Elsevier

Methods

Volume 40, Issue 4, December 2006, Pages 344-352
Methods

Quantitative genetic analysis in Saccharomyces cerevisiae using epistatic miniarray profiles (E-MAPs) and its application to chromatin functions

https://doi.org/10.1016/j.ymeth.2006.07.034Get rights and content

Abstract

The use of the budding yeast Saccharomyces cerevisiae as a simple eukaryotic model system for the study of chromatin assembly and regulation has allowed rapid discovery of genes that influence this complex process. The functions of many of the proteins encoded by these genes have not yet been fully characterized. Here, we describe a high-throughput methodology that can be used to illuminate gene function and discuss its application to a set of genes involved in the creation, maintenance and remodeling of chromatin structure. Our technique, termed E-MAPs, involves the generation of quantitative genetic interaction maps that reveal the function and organization of cellular proteins and networks.

Introduction

The creation, maintenance and remodeling of chromatin structure occur through highly complex and regulated processes involving many transient protein interactions and modifications. These associations can be difficult to detect using standard techniques, thus necessitating the development of additional methods to study the relevant signal transduction pathways and their consequences. Genetic interactions, or the extent to which mutations in one gene modulate the phenotype of a second mutation, provide a functional view of cellular processes that is complementary to the picture provided by physical interactions [1], [2]. In order to systematically collect information on these functional relationships, high-throughput technologies in Saccharomyces cerevisiae have been developed to qualitatively identify synthetic sick/lethal (SSL) (aggravating) genetic interactions between gene pairs on a genome-wide scale [3], [4], [5]. While these methods have proven very powerful, it has become clear that individual SSL relationships between non-essential genes are often hard to interpret since they usually identify genes that function in different, potentially parallel pathways [6], [7], [8]. Quantitative information on the entire spectrum of genetic interactions would provide a more comprehensive view of the cellular effects of gene mutation. This includes interactions that are alleviating (buffering or suppressing), where the double mutants grow more rapidly than would be expected given the growth rate of each of the single mutants. Such interactions often occur between pairs of genes working in the same cellular pathway [7], [8], [9]. To enable high-throughput analysis of the entire spectrum of interactions for large groups of genes we developed the E-MAPs (epistatic miniarray profiles) [7] technology that allows for the collection of quantitative genetic interaction data on logically selected subsets of genes.

To study chromatin functions, an E-MAP was generated that includes genes associated with chromatin functions, as well as those involved in transcription, DNA repair/recombination, DNA replication, chromosome segregation, and telomere function (Collins et al., submitted for publication). Several important predictions arising from this E-MAP for novel gene functions have recently been studied and confirmed [10], [11], [12], [13], [14], [15], [16], [17]. One such prediction was a connection between SET2, a histone methyltransferase, and Rpd3C(S), a histone deacetylation complex that contains the chromodomain protein Eaf3p as well as Rco1p. E-MAP analysis of their genetic interactions showed a striking correlation between each of their profiles and that of SET2 (Fig. 1A). Consistent with this result, the Δset2, Δeaf3, and Δrco1 strains all display alleviating genetic interactions with each other (Fig. 1B), suggesting that they function in the same cellular pathway. Indeed, methylation of histone H3 by Set2p has now been shown to be required for the recruitment and subsequent deacetylation activity of the small, Eaf3p and Rco1p containing Rpd3 complex to suppress spurious transcription initiation inside the coding regions of genes [11], [18], [19]. This example highlights the power of genetic interactions for revealing functional dependencies that could not have been observed by protein–protein interaction data alone.

An important aspect of E-MAPs is their quantitative scoring system that identifies both aggravating and alleviating interactions. This ability is dependent on the presence of a high density of interactions [7], [20], which improves the signal-to-noise ratio relative to full genome screens. Indeed, screening a single query gene against the whole-genome may actually result in the detection of fewer genetic interactions than screening against a smaller set of functionally related genes despite the larger number of potential interactors. This point can be illustrated by observing the drastic decrease in the confidence assigned to each interaction (as a function of interaction score) when a whole-genome screen situation is simulated (tenfold increase in the number of non-interacting gene pairs) (Fig. 2). Hence, it is of great value to select small, comprehensive, subsets of functionally related genes for quantitative E-MAP analysis. Additionally, it is now feasible to include all relevant essential genes in the analysis by employing DAmP (decreased abundance by mRNA perturbation) technology [7].

Here we discuss the basic approach for generating an E-MAP. Specifically, we review the considerations in choosing a set of genes for analysis by E-MAP, the strain construction procedures for creating both gene deletions and DAmP alleles, and the manual pinning protocols that allow the generation of all double mutant combinations. Many of these protocols rely on previously published synthetic genetic array (SGA) protocols [21] and can also be found on our website (http://www.weissmanlab.ucsf.edu). The analytical tools and software (which is freely distributed), necessary for computing quantitative genetic interaction scores once colonies of double mutant strains have been obtained, have been described in detail elsewhere [20].

Section snippets

Selection of genes for E-MAP analysis

Previous efforts to gather genome-wide genetic interaction data [3], [5] have demonstrated that most genes only show strong aggravating interactions with a relatively small percentage of the genome, and that these interactions tend to be with functionally related genes. Based on these considerations, E-MAPs were developed to measure all genetic interactions between 384 functionally related mutations. This number is compatible with commercially available hand-pinners, which can be used for rapid

Deletion strains

Once the set of non-essential genes to analyze has been chosen, it is necessary to generate complementary MATa and MATα deletion strains. The MATa set can be obtained from the commercially available yeast deletion consortium library (his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) that contains individual non-essential genes deleted with a kanamycin resistance (Kanr) cassette [26]. However, these strains lack any of the markers necessary for performing the mating and spore selection steps in high-throughput [4],

Creation of double mutant strains

Once the strains have been made, the creation of double mutants can be performed in high-throughput using the previously published protocols [21]. However, to allow this method to be widely used, we have optimized our protocols to use only manual pinning tools and grids (V & P scientific, San Diego, CA). Most of the steps are performed using pin tools with large-diameter pins so that adequate amounts of cells are carried over from step to step (VP384F6 alongside library copiers for liquid VP381

Perspective

Although a large number of accurate predictions have already been generated using our E-MAP focused on chromatin-related functions, many additional discoveries of gene function have yet to be made using these data. The E-MAP approach, however, is general and allows the study of any group of functionally related genes. Due to the optimization of this protocol to fit manual pinning tools, it is now feasible for any yeast lab to create an E-MAP tailored to their genes/ processes of interest. The

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