bioRxivGeneticsPreprint
Rare coding variants in NOX4 link high superoxide levels to psoriatic arthritis mutilans
Sailan WangiD, Pernilla NikamoiD, Leena Laasonen, Björn GuðbjörnssoniD, L. Ejstrup, Lars IverseniD, Ulla R C LindqvistiD, Jessica J. AlmiD, Jesper EisfeldtiD, Xiaowei ZhengiD, Sergiu‐Bogdan CatrinaiD, Fulya TaylaniD, Raquel VaziD, Mona StåhleiD, Isabel Tapia‐PáeziD
Abstract
Psoriatic arthritis mutilans (PAM) is the rarest and most severe form of psoriatic arthritis. PAM is characterized by erosions of the small joints of hands and feet and osteolysis leading to joint disruption. Despite its severity, the underlying mechanisms are unknown, and no candidate susceptibility genes have hitherto been identified. We aimed to investigate the genetic basis of PAM. We performed massive parallel sequencing of sixty-one patients genomes from the PAM Nordic cohort. We validated the rare variants found by Sanger sequencing and genotyped additional psoriasis, psoriatic arthritis, and control cohorts. We then tested the role of the variants using in vivo and in vitro models. We found rare variants with a minor allele frequency (MAF) below 0.0001 in the NADPH oxidase 4 (NOX4) in four patients. In silico predictions show that the identified variants are potentially damaging. NOXs are the only enzymes producing reactive oxygen species (ROS). ROS are highly reactive molecules important role in the regulation of signal transduction. NOX4 is specifically involved in the differentiation of osteoclasts, the cells implicated in bone resorption. Functional follow-up studies using cell culture, zebrafish models, and measurement of ROS in patients uncovered that the NOX4 variants found in this study increase the levels of ROS both in vitro and in vivo. We propose NOX4 as the first candidate susceptibility gene for PAM. Our study links high levels of ROS caused by NOX4 variants to the development of PAM, opening the possibility for a potential therapeutic target.
- organism: human
- assay: whole-exome sequencing
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 0
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