Several studies recently showed that ex vivo fibrils from patient or animal tissue were structurally different from in vitro formed fibrils from the same polypeptide chain. Analysis of serum amyloid A (SAA) and Ab-derived amyloid fibrils additionally revealed that ex vivo fibrils were more protease stable than in vitro fibrils. These observations gave rise to the proteolytic selection hypothesis that suggested that disease-associated amyloid fibrils were selected inside the body by their ability to resist endogenous clearance mechanisms. We here show, for more than twenty different fibril samples, that ex vivo fibrils are more protease stable than in vitro fibrils. These data support the idea of a proteolytic selection of pathogenic amyloid fibril morphologies and help to explain why only few amino acid sequences lead to amyloid diseases, although many, if not all, polypeptide chains can form amyloid fibrils in vitro.

Several studies showed that seeding of solutions of monomeric fibril proteins with ex vivo amyloid fibrils accelerated the kinetics of fibril formation in vitro but did not necessarily replicate the seed structure. In this research we use cryo-electron microscopy and other methods to analyze the ability of serum amyloid A (SAA)1.1-derived amyloid fibrils, purified from systemic AA amyloidosis tissue, to seed solutions of recombinant SAA1.1 protein. We show that 98% of the seeded fibrils remodel the full fibril structure of the main ex vivo fibril morphology, which we used for seeding, while they are notably different from unseeded in vitro fibrils. The seeded fibrils show a similar proteinase K resistance as ex vivo fibrils and are substantially more stable to proteolytic digestion than unseeded in vitro fibrils. Our data support the view that the fibril morphology contributes to determining proteolytic stability and that pathogenic amyloid fibrils arise from proteolytic selection.

Several studies recently showed that ex vivo fibrils from patient or animal tissue were structurally different from in vitro formed fibrils from the same polypeptide chain. Analysis of serum amyloid A (SAA) and Aβ-derived amyloid fibrils additionally revealed that ex vivo fibrils were more protease stable than in vitro fibrils. These observations gave rise to the proteolytic selection hypothesis that suggested that disease-associated amyloid fibrils were selected inside the body by their ability to resist endogenous clearance mechanisms. We here show, for more than twenty different fibril samples, that ex vivo fibrils are more protease stable than in vitro fibrils. These data support the idea of a proteolytic selection of pathogenic amyloid fibril morphologies and help to explain why only few amino acid sequences lead to amyloid diseases, although many, if not all, polypeptide chains can form amyloid fibrils in vitro.

Systemic AA amyloidosis is a debilitating protein misfolding disease in humans and animals. In humans, it occurs in two variants that are called ‘vascular’ and ‘glomerular’, depending on the main amyloid deposition site in the kidneys. Using cryo electron microscopy, we here show the amyloid fibril structure underlying the vascular disease variant. Fibrils purified from the tissue of such patients are mainly left-hand twisted and contain two non-equal stacks of fibril proteins. They contrast in these properties to the fibrils from the glomerular disease variant which are right-hand twisted and consist of two structurally equal stacks of fibril proteins. Our data demonstrate that the different disease variants in systemic AA amyloidosis are associated with different fibril morphologies.

Fast assessment of the composition of amyloid fibril samples from cryo-EM data poses a serious challenge to existing image analysis tools. We develop a method for automated segmentation of single fibrils requiring only little user input during the training process. This is achieved by combining a binary segmentation based on a convolutional neural network with preprocessing steps to allow for easy manual generation of training data. Subsequent skeletonization turns the binary segmentation into a single-object segmentation. Then, we compute properties of shape and texture of each segmented fibril, including an estimation of the fibril width. We discuss the composition of the sample based on the distributions of these computed properties and outline how a classification of fibril morphologies might be performed using these properties.