Bashar S Janna, Zheng Zihao, Mergaert Aisha M, Adyniec Ryan R, Gupta Srishti, Amjadi Maya F, McCoy Sara S, Newton Michael A, Shelef Miriam A
Department of Medicine, University of Wisconsin-Madison, Madison, WI 53705, USA.
Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA.
Antibodies (Basel). 2024 Oct 12;13(4):87. doi: 10.3390/antib13040087.
Autoantibodies are commonly used as biomarkers in autoimmune diseases, but there are limitations. For example, autoantibody biomarkers have poor sensitivity or specificity in systemic lupus erythematosus and do not exist in the spondyloarthropathies, impairing diagnosis and treatment. While autoantibodies suitable for strong biomarkers may not exist in these conditions, another possibility is that technology has limited their discovery. The purpose of this study was to use a novel high-density peptide array that enables the evaluation of IgG binding to every possible linear antigen in the entire human peptidome, as well as a novel machine learning approach that incorporates ELISA validation predictability in order to discover autoantibodies that could be developed into sensitive and specific markers of lupus or spondyloarthropathy.
We used a peptide array containing the human peptidome, several viral peptidomes, and key post-translational modifications (6 million peptides) to quantify IgG binding in lupus, spondyloarthropathy, rheumatoid arthritis, Sjögren's disease, and control sera. Using ELISA data for 70 peptides, we performed a random forest analysis that evaluated multiple array features to predict which peptides might be good biomarkers, as confirmed by ELISA. We validated the peptide prediction methodology in rheumatoid arthritis and COVID-19, conditions for which the antibody repertoire is well-understood, and then evaluated IgG binding by ELISA to peptides that we predicted would be highly bound specifically in lupus or spondyloarthropathy.
Our methodology performed well in validation studies, but peptides predicted to be highly and specifically bound in lupus or spondyloarthropathy could not be confirmed by ELISA.
In a comprehensive evaluation of the entire human peptidome, highly sensitive and specific IgG autoantibodies were not identified in lupus or spondyloarthropathy. Thus, the pathogenesis of lupus and spondyloarthropathy may not depend upon unique autoantigens, and other types of molecules should be sought as optimal biomarkers in these conditions.
自身抗体常用于自身免疫性疾病的生物标志物,但存在局限性。例如,自身抗体生物标志物在系统性红斑狼疮中敏感性或特异性较差,而在脊柱关节炎中不存在,这影响了诊断和治疗。虽然在这些情况下可能不存在适合作为强生物标志物的自身抗体,但另一种可能性是技术限制了它们的发现。本研究的目的是使用一种新型高密度肽阵列,该阵列能够评估IgG与整个人类肽组中每个可能的线性抗原的结合,以及一种结合ELISA验证可预测性的新型机器学习方法,以发现可发展成为狼疮或脊柱关节炎敏感和特异性标志物的自身抗体。
我们使用了一种包含人类肽组、几种病毒肽组和关键翻译后修饰(600万个肽)的肽阵列,以量化狼疮、脊柱关节炎、类风湿性关节炎、干燥综合征和对照血清中的IgG结合。利用70个肽的ELISA数据,我们进行了随机森林分析,评估了多个阵列特征,以预测哪些肽可能是良好的生物标志物,ELISA证实了这一点。我们在类风湿性关节炎和COVID-19中验证了肽预测方法,这两种疾病的抗体库已得到充分了解,然后通过ELISA评估IgG与我们预测在狼疮或脊柱关节炎中会高度特异性结合的肽的结合情况。
我们的方法在验证研究中表现良好,但ELISA无法证实预测在狼疮或脊柱关节炎中高度特异性结合的肽。
在对整个人类肽组的综合评估中,未在狼疮或脊柱关节炎中鉴定出高度敏感和特异性的IgG自身抗体。因此,狼疮和脊柱关节炎的发病机制可能不依赖于独特的自身抗原,在这些情况下应寻找其他类型的分子作为最佳生物标志物。