THU0509 MONOCYTES PROTEOMIC PROFILE OF PATIENTS WITH DIFFERENT AUTOINFLAMMATORY DISEASES: A NEW APPROACH TO CHARACTERIZE THESE DISEASES

Background: Autoinflammatory diseases are a group of inherited diseases characterized by early onset and systemic inflammation. These pathologies are caused by mutations in genes involved in the regulation of innate immune response with a consequent inflammatory phenotype. The most common genetically defined periodic fevers are Familial Mediterranean Fever (FMF), Cryopyrin-associated periodic syndromes (CAPS), TNF receptor-associated periodic syndrome (TRAPS) and mevalonate kinase deficiency (MKD/HIDS). Some patients show clinical features similar to autoinflammatory diseases but no genetic mutation has been found. Objectives: Our aim is to evaluate the differences in the expression of proteins or pathway in monocytes, and plasma metabolites in patients with autoinflammatory diseases compared with healthy subjects to clusterize and better understand the mechanisms underlying different genetically defined disorders and try to characterize the genetically undefined pathologies. Methods: Monocytes, purified from peripheral blood and incubated with or without LPS, were collected from patients and healthy donors; samples have been processed by iST protocol. Each digested sample was analyzed by high-resolution liquid chromatography and tandem mass spectrometry (LC-MS/MS) based on Orbitrap technology. The quantification strategy is a label-free approach (LFQ) available in MaxQuant suite. Results: Here we identified a median of about 5000 proteins from the monocyte samples of each 4000 are quantified by LFQ approach. PCA analysis and Person’s correlation show good reproducibility of data and a good separation between the different groups. The data were then submitted to an appropriate statistic. T-Tests highlighted differentially expressed proteins and through Cytoscape with the ClueGo app we obtained the differently regulated pathways in the different conditions. It has also been constructed, starting from significative proteins, a network, related to disease using the information of String Disease db. We observed that the expression of proteins is differently enriched according to the different conditions. For each autoinflammatory disease, a list of significantly modulated proteins was obtained: some of which are already known to be related to the disorders, while others have not yet been described. In FMF, MEFV, RhoA and some related proteins were significantly up-regulated together with genes linked to the interferon pathway. In TRAPS relevant proteins turn up related to the maintenance of Golgi and cellular trafficking. The bioinformatics analysis allows us to better understand the functional interaction between these monocytes proteins and map which are involved in the diseases. Conclusion: Here, we addressed how a high-resolution proteomics approach could be used to better understand the biology of autoinflammatory diseases. The characterization of a broad spectrum of proteins and their interaction network will allow us to identify new biomarkers for the different pathologies and better comprehend and recognize the genetically undefined disorders. References: [1] Lucherini O M, Rigante D, Sota J, et al. Update overview of molecular pathways involved in the most common monogenic autoinflammatory diseases. Clin Exp Rheumatol 2018. [2] Rieckmann JC, Geiger R, Hornburg D, et al. Social network architecture of human immune cells unveiled by quantitative proteomics. Nat Immunol 2017. Disclosure of Interests: Federica Penco: None declared, Andrea Petetto: None declared, Chiara Lavarello: None declared, Ilaria Gueli: None declared, Arinna Bertoni: None declared, Alessia Omenetti: None declared, Claudia Pastorino: None declared, Marco Gattorno Grant/research support from: MG has received unrestricted grants from Sobi and Novartis