Multimodal Interrogation of Autoimmune Disease Patients Reveals Highly Diverse Biomarker Signatures 2259778

Abstract

Abstract Introduction Autoimmune diseases (AIDs) like multiple sclerosis (MS), systemic lupus erythematosus (SLE) and inflammatory bowel disease (IBD) share common etiologies and are characterized by dysregulation of the immune system. Nonetheless, they are highly heterogenous disorders that are caused by distinct underlying mechanisms—termed endotypes. AID endotypes—each associated with genetic predisposition, treatment response, and altered molecular pathways—are emerging as clinical phenotypes. Endotype-driven diagnosis, prognosis and treatment rely on biomarkers to select the optimal therapeutic intervention. Methods In this study, we interrogated human plasma samples from AID patients (i.e., MS, SLE, and IBD) and age- and sex-matched healthy controls for biomarker signatures using multiple platforms. We assessed multiple serum protein using a Meso Scale Discovery (MSD)® platform, a high-sensitivity Quanterix® platform as well as O-Link® technology based on proximity extension. In addition, we employed unbiased mass spectrometry to evaluate the lipidome and the metabolome of the samples. Eventually, we integrated the obtained experimental data with available clinical metadata using DIABLO and analyzed the dataset to extract key predictive features with multiple regularization approaches such as elastic net. Results We demonstrated the establishment of a workflow for end-to-end data generation, from human sample acquisition to biomarker discovery and potential patient stratification, including an in-silico framework for data integration and analysis. In addition, we identified three inflammatory clusters among the analyzed patient samples indicating the underlying AID endotypes. Moreover, we found analytes that have significant discrimination power for biomarker discovery. Conclusion Multimodal characterization and analysis enable identification of relevant disease endotypes and clinically relevant biomarkers to support patient stratification for clinical studies. Funding Source n/a Topic Categories Translational and Interventional Immunology (TI)