Abstract
Cystic fibrosis (CF) is caused by mutations in the CFTR gene. The CFTR gene encodes for the CFTR protein, which is an ion channel and indirectly responsible for fluid transport in various organs. People with CF suffer from various symptoms, including recurrent airway infections, pancreatic insufficiency and intestinal malabsorption. There are over 2000 different variants of the CFTR gene known, these variants all differentially affect CFTR quantity or function. Additionally, response to CFTR modulator treatment also differs per mutation.
In this thesis we use PDIOs to study rare CFTR variants and aim to predict the effect of the CFTR intragenic profile and CFTR modulator response on the in vivo disease expression. First, we use PDIOs to identify pwCF with CFTR mutations that are currently not eligible for CFTR modulator therapy that could benefit from this treatment. Next, we gain insight in the complexity of the CFTR gene and investigate the use of PDIOs for the linking of rare, complex genotypes to CFTR function and disease phenotype. Finally, we developed new assay models to increase the dynamic range of functional CFTR measurements in PDIOs, and potentially improve automation and accessibility of CFTR function measurements.
In this thesis we use PDIOs to study rare CFTR variants and aim to predict the effect of the CFTR intragenic profile and CFTR modulator response on the in vivo disease expression. First, we use PDIOs to identify pwCF with CFTR mutations that are currently not eligible for CFTR modulator therapy that could benefit from this treatment. Next, we gain insight in the complexity of the CFTR gene and investigate the use of PDIOs for the linking of rare, complex genotypes to CFTR function and disease phenotype. Finally, we developed new assay models to increase the dynamic range of functional CFTR measurements in PDIOs, and potentially improve automation and accessibility of CFTR function measurements.
Original language | English |
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Award date | 12 Mar 2024 |
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Print ISBNs | 978-94-6483-744-5 |
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Publication status | Published - 12 Mar 2024 |
Keywords
- Cystic fibrosis
- Organoids
- Genetic variation
- Theratpying
- Targeted locus amplification
- Deep learning