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τ-SGA: synthetic genetic array analysis for systematically screening and quantifying trigenic interactions in yeast

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.

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Fig. 1: Overview of the τ-SGA screening and quantification methodology.
Fig. 2: Classes of trigenic interactions.
Fig. 3: An overview of the τ-SGA experimental pipeline.
Fig. 4: τ-SGA experimental pipeline.
Fig. 5: An example of a trigenic interaction.
Fig. 6: Schematic of the steps involved in processing and scoring high-throughput trigenic interaction screens by using a τ-SGA scoring pipeline.
Fig. 7: Schematic of the steps for processing and scoring small-scale trigenic interaction screens by using the SGAtools platform.
Fig. 8: Representative examples of clustering and visualization of τ-SGA scores.

Data availability

A sample dataset for quantifying MTC1-MDY2 trigenic interactions and the corresponding MTC1 and MDY2 digenic interactions is available at https://github.com/ElenaK35/TrigenicSGA_CaseStudy. τ-SGA scores, which are included for comparison, are from a previous study11. Trigenic interaction datasets can be browsed interactively at http://boonelab.ccbr.utoronto.ca/supplement/kuzmin2018/supplement.html and http://boonelab.ccbr.utoronto.ca/paralogs/. They were also deposited in the DRYAD Digital Repository (https://datadryad.org/stash/dataset/doi:10.5061/dryad.tt367) and (https://datadryad.org/stash/dataset/doi:10.5061/dryad.g79cnp5m9), respectively.

Code availability

Scripts for τ-SGA scoring pipeline are available on GitHub at https://github.com/csbio/SGA_Public. The release for τ-SGA interaction scoring is available at https://github.com/csbio/SGA_Public/releases/tag/tau_score_v1.1.0.

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Acknowledgements

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).

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Contributions

E.K., B.V., C.L.M., B.J.A. and C.B. conceived the project. E.K., B.J.A. and C.B. designed the experimental pipeline with assistance from M.C., B.V. and C.L.M. B.V. and C.L.M. designed the high-throughput analysis pipeline with assistance from E.K., M.C., B.J.A., and C.B. M.R. performed user testing of the high-throughput screening analysis software. E.K. designed and implemented the smaller-scale screening analysis pipeline. E.K. and M.R. wrote the manuscript with input and editing from all authors.

Corresponding authors

Correspondence to Elena Kuzmin or Chad L. Myers or Brenda J. Andrews or Charles Boone.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Reiko Sugiura and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Kuzmin, E. et al. Science 360, eaao1729 (2018): https://doi.org/10.1126/science.aao1729

Kuzmin, E. et al. Science 368, eaaz5667 (2020): https://doi.org/10.1126/science.aaz5667

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Kuzmin, E., Rahman, M., VanderSluis, B. et al. τ-SGA: synthetic genetic array analysis for systematically screening and quantifying trigenic interactions in yeast. Nat Protoc 16, 1219–1250 (2021). https://doi.org/10.1038/s41596-020-00456-3

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