THU0225 INTEGRATIVE PLASMA METABOLOME AND TRANSCRIPTOME ANALYSIS REVEALED THE IMPORTANCE OF HISTIDINE HOMEOSTASIS IN SLE PATHOGENESIS WITH POTENTIAL FOR IMPROVED SLE PATIENTS STRATIFICATION. (2nd June 2020)

• Record Type: Journal Article
• Authors: Iwasaki, Y.; Takeshima, Y.; Nakano, M.; Ota, M.; Nagafuchi, Y.; Suzuki, A.; Kochi, Y.; Okamura, T.; Endo, T.; Miki, I.; Sakurada, K.; Yamamoto, K.; Fujio, K.
• Is Part Of: Annals of the rheumatic diseases. Volume 79(2020)Supplement 1
• Page Start: 339
• Page End: 340
• Publication Date: 2020-06-02
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2020-eular.183
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Recently, immunometabolism has gathered attention of many immunologists. It has been widely recognized that metabolic reprogramming in each immune cell brings different effects on different cells and is important for regulating their functions. Along with the progress of statistical genetics, serum metabolites were shown to be under genetic regulations1). Metabolic changes are now considered not only to be mere phenotypes of cells but also to be key factors for controlling immune cell differentiation, proliferation and function through regulating gene expressions eventually. Although genome-wide association studies have brought deep insights into SLE pathogenesis, the precise pathway from genome to metabolome has been largely unknown, and vice versa. Objectives: The aim of this study is to investigate metabolomic regulation in SLE in relation to gene expressions by integrating plasma metabolome data and transcriptome data. Methods: We collected plasma samples from patients with SLE (n=57) who met the 1997 American College of Rheumatology criteria for SLE. Gender- and age-matched healthy controls (HCs) (n=56) were recruited. Metabolic profiles focusing on 39 amino acids were analyzed with liquid chromatography (LC)-mass spectrometry. Transcriptome data of SLE patients were obtained from our RNA-sequencing data of each immune cell subset (total 19 subsets). Whole-genome sequencing was also performed. Results: Our previous experiment showed that about 160 peaks were detected from comprehensive LC-TOFMS and amino acids were useful for distinguishing SLE patients from HCs. Both partial least squares discriminant analysis (PLS-DA) and random forest, a machine learning algorithm, revealed the importance of histidine (His), one of the essential amino acids, to classify SLE patients from HCs, whose plasma level was lower in SLE patients. In addition, inverse correlation between His level and titer of ds-DNA as well as damage index (SDI) was detected. His level was correlated neither with PSL dosage nor with type I interferon (IFN) signature. Receiver operating characteristic (ROC) analysis showed the best predictability for SLE with the combination of specific amino acids including His. Our transcriptome analysis has revealed the significance of oxidative phosphorylation (OXPHOS) in B cells for SLE pathogenesis. Interestingly, OXPHOS signature was inversely correlated with His level in SLE B cells. Conclusion: His may be an important factor for SLE pathogenesis especially in B cells independently from IFN signal. SLC15A4, a transporter of His on lysosome, is one of the SLE GWAS SNPs and has been reported to play an important role in IFN production in B cells through regulation of TLR7/9 activation 2). We also identified that SLE patients with risk allele of SLC15A4 had tendency to show higher plasma His level, indicating His homeostasis could become a novel treatment target for SLE. Moreover, the inverse correlation of His level to SDI as well as OXPHOS signature suggests that His might play a key role for promoting organ damages in SLE. References: [1] Nat Genet. 2017;49:568. 2) Immunity . 2014;41:375. 3) Semin Arthritis Rheum. 2019;48:1142 Disclosure of Interests: Yukiko Iwasaki: None declared, Yusuke Takeshima: None declared, Masahiro Nakano: None declared, Mineto Ota: None declared, Yasuo Nagafuchi: None declared, Akari Suzuki: None declared, Yuta Kochi: None declared, Tomohisa Okamura: None declared, Takaho Endo: None declared, Ichiro Miki: None declared, Kazuhiro Sakurada: None declared, Kazuhiko Yamamoto Grant/research support from: Astellas, BMS, MitsubishiTanabe, Pfizer, Ayumi, Takeda, Chugai, Eisai, Taisho Toyama, UCB, and ImmunoFuture, Keishi Fujio Grant/research support from: Astellas, BMS, MitsubishiTanabe, Pfizer, Ayumi, Takeda, Chugai, Eisai, Taisho Toyama, Eli Lilly, Sanofi, and UCB