Toshihiro Kishikawa1, Noriko Arase2, Shigeyoshi Tsuji3, Yuichi Maeda4, Takuro Nii4, Jun Hirata5, Ken Suzuki5, Kenichi Yamamoto6, Tatsuo Masuda7, Kotaro Ogawa8, Shiro Ohshima9, Hidenori Inohara10, Atsushi Kumanogoh11, Manabu Fujimoto12, Yukinori Okada13. 1. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan; Department of Otorhinolaryngology-Head and Neck Surgery, Osaka University Graduate School of Medicine, Suita, Japan. Electronic address: tkishikawa@ent.med.osaka-u.ac.jp. 2. Department of Dermatology, Osaka University Graduate School of Medicine, Suita, Japan. 3. Department of Orthopedics/Rheumatology, National Hospital Organization Osaka Minami Medical Center, Kawachinagano, Japan. 4. Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan; Department of Laboratory of Immune Regulation, Department of Microbiology and Immunology, Osaka University Graduate School of Medicine, Suita, Japan. 5. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan. 6. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan; Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan. 7. Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Suita, Japan; StemRIM Institute of Regeneration-Inducing Medicine, Osaka University, Osaka, Japan. 8. Department of Neurology, Osaka University Graduate School of Medicine, Suita, Japan; Department of Neurology, Japan Community Health care Organization (JCHO) Hoshigaoka Medical Center, Hirakata, Japan. 9. Rheumatology and Allergology, National Hospital Organization Osaka Minami Medical Center, Kawachinagano, Japan; Clinical Research, National Hospital Organization Osaka Minami Medical Center, Kidohigasi-machi, Kawachinagano, Osaka, Japan. 10. Department of Otorhinolaryngology-Head and Neck Surgery, Osaka University Graduate School of Medicine, Suita, Japan. 11. Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan; Department of Immunopathology, Immunology Frontier Research Center, Osaka University, Suita, Japan. 12. Department of Dermatology, Osaka University Graduate School of Medicine, Suita, Japan; Laboratory of Cutaneous Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan. 13. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan; Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan. Electronic address: yokada@sg.med.osaka-u.ac.jp.
Abstract
BACKGROUND: Psoriasis is an immune-mediated skin disease for which the crosstalk between genetic and environmental factors is responsible. To date, no definitive diagnostic criteria for psoriasis yet, and specific biomarkers are required. OBJECTIVE: We performed metabolome analysis to identify metabolite biomarkers of psoriasis and its subtypes such as psoriatic arthritis (PsA) and cutaneous psoriasis (PsC). METHODS: We constructed metabolomics profiling of 130 plasma samples (42 PsA patients, 50 PsC patients, and 38 healthy controls) using a nontargeted metabolomics approach. RESULTS: Psoriasis-control association tests showed that one metabolite (ethanolamine phosphate) was significantly increased in psoriasis samples than in the controls, whereas three metabolites decreased (false discovery rate [FDR] < 0.05; XA0019, nicotinic acid, and 20α-hydroxyprogesterone). In the association test between PsA and PsC, tyramine significantly increased in PsA than in PsC, whereas mucic acid decreased (FDR < 0.05). Molecular pathway analysis of the PsA-PsC association test identified enrichment of vitamin digestion and absorption pathway in PsC (P = 1.3 × 10-4). Correlation network analyses elucidated that a subnetwork centered on aspartate was constructed among the psoriasis-associated metabolites; meanwhile, the major subnetwork among metabolites with differences between PsA and PsC was primarily formed from saturated fatty acids. CONCLUSION: Our large-scale metabolome analysis highlights novel characteristics of plasma metabolites in psoriasis and the differences between PsA and PsC, which could be used as potential biomarkers of psoriasis and its clinical subtypes. These findings contribute to our understanding of psoriasis pathophysiology.
BACKGROUND:Psoriasis is an immune-mediated skin disease for which the crosstalk between genetic and environmental factors is responsible. To date, no definitive diagnostic criteria for psoriasis yet, and specific biomarkers are required. OBJECTIVE: We performed metabolome analysis to identify metabolite biomarkers of psoriasis and its subtypes such as psoriatic arthritis (PsA) and cutaneous psoriasis (PsC). METHODS: We constructed metabolomics profiling of 130 plasma samples (42 PsA patients, 50 PsC patients, and 38 healthy controls) using a nontargeted metabolomics approach. RESULTS:Psoriasis-control association tests showed that one metabolite (ethanolamine phosphate) was significantly increased in psoriasis samples than in the controls, whereas three metabolites decreased (false discovery rate [FDR] < 0.05; XA0019, nicotinic acid, and 20α-hydroxyprogesterone). In the association test between PsA and PsC, tyramine significantly increased in PsA than in PsC, whereas mucic acid decreased (FDR < 0.05). Molecular pathway analysis of the PsA-PsC association test identified enrichment of vitamin digestion and absorption pathway in PsC (P = 1.3 × 10-4). Correlation network analyses elucidated that a subnetwork centered on aspartate was constructed among the psoriasis-associated metabolites; meanwhile, the major subnetwork among metabolites with differences between PsA and PsC was primarily formed from saturated fatty acids. CONCLUSION: Our large-scale metabolome analysis highlights novel characteristics of plasma metabolites in psoriasis and the differences between PsA and PsC, which could be used as potential biomarkers of psoriasis and its clinical subtypes. These findings contribute to our understanding of psoriasis pathophysiology.