Search bioRxiv⌕ Search

Biology subjects

Van Nieuwenhove, E.

Publications and source records attributed to Van Nieuwenhove, E..

2 recordsLinked to original sources

Enhanced cGAS-STING-dependent interferon signaling associated with mutations in ATAD3A

Mitochondrial DNA (mtDNA) has been suggested to drive immune system activation, but the induction of interferon signaling by mtDNA has not been demonstrated in a Mendelian mitochondrial disease. We initially ascertained two patients, one with a purely neurological phenotype, and one with features suggestive of systemic sclerosis in a syndromic context, and found them both to demonstrate enhanced interferon-stimulated gene (ISG) expression in blood. We determined each to harbor a previously described de novo dominant-negative heterozygous mutation in ATAD3A, encoding ATPase family AAA domain-containing protein 3A (ATAD3A). We identified five further patients with mutations in ATAD3A, and recorded up-regulated ISG expression and interferon alpha protein in four of them. Knockdown of ATAD3A in THP-1 cells resulted in increased interferon signaling, mediated by cyclic GMP-AMP synthase (cGAS) and stimulator of interferon genes (STING). Enhanced interferon signaling was abrogated in THP-1 cells and patient fibroblasts depleted of mtDNA. Thus, mutations in the mitochondrial membrane protein ATAD3A define a novel type I interferonopathy. SummaryDominant-negative mutations in ATAD3A, a ubiquitously expressed mitochondrial protein, cause mitochondrial DNA-dependent up-regulation of type I interferon signaling in the context of neurological disease and autoimmunity, thereby defining a novel type I interferonopathy.

immunology↗

Machine learning identifies the immunological signature of Juvenile Idiopathic Arthritis

Juvenile idiopathic arthritis (JIA) is the most common childhood rheumatic disease, with a strongly debated pathophysiological origin. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed. Here we profiled the adaptive immune system of 85 JIA patients and 43 age-matched controls, identifying immunological changes unique to JIA and others common across a broad spectrum of childhood inflammatory diseases. The JIA immune signature was shared between clinically distinct subsets, but was accentuated in the systemic JIA patients and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and JIA patients, machine learning analysis of the dataset proved capable of diagnosis of JIA patients with ~90% accuracy. These results pave the way for large-scale longitudinal studies of JIA, where machine learning could be used to predict immune signatures that correspond to treatment response group.

immunology↗