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.

The genetic landscape for amyloid beta fibril nucleation accurately discriminates familial Alzheimer’s disease mutations

“…Previously we identified a principal component of aa properties (principal component 1 [PC1], related to changes in hydrophobicity) that predicts the aggregation and toxicity of the amyotrophic lateral sclerosis (ALS) protein TDP-43 when it is expressed in yeast ( Bolognesi et al, 2019 ). PC1 is also not a good predictor of Aß nucleation ( Figure 1D ) but it does predict the previously reported changes in Aß solubility ( Figure 1E ), suggesting that Aß is aggregating by a similar process to TDP-43 in this alternative selection assay ( Gray et al, 2019 ) but by a different mechanism in the nucleation selection.…”

“…These 12 known disease mutations are not well discriminated by commonly used computational variant effect predictors ( Figure 4 and Figure 4—figure supplement 1A ) or by computational predictors of protein aggregation and solubility ( Figure 4 and Figure 4—figure supplement 1B ). They are also poorly predicted by the previous deep mutational scan of Aß designed to quantify changes in protein solubility, suggesting the disease is unrelated to the biophysical process quantified in this assay ( Gray et al, 2019 ; Figure 4—figure supplement 1C ).…”

“…Weighted Pearson correlation coefficient and p-value are indicated. ( E ) Solubility scores ( Gray et al, 2019 ) as a function of PC1 changes ( Bolognesi et al, 2019 ) for n = 895 single and double mutants. Pearson correlation coefficient and p-value are indicated.…”

“…A prior deep mutational scan quantified the effects of mutations on the abundance of Aß fused to an enzymatic reporter ( Gray et al, 2019 ). These ‘solubility scores’ do not predict the effects of mutations on Aß nucleation ( Figure 1—figure supplement 1D ).…”

Deep mutational scanning reveals the structural basis for α-synuclein activity

“…In DMS, a library of protein missense variants is screened for relative activity in competitive selection; by applying modest selective pressures, the relative effect of many mutations can be quantified using deep sequencing. DMS has typically been applied to probe structure and function of proteins of known three-dimensional structures, and it has recently been extended to identify residues that are important for the activities of intrinsically disordered proteins 6 , 7 .…”