Dataset Information

Elucidating the Molecular Determinants of A? Aggregation with Deep Mutational Scanning.

ABSTRACT: Despite the importance of A? 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 A?40 proposed core regions important for aggregation. However, we lack even this limited mutagenesis data for the more disease-relevant A?42 Thus, to better understand the molecular determinants of A?42 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 A?42 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 A? aggregation. These critical positions were similar but not identical to critical positions identified in previous A? mutagenesis studies. Finally, we analyzed our large-scale mutagenesis data in the context of different A? aggregate structural models, finding that the mutagenesis data agreed best with models derived from fibrils seeded using brain-derived A? aggregates.

SUBMITTER: Gray VE

PROVIDER: S-EPMC6829127 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Elucidating the Molecular Determinants of Aβ Aggregation with Deep Mutational Scanning.

Gray Vanessa E VE Sitko Katherine K Kameni Floriane Z Ngako FZN Williamson Miriam M Stephany Jason J JJ Hasle Nicholas N Fowler Douglas M DM

G3 (Bethesda, Md.) 20191105 11

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</ ...[more]

PMID: 31558564

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Project description:This dataset provides allele counts and raw fastqs for deep mutational scanning of the HIV-1 genes tat and rev when not-overlapped with one another (placed in the nef locus) as described in Fernandes et al. Functional segregation of overlapping genes in HIV Cell 2016 (in revision). Preselection (input) and post selection (replicate 1/2) files for every possible point mutant of these two HIV proteins from the NL4-3 background are given.Tab delimited files including codon counts across the amplicons are also included and are probably the most useful thing to most researchers. The data here was used to generate Figures 3 and 4 and 7 and might be of general use for people interested in deep mutational scanning, looking for signatures of epistasis in rev or tat, or reanalyzing and mining the data. FAQ: Why do the ends of each amplicon have such variation? In order to increase diversity across the flowcell, I pooled standard primers with N, NN, and NNN extensions to throw amplicons out of phase. When aligning you should trim the ends or ignore them. This means that the overlap between PE's can vary by 3 nt. Why are the filenames not easy to deal with? The filenames are tied to separate MiSeq runs. I hope to clean up the nomenclature and update this entry in the future while preserving the run information. You can get a sense of that as different residues will vary in Q-score, and that is mostly tied to the run they were pooled on and not any interesting biology. While this is makes it a little harder to follow, I think it's good to get a sense that doing this kind of analysis in high-throughput fashion leads to a reasonable amount of failure (i.e. RNA isolation, RT, fail) that led to repetition until we had good data for every position. Can you help me deal with this dataset? Yes. Please email me at jferna10@ucsc.edu, or contact me on twitter @jdf_ev. For reagents please contact Alan Frankel at frankel@cgl.ucsf.edu. Do you have the analysis scripts you used to process the data? Yes they are on github. https://github.com/nbstrauli/allele_frequency_trajectory_sim

Dataset Information

Elucidating the Molecular Determinants of A? Aggregation with Deep Mutational Scanning.

Publications

Elucidating the Molecular Determinants of Aβ Aggregation with Deep Mutational Scanning.

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