Kai-Leun Tsai1,2, Che-Chang Chang3, Yu-Sheng Chang1,2, Yi-Ying Lu4, I-Jung Tsai4, Jin-Hua Chen5,6, Sheng-Hong Lin1, Chih-Chun Tai7, Yi-Fang Lin7, Hui-Wen Chang8,9, Ching-Yu Lin10,11,12, Emily Chia-Yu Su13,14. 1. Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, 23561, Taiwan. 2. Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan. 3. Graduate Institute of Translational Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, 11031, Taiwan. 4. School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan. 5. Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, 11031, Taiwan. 6. Research Center of Biostatistics, College of Management, Taipei Medical University, Taipei, 11031, Taiwan. 7. Department of Laboratory Medicine, Taipei Medical University-Shuang-Ho Hospital, Taipei Medical University, New Taipei City, 23561, Taiwan. 8. Department of Medical Laboratory, Taipei Medical University Hospital, Taipei, 11031, Taiwan. 9. PhD Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei, 11031, Taiwan. 10. School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan. cylin@tmu.edu.tw. 11. PhD Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei, 11031, Taiwan. cylin@tmu.edu.tw. 12. Department of Biotechnology and Animal Science, National Ilan University, Ilan, 26047, Taiwan. cylin@tmu.edu.tw. 13. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 11031, Taiwan. emilysu@tmu.edu.tw. 14. Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, 11031, Taiwan. emilysu@tmu.edu.tw.
Abstract
BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune disorder with systemic inflammation and may be induced by oxidative stress that affects an inflamed joint. Our objectives were to examine isotypes of autoantibodies against 4-hydroxy-2-nonenal (HNE) modifications in RA and associate them with increased levels of autoantibodies in RA patients. METHODS: Serum samples from 155 female patients [60 with RA, 35 with osteoarthritis (OA), and 60 healthy controls (HCs)] were obtained. Four novel differential HNE-modified peptide adducts, complement factor H (CFAH)1211-1230, haptoglobin (HPT)78-108, immunoglobulin (Ig) kappa chain C region (IGKC)2-19, and prothrombin (THRB)328-345, were re-analyzed using tandem mass spectrometric (MS/MS) spectra (ProteomeXchange: PXD004546) from RA patients vs. HCs. Further, we determined serum protein levels of CFAH, HPT, IGKC and THRB, HNE-protein adducts, and autoantibodies against unmodified and HNE-modified peptides. Significant correlations and odds ratios (ORs) were calculated. RESULTS: Levels of HPT in RA patients were greatly higher than the levels in HCs. Levels of HNE-protein adducts and autoantibodies in RA patients were significantly greater than those of HCs. IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgM anti-IGKC2-19 HNE may be considered as diagnostic biomarkers for RA. Importantly, elevated levels of IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgG anti-THRB328-345 were positively correlated with the disease activity score in 28 joints for C-reactive protein (DAS28-CRP). Further, the ORs of RA development through IgM anti-HPT78-108 HNE (OR 5.235, p < 0.001), IgM anti-IGKC2-19 (OR 12.655, p < 0.001), and IgG anti-THRB328-345 (OR 5.761, p < 0.001) showed an increased risk. Lastly, we incorporated three machine learning models to differentiate RA from HC and OA, and performed feature selection to determine discriminative features. Experimental results showed that our proposed method achieved an area under the receiver operating characteristic curve of 0.92, which demonstrated that our selected autoantibodies combined with machine learning can efficiently detect RA. CONCLUSIONS: This study discovered that some IgG- and IgM-NAAs and anti-HNE M-NAAs may be correlated with inflammation and disease activity in RA. Moreover, our findings suggested that IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgG anti-THRB328-345 may play heavy roles in RA development.
BACKGROUND:Rheumatoid arthritis (RA) is an autoimmune disorder with systemic inflammation and may be induced by oxidative stress that affects an inflamed joint. Our objectives were to examine isotypes of autoantibodies against 4-hydroxy-2-nonenal (HNE) modifications in RA and associate them with increased levels of autoantibodies in RApatients. METHODS: Serum samples from 155 female patients [60 with RA, 35 with osteoarthritis (OA), and 60 healthy controls (HCs)] were obtained. Four novel differential HNE-modified peptide adducts, complement factor H (CFAH)1211-1230, haptoglobin (HPT)78-108, immunoglobulin (Ig) kappa chain C region (IGKC)2-19, and prothrombin (THRB)328-345, were re-analyzed using tandem mass spectrometric (MS/MS) spectra (ProteomeXchange: PXD004546) from RApatients vs. HCs. Further, we determined serum protein levels of CFAH, HPT, IGKC and THRB, HNE-protein adducts, and autoantibodies against unmodified and HNE-modified peptides. Significant correlations and odds ratios (ORs) were calculated. RESULTS: Levels of HPT in RApatients were greatly higher than the levels in HCs. Levels of HNE-protein adducts and autoantibodies in RApatients were significantly greater than those of HCs. IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgM anti-IGKC2-19 HNE may be considered as diagnostic biomarkers for RA. Importantly, elevated levels of IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgG anti-THRB328-345 were positively correlated with the disease activity score in 28 joints for C-reactive protein (DAS28-CRP). Further, the ORs of RA development through IgM anti-HPT78-108 HNE (OR 5.235, p < 0.001), IgM anti-IGKC2-19 (OR 12.655, p < 0.001), and IgG anti-THRB328-345 (OR 5.761, p < 0.001) showed an increased risk. Lastly, we incorporated three machine learning models to differentiate RA from HC and OA, and performed feature selection to determine discriminative features. Experimental results showed that our proposed method achieved an area under the receiver operating characteristic curve of 0.92, which demonstrated that our selected autoantibodies combined with machine learning can efficiently detect RA. CONCLUSIONS: This study discovered that some IgG- and IgM-NAAs and anti-HNE M-NAAs may be correlated with inflammation and disease activity in RA. Moreover, our findings suggested that IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgG anti-THRB328-345 may play heavy roles in RA development.
Authors: Laura M Shireman; Kimberly A Kripps; Larissa M Balogh; Kip P Conner; Dale Whittington; William M Atkins Journal: Arch Biochem Biophys Date: 2010-09-15 Impact factor: 4.013
Authors: F C Arnett; S M Edworthy; D A Bloch; D J McShane; J F Fries; N S Cooper; L A Healey; S R Kaplan; M H Liang; H S Luthra Journal: Arthritis Rheum Date: 1988-03
Authors: Marianna M Newkirk; Raphaela Goldbach-Mansky; Jennifer Lee; Joseph Hoxworth; Angie McCoy; Cheryl Yarboro; John Klippel; Hani S El-Gabalawy Journal: Arthritis Res Ther Date: 2003-01-06 Impact factor: 5.156