Atherothrombosis is the leading cause of disease and death worldwide despite advances in medicine. The multifaceted pathomechanisms of this disease are only inadequately addressed by currently available therapeutics and about 95% of the candidates tested in trials fail in the early clinical development phase.

The biosignATure project focuses on therapeutic targets that are central to the onset and progression of the disease and have the potential for the development of drugs with add-on effects to conventional treatments in the project, state-of-the-art analytical artificial intelligence methods are applied to identify novel therapeutic targets from biodatabases with deep clinical phenotyping and multiomics data at sequential time points. The resulting pipeline is complemented by data from experimental mechanistic research.

In biosignATure, a multidisciplinary panel translational research, drug development, and clinical application evaluates results at all steps of development for clinical use of innovative RNA technology and the use of biomarker signatures.

The goal of curATarget is to identify and prioritize thrombo-inflammatory targets for loco-regional reprogramming of immune reactions in athero-thrombotic diseases.

Thrombo-inflammation, the interplay of inflammatory and thrombotic processes, contributes significantly to the residual risk in the context of current therapies for cardiovascular diseases. These atherogenic processes locally influence immune responses in the vessel wall. This project combines high-resolution single-cell analyses in preclinical models and clinical samples to molecularly define pro- and anti-atherogenic immune cell functions and relate these to clinical biomarker profiles of atherothrombosis. Standardized bioinformatics pipelines and AI-based methods will be developed for the classification of immunological alterations in atherothrombotic lesions and for the identification of new therapeutic targets for cell type specific reprogramming as part of the overall project.

The microbAIome project investigates the role of the microbiome in atherothrombosis. In particular, bioinformatic analysis of RNA sequencing results is performed. In a complementary approach, clinical microbiomes from patients, that took part in population studies and show cardiovascular diseases, are transplanted into germ-free recipient mice to investigate the role of these microbiomes in arterial thrombosis. In addition, potential targets within the sphingolipid synthesis pathway will be investigated with regard to the development of atherothrombosis. Together with the analysis of stool samples from patients with cardiovascular diseases, this translational project will help us to learn about the effects of the gut microbiome and its impact towards cardiovascular diseases, which will be helpful in the future for optimal diagnosis, stratification and treatment of patients.

In the course of the curABodies project, we want to use a technology platform developed by TRON and BioNTech together with the findings from high-quality disease and cohort studies, as well as the epidemiological and systems medicine expertise of Unimedizin Mainz, to identify new disease-relevant autoantibody signatures for cardiovascular diseases on an unprecedented scale and resolution. The aim here is to make the autoantibody signatures usable as biomarkers or target structures for the development of new diagnostic and therapeutic applications.

The diAMs platform uses high-resolution quantitative data-independent mass spectrometry technologies established within the Mainz research core DIASyM. The generated multidimensional datasets at protein and lipid levels, together with bioinformatics workflows, machine learning and multi-OMICS data integration, enable comprehensive characterization of clinical samples and deciphering of complex pathophysiological mechanisms in different diseases.

The diAMs platform develops and optimizes highly sensitive methods for the quantitative characterization of complex proteomes and post-translational protein modifications and for the characterization of lipid immunomodulators. These are used to identify multi-OMICs-based signatures induced during macrophage reprogramming and platelet activation. The diAMs platform utilizes state-of-the-art bioinformatics workflows for the integration of multi-OMICs datasets and their annotation and interpretation using biological network and pathway data analysis for patient stratification and biomarker discovery.

Within the framework of curAIntervent, the preclinical foundation for innovative therapy options against atherothrombosis will be developed by using the RNA technology established in TRON. This will create the basis for industrial drug development. We want to achieve this goal together with our industrial partner, resano GmbH, which is aimed towards the implementation of clinical studies. The use of nanoparticle-formulated, systemically applied RNA is in the foreground here. Through the cooperation with the group of Prof. Lutz Nuhn (University of Würzburg), the respective nanoparticle formulation is going to be developed making targeted therapies against atherothrombosis possible. Potential target structures will be identified by applying methods from artificial intelligence to multi-omics data from large clinical cohorts together with population-based studies of our partner, University Medical Center Mainz. The focus is placed here on target structures that are clinically relevant and accessible to drug therapy.

Cardiovascular diseases cause one third of all deaths worldwide. Among these, atherothrombosis is the main cause of heart attack and stroke. Here, part of a thickened, fatty blood vessel wall detaches and the resulting blood clot can lead to vessel occlusion and sudden interruption of the blood supply. Despite advances in the treatment of atherothrombosis, vascular occlusion continues to occur even within optimized therapies.

The aim of this project is to develop a novel therapeutic approach that specifically addresses the interplay of blood coagulation and inflammatory processes in atherothrombosis. Various thrombocyte functions play a central role in this. Together with our project partners, we want to reprogram precursors of thrombocytes in the bone marrow (megakaryocytes) using nucleic acid-based agents for the generation of therapeutically effective thrombocytes. Therefore, the first phase of the project is aimed at the establishment of techniques for the optimization of the packaging and specificity of the active substances our project partners. We further want to design and synthesize new nucleic acid-based active substances and investigate their efficacy.

heartATech addresses monocyte-macrophage reprogramming to prevent ischemic heart failure (IHF) after myocardial infarction (MI) using synthesized mRNA with Lipoplex formulation.

Monocytes are addressable in cardiac remodeling by synthesized mRNA with Lipoplex formulation and can act as a therapeutic shuttle to prevent the development of IHF. Ribotherapeutics can thus address appropriate signaling pathways in a tissue-specific manner to achieve low side-effect, efficient immunomodulatory therapy after MI. We aim to investigate the role of coagulation proteases and their receptors in monocyte-macrophage driven cardiac remodeling after MI. These functions are essential for mediating excessive cardiac fibrosis. We plan to study mice lacking the acid-sensing receptors, thus preventing reprogramming of proinflammatory cells toward tissue-repairing cells. Both receptors may be addressable targets to attenuate cardiac and vascular dysfunction in IHF.

The goal of endoTArget is to develop therapeutic strategies in autoimmune thrombosis. 

Autoimmune diseases lead to cardiovascular complications and thrombosis especially in the antiphospholipid syndrome. The discovery of the pathogenic target for antiphospholipid antibodies (aPL) opens new therapeutic approaches for thrombo-inflammatory diseases. The association of aPL with severe progression in COVID-19 and evidence of vascular bed-specific uptake of SARS-CoV-2 virus suggest overlapping mechanisms of autoimmune disease and viral infection in thrombo-inflammatory endothelial dysfunction. This project will characterize these prothrombotic mechanisms in endothelial cells, elucidate the repertoire of autoantibodies with respect to endothelial targets using an innovative biomarker platform, and define the endothelium-protective effects of targeted preclinical intervention in prothrombotic autoimmune mechanisms.

curAIscid aims to develop widely applicable solutions to cross-project AI problems. The following four topics have been identified as essential in advance:

Small data: The problem of small datasets is to be addressed through approaches such as transfer learning, data augmentation as well as knowledge-intensive machine learning.

Explainability: AI-based predictions may be due to purely associative rather than causal factors, highlighting the need to combine both explainable AI and formal causal inference methods.

Representation Learning and Phenotyping: The use of e.g. autoencoder-assisted dimensionality reduction can reduce complexity and noise in multidimensional data.

Privacy and Fairness: In order to ensure data protection and confidentiality, the project will investigate concepts of differential privacy.

curAIvasc aims to develop innovative technologies and pipelines for the analysis of multi-dimensional biodata on vascular function and structure and to transfer them into patient-oriented research.

In close collaboration, the German Research Centre for Artificial Intelligence and the University Medical Center Mainz will analyse vascular imaging data from the large-scale y Gutenberg Health Study and disease-specific cohorts. Standardised pre-processing steps, harmonisation processes and quality controls create the basis for an AI-based cross-cohort exploration of the data, leading to the development ofa modern Deep Learning-based multi-dimensional pipeline for image-based prediction of individual disease progression and personalised risk assessment. The identification of the most influential variables will help to transfer new biologically based influencing factors for risk assessment into medical research and to create a better understanding of the molecular processes of atherothrombosis.

Echocardiography is a clinically established procedure for determining structure and functional parameters of the heart for classification and risk assessment of cardiovascular diseases. This is a laborious process, which in the clinic has so far been based on the visual evaluation of recorded video loops by clinical staff. Current methods still rely on the selection of defined image regions by medical staff and depend heavily on expertise and experience.

In curAIheart, innovative machine learning methods will be used to automate deep analysis of echocardiographic images and digitally recorded image information. The analysis of the recordings will incorporate the image loops themselves as well as data from molecular biology that will enable a deeper analysis. The pipeline to be developed will improve risk assessment and early detection of cardiovascular disease and will allow integration with other diagnostic data.

curAIsig develops innovative technologies in systems-oriented biomedical research. In today's clinical practice, analytical methods are used that can lead to incorrect interpretations of results, especially in the case of data outliers and small sample sizes placed in relation to dimensionality. We will therefore develop and apply robust signal processing and statistical learning methods to discover novel and reproducible biomarker signatures.

The first contribution of curAIsig deals with autonomic dysfunctions leading to the development of atherosclerotic diseases and their consequences. New methods will be developed to extract clinically robust biomarkers from heart rate variability measurements. Furthermore, novel learning methods will be explored to develop clinically interpretable models from high-dimensional multi-omics and resulting data. We will apply these to biodatabases of the University Medical Center Mainz and will evaluate them in the context of the pathophysiology of atherothrombosis.

curAIknow deals with the development of knowledge-based machine learning methods based on a very large biomedical knowledge graph for use within the curATime cluster. An existing knowledge graph, Ontosight, covering almost all relevant public knowledge in the biosciences, will be combined with large-scale data from healthy individuals and patients for integrated analysis. Two directions will be pursued: On the one hand, the use of knowledge a priori in a machine learning algorithm. On the other hand, the use of knowledge a posteriori to retrospectively verify purely data-driven learned machine learning models in light of existing knowledge. Part of this task is the development of a so-called discovery agent that discovers or evaluates connections between two entities in the knowledge graph.

The curAHub project is an initiative to develop a concept for a professional data analysis and technology platform for translational biomedical research.

Central to the concept is the regulated use of high-quality, multidimensional data from longitudinal cohort and patient studies as well as external data. State-of-the-art AI tools and statistical methods, as well as scientific advice on data selection and analysis techniques aim to facilitate the translation of biomedical findings into clinical application. The platform will provide a legally compliant, data safe and -secure digital environment that fosters interdisciplinary collaboration between science and industry, enabling the synergistic use of various competencies. Additionally, legal advice on the market launch of new medical products, patents, and spin-offs will be offered. The commercialization of the platform concept is intended to ensure its long-term sustainability.

The goal of curAEducate is to establish a comprehensive network that educates and trains talents and specialists in an interdisciplinary, intersectoral and translational manner and binds them to the RMP region through excellent career perspectives. For this purpose, curAEducate is building an exchange platform in which knowledge transfer takes place by bringing together researchers of all levels of experience in the disciplines of medicine, life sciences and AI. Fellowships will be awarded in order to promote cross-locational research. A Young Investigator Academy will be established, including workshops tailored to curATime needs. An additional certificate for the acquisition of translational expertise will be established and prospectively integrated into a master's degree program. A newly developed manual explaining translational case studies will serve as learning material. Within the framework of hackathons, curATime-specific questions are worked on. In order to bind cluster members to curATime and the RMP region in the long term, a recruiting platform is being established for the job applications of partners and specialists seeking employment.

curACulture sets the basis for the long-term creation of a sustainable, joint, open, equal and innovative cluster culture of all active and associated partners. The concept serves to create a cluster identity for itself as well as to anchor the network in the region and to establish a network inside and outside Germany. In addition to an open and transparent communication culture, versatile and applicable research results are generated by promoting creativity and excellence.

To achieve this, our curAMeet cluster conferences and the curATalk seminar series are held at regular intervals as part of the project, and press and public relations work is coordinated. With the targeted mentoring programme curAMent and the Equality Reporting, we want to improve equal opportunities and thus promote a more balanced gender distribution and diversity in leadership positions. In addition, we want to actively support and encourage researchers to address population heterogeneities as well as gender differences.