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Investigating Pathogen-Induced Auto-Antibody Repertoires in Alzheimer’s Disease Using LLDEEP PhIP-Seq Data

Alzheimer’s disease (AD) has repeatedly been linked to chronic pathogen exposure and the humoral immune response it elicits, yet whether pathogen-induced autoantibodies contribute mechanistically to neurodegeneration or instead reflect the broader immune remodeling of aging remains unresolved. The LLD PhIP-Seq dataset (EGAD00001010104) profiles the antibody epitope repertoire of 1,783 plasma samples from Lifelines-DEEP participants, including 340 with a second time point after four years of follow-up. Cross-referencing these oligopeptide-derived antibody profiles against the cohort’s longitudinal phenotypic, microbiome, and genetic data will allow us to test whether specific pathogen exposures generate cross-reactive autoantibodies through molecular mimicry, and whether the resulting antibody signatures track with markers of neurodegeneration and age-related decline.
Because causal inference from serological association is inherently limited, our analytical pipeline is designed to adjudicate between competing explanations rather than assume molecular mimicry a priori. In parallel with the primary hypothesis, we will test whether antibody positivity is better explained by age-related loss of barrier integrity — gut epithelial or blood–brain barrier permeability — or by generalized immunosenescence, exploiting the cohort’s longitudinal and multi-omic structure to distinguish pathogen-specific signals from broader markers of immune aging. This comparative framework situates the project within the study of mechanisms of healthy versus pathological aging, consistent with the dataset’s approved scope.
We request the LLD PhIP-Seq sequence data (EGAD00001010104) together with the linked phenotypic, microbiome, and genetic variation data required for this multi-omic analysis. All analyses will be conducted for non-commercial research purposes, and results will be shared with the Lifelines-DEEP microbiome project ahead of publication as required by the Data Access Agreement.

Year of approval

2026

Institute

TAU- Gray Faculty of Medical & health sciences (ISR)

Primary applicant

Bar, D.