Background:To date, although multiple molecular approaches have illustrated the various aspects of Primary Antiphospholipid Syndrome (APS), systemic lupus erythematosus (SLE) and antiphospholipid syndrome plus lupus (APS plus SLE), no study has so far fully characterized the potential role of posttranscriptional regulatory mechanisms such as the alternative splicing.Objectives:To identify shared and differential changes in the splicing machinery of immune cells from APS, SLE and APS plus SLE patients, and their involvement in the activity and clinical profile of these autoimmune disorders.Methods:Monocytes, lymphocytes and neutrophils from 80 patients (22 APS, 35 SLE and 23 APS plus SLE) and 50 healthy donors (HD) were purified by immunomagnetic selection. Then, selected elements of the splicing machinery were evaluated using a microfluidic qPCR array (Fluidigm). In parallel, extensive clinical/serological evaluation was performed, comprising disease activity, thrombosis and renal involvement, along with autoantibodies, acute phase reactants, complement and inflammatory molecules. Molecular clustering analyses and correlation/association studies were developed.Results:Patients with primary APS, SLE and APS plus SLE displayed significant and specific alterations in the splicing machinery components in comparison with HD, that were further specific for each leukocyte subset. Besides, these alterations were associated with distinctive clinical features.Hence, in APS, clustering analysis allowed to identify two sets of patients representing different molecular profile groups with respect to the expression levels of splicing machinery components. Principal component analyses confirmed a clear separation between patients. Clinically, cluster 1 characterized patients with higher thrombotic episodes and recurrences than cluster 2 and displayed a higher adjusted global APS score (aGAPSS). Accordingly, these patients showed higher levels of inflammatory mediators than cluster 2.Similarly, in patients with APS plus SLE, clustering analysis allowed to identify two sets of patients showing differential expression of splicing machinery components. Clinical and laboratory profiles showed that cluster 2 characterized patients that had suffered more thrombotic recurrences, most of them displaying an aGAPSS over 12 points and expressing higher levels of inflammatory mediators than cluster 1. The incidence of lupus nephropathy was similarly represented in both clusters.Lastly, in SLE patients, molecular clustering analysis identified two sets of patients showing distinctive clinical features. One cluster characterized most of the patients positive for anti-dsDNA antibodies, further suffering lupus nephropathy, and a high proportion of them also presenting atheroma plaques and high levels of inflammatory mediators.Correlation studies further demonstrated that several deranged splicing machinery components in immune cells (i.e. SF3B1tv1, PTBP1, PRP8 and RBM17) were linked to the autoimmune profile of the three autoimmune diseases, albeit in a specific way on each disorder. Accordingly, in vitro treatment of HD lymphocytes with aPL-IgG or anti-dsDNA-IgG changed the expression of spliceosome components also found altered in vivo in the three autoimmune diseases. Finally, the induced over/downregulated expression of selected spliceosome components in leukocytes modulated the expression of inflammatory cytokines, changed the procoagulant/adhesion activities of monocytes and regulated NETosis in neutrophils.Conclusion:1) The splicing machinery, profoundly altered in leukocytes from APS, APS plus SLE and SLE patients, is closely related to the activity of these diseases, their autoimmune and inflammatory profiles. 2) The analysis of the splicing machinery allows the segregation of APS, APS plus SLE and SLE, with specific components explaining the CV risk and renal involvement in these highly related autoimmune disorders.Acknowledgements:Funded by ISCIII, PI18/00837 and RIER RD16/0012/0015 co-funded with FEDERDisclosure of Interests:None declared
Segmentasi Pelanggan Produk Digital Service Indihome Menggunakan Algoritma K-Means Berbasis Python
