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Network of epistatic interactions within a yeast snoRNA.

ABSTRACT: Epistatic interactions play a fundamental role in molecular evolution, but little is known about the spatial distribution of these interactions within genes. To systematically survey a model landscape of intragenic epistasis, we quantified the fitness of ~60,000 Saccharomyces cerevisiae strains expressing randomly mutated variants of the 333-nucleotide-long U3 small nucleolar RNA (snoRNA). The fitness effects of individual mutations were correlated with evolutionary conservation and structural stability. Many mutations had small individual effects but had large effects in the context of additional mutations, which indicated negative epistasis. Clusters of negative interactions were explained by local thermodynamic threshold effects, whereas positive interactions were enriched among large-effect sites and between base-paired nucleotides. We conclude that high-throughput mapping of intragenic epistasis can identify key structural and functional features of macromolecules.

SUBMITTER: Puchta O

PROVIDER: S-EPMC5137784 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Network of epistatic interactions within a yeast snoRNA.

Puchta Olga O Cseke Botond B Czaja Hubert H Tollervey David D Sanguinetti Guido G Kudla Grzegorz G

Science (New York, N.Y.) 20160414 6287

Epistatic interactions play a fundamental role in molecular evolution, but little is known about the spatial distribution of these interactions within genes. To systematically survey a model landscape of intragenic epistasis, we quantified the fitness of ~60,000 Saccharomyces cerevisiae strains expressing randomly mutated variants of the 333-nucleotide-long U3 small nucleolar RNA (snoRNA). The fitness effects of individual mutations were correlated with evolutionary conservation and structural s ...[more]

PMID: 27080103

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Project description:Lethal mutagenesis is a broad-spectrum antiviral strategy that employs mutagenic nucleoside analogs to exploit the high mutation rate and low mutational tolerance of many RNA viruses. Studies of mutagen-resistant viruses have identified determinants of replicative fidelity and the importance of mutation rate to viral population dynamics. We have previously demonstrated the effective lethal mutagenesis of influenza A virus using three nucleoside analogs as well as the virus's high genetic barrier to mutagen resistance. Here, we investigate the mutagen-resistant phenotypes of mutations that were enriched in drug-treated populations. We find that PB1 T123A has higher replicative fitness than the wild type, PR8, and maintains its level of genome production during 5-fluorouracil (2,4-dihydroxy-5-fluoropyrimidine) treatment. Surprisingly, this mutagen-resistant variant also has an increased baseline rate of C-to-U and G-to-A mutations. A second drug-selected mutation, PA T97I, interacts epistatically with PB1 T123A to mediate high-level mutagen resistance, predominantly by limiting the inhibitory effect of nucleosides on polymerase activity. Consistent with the importance of epistatic interactions in the influenza virus polymerase, our data suggest that nucleoside analog resistance and replication fidelity are strain dependent. Two previously identified ribavirin {1-[(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)oxolan-2-yl]-1H-1,2,4-triazole-3-carboxamide} resistance mutations, PB1 V43I and PB1 D27N, do not confer drug resistance in the PR8 background, and the PR8-PB1 V43I polymerase exhibits a normal baseline mutation rate. Our results highlight the genetic complexity of the influenza A virus polymerase and demonstrate that increased replicative capacity is a mechanism by which an RNA virus can counter the negative effects of elevated mutation rates. IMPORTANCE RNA viruses exist as genetically diverse populations. This standing genetic diversity gives them the potential to adapt rapidly, evolve resistance to antiviral therapeutics, and evade immune responses. Viral mutants with altered mutation rates or mutational tolerance have provided insights into how genetic diversity arises and how it affects the behavior of RNA viruses. To this end, we identified variants within the polymerase complex of influenza virus that are able to tolerate drug-mediated increases in viral mutation rates. We find that drug resistance is highly dependent on interactions among mutations in the polymerase complex. In contrast to other viruses, influenza virus counters the effect of higher mutation rates primarily by maintaining high levels of genome replication. These findings suggest the importance of maintaining large population sizes for viruses with high mutation rates and show that multiple proteins can affect both mutation rate and genome synthesis.

Project description:The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fashion. Experimental identification of the full landscape of genetic interactions by perturbing all gene combinations is prohibitive due to the exponential growth of testable hypotheses. Here we present a model for the inference of pairwise epistatic, including synthetic lethal, gene interactions from siRNA-based perturbation screens. The model exploits the combinatorial nature of siRNA-based screens resulting from the high numbers of sequence-dependent off-target effects, where each siRNA apart from its intended target knocks down hundreds of additional genes. We show that conditional and marginal epistasis can be estimated as interaction coefficients of regression models on perturbation data. We compare two methods, namely glinternet and xyz, for selecting non-zero effects in high dimensions as components of the model, and make recommendations for the appropriate use of each. For data simulated from real RNAi screening libraries, we show that glinternet successfully identifies epistatic gene pairs with high accuracy across a wide range of relevant parameters for the signal-to-noise ratio of observed phenotypes, the effect size of epistasis and the number of observations per double knockdown. xyz is also able to identify interactions from lower dimensional data sets (fewer genes), but is less accurate for many dimensions. Higher accuracy of glinternet, however, comes at the cost of longer running time compared to xyz. The general model is widely applicable and allows mining the wealth of publicly available RNAi screening data for the estimation of epistatic interactions between genes. As a proof of concept, we apply the model to search for interactions, and potential targets for treatment, among previously published sets of siRNA perturbation screens on various pathogens. The identified interactions include both known epistatic interactions as well as novel findings.

Dataset Information

Network of epistatic interactions within a yeast snoRNA.

Publications

Network of epistatic interactions within a yeast snoRNA.

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