SAT0035 IS PHARMACOLOGICAL CLINICAL REMISSION SYNONYMOUS WITH BIOLOGICAL INACTIVE DISEASE? DIFFERENTIAL GENE EXPRESSION ANALYSIS IN RHEUMATOID ARTHRITISAND HEALTY CONTROLS

Background: Remission is an important goal of therapy in rheumatoid arthritis (RA), but data on molecular players of clinical remission and effective disease inactivation are scarce. Gene expression profiling analysis is useful to elucidate the pathogenic mechanisms of diseases, and differential gene expression analysis between diverse disease conditions produces gene signatures characteristic of the state or disease being studied. Objectives: Our aim was to compare the transcriptional profiles of patients with clinically active versus inactive (remission state) RA, and healthy controls (HCs). Methods: From a cohort of around 1000 patients affected by RA according to ACR-EULAR 2010 criteria, we first selected 20 patients with active disease state (without biologic treatment ongoing) (A) and 20 patients with >1-year remission induced by TNFα antagonism (Etanercept) (R), as assessed by DAS28(PCR) scores, and from 20 HCs matching for age and gender ratio. Both RA groups were not on corticosteroid treatment. RNA from peripheral blood was extracted and, following quality analysis by Agilent Bioanalyzer, each condition has been profiled using RNAs pools in biological duplicates by distinct Affymetrix Human GeneChip HTA 2.0, for a total of 6 arrays. Data analysis was performed using the commercial software Partek Genomics Suite, V 6.6. To identify a transcript as differentially expressed, a value of fold change 1.5 and p-value 0.05 has been set. Results: The Venn diagram shows all comparative groups (A vs R, A vs HC, R vs HC) with their relative amount of transcripts differentially expressed, generated using abovementioned parameters, and the relationship between sets (fig1, panel A). Using the list of transcripts differentially expressed in at least one of the aforementioned comparison, a hierarchical clustering was carried out to view the intra-condition expression profile. Here, we have identified (arbitrarily) 4 clusters of transcripts with analogous transcriptional profile and to each of them a color code has been assigned (Heatmap in Fig1, panel B). For these clusters and for all lists of transcripts differentially expressed founded by our comparative study, we carried out the Gene Set Enrichment Analysis by Gene Ontology (GO), in order to identify how molecular functions, cellular components or biological processes occurs more frequently than expected in a reference list of transcripts. Conclusion: Considering the amount of differentially expressed transcripts and the hierarchical clustering analysis, is evident that the drug-induced remission (R) is more similar with HCs condition, while active disease state (A) has a different profile; however “similar” profile does not mean “identical”. In fact, the Gene Set Enrichment Analysis Score showed us that mRNA transcripts dysregulated in the R condition vs HCs, are involved in several biological processes regarding the immune system, response to stimulus, biological regulation, locomotion and others. Our next step will be to validate, by Real Time PCR in a large cohort of patients, the most interesting dysregulated genes covering biological functions eventually sustaining disease activity.