Project acronym AI-PREVENT
Project A nationwide artificial intelligence risk assessment for primary prevention of cardiometabolic diseases
Researcher (PI) Andrea Ganna
Host Institution (HI) HELSINGIN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS7, ERC-2020-STG
Summary Diabetes, stroke and coronary artery disease (cardiometabolic diseases) are the leading cause of death in Europe. Given that effective pharmacological and lifestyle interventions are available, it is important to identify high risk individuals at an early stage. Traditionally, this is done using clinical prediction models. However, the established models have substantial limitations: they are often used by doctors only when an underlying disease is already suspected, they are not developed on updated nationally-representative data and they require time-consuming clinical measurements. Thus, a substantial part of the population is not provided with risk assessment. I propose to revolutionize the existing approaches to primary prevention by providing risk assessment of cardiometabolic diseases before an individual even steps into the doctor’s office for a visit. To this end my project has three main objectives:
1) Development of artificial intelligence (AI) approaches to model health trajectories based on nationwide registry data on medications, diagnoses, familial risk and socio-demographic information to obtain accurate risk estimates for cardiometabolic disease. I will integrate high quality data from selected countries that have long traditions of registry data (Finland and Sweden, over 7.5 million individuals).
2) To identify health trajectories that maximize the clinical utility of genetic scores by integrating genetic and registry-based data on > 1 million people to identify subgroups of individuals for whom genetic information might improve risk prediction.
3) Validation of AI and genetic-based risk assessment as first-stage screening via a clinical study in 2800 individuals.
My project leverages the latest developments in AI and high-quality data of unprecedented scale to deliver a paradigm shift with important public health consequences by potentially changing the way cardiometabolic disease risk is assessed.
Summary
Diabetes, stroke and coronary artery disease (cardiometabolic diseases) are the leading cause of death in Europe. Given that effective pharmacological and lifestyle interventions are available, it is important to identify high risk individuals at an early stage. Traditionally, this is done using clinical prediction models. However, the established models have substantial limitations: they are often used by doctors only when an underlying disease is already suspected, they are not developed on updated nationally-representative data and they require time-consuming clinical measurements. Thus, a substantial part of the population is not provided with risk assessment. I propose to revolutionize the existing approaches to primary prevention by providing risk assessment of cardiometabolic diseases before an individual even steps into the doctor’s office for a visit. To this end my project has three main objectives:
1) Development of artificial intelligence (AI) approaches to model health trajectories based on nationwide registry data on medications, diagnoses, familial risk and socio-demographic information to obtain accurate risk estimates for cardiometabolic disease. I will integrate high quality data from selected countries that have long traditions of registry data (Finland and Sweden, over 7.5 million individuals).
2) To identify health trajectories that maximize the clinical utility of genetic scores by integrating genetic and registry-based data on > 1 million people to identify subgroups of individuals for whom genetic information might improve risk prediction.
3) Validation of AI and genetic-based risk assessment as first-stage screening via a clinical study in 2800 individuals.
My project leverages the latest developments in AI and high-quality data of unprecedented scale to deliver a paradigm shift with important public health consequences by potentially changing the way cardiometabolic disease risk is assessed.
Max ERC Funding
1 550 057 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym AIM.imaging.CKD
Project AI-augmented, Multiscale Image-based Diagnostics of Chronic Kidney Disease
Researcher (PI) Peter BOOR
Host Institution (HI) UNIVERSITAETSKLINIKUM AACHEN
Country Germany
Call Details Consolidator Grant (CoG), LS7, ERC-2020-COG
Summary Chronic kidney disease (CKD) is a major global health problem, affecting 10% of the world population and projected to be the fifth major cause of death in 2040. CKD patients are one of the most complex and multi-morbid populations in internal medicine while at the same time having the least translational randomized clinical trials and limited treatment options. One of the major reasons for this is the lack of reproducible approaches specifically reflecting intrarenal pathological processes and disease activity. The overall goal of AIM.imaging.CKD is to specifically address this unmet need by developing, validating and integrating image-based diagnostics for CKD. The integration of broad interdisciplinary expertise will enable to develop a multiscale approach from nano- to micro- to macromorphological and molecular diagnostics. Specifically, the project will develop augmented full-spectrum ultrastructural (“nano”) and histological (“micro”) renal biopsy diagnostics, focusing on reproducible, quantitative nephropathological analyses and prediction of clinically relevant outcome parameters. The project will also explore macro-morphological and molecular imaging in CKD, focusing on translatable non-invasive approaches. The central feature will be the development of advanced, scalable and modular image analyses models utilizing artificial intelligence (AI), particularly machine and deep learning. Using preclinical testing and clinical validation, the main emphasis will be on accelerated or, whenever possible, direct implementation into the clinical practice. The integration of the above-mentioned tools and technologies provides a comprehensive multiscale and multiplex approach for improved diagnostics of CKD patients and facilitate future randomized clinical trials. At each level, and even more so when integrated, the results are expected to augment and transform image-based diagnostics of kidney diseases, and thereby lead to improved patient management and outcome.
Summary
Chronic kidney disease (CKD) is a major global health problem, affecting 10% of the world population and projected to be the fifth major cause of death in 2040. CKD patients are one of the most complex and multi-morbid populations in internal medicine while at the same time having the least translational randomized clinical trials and limited treatment options. One of the major reasons for this is the lack of reproducible approaches specifically reflecting intrarenal pathological processes and disease activity. The overall goal of AIM.imaging.CKD is to specifically address this unmet need by developing, validating and integrating image-based diagnostics for CKD. The integration of broad interdisciplinary expertise will enable to develop a multiscale approach from nano- to micro- to macromorphological and molecular diagnostics. Specifically, the project will develop augmented full-spectrum ultrastructural (“nano”) and histological (“micro”) renal biopsy diagnostics, focusing on reproducible, quantitative nephropathological analyses and prediction of clinically relevant outcome parameters. The project will also explore macro-morphological and molecular imaging in CKD, focusing on translatable non-invasive approaches. The central feature will be the development of advanced, scalable and modular image analyses models utilizing artificial intelligence (AI), particularly machine and deep learning. Using preclinical testing and clinical validation, the main emphasis will be on accelerated or, whenever possible, direct implementation into the clinical practice. The integration of the above-mentioned tools and technologies provides a comprehensive multiscale and multiplex approach for improved diagnostics of CKD patients and facilitate future randomized clinical trials. At each level, and even more so when integrated, the results are expected to augment and transform image-based diagnostics of kidney diseases, and thereby lead to improved patient management and outcome.
Max ERC Funding
1 999 375 €
Duration
Start date: 2021-05-01, End date: 2026-04-30
Project acronym ALLERGENE
Project Allergic multimorbidity from birth to young adulthood: determinants, epigenetic regulation and inflammatory processes
Researcher (PI) Marie Standl
Host Institution (HI) HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUNGSZENTRUM FUER GESUNDHEIT UND UMWELT GMBH
Country Germany
Call Details Starting Grant (StG), LS7, ERC-2020-STG
Summary The prevalence of allergic diseases, such as atopic eczema, asthma and rhinitis, has increased over the past decades and is currently estimated to be up to 40%. Prevention strategies play a pivotal role, as there are no curative treatments available. Therefore, the aim of ALLERGENE is to understand the complex interplay of genetic, environmental and lifestyle factors and to identify involved mechanisms that distinguish between young adults free of allergic diseases and those suffering from allergic multimorbidity. Therefore, the aims of the present project are to: 1. Define allergic disease trajectories from birth to young adulthood, describe their determinants and identify risk and protective early-life environmental and lifestyle factors contributing to progression towards allergic multimorbidity or remission of allergic diseases. 2. Investigate molecular mechanisms of epigenetic regulation of allergic disease trajectories and test effect modification by inclusion of selected environmental and lifestyle factors. 3. Characterise the underlying inflammation profile of allergic disease trajectories and determine interactions with environmental and lifestyle factors The project makes use of two long-standing, prospective German birth cohort studies, GINIplus and LISA, with available data from birth to young adulthood, and an extensive examination planned at age 25. Within this project, a comprehensive characterisation of allergic disease trajectories, their determinants, comorbidities, risk and protective factors across the life-course will be obtained. ALLERGENE will enhance the understanding of how modifiable factors contribute to allergic disease aetiology. This will be an essential prerequisite to develop effective early intervention strategies for susceptible populations and to identify disease-specific biomarkers for the development and progression of allergic diseases in the future.
Summary
The prevalence of allergic diseases, such as atopic eczema, asthma and rhinitis, has increased over the past decades and is currently estimated to be up to 40%. Prevention strategies play a pivotal role, as there are no curative treatments available. Therefore, the aim of ALLERGENE is to understand the complex interplay of genetic, environmental and lifestyle factors and to identify involved mechanisms that distinguish between young adults free of allergic diseases and those suffering from allergic multimorbidity. Therefore, the aims of the present project are to: 1. Define allergic disease trajectories from birth to young adulthood, describe their determinants and identify risk and protective early-life environmental and lifestyle factors contributing to progression towards allergic multimorbidity or remission of allergic diseases. 2. Investigate molecular mechanisms of epigenetic regulation of allergic disease trajectories and test effect modification by inclusion of selected environmental and lifestyle factors. 3. Characterise the underlying inflammation profile of allergic disease trajectories and determine interactions with environmental and lifestyle factors The project makes use of two long-standing, prospective German birth cohort studies, GINIplus and LISA, with available data from birth to young adulthood, and an extensive examination planned at age 25. Within this project, a comprehensive characterisation of allergic disease trajectories, their determinants, comorbidities, risk and protective factors across the life-course will be obtained. ALLERGENE will enhance the understanding of how modifiable factors contribute to allergic disease aetiology. This will be an essential prerequisite to develop effective early intervention strategies for susceptible populations and to identify disease-specific biomarkers for the development and progression of allergic diseases in the future.
Max ERC Funding
1 493 330 €
Duration
Start date: 2021-03-01, End date: 2026-02-28
Project acronym CARsen
Project Senolytic CAR T cells as novel therapeutic concept for solid tumors and senescence-associated diseases.
Researcher (PI) Judith Feucht
Host Institution (HI) EBERHARD KARLS UNIVERSITAET TUEBINGEN
Country Germany
Call Details Starting Grant (StG), LS7, ERC-2020-STG
Summary The adoptive transfer of T cells expressing CD19-directed chimeric antigen receptors (CARs) has yielded remarkable efficacy in patients with hematological B-cell malignancies. CARs are a class of synthetic receptors that reprogram T cell specificity, function and metabolism. Engineered T cells are applicable in principle to other cancers and diseases, but clinical success will critically depend on further progress to overcome current limitations such as antigenic heterogeneity or impaired T cell trafficking and function. We propose to develop CAR T cells targeting senescent cells as a novel therapeutic concept for cancer and senescence-associated diseases. Cellular senescence is a stress-response program characterized by stable cell cycle arrest that serves as a potent tumor-suppressive mechanism. Conversely, accumulation of senescent cells generates a chronic inflammatory milieu, which contributes to a plethora of pathologies, such as liver or lung fibrosis and can even promote tumor progression.
Our preliminary data demonstrate that CAR T cells can efficiently clear senescent cells, providing therapeutic benefit in a murine model of liver fibrosis. We thus firmly believe that senolytic CAR T cells have broad therapeutic potential. To this end, we will apply innovative engineering strategies to develop modular CAR designs tailored to senescence-specific requirements. We will determine safety and efficacy of senolytic CAR T cells in murine models of cellular senescence and solid tumors. Importantly, we will evaluate combined treatment approaches of senescence-inducing therapies with CAR T cells targeting senescent and proliferating tumor cells. Finally, we will investigate engineering tools to optimally direct senolytic CAR activity to mediate durable tumor regression.
This project combines two emerging concepts of anticancer therapies and goes beyond current applications of CAR therapies. The efforts may lead to promising new therapeutic avenues.
Summary
The adoptive transfer of T cells expressing CD19-directed chimeric antigen receptors (CARs) has yielded remarkable efficacy in patients with hematological B-cell malignancies. CARs are a class of synthetic receptors that reprogram T cell specificity, function and metabolism. Engineered T cells are applicable in principle to other cancers and diseases, but clinical success will critically depend on further progress to overcome current limitations such as antigenic heterogeneity or impaired T cell trafficking and function. We propose to develop CAR T cells targeting senescent cells as a novel therapeutic concept for cancer and senescence-associated diseases. Cellular senescence is a stress-response program characterized by stable cell cycle arrest that serves as a potent tumor-suppressive mechanism. Conversely, accumulation of senescent cells generates a chronic inflammatory milieu, which contributes to a plethora of pathologies, such as liver or lung fibrosis and can even promote tumor progression.
Our preliminary data demonstrate that CAR T cells can efficiently clear senescent cells, providing therapeutic benefit in a murine model of liver fibrosis. We thus firmly believe that senolytic CAR T cells have broad therapeutic potential. To this end, we will apply innovative engineering strategies to develop modular CAR designs tailored to senescence-specific requirements. We will determine safety and efficacy of senolytic CAR T cells in murine models of cellular senescence and solid tumors. Importantly, we will evaluate combined treatment approaches of senescence-inducing therapies with CAR T cells targeting senescent and proliferating tumor cells. Finally, we will investigate engineering tools to optimally direct senolytic CAR activity to mediate durable tumor regression.
This project combines two emerging concepts of anticancer therapies and goes beyond current applications of CAR therapies. The efforts may lead to promising new therapeutic avenues.
Max ERC Funding
1 818 637 €
Duration
Start date: 2021-08-01, End date: 2026-07-31
Project acronym COMBAT-RES
Project Predicting potent drug combinations by exploiting monotherapy resistance
Researcher (PI) Michael Menden
Host Institution (HI) HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUNGSZENTRUM FUER GESUNDHEIT UND UMWELT GMBH
Country Germany
Call Details Starting Grant (StG), LS7, ERC-2020-STG
Summary Personalising treatments based on tumour genetic profiles enables cancer precision medicine. However, treating cancers using targeted therapies often fails due to the emergence of drug resistance. Here, my goal is to use drug high-throughput screens (HTS) combined with computational methods to identify resistance and its biomarkers, and to overcome it with smart drug combinations to empower cancer precision medicine.
Identifying resistance in HTS is challenging: dissecting meaningful drug responses at high concentrations is impossible due to cytotoxicity, making non-responders and resistant cell lines indistinguishable, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, I will employ three approaches: 1) systematically identify non-responding cell lines carrying low-frequency resistance markers; 2) reveal intrinsic resistance driven by gene expression plasticity by conducting my own RNA sequencing experiments and modelling the maximal effect at high drug concentration; 3) identify drugs which increase cell viability, combined with drugs targeting fast proliferating cells. My paradigm shift, that resistance biomarkers become synergy markers, empowers smart drug combinations.
Additionally, I aim to predict drug synergy based on multi-task deep learning using molecular characterisation, QSAR modelling and monotherapies; and, to boost biomarker discovery by identifying clinically-relevant cancer subtypes based on transfer and reinforcement learning.
COMBAT-RES will benefit from data access to a phase III clinical trial in colorectal cancer (COREAD) and access to the largest human pancreas adenocarcinoma (PAAD) combination HTS (currently unpublished) accelerating the delivery of medicine for COREAD and PAAD patients. COMBAT-RES will interrogate the underpinnings of drug resistance, clinically-relevant subtypes and overcome it with highly synergistic drug combinations, enabling the next generation of precision medicine.
Summary
Personalising treatments based on tumour genetic profiles enables cancer precision medicine. However, treating cancers using targeted therapies often fails due to the emergence of drug resistance. Here, my goal is to use drug high-throughput screens (HTS) combined with computational methods to identify resistance and its biomarkers, and to overcome it with smart drug combinations to empower cancer precision medicine.
Identifying resistance in HTS is challenging: dissecting meaningful drug responses at high concentrations is impossible due to cytotoxicity, making non-responders and resistant cell lines indistinguishable, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, I will employ three approaches: 1) systematically identify non-responding cell lines carrying low-frequency resistance markers; 2) reveal intrinsic resistance driven by gene expression plasticity by conducting my own RNA sequencing experiments and modelling the maximal effect at high drug concentration; 3) identify drugs which increase cell viability, combined with drugs targeting fast proliferating cells. My paradigm shift, that resistance biomarkers become synergy markers, empowers smart drug combinations.
Additionally, I aim to predict drug synergy based on multi-task deep learning using molecular characterisation, QSAR modelling and monotherapies; and, to boost biomarker discovery by identifying clinically-relevant cancer subtypes based on transfer and reinforcement learning.
COMBAT-RES will benefit from data access to a phase III clinical trial in colorectal cancer (COREAD) and access to the largest human pancreas adenocarcinoma (PAAD) combination HTS (currently unpublished) accelerating the delivery of medicine for COREAD and PAAD patients. COMBAT-RES will interrogate the underpinnings of drug resistance, clinically-relevant subtypes and overcome it with highly synergistic drug combinations, enabling the next generation of precision medicine.
Max ERC Funding
1 499 991 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym DCUBATION
Project Redefining the term 'Incubation Period' using large-scale digital data
Researcher (PI) Dan Yamin
Host Institution (HI) TEL AVIV UNIVERSITY
Country Israel
Call Details Starting Grant (StG), LS7, ERC-2020-STG
Summary Infectious diseases pose one of the greatest risks for a global catastrophe. Just like controlling the spread of wildfires, an early detection of infectious diseases is instrumental to containing outbreaks. Nearly all infections start silently, and gradually progress until clinical symptoms appear. In this silent period, the incubation period (IP), pathogens inhibit major pathways of the innate immune system, allowing an extended period of unhindered replication. The rate of replication, as well as the type and length of suppressed symptoms vary considerably between pathogens, creating a unique signature for each pathogen. Thus, improved understanding of the IP is pivotal for early detection, prevention and control of infectious diseases. Previous studies estimating IPs have used aggregated retrospective data, and are subject to the biases of patient self-reporting. I hypothesise that the actual onset of clinical symptoms occurs earlier than previously known, can be identified more accurately, and can be used in real-time for patient empowerment. Focusing on respiratory infections, my methodological approach includes: 1) real-time evaluation of the prior risk for respiratory infections by integrating transmission models with individual-level data from electronic medical records of 4.5 Million individuals, 2) identification of micro-changes in patients’ behaviour during the early phase of an infection by prospectively analysing digital sensory data from wearable devices and mobile phones of 5000 selected participants, 3) early detection of the causing pathogen validated with self-swab kits that are tested using RT-PCR. Our preliminary work that combined an analysis of EMR and transmission modelling led to a change in public health policy in Israel. The proposed study has the potential to open new research directions on the hidden side of infectious diseases and to initiate a new era of personalized medicine through dramatic changes in patient-doctor interaction.
Summary
Infectious diseases pose one of the greatest risks for a global catastrophe. Just like controlling the spread of wildfires, an early detection of infectious diseases is instrumental to containing outbreaks. Nearly all infections start silently, and gradually progress until clinical symptoms appear. In this silent period, the incubation period (IP), pathogens inhibit major pathways of the innate immune system, allowing an extended period of unhindered replication. The rate of replication, as well as the type and length of suppressed symptoms vary considerably between pathogens, creating a unique signature for each pathogen. Thus, improved understanding of the IP is pivotal for early detection, prevention and control of infectious diseases. Previous studies estimating IPs have used aggregated retrospective data, and are subject to the biases of patient self-reporting. I hypothesise that the actual onset of clinical symptoms occurs earlier than previously known, can be identified more accurately, and can be used in real-time for patient empowerment. Focusing on respiratory infections, my methodological approach includes: 1) real-time evaluation of the prior risk for respiratory infections by integrating transmission models with individual-level data from electronic medical records of 4.5 Million individuals, 2) identification of micro-changes in patients’ behaviour during the early phase of an infection by prospectively analysing digital sensory data from wearable devices and mobile phones of 5000 selected participants, 3) early detection of the causing pathogen validated with self-swab kits that are tested using RT-PCR. Our preliminary work that combined an analysis of EMR and transmission modelling led to a change in public health policy in Israel. The proposed study has the potential to open new research directions on the hidden side of infectious diseases and to initiate a new era of personalized medicine through dramatic changes in patient-doctor interaction.
Max ERC Funding
1 986 875 €
Duration
Start date: 2020-11-01, End date: 2025-10-31
Project acronym De-StunHeartAttacks
Project Re-defining clinical care and reasoning in ST-elevation myocardial infarction (STEMI) by shifting focus to ischemic myocardial stunning - Mechanisms prognostic implications and a new treatment
Researcher (PI) Bjoern Redfors
Host Institution (HI) GOETEBORGS UNIVERSITET
Country Sweden
Call Details Starting Grant (StG), LS7, ERC-2020-STG
Summary Acute ischemic heart failure (AIHF) after ST-elevation myocardial infarction (STEMI) is a common and life-threatening condition for which the prognosis has not improved over the past decade, and for which no effective treatment option exists. Women, who have been underrepresented in most STEMI studies are more likely than men to develop AIHF after STEMI. My research aims to shift focus from traditional treatment targets (increased cardiac work and maintenance of blood pressure) to manipulation of myocardial stunning, the physiological phenomenon in which viable myocardium suddenly lose its function (becomes ‘stunned’) when exposed to a stressor (e.g. ischemia). Specifically, I aim to: 1) compare how stunning and propensity for AIHF affect prognosis for women vs. men with STEMI – using causal mediation analysis applied to a unique nationwide STEMI cohort (around 40,000 patients); 2) prospectively compare the rate of stunning resolution in STEMI patients with vs. without microvascular obstruction, in patients with STEMI vs. patients with takotsubo (another form of myocardial stunning with more favorable prognosis), and for women with STEMI vs. men with STEMI; 3) conduct a nationwide multicenter randomized registry trial to determine if adenosine, which has anti-inflammatory, vasodilatory and cytoprotective effects, improves resolution of myocardial stunning and clinical outcomes in STEMI; and 4) determine if pre-existing takotsubo-like stunning is protective in experimental AIHF. If my main hypothesis is correct, my this project could change the way researchers and clinicians approach the pathophysiology and treatment in STEMI. Irrespective of whether my main hypothesis is correct, my research proposal is designed to provide important insight into the pathophysiology and prognosis of myocardial stunning in STEMI, with a clear sex perspective that can help mitigate the sex gap in the level of evidence supporting the use and benefit of current treatments for STEMI.
Summary
Acute ischemic heart failure (AIHF) after ST-elevation myocardial infarction (STEMI) is a common and life-threatening condition for which the prognosis has not improved over the past decade, and for which no effective treatment option exists. Women, who have been underrepresented in most STEMI studies are more likely than men to develop AIHF after STEMI. My research aims to shift focus from traditional treatment targets (increased cardiac work and maintenance of blood pressure) to manipulation of myocardial stunning, the physiological phenomenon in which viable myocardium suddenly lose its function (becomes ‘stunned’) when exposed to a stressor (e.g. ischemia). Specifically, I aim to: 1) compare how stunning and propensity for AIHF affect prognosis for women vs. men with STEMI – using causal mediation analysis applied to a unique nationwide STEMI cohort (around 40,000 patients); 2) prospectively compare the rate of stunning resolution in STEMI patients with vs. without microvascular obstruction, in patients with STEMI vs. patients with takotsubo (another form of myocardial stunning with more favorable prognosis), and for women with STEMI vs. men with STEMI; 3) conduct a nationwide multicenter randomized registry trial to determine if adenosine, which has anti-inflammatory, vasodilatory and cytoprotective effects, improves resolution of myocardial stunning and clinical outcomes in STEMI; and 4) determine if pre-existing takotsubo-like stunning is protective in experimental AIHF. If my main hypothesis is correct, my this project could change the way researchers and clinicians approach the pathophysiology and treatment in STEMI. Irrespective of whether my main hypothesis is correct, my research proposal is designed to provide important insight into the pathophysiology and prognosis of myocardial stunning in STEMI, with a clear sex perspective that can help mitigate the sex gap in the level of evidence supporting the use and benefit of current treatments for STEMI.
Max ERC Funding
1 500 000 €
Duration
Start date: 2020-11-01, End date: 2025-10-31
Project acronym DEFEND
Project Dietary shaping of the early life metabolome and its role in healthy lung and brain development
Researcher (PI) Bo Chawes
Host Institution (HI) REGION HOVEDSTADEN
Country Denmark
Call Details Starting Grant (StG), LS7, ERC-2020-STG
Summary Asthma and behavioral problems related to neurodevelopmental delay such as ADHD and autism are common childhood disorder with lack of insight in disease mechanisms and no preventive measures, which is an unmet medical and societal need. In the Danish COPSAC mother-child cohort, I showed in two randomized trials that pregnancy supplements with n-3 long-chained polyunsaturated fatty acids (n-3 LCPUFA) and high-dose vitamin D reduced offspring asthma risk, improved cognitive scores and accelerated language and milestone achievement, which was replicated in the American VDAART vitamin D trial. This holds the promise for primary prevention but establishing underlying biochemical mechanisms and effect modification by maternal and early life diet and host genetics remains to achieve a personalized preventive strategy targeting supplements only to those women, whose offspring will benefit from it. This research project aims to understand how dietary supplements and diet in pregnancy and early life shapes the child’s metabolism and how this is linked to asthma and neurodevelopment, using longitudinal untargeted and targeted metabolomics profiles from more than 1,500 mother-child pairs in COPSAC and VDAART, detailed data on maternal and infant diet, the early life exposome, and genetics and genomics data. The objective is to identify metabolites and biochemical pathways of importance for asthma and neurodevelopment, focusing on n-3/n-6 LCPUFAs, sphingolipids and tryptophan, with the ultimate goal to achieve healthy lung and brain development through personalized diet interventions during pregnancy. The transdisciplinary study group lead by the applicant consisting of MDs with pediatric, respiratory and psychiatric expertise, wet and dry lab metabolomics experts and bioinformaticians will bridge basic and clinical sciences in an international collaboration between COPSAC, Denmark, and VDAART at Harvard Medical School, US.
Summary
Asthma and behavioral problems related to neurodevelopmental delay such as ADHD and autism are common childhood disorder with lack of insight in disease mechanisms and no preventive measures, which is an unmet medical and societal need. In the Danish COPSAC mother-child cohort, I showed in two randomized trials that pregnancy supplements with n-3 long-chained polyunsaturated fatty acids (n-3 LCPUFA) and high-dose vitamin D reduced offspring asthma risk, improved cognitive scores and accelerated language and milestone achievement, which was replicated in the American VDAART vitamin D trial. This holds the promise for primary prevention but establishing underlying biochemical mechanisms and effect modification by maternal and early life diet and host genetics remains to achieve a personalized preventive strategy targeting supplements only to those women, whose offspring will benefit from it. This research project aims to understand how dietary supplements and diet in pregnancy and early life shapes the child’s metabolism and how this is linked to asthma and neurodevelopment, using longitudinal untargeted and targeted metabolomics profiles from more than 1,500 mother-child pairs in COPSAC and VDAART, detailed data on maternal and infant diet, the early life exposome, and genetics and genomics data. The objective is to identify metabolites and biochemical pathways of importance for asthma and neurodevelopment, focusing on n-3/n-6 LCPUFAs, sphingolipids and tryptophan, with the ultimate goal to achieve healthy lung and brain development through personalized diet interventions during pregnancy. The transdisciplinary study group lead by the applicant consisting of MDs with pediatric, respiratory and psychiatric expertise, wet and dry lab metabolomics experts and bioinformaticians will bridge basic and clinical sciences in an international collaboration between COPSAC, Denmark, and VDAART at Harvard Medical School, US.
Max ERC Funding
1 499 952 €
Duration
Start date: 2021-02-01, End date: 2026-01-31
Project acronym DELIVER
Project Release of engineered extracellular vesicles for delivery of biotherapeutics
Researcher (PI) Samir EL-ANDALOUSSI
Host Institution (HI) KAROLINSKA INSTITUTET
Country Sweden
Call Details Consolidator Grant (CoG), LS7, ERC-2020-COG
Summary Nucleic acid-based medicines have opened a new avenue in drug discovery to target currently undruggable genes and to express therapeutic proteins, unlocking novel therapeutic options for a range of diseases, including neurodegeneration. However, they need to be encapsulated in nanocarriers to ensure their stability and efficient uptake into cells and tissues. Synthetic nanoparticles based on cell-penetrating peptides (CPPs) and, particularly, lipid nanoparticles (LNPs) have recently emerged as potent vectors for hepatic delivery. However, these systems fail to robustly target other organs in a safe manner.
Another promising nanocarrier for advanced drug delivery is extracellular vesicles (EVs) that have the ability to efficiently convey macromolecules into cells. As native nanoparticles, EVs benefit from immune tolerance as well as the ability to cross biological barriers to reach, for example, the brain. We have developed advanced strategies to bioengineer cells to generate EVs loaded with therapeutic RNAs and proteins. However, their production at scale is cumbersome and time consuming.
Here, I propose a platform development using synthetic nanocarriers to transiently engineer hepatic cells in vivo and harness EVs to functionally DELIVER biotherapeutics to currently unreachable, distant organs, focusing on brain. To achieve this, genetic constructs will be developed that allow for transient in situ engineering of cells in vivo and release of cargo (e.g. CRE)- laden EVs, displaying CNS-specific peptides, that can be functionally transported to distant organs, including brain. We will exploit the same strategy using CPP-based nanoformulations, recently developed in my lab, injected locally in brain to secrete EVs loaded with the disease-relevant protein GBA1 as a treatment strategy for Parkinson´s disease.
Long-term this novel project has enormous potential, as any engineered EV could be produced in situ and be used for delivery of virtually any biotherapeutics.
Summary
Nucleic acid-based medicines have opened a new avenue in drug discovery to target currently undruggable genes and to express therapeutic proteins, unlocking novel therapeutic options for a range of diseases, including neurodegeneration. However, they need to be encapsulated in nanocarriers to ensure their stability and efficient uptake into cells and tissues. Synthetic nanoparticles based on cell-penetrating peptides (CPPs) and, particularly, lipid nanoparticles (LNPs) have recently emerged as potent vectors for hepatic delivery. However, these systems fail to robustly target other organs in a safe manner.
Another promising nanocarrier for advanced drug delivery is extracellular vesicles (EVs) that have the ability to efficiently convey macromolecules into cells. As native nanoparticles, EVs benefit from immune tolerance as well as the ability to cross biological barriers to reach, for example, the brain. We have developed advanced strategies to bioengineer cells to generate EVs loaded with therapeutic RNAs and proteins. However, their production at scale is cumbersome and time consuming.
Here, I propose a platform development using synthetic nanocarriers to transiently engineer hepatic cells in vivo and harness EVs to functionally DELIVER biotherapeutics to currently unreachable, distant organs, focusing on brain. To achieve this, genetic constructs will be developed that allow for transient in situ engineering of cells in vivo and release of cargo (e.g. CRE)- laden EVs, displaying CNS-specific peptides, that can be functionally transported to distant organs, including brain. We will exploit the same strategy using CPP-based nanoformulations, recently developed in my lab, injected locally in brain to secrete EVs loaded with the disease-relevant protein GBA1 as a treatment strategy for Parkinson´s disease.
Long-term this novel project has enormous potential, as any engineered EV could be produced in situ and be used for delivery of virtually any biotherapeutics.
Max ERC Funding
2 000 000 €
Duration
Start date: 2021-03-01, End date: 2026-02-28
Project acronym DIVERGE
Project Depression in diverse populations: Unravelling the interplay between genes and environment
Researcher (PI) Karoline Kuchenbaecker
Host Institution (HI) UNIVERSITY COLLEGE LONDON
Country United Kingdom
Call Details Starting Grant (StG), LS7, ERC-2020-STG
Summary Depression affects 300 million people and represents one of the biggest challenges to human health to date. Of the burden, 80% pertains to low- and middle-income countries. It is thus imperative to understand the global causes of depression to design effective targeted interventions. DIVERGE will build the first ancestrally diverse data resource for depression, generated from biobanks (N=1.8M) and new studies in Pakistan (N=20K) as well as sub-Saharan Africa (N=13K) with deep phenotyping and genotyping.
Differences in depression risk between populations have been shaped by the environment, demography and diverging evolutionary history. Using the novel perspective of evolutionary psychiatry, DIVERGE will comprehensively characterise the genetic architecture of depression and assess how it has been shaped by natural selection. Thereby, I will illuminate how heritability, environmental factors and their interplay affect disease development. I will develop a new method, trans-ethnic colocalization, to address the fundamental question whether genetic risk factors are transferable across populations. This is important to ensure that health benefits of precision medicine can be shared within and across populations.
In addition to the big picture approach, I aim to identify specific causes of the disorder. The diversity of the data together with the application of population-matched inheritance models will empower the discovery of novel genetic loci for depression. I will develop and apply cutting-edge methods, including trans-ethnic fine-mapping with functional annotations to uncover biological mechanisms underlying depression loci. Trauma, such as exposure to violence, is a strong risk factor for depression. DIVERGE will investigate the interplay between traumatic life events and genetic susceptibility which could help understand how mental illness differs across groups. These innovations will lead to a step change in our understanding of the aetiology of depression.
Summary
Depression affects 300 million people and represents one of the biggest challenges to human health to date. Of the burden, 80% pertains to low- and middle-income countries. It is thus imperative to understand the global causes of depression to design effective targeted interventions. DIVERGE will build the first ancestrally diverse data resource for depression, generated from biobanks (N=1.8M) and new studies in Pakistan (N=20K) as well as sub-Saharan Africa (N=13K) with deep phenotyping and genotyping.
Differences in depression risk between populations have been shaped by the environment, demography and diverging evolutionary history. Using the novel perspective of evolutionary psychiatry, DIVERGE will comprehensively characterise the genetic architecture of depression and assess how it has been shaped by natural selection. Thereby, I will illuminate how heritability, environmental factors and their interplay affect disease development. I will develop a new method, trans-ethnic colocalization, to address the fundamental question whether genetic risk factors are transferable across populations. This is important to ensure that health benefits of precision medicine can be shared within and across populations.
In addition to the big picture approach, I aim to identify specific causes of the disorder. The diversity of the data together with the application of population-matched inheritance models will empower the discovery of novel genetic loci for depression. I will develop and apply cutting-edge methods, including trans-ethnic fine-mapping with functional annotations to uncover biological mechanisms underlying depression loci. Trauma, such as exposure to violence, is a strong risk factor for depression. DIVERGE will investigate the interplay between traumatic life events and genetic susceptibility which could help understand how mental illness differs across groups. These innovations will lead to a step change in our understanding of the aetiology of depression.
Max ERC Funding
2 495 950 €
Duration
Start date: 2021-02-01, End date: 2026-01-31
