Isotypes of autoantibodies against novel differential 4-hydroxy-2-nonenal-modified peptide adducts in serum is associated with rheumatoid arthritis in Taiwanese women

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.

Clinical Assay for the Early Detection of Colorectal Cancer Using Mass Spectrometric Wheat Germ Agglutinin Multiple Reaction Monitoring

“…In a previous study, WGA was used to remove abundant proteins such as albumin and enriched glycoproteins [28]. A pooling strategy by equalizing samples proved to be useful in biomarker discovery [36]. A pooling strategy can help overcome resource constraints while many individuals are analyzed; further, the variation in biological samples should [54][55][56][57][58][59][60][61][62] ), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4 [429][430][431][432][433][434][435][436][437][438] ), apolipoprotein E (APOE [198][199][200][201][202][203][204][205][206][207] ), and combinations of the three biomarkers with logistic regression and random forest in healthy controls (HCs) versus early-stage colorectal cancer (CRC), HC versus late-stage CRC, and HC versus all-stage CRC.…”

“…In a previous study, WGA was used to remove abundant proteins such as albumin and enriched glycoproteins [ 28 ]. A pooling strategy by equalizing samples proved to be useful in biomarker discovery [ 36 ]. A pooling strategy can help overcome resource constraints while many individuals are analyzed; further, the variation in biological samples should be reduced and should provide increased power for detecting differences [ 37 ].…”

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“…Further, the random forest classifier has been applied to discover the association between disease and the features [50]. Moreover, in a previous study, feature selection was used to optimize the model performance [51]. In this study, IgG anti-IGKC 76-99 , IgM anti-IGKC 76-99 MDA and IgM anti-A1AT 284-298 MDA were selected as the most frequent during random forest classifier training with forward selection.…”

Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data

“…3.8.3) ( Table 2 ). Furthermore, optimized models were used to conduct a forward selection, as mentioned in a previous study [ 45 ]. The models we built in this study were based on decision tree (DT), random forest (RF), support vector machine (SVM), XGBoost, and lightGBM, with 10-fold cross validation with scikit-learn (vers.…”