Abstract
Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic interactions from ordered arrays of yeast strains grown on agar plates as individual colonies. The protocol instructs users on the trigenic synthetic genetic array analysis technique, τ-SGA, for high-throughput screens. The steps describe construction of the double-mutant query strains and the corresponding single-mutant control query strains, which are screened in parallel in two replicates. The screening experimental set-up consists of sequential replica-pinning steps that enable automated mating, meiotic recombination and successive haploid selection steps for the generation of triple mutants, which are scored for colony size as a proxy for fitness, which enables the calculation of trigenic interactions. The procedure described here was used to conduct 422 trigenic interaction screens, which generated ~460,000 yeast triple mutants for trigenic interaction analysis. Users should be familiar with robotic equipment required for high-throughput genetic interaction screens and be proficient at the command line to execute the scoring pipeline. Large-scale screen computational analysis is achieved by using MATLAB pipelines that score raw colony size data to produce τ-SGA interaction scores. Additional recommendations are included for optimizing experimental design and analysis of smaller-scale trigenic interaction screens by using a web-based analysis system, SGAtools. This protocol provides a resource for those who would like to gain a deeper, more practical understanding of trigenic interaction screening and quantification methodology.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1219-1250 |
| Number of pages | 32 |
| Journal | Nature Protocols |
| Volume | 16 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2021 |
Bibliographical note
Funding Information:This work was primarily supported by the National Institutes of Health (R01HG005853; to C.B., B.J.A. and C.L.M.), Canadian Institutes of Health Research (FDN-143264 and FDN-143265; to C.B. and B.J.A.), National Institutes of Health (R01HG005084 and R01GM104975); to C.L.M.) and the National Science Foundation (DBI\0953881; to C.L.M.). Computing resources and data storage services were partially provided by the Minnesota Supercomputing Institute and the University of Minnesota Office of Information Technology, respectively. Additional support was provided by Natural Science and Engineering Research Council of Canada Postgraduate Scholarship-Doctoral PGS D2 (to E.K.), a University of Toronto Open Fellowship (to E.K.) and a University of Minnesota Doctoral Dissertation Fellowship (to B.V.). C.B. is a fellow of the Canadian Institute for Advanced Research (CIFAR).
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature Limited.