RESUMEN
The data in this article are associated with the research paper "GigaAssay - an adaptable high-throughput saturation mutagenesis assay" [1]. The raw data are sequence reads of HIV-1 Tat cDNA amplified from cellular genomic DNA in a new single-pot saturation mutagenesis assay designated the "GigaAssay". A bioinformatic pipeline and parameters used to analyze the data. Raw, processed, analyzed, and filtered data are reported. The data is processed to calculate the Tat-driven transcription activity for cells with each possible single amino acid substitution in Tat. This data can be reused to interpret Tat intermolecular interactions and HIV latency. This is one of the largest and most complete datasets regarding the impact of amino acid substitutions within a single protein on a molecular function.
RESUMEN
High-throughput assay systems have had a large impact on understanding the mechanisms of basic cell functions. However, high-throughput assays that directly assess molecular functions are limited. Herein, we describe the "GigaAssay", a modular high-throughput one-pot assay system for measuring molecular functions of thousands of genetic variants at once. In this system, each cell was infected with one virus from a library encoding thousands of Tat mutant proteins, with each viral particle encoding a random unique molecular identifier (UMI). We demonstrate proof of concept by measuring transcription of a GFP reporter in an engineered reporter cell line driven by binding of the HIV Tat transcription factor to the HIV long terminal repeat. Infected cells were flow-sorted into 3 bins based on their GFP fluorescence readout. The transcriptional activity of each Tat mutant was calculated from the ratio of signals from each bin. The use of UMIs in the GigaAssay produced a high average accuracy (95%) and positive predictive value (98%) determined by comparison to literature benchmark data, known C-terminal truncations, and blinded independent mutant tests. Including the substitution tolerance with structure/function analysis shows restricted substitution types spatially concentrated in the Cys-rich region. Tat has abundant intragenic epistasis (10%) when single and double mutants are compared.
Asunto(s)
VIH-1 , Productos del Gen tat del Virus de la Inmunodeficiencia Humana , Línea Celular , Duplicado del Terminal Largo de VIH , VIH-1/genética , Mutagénesis , Activación Transcripcional , Productos del Gen tat del Virus de la Inmunodeficiencia Humana/genética , Productos del Gen tat del Virus de la Inmunodeficiencia Humana/metabolismoRESUMEN
The phenotypic effect of an allele at one genetic site may depend on alleles at other sites, a phenomenon known as epistasis. Epistasis can profoundly influence the process of evolution in populations and shape the patterns of protein divergence across species. Whereas epistasis between adaptive substitutions has been studied extensively, relatively little is known about epistasis under purifying selection. Here we use computational models of thermodynamic stability in a ligand-binding protein to explore the structure of epistasis in simulations of protein sequence evolution. Even though the predicted effects on stability of random mutations are almost completely additive, the mutations that fix under purifying selection are enriched for epistasis. In particular, the mutations that fix are contingent on previous substitutions: Although nearly neutral at their time of fixation, these mutations would be deleterious in the absence of preceding substitutions. Conversely, substitutions under purifying selection are subsequently entrenched by epistasis with later substitutions: They become increasingly deleterious to revert over time. Our results imply that, even under purifying selection, protein sequence evolution is often contingent on history and so it cannot be predicted by the phenotypic effects of mutations assayed in the ancestral background.
Asunto(s)
Evolución Molecular , Proteínas/genética , Epistasis Genética , Modelos Teóricos , Mutación , Estabilidad Proteica , TermodinámicaRESUMEN
Meta-analysis of data from genome-wide association studies (GWAS) of Alzheimer's disease (AD) has confirmed the high risk of APOE and identified twenty other risk genes/loci with moderate effect size. However, many more risk genes/loci remain to be discovered to account for the missing heritability. The contributions from individual singe-nucleotide polymorphisms (SNPs) have been thoroughly examined in traditional GWAS data analysis, while SNP-SNP interactions can be explored by a variety of alternative approaches. Here we applied generalized multifactor dimensionality reduction to the re-analysis of four publicly available GWAS datasets for AD. When considering 4-order intragenic SNP interactions, we observed high consistency of discovered potential risk genes among the four independent GWAS datasets. Ten potential risk genes were observed across all four datasets, including PDE1A, RYR3, TEK, SLC25A21, LOC729852, KIRREL3, PTPN5, FSHR, PARK2, and NR3C2. These potential risk genes discovered by generalized multifactor dimensionality reduction are highly relevant to AD pathogenesis based on multiple layers of evidence. The genetic contributions of these genes warrant further confirmation in other independent GWAS datasets for AD.