Mapping DNA damage‐dependent genetic interactions in yeast via party mating and barcode fusion genetics
2018; Springer Nature; Volume: 14; Issue: 5 Linguagem: Inglês
10.15252/msb.20177985
ISSN1744-4292
AutoresJ. Javier Díaz-Mejía, Albi Celaj, Joseph Mellor, Atina G. Coté, Attila Balint, Brandon Ho, Pritpal Bansal, Fatemeh Shaeri, Marinella Gebbia, Jochen Weile, Marta Verby, Anna A. Karkhanina, Yifan Zhang, Cassandra J. Wong, Justin Rich, D’Arcy Prendergast, Gaurav Gupta, Sedide Öztürk, Daniel Durocher, Grant W. Brown, Frederick P. Roth,
Tópico(s)Fermentation and Sensory Analysis
ResumoMethod28 May 2018Open Access Source DataTransparent process Mapping DNA damage-dependent genetic interactions in yeast via party mating and barcode fusion genetics J Javier Díaz-Mejía Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Department of Computer Science, University of Toronto, Toronto, ON, Canada Search for more papers by this author Albi Celaj orcid.org/0000-0002-5888-772X Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Department of Computer Science, University of Toronto, Toronto, ON, Canada Search for more papers by this author Joseph C Mellor Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Department of Computer Science, University of Toronto, Toronto, ON, Canada Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA Search for more papers by this author Atina Coté Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Attila Balint orcid.org/0000-0002-6481-1772 Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Biochemistry, University of Toronto, Toronto, ON, Canada Search for more papers by this author Brandon Ho Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Biochemistry, University of Toronto, Toronto, ON, Canada Search for more papers by this author Pritpal Bansal Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Fatemeh Shaeri Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Marinella Gebbia Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Search for more papers by this author Jochen Weile orcid.org/0000-0003-1628-9390 Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Marta Verby Donnelly Centre, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Anna Karkhanina Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author YiFan Zhang Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Cassandra Wong Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Justin Rich Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author D'Arcy Prendergast Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Gaurav Gupta Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Sedide Öztürk Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA Search for more papers by this author Daniel Durocher Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Grant W Brown Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Biochemistry, University of Toronto, Toronto, ON, Canada Search for more papers by this author Frederick P Roth Corresponding Author [email protected] orcid.org/0000-0002-6628-649X Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Department of Computer Science, University of Toronto, Toronto, ON, Canada Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Canadian Institute for Advanced Research, Toronto, ON, Canada Search for more papers by this author J Javier Díaz-Mejía Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Department of Computer Science, University of Toronto, Toronto, ON, Canada Search for more papers by this author Albi Celaj orcid.org/0000-0002-5888-772X Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Department of Computer Science, University of Toronto, Toronto, ON, Canada Search for more papers by this author Joseph C Mellor Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Department of Computer Science, University of Toronto, Toronto, ON, Canada Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA Search for more papers by this author Atina Coté Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Attila Balint orcid.org/0000-0002-6481-1772 Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Biochemistry, University of Toronto, Toronto, ON, Canada Search for more papers by this author Brandon Ho Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Biochemistry, University of Toronto, Toronto, ON, Canada Search for more papers by this author Pritpal Bansal Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Fatemeh Shaeri Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Marinella Gebbia Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Search for more papers by this author Jochen Weile orcid.org/0000-0003-1628-9390 Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Marta Verby Donnelly Centre, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Anna Karkhanina Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author YiFan Zhang Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Cassandra Wong Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Justin Rich Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author D'Arcy Prendergast Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Gaurav Gupta Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Sedide Öztürk Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA Search for more papers by this author Daniel Durocher Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Search for more papers by this author Grant W Brown Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Biochemistry, University of Toronto, Toronto, ON, Canada Search for more papers by this author Frederick P Roth Corresponding Author [email protected] orcid.org/0000-0002-6628-649X Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Department of Computer Science, University of Toronto, Toronto, ON, Canada Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Canadian Institute for Advanced Research, Toronto, ON, Canada Search for more papers by this author Author Information J Javier Díaz-Mejía1,2,3,4, Albi Celaj1,2,3,4, Joseph C Mellor1,2,3,4,5,†, Atina Coté1,2,3, Attila Balint1,6,†, Brandon Ho1,6, Pritpal Bansal1,2,3, Fatemeh Shaeri1,2,3, Marinella Gebbia1,2, Jochen Weile1,2,3, Marta Verby1,3, Anna Karkhanina1,2,3, YiFan Zhang1,2,3, Cassandra Wong3, Justin Rich1,2,3, D'Arcy Prendergast1,2,3, Gaurav Gupta1,2,3, Sedide Öztürk5,†, Daniel Durocher2,3, Grant W Brown1,6 and Frederick P Roth *,1,2,3,4,5,7,8 1Donnelly Centre, University of Toronto, Toronto, ON, Canada 2Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada 3Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada 4Department of Computer Science, University of Toronto, Toronto, ON, Canada 5Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA 6Department of Biochemistry, University of Toronto, Toronto, ON, Canada 7Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA 8Canadian Institute for Advanced Research, Toronto, ON, Canada †Present address: SeqWell, Inc., Beverly, MA, USA †Present address: Department of Cellular and Molecular Medicine, Center for Chromosome Stability, University of Copenhagen, Copenhagen, Denmark †Present address: Roche Sequencing Solutions, Pleasanton, CA, USA *Corresponding author. Tel: +1 416 946 5130; E-mail: [email protected] Mol Syst Biol (2018)14:e7985https://doi.org/10.15252/msb.20177985 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Condition-dependent genetic interactions can reveal functional relationships between genes that are not evident under standard culture conditions. State-of-the-art yeast genetic interaction mapping, which relies on robotic manipulation of arrays of double-mutant strains, does not scale readily to multi-condition studies. Here, we describe barcode fusion genetics to map genetic interactions (BFG-GI), by which double-mutant strains generated via en masse "party" mating can also be monitored en masse for growth to detect genetic interactions. By using site-specific recombination to fuse two DNA barcodes, each representing a specific gene deletion, BFG-GI enables multiplexed quantitative tracking of double mutants via next-generation sequencing. We applied BFG-GI to a matrix of DNA repair genes under nine different conditions, including methyl methanesulfonate (MMS), 4-nitroquinoline 1-oxide (4NQO), bleomycin, zeocin, and three other DNA-damaging environments. BFG-GI recapitulated known genetic interactions and yielded new condition-dependent genetic interactions. We validated and further explored a subnetwork of condition-dependent genetic interactions involving MAG1, SLX4, and genes encoding the Shu complex, and inferred that loss of the Shu complex leads to an increase in the activation of the checkpoint protein kinase Rad53. Synopsis A new method, Barcode Fusion Genetics to Map Genetic Interactions (BFG-GI) allows generating double mutants and measuring condition-dependent genetic interactions en masse. Application of BFG-GI to DNA repair genes reveals a new function for the Shu complex. BFG-GI involves generating double-mutant-specific fused barcodes, enabling to measure the abundance of double mutants en masse by next generation sequencing. Once a double mutant BFG-GI pool has been generated genetic interactions can be tested in new growth conditions. BFG-GI is applied to 26 genes related to DNA damage repair in nine different conditions, including seven DNA-damaging agents. A novel relationship is reported between the Shu complex and the checkpoint protein kinase Rad53. Introduction The importance of condition-dependent genetic interactions Genetic interactions, defined by a surprising phenotype that is observed when mutations in two genes are combined (Mani et al, 2008), are powerful tools to infer gene and pathway functions (Baryshnikova et al, 2010; Ideker & Krogan, 2012). Of the genetic interactions currently known in any species, the vast majority were found using Synthetic Genetic Array (SGA) technology in Saccharomyces cerevisiae (Bandyopadhyay et al, 2010; Costanzo et al, 2010, 2016; van Leeuwen et al, 2016) and these studies have yielded a rich landscape of genetic interactions. The sign of genetic interaction (defined to be negative when mutants are synergistically deleterious, and positive when the combination is less severe than would be expected from independent effects) provides clues about whether the genes act in parallel or in a concerted or serial fashion. Measuring similarity between genetic interaction profiles, both at the level of single genes and of clusters of genes, has revealed a hierarchical map of eukaryotic gene function (Costanzo et al, 2010, 2016). However, the vast majority of genetic interaction mapping has been conducted under a single standard culture condition. The importance and qualitative nature of gene function can change with environmental fluctuation, so that a complete understanding of genetic interactions will require mapping under multiple conditions. For example, pairs of DNA repair genes had 2–4 times more genetic interactions between DNA repair genes under MMS treatment compared with rich media alone (St Onge et al, 2007; Bandyopadhyay et al, 2010; Ideker & Krogan, 2012), so that a plethora of condition-dependent genetic interactions remain to be uncovered via gene × gene × environment studies. Current genetic interaction discovery technologies Essentially every large-scale genetic interaction mapping strategy in S. cerevisiae uses a genetic marker system developed for the SGA technique, which works by mating a single-gene deletion query strain with an array of different single-gene deletion strains from the Yeast Knockout Collection (YKO) (Giaever et al, 2002). The SGA system provides genetic markers by which mated diploids can be subjected to a series of selections to ultimately yield haploid double mutants. In "standard" SGA mapping, the fitness of the resulting double mutants is determined by statistical analysis of the images from each plate, yielding cell growth estimates for each separately arrayed strain (Tong & Boone, 2005). SGA has also been used to study genetic interactions within functionally enriched gene groups (Collins et al, 2006) and has been applied to detect environment-dependent interactions (St Onge et al, 2007; Bandyopadhyay et al, 2010). For example, St Onge et al (2007) used the SGA markers to generate all pairwise double mutants between 26 DNA repair genes in yeast. The authors cultured each double mutant individually in microplates and monitored cell density over time to infer the fitness of double mutants and thereby identify genetic interactions in the presence and absence of MMS. Others have measured genetic interactions via competition-based fitness measurements in liquid cultures, adding fluorescent markers for tracking cell viability, and using robotic manipulation to inoculate and measure cell growth (DeLuna et al, 2008; Garay et al, 2014). A recent technique called iSeq incorporated barcodes into single-mutant strains, such that pairs of barcodes identifying corresponding pairs of deleted genes could be fused by Cre-mediated recombination (Jaffe et al, 2017). The authors demonstrated the method, showing that a pool corresponding to nine gene pairs could be sequenced to monitor competitive growth of double mutants en masse in different environments (Jaffe et al, 2017). Cre-mediated approaches have been used similarly to map protein–protein interactions (Hastie & Pruitt, 2007; Yachie et al, 2016; Schlecht et al, 2017). For each of the above genetic interaction methods, double mutants were generated by individual mating of two specific yeast strains, requiring at least one distinct location for each double-mutant strain on an agar or microwell plate and necessitating robotic strain manipulation to achieve large scale. By contrast, other methods to map genetic interactions generated double mutants in a "one-by-many" fashion. For example, diploid-based synthetic lethality analysis on microarrays (dSLAM) (Pan et al, 2004) disrupted a single "query" gene by homologous recombination via transformation of a marker into a pool of diploid heterozygous deletion strains bearing the SGA marker. After selecting for double-mutant haploids from such a one-by-many haploid double-mutant pool, barcodes were PCR-amplified from extracted double-mutant DNA and hybridized to microarrays to infer the relative abundance and fitness of each double mutant. Another method, genetic interaction mapping (GIM) (Decourty et al, 2008), generated a one-by-many pool of barcoded double mutants by en masse mating a single query strain to a pool of haploid gene deletion strains. Like dSLAM, GIM inferred strain abundance and fitness via barcode hybridization to microarrays. Despite the efficiency of generating one-by-many double-mutant pools, a matrix involving thousands of query strains would require thousands of such pools to be generated. Each of the above methods has advantages and disadvantages. For example, measuring a growth time-course for each double-mutant strain provides high-resolution fitness measurements (St Onge et al, 2007; Garay et al, 2014), but scalability is low. Standard SGA is high-throughput, but requires specialized equipment for robotic manipulation, and these manipulations must be repeated to test genetic interactions in new environments. The iSeq method shares the scaling challenge of SGA in strain construction, in that it requires many pairwise mating operations; however, once a double-mutant pool has been generated, it represents a promising strategy for measurement of competitive pools in different environments. The dSLAM and GIM methods allow generation of one-by-many pools, which reduces the number of mating operations, but both methods require customized microarrays as well as pool generation and microarray hybridization steps for every query mutation in the matrix. Barcode fusion genetics to map genetic interactions (BFG-GI) Here, we describe BFG-GI, which borrows elements from several previous approaches. Like iSeq, BFG-GI requires generation of barcoded single-mutant strains, with only minimal use of robotics. To generate double-mutant pools, BFG-GI uses the SGA marker system and, like the GIM strategy, BFG-GI employs en masse mating. Unlike GIM and all other previous genetic interaction mapping strategies, BFG-GI employs many-by-many "party mating" to generate all double mutants for a matrix of genes in a single mating step. All successive steps—including barcode fusion, sporulation, selection of haploid double mutants, and measurement of relative strain abundance—are also conducted en masse. We show that double mutants can be generated and monitored in competitive pools using BFG-GI. Like iSeq, BFG-GI infers double-mutant fitness in competitively grown strain pools using next-generation sequencing of fused barcodes, and BFG-GI double-mutant pools can be aliquoted and stored. Aliquots can be thawed later and challenged under specific environments (e.g., drugs) to detect condition-dependent genetic interactions without having to regenerate the double-mutant strains. We assessed BFG-GI by mapping genetic interactions of DNA repair-related genes under multiple DNA-damaging conditions, revealing many condition-dependent interactions and a discovery that perturbation of the Shu complex leads to increased activation of the Rad53 checkpoint protein kinase. Results BFG-GI experimental design overview The first step in the BFG-GI process is generating uniquely barcoded donor and recipient strains with complementary mating types. Each donor and recipient strain contains a unique barcode locus. In the donor strain, this barcode is flanked by two distinct site-specific recombination sites (loxP/2272 sites), while in the recipient strain, both recombination sites lie on the same side of the unique recipient barcode. After the mating step, these sites mediate barcode fusion via the Cre/Lox system, yielding chimeric barcode sites that uniquely identify specific deletion combinations. We created donors by crossing individual gene deletion strains from the YKO collection with proDonor strains that contained newly constructed pDonor plasmids (Figs 1A and EV1, and Materials and Methods). We generated recipient strains by crossing individual gene deletion strains from the SGA query collection with proRecipient strains (Figs 1B and EV2, and Materials and Methods). Haploid selection of double mutants followed mating of donor and recipient strains, sporulation, and in vivo fusion of barcodes using Cre/Lox recombination (Fig 1C). Figure 1. BFG-GI pipeline summary Construction of donors with unique barcodes representing each gene deletion in parental strains from the YKO collection. Construction of recipients also with unique barcodes representing genes of interest in parental strains from the SGA query collection. Pairs of recombination sites (loxP and lox2272) were located at the barcode loci of donor and recipient strains to enable in vivo intracellular fusion of barcode pairs at the recipient barcode locus. Donors and recipients were mated with each other to generate heterozygous diploid double mutants, and barcodes were fused in vivo by the Cre/Lox system. The relic plasmid remaining in donors after Cre/Lox recombination was counter-selected after barcode fusion. Sporulation was induced to select for the MATa progeny and haploid double mutants. BFG-GI was conducted en masse to generate "many-by-many" pools for a set of 26 DNA repair and 14 neutral genes. The resulting pool of haploid double mutants was stored as aliquots of glycerol stock. Thawed aliquots were used to inoculate media containing different chemical agents ("drugs"). Genomic DNA was extracted and fused barcodes were amplified and sequenced to monitor double-mutant abundance and to infer genetic interactions. Details of donor and recipient strain construction are shown in Figs EV1 and EV2, respectively. Media details are shown in Fig EV3. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Donor toolkit construction Two fragments were built to generate proDonor plasmids. The first, preD1, contained loxP/lox2272 sites flanking two 20-bp unique barcodes and a hygromycin resistance marker. In this study, only the upstream barcode was used for further steps, and for simplification, the downstream barcode was omitted from Fig 1. The second, preD2, contained the Cre recombinase driven by the doxycycline-inducible tetO-CMV, and a URA3 marker. The two fragments were assembled in vivo in yeast to generate pDonors. pDonors were arrayed and Sanger-sequenced to confirm the integrity of the preD1 fragment. ProDonors with confirmed preD1 fragments were mated with YKO strains to generate strains carrying both a uniquely barcoded pDonor and a gene deletion of interest. Then, they were sporulated and the haploid MATalpha progeny were selected using the mating type maker indicated in panel (C). Details on selective media are shown in Fig EV3. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Recipient toolkit construction Two constructs were built to generate recipients. The first fragment, preR1, contained loxP/lox2272 sites flanking a klURA3 marker and two 20-bp unique barcodes flanking these loci. In this study, only the upstream barcode was used for further steps, and for simplification, the downstream barcode was omitted from Fig 1. The second construct, preR2, contained the can1Δ::PSTE2-spHis5-TSTE2 mating type marker. The two fragments were assembled in vivo using a derivative of the delitto perfetto construct. Resulting proRecipients were arrayed and Sanger-sequenced to confirm integrity of preR1 loci. ProRecipients with confirmed preR1 loci were mated with SGA query strains to generate strains carrying both a uniquely barcoded recipient construct and a gene deletion of interest. Then, they were sporulated and the haploid MATa progeny were selected using the mating type maker indicated in panel (C). Details on selective media are shown in Fig EV3. Download figure Download PowerPoint We confirmed that barcode fusion was successful using two control strains carrying markers at likely-neutral loci. Specifically, we crossed a MATalpha Donor hoΔ::kanMX to a MATa Recipient ylr179cΔ::natMX and induced Cre/Lox recombination to fuse their barcodes. After sporulation and selection of the MATalpha haploid double-mutant progeny (Materials and Methods), we extracted genomic DNA, amplified barcode fusions by PCR, and confirmed their integrity by Sanger sequencing (Fig 1C). To scale up the BFG-GI process, we optimized mating and sporulation steps to generate double mutants with unique barcodes that had been fused en masse (Materials and Methods). We selected hundreds of double mutants using a series of marker selection steps in a many-by-many fashion. Intermediate selection steps allowed us to fuse barcodes representing each donor and recipient parental pair within each double-mutant cell (Fig 1D and Materials and Methods). Once we generated the pool of fused-barcode double mutants, aliquots were stored at −80°C for future experiments. Amplification and next-generation sequencing of fused barcodes in the pool allowed us to infer the relative abundance of each double mutant in each condition of interest (Fig 1D and Materials and Methods). In addition to haploid double-mutant pools, we sequenced fused barcodes from the heterozygous diploid double-mutant pools and used those as reference ("time zero") controls for fitness and genetic interaction calculations (Materials and Methods). BFG
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