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• W3168292662 abstract "Article7 June 2021Open Access Transparent process A genome-scale yeast library with inducible expression of individual genes Yuko Arita Yuko Arita Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada RIKEN Centre for Sustainable Resource Science, Wako, Saitama, Japan Search for more papers by this author Griffin Kim Griffin Kim Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Zhijian Li Zhijian Li Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada Search for more papers by this author Helena Friesen Helena Friesen Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada Search for more papers by this author Gina Turco Gina Turco Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Rebecca Y Wang Rebecca Y Wang Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Dale Climie Dale Climie Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada Search for more papers by this author Matej Usaj Matej Usaj Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada Search for more papers by this author Manuel Hotz Manuel Hotz Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Emily H Stoops Emily H Stoops Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Anastasia Baryshnikova Anastasia Baryshnikova Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Charles Boone Corresponding Author Charles Boone [email protected] orcid.org/0000-0002-3542-6760 Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada RIKEN Centre for Sustainable Resource Science, Wako, Saitama, Japan Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Search for more papers by this author David Botstein Corresponding Author David Botstein [email protected] orcid.org/0000-0001-9499-4883 Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Brenda J Andrews Corresponding Author Brenda J Andrews [email protected] orcid.org/0000-0001-6427-6493 Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Search for more papers by this author R Scott McIsaac Corresponding Author R Scott McIsaac [email protected] orcid.org/0000-0002-5339-6032 Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Yuko Arita Yuko Arita Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada RIKEN Centre for Sustainable Resource Science, Wako, Saitama, Japan Search for more papers by this author Griffin Kim Griffin Kim Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Zhijian Li Zhijian Li Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada Search for more papers by this author Helena Friesen Helena Friesen Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada Search for more papers by this author Gina Turco Gina Turco Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Rebecca Y Wang Rebecca Y Wang Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Dale Climie Dale Climie Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada Search for more papers by this author Matej Usaj Matej Usaj Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada Search for more papers by this author Manuel Hotz Manuel Hotz Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Emily H Stoops Emily H Stoops Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Anastasia Baryshnikova Anastasia Baryshnikova Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Charles Boone Corresponding Author Charles Boone [email protected] orcid.org/0000-0002-3542-6760 Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada RIKEN Centre for Sustainable Resource Science, Wako, Saitama, Japan Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Search for more papers by this author David Botstein Corresponding Author David Botstein [email protected] orcid.org/0000-0001-9499-4883 Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Brenda J Andrews Corresponding Author Brenda J Andrews [email protected] orcid.org/0000-0001-6427-6493 Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Search for more papers by this author R Scott McIsaac Corresponding Author R Scott McIsaac [email protected] orcid.org/0000-0002-5339-6032 Calico Life Sciences LLC, South San Francisco, CA, USA Search for more papers by this author Author Information Yuko Arita1,2,†, Griffin Kim3,†, Zhijian Li1,†, Helena Friesen1, Gina Turco3, Rebecca Y Wang3, Dale Climie1, Matej Usaj1, Manuel Hotz3, Emily H Stoops3, Anastasia Baryshnikova3, Charles Boone *,1,2,4, David Botstein *,3, Brenda J Andrews *,1,4 and R Scott McIsaac *,3 1Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada 2RIKEN Centre for Sustainable Resource Science, Wako, Saitama, Japan 3Calico Life Sciences LLC, South San Francisco, CA, USA 4Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada †These authors contributed equally to this work *Corresponding author. Tel: +416 946 7261; E-mail: [email protected] *Corresponding author. Tel: +650 769 5510; E-mail: [email protected]colabs.com *Corresponding author. Tel: +416 978 8562; E-mail: [email protected] *Corresponding author. Tel: +650 769 5535; E-mail: [email protected] Molecular Systems Biology (2021)17:e10207https://doi.org/10.15252/msb.202110207 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 The ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which > 5,600 yeast genes are engineered for transcriptional inducibility with single-gene precision at their native loci and without plasmids. Each strain contains SGA screening markers and a unique barcode, enabling high-throughput genetics. We characterized YETI using growth phenotyping and BAR-seq screens, and we used a YETI allele to identify the regulon of Rof1, showing that it acts to repress transcription. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from eukaryotic and prokaryotic systems shows that native expression is a variable that can bias promoter-perturbing screens, including CRISPRi. We engineered a second expression system, Z3EB42, that gives lower expression than Z3EV, a feature enabling conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library. Synopsis A conditional expression system is used to make >5,600 genes inducible at their native loci with a small molecule that is otherwise inert in yeast. The resulting strain collection is systematically characterized using high-throughput assays. All strains contain the SGA markers for high-throughput genetics as well as unique molecular barcodes for functional genomics. A gene’s native expression level is a critical parameter that can influence growth-based assays in multiple species. Two systems Z3EV and Z3EB42, enable different types of growth-based screens, and Z3EV is ideal for studying transcriptional regulatory networks. The transcription factor Rof1 inhibits growth when overexpressed, and is a transcriptional repressor. Introduction With its facile genetics and rapid division rate, the budding yeast Saccharomyces cerevisiae has been a leading model system for systematic studies of gene function (Botstein & Fink, 2011). To date, the most common genetic approach for exploring the biological roles of genes is to study phenotypes associated with loss-of-function alleles. The first genome-wide gene deletion strain collection was constructed in budding yeast, enabling a broad range of functional profiling studies (Giaever et al, 2002). Because ~ 20% of all genes are essential, causing lethality when deleted in a haploid cell (Giaever et al, 2002), exploring loss-of-function of essential genes in this context requires the generation of conditional alleles. Conditional knockdown of gene function using degrons, transcriptional repression, and temperature sensitivity has been employed to investigate the role of essential genes, but each strategy has its own drawbacks, one of the most serious of which is general perturbations to cellular physiology associated with changes of environmental conditions (Kanemaki et al, 2003; Mnaimneh et al, 2004; Li et al, 2011; McIsaac et al, 2011; Snyder et al, 2019). Another systems-wide approach for studying gene function is gene overexpression, which can produce gain-of-function phenotypes and be used to study both essential and non-essential genes. Several gene overexpression plasmid collections with genes under the control of their endogenous promoters on high copy plasmids have been constructed (Jones et al, 2008; Ho et al, 2009). Gene expression in these collections is not conditional, limiting phenotypic analysis and precluding the study of genes whose overexpression inhibits growth. To observe more dynamic overexpression phenotypes, the GAL1 promoter, a strong inducible promoter that can be easily activated by the addition of galactose to glucose-free culture medium, has been used to construct a number of gene overexpression plasmid collections (Sopko et al, 2006; Douglas et al, 2012). However, GAL1 promoter-based overexpression systems also have significant drawbacks, including the cell-to-cell variation of expression associated with replicative plasmids and the requirement of a metabolic signal for activation (Karim et al, 2013). To address issues with yeast gene overexpression systems, we previously engineered a genome-integrated, conditional β-estradiol-inducible gene expression system in budding yeast (McIsaac et al, 2013a,b, 2014). In this system, an artificial transcription factor consisting of the modular zinc finger DNA-binding domain, human estrogen receptor, and VP16 activation domain is constitutively expressed. We refer to these artificial transcription factors as “ZEVs”. A synthetic promoter, which contains binding sites for ZEV variants, is inserted upstream of an open reading frame, displacing the endogenous promoter. The level of activity of the artificial transcription factor is controlled by β-estradiol concentration and enables regulated expression from the corresponding promoter. Since β-estradiol is not a yeast metabolite or signaling molecule, cellular metabolism is not perturbed (McIsaac et al, 2013b). ZEVs have been widely used for basic and applied research, including studies of gene regulatory networks (Hackett et al, 2020; Kang et al, 2020; Ma & Brent, 2020), individual gene function (Elfving et al, 2014; Lyon et al, 2016; Weir et al, 2017; Tran et al, 2018; Kim et al, 2019; Smith et al, 2020; Wang et al, 2020; Kira et al, 2021), gene regulation (Carey, 2015; Hendrickson et al, 2018a; Schikora-Tamarit et al, 2018; Lutz et al, 2019; Brion et al, 2020; preprint: Leydon et al, 2021), metabolic engineering (Liu et al, 2020), synthetic biology (Schikora-Tamarit et al, 2016; Aranda-Díaz et al, 2017; Gander et al, 2017; Pothoulakis & Ellis, 2018; Bashor et al, 2019; Kotopka & Smolke, 2020; Shaw et al, 2019; Yang et al, 2019), biocontainment (Agmon et al, 2017), living materials (Gilbert et al, 2021), high-throughput screening (Younger et al, 2017; Staller et al, 2018), and they have also been adapted to fission yeast (Ohira et al, 2017; Gómez-Gil et al, 2020; Nuckolls et al, 2020) and Pichia pastoris (Perez-Pinera et al, 2016). ZEVs allow the rapid induction of a single gene in any environment, which provides a system for tracking how induction of gene expression is directly linked to a cellular response, something that cannot be achieved with deletion mutants (McIsaac et al, 2012; Hackett et al, 2020; Kang et al, 2020). To generate a systems-level reagent set for molecular and cellular analysis using the ZEV system, we constructed the YETI collection, in which nearly every gene in budding yeast is inducible with Z3EV, a ZEV variant that utilizes the 3-finger, Zif268 zinc finger from mouse to bind DNA. We thoroughly characterize these strains and provide details for researchers interested in using this collection. In total, we integrated a uniquely barcoded β-estradiol-regulated promoter in front of 4,668 non-essential genes and 1,022 essential genes in a heterozygous diploid background (as confirmed by PCR) and recovered 4,655 Z3EV-driven non-essential genes in a haploid background using the synthetic genetic array (SGA) selection system (Tong et al, 2001). By combining this collection with automated yeast genetics and dynamic growth profiling, we identified 987 genes whose overproduction reduces cell fitness at higher levels of β-estradiol. Additionally, we identified 46 genes whose expression levels affect fitness in a non-monotonic fashion, demonstrating the utility of this collection for genome-scale exploration of fitness landscapes. While more than half of strains with Z3EV-driven essential genes were not able to grow as haploids in the absence of β-estradiol, another subset was viable even without inducer. These findings motivated us to develop a second expression system—the Z3EB42 system—which involved re-engineering Z3EV as well as its target promoter to generate a gene regulation system that gives lower expression and is more extensively repressed in the absence of inducer. Together, the YETI strain collection and the Z3EB42 system provide a comprehensive platform for interrogating yeast gene function and dynamics. Results A genome-scale collection of inducible alleles To construct a genome-scale collection of strains expressing β-estradiol-inducible alleles, we first constructed a parental strain expressing the Z3EV transcription factor. Our diploid parental strain, Y14789, was based on the RCY1972 strain, a derivative of S288C. We chose RCY1972 as it is deleted for the HIS3 locus, making it compatible with SGA methodology, but is otherwise prototrophic, enabling studies of yeast cell growth and other phenotypes in a variety of conditions (Brauer et al, 2008). The strain also carries a functional HAP1 gene, which encodes a transcription factor that localizes to both the mitochondria and nucleus and is required for regulation of genes involved in respiration and the response to oxygen levels (Gaisne et al, 1999). Strains derived from S288C typically carry a Ty1 element insertion in the 3’ region of the HAP1 coding sequence, creating a HAP1 allele that acts as a null for cytochrome c expression and leads to mitochondrial genome instability (Gaisne et al, 1999). Previous work has shown that removal of the Ty element, which repairs the HAP1 gene, increases sporulation efficiency dramatically (Harvey et al, 2018). To select for strains that carried a functional HAP1 gene, the gene encoding the Z3EV transcription factor was integrated next to a functional HAP1 together with a natMX selectable marker in Y14789. Each strain also carries the SGA marker system (can1∆::STE2pr-Sphis5 and lyp1∆), enabling automated, array-based genetics. Combining SGA selection with a nourseothricin selection ensures that haploid YETI strains contain Z3EV as well as a functional HAP1 allele. Finally, we engineered a DNA template on which the URA3 gene is linked to Z3pr for creating genomically integrated promoter fusions. The components of the β-estradiol gene expression system are outlined in Fig 1A. Figure 1. The Z3EV system Outline of the β-estradiol-inducible gene expression system. Z3EV is composed of a 3-zinc finger DNA-binding domain (Zif268), human estrogen receptor domain (hER), and transcription activation domain (VP16). β-estradiol displaces Hsp90 from the estrogen receptor, allowing Z3EV to translocate to the nucleus and induce gene expression. Zif268 binds preferentially to a sequence that is present in six copies in Z3pr. In the strain collection, gene-specific DNA barcodes are flanked by universal primer sequences: 5’-GCACCAGGAACCATATA-3’ and 5’-GATCCGCTCGCACCG-3’. GFP intensity as a function of β-estradiol concentration. Strains with an integrated Z3pr driving GFP (Y15292; blue) and a control strain (Y15483; gray) were incubated with a concentration series of β-estradiol in YNB for 6 h, and then cells were fixed. GFP intensity was measured by flow cytometry. Error bars represent standard deviation for three replicates. Y15292 (blue) and Y15477 (gray) cultures were induced with 10 nM β-estradiol for 6 h. Cells were washed, and β-estradiol was removed from the medium at time = 0 h on the figure. Error bars represent ± 1 standard deviation for three biological replicates. Download figure Download PowerPoint To test the β-estradiol concentration- and time-dependent expression of a gene regulated by the Z3EV transcription factor in our strain background, we first inserted the Z3pr in front of GFP and measured GFP fluorescence intensity by flow cytometry. Expression of the Z3pr-GFP reporter gene increased in a concentration-dependent and graded manner (Fig 1B, Appendix Fig S1A). Following removal of β-estradiol, the GFP signal decreased by ~ 50% within 3 h and decreased to near-background levels within 24 h, demonstrating that expression of the GFP reporter gene was dependent on β-estradiol concentration and treatment time (Fig 1C). We also tested whether mutant phenotypes could be complemented by cognate genes expressed from the Z3 promoter. We engineered strains in which LEU2 or TPS2 were placed downstream of Z3pr and in the absence of β-estradiol, the resultant strains displayed known phenotypes associated with leu2∆ (Appendix Fig S1B) and tps2∆ (Appendix Fig S1C), leucine auxotrophy (Toh-e et al, 1980) and heat sensitivity (Gibney et al, 2015), respectively, but grew equivalently to WT cells in the presence of β-estradiol. Following these successful characterizations of our constructs, we engineered a genome-wide collection in which the endogenous promoters of individual genes were replaced by inserting the Z3pr just upstream of the start codon of each ORF in a heterozygous diploid strain carrying the Z3EV transcription factor under the control of the constitutive ACT1 promoter, which we linked to a natMX marker. The URA3 gene marking the Z3 promoter is expressed divergently from the Z3pr-controlled target gene. Importantly, the URA3 marker gene in each strain is linked to a unique DNA molecular barcode such that the resulting genome-wide β-estradiol-inducible strain collection is compatible with pooled screening approaches (Fig 1A). Promoter insertions were placed directly upstream of the first ATG of each ORF and did not remove any native DNA. Rather than removing the native promoter sequence from the genome, which we believe is likely to disrupt the expression of neighboring genes, native promoters were simply displaced by ~ 2 kb. Additionally, yeast does not have “transcriptional activation at a distance”. From the work of Dobi and Winston (2007), once an activation sequence was placed 690+ bp from a target gene, it was no longer regulatory. Thus, we expect that displacement of the native promoter by ~ 2 kb should be sufficient for removing its regulatory potential. Promoter insertions were confirmed by PCR, and further quality control with a subset of specific strains was carried out using whole-genome sequencing (see Appendix, Dataset EV1). In total, we constructed 1,022 diploid strains carrying Z3pr alleles of essential genes (corresponding to 97.1% of essential genes) (Dataset EV2A) and 4,668 diploid strains expressing Z3pr alleles of non-essential genes (corresponding to 98% of non-essential genes) (Dataset EV2B). For the non-essential genes, we recovered haploid derivatives by sporulating the heterozygous diploid strains and germinating haploid meiotic progeny on SGA selection medium (Dataset EV2C). We refer to these haploid strains as the YETI non-essential (YETI-NE) panel. The set of strains with essential genes under the control of the β-estradiol-inducible promoter are maintained as diploids. We refer to these strains as the YETI essential (YETI-E) panel. In total, we were unable to construct 127 strains (30 YETI-E diploids, 84 YETI-NE diploids, and 13 YETI-NE haploids; Dataset EV2D). Quantifying the relationship between inducer level and transcript level A useful feature of the Z3EV system is the potential to either knock down or induce a gene of interest depending on the level of β-estradiol inducer, which enables comprehensive analysis of the relationship between gene expression and phenotype by finely tuning transcription. To assess this property of the collection, we selected Z3pr alleles of 18 non-essential genes with a range of native expression levels (from 3 to 2,173 TPM [transcripts per million]). Following growth in the presence of various concentrations of β-estradiol for 30 min, we quantified mRNA expression levels of the β-estradiol-regulated genes using RNA-seq. All genes had qualitatively similar responses to β-estradiol concentration: very low expression at β-estradiol concentrations from 0–1 nM and then an increase in expression between 4 and 16 nM, followed by a plateau at β-estradiol concentrations beginning at 16–64 nM (Fig EV1A). However, the actual level of transcript produced at each β-estradiol concentration varied. For this gene panel, maximum expression levels ranged from ~ 30–40 TPM for some genes (e.g., ATG4, SNT1, and SRS2) to over 1,000 TPM for others (e.g., ASC1, BAT1, THR1, and VMA3) at > 16 nM β-estradiol. Peak expression levels were correlated with native transcript levels (Pearson correlation = 0.74), which is reminiscent of the gene-dependent expression variation reported with a GALpr plasmid collection (Gelperin et al, 2005). For genes in the panel whose native expression was < 250 TPM, the maximum expression output from Z3EV and the level of native expression followed a simple linear relationship (Fig EV1B). Linearity broke down for the two highly expressed genes we tested, ASC1 and VMA3; even at saturating concentrations of β-estradiol, Z3EV-induced expression was lower than or equal to native expression levels. We conclude that Z3EV can be used for titrating gene expression, but the exact number of transcripts produced depends on the target gene open reading frame and its genomic context. Additionally, since we used RNA-seq, we explored the transcriptome landscape of these strains. Our most striking observation was that Bat1 induction resulted in the repression of a variety of amino acid biosynthesis genes in a dose-dependent fashion, including all of the ILV (IsoLeucine-plus-Valine requiring) genes, which are upstream of Bat1 and are members of the superpathway of branched-chain amino acid biosynthesis (Appendix Fig S2). Since Bat1 catalyzes the terminal reactions in this superpathway, the transcriptional responses are consistent with end-product inhibition. Click here to expand this figure. Figure EV1. Transcriptional induction of 18 YETI-NE strains Expression data of 18 strains during exponential growth in 1 ml batch culture of SC medium after 30 min of induction with varying concentrations of β-estradiol in a deep-well 96-well plate. The y-axis shows transcripts per million (TPM) values from RNA-seq experiments, and the x-axis shows concentrations of β-estradiol (nM). The dotted line indicates the endogenous level of expression for each gene in this experiment, calculated as the average TPM value from all strains in the panel that contain a native copy of the allele. Comparing Z3EV inducibility to endogenous gene expression. The graph shows maximum TPM values after β-estradiol induction for the 18 genes assayed in (A) (y-axis) versus native expression (x-axis). For genes with maximum endogenous expression level of < 250 TPM, a linear model was fit (y = 87.96 + 4.79x, R2 = 0.76). Download figure Download PowerPoint Growth patterns associated with β-estradiol-dependent regulation of essential genes To begin our characterization of the YETI strain collection, we wanted to describe the possible behaviors of the β-estradiol-regulated promoter alleles. We first explored the growth characteristics of the essential gene alleles (YETI-E) using high-resolution time-lapse imaging (see Materials and Methods for details) at twelve β-estradiol concentrations since, unlike non-essential genes, alterations in essential gene expression are expected to lead to an easily assayed growth phenotype (Fig 2). YETI-E strains were grown as heterozygous diploids, sporulated and haploid YETI-E strains were selected on medium with various β-estradiol concentrations. Colony growth was measured over time and growth curves were quantified by determining the area under the growth curve (AUGC) (Fig EV2). We utilized this metric because it is insensitive to specific data parametrizations. Normalized AUGC values were then hierarchically clustered using a Chebyshev distance metric, which revealed five distinct clusters or promoter behaviors (Fig 2, Dataset EV3A). The largest cluster contained 49% of YETI-E strains, each of which showed a dosage-dependent growth response, with no growth in the absence of inducer, and improved growth with increasing β-estradiol concentrations (Cluster 5, Fig 2). A second smaller cluster showed a similar initial behavior, with growth depending on the presence and concentration of inducer, but with growth inhibition at higher β-estradiol concentrations, indicating dosage toxicity (Cluster 4 with 4.7% of strains). Twenty-six out of 46 genes in Cluster 4 have been shown to be toxic upon overexpression in one or more plasmid collections under control of the GAL1 promoter (Gelperin et al, 2005; Sopko et al, 2006; Douglas et al, 2012). In total, more than half (53.7%) of the YETI-E strains exhibited β-estradiol-dependent growth that is “tunable” by inducer concentration. Figure 2. Hierarchical clustering growth patterns of YETI-E haploid strains The YETI-E diploid strains were sporulated, and haploids were selected on SC SGA selection medium with monosodium glutamate as a nitrogen source at 12 β-estradiol concentrations (0, 0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100, 300, 1,000 nM) and clustered by growth profile. Growth was measured as the area under the growth curve (AUGC); blue represents little growth and yellow represents more growth. AUGC values were quantified for sixteen separate colonies per genotype and dose (see Materials and Methods). Strains were clustered using a Chebyshev distance metric, resulting in five clusters (numbered 1–5). Representative strains for each of the five clusters and their growth patterns are plotted to the right of the clustergram. Error bars represent ± 1 standard deviation. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Growth measurements for YETI alleles of ALG1, ULP2, CDC48, and ADE13 LOESS curve fits are shown (blue lines) from individual measurements of four separate colonies (gray dots). Download figure Download PowerPoint Most remaining strains in the YETI-E collection had a dosage-independent growth response, including a large set of strains (33%) that grew well in the absence of β-estradiol (Cluster 1); many of these had a mild growth impairment in high β-estradiol concentrations. A smaller set (9.2%) grew in the absence of inducer, with more substantial growth inhibition at higher concentrations (Cluster 2—dosage toxicity). Finally, a small group of strains (4.2%), with no obvious functional features in common, failed to grow well regardless of the inducer concentration, suggesting these strains have a non-functional promoter (Cluster 3—dosage-independent lethality) (Dataset EV3B). Roughly half of YETI-E strains grew in the absence of β-estradiol, indicating sufficient basal gene expression to support essential gene function (Fig 2, Dataset EV3A). Our transcriptome analysis of a panel of 18 Z3pr-driven alleles showed that transcript levels were reduced to 30%, on average, of their native levels in the absence of inducer (Appendix Fig EV1A, Appendix Fig S3), and similar results were found with 201 β-estradiol-control" @default.
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• W3168292662 title "A genome‐scale yeast library with inducible expression of individual genes" @default.
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