Elucidating the Molecular Determinants of Aβ Aggregation with Deep Mutational Scanning

Abstract

Despite the importance of Aβ aggregation in Alzheimer's disease etiology, our understanding of the sequence determinants of aggregation is sparse and largely derived from in vitro studies. For example, in vitro proline and alanine scanning mutagenesis of Aβ₄₀ proposed core regions important for aggregation. However, we lack even this limited mutagenesis data for the more disease-relevant Aβ₄₂ Thus, to better understand the molecular determinants of Aβ₄₂ aggregation in a cell-based system, we combined a yeast DHFR aggregation assay with deep mutational scanning. We measured the effect of 791 of the 798 possible single amino acid substitutions on the aggregation propensity of Aβ₄₂ We found that ∼75% of substitutions, largely to hydrophobic residues, maintained or increased aggregation. We identified 11 positions at which substitutions, particularly to hydrophilic and charged amino acids, disrupted Aβ aggregation. These critical positions were similar but not identical to critical positions identified in previous Aβ mutagenesis studies. Finally, we analyzed our large-scale mutagenesis data in the context of different Aβ aggregate structural models, finding that the mutagenesis data agreed best with models derived from fibrils seeded using brain-derived Aβ aggregates.

Keywords: Amyloid; Amyloid beta; Deep mutational scanning; Protein aggregation.

Figure 1A-D.

A yeast-based aggregation assay distinguishes between soluble and aggregation-prone variants of Aβ. A schematic of the assay shows plasmid-based expression of Aβ-DHFR and a nonaggregating variant of Aβ fused to DHFR, which lead to slow and fast yeast growth in the presence of methotrexate, respectively (A). A stacked bar graph shows the percentage of Aβ-DHFR and Aβ¹⁹_FD-DHFR in co-culture (y-axis) every 12 hr for 48 hr (x-axis; B). Fluorescence light microscopy shows the aggregation patterns of Aβ-GFP (WT) and Aβ¹⁹_FD-GFP (¹⁹FD) 16h after induction of expression (C). A bar graph shows the percentage of yeast cells with punctae (y-axis) in five fluorescence microscopy images of Aβ-DHFR (WT) or Aβ¹⁹_FD-DHFR (¹⁹FD; x-axis; D).

Figure 2A-F.

Solubility scores for 791 Aβ variants. Solubility scores reliably measure the effects of Aβ sequence on aggregation propensity. A scatter plot shows the correlation between two of three biological replicates that were averaged to yield final solubility scores (A; Figure S1A). The distribution of solubility scores (x-axis) of synonymous variants was used to determine cutoffs that define variants that are wild-type-like or more/less aggregation-prone than wild-type. The density plot shows distributions of nonsynonymous (light gray) and synonymous (dark gray) variants and the white lines show the lower (-0.26) and upper (0.39) bounds for wild-type-like variants (B). The scatterplot shows the correlation between our solubility scores (y-axis) and a low-throughput yeast growth assay that measured yeast growth rate as a proxy for Aβ solubility (C; Figure S1B). The heatmap shows the effect of 791 Aβ variants on solubility with Aβ positions on the x-axis and mutant amino acids on the y-axis. A variant’s color denotes its solubility: red is most soluble, white is wild-type-like and, dark blue is most aggregated, whereas yellow variants are missing from our variant library and dots denote the wild-type amino acid at a given position. The annotation tracks on the x- and y-axes display the hydrophobicity of each wild-type and mutant amino acid, respectively. The heatmap’s y-axis has been re-ordered using hierarchical clustering on the solubility score vectors (D). For each position, the mean solubility score at each position is depicted using the same color scheme as the main heatmap. Additionally, the mean solubility scores for all hydrophobic and polar substitutions are shown (E; Figure S2A). Heirarchical clustering on the x-axis yielded 6 distinct clusters: 1 (red), 2 (orange), 3 (yellow), 4 (green), 5 (light blue), and 6 (dark blue; F; Figure S2B-C).

Figure 3

Comparison of yeast cell-based solubility scores to in vitro aggregation measurements and Aβ structural models. The scatterplot shows the correlation between our solubility scores (y-axis) and two single amino acid scans that measured the effect of proline (orange) or alanine (teal) variants on the thermodynamic stability of aggregates, relative to wild type (ΔΔG) (A; Figure S3). The first two tracks show unmeasured mutations (dashed gray) and the Aβ buried β-strand positions (black) suggested by proline scanning alone, or by proline and alanine scanning together (Williams et al. 2004; Williams, Shivaprasad, and Wetzel 2006). The third track shows positions with the greatest increase in solubility when mutated in our large-scale mutagenesis study, found in cluster 1 (B). The next nine tracks show the secondary structure of nine models of Aβ aggregate structure for each Aβ position (x-axis; C). The Aβ wild-type sequence is shown at the top.