τ-SGA: synthetic genetic array analysis for systematically screening and quantifying trigenic interactions in yeast.
Alleles
Computational Biology
/ methods
Gene Regulatory Networks
/ genetics
Genetic Techniques
Genetic Testing
/ methods
Haploidy
High-Throughput Nucleotide Sequencing
/ methods
Meiosis
/ genetics
Oligonucleotide Array Sequence Analysis
/ methods
Saccharomyces cerevisiae
/ genetics
Yeasts
/ genetics
Journal
Nature protocols
ISSN: 1750-2799
Titre abrégé: Nat Protoc
Pays: England
ID NLM: 101284307
Informations de publication
Date de publication:
02 2021
02 2021
Historique:
received:
01
04
2020
accepted:
28
10
2020
pubmed:
20
1
2021
medline:
9
3
2021
entrez:
19
1
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33462440
doi: 10.1038/s41596-020-00456-3
pii: 10.1038/s41596-020-00456-3
pmc: PMC9127509
mid: NIHMS1805060
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1219-1250Subventions
Organisme : NHGRI NIH HHS
ID : R01 HG005084
Pays : United States
Organisme : NHGRI NIH HHS
ID : R01 HG005853
Pays : United States
Organisme : Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada)
ID : FDN-143264
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R01HG005853
Organisme : Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada)
ID : FDN-143265
Organisme : National Science Foundation (NSF)
ID : DBI\0953881
Organisme : NIGMS NIH HHS
ID : R01 GM104975
Pays : United States
Références
Bateson, W. R. S. E., Punnett, R. C. & Hurst, C. C. Reports to the Evolution Committee of the Royal Society, Report II (Harrison and Sons, 1905).
Baryshnikova, A. et al. Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nat. Methods 7, 1017–1024 (2010).
pubmed: 21076421
pmcid: 3117325
doi: 10.1038/nmeth.1534
Novick, P. & Botstein, D. Phenotypic analysis of temperature-sensitive yeast actin mutants. Cell 40, 405–416 (1985).
pubmed: 3967297
doi: 10.1016/0092-8674(85)90154-0
Bender, A. & Pringle, J. R. Use of a screen for synthetic lethal and multicopy suppressee mutants to identify two new genes involved in morphogenesis in Saccharomyces cerevisiae. Mol. Cell. Biol. 11, 1295–1305 (1991).
pubmed: 1996092
pmcid: 369400
Srivas, R. et al. A network of conserved synthetic lethal interactions for exploration of precision cancer therapy. Mol. Cell 63, 514–525 (2016).
pubmed: 27453043
pmcid: 5209245
doi: 10.1016/j.molcel.2016.06.022
Fong, P. C. et al. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N. Engl. J. Med. 361, 123–134 (2009).
pubmed: 19553641
doi: 10.1056/NEJMoa0900212
Costanzo, M. et al. Global genetic networks and the genotype-to-phenotype relationship. Cell 177, 85–100 (2019).
pubmed: 30901552
doi: 10.1016/j.cell.2019.01.033
Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).
pubmed: 27708008
pmcid: 5661885
doi: 10.1126/science.aaf1420
Tong, A. H. et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364–2368 (2001).
pubmed: 11743205
doi: 10.1126/science.1065810
van Leeuwen, J. et al. Exploring genetic suppression interactions on a global scale. Science 354, aag0839 (2016).
pubmed: 27811238
pmcid: 5562937
doi: 10.1126/science.aag0839
Kuzmin, E. et al. Systematic analysis of complex genetic interactions. Science 360, eaao1729 (2018).
pubmed: 29674565
pmcid: 6215713
doi: 10.1126/science.aao1729
Kuzmin, E. et al. Exploring whole-genome duplicate gene retention with complex genetic interaction analysis. Science 368, eaaz5667 (2020).
pubmed: 32586993
pmcid: 7539174
doi: 10.1126/science.aaz5667
Bowers, J. E., Chapman, B. A., Rong, J. & Paterson, A. H. Unravelling angiosperm genome evolution by phylogenetic analysis of chromosomal duplication events. Nature 422, 433–438 (2003).
pubmed: 12660784
doi: 10.1038/nature01521
Dehal, P. & Boore, J. L. Two rounds of whole genome duplication in the ancestral vertebrate. PLoS Biol. 3, e314 (2005).
pubmed: 16128622
pmcid: 1197285
doi: 10.1371/journal.pbio.0030314
Kuzmin, E., Costanzo, M., Andrews, B. & Boone, C. Synthetic genetic arrays: automation of yeast genetics. Cold Spring Harb. Protoc. 2016, pdb.top086652 (2016).
pubmed: 27037078
doi: 10.1101/pdb.top086652
Kuzmin, E., Costanzo, M., Andrews, B. & Boone, C. Synthetic genetic array analysis. Cold Spring Harb. Protoc. 2016, pdb.prot088807 (2016).
pubmed: 27037072
doi: 10.1101/pdb.prot088807
Kuzmin, E. et al. Synthetic genetic array analysis for global mapping of genetic networks in yeast. Methods Mol. Biol. 1205, 143–168 (2014).
pubmed: 25213244
doi: 10.1007/978-1-4939-1363-3_10
Richardson, H. E., Wittenberg, C., Cross, F. & Reed, S. I. An essential G1 function for cyclin-like proteins in yeast. Cell 59, 1127–1133 (1989).
pubmed: 2574633
doi: 10.1016/0092-8674(89)90768-X
Sugawara, N., Wang, X. & Haber, J. E. In vivo roles of Rad52, Rad54, and Rad55 proteins in Rad51-mediated recombination. Mol. Cell 12, 209–219 (2003).
pubmed: 12887906
doi: 10.1016/S1097-2765(03)00269-7
Haber, J. E. et al. Systematic triple-mutant analysis uncovers functional connectivity between pathways involved in chromosome regulation. Cell Rep. 3, 2168–2178 (2013).
pubmed: 23746449
pmcid: 3718395
doi: 10.1016/j.celrep.2013.05.007
Moura de Sousa, J., Balbontin, R., Durao, P. & Gordo, I. Multidrug-resistant bacteria compensate for the epistasis between resistances. PLoS Biol. 15, e2001741 (2017).
pubmed: 28419091
pmcid: 5395140
doi: 10.1371/journal.pbio.2001741
Taylor, M. B. & Ehrenreich, I. M. Genetic interactions involving five or more genes contribute to a complex trait in yeast. PLoS Genet. 10, e1004324 (2014).
pubmed: 24784154
pmcid: 4006734
doi: 10.1371/journal.pgen.1004324
Celaj, A. et al. Highly combinatorial genetic interaction analysis reveals a multi-drug transporter influence network. Cell Syst. 10, 25–38.e10 (2020).
pubmed: 31668799
doi: 10.1016/j.cels.2019.09.009
Li, Z. et al. Systematic exploration of essential yeast gene function with temperature-sensitive mutants. Nat. Biotechnol. 29, 361–367 (2011).
pubmed: 21441928
pmcid: 3286520
doi: 10.1038/nbt.1832
Yan, Z. et al. Yeast Barcoders: a chemogenomic application of a universal donor-strain collection carrying bar-code identifiers. Nat. Methods 5, 719–725 (2008).
pubmed: 18622398
doi: 10.1038/nmeth.1231
McIsaac, R. S. et al. Synthetic gene expression perturbation systems with rapid, tunable, single-gene specificity in yeast. Nucleic Acids Res. 41, e57 (2013).
pubmed: 23275543
doi: 10.1093/nar/gks1313
Smith, J. D. et al. Quantitative CRISPR interference screens in yeast identify chemical-genetic interactions and new rules for guide RNA design. Genome Biol. 17, 45 (2016).
pubmed: 26956608
pmcid: 4784398
doi: 10.1186/s13059-016-0900-9
Chong, Y. T. et al. Yeast proteome dynamics from single cell imaging and automated analysis. Cell 161, 1413–1424 (2015).
pubmed: 26046442
doi: 10.1016/j.cell.2015.04.051
Mattiazzi Usaj, M. et al. Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability. Mol. Syst. Biol. 16, e9243 (2020).
pubmed: 32064787
pmcid: 7025093
doi: 10.15252/msb.20199243
Styles, E. B. et al. Exploring quantitative yeast phenomics with single-cell analysis of DNA damage foci. Cell Syst. 3, 264–277.e10 (2016).
pubmed: 27617677
pmcid: 5689480
doi: 10.1016/j.cels.2016.08.008
Gottert, H., Mattiazzi Usaj, M., Rosebrock, A. P. & Andrews, B. J. Reporter-based synthetic genetic array analysis: a functional genomics approach for investigating transcript or protein abundance using fluorescent proteins in Saccharomyces cerevisiae. Methods Mol. Biol. 1672, 613–629 (2018).
pubmed: 29043651
doi: 10.1007/978-1-4939-7306-4_40
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).
pubmed: 28753430
pmcid: 5667678
doi: 10.1016/j.cell.2017.06.010
Gonatopoulos-Pournatzis, T. et al. Genetic interaction mapping and exon-resolution functional genomics with a hybrid Cas9–Cas12a platform. Nat. Biotechnol. 38, 638–648 (2020).
pubmed: 32249828
doi: 10.1038/s41587-020-0437-z
Mair, B. et al. High-throughput genome-wide phenotypic screening via immunomagnetic cell sorting. Nat. Biomed. Eng. 3, 796–805 (2019).
pubmed: 31548591
doi: 10.1038/s41551-019-0454-8
Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).
pubmed: 28099430
pmcid: 5334791
doi: 10.1038/nmeth.4177
McFaline-Figueroa, J. L. et al. A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition. Nat. Genet. 51, 1389–1398 (2019).
pubmed: 31477929
pmcid: 6756480
doi: 10.1038/s41588-019-0489-5
Replogle, J. M. et al. Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing. Nat. Biotechnol. 38, 954–961 (2020).
pubmed: 32231336
pmcid: 7416462
doi: 10.1038/s41587-020-0470-y
Braberg, H. et al. Quantitative analysis of triple-mutant genetic interactions. Nat. Protoc. 9, 1867–1881 (2014).
pubmed: 25010907
pmcid: 4167031
doi: 10.1038/nprot.2014.127
Collins, S. R., Schuldiner, M., Krogan, N. J. & Weissman, J. S. A strategy for extracting and analyzing large-scale quantitative epistatic interaction data. Genome Biol. 7, R63 (2006).
pubmed: 16859555
pmcid: 1779568
doi: 10.1186/gb-2006-7-7-r63
Keil, C. et al. Treeview 3.0 (beta 1)—visualization and analysis of large data matrices. Zenodo. https://zenodo.org/record/1303402#.X8fANxNKj-Y (2018).
Baryshnikova, A. Systematic functional annotation and visualization of biological networks. Cell Syst. 2, 412–421 (2016).
pubmed: 27237738
doi: 10.1016/j.cels.2016.04.014
Gietz, R. D., Schiestl, R. H., Willems, A. R. & Woods, R. A. Studies on the transformation of intact yeast cells by the LiAc/SS-DNA/PEG procedure. Yeast 11, 355–360 (1995).
pubmed: 7785336
doi: 10.1002/yea.320110408
Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).
pubmed: 12140549
doi: 10.1038/nature00935
Sung, M. K., Ha, C. W. & Huh, W. K. A vector system for efficient and economical switching of C-terminal epitope tags in Saccharomyces cerevisiae. Yeast 25, 301–311 (2008).
pubmed: 18350525
doi: 10.1002/yea.1588
Wagih, O. et al. SGAtools: one-stop analysis and visualization of array-based genetic interaction screens. Nucleic Acids Res. 41, W591–W596 (2013).
pubmed: 23677617
pmcid: 3692131
doi: 10.1093/nar/gkt400
Usaj, M. et al. TheCellMap.org: a web-accessible database for visualizing and mining the global yeast genetic interaction network. G3 (Bethesda) 7, 1539–1549 (2017).
doi: 10.1534/g3.117.040220
Amberg, D. C., Burke, D. J. & Strathern, J. N. Tetrad dissection. Cold Spring Harb. Protoc. 2006, pdb.prot4181 (2006).
doi: 10.1101/pdb.prot4181
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
pubmed: 14597658
pmcid: 403769
doi: 10.1101/gr.1239303
Baryshnikova, A. Exploratory analysis of biological networks through visualization, clustering, and functional annotation in Cytoscape. Cold Spring Harb. Protoc. 2016, pdb.prot077644 (2016).
doi: 10.1101/pdb.prot077644
Baryshnikova, A. Spatial analysis of functional enrichment (SAFE) in large biological networks. Methods Mol. Biol. 1819, 249–268 (2018).
pubmed: 30421408
doi: 10.1007/978-1-4939-8618-7_12