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  • Introduction Determining how genes function

    2019-09-16

    Introduction Determining how genes function together as biological systems is a defining challenge of the genomic era. While genome sequences reveal the DNA blueprint of organisms, deciphering how this blueprint leads to biological function is challenging due in large part to the complexity of protein interaction networks [1], [2]. For example, many phenotypes are mediated by multiple genes [3], and numerous genes exhibit pleiotropy [4]. Tremendous progress has been made in mapping the connections (also known as edges) between genes and gene products by both genetic [5], [6], [7] and biochemical approaches [8], [9]. Epistatic analyses of gene knockout combinations have provided a broad understanding of the impacts of node deletions on network function [5]. In addition, approaches have been developed to analyze the effects of disrupting individual network edges by identifying mutations that eliminate a specific interaction [10], [11], [12]. However, for most complex biological networks, the elasticity function [13], [14] relating network edge strength (e.g., the affinity of a specific protein–protein interaction) to overall network function (e.g., growth rate) is poorly understood. To address this challenge, we developed a high-throughput strategy to analyze how all point mutations in a central gene impact both an edge to a directly connected node in its network and an overall network function. Of note, we assess overall network function by quantifying yeast growth rate as a measure of experimental fitness under defined environmental conditions. In this work, we report experiments with ubiquitin and the E1 enzyme that provide fundamental insights into regulated protein degradation in eukaryotes. Systematic investigations of the relationships among gene or protein sequence, function, and fitness provide new opportunities to bridge molecular, systems, and evolutionary biology [15], [16], [17], [18]. While a wealth of studies demonstrate that the fitness effects of mutations are mediated by biochemical changes [19], [20], [21], [22], [23], [24], [25], most systematic studies of mutants have focused predominantly on either growth effects [14], [26], [27] or biochemical effects [11], [28], [29], [30]. The relationships between mutant effects on biochemical properties and experimental fitness under defined conditions have been studied using traditional approaches for a handful of genes, almost all of which encode acetylcholine inhibitor that catalyze a single critical chemical transformation. In many of these systems [24], [31], [32], [33], the experimental fitness effects of a set of mutants can be accurately predicted based on both the proficiency of the mutant enzyme and physiological models of biochemical fluxes [19]. However, for the majority of genes (particularly those that perform multiple functions or whose functions are not fully appreciated), the relationships between a mutation\'s impact on biochemical properties and fitness remain unclear. In theory, each activity of a multi-functional protein may contribute independently to fitness and may be predicted based on flux models, or the contributions of each activity to function may be interdependent, likely depending on the molecular and evolutionary context of each particular gene product. Distinguishing these possibilities provides insights into network function and can be accomplished by systematically investigating the effects of mutations on both biochemical function and experimental fitness. We determined the effects of all possible point mutants in ubiquitin on activation by the E1 enzyme using a biochemical assay and compared this new functional map to a corresponding map of experimental fitness effects in yeast that we had previously determined [27]. Through its ability to covalently link to other proteins, ubiquitin contributes to multiple important cellular processes including regulated protein degradation [34]. The covalent attachment of ubiquitin is mediated by a series of enzymes, with E1 activation serving as the first step in this process. E1 activates ubiquitin by first adenylating the C-terminus of ubiquitin and subsequent covalent attachment via a catalytic cysteine in E1 [35], [36]. In this work, we developed a biochemical screen for the relative effects of ubiquitin mutations on E1 reactivity. We find that most ubiquitin variants that were deficient for E1 activation in this screen also failed to support robust yeast growth, consistent with the essential role of this reaction [37]. However, our results also demonstrate that activation of wild-type ubiquitin is far more efficient than required to support robust growth and that the relationship between the E1 reactivity of an ubiquitin mutant and yeast growth rate is non-linear. Despite this non-linear elasticity function [13], [14], the effects of most ubiquitin mutants on E1 activation were similar to their effects on yeast growth rate. These observations suggest that most ubiquitin mutations that lead to defects in E1 activation also lead to defects in other ubiquitin network edges (e.g., binding to the proteasome) and that the combined biochemical defects of these ubiquitin acetylcholine inhibitor mutations are responsible for the observed fitness defect.