bioRxivImmunologyPreprint
Integrated Multi-omics Analyses of NFKB1 patients B cells points towards an up regula-tion of NF-κB network inhibitors
N. Camacho-Ordonez, N. Ramirez, S. Posadas-Cantera, A. Caballero-Oyteza, M. Fliegauf, F. Zhao, M. Guarini, V. Gernedl, M. Pecoroaro, K. Warnatz, C. Bock, E. Ballestar, R. Geiger, M. Proietti, B. Grimbacher
Abstract
The transcription factor NF-{kappa}B plays a pivotal role in the adaptive immune response. Pathogenic variants in NFKB1 are the most common genetic etiology of common variable immunodeficiency (CVID). Patients frequently present with impaired terminal B cell differentiation, autoimmunity, and hyperinflammatory immune dysregulation. NF-{kappa}B signaling and target gene expression are expected to be dysregulated in NFKB1-mutated patients. Here, we performed a multi-omics characterization of B cells from a cohort of clinically affected and unaffected NFKB1 mutation carriers. Our analysis identified specific epigenetic dysregulation and gene expression differences on B cells from NFKB1-mutated patients. We observed an aberrant expression of negative regulators of NF-{kappa}B signaling in NFKB1 mutation carriers, which may be a key factor for the autoinflammatory phenotype of these patients. Moreover, our analysis points towards a dysregulation of XBP1 and BCL3, key players of B cell activation and proliferation at different stages of B cell differentiation. The reduced expression of negative regulators of the NF-{kappa}B network is likely to be one of several mechanisms responsible for the aberrant NF-{kappa}B signaling, which impairs the maintenance of a normal humoral immune response. In summary, our findings highlight epigenetic and gene expression changes in B cells associated with NFKB1 mutations. Our data give insight into future therapeutic opportunities for patients with NFKB1 (haplo)insufficiency.
- organism: human
- assay: bulk RNA-seq
- assay: DNA methylation
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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