Igome Signatures in the IgM Repertoire Characterize Alzheimer's Disease and Differentiate it from Frontotemporal Dementia

Abstract

The early diagnosis of Alzheimer's disease (AD) holds promise for timely therapeutic intervention and the search for appropriate biomarkers is ongoing. We propose the use of the global antibody reactivity profiling (igome) to discover serum biomarkers that can diagnose AD and differentiate it from other neurodegenerative diseases. Leveraging our optimized IgM mimotope library, we assessed the antibody repertoire of 16 patients with AD or frontotemporal dementia (FTD) and 8 healthy controls. This was performed using oriented, in situ synthesized peptide microarrays followed by recursive feature elimination for optimal diagnostic profile identification. We created reactivity graphs to map partial cross-reactivities between mimotopes, differentiating clusters significantly related to each disease. Unlike previous studies focused mainly on IgG repertoires and random peptides, our approach capitalizes on the natural biosensor properties of IgM and uses artificial intelligence-based biomarker mining to overcome limitations such as compositional bias and intra-individual variability. The identified mimotopes associated with AD or FTD offer potential as diagnostic probes for disease prediction and staging. Our study presents a proof of principle of exploiting the igome as a sensitive, low-cost tool for early AD diagnosis.