As all the sequences inside a given component shared significant homology, it was reasonable to assume that all the antibodies encoded by those sequences would react to the same epitope. gene conversion of B cells encoding HGF-reactive antibodies. One component showed significant increases in the number and frequencies of unique sequences and harbored genes encoding antibodies that were reactive to human HGF and competitive with YYB-101 for HGF binding. Some of the antibodies also reacted to mouse HGF. The selected sequences shared 98.3% identity and 98.9% amino acid similarity. It is therefore likely that this antibodies encoded by them all react to the epitope targeted by YYB-101. Keywords: next-generation sequencing, hepatocyte growth factor, B cell receptor, immune profiling 1. Introduction Hepatocyte growth factor (HGF), also known as scatter factor, is usually a ligand for c-MET and was initially identified as a growth factor for fibroblast-derived cell motility factor and hepatocytes [1]. Secreted by fibroblasts and mesenchymal cells, and also by epithelial cells under special circumstances [2], HGF is produced in an inactive TSPAN9 state and is converted by proteolysis into its active heterodimeric form consisting of a 69 kDa -chain subunit and a 34 kDa -chain linked by a disulfide bond [3]. The active form consists of an amino (N) domain name, four Kringle domains (K1CK4) in the -chain, and a serine proteases homology domain name (SPH) in the -chain [4,5]. The binding of active HGF to c-MET activates signaling cascades leading to cancer progression, invasion, and metastasis [6]. Currently, there are four anti-HGF antibodies in clinical trials, including rilotumumab (AMG-102), ficlatuzumab (AV-299), HuL2G7 (TAK-701), and YYB-101 [7,8,9,10]. Antibodies that are cross-reactive to mouse HGF are needed to test the in vivo effects of antibody-mediated HGF neutralization. Despite 90.3% identity and 95.6% similarity PF-04880594 between human and mouse HGF, YYB-101 and the other antibodies in clinical trials are not reactive to mouse HGF. There has been no report of a mouse HGF-neutralizing monoclonal antibody. Therefore, a knock-in mouse with human HGF in an immunodeficient NOD scid gamma (NSG) background was generated and used for in vivo study [11]. Recently, it was shown that neutrophils recruited to T cell-inflamed microenvironments rapidly acquired immunosuppressive properties [12]. The inhibition of HGF/c-MET signaling impaired those acquired immunosuppressive properties and also reduced the exhaustion of cytotoxic T cells [13]. In patients with cancer, high serum levels of HGF were correlated with increasing neutrophil counts and unresponsiveness to anti-PD-1 checkpoint blockade [12]. All of those observations suggested that treatment with HGF-neutralizing antibodies might potentiate the efficacy of immune checkpoint inhibitors. That hypothesis cannot be tested in human-HGF knock-in NSG mice, however, because NSG mice lack T cells. The most accurate way to test the combinational therapeutic effects of YYB-101 would be to treat immunologically intact mice with a mouse HGF-neutralizing antibody that binds to the homologous epitope on human HGF. To obtain an antibody with those characteristics, we immunized a chicken with human HGF, monitored the chronological change in the B cell receptor repertoire using next-generation sequencing (NGS), and analyzed the change in the B cell repertoire using an algorithm developed in this study. We identified groups of variable heavy-chain (genes that had changed significantly following the immunization. From those reactive clones, we could successfully select antibodies that were reactive to both human and mouse HGF and competitive with YYB-101 for binding to human HGF. 2. Results We immunized one chicken with human HGF and boosted the immunization twice at the second week and the fourth week, as shown in Table 1. Peripheral blood was collected before the immunization (week 0), at the times of the first and second boosters (weeks 2 and 4), and one week after the second booster (week 5), just before sacrifice. After isolation of mononuclear cell fraction from the blood, RNA was prepared to produce cDNA. We used the cDNA and specific primers to amplify the gene, which we sequenced around the Illumina MiSeq NGS platform. NGS data was preprocessed by quality-based filtering and error correction based on hierarchical clustering as described previously [15]. Thus, we obtained 133,312 unique PF-04880594 PF-04880594 nucleotide sequences from the four transcript sets corresponding to each of the blood samples, respectively. Table 1 Blood sampling, next-generation sequencing (NGS) analysis, and immunization/boosting. Genenucleotide sequences, we used network analysis tools described previously [16,17,18]. Each individual transcript and its read count were.