Towards a PREcise DIagnosis in Ciliopathies – PREDICT
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.
Madame Ruxandra Bachmann-Gagescu ()
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
IMAGINE IHU IMAGINE - INSTITUT DES MALADIES GENETIQUES
Help of the ANR 1,500,856 euros
Beginning and duration of the scientific project: June 2023 - 36 Months