Elucidating the molecular determinants of Aβ aggregation with deep mutational scanning
Preprint posted on June 06, 2019 https://www.biorxiv.org/content/10.1101/662213v1
Aggregation of the amyloid β (Aβ) peptide in the brain is thought to play a critical role in the formation of amyloid plaques and the pathogenesis of Alzheimer’s disease. There are several Aβ isoforms that are generated by imprecise cleavage of the Aβ precursor protein at different positions. One of these isoforms, Aβ42, is more prone to aggregation and is the major component of amyloid plaques in Alzheimer’s disease brains. However, our understanding of the relationships between peptide sequence and structure and Aβ aggregation has been limited to in vitro mutagenesis studies of Aβ, and these were not carried out with the disease-relevant Aβ42.
Here, the authors use an unbiased technique called deep mutational scanning to interrogate most if not all possible single amino acid substitutions at every residue of Aβ42 and their effects on aggregation. This powerful approach uses high-throughput sequencing to assess the function of a large number of unique protein variants in parallel (Fowler and Fields 2014; Shin and Cho 2015; Kinney and McCandlish 2019). In general, a library of many protein variants is introduced into a system where the genotype of each variant is linked to a selectable phenotype, and selection is imposed. Variants with high activity increase in frequency, whereas variants with low activity decrease in frequency. The change in the frequency of each variant is measured using high-throughput sequencing and serves as a measure of function. In this study the authors develop a novel yeast-based system where Aβ42 is fused to the essential dihydrofolate reductase (DHFR) protein. Aβ42 aggregation reduces DHFR activity, which slows the growth of yeast cells in the presence of the DHFR inhibitor methotrexate. Thus, cells containing aggregating variants grow slowly upon selection with methotrexate while those containing soluble variants grow rapidly (Figure 1). The frequencies of different Aβ42 variants can be measured before and after methotrexate selection using sequencing and will reflect their solubilities.
- The group combine a yeast cell-based aggregation assay with deep mutational scanning for the first time, which could be used in the future to study other disease relevant aggregation-prone proteins such a-synuclein (Parkinson’s disease) and transthyretin (transthyretin amyloidosis; ATTR).
- 791 out of a possible 798 single amino acid substitutions across the 42 residues of Aβ42 were interrogated for their effects on aggregation, resulting in a comprehensive Aβ42 sequence-aggregation map.
- While most (75%) substitutions maintained or increased Aβ42 aggregation, the approach revealed 11 positions whose substitution strongly disrupted Aβ42 aggregation, particularly when changed to hydrophilic or charged residues.
- There is some disagreement between the results presented here and previous in vitro mutagenesis studies of different Aβ isoforms. The results of this study are more consistent with structural models based on in vivo derived fibrils than those based on laboratory grown fibrils. These findings therefore highlight the utility of studying the disease-relevant isoform and using a cell-based assay.
What I like about this preprint/why I think it is important:
Mutagenesis can provide functional insight into proteins, but the ability to choose the most informative mutations to make can be limited, for example in highly divergent proteins that lack homology to known protein domains. Unbiased and saturating deep mutational scanning overcomes this limitation, allows many variants to be studied in parallel, and may reveal unexpected results. I believe that this is a powerful approach that is widely applicable to diverse proteins in different systems, as long as function can be assayed in a manner that couples genotype and phenotype. For example, it has been used to probe protein-protein (Starita et al. 2015), protein-nucleic acid, and protein-ligand (Tinberg et al. 2013) interactions, protein stabilities (Matreyek et al. 2018), and enzymatic activities (Starita et al. 2015). More recently, it has been used to understand the phenotypic consequences of many possible missense mutations in human disease genes (Starita et al. 2015; Weile et al. 2017; Matreyek et al. 2018; Stein et al. 2019), to profile mutations that result in drug resistance (Dingens et al. 2019), and to profile viral host tropism (Setoh et al. 2019), replication (Haddox et al. 2016) and evolution (Lee et al. 2018). Furthermore, it is also becoming clear that these types of experiments are key to realizing the potential of directed protein evolution and de novo protein design (Acevedo-Rocha et al. 2018). Elegant assay development is critical for the success of this approach and I am excited to see an example of a novel functional assay described in this preprint, which could be used to study other disease relevant aggregation-prone proteins in the future. This study also highlights the utility of deep mutational scanning data for the evaluation and prediction of protein structure and structural models. This provides an important layer of data that can be potentially added to recently developed integrative structural modeling tools (Hicks et al. 2019; Wollacott et al. 2019).
Future directions and questions for the authors:
- Most of the discussion focuses on the substitutions that result in decreased Aβ42 aggregation. Are any of substitutions associated with increased aggregation in this assay also associated with cases of familial or sporadic Alzheimer’s disease?
- Do you think that this work could be used to rationally design therapeutic peptides or monoclonal antibodies that could potentially inhibit Aβ42 aggregation? Your cell-based assay could also be used to assess the effects of such peptides and antibodies on aggregation, and even to evolve them further using deep mutational scanning.
- Does your deep mutational scanning data, particularly for the C-terminus of the peptide, provide any insight or support any hypothesis as to why Aβ42 is more prone to aggregation than other Aβ isoforms?
Acevedo-Rocha CG, Ferla M, Reetz MT. 2018. Directed Evolution of Proteins Based on Mutational Scanning. Methods in molecular biology 1685: 87-128.
Dingens AS, Arenz D, Overbaugh J, Bloom JD. 2019. Massively Parallel Profiling of HIV-1 Resistance to the Fusion Inhibitor Enfuvirtide. Viruses 11.
Fowler DM, Fields S. 2014. Deep mutational scanning: a new style of protein science. Nat Methods 11: 801-807.
Haddox HK, Dingens AS, Bloom JD. 2016. Experimental Estimation of the Effects of All Amino-Acid Mutations to HIV’s Envelope Protein on Viral Replication in Cell Culture. PLoS Pathog 12: e1006114.
Hicks M, Bartha I, di Iulio J, Venter JC, Telenti A. 2019. Functional characterization of 3D protein structures informed by human genetic diversity. Proceedings of the National Academy of Sciences of the United States of America 116: 8960-8965.
Kinney JB, McCandlish DM. 2019. Massively Parallel Assays and Quantitative Sequence-Function Relationships. Annu Rev Genomics Hum Genet.
Lee JM, Huddleston J, Doud MB, Hooper KA, Wu NC, Bedford T, Bloom JD. 2018. Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human H3N2 influenza variants. Proceedings of the National Academy of Sciences of the United States of America 115: E8276-E8285.
Matreyek KA, Starita LM, Stephany JJ, Martin B, Chiasson MA, Gray VE, Kircher M, Khechaduri A, Dines JN, Hause RJ et al. 2018. Multiplex assessment of protein variant abundance by massively parallel sequencing. Nat Genet 50: 874-882.
Setoh YX, Amarilla AA, Peng NYG, Griffiths RE, Carrera J, Freney ME, Nakayama E, Ogawa S, Watterson D, Modhiran N et al. 2019. Determinants of Zika virus host tropism uncovered by deep mutational scanning. Nat Microbiol 4: 876-887.
Shin H, Cho BK. 2015. Rational Protein Engineering Guided by Deep Mutational Scanning. Int J Mol Sci 16: 23094-23110.
Starita LM, Young DL, Islam M, Kitzman JO, Gullingsrud J, Hause RJ, Fowler DM, Parvin JD, Shendure J, Fields S. 2015. Massively Parallel Functional Analysis of BRCA1 RING Domain Variants. Genetics 200: 413-422.
Stein A, Fowler DM, Hartmann-Petersen R, Lindorff-Larsen K. 2019. Biophysical and Mechanistic Models for Disease-Causing Protein Variants. Trends in biochemical sciences.
Tinberg CE, Khare SD, Dou J, Doyle L, Nelson JW, Schena A, Jankowski W, Kalodimos CG, Johnsson K, Stoddard BL et al. 2013. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501: 212-216.
Weile J, Sun S, Cote AG, Knapp J, Verby M, Mellor JC, Wu Y, Pons C, Wong C, van Lieshout N et al. 2017. A framework for exhaustively mapping functional missense variants. Mol Syst Biol 13: 957.
Wollacott AM, Robinson LN, Ramakrishnan B, Tissire H, Viswanathan K, Shriver Z, Babcock GJ. 2019. Structural prediction of antibody-APRIL complexes by computational docking constrained by antigen saturation mutagenesis library data. J Mol Recognit 32: e2778.
Posted on: 18th July 2019Read preprint
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