Medical and health sciences
- Computational transcriptomics and epigenomics
- Molecular diagnostics
- Adaptive immunology
- Laboratory medicine not elsewhere classified
- Medical epigenomics
Common variable immunodeficiencies (CVID) are the most common primary immunodeficiency. The dysfunctional immune system of CVID patients is unable to protect them against infections. A subgroup of the CVID patients develop a severe disease with distinct comorbidities, associated with a poor outcome. In the majority of the CVID no monogenic cause is found. Flow cytometry (FCM) and to a lesser extent genetic analysis are currently used to define disease subsets and association to severe clinical phenotypes, but it remains insufficient to elucidate the full disease heterogeneity. There is published evidence that epigenetic reprogramming is an important player in the CVID pathogenesis and highlight the pressing need for a more thorough exploration of the epigenomic alteration in CVID. In this project, I want to achieve a deeper understanding of the DNA methylome fingerprint underpinning CVID heterogeneity. I will explore differential methylation patterns in sorted B cell fractions of CVID patients with distinct clinical phenotypes. Further, using a RRBS-deconvolution pipeline and (un)supervised machine learning on a large heterogeneous CVID population, I will unravel the potential of DNAm based immune profiles to fine-tune CVID subclassification and in association with clinical phenotypes. Finally, I will document DNA methylation patterns in relation with the clinical and immunological phenotype in two specific subsets of monogenic CVID (PI3KCD variant and IKZF1 variant).