98 CHAPTER 5 jective was to assess predictive factors of anti-drug antibodies (ADA) development (www.abirisk.eu/) [5,6]. The treatment protocol consisted of two intravenous injections of 1000 mg rituximab (Roche, Woerden, The Netherlands) 15 days apart. A second cycle of treatment was allowed after 6 months at the treating physician’s discretion (n = 6). Concomitant medications allowed for RA treatment were Disease-modifying antirheumatic drugs (DMARDs), Non-steroidal anti-inflammatory drugs (NSAID) and corticosteroids. No other biologicals were allowed. Patient visits were at baseline and at one, three, six and twelve months after treatment for sample collection and assessment of disease activity using the Disease Activity Score 28 joints (DAS28) based on CRP, or ESR when CRP was not available. Clinical response was assessed using EULAR response criteria [7]. Peripheral blood for BCR repertoire analysis was collected using PAXGene Blood RNA tubes (PreAnalytiX, Breda, The Netherlands) and stored at -80°C. Serum for Anti-Drug Antibodies (ADA) testing was collected in BD SST vacutainers, left to coagulate for at least 30 min, centrifuged at 1,500 g for 10 min at 4°C and then stored at -20°C. The study protocol received ethical approval in all patient recruiting centers and was performed according to the Declaration of Helsinki. All patients gave written informed consent before participation. Next-generation sequencing of the B-cell receptor repertoire RNA extraction was performed using PAXgene isolation kit (Qiagen) according to manufacturer’s instructions. Amplification of the B-cell receptor repertoire was performed as previously described and reported in online supplementary figure S1A [8,9]. In case no amplification product was obtained, the amplification was repeated with the addition of carrier RNA from the non BCR-expressing cell line HEK939T. This addition did not alter the sample’s BCR repertoire (online supplementary figure S1B-C). Processing of raw sequences and final dataset construction Reads were processed using pRESTO [10]. Low quality reads (phred score ≤ 25) were filtered out. IGHV and IGHJ primer sequences were masked and cut off respectively using the MaskPrimers.py function, UMI-based consensus sequences created using BuildConsensus.py (max.error = 0.1) and paired-end reads assembled. Unique UMIbased consensus sequences represented by at least 3 different UMIs were aligned using IMGT/HighV-QUEST [11]. Functional rearrangements were further processed for germline reconstruction and clonal clustering using Change-O [12].
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