Preclinical type 1 diabetes (T1D) research has made important advances in recent years, but less progress has been made in translating findings from in vitro and animal models into effective clinical interventions. INNODIA aims to achieve a breakthrough in the way in which we study T1D to enable us to move closer towards prevention and cure of T1D. To this end, INNODIA joins together the leading European experts from the fields of basic and clinical T1D research, four leading pharmaceutical companies with strong expertise in the discovery and development of diabetes medicines and the two leading public organizations involved in T1D research into one comprehensive collaborative consortium. The clinicians in INNODIA oversee T1D registries and have access to large populations of children and adults with T1D and family members at increased risk of developing the disease. The basic science researchers are experts in beta-cell pathophysiology, immunology, biomarker discovery, bioinformatics, systems biology and clinical trial design. INNODIA will accelerate understanding of T1D through coordinated studies of unique clinical samples and translation-oriented preclinical models. This should deliver novel biomarkers and interventions for testing in appropriately designed trials, to be developed in active collaboration with regulators and patients. INNODIA provides access to unique historical biorepositories and will create the Clinical Sample Network, a clinical EU infrastructure to recruit T1D subjects at diagnosis and at-risk relatives. These individuals will be deep-phenotyped and will provide biosamples, allowing the establishment of a ‘living biobank’ of subjects consented for recall. They will be characterized using standardized clinical, genetic and metabolic phenotyping procedures, including prospective, longitudinal sample collection to facilitate novel biomarker discovery. Diverse biological samples (blood, plasma, serum, urine, stools, etc.) will be collected at

In many European countries the recent rise in incidence of esophageal adenocarcinoma (EAC) is without precedent. EAC is notorious for its highly aggressive biological behavior leading to invasive disease and early metastases. The only way to reduce mortality is through treatment in early stage of the cancer. EAC has a well recognized premalignant precursor lesion identified as esophageal metaplasia, or Barrett’s Esophagus (BE), which offers important opportunities for treatment in early stages of cancer which may reach 5 years survival rates up to 80%. However, these patients need to be monitored constantly for timely intervention in case of disease recurrence or metastases. The problem is that after endoscopic treatment up to 30% of cases will develop recurring cancers or even present with metastases, which requires additional endoscopic treatments or surgery. Currently it is impossible to predict which of the treated BE patients with will have stable disease and which will recur or progress to invasive cancer. As a consequence all treated patients need to remain in frequent endoscopic surveillance. This leads to over-treatment of a large group of BE patients and under-treatment of those with more aggressive disease. There is a low cost effectiveness of endoscopic therapies, low quality of life of patients and poor satisfaction of care providers. An accurate risk stratification method for early AEC in BE patients is therefore an unmet clinical need. The ambition of the ENDEAVOR consortium is to implement an innovative risk stratification method, which encompasses minimally invasive cell collection supplemented by single cell genomic analysis to address this specific need. Taking into account patient characteristics, gender dimensions, an optimal model model will be tested in a randomized controlled prospective trial. Future implementation of this method will reduce health care costs, increase quality of life and satisfaction of health care providers. This action is part of the Cancer Mission cluster of projects on Diagnostics and Treatment (diagnostics).

Cilia-AI will train a new generation of multidisciplinary biomedical researchers and entrepreneurs, and those specializing in emerging machine learning technologies, a subset of AI. The focus is the study of primary cilia, microtubule-based projections on cell surfaces that play a pivotal role in coordinating cellular signalling pathways during development and homeostasis of cells, tissues and organs. These tiny structures are essential for various physiological functions such as hearing, smell, respiration, excretion and reproduction. Dysfunctional cilia can lead to >35 severe human diseases known as ciliopathies, exhibiting diverse and overlapping phenotypes, affecting up to 1 in 400 people. To unravel the multi-level organisation and regulation of cilia in health and disease, Cilia-AI employs a multidisciplinary approach, integrating cutting edge technologies like structural biology, omics- and organoid technologies. Advanced imaging techniques, including super-resolution microscopy, cryo-electron tomography and expansion microscopy, will be used to generate high-resolution and versatile datasets. Processing such data requires sophisticated computational methods. Cilia-AI is at the forefront of implementing and developing machine learning approaches to decipher these high-content datasets and integrate diverse multidisciplinary data. Cilia-AI offers unparalleled training opportunities for 14 Doctoral Candidates (DCs) in both academic and industrial settings. The training involves individual research projects, secondments, and network-wide sessions. This training equips DCs with skills attractive to both industrial and academic sectors, enhancing their career prospects in these domains. Overall, Cilia-AI’s research and training activities contribute to advancing the understanding of cilia in health and disease while fostering a new generation of skilled professionals with broad competences.

Endometriosis is characterized by the presence of endometrium-like tissue outside the uterus and chronic inflammation. Worldwide, >10% of reproductive-age women suffer from pain and/or infertility caused by this disease. Recently, cancer driver mutations have been identified in both the eutopic and ectopic endometrium of endometriosis patients. Moreover, it has been shown in cancer studies that cancer driver mutations can alter the local immune microenvironment. Nevertheless, it has never been studied how cancer driver mutations present in the eutopic endometrium of endometriosis patients contribute to the pathogenesis of this disease. Therefore, I propose to investigate how these mutations that are present in the eutopic endometrium alter the local eutopic immune microenvironment and thereby drive endometriosis development. This theory is what the acronym MERMAID in this proposal stands for: Mutations in Endometrial Regions and Microenvironmental Adaptations Influence Disease. To this end, I will analyze cell viability and immune cell signature in the menstrual fluid of people with and without endometriosis and identify which cancer driver mutations are present in their endometrial epithelial cells. In addition, I will create a mouse model to study the role of these mutations on the local immune microenvironment and in early endometriosis development. The outlined proposal will not only improve our understanding about the pathogenesis of endometriosis, but it will also generate potential new diagnostic methods and lead to novel personalized treatment options. Moreover, successfully managing this project will provide me with the additional scientific and translational skills necessary for moving up the career ladder towards a position as assistant professor in academia or senior scientist in industry.

Challenges. The incidence of cardiovascular diseases (CVD) increases after infections, but causal mechanisms are not understood yet. Pneumonia, which can be acquired in the community (such as flu and COVID-19) or during hospitalization, is a leading cause of infectious diseases. The main idea of the Homi-lung project is to investigate the causal relationship between CVD progression and the immune and microbiome alterations observed after pneumonia. Objectives. During the Homi-lung project, we aim to i) define the medical and societal, and patient needs; ii) increase medical doctor’s knowledge of the physiological processes linking pneumonia and CVD; iii) enable early identification of patients at risk of CVD progression; and iv) preclinically develop new treatments. Implementation. The Homi-lung project will address this challenge by comparing CVD rates between patients cured of pneumonia and matched patients who had not developed pneumonia during a prospective 3-year follow-up. We will analyze longitudinal samples collected in these populations and develop new algorithms by artificial intelligence to associate host-microbiome interactions with CVD progression. We will also demonstrate the causal link between CVD progression and host-microbiome interactions in preclinical pneumonia models. The interdisciplinary and ambitious Homi-lung project brings together 8 partners from 5 EU countries, with expertise in pneumonia, CVD, immunology, microbiome, and artificial intelligence and is uniquely placed to reach these objectives. Impacts. The project will provide clinicians with robust evidence contributing to identifying patients at risk of CVD after pneumonia. By developing new biomarkers and preclinical validating treatments to TRL4, the project will contribute to improving patients’ recovery and reducing the burden of infections. This project, particularly timely after the COVID-19 pandemic crisis, will also increase European preparedness for the next pandemic.