BackgroundmicroRNAs (miRNAs) are small non-coding RNAs that can regulate gene expression and mirror the patient’s health condition. miRNAs deregulation is considered a crucial factor in the development and progression of various diseases, including axial spondyloarthritis (axSpA) [1].ObjectivesThe aim of the study was to profile the miRNome of peripheral blood mononuclear cells (PBMCs), to identify specific miRNAs and their association with several cytokines, axSpA disease activity and spinal impairment.MethodsMassive parallel sequencing (MPS, Ilumina) was performed for miRNAs profiling in 96 subjects (38 patients with non-radiographic (nr-) axSpA, 38 patients with radiographic (r-) axSpA and 20 healthy controls (HC)). The expression of candidate miRNAs was validated using the qRT-PCR system (SmartChip) on a new cohort of 47 patients with nr-axSpA, 44 patients with r-axSpA and 50 HC. Disease activity was determined using C-reactive protein (CRP) and Ankylosing Spondylitis Disease Activity Score (ASDAS). Radiographs of the cervical and lumbar spine were assessed by two independent blinded readers using modified Stoke Ankylosing SpondylitisSpineScore (mSASSS). We employed DESeq2 and generalized linear modelling with a negative binomial assumption (GLM-NB) to evaluate the association of candidate miRNAs to the radiographic form, ASDAS and CRP. Linear modelling was used to determine the association between miRNAs and laboratory/clinical parameters adjusted for CRP, age and sex.ResultsMPS detected 432 miRNAs; however, only 90 miRNAs passed through the selection criteria (p<0.05, BaseMean>10, the difference in log2FC>0.5). We selected 45 miRNAs for validation based on the selection criteria and the literature. We validated miR-1-3p (p=0.006, FC=1.757) to be upregulated and miR-1248 (p=0.002, FC=-1.125) and miR-1246 (p=0.002, FC = -1.125) to be downregulated in patients with axSpA compared to HC. In addition, the expression of miR-1-3p correlated with the plasma levels of IL-17 (p=0.016, τ=0.25) and TNF (p=0.028, τ=0.22), but not with the gene expression of IL-17 or TNF in PBMCs. miR-1-3p (p=0.039, β=0.665) as well as miR-1248 (p<0.001, β=-0.207) correlated with the IL-6 gene expression in PBMCs. None of the miRNAs distinguished between radiographic and non-radiographic disease or correlated with disease activity or radiographic spinal impairment.ConclusionThis cross-sectional study failed to demonstrate association between cellular miRNAs, disease activity or spinal impairment, but the association between miR-1-3p, IL-17 and TNF may suggest its role in the pathogenesis of axSpA.Reference[1]Prajzlerová K, Grobelná K, Hušáková M, et al. Association between circulating miRNAs and spinal involvement in patients with axial spondyloarthritis. PLoS One. 2017 Sep 22;12(9):e0185323.AcknowledgementsSupported by MHCR No. 023728, BBMRI-CZ LM2018125 and SVV 260 523.Disclosure of InterestsNone Declared.
BackgroundmicroRNAs (miRNAs) are small non-coding RNAs regulating up to 60 % of human mRNAs, including genes related to axial spondyloarthritis (axSpA) (1).ObjectivesThis study aims to profile miRNome and to identify candidate miRNAs determining disease severity in patietns with non-radiographic (nr) and radiographic (r) axSpA.MethodsThe miRNome profiling experiment included peripheral blood mononuclear cells (PBMCs) of 96 subjects (38 patients with nr-axSpA, 38 patients with r-axSpA and 20 healthy controls). Firstly, massive parallel sequencing on NextSeq 500 (MPS, Illumina) was performed for miRNA screening. Selected candidate miRNAs were further validated using the qRT-PCR system (SmartChip) on the validation cohort of 141 subjects (47 patients with nr-axSpA, 44 patients with r-axSpA and 50 healthy controls). We employed the DESeq2 algorithm and generalized linear modelling with a negative binomial assumption (GLM-NB) to evaluate the association of candidate miRNAs to axSpA subtype and clinical disease activity (ASDAS and CRP).ResultsMPS revealed 432 unique miRNAs in all samples. We identified 13 differently expressed miRNAs in axSpA patients compared to healthy controls, and 14 differently expressed miRNAs in axSpA patients with high to very high ASDAS compared to patients with inactive disease. Data from validation cohort revealed that the expression level of miR-4286 was higher in patients with very high disease activity compared to patients with inactive disease. Simultaneously, miR-4286 positively correlated with ASDAS. miR-4286 has been recently associated with osteogenesis and angiogenesis (2). None of the validated miRNAs was associated with the levels of CRP.ConclusionIn this study, we identified that miR-4286 is related to disease activity and could play a role in the pathogenesis of axSpA.References[1]Prajzlerová K, Grobelná K, Hušáková M, et al. Association between circulating miRNAs and spinal involvement in patients with axial spondyloarthritis. PLoS One. 2017 Sep 22;12(9):e0185323.[2]Yu H, Wang K, Liu P, et al. miR-4286 functions in osteogenesis and angiogenesis via targeting histone deacetylase 3 and alleviates alcohol-induced bone loss in mice. Cell Prolif. 2021 Jun;54(6):e13054.AcknowledgementsSupported by MHCR No. 023728, BBMRI-CZ LM2018125 and SVV 260 523.Disclosure of InterestsNone declared
BackgroundThe web-based Spondyloarthritis Research Consortium of Canada (SPARCC) real-time iterative calibration (RETIC) modules for scoring MRI lesions in axial spondyloarthritis (axSpA) have been created by SPARCC developers to enable remote training of readers to appropriately use the SPARCC MRI inflammation and structural damage instruments and to attain adequate scoring proficiency.ObjectivesWe aimed to test the performance of these modules in enhancing scoring proficiency in comparison to SPARCC developers.MethodsThe SPARCCRETIC SIJ inflammation and structural damage modules are each comprised of 50 DICOM axSpA cases with baseline and follow up scans and an online scoring interface based on SIJ quadrants. Continuous visual real-time feedback regarding concordance/discordance of scoring per SIJ quadrant with expert readers is provided by a color-coding scheme. Reliability is assessed in real-time by intra-class correlation coefficient (ICC), ICC data being provided every 10 cases, which are scored until proficiency targets for ICC are attained. In the present exercise, participants (n=15) from the EuroSpA Imaging project were randomized, stratified by reader expertise in scoring with SPARCC, to one of two reader training strategies (groups A and B) that each comprised 3 stages (25 patients per stage, 2 timepoints, blinded to chronology; independent assessment of Inflammatory and structural lesions): Group A. 1. Review of original SPARCC manuscript describing scoring method. 2. Review of PowerPoint summary of SPARCC method plus completion of SPARCCRETIC module. 3. Re-review of PowerPoint summary. Group B. Same 3-step strategy as A except SPARCCRETIC module completed at stage 3. The reliability of scoring was compared to an expert radiologist (SPARCC developer).ResultsVery good scoring proficiency for status and change scores was evident for SPARCC BME even by non-experienced readers with similar levels of reliability irrespective of prior expertise. The beneficial impact of the SPARCCRETIC module on scoring proficiency was most consistently evident for the scoring of structural lesions and for Strategy B, where the impact was evident for all structural lesions, level of reader expertise, and status as well as change scores (Table 1). Scoring proficiency improved the most for the least experienced readers (Figure 1).Table 1.Inter-rater reliability (Status/Change ICC) compared to radiologist SPARCC developerMRI LesionReader expertiseStrategy AStrategy BStage 1 cases (n=25)Stage 2 cases (n=25)Stage 3 cases (n=25)Stage 1 cases (n=25)Stage 2 cases (n=25)Stage 3 cases (n=25)BMENone (n=4)0.91 / 0.940.83/0.820.77/0.780.82/0.880.65/0.820.88/0.90Intermediate (n=6)0.88/0.880.90/0.900.85/0.900.93/0.940.78/0.800.83/0.80Experienced (n=5)0.92/0.940.90/0.880.92/0.930.83/0.880.84/0.900.89/0.89ANKYLOSISNone (n=4)0.86/0.660.83/0.280.86/0.780.66/0.410.69/0.340.88/0.80Intermediate (n=6)0.89/0.570.83/0.370.92/0.810.82/0.680.74/0.470.93/0.84Experienced (n=5)0.96/0.760.93/0.640.94/0.860.97/0.240.83/0.410.91/0.79BACKFILLNone (n=4)-0.08/-0.050.38/0.220.59/0.380.64/0.130.05/-0.090.47/0.27Intermediate (n=6)0.41/0.130.44/0.420.69/0.390.50/0.220.30/0.300.70/0.42Experienced (n=5)0.82/0.380.55/0.400.91/0.640.65/0.240.21/0.260.71/0.30EROSIONNone (n=4)0.13/-0.080.67/0.420.51/0.330.34/0.330.23/0.080.38/0.37Intermediate (n=6)0.42/0.180.56/0.120.51/0.440.33/0.270.45/0.180.53/0.39Experienced (n=5)0.61/0.330.64/0.340.64/0.420.51/0.270.58/0.110.62/0.31FAT METAPLASIANone (n=4)0.62/0.540.30/0.170.57/0.290.43/0.530.38/0.070.83/0.63Intermediate (n=6)0.49/0.380.59/0.300.79/0.510.57/0.780.50/0.420.81/0.47Experienced (n=5)0.75/0.620.81/0.340.91/0.700.84/0.900.56/0.130.78/0.37ConclusionAttaining scoring proficiency for MRI structural lesions in axSpA is difficult but can be consistently improved by using the SPARCCRETIC module, even for experienced readers.Figure 1.Disclosure of InterestsWalter P Maksymowych Speakers bureau: Abbvie, Janssen, Novartis, Pfizer, UCB, Consultant of: Abbvie, Boehringer Ingelheim, Celgene, Eli-Lilly, Galapagos, Novartis, Pfizer, UCB, Grant/research support from: Abbvie, Novartis, Pfizer, UCB, Anna Enevold Fløistrup Hadsbjerg Grant/research support from: Novartis, Mikkel Østergaard Consultant of: AbbVie, BMS, Boehringer-Ingelheim, Celgene, Eli Lilly and Company, Galapagos, Gilead, Hospira, Janssen, Merck, Novartis, Novo, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, UCB, Grant/research support from: AbbVie, BMS, Merck, Celgene, Novartis, Raphael Micheroli: None declared, Susanne Juhl Pedersen Grant/research support from: Novartis, Adrian Ciurea: None declared, Nora Vladimirova Grant/research support from: Novartis, Michael J Nissen Speakers bureau: Eli-Lilly, Janssen, Novartis, Consultant of: Abbvie, Celgene, Eli-Lilly, Janssen, Novartis, Pfizer, Kristyna Bubova: None declared, Stephanie Wichuk: None declared, Manouk de Hooge: None declared, Ashish Jacob Mathew Grant/research support from: Novartis, Karlo Pintaric: None declared, Monika Gregová: None declared, Ziga Snoj: None declared, Marie Wetterslev: None declared, Karel Gorican: None declared, Joel Paschke: None declared, Iris Eshed: None declared, Robert G Lambert Paid instructor for: Novartis
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