OP0234 RNA-Seq IN PERIPHERAL BLOOD IMMUNE CELLS IDENTIFIES MODULAR NETWORKS PREDICTIVE AND PROTECTIVE FOR PROGRESSION FROM ANA POSITIVITY TO CLASSIFIABLE SYSTEMIC AUTOIMMUNE DISEASE.

Abstract

BackgroundAnti-nuclear antibody (ANA) positivity represents a complex ‘At-Risk’ state for development of connective tissue disease (CTD). While ANA may become positive years in advance of clinically manifest CTD, they are also harboured in at least low titre by up to 25% of the wider population, of whom only a small fraction ultimately develop systemic autoimmunity. Complex immune disturbances including plasmacytoid dendritic cell exhaustion and non-haematopoietic interferon (IFN) production are evident even among ANA positive individuals who do not ultimately progress to overt disease [1].In a prospective observational cohort of ANA positive individuals At-Risk for CTD we have shown that a validated blood IFN-Score was predictive of progression to classifiable SLE [2]. However, the wider transcriptional fingerprint of the At-Risk state and other factors modifying risk of progression are not known. We hypothesise that diverse immune processes, both independent and interacting with IFN pathway activation, could modulate risk of progression.ObjectivesTo investigate how peripheral blood immune cell transcriptional signatures derived by RNA Seq associate with progression or non-progression from At-Risk ANA positivity to clinically apparent CTD.MethodsPeripheral blood mononuclear cells (PBMCs) were isolated at baseline from ANA-positive At-Risk individuals demonstrating ≤1 clinical criterion for classifiable CTD, symptom duration <12 months and naive of glucocorticoid or immunosuppressive therapy. Progression was prospectively adjudicated at 12 months and defined as accrual of clinical/ immunological criteria sufficient to meet classification for SLE (SLICC 2012) or other relevant CTDs. Bulk RNASeq was performed on PMBCs from 16 progressors and 19 non-progressors. Weighted gene co-expression network analysis (WGCNA) was performed using WGCNA package and gene ontology enrichment was evaluated using ClusterProfiler, in R Bioconductor. The top 20% genes ranked by connectivity were defined as hub genes. Major cell subsets were quantified in parallel by multiparameter flow cytometry.Results29 modules were identified by WGCNA. Eigengenes for 3 modules were significantly associated with progression status. A single, 152 gene module showed strong positive correlation with progression (R=0.55, p<0.001). Hub genes were significantly enriched for type I IFN-signalling pathway and included established interferon stimulated genes such as IFI44 and IRF7.Two further modules had a negative, i.e. protective, association with progression; a smaller 37 gene module, correlated negatively with both blood interferon score (R=-0.46, p=0.005) and with progression (R=-0.43, p=0.01). A larger 252 gene module was also negatively related to progression (R=-0.43, p=0.009) and demonstrated significant pathway enrichment for regulation of cell morphogenesis and actin cyctoskeleton organisation.ConclusionWe identify novel modular transcriptomic signatures implicated in SLE disease initiation. We show (i) IFN-pathway activation is the single strongest transcriptomic risk marker of progression from the ANA positive At Risk state and (ii) we identify 2 novel protective signatures in peripheral blood immune cells for which further network-based characterization is ongoing.