Joint Transnational Call 2022 (JTC2022)

PREDICT: Towards a PREcise DIagnosis in Ciliopathies

Ciliopathies are rare Mendelian disorders caused by dysfunction of primary cilia, sensory organelles protruding from the cell surface playing a crucial role in signal transduction during development and cell function. Clinically, ciliopathies can involve most organ systems, displaying substantial phenotypic variability and overlap between ciliopathy disorders. This makes predicting the precise outcome for a given patient particularly difficult, especially for progressive features. This project aims at providing a precise diagnosis to patients, to enable tailored surveillance and treatment, focusing on progressive retinal and renal phenotypes in patients with selected ciliopathies and recurrent causal mutations. PREDICT will rely on large previously collected cohorts of patients with ciliopathies to generate a comprehensive dataset for selected patients, combining detailed phenotypic information with whole genome sequencing and transcriptomic analyses to provide a holistic view for selected individuals. Using this patient-derived information in combination with the extensive publicly available data in databases and the literature, PREDICT will use artificial intelligence to generate predictive models for specific endorgan involvement. These models will be tested in vitro using simple cellular assays based on fibroblasts and/or human urinary epithelial cells which will provide a quantitative measure of ciliary dysfunction. Predictions will further be tested in more complex cellular models based on induced pluripotent stem cell (iPSC)-derived retinal and renal organoids, as well as in vivo using zebrafish models. Taking advantage of the wealth of biological information available on ciliopathies, in particular concerning protein interaction networks, these validations through functional assays will circumvent the limitation of poor statistical power inherent to rare disorders. Cellular assays will further serve as diagnostic tools for improved classification of ciliopathies, using high-content imaging and transcriptional signatures. Models and algorithms developed in PREDICT may serve as paradigms for other rare Mendelian disorders to improve our ability to provide an accurate prognosis, which is a requirement for precision medicine.
  • Bachmann-Gagescu, Ruxandra (Coordinator)
    Institute of Medical Genetics and Department of Molecular Life Sciences

    [SWITZERLAND]

  • Dollfus, Hélène

    Laboratoire de génétique médicale U1112, Université de Strasbourg AND Hôpitaux Universitaires de Strasbourg, Service de génétique médicale et CARGO
    [FRANCE]

  • Saunier, Sophie
    Institut Imagine
    [FRANCE]

  • Roepman, Ronald
    Genome Research Division

    [THE NETHERLANDS]

  • Russell, Rob
    Bloquant
    [GERMANY]

  • Tory, Kàlmàn
    Ist Department of Pediatrics
    [HUNGARY]

  • Bendert, de Graaf
    Patient advocacy organisation 
    [THE NETHERLANDS]

  • First Karalar, Elif Nur
    School of Medicine
    [TURKEY]
  • Patient advocacy organisation 
    [THE NETHERLANDS]