Transmission of the amyloid fold between alpha-synuclein and other protein species

Amyloid aggregation of α-Synuclein (aSyn) is a central pathological hallmark of synucleinopathies, including Parkinson’s disease, yet the contribution of exogenous proteins and environmental amyloids to this process remains insufficiently understood. While most studies have focused on intrinsic aggregation mechanisms, this thesis explores how heterologous protein species can trigger or modulate aSyn aggregation through the transmission of the amyloid fold. To address this question, a multidisciplinary approach combining biophysical experiments, bioinformatics analyses, machine learning strategies, and diagnostic assay development was employed.

Using amyloid fibrils derived from food proteins and structurally unrelated amyloidogenic species, this work demonstrates that heterologous cross-seeding of aSyn is a highly selective process. Rather than being a universal property of amyloids, cross-seeding efficiency is shown to depend on specific surface-exposed physicochemical features, in particular defined charge patterns, while overall amyloid architecture alone is insufficient to predict seeding capacity. These findings provide experimental support for the hypothesis that dietary or environmental amyloids may contribute to early aggregation events, potentially along the gut–brain axis.

Beyond fibrillar interactions, the role of soluble monomeric proteins in modulating aSyn aggregation was investigated. A supervised machine learning model based on sequence-derived physicochemical descriptors was developed and successfully discriminated aggregation-inducing from non-inducing proteins, establishing a predictive computational framework to identify potential biological interaction partners involved in pathological aggregation processes.

In parallel, a highly sensitive seed amplification assay based on real-time quaking-induced conversion (RT-QuIC) was developed to detect and quantify trace amounts of aSyn aggregates in biological fluids such as blood and cerebrospinal fluid, approaching single-fibril detection sensitivity. Together, these results provide new mechanistic insights into cross-seeding phenomena, introduce predictive tools for protein–protein interactions in amyloid formation, and advance diagnostic strategies, thereby contributing to a more comprehensive understanding of amyloid-driven neurodegenerative disease processes.