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Gout, which results from elevated serum uric acid (SUA), is a common form of arthritis that is induced by urate crystals. A single nucleotide polymorphism, rs2544390, of LDL receptor related protein 2 (LRP2/Megalin), has previously been reported to be associated with SUA by a genome-wide association study in a Japanese population. However, it was controversial as to whether rs2544390 is associated with gout in a Japanese population, since previous studies with Japanese populations have reported an association between gout and rs2544390 both with and without significance. This prompted us to investigate the association between gout and rs2544390 of LRP2. Using 1208 clinically diagnosed gout patients and 1223 controls in a Japanese male population, our results showed that while rs2544390 did not show a significant association with gout susceptibility in the present study (p = 0.0793, odds ratio [OR] with 95% confidential interval [CI] 1.11 [0.99–1.24]). However, a meta-analysis using previous studies on Japanese populations revealed a significant association with gout (pmeta = 0.0314, OR with 95% CI 1.09 [1.01–1.18]). We have therefore for the first time confirmed a positive association between rs2544390 and gout with only a Japanese male population. Our study provides clues to a better understanding of the pathogenesis of gout and has the potential to lead to novel therapeutic strategies against gout using LRP2 as a molecular target.

Organic anion transporter 10 (OAT10), also known as SLC22A13, has hitherto been identified as a urate transporter by in vitro analyses.1 Despite the reported expression of OAT10 on the apical membrane of the renal proximal tubular cells,1 the physiological impact of OAT10 on urate handling in humans remains to be elucidated. Accumulating evidence suggests that functional variants of already-characterised, physiologically important urate transporters—URAT1/SLC22A12, GLUT9/SLC2A9, BCRP/ABCG2 and NPT1/SLC17A1—affect serum uric acid (SUA) levels and susceptibility of gout,2–6 the most common form of inflammatory arthritis. However, there are no reports on the association between OAT10 gene and either hyperuricaemia or gout. Here, for the first time, we reveal that a dysfunctional variant of OAT10 decreases both gout risk and SUA levels, suggesting OAT10 to be physiologically involved in urate reabsorption in the human kidney, as described below. To explore exonic variants in OAT10 potentially associated with gout susceptibility, we sequenced all exons of OAT10 in 480 gout cases and 480 controls of Japanese male6 and conducted an association analysis (see online supplementary tables S1 and S2), followed by a replication study on 924 gout cases and 2113 controls (see online supplementary figure S1). In two identified OAT10 variants with minor allele frequency (MAF) >0.5%, only rs117371763 (c.1129C>T; p.Arg377Cys [R377C]) was significantly associated with gout susceptibility after Bonferroni correction (p=0.014). The significant association between rs117371763 and gout susceptibility was replicated, and our meta-analysis showed a significant protective effect of rs117371763 on gout susceptibility (OR=0.67; 95% CI 0.53 to 0.85; pmeta …

Objectives: High coverage expression profiling (HiCEP) is an AFLP-based comprehensive gene expression analysis invented in Japan. There are two advantages of HiCEP compared with existing methods, such as DNA microarrays and RNA sequencing. First, it can efficiently detect an especially low amount of mRNA with high sensitivity and reliability, and second, it enables us to analyze mRNA expression much more quantitatively and reproducibly. On the other hand, it requires complicated processes, including TA cloning of isolated transcripts, to obtain the sequence information of the detected peaks. In order to solve this problem, we established the gene expression database of human cancers by combining the next-generation sequencing (NGS) with HiCEP method. Materials and methods: We applied the NGS-combined HiCEP method to analyze pancreatic ductal adenocarcinoma(PDAC) cases in this study, and tried to establish the gene expression database of PDAC to identify effective tumor markers. We collected samples of both the cancerous and macroscopically non-cancerous tissues from 49 patients diagnosed with PDAC who underwent surgical resection at our institute. Among them, four cases were analyzed by HiCEP. Total RNA was extracted from the cancerous and normal tissues of the four PDAC cases, and transcribed to cDNA. The cDNA was synthesized and subjected to digestion with the restriction enzymes, MspI and MseI, followed by adapter ligation. Selective PCR by 256 kinds of primer pairs was used to amplify the HiCEP fragments, and products with fluorescently-labeled primer were then analyzed by capillary electrophoresis. HiCEP fragments were sequenced by the next-generation sequencer (ion PGM, Thermo Fisher Scientific). Furthermore, we compared the expression levels of HiCEP peaks in cancerous tissues with those in normal tissues. Results: We detected multiple HiCEP peaks that showed higher expression in cancerous tissues than in normal tissues in all four cases, and also found out different peaks showing higher expression in cancerous tissues of cases which had a recurrence of cancer after surgery than in cancerous tissues of cases without recurrences. We determined the sequences of the HiCEP fragments by NGS, and developed the first ever HiCEP fragment database for PDAC. Conclusion: We successfully established a PDAC gene expression database by NGS-combined HiCEP method. We are now performing replication analyses with the other PDAC cases, and further analyses of blood samples from the same PDAC cases, aiming to identify diagnostic and prognostic markers of PDAC. Citation Format: Mikiya Takao, Hirotaka Matsuo, Ryoko Araki, Seiko Shimizu, Makoto Kawaguchi, Akiyoshi Nakayama, Yosuke Kitamura, Yusuke Kawamura, Kazuki Maehara, Masumi Abe, Keiichi Ito, Mayumi Hoshikawa, Junji Yamamoto, Yoji Kishi, Nariyoshi Shinomiya. Development of a gene expression database of pancreatic ductal adenocarcinoma cases by NGS-combined HiCEP to identify tumor markers [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2484.

Objectives: High coverage expression profiling (HiCEP) is an AFLP-based comprehensive gene expression analysis technique invented in Japan. HiCEP has two unique characteristics. First, it can detect low amounts of mRNA with high sensitivity and reliability. Second, HiCEP enables highly quantitative and reproducible mRNA expression analyses. However, it requires complicated processes, including TA cloning of isolated transcripts, to obtain sequence information on detected peaks. We performed next-generation sequencing (NGS)-combined HiCEP and tried to establish a gene expression database of renal cell carcinoma (RCC) cases to identify effective tumor markers. Materials and methods: We collected cancerous and macroscopically non-cancerous regions from 83 RCC cases. Of these, six cases with clear cell RCC were analyzed by HiCEP. Total RNA was extracted from the cancerous and non-cancerous tissues of six clear cell RCC cases, and transcribed to cDNA. The cDNA was synthesized and subjected to digestion with the restriction enzymes MspI or MseI, followed by adapter ligation. Selective PCR by 256 kinds of primer pairs was used to amplify the HiCEP fragments, and products with fluorescently-labeled primer were then analyzed by capillary electrophoresis. HiCEP fragments were sequenced using a next-generation sequencer (ion PGM, Thermo Fisher Scientific). We compared the expression levels of HiCEP peaks in cancerous tissues with those in non-cancerous tissues. Results: We detected several HiCEP peaks in cancerous tissues that showed five times higher expression than in normal tissues. We determined the sequences of the HiCEP fragments by NGS, and developed the first ever cancerous tissue HiCEP fragment database. Conclusion: We successfully established an RCC gene expression database by NGS-combined HiCEP. We are now performing replication analyses and further analyses of blood samples from the same RCC cases to be able to identify diagnostic and prognostic markers of RCC. Citation Format: Makoto Kawaguchi, Hirotaka Matsuo, Ryoko Araki, Seiko Shimizu, Mikiya Takao, Akiyoshi Nakayama, Yosuke Kitamura, Masumi Abe, Keiichi Ito, Nariyoshi Shinomiya. Development of a gene expression database of renal cell carcinoma cases by NGS-combined HiCEP to identify tumor markers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5237.