The classification, genetic diagnosis and modelling of monogenic autoinflammatory disorders

• Published in: Clinical science (1979) Vol. 132; no. 17; pp. 1901 - 1924
• Main Authors: Moghaddas, Fiona, Masters, Seth L
• Format: Journal Article
• Language: English
• Published: England Portland Press Ltd 14.09.2018
• Subjects: Cytokines - genetics; Cytokines - immunology; Cytokines - metabolism; Genetic Association Studies; Genetic Predisposition to Disease - genetics; Hereditary Autoinflammatory Diseases - classification; Hereditary Autoinflammatory Diseases - genetics; Hereditary Autoinflammatory Diseases - immunology; Humans; Immunity, Innate - genetics; Immunity, Innate - immunology; Inflammasomes - genetics; Inflammasomes - immunology; Inflammasomes - metabolism; Inflammation - genetics; Inflammation - immunology; Inflammation - metabolism; Mutation; Review; Interferonopathy; interferon; Autoinflammatory; Periodic Fever Syndrome; inflammasome
• ISSN: 0143-5221; 1470-8736
• DOI: 10.1042/CS20171498

Monogenic autoinflammatory disorders are an increasingly heterogeneous group of conditions characterised by innate immune dysregulation. Improved genetic sequencing in recent years has led not only to the discovery of a plethora of conditions considered to be 'autoinflammatory', but also the broadening of the clinical and immunological phenotypic spectra seen in these disorders. This review outlines the classification strategies that have been employed for monogenic autoinflammatory disorders to date, including the primary innate immune pathway or the dominant cytokine implicated in disease pathogenesis, and highlights some of the advantages of these models. Furthermore, the use of the term 'autoinflammatory' is discussed in relation to disorders that cross the innate and adaptive immune divide. The utilisation of next-generation sequencing (NGS) in this population is examined, as are potential and methods of modelling to determine pathogenicity of novel genetic findings. Finally, areas where our understanding can be improved are highlighted, such as phenotypic variability and genotype-phenotype correlations, with the aim of identifying areas of future research.