Back

Exogenous Amyloid Sequences: Their Role in Amyloid-Beta Heterotypic Aggregation

Seira Curto, J.; Fernandez Gallegos, M. R.; Villegas Hernandez, S.; Sanchez de Groot, N.

2025-01-24 biochemistry Community evaluation
10.1101/2025.01.24.634659 bioRxiv
Show abstract

Protein aggregation is a complex process influenced by environmental conditions and interactions between multiple molecules, including those of exogenous origin. Although in vitro simulations of aggregation are crucial for advancing research, few studies explore cross-seeding as a repeating event, despite the potential for such events when proteins circulate through the body. Here, we investigated the impact of exogenous amyloid sequences derived from the gut microbiota on the heterotypic aggregation of A{beta} peptides. We utilized ten 21-amino acid peptides derived from bacterial genomes, previously shown to interfere with A{beta}40 aggregation and induce memory loss in Caenorhabditis elegans. Through consecutive cross-seeding assays with A{beta}40 and A{beta}42, we analyzed the effects of these peptides on aggregation kinetics and seed propagation. Our findings indicate that exogenous molecules can influence A{beta}s aggregation process, altering the fibrils properties. Based on this, we introduce the "Interaction History" concept, where prior interactions shape the aggregation and propagation of A{beta} peptides. This work supports the idea that environmental factors, such as microbial amyloids, can contribute to the heterogeneity and progression of amyloid-related diseases. Our results highlight the need for therapeutic strategies targeting diverse amyloid configurations and reinforce the importance of considering exogenous sequences as additional triggers in AD pathology.

Matching journals

The top 14 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.