compounds in response to treatment, stratified per UL subclass. These efforts will 1) provide biological insight into molecular mechanisms driving the UL genesis and lay the scientific basis of their molecular classification, 2) describe the key characteristics of each class, 3) provide key biomarkers and molecular tools for routine diagnosis of UL subclasses, as well as clues to their targeted treatment, and 4) produce tools for detection of hereditary predisposition to ULs. This ERC project will be an important step towards non-invasive management of ULs. Reaching this goal would benefit hundreds of millions of women.
Summary
Every fourth woman suffers from uterine leiomyomas (ULs) – benign tumors of the uterine smooth muscle wall - at some point in premenopausal life. ULs, also called myomas or fibroids, cause a substantial health burden through symptoms such as excessive uterine bleeding, abdominal pain and infertility. These tumors are the most common cause of hysterectomy. Considering the impact that ULs have to women’s health, they are severely understudied. Our breakthrough work has shed important new light on the biology and genesis of ULs. In this ERC proposal we hypothesize that ULs can emerge through several distinct mechanisms and anticipate that each mechanism contributes to somewhat different tumor biology, clinicopathological features, and response to treatment. Also, we hypothesize that predisposing genetic variants may confer susceptibility to a particular UL subclass. To test these hypotheses, we shall create multiple layers of high-throughput data on clinicopathologically characterized ULs, including copy number variation, whole genome sequence, gene expression, and methylome profiles. Integration of these data should establish the existence and key characteristics of the different UL subclasses. Finally, we shall examine the effect of currently used drugs as well as new lead compounds in response to treatment, stratified per UL subclass. These efforts will 1) provide biological insight into molecular mechanisms driving the UL genesis and lay the scientific basis of their molecular classification, 2) describe the key characteristics of each class, 3) provide key biomarkers and molecular tools for routine diagnosis of UL subclasses, as well as clues to their targeted treatment, and 4) produce tools for detection of hereditary predisposition to ULs. This ERC project will be an important step towards non-invasive management of ULs. Reaching this goal would benefit hundreds of millions of women.
Max ERC Funding
2 499 099 €
Duration
Start date: 2016-06-01, End date: 2021-05-31
Project acronym PeptiCrad
Project Personalized oncolytic vaccines for cancer immunotherapy
Researcher (PI) Vincenzo Cerullo
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Consolidator Grant (CoG), LS7, ERC-2015-CoG
Summary This grant application proposes to develop a novel, customizable and personalized anti-cancer vaccine: peptide-coated conditionally replicating adenovirus (PeptiCrad).
Anti-cancer vaccines represent a promising approach for cancer treatment because they elicit durable and specific immune response that destroys primary tumors and distant metastases. Oncolytic viruses (OVs) are of significant interest because in addition to cytolysis they stimulate anti-tumor immune responses, thereby functioning as anti-tumor vaccines. However, their efficacy among cancer patients has been modest. One reason for this shortcoming is that the immune responses generated by virus infection primarily target the virus rather than the tumor. In addition, tumors differ across patients. Specific and personalized approaches (rather than generic virus infection strategies) are required to optimize therapy. To this end we propose to develop a novel vaccine platform that combines the strengths of OVs with the specificity of vaccines. Our technology is called PeptiCrad. PeptiCrad is a virus “dressed as a tumor”. It directly kills cancer cells (i.e., oncolytic viruses) and expresses immunomodulatory molecules (i.e., cytokines or the immune checkpoint inhibitors anti-CTLA4 or anti-PDL1); most importantly, it diverts immunity toward the tumor (i.e., the capsid becomes covered with MHC-I-restricted tumor-specific peptides).
The method that we have developed to cover the virus with tumor peptides is novel and exceeds current state-of-the-art. Importantly, it is fast and does not require genetic or chemical manipulation of the virus; this feature has a significant impact on the translational capability of the project.
Our preliminary results show great potential but significant questions regarding the development and the personalization of PeptiCrad remain to be studied. In this grant I propose two lines of research, one focused on the development and the other one on the personalization of PeptiCrad.
Summary
This grant application proposes to develop a novel, customizable and personalized anti-cancer vaccine: peptide-coated conditionally replicating adenovirus (PeptiCrad).
Anti-cancer vaccines represent a promising approach for cancer treatment because they elicit durable and specific immune response that destroys primary tumors and distant metastases. Oncolytic viruses (OVs) are of significant interest because in addition to cytolysis they stimulate anti-tumor immune responses, thereby functioning as anti-tumor vaccines. However, their efficacy among cancer patients has been modest. One reason for this shortcoming is that the immune responses generated by virus infection primarily target the virus rather than the tumor. In addition, tumors differ across patients. Specific and personalized approaches (rather than generic virus infection strategies) are required to optimize therapy. To this end we propose to develop a novel vaccine platform that combines the strengths of OVs with the specificity of vaccines. Our technology is called PeptiCrad. PeptiCrad is a virus “dressed as a tumor”. It directly kills cancer cells (i.e., oncolytic viruses) and expresses immunomodulatory molecules (i.e., cytokines or the immune checkpoint inhibitors anti-CTLA4 or anti-PDL1); most importantly, it diverts immunity toward the tumor (i.e., the capsid becomes covered with MHC-I-restricted tumor-specific peptides).
The method that we have developed to cover the virus with tumor peptides is novel and exceeds current state-of-the-art. Importantly, it is fast and does not require genetic or chemical manipulation of the virus; this feature has a significant impact on the translational capability of the project.
Our preliminary results show great potential but significant questions regarding the development and the personalization of PeptiCrad remain to be studied. In this grant I propose two lines of research, one focused on the development and the other one on the personalization of PeptiCrad.
Max ERC Funding
1 975 705 €
Duration
Start date: 2016-07-01, End date: 2021-06-30
Project acronym whyBOTher
Project Why does Clostridium botulinum kill? – In search for botulinum neurotoxin regulators
Researcher (PI) Miia Kristina Lindstrom
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Consolidator Grant (CoG), LS9, ERC-2015-CoG
Summary Bacterial toxins cause devastating diseases in humans and animals, ranging from necrotic enteritis to gas gangrene and tetraplegia. While toxin synthesis probably endows these bacteria with a selective advantage in their natural habitats, toxigenesis is likely to represent a fitness cost. It is thus plausible that mild environments encourage bacteria to give up toxin production, or reduce the number of toxigenic cells in populations. The cellular strategies bacteria use to silence toxin production and to establish stably non-toxigenic subpopulations represent targets for innovative antitoxin and vaccine strategies that can be utilized by the food, feed, medical, and agricultural sectors. I have found the first repressor that blocks the production of the most poisonous substance known to mankind, botulinum neurotoxin (BOT). This toxin, also known as “botox”, kills in nanogram quantities and is produced by the notorious food pathogen, Clostridium botulinum. In whyBOTher, I will extend the knowledge from this single regulator to comprehensive understanding of how C. botulinum cultures coordinate BOT production between single cells and cell subpopulations in response to their physical and social environment, and which genetic and plastic cellular strategies the cells take to attenuate BOT production in short and long term. I will experimentally force evolution of BOT-producing and non-producing cell lines, and explore the genetic, epigenetic, and cellular factors that explain the emergence of the two cell lines. To achieve this goal, I will extend the research on C. botulinum biology in two dimensions: from population level to fluorescent single-cell biology, and from genomic information to functional analysis of regulatory and metabolic networks controlling BOT production. whyBOTher represents an unprecedented research effort into regulation of bacterial toxins, and introduces a shift in paradigm from population-level observations to the life of single bacterial cells.
Summary
Bacterial toxins cause devastating diseases in humans and animals, ranging from necrotic enteritis to gas gangrene and tetraplegia. While toxin synthesis probably endows these bacteria with a selective advantage in their natural habitats, toxigenesis is likely to represent a fitness cost. It is thus plausible that mild environments encourage bacteria to give up toxin production, or reduce the number of toxigenic cells in populations. The cellular strategies bacteria use to silence toxin production and to establish stably non-toxigenic subpopulations represent targets for innovative antitoxin and vaccine strategies that can be utilized by the food, feed, medical, and agricultural sectors. I have found the first repressor that blocks the production of the most poisonous substance known to mankind, botulinum neurotoxin (BOT). This toxin, also known as “botox”, kills in nanogram quantities and is produced by the notorious food pathogen, Clostridium botulinum. In whyBOTher, I will extend the knowledge from this single regulator to comprehensive understanding of how C. botulinum cultures coordinate BOT production between single cells and cell subpopulations in response to their physical and social environment, and which genetic and plastic cellular strategies the cells take to attenuate BOT production in short and long term. I will experimentally force evolution of BOT-producing and non-producing cell lines, and explore the genetic, epigenetic, and cellular factors that explain the emergence of the two cell lines. To achieve this goal, I will extend the research on C. botulinum biology in two dimensions: from population level to fluorescent single-cell biology, and from genomic information to functional analysis of regulatory and metabolic networks controlling BOT production. whyBOTher represents an unprecedented research effort into regulation of bacterial toxins, and introduces a shift in paradigm from population-level observations to the life of single bacterial cells.
Max ERC Funding
2 000 000 €
Duration
Start date: 2017-01-01, End date: 2021-12-31
