Since the spectacular demonstration of suppression of nonsense mutations and its application to T4 development (Epstein et al., 1963), means to express genes conditionally to permit observation of the phenotype have remained central to biological experimentation and discovery. During the 20th century, workhorse methods to ensure the presence or absence of gene products have included use of temperature-sensitive (ts) and cold-sensitive (cs) mutations within genes, for example to give insight into ordinality of cell biological events (Hereford and Hartwell, 1974). After the advent of recombinant DNA methods, conditional expression of genes into proteins, for example by derepression of lac promoter derivatives (Goeddel et al., 1979), also found application in biotechnology for production of therapeutics and industrial products (Sochor et al., 2015). In 2021, contemporaneous approaches to conditional expression in wide use include construction of transgenes activated by chimeric activators controlled by promoters whose expression is temporally and spatially restricted to different cell lineages (Brand and Perrimon, 1993), hundreds of approaches based on production of DNA rearrangements by phage-derived site specific recombination (Sauer, 1987), and triggered induction of engineered genes by chimeric transcription regulators with DNA-binding moieties based on derivatives of TetR from Tn10 (Gossen et al., 1995; Garí et al., 1997). Most of these approaches are all-or-none, in the sense that they are not intended to bring about expression of intermediate levels of protein; and the observations they enable are often qualitative.

But it has long been recognized that adjustment of protein dosage can provide additional insight into function that cannot be gained from all-or-none expression. For example, controlled expression of the bacteriophage λ cI and cro gene products was key to understanding how changes in the level of those proteins regulated the phage’s decision to undergo lytic or lysogenic growth (Maurer et al., 1980; Meyer et al., 1980; Meyer and Ptashne, 1980). In S. cerevisiae, contemporaneous means to tune dosage include metabolite induced promoters, such as P_GAL1, P_MET3, P_CUP1 (Maya et al., 2008), in which expression is controlled by growth media composition, and small molecule induced systems, such as the β-estradiol-induced LexA-hER-B112 system (Ottoz et al., 2014). Many of these depend on fusions between eukaryotic and viral activator domains and prokaryotic proteins (Garí et al., 1997; Ottoz et al., 2014; McIsaac et al., 2013; McIsaac et al., 2014) that bind sites on engineered promoters (Brent and Ptashne, 1985). These methods suffer from a number of drawbacks, including basal expression when not induced (Bellí et al., 1998; Garí et al., 1997; Ottoz et al., 2014), deleterious effects on cell growth due to sequestration of cellular components by the activation domain (Gill and Ptashne, 1988) induction of genes in addition to the controlled gene (McIsaac et al., 2013), and high cell-to-cell variation in expression of the controlled genes (Meurer et al., 2017; Elison et al., 2017; Ottoz et al., 2014).

These inducible systems rely on 'activation by recruitment' (Ptashne and Gann, 1997); the activator binds a site on DNA upstream of a yeast gene and recruits general transcription factors and regulators of the Pre-Initiation Complex (PIC). These assemble downstream at the 'core promoter’ and recruit RNA polymerase II to induce transcription (Hahn and Young, 2011). An alternative to inducible activation would be to engineer reversible repression of yeast transcription by prokaryotic repressors (Brent and Ptashne, 1984; Hu and Davidson, 1987; Brown et al., 1987). For TATA-containing promoters, binding of prokaryotic proteins such as LexA and the lac repressor near the TATA sequence can repress transcription (Brent and Ptashne, 1984; Murphy et al., 2007; Wedler and Wambutt, 1995), presumably by interference with the formation of the PIC, transcription initiation, or early elongation. It has long been recognized (Brent, 1985) that prokaryotic repressors likely work through different mechanisms than mechanisms used by repressors native to eukaryotes (Wang et al., 2011; Gaston and Jayaraman, 2003).

We envisioned that an ideal conditional expression system to support genetic and quantitative experimentation would: (1) function in all growth media, (2) be inducible by an exogenous small molecule with minimal other effects on the cell, (3) manifest no basal expression of the controlled gene in absence of inducer, allowing generation of null phenotypes, (4) enable a very large range of precisely adjustable expression, and (5) drive very high maximum expression, allowing generation of overexpression phenotypes. Moreover, since differences in global ability to express genes into proteins (Colman-Lerner et al., 2005) lead to differences in allelic penetrance and expressivity (Burnaevskiy et al., 2019), the ideal controller should (6) exhibit low cell-to-cell variation at any set output, facilitating detection of phenotypes that depend on thresholds of protein dosage, and other inferences of single-cell behaviors from population responses.

Here, we describe the development of a prokaryotic repressor-based transcriptional controller of gene expression, Well-tempered Controller₈₄₆ (WTC₈₄₆), that fulfils the criteria outlined above. This development had three main stages. We first engineered a powerful eukaryotic promoter that is repressed by the prokaryotic repressor TetR and induced by the chemical tetracycline and its analogue Anhydrotetracycline (aTc), to use as the promoter of the controlled gene. Next, we used instances of this promoter to construct a configuration of genetic elements that show low cell-to-cell variation in expression of the controlled gene, by creating an autorepression loop in which TetR repressed its own synthesis. Third and last, we abolished basal expression of the controlled gene in the absence of the inducer, by engineering a weakly expressed 'zeroing' repressor, a chimera between TetR and an active yeast repressor Tup1. With WTC₈₄₆, adjusting the extracellular concentration of aTc can precisely set the expression level of the controlled gene in different growth media, over time and over cell cycle stage. The gene is then 'expression clamped' with low cell-to-cell variation at a certain protein dosage, which can range from undetectable to greater abundance than wild type. We showed that strains carrying WTC₈₄₆ allelic forms of essential genes recapitulated known knockout phenotypes, and one demonstrated a novel overexpression phenotype. We constructed strains bearing WTC₈₄₆ alleles of genes involved in size control, growth rate, and cell cycle state and showed that these allowed precise experimental control of these fundamental aspects of cell physiology. We expect that WTC₈₄₆ alleles will find use in biological engineering and in discovery research, in assessment of phenotypes now incompletely penetrant due to cell-to-cell variation of the causative gene, in hypothesis directed cell biological research, and in genome-wide studies such as gene-by-gene epistasis screens.

Conditional expression of genes and observation of phenotype remain central to biological discovery. Many methods used historically, such as suppression of nonsense mutations, or conditional inactivation of temperature sensitive mutations, do not facilitate titration of graded or intermediate doses of protein. More current methods for graded expression do not allow experimenters to adjust and set protein levels and show high cell-to-cell variation of protein expression in cell populations, limiting their utility for elucidating protein-dosage-dependent phenotypes. Moreover, most such methods also have secondary consequences including slowing of cell growth. In order to overcome these limitations, we developed for use in S. cerevisiae a 'Well-tempered Controller'. This controller, WTC₈₄₆, is an autorepression-based transcriptional controller of gene expression. It can set protein levels across a large input and output dynamic range. As assessed by Citrine fluorescence readout, WTC₈₄₆ alleles display no uninduced basal expression, and uninduced WTC₈₄₆ alleles of poorly expressed proteins display complete null phenotypes. WTC₈₄₆ alleles also exhibit high maximum expression, low cell-to-cell variation, and operation in different media conditions without adverse effects on cell physiology.

The central component of WTC₈₄₆ is an engineered TATA containing promoter, P_7tet.1. We and others had shown that prokaryotic repressors including LexA (Brent and Ptashne, 1984), TetR (Murphy et al., 2007), λcI (Wedler and Wambutt, 1995) and LacI (Hu and Davidson, 1987; Figge et al., 1988) can block transcription from engineered TATA-containing eukaryotic promoters, when those promoters contain binding sites between the UAS (or, for vertebrate cells, the enhancer) and the TATA (Brent and Ptashne, 1984) or downstream of or flanking the TATA. To develop P_7tet.1, we placed seven tetO₁ TetR-binding sites in the promoter of the strongly expressed yeast gene TDH3. Two of the sites flank the TATA sequence, the other five abut binding sites for an engineered UAS that binds the transcription activators Rap1 and Gcr1. In WTC₈₄₆, one instance of P_7tet.1 drives expression of the controlled gene, while a second instance of P_7tet.1 drives expression of the TetR repressor, which thus represses its own synthesis.

We believe that repression of P_7tet.1 by TetR is due mainly to its action at the two tetO₁ sites flanking the TATA sequence, because TetR represses a precursor promoter that only carries such sites to the same extent. The mechanism(s) by which binding of repressors near the TATA might interfere with PIC formation, transcription initiation, or early elongation remain unknown, as well as why binding of larger presumably transcriptionally inert TetR fusion proteins results in stronger repression. However, examination of the Cryo-EM structure of TBP and TFIID bound to mammalian TATA promoters (Nogales et al., 2017) suggests that binding of TetR and larger derivatives of it to these sites might simply block PIC assembly. Studies of repression of native Drosophila melanogaster promoters by the en and eve homeobox proteins show that a similar, steric occlusion based mechanism can block eukaryotic transcription by binding of the repressors to sites close to the TATA sequence (Ohkuma et al., 1990; Austin and Biggin, 1995).

In WTC₈₄₆, when inducer is absent, measured basal expression of the controlled gene is abolished by a second TetR derivative, a fusion bearing an active repressor protein native to yeast. Because the same TetR-nls-Tup1 fusion protein fully represses a precursor promoter that only carries TetR operators in the UAS, we believe that the main zeroing effect of TetR-nls-Tup1 is manifested through binding the tetO₁ sites in the UAS. Native Tup1 repressor complexes with Ssn6 (also called Cyc8) to form a 420 kDa protein complex (Varanasi et al., 1996), and TetR binds DNA as a dimer. In gcr1Δ cells, in which transcription from P_TDH3 is severely diminished, the native P_TDH3 promoter has two nucleosomes positioned between the UAS and the transcription start site (Pavlović and Hörz, 1988). It is thus possible that in the UAS as many as five very large dimeric TetR-nls-Tup1 complexes block binding of Gcr1 and Rap1, mask their activating domains, or some combination of these, resulting in similar placement of two nucleosomes in P_7tet.1. One of these nucleosomes could then be positioned at the 294nt stretch between the UAS and the TATA sequence. It also seems possible that binding of the TetR-nls-Tup1 repressor might shift the position of the second nucleosome further downstream, so that it obscures the transcription start site.

Both the increased input dynamic range and the lower cell-to-cell variation in expression from WTC₈₄₆ arise from the fact that the TetR protein that represses the controlled gene also represses its own synthesis. This autorepression architecture is common in prokaryotic regulons (Smith and Magasanik, 1971; Brent and Ptashne, 1980) including Tn10, the source of the TetR gene used here, and it has been engineered into eukaryotic systems (Becskei and Serrano, 2000; Nevozhay et al., 2009). In self-repressing TetR systems, the input (here, aTc) and TetR output together function as a comparator-adjustor (Andrews et al., 2016). In such systems, aTc diffuses into the cell. Intracellular aTc concentration is limited by entry. Inside the cell, aTc and TetR^free concentrations are continuously compared by their binding interaction. If TetR^free is in excess, it represses TetR expression, and total intracellular TetR concentration is reduced by dilution, cell division, and active degradation of DNA-bound TetR until an equilibrium determined by the intracellular aTc concentration is again reached. The consequence of this autorepression is that the WTC₈₄₆ requires more aTc to reach a given level of controlled gene expression than strains in which TetR is expressed only constitutively. Autorepression flattens the dose response curve, increases the range of aTc doses where a change in promoter activity can be observed, and buffers the effects of stochastic cell-to-cell variations in TetR concentration, thereby reducing cell-to-cell variation in expression of the controlled gene throughout the input dynamic range.

We further extended the output dynamic range of WTC₈₄₆-controlled genes by developing three different Kozak sequences, K1, K2, and K3 (Li et al., 2017), to allow controlled genes to be translated at different levels. We used these sequences to construct strains bearing conditional alleles of the essential genes CDC28, IPL1, TOR2, CDC20, CDC42, PMA1, and PBR1. These strains all showed graded expression of growth and other phenotypes, from lethality at zero expression to penetrant expression of previously reported phenotypes at higher protein dosage (Mnaimneh et al., 2004; Yu et al., 2006; Dechant et al., 2014; Muñoz-Barrera and Monje-Casas, 2014). We used controlled expression in the WTC₈₄₆::CDC20 strain to bring about G2/M arrest followed by synchronous release, with low cell-to-cell variation in induction timing, demonstrating that WTC₈₄₆ can be used in experimental approaches that require dynamic control of gene expression.

Both induction and shutoff with WTC₈₄₆ are rapid, as both Citrine and Cdc20 expression occur within 30 min of induction, and shutoff of Citrine expression is observed within 60 min. However, time to steady state expression after induction, reached when degradation and dilution through cell division balance new synthesis, takes longer. Time to steady state will depend on the stability of the controlled protein. This is 6–7 hr for the stable protein Citrine, and the majority of yeast proteins have similar stability (Wiechecki et al., 2018). Those proteins with shorter half-lives will reach steady state faster. Furthermore, we showed in WTC₈₄₆::IPL1 strains that high level expression of this spindle assembly checkpoint kinase arrests cells at G2/M with 2 n or higher DNA content. This phenotype, thought to be due to disruption of kinetichore microtubule attachments, is displayed in mammalian cells when the homologous Aurora B is overexpressed (González-Loyola et al., 2015), but had not been observed previously in S. cerevisiae when Ipl1 was overexpressed from P_GAL1 (Muñoz-Barrera and Monje-Casas, 2014). We also showed that in WTC₈₄₆::WHI5 strains, different levels of controlled expression of Whi5 can constrain cell sizes within different limits.

Cell-cell variation in WTC₈₄₆-driven expression is highest at low aTc levels, because control in this regime depends mostly on the higher variation Simple Repression by TetR-Tup1 expressed from the P_RNR2. This variation at low input doses in the WTC₈₄₆represented a trade-off between the design goals of abolition of basal expression and suppression of cell-to-cell variation. The Autorepression (AR) architecture better suppressed cell-to-cell variation in controlled gene expression at low inducer inputs, but, because of the fact that TetR and the controlled gene were both under the control of the same repressible promoter, the controlled gene still showed considerable basal expression when uninduced. Given that suppression of basal expression of the controlled gene was critical to generating 'reversible null' phenotypes, we developed the AR architecture further. The resulting cAR configuration of WTC₈₄₆, had low cell-to-cell variation, equivalent to the variation at the lowest expression levels that AR could achieve. Importantly, because transcription of WTC₈₄₆-controlled genes is synchronized to that of the autorepressing TetR gene, transcription and mRNA abundance of WTC₈₄₆-controlled genes should be steady throughout the cell cycle. This autorepressing circuitry operationally defines WTC₈₄₆ as an 'expression clamp', a device for adjusting and setting gene expression at desired levels, and maintaining it with low cell-to-cell variation, and so allowing expressed protein dosage in individual cells to closely track the population average.

Taken together, our results show that WTC₈₄₆ controlled genes define a new type of conditional allele, one that allows precise control of gene dosage. We anticipate that WTC₈₄₆ alleles will find use in cell biological experimentation, for example in assessment of phenotypes now incompletely penetrant due to variable dosage of the causative gene products (Casanueva et al., 2012), and for sharpening the thresholds at which dosage dependent phenotypes manifest. We also hope that genome wide collections of WTC₈₄₆ alleles might enable genome wide gene-by-gene and gene-by-chemical epistasis for interactions that depend on gene dosage. In S. cerevisiae, recent development of strains and methods (such as the SWAP-Tag [Weill et al., 2018]) that facilitate installation of defined N and C terminal genetic elements after cycles of mating, sporulation, and selection of desired haploids should allow generation of whole genome WTC₈₄₆ strains for this purpose. Epistasis screens rely on measurement of colony size on plates or culture density in liquid media. For two proteins whose effect on growth rate was identical, a one-generation difference in achievement of steady state expression could result in a twofold difference in number of cells in a colony or well, and thus in a 1.26-fold difference in colony diameter. We therefore suggest that growth rate-based assays using WTC₈₄₆ or any other inducible system pre-induce cells several generations before plating or pinning. WTC₈₄₆ alleles may find use in engineering applications such affinity maturation of antibodies expressed by yeast surface display, where precise ability to lower surface concentration should aid selection for progressively higher affinity binders. WTC₈₄₆ can also be a useful complement to boost the efficiency of methods that act at the protein level such as induced degradation or AnchorAway techniques. Such techniques could be used in conjunction with WTC₈₄₆ to achieve rapid and sustained shutoff from a well-maintained steady state level. This would also allow fast step function decreases in abundance. For example, an experimenter might simultaneously induce depletion of the product of a controlled gene by such a method while adjusting aTc downward to rapidly reset the level of an expressed protein to a new, lower level. Implementation of the WTC₈₄₆ control logic in mammalian cells and in engineered multicellular organisms should allow similar experimentation now impossible due to cell-to-cell variation and imprecise control.

All relevant sequences are included in the supporting files for reproducibility. All raw flow cytometry data is publicly available at https://doi.org/10.3929/ethz-b-000488967. All other source data is included in the manuscript and supporting files.

1. 1. Azizoglu A

   (2021) ETH Library research collection

   A precisely adjustable, variation-suppressed eukaryotic transcriptional controller to enable genetic discovery.

   https://doi.org/10.3929/ethz-b-000488967

1. 1. Ambesi A
   2. Miranda M
   3. Petrov VV
   4. Slayman CW

   (2000) Biogenesis and function of the yeast plasma-membrane H(+)-ATPase

   Journal of Experimental Biology 203:155–160.

   https://doi.org/10.1242/jeb.203.1.155

   • PubMed
   • Google Scholar
2. 1. Andrews SS
   2. Peria WJ
   3. Yu RC
   4. Colman-Lerner A
   5. Brent R

   (2016) Push-Pull and feedback mechanisms can align signaling system outputs with inputs

   Cell Systems 3:444–455.

   https://doi.org/10.1016/j.cels.2016.10.002

   • PubMed
   • Google Scholar
3. 1. Austin RJ
   2. Biggin MD

   (1995) A domain of the even-skipped protein represses transcription by preventing TFIID binding to a promoter: repression by cooperative blocking

   Molecular and Cellular Biology 15:4683–4693.

   https://doi.org/10.1128/MCB.15.9.4683

   • PubMed
   • Google Scholar
4. 1. Bartlett K
   2. Kim K

   (2014) Insight into Tor2, a budding yeast microdomain protein

   European Journal of Cell Biology 93:87–97.

   https://doi.org/10.1016/j.ejcb.2014.01.004

   • PubMed
   • Google Scholar
5. 1. Becskei A
   2. Serrano L

   (2000) Engineering stability in gene networks by autoregulation

   Nature 405:590–593.

   https://doi.org/10.1038/35014651

   • PubMed
   • Google Scholar
6. 1. Bellí G
   2. Garí E
   3. Piedrafita L
   4. Aldea M
   5. Herrero E

   (1998) An activator/repressor dual system allows tight tetracycline-regulated gene expression in budding yeast

   Nucleic Acids Research 26:942–947.

   https://doi.org/10.1093/nar/26.4.942

   • PubMed
   • Google Scholar
7. 1. Benaglia T
   2. Chauveau D
   3. Hunter DR
   4. Young DS

   (2009) Mixtools: an R package for analyzing finite mixture models

   Journal of Statistical Software 32:1–29.

   https://doi.org/10.18637/jss.v032.i06

   • Google Scholar
8. 1. Bertram R
   2. Hillen W

   (2008) The application of tet repressor in prokaryotic gene regulation and expression

   Microbial Biotechnology 1:2–16.

   https://doi.org/10.1111/j.1751-7915.2007.00001.x

   • PubMed
   • Google Scholar
9. 1. Brand AH
   2. Perrimon N

   (1993) Targeted gene expression as a means of altering cell fates and generating dominant phenotypes

   Development 118:401–415.

   https://doi.org/10.1242/dev.118.2.401

   • PubMed
   • Google Scholar
10. 1. Brent R

    (1985) Repression of transcription in yeast

    Cell 42:3–4.

    https://doi.org/10.1016/S0092-8674(85)80091-X

    • PubMed
    • Google Scholar
11. 1. Brent R
    2. Ptashne M

    (1980) The lexA gene product represses its own promoter

    PNAS 77:1932–1936.

    https://doi.org/10.1073/pnas.77.4.1932

    • PubMed
    • Google Scholar
12. 1. Brent R
    2. Ptashne M

    (1984) A bacterial repressor protein or a yeast transcriptional terminator can block upstream activation of a yeast gene

    Nature 312:612–615.

    https://doi.org/10.1038/312612a0

    • PubMed
    • Google Scholar
13. 1. Brent R
    2. Ptashne M

    (1985) A eukaryotic transcriptional activator bearing the DNA specificity of a prokaryotic repressor

    Cell 43:729–736.

    https://doi.org/10.1016/0092-8674(85)90246-6

    • PubMed
    • Google Scholar
14. 1. Brown M
    2. Figge J
    3. Hansen U
    4. Wright C
    5. Jeang KT
    6. Khoury G
    7. Livingston DM
    8. Roberts TM

    (1987) Lac repressor can regulate expression from a hybrid SV40 early promoter containing a lac operator in animal cells

    Cell 49:603–612.

    https://doi.org/10.1016/0092-8674(87)90536-8

    • PubMed
    • Google Scholar
15. 1. Burnaevskiy N
    2. Sands B
    3. Yun S
    4. Tedesco PM
    5. Johnson TE
    6. Kaeberlein M
    7. Brent R
    8. Mendenhall A

    (2019) Chaperone biomarkers of lifespan and penetrance track the dosages of many other proteins

    Nature Communications 10:5725.

    https://doi.org/10.1038/s41467-019-13664-7

    • PubMed
    • Google Scholar
16. 1. Casanueva MO
    2. Burga A
    3. Lehner B

    (2012) Fitness trade-offs and environmentally induced mutation buffering in isogenic C. elegans

    Science 335:82–85.

    https://doi.org/10.1126/science.1213491

    • PubMed
    • Google Scholar
17. 1. Chen K
    2. Wilson MA
    3. Hirsch C
    4. Watson A
    5. Liang S
    6. Lu Y
    7. Li W
    8. Dent SY

    (2013) Stabilization of the promoter nucleosomes in nucleosome-free regions by the yeast Cyc8-Tup1 corepressor

    Genome Research 23:312–322.

    https://doi.org/10.1101/gr.141952.112

    • PubMed
    • Google Scholar
18. 1. Cid A
    2. Perona R
    3. Serrano R

    (1987) Replacement of the promoter of the yeast plasma membrane ATPase gene by a galactose-dependent promoter and its physiological consequences

    Current Genetics 12:105–110.

    https://doi.org/10.1007/BF00434664

    • PubMed
    • Google Scholar
19. 1. Colman-Lerner A
    2. Gordon A
    3. Serra E
    4. Chin T
    5. Resnekov O
    6. Endy D
    7. Pesce CG
    8. Brent R

    (2005) Regulated cell-to-cell variation in a cell-fate decision system

    Nature 437:699–706.

    https://doi.org/10.1038/nature03998

    • PubMed
    • Google Scholar
20. 1. Cookson NA
    2. Cookson SW
    3. Tsimring LS
    4. Hasty J

    (2010) Cell cycle-dependent variations in protein concentration

    Nucleic Acids Research 38:2676–2681.

    https://doi.org/10.1093/nar/gkp1069

    • PubMed
    • Google Scholar
21. 1. Cosma MP
    2. Tanaka T
    3. Nasmyth K

    (1999) Ordered recruitment of transcription and chromatin remodeling factors to a cell cycle- and developmentally regulated promoter

    Cell 97:299–311.

    https://doi.org/10.1016/S0092-8674(00)80740-0

    • PubMed
    • Google Scholar
22. 1. de Bruin RA
    2. McDonald WH
    3. Kalashnikova TI
    4. Yates J
    5. Wittenberg C

    (2004) Cln3 activates G1-specific transcription via phosphorylation of the SBF bound repressor Whi5

    Cell 117:887–898.

    https://doi.org/10.1016/j.cell.2004.05.025

    • PubMed
    • Google Scholar
23. 1. Dechant R
    2. Saad S
    3. Ibáñez AJ
    4. Peter M

    (2014) Cytosolic pH regulates cell growth through distinct GTPases, Arf1 and Gtr1, to promote ras/PKA and TORC1 activity

    Molecular Cell 55:409–421.

    https://doi.org/10.1016/j.molcel.2014.06.002

    • PubMed
    • Google Scholar
24. 1. Deuschle U
    2. Meyer WK
    3. Thiesen HJ

    (1995) Tetracycline-reversible silencing of eukaryotic promoters

    Molecular and Cellular Biology 15:1907–1914.

    https://doi.org/10.1128/MCB.15.4.1907

    • PubMed
    • Google Scholar
25. 1. Ducker CE

    (2000) The organized chromatin domain of the repressed yeast a cell-specific gene STE6 contains two molecules of the corepressor Tup1p per nucleosome

    The EMBO Journal 19:400–409.

    https://doi.org/10.1093/emboj/19.3.400

    • Google Scholar
26. 1. Elison GL
    2. Song R
    3. Acar M

    (2017) A precise genome editing method reveals insights into the activity of eukaryotic promoters

    Cell Reports 18:275–286.

    https://doi.org/10.1016/j.celrep.2016.12.014

    • PubMed
    • Google Scholar
27. Software

    1. Ellis B
    2. Haaland P
    3. Hahne F
    4. Meur NL
    5. Gopalakrishnan N

    (2009) flowCore: Basic structures for flow cytometry data, version 2.4.0

    flowCore.

    http://bioconductor.org/
28. Conference

    1. Epstein RH
    2. Bolle A
    3. Steinberg CM
    4. Kellenberger E
    5. Boy de la Tour E
    6. Chevalley R
    7. Edgar RS
    8. Susman M
    9. Denhardt GH
    10. Lielausis A

    (1963) Physiological studies of conditional lethal mutants of bacteriophage T4D

    Cold Spring Harbor Symposia on Quantitative Biology. pp. 375–394.

    https://doi.org/10.1101/SQB.1963.028.01.053

    • Google Scholar
29. 1. Ewald JC
    2. Kuehne A
    3. Zamboni N
    4. Skotheim JM

    (2016) The yeast Cyclin-Dependent kinase routes carbon fluxes to fuel cell cycle progression

    Molecular Cell 62:532–545.

    https://doi.org/10.1016/j.molcel.2016.02.017

    • PubMed
    • Google Scholar
30. 1. Figge J
    2. Wright C
    3. Collins CJ
    4. Roberts TM
    5. Livingston DM

    (1988) Stringent regulation of stably integrated chloramphenicol acetyl transferase genes by E. coli lac repressor in monkey cells

    Cell 52:713–722.

    https://doi.org/10.1016/0092-8674(88)90409-6

    • PubMed
    • Google Scholar
31. 1. Fraenkel DG

    (2003) The top genes: on the distance from transcript to function in yeast glycolysis

    Current Opinion in Microbiology 6:198–201.

    https://doi.org/10.1016/S1369-5274(03)00023-7

    • PubMed
    • Google Scholar
32. 1. Garí E
    2. Piedrafita L
    3. Aldea M
    4. Herrero E

    (1997) A set of vectors with a tetracycline-regulatable promoter system for modulated gene expression in Saccharomyces cerevisiae

    Yeast 13:837–848.

    https://doi.org/10.1002/(SICI)1097-0061(199707)13:9<837::AID-YEA145>3.0.CO;2-T

    • PubMed
    • Google Scholar
33. 1. Gaston K
    2. Jayaraman PS

    (2003) Transcriptional repression in eukaryotes: repressors and repression mechanisms

    Cellular and Molecular Life Sciences 60:721–741.

    https://doi.org/10.1007/s00018-003-2260-3

    • PubMed
    • Google Scholar
34. 1. Gibson DG
    2. Young L
    3. Chuang RY
    4. Venter JC
    5. Hutchison CA
    6. Smith HO

    (2009) Enzymatic assembly of DNA molecules up to several hundred kilobases

    Nature Methods 6:343–345.

    https://doi.org/10.1038/nmeth.1318

    • PubMed
    • Google Scholar
35. 1. Gill G
    2. Ptashne M

    (1988) Negative effect of the transcriptional activator GAL4

    Nature 334:721–724.

    https://doi.org/10.1038/334721a0

    • PubMed
    • Google Scholar
36. 1. Ginestet C

    (2011) ggplot2: elegant graphics for data analysis

    Journal of the Royal Statistical Society: Series A 174:245–246.

    https://doi.org/10.1111/j.1467-985X.2010.00676_9.x

    • Google Scholar
37. 1. Gnügge R
    2. Liphardt T
    3. Rudolf F

    (2016) A shuttle vector series for precise genetic engineering of Saccharomyces cerevisiae

    Yeast 33:83–98.

    https://doi.org/10.1002/yea.3144

    • PubMed
    • Google Scholar
38. 1. Goeddel DV
    2. Heyneker HL
    3. Hozumi T
    4. Arentzen R
    5. Itakura K
    6. Yansura DG
    7. Ross MJ
    8. Miozzari G
    9. Crea R
    10. Seeburg PH

    (1979) Direct expression in Escherichia coli of a DNA sequence coding for human growth hormone

    Nature 281:544–548.

    https://doi.org/10.1038/281544a0

    • PubMed
    • Google Scholar
39. 1. González-Loyola A
    2. Fernández-Miranda G
    3. Trakala M
    4. Partida D
    5. Samejima K
    6. Ogawa H
    7. Cañamero M
    8. de Martino A
    9. Martínez-Ramírez Á
    10. de Cárcer G
    11. Pérez de Castro I
    12. Earnshaw WC
    13. Malumbres M

    (2015) Aurora B overexpression causes aneuploidy and p21Cip1 repression during tumor development

    Molecular and Cellular Biology 35:3566–3578.

    https://doi.org/10.1128/MCB.01286-14

    • PubMed
    • Google Scholar
40. 1. Gordon A
    2. Colman-Lerner A
    3. Chin TE
    4. Benjamin KR
    5. Yu RC
    6. Brent R

    (2007) Single-cell quantification of molecules and rates using open-source microscope-based cytometry

    Nature Methods 4:175–181.

    https://doi.org/10.1038/nmeth1008

    • PubMed
    • Google Scholar
41. 1. Gossen M
    2. Freundlieb S
    3. Bender G
    4. Müller G
    5. Hillen W
    6. Bujard H

    (1995) Transcriptional activation by tetracyclines in mammalian cells

    Science 268:1766–1769.

    https://doi.org/10.1126/science.7792603

    • PubMed
    • Google Scholar
42. 1. Griesbeck O
    2. Baird GS
    3. Campbell RE
    4. Zacharias DA
    5. Tsien RY

    (2001) Reducing the environmental sensitivity of yellow fluorescent protein. mechanism and applications

    The Journal of Biological Chemistry 276:29188–29194.

    https://doi.org/10.1074/jbc.M102815200

    • PubMed
    • Google Scholar
43. 1. Hahn S
    2. Young ET

    (2011) Transcriptional regulation in Saccharomyces cerevisiae: transcription factor regulation and function, mechanisms of initiation, and roles of activators and coactivators

    Genetics 189:705–736.

    https://doi.org/10.1534/genetics.111.127019

    • PubMed
    • Google Scholar
44. 1. Hereford LM
    2. Hartwell LH

    (1974) Sequential gene function in the initiation of Saccharomyces cerevisiae DNA synthesis

    Journal of Molecular Biology 84:445–461.

    https://doi.org/10.1016/0022-2836(74)90451-3

    • PubMed
    • Google Scholar
45. 1. Ho B
    2. Baryshnikova A
    3. Brown GW

    (2018) Unification of protein abundance datasets yields a quantitative Saccharomyces cerevisiae proteome

    Cell Systems 6:192–205.

    https://doi.org/10.1016/j.cels.2017.12.004

    • PubMed
    • Google Scholar
46. 1. Hu MC
    2. Davidson N

    (1987) The inducible iac operator-repressor system is functional in mammalian cells

    Cell 48:555–566.

    https://doi.org/10.1016/0092-8674(87)90234-0

    • PubMed
    • Google Scholar
47. 1. Huie MA
    2. Scott EW
    3. Drazinic CM
    4. Lopez MC
    5. Hornstra IK
    6. Yang TP
    7. Baker HV

    (1992) Characterization of the DNA-Binding activity of GCR1 : in vivo evidence for two GCR1-binding sites in the upstream activating sequence of TPI of Saccharomyces cerevisiae

    Molecular and Cellular Biology 12:2690–2700.

    https://doi.org/10.1128/mcb.12.6.2690-2700.1992

    • PubMed
    • Google Scholar
48. 1. Hurtado-Guerrero R
    2. van Aalten DM

    (2007) Structure of Saccharomyces cerevisiae chitinase 1 and screening-based discovery of potent inhibitors

    Chemistry & Biology 14:589–599.

    https://doi.org/10.1016/j.chembiol.2007.03.015

    • PubMed
    • Google Scholar
49. 1. Janke C
    2. Magiera MM
    3. Rathfelder N
    4. Taxis C
    5. Reber S
    6. Maekawa H
    7. Moreno-Borchart A
    8. Doenges G
    9. Schwob E
    10. Schiebel E
    11. Knop M

    (2004) A versatile toolbox for PCR-based tagging of yeast genes: new fluorescent proteins, more markers and promoter substitution cassettes

    Yeast 21:947–962.

    https://doi.org/10.1002/yea.1142

    • PubMed
    • Google Scholar
50. 1. Juanes MA

    (2017) Methods of synchronization of yeast cells for the analysis of cell cycle progression

    Methods in Molecular Biology 1505:19–34.

    https://doi.org/10.1007/978-1-4939-6502-1_2

    • PubMed
    • Google Scholar
51. 1. Kafadar K
    2. Koehler JR
    3. Venables WN
    4. Ripley BD

    (1999) Modern applied statistics with S-Plus

    The American Statistician 53:86.

    https://doi.org/10.2307/2685660

    • Google Scholar
52. 1. Kuroda S
    2. Otaka S
    3. Fujisawa Y

    (1994) Fermentable and nonfermentable carbon sources sustain constitutive levels of expression of yeast triosephosphate dehydrogenase 3 gene from distinct promoter elements

    Journal of Biological Chemistry 269:6153–6162.

    https://doi.org/10.1016/S0021-9258(17)37582-8

    • PubMed
    • Google Scholar
53. 1. Li J
    2. Liang Q
    3. Song W
    4. Marchisio MA

    (2017) Nucleotides upstream of the Kozak sequence strongly influence gene expression in the yeast S. cerevisiae

    Journal of Biological Engineering 11:25.

    https://doi.org/10.1186/s13036-017-0068-1

    • Google Scholar
54. 1. Maurer R
    2. Meyer B
    3. Ptashne M

    (1980) Gene regulation at the right operator (OR) bacteriophage lambda. I. OR3 and autogenous negative control by repressor

    Journal of Molecular Biology 139:147–161.

    https://doi.org/10.1016/0022-2836(80)90302-2

    • PubMed
    • Google Scholar
55. 1. Maya D
    2. Quintero MJ
    3. de la Cruz Muñoz-Centeno M
    4. Chávez S

    (2008) Systems for applied gene control in Saccharomyces cerevisiae

    Biotechnology Letters 30:979–987.

    https://doi.org/10.1007/s10529-008-9647-z

    • PubMed
    • Google Scholar
56. 1. McIsaac RS
    2. Oakes BL
    3. Wang X
    4. Dummit KA
    5. Botstein D
    6. Noyes MB

    (2013) Synthetic gene expression perturbation systems with rapid, tunable, single-gene specificity in yeast

    Nucleic Acids Research 41:e57.

    https://doi.org/10.1093/nar/gks1313

    • PubMed
    • Google Scholar
57. 1. McIsaac RS
    2. Gibney PA
    3. Chandran SS
    4. Benjamin KR
    5. Botstein D

    (2014) Synthetic biology tools for programming gene expression without nutritional perturbations in Saccharomyces cerevisiae

    Nucleic Acids Research 42:e48.

    https://doi.org/10.1093/nar/gkt1402

    • PubMed
    • Google Scholar
58. 1. Mendenhall AR
    2. Tedesco PM
    3. Sands B
    4. Johnson TE
    5. Brent R

    (2015) Single cell quantification of reporter gene expression in live adult Caenorhabditis elegans reveals reproducible Cell-Specific expression patterns and underlying biological variation

    PLOS ONE 10:e0124289.

    https://doi.org/10.1371/journal.pone.0124289

    • PubMed
    • Google Scholar
59. 1. Mennella TA
    2. Klinkenberg LG
    3. Zitomer RS

    (2003) Recruitment of Tup1-Ssn6 by yeast hypoxic genes and chromatin-independent exclusion of TATA binding protein

    Eukaryotic Cell 2:1288–1303.

    https://doi.org/10.1128/EC.2.6.1288-1303.2003

    • PubMed
    • Google Scholar
60. 1. Metzger BP
    2. Yuan DC
    3. Gruber JD
    4. Duveau F
    5. Wittkopp PJ

    (2015) Selection on noise constrains variation in a eukaryotic promoter

    Nature 521:344–347.

    https://doi.org/10.1038/nature14244

    • PubMed
    • Google Scholar
61. 1. Meurer M
    2. Chevyreva V
    3. Cerulus B
    4. Knop M

    (2017) The regulatable MAL32 promoter in Saccharomyces cerevisiae: characteristics and tools to facilitate its use

    Yeast 34:39–49.

    https://doi.org/10.1002/yea.3214

    • PubMed
    • Google Scholar
62. 1. Meyer BJ
    2. Maurer R
    3. Ptashne M

    (1980) Gene regulation at the right operator (OR) of bacteriophage lambda. II. OR1, OR2, and OR3: their roles in mediating the effects of repressor and cro

    Journal of Molecular Biology 139:163–194.

    https://doi.org/10.1016/0022-2836(80)90303-4

    • PubMed
    • Google Scholar
63. 1. Meyer BJ
    2. Ptashne M

    (1980) Gene regulation at the right operator (OR) of bacteriophage λ

    Journal of Molecular Biology 139:195–205.

    https://doi.org/10.1016/0022-2836(80)90304-6

    • Google Scholar
64. 1. Mnaimneh S
    2. Davierwala AP
    3. Haynes J
    4. Moffat J
    5. Peng WT
    6. Zhang W
    7. Yang X
    8. Pootoolal J
    9. Chua G
    10. Lopez A
    11. Trochesset M
    12. Morse D
    13. Krogan NJ
    14. Hiley SL
    15. Li Z
    16. Morris Q
    17. Grigull J
    18. Mitsakakis N
    19. Roberts CJ
    20. Greenblatt JF
    21. Boone C
    22. Kaiser CA
    23. Andrews BJ
    24. Hughes TR

    (2004) Exploration of essential gene functions via titratable promoter alleles

    Cell 118:31–44.

    https://doi.org/10.1016/j.cell.2004.06.013

    • PubMed
    • Google Scholar
65. 1. Mogno I
    2. Vallania F
    3. Mitra RD
    4. Cohen BA

    (2010) TATA is a modular component of synthetic promoters

    Genome Research 20:1391–1397.

    https://doi.org/10.1101/gr.106732.110

    • PubMed
    • Google Scholar
66. 1. Muñoz-Barrera M
    2. Monje-Casas F

    (2014) Increased Aurora B activity causes continuous disruption of kinetochore-microtubule attachments and spindle instability

    PNAS 111:E3996–E4005.

    https://doi.org/10.1073/pnas.1408017111

    • PubMed
    • Google Scholar
67. 1. Murphy KF
    2. Balázsi G
    3. Collins JJ

    (2007) Combinatorial promoter design for engineering noisy gene expression

    PNAS 104:12726–12731.

    https://doi.org/10.1073/pnas.0608451104

    • PubMed
    • Google Scholar
68. 1. Nagai T
    2. Ibata K
    3. Park ES
    4. Kubota M
    5. Mikoshiba K
    6. Miyawaki A

    (2002) A variant of yellow fluorescent protein with fast and efficient maturation for cell-biological applications

    Nature Biotechnology 20:87–90.

    https://doi.org/10.1038/nbt0102-87

    • PubMed
    • Google Scholar
69. 1. Nevozhay D
    2. Adams RM
    3. Murphy KF
    4. Josic K
    5. Balázsi G

    (2009) Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression

    PNAS 106:5123–5128.

    https://doi.org/10.1073/pnas.0809901106

    • PubMed
    • Google Scholar
70. 1. Nogales E
    2. Louder RK
    3. He Y

    (2017) Structural insights into the eukaryotic transcription initiation machinery

    Annual Review of Biophysics 46:59–83.

    https://doi.org/10.1146/annurev-biophys-070816-033751

    • PubMed
    • Google Scholar
71. 1. Ohkuma Y
    2. Horikoshi M
    3. Roeder RG
    4. Desplan C

    (1990) Engrailed, a homeodomain protein, can repress in vitro transcription by competition with the TATA box-binding protein transcription factor IID

    PNAS 87:2289–2293.

    https://doi.org/10.1073/pnas.87.6.2289

    • PubMed
    • Google Scholar
72. 1. Ottoz DS
    2. Rudolf F
    3. Stelling J

    (2014) Inducible, tightly regulated and growth condition-independent transcription factor in Saccharomyces cerevisiae

    Nucleic Acids Research 42:e130.

    https://doi.org/10.1093/nar/gku616

    • PubMed
    • Google Scholar
73. 1. Pavlović B
    2. Hörz W

    (1988) The chromatin structure at the promoter of a glyceraldehyde phosphate dehydrogenase gene from Saccharomyces cerevisiae reflects its functional state

    Molecular and Cellular Biology 8:5513–5520.

    https://doi.org/10.1128/MCB.8.12.5513

    • PubMed
    • Google Scholar
74. 1. Pesce CG
    2. Zdraljevic S
    3. Peria WJ
    4. Bush A
    5. Repetto MV
    6. Rockwell D
    7. Yu RC
    8. Colman-Lerner A
    9. Brent R

    (2018) Single-cell profiling screen identifies microtubule-dependent reduction of variability in signaling

    Molecular Systems Biology 14:e7390.

    https://doi.org/10.15252/msb.20167390

    • PubMed
    • Google Scholar
75. 1. Pesin JA
    2. Orr-Weaver TL

    (2008) Regulation of APC/C activators in mitosis and meiosis

    Annual Review of Cell and Developmental Biology 24:475–499.

    https://doi.org/10.1146/annurev.cellbio.041408.115949

    • PubMed
    • Google Scholar
76. 1. Ptashne M
    2. Gann A

    (1997) Transcriptional activation by recruitment

    Nature 386:569–577.

    https://doi.org/10.1038/386569a0

    • PubMed
    • Google Scholar
77. 1. Qu Y
    2. Jiang J
    3. Liu X
    4. Wei P
    5. Yang X
    6. Tang C

    (2019) Cell cycle inhibitor Whi5 records environmental information to coordinate growth and division in yeast

    Cell Reports 29:987–994.

    https://doi.org/10.1016/j.celrep.2019.09.030

    • PubMed
    • Google Scholar
78. Software

    1. R Development Core Team

    (2013) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org/
79. 1. Ritz C
    2. Baty F
    3. Streibig JC
    4. Gerhard D

    (2015) Dose-Response analysis using R

    PLOS ONE 10:e0146021.

    https://doi.org/10.1371/journal.pone.0146021

    • PubMed
    • Google Scholar
80. 1. Sauer B

    (1987) Functional expression of the cre-lox site-specific recombination system in the yeast Saccharomyces cerevisiae

    Molecular and Cellular Biology 7:2087–2096.

    https://doi.org/10.1128/MCB.7.6.2087

    • PubMed
    • Google Scholar
81. 1. Schindelin J
    2. Arganda-Carreras I
    3. Frise E
    4. Kaynig V
    5. Longair M
    6. Pietzsch T
    7. Preibisch S
    8. Rueden C
    9. Saalfeld S
    10. Schmid B
    11. Tinevez JY
    12. White DJ
    13. Hartenstein V
    14. Eliceiri K
    15. Tomancak P
    16. Cardona A

    (2012) Fiji: an open-source platform for biological-image analysis

    Nature Methods 9:676–682.

    https://doi.org/10.1038/nmeth.2019

    • PubMed
    • Google Scholar
82. 1. Schmoller KM
    2. Turner JJ
    3. Kõivomägi M
    4. Skotheim JM

    (2015) Dilution of the cell cycle inhibitor Whi5 controls budding-yeast cell size

    Nature 526:268–272.

    https://doi.org/10.1038/nature14908

    • PubMed
    • Google Scholar
83. 1. Shirayama M
    2. Zachariae W
    3. Ciosk R
    4. Nasmyth K

    (1998) The Polo-like kinase Cdc5p and the WD-repeat protein Cdc20p/fizzy are regulators and substrates of the anaphase promoting complex in Saccharomyces cerevisiae

    The EMBO Journal 17:1336–1349.

    https://doi.org/10.1093/emboj/17.5.1336

    • PubMed
    • Google Scholar
84. 1. Smith GR
    2. Magasanik B

    (1971) Nature and self-regulated synthesis of the repressor of the hut operons in Salmonella typhimurium

    PNAS 68:1493–1497.

    https://doi.org/10.1073/pnas.68.7.1493

    • PubMed
    • Google Scholar
85. 1. Sochor MA
    2. Vasireddy V
    3. Drivas TG
    4. Wojno A
    5. Doung T
    6. Shpylchak I
    7. Bennicelli J
    8. Chung D
    9. Bennett J
    10. Lewis M

    (2015) An autogenously regulated expression system for gene therapeutic ocular applications

    Scientific Reports 5:17105.

    https://doi.org/10.1038/srep17105

    • PubMed
    • Google Scholar
86. 1. Taberner FJ
    2. Quilis I
    3. Igual JC

    (2009) Spatial regulation of the start repressor Whi5

    Cell Cycle 8:3013–3022.

    https://doi.org/10.4161/cc.8.18.9621

    • Google Scholar
87. 1. Tavormina PA
    2. Burke DJ

    (1998) Cell cycle arrest in cdc20 mutants of Saccharomyces cerevisiae is independent of Ndc10p and kinetochore function but requires a subset of spindle checkpoint genes

    Genetics 148:1701–1713.

    https://doi.org/10.1093/genetics/148.4.1701

    • PubMed
    • Google Scholar
88. 1. Tzamarias D
    2. Struhl K

    (1994) Functional dissection of the yeast Cyc8-Tupl transcriptional co-repressor complex

    Nature 369:758–761.

    https://doi.org/10.1038/369758a0

    • PubMed
    • Google Scholar
89. 1. Varanasi US
    2. Klis M
    3. Mikesell PB
    4. Trumbly RJ

    (1996) The Cyc8 (Ssn6)-Tup1 corepressor complex is composed of one Cyc8 and four Tup1 subunits

    Molecular and Cellular Biology 16:6707–6714.

    https://doi.org/10.1128/MCB.16.12.6707

    • PubMed
    • Google Scholar
90. 1. Wang X
    2. Bai L
    3. Bryant GO
    4. Ptashne M

    (2011) Nucleosomes and the accessibility problem

    Trends in Genetics 27:487–492.

    https://doi.org/10.1016/j.tig.2011.09.001

    • PubMed
    • Google Scholar
91. 1. Wedler H
    2. Wambutt R

    (1995) A temperature-sensitive lambda cI repressor functions on a modified operator in yeast cells by masking the TATA element

    Molecular and General Genetics MGG 248:499–505.

    https://doi.org/10.1007/BF02191651

    • PubMed
    • Google Scholar
92. 1. Weill U
    2. Yofe I
    3. Sass E
    4. Stynen B
    5. Davidi D
    6. Natarajan J
    7. Ben-Menachem R
    8. Avihou Z
    9. Goldman O
    10. Harpaz N
    11. Chuartzman S
    12. Kniazev K
    13. Knoblach B
    14. Laborenz J
    15. Boos F
    16. Kowarzyk J
    17. Ben-Dor S
    18. Zalckvar E
    19. Herrmann JM
    20. Rachubinski RA
    21. Pines O
    22. Rapaport D
    23. Michnick SW
    24. Levy ED
    25. Schuldiner M

    (2018) Genome-wide SWAp-Tag yeast libraries for proteome exploration

    Nature Methods 15:617–622.

    https://doi.org/10.1038/s41592-018-0044-9

    • PubMed
    • Google Scholar
93. Software

    1. Wickham H
    2. Romain F
    3. Henry L
    4. Müller K
    5. RStudio

    (2018) dplyr: A Grammar of Data Manipulation, version 0.8.0.1

    Dplyr.

    https://dplyr.tidyverse.org
94. 1. Wiechecki K
    2. Manohar S
    3. Silva G
    4. Tchourine K
    5. Jacob S
    6. Valleriani A
    7. Vogel C

    (2018) Integrative Meta-Analysis reveals that most yeast proteins are very stable

    SSRN Electronic Journal 234:36.

    https://doi.org/10.2139/ssrn.3155916

    • Google Scholar
95. 1. Wong KH
    2. Struhl K

    (2011) The Cyc8-Tup1 complex inhibits transcription primarily by masking the activation domain of the recruiting protein

    Genes & Development 25:2525–2539.

    https://doi.org/10.1101/gad.179275.111

    • PubMed
    • Google Scholar
96. 1. Yagi S
    2. Yagi K
    3. Fukuoka J
    4. Suzuki M

    (1994) The UAS of the yeast GAPDH promoter consists of multiple general functional elements including RAP1 and GRF2 binding sites

    Journal of Veterinary Medical Science 56:235–244.

    https://doi.org/10.1292/jvms.56.235

    • Google Scholar
97. 1. Yu L
    2. Peña Castillo L
    3. Mnaimneh S
    4. Hughes TR
    5. Brown GW

    (2006) A survey of essential gene function in the yeast cell division cycle

    Molecular Biology of the Cell 17:4736–4747.

    https://doi.org/10.1091/mbc.e06-04-0368

    • PubMed
    • Google Scholar
98. 1. Zhang Z
    2. Reese JC

    (2004) Redundant mechanisms are used by Ssn6-Tup1 in repressing chromosomal gene transcription in Saccharomyces cerevisiae

    Journal of Biological Chemistry 279:39240–39250.

    https://doi.org/10.1074/jbc.M407159200

    • PubMed
    • Google Scholar

[Editors' note: this paper was reviewed by Review Commons.]

Summary review comments and responses

Both reviewers were obviously expert and their comments positive and constructive. Taking them together, the major comments we received on our manuscript fall into three different categories.

1. Need for clarification, either in terms of wording (Reviewer 1, #1) or more detailed explanation of the design process (R1, #2)

2. Requests for us to further elaborate on our results and their implications (R1, #3,4,6,7,9,10,11)

3. Requests for additional characterization of the system (R1, #5,8). In this category there was also one minor comment from Reviewer 2 that requested additional controls.

Actions we have taken.

We have addressed all major and minor comments and performed additional experiments where necessary.

1. We adjusted the wording as suggested and added text to clarify the design process.

2. We responded to each comment in this category in detail.

a. R1#3, we added text and a simple computational model for clarification.

b. R1#4,7,9,10,11 we added text to further elaborate on the points raised.

c. R1#6, we added text and performed an additional experiment to further elaborate on our results.

3. We performed all the requested additional characterization and control experiments.

a. R1#5, we performed two additional experiments to further characterize the shut-off speed of the system.

b. R1#8, as requested, we characterized two other transcriptional control systems and compared them to our system in terms of cell-to-cell variation.

c. R2#m1, we performed the control experiment requested.

In addition, we addressed all minor comments as suggested by the reviewers.

Detailed comments and responses

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary

Azizoglu et al. describe a novel system for highly regulated, conditional expression of any gene in budding yeast, with the goal of facilitating quantitative functional genetic analysis. The authors aimed to meet 6 criteria in their system: (1) operation in all growth media; (2) regulation by an exogenous small molecule with minimal effect on cell function; (3) zero basal expression to obtain null phenotypes; (4) large range of precisely adjustable expression; (5) very high maximal expression; and (6) low cell-to-cell variability at any output level. The data shown indicate that they have more or less achieved these aims and that the system they describe is an advance on current methods. The logic behind the development of the system is very clearly presented, as well as its relationship to previous work. In addition, the authors place their work in a very broad historical perspective, which is unusual but much appreciated by this reviewer. Finally, they complement their description of the system (which employs a fluorescence read-out) with several case studies on a variety of different yeast genes that demonstrate its utility and highlights its advantages relative to the current state-of-the-art methods. This study thus provides an important new tool for quantitative analysis of the effect of expression level on phenotype that should be of wide interest in the yeast genetics community as a discovery tool.

We appreciate this generous summary.

We note that the approach to setpoint gene regulation is general beyond yeast. We expect it to be applied to vertebrate systems and we expect it to be applied to plants. We also note that, although its primary application is genetic discovery, it is finding use in some biotechnology applications, for example for tuning the expression of genes in multistep biochemical pathways to find a level that results in maximum synthesis of an end product.

Primary comments and suggestions

1. The phrase "Expression of genes conditionally into phenotype…", used in the Abstract, and in other guises elsewhere in the text, is awkward and confusing. A more comprehensible alternative could be: "The relationship between gene expression level and phenotype remains central to biological research. Current methods enable either on/off or imprecisely controlled graded expression…"

We thank the reviewer. In response, we changed the wording.

New abstract now reads "Conditional expression of genes and observation of phenotype remain central to biological discovery".

The variant of the awkward phrase occurs in one other place, in the first sentence of the introduction. In response to the reviewer, we changed the revised text to read

“… means to express genes conditionally to permit observation of the phenotype have remained.…”

Old text read:

“… means to express genes conditionally into phenotype have remained.…”

2. It seems to me that the AR strain (Figure 2A, and Supplementary Figure 4) displayed less cellto-cell variation than the cAR variant. Why was this strain not pursued further as an alternative for cases in which this feature might be more desirable?

We thank the reviewer for this question. We should have been clearer.

Yes, the AR strain displays lower cell-to-cell variation compared to the cAR strain in Figure 2A, especially at lower doses of aTc. But this was not true for the final cAR constructions that comprise WTC₈₄₆ (compare Figure 4 and the AR strain in Figure 2). When comparing the AR strain in Figure 2 and the final WTC₈₄₆ strain in Figure 4, at lowest expression levels, cell-to-cell variation is almost the same.

There are two differences between the cAR strain presented in Figure

2 and the final WTC₈₄₆ strain: the former has TetR instead of TetRTup1 as the constitutively expressed element of the architecture, and more importantly, the promoter for this constitutively expressed repressor in the original cAR strain (P_ACT1) is much stronger than in the final WTC₈₄₆ strain (P_RNR2). As a consequence, the starting cAR strain constitutively expressed a much larger amount of repressor, which was not subject to the cell-to-cell variation reducing effects of the negative feedback loop. The lower constitutive expression of repressor in the WTC₈₄₆ strain means that a greater proportion of the cellular complement of repressor is autorepressed, and variation is suppressed.

The answer to why we pursued the cAR architecture after the experiments in Figure 2 lies in the initial criteria we had set for our ideal transcriptional controller. These are enumerated in the Introduction section. The two relevant criteria read: "(3) manifest no basal expression of the controlled gene in absence of inducer, allowing generation of null phenotypes” and "(6) exhibit low cell-to-cell variability at any set output…” In the case of our AR strain, we reasoned we could never fulfil criterion (3), as long as both the repressor and the controlled gene were expressed from the same TetR repressible promoter. If we wished the gene of interest to have no basal expression, then by definition TetR should have no basal expression, which would then mean there is no TetR to repress the promoters.

The cAR strain therefore embodies a trade-off between being able to supress basal expression and allowing higher cell-to-cell variation within a small portion of the dynamic range of the controller. We went with this trade-off because we judged that the ability to zero out basal expression was important for generating “reversible knockout” phenotypes, and perhaps in some synthetic biology applications where complete absence of a protein might be required for proper function of an engineered synthetic genetic circuit or to keep a kill switch off. Additionally, we went with the trade off because the means by which we would achieve it, the "zeroing repressor" concept, would be applicable to other WTC₈₄₆-like controlled systems. As a backup, we made parts available via Addgene that enable construction of strains with the other architecture, AR, by one step cloning.

In response to the reviewer comments, we made the following changes.

Last paragraph of the Results section titled “Complex autorepressing (cAR) controller architecture expands the input dynamic range and reduces cell-to-cell variation”:

Previously the text read:

“Compared with cells bearing the SR architecture, otherwise-isogenic cells bearing the AR architecture showed increased basal expression (6.3 vs. 4.1fold over autouorescence background). […] We therefore picked this cAR architecture for our controller.”

The text in the Results section now reads:

“Compared with cells bearing the SR architecture, otherwise-isogenic cells bearing the AR architecture showed increased basal expression (6.3 vs. 4.1fold over autofluorescence background). […] We therefore picked this cAR architecture for our controller.”

We also made the following addition to the Discussion:

“Cell-cell variation in WTC₈₄₆-driven expression is highest at low aTc levels, because control in this regime depends mostly on the higher variability Simple Repression by TetR-Tup1 expressed from P_RNR2. […] This autorepressing circuitry operationally defines WTC₈₄₆ as an ”expression clamp”, a device for adjusting and setting gene expression at desired levels and maintaining it with low cell-to-cell variation, and so allowing expressed protein dosage in individual cells to closely track the population average.”

We also made the following addition to the legend in the Plasmids table (Supplementary Table 2), to make it clear that we have provided information and materials sufficient to make strains with the other architectures used in this study.

“Plasmids used in this study. * indicates plasmids available through Addgene. These plasmids are sufficient to allow construction of WTC₈₄₆ strains carrying the cAR architecture without any further construction. […] Construction of SR strains would require deletion of the negative feedback-controlled TetR from (P2365/2370/2371/2372/2374), and construction of AR strains would require deletion of the constitutively expressed TetR-Tup1 from the same plasmids.”

3. Induction with the WTC₈₄₆ system using aTc is a relatively slow process, at least judged by the Citrine read-out (Figure 4B), taking place over >360 minutes (>3 cell divisions in rich medium). The authors should discuss why this is so, with reference to known transcriptional and/or translational kinetics in this complex feedback system.

We thank the reviewer for directing us to address induction or induction kinetics. We had not done so previously, partly because the story is complicated, and partly because what we knew was probative, not dispositive.

We need to distinguish between speed of induction and time to steady state. Reviewer notes that it takes >3 cell divisions to reach steady state after induction. In WTC₈₄₆ strains, induction, judged by Citrine fluorescence signal (Figure 4B) is detected within 30 minutes after aTc addition, and the release after Cdc20 depletion (Figure 5B) occurs within 35 minutes after aTc addition. Given that induction is rather fast, why do WTC₈₄₆ strains require 6 hours to reach steady state? Expression of the controlled gene is synchronized to expression of the autorepressing TetR. And yet, we know that the long time to steady state is not a consequence of the autorepressing feedback, as we observe that Simple Repression (SR) configurations also require a similar length of time (6 hours) to reach steady state (See Supplementary Figure 6). This time to steady-state derepressed expression is also comparable to that for a previously described TetR based system, which depends on galactose for activation, which required around 400 minutes to reach steady state (doi: 10.1038/nature01546). This time to steady state is still short compared to activation-based systems like the β-estradiol dependent system previously constructed in our lab (doi: 10.1093/nar/gku616).

Our explanation for the long time to equilibrium is that steady state concentration of the controlled protein (Citrine) is reached when degradation of existing protein and its dilution by partition into daughter cells, balances new synthesis. Therefore, the time to steady state will depend on the stability of the controlled protein. If the half-life is longer than doubling time, steady state will be reached in 6-7 hours. If the protein has a half-life shorter than doubling time, steady state will be reached faster. Citrine is long lived and is lost mainly due to dilution by cell division (the longevity of Citrine is shown in our Supplementary Figure S25). Estimates vary as to the stability of the yeast proteome. A recent meta-analysis of 4 different datasets measuring half-lives suggests most proteins (between 50% and 85%) are just as stable and are lost mainly due to dilution by cell division (http://dx.doi.org/10.2139/ssrn.3155916).

Equation 1 is a simple ODE model where a protein is produced at constant rate a and lost with a lumped linear rate (dilution + degradation) d. The analytical solution (Equation 2) of this model shows that the only constant that affects the time variable is the degradation + dilution rate. Dependence on this variable is also evident in the simulations we provide in Appendix 4—figure 1, based on this model. The smaller d is, (i.e., the more stable the protein is), the longer it takes to reach steady state. On the other hand, changes in the production rate a have no effect on time to steady state, although both rates affect the maximum level as one would expect.

In order to make the distinction between induction and steady state kinetics clearer, we made the following changes in the first paragraph of the 4^th subsection of results:

Old text read:

“After induction, signal appeared within 30 minutes and reached steady state within 7 hours.”

New text reads:

“After induction, signal appeared within 30 minutes. Time to reach steady state, which will be shorter for proteins that degrade more quickly (see Appendix 4), was 7 hours for the stable protein Citrine.”

We also added the simulations mentioned above as Appendix 4—figure 1, and we refer to it as seen below:

“Time to steady state depends on the stability of the controlled protein. In the experiments in Figure 4, WTC₈₄₆-controlled Citrine takes around 7 hours to reach steady state concentration. […] Therefore most WTC₈₄₆-controlled endogenous proteins will likely have a time to steady state around 7 hours, except those with a shorter half-life which will exhibit a shorter time to steady state.

$\frac{d[P]}{\mathrm{\text{dt}}}=a-d•P$ 𝑃 (1)

${\displaystyle \left[P\right]=\frac{a}{d}(1-e^{-dt})}$ (2)”

We also made the following addition to the 7^th paragraph of the Discussion:

“Both induction and shutoff with WTC₈₄₆ are rapid, as both Citrine and

Cdc20 expression occur within 30 minutes of induction, and shutoff of Citrine expression is observed within 60 minutes. […] Those proteins with shorter halflives will reach steady state faster.”

4. They should also discuss the potential implications of this for screens based upon plate measurements of single-colony growth rates or growth of robotically "pinned" cell cultures. They show a limited number of growth ("spot") assays on solid media that would appear to indicate abrupt cut-offs for colony formation, though the data show only one time point in the growth assay and might obscure growth rate differences.

First, viability experiments. Our plate-based assays using WTC₈₄₆, were not meant to measure growth rates but only the ability of single cells to divide and form colonies (a measure of cell viability) at a terminal time point.

The revised results text in the 3^rd paragraph of the last Results section now reads:

“We spotted serial dilutions of cultures of the final seven strains on YPD, YPE, SD, S Glycerol and SD Proline plates, with and without inducer, and assessed the strains' ability to grow into visible colonies at a single time point, at which cells of the parent strain formed colonies in all serially diluted spots(24h for YPD and SD, 42h for others.).”

But this is not what the reviewer asks. We would like to see WTC₈₄₆ used in plate based and robotically pinned growth dependent screens. We know there are differences in time to approach steady state expression for different strains expressing different proteins from WTC₈₄₆ – or by any other inducible system. These differences will lead to different effects on growth rate for the first doublings on solid medium or liquid culture. This fact admits to an obvious workaround, which is to suggest that experimenters pre-induce the strains before plating cells or pinning them robotically into wells on a dish.

In response, we added wording to the last paragraph of the Discussion section about the suitability of WTC₈₄₆ for pinned growth rate experiments. The text now reads:

“Taken together, our results show that WTC₈₄₆ controlled genes define a new type of conditional allele, one that allows precise control of gene dosage.[…] WTC₈₄₆- alleles may find use in engineering applications, for example in directed evolution of higher affinity antibodies expressed in yeast surface display, where precise ability to lower surface concentration should aid selection of….”

5. The authors should examine shut-off kinetics, ideally by as direct a measure of RNA Pol II initiation as possible. This could be of interest in exploring the function of essential genes, for example.

Yes. The reason we were reluctant to explore shutoff kinetics is that it is another complex story. In WTC₈₄₆, elimination of protein from the cell (which is what we care about for assessment of phenotype) depends both on shutoff of new expression and on perdurance.

We expected (and expect) that the speed of shutoff of WTC₈₄₆ controlled genes would be affected primarily by the rates of 3 reversible processes: aTc diffusion out of the cell, TetR binding to its operators, and sequestration of free aTc by newly synthesized TetR. These processes are fast: TetR binding and unbinding to DNA is fast (all binding sites were occupied within ~5 minutes of addition of doxycycline to a reverse TetR system in mammalian cells) (doi: 10.1038/ncomms8357 (2015)). Moreover, in the same experiment, reverse TetR started associating with specific sites seconds after addition of doxycycline, indicating that, at least in mammalian cells inducer diffusion and inducer-TetR binding are also rapid. For this reason (although it is possible that diffusion out of yeast cells might take slightly longer due to the cell wall), we expect that shutoff of WTC₈₄₆-controlled phenotypes will be limited by perdurance, governed by the degradation kinetics of the mRNA and the encoded protein. Given the rapidity of induction (again, fluorescence signal after 30 minutes, Figure 4B, release from CDC20 arrest within 35 minutes, Figure 5E), we reasoned that the same reversible phenomena that govern WTC₈₄₆ induction (namely aTc diffusion, TetR-DNA interactions, and sequestration by newly synthesized TetR) will govern shutoff, so that shutoff of the WTC₈₄₆ expressed phenotype will be dominated by perdurance of the controlled gene products. The consequence would be that for controlled genes (as in Figure 5E with CDC20), the predominant factor in achieving zero protein and observing the associated phenotype in the cell upon aTc removal will be the stability of the mRNA and protein of interest.

Given the relative complexity of this explanation, in response to this reviewer we performed additional direct experiments to (a) determine how quickly WTC₈₄₆ stopped producing the protein of interest upon aTc removal and (b) confirm that perdurance is the predominant factor in how quickly the desired loss of function is seen.

For (a) we grew a strain in which Citrine expression was under WTC₈₄₆ control to exponential phase and then induced with a high concentration (600ng/mL) of aTc. We then measured fluorescence signal every 30 minutes in flow cytometry. Additionally, after 30, 90, 150, and 210 minutes, we removed, washed, and resuspended a sample from the culture in (a) medium without aTc (to shut off WTC₈₄₆) and (b) without aTc but with cyclohexamide (to shut off both WTC₈₄₆ and new protein synthesis). After shutoff, we expected to see an initial increase in signal, followed by decline from this peak. The initial increase in fluorescence after shutoff would be due to 3 factors: the time it took for WTC₈₄₆ to stop producing new transcripts, the time it took for the existing mRNA to be degraded, and continued fluorophore formation by already-synthesized but immature Citrine proteins. Whatever increase in fluorescence we observed above that found in the cycloheximide sample would be due to the other 2 factors outlined above, namely WTC₈₄₆ shutoff speed and mRNA degradation speed.

Our results are presented as a supplementary figure in the revised submission. Shutoff of WTC₈₄₆ directed expression is rapid, as we see stabilization of fluorescence within 60 minutes. And assuming a ~30 minute maturation time for Citrine (since its parent EYFP matures in ~30 minutes after denaturation (https://doi.org/10.1038/nbt0102-87)), WTC₈₄₆ shutoff likely occurs within the first 30 minutes. These results also show that a stable protein like Citrine takes a long time to be eliminated from the cells, given that its level is halved every 90-120 minutes, consistent with dilution through cell division.

In (b), we revisited cell cycle arrest and release by Cdc20. In the original experiment in Figure 5E, arrest of the WTC₈₄₆::CDC20 strain took 8 hours (See Materials and Methods). Given that CDC20 is a very unstable protein, we attributed this long time to arrest to the high aTc concentration in which we precultured the cells (20ng/mL), which likely brought about high pre-arrest concentrations of Cdc20 that took a long time to become depleted. To confirm this hypothesis, we precultured the WTC₈₄₆::CDC20 strain at a much lower aTc concentration (3ng/mL) and then placed cells in medium without aTc. At this lower concentration, arrest began at 1.5 hours after removal of aTc, consistent with the shutoff time of WTC₈₄₆ established above, and between 3-4 hours all cells had arrested. The large difference between the arrest times of cells grown with 3 and 20ng/mL aTc, as well as the short shutoff time of WTC₈₄₆ [WTC_{846 –} Citrine expression] presented above, support the notion that perdurance of protein of interest will in most cases dominate the time to complete “phenotypic” shutoff, i.e. the time needed to see the zero expression "null" phenotype.

We’ve included both experiments as supplementary figures (also presented here) and added the following text to the manuscript.

As the second paragraph of the Results section titled “WTC₈₄₆ fulfils the criteria of an ideal transcriptional controller” we added:

“To better characterize the system, we also measured the shutoff speed of WTC₈₄₆ driven Citrine expression. […] Overall, we conclude that WTC₈₄₆ shutoff is rapid, but the time required to see the phenotypic effects of the absence of the controlled gene product will primarily depend on the stability of the mRNA and expressed protein.”

And we made the following changes in the last paragraph of the Results section titled “WTC₈₄₆ allows precise control over protein dosage and cellular physiology”.

“Finally, we tested the ability of WTC₈₄₆ to exert dynamic control of gene expression by constructing a WTC₈₄₆-K3::CDC20 strain (Y2837) and using this allele to synchronize cells in batch culture by setting Cdc20 expression to zero and then restoring it. […] Given this, and the rapid shutoff kinetics of WTC₈₄₆ presented in Figure 4—figure supplement 8, we conclude that the shutoff dynamics of WTC₈₄₆ controlled phenotypes depend mostly on the speed of degradation of the controlled protein.”

We also added a short comment on the speed of shutoff within the third to last paragraph of the Discussion section:

“Both induction and shutoff with WTC₈₄₆ are rapid, as both Citrine and Cdc20 expression occur within 30 minutes of induction, and shutoff of Citrine expression is observed within 60 minutes.”

We also adjusted our Materials and methods to explain these additional experiments.

6. The expected results for the Whi5 experiment and how they might differ from the actual findings (Figure 5D, pg. 10) are not clear and should be developed further, perhaps in the Results section itself. As the authors point out: "Whi5 controls cell volume by a complex mechanism", but they don't explain what I believe is a key feature, namely that its translation does not scale with cell volume (according to the Skotheim lab), which is an unusual feature. It would be interesting to examine WTC₈₄₆ regulation of other genes implicated in cell size control.

We thank the reviewer for the suggested clarification. We agree that the decoupling of the amount of translation from cell volume is a key feature of Whi5 control. We changed the results (6^th paragraph of the last Results section). Revised text now reads.

“Whi5 controls cell volume by a complex mechanism and unlike most other proteins its abundance does not scale with cell volume. […] To test whether we could control cell volume by controlling Whi5, we constructed haploid and diploid WTC₈₄₆::whi5 strains (Y2791, Y2929)…”

In terms of our results, we believe that while we alter the cell cycle stage at which Whi5 mRNA is expressed (by making it expressed continually), the nuclear import/export regulation of Whi5 remains intact. The continued import/ export regulation allows cell size regulation by Whi5 to remain functional, and so that cell volume simply scales with the amount of Whi5 expression we induce via WTC_846. We clarified this point by adding the text below to the Results:

“Here, we constructed haploid and diploid WTC₈₄₆::WHI5 strains (Y2791, Y2929). […] We expected that the volume of these cells should scale with the concentration of the aTc inducer.”

While we observe that the cell size scaled with aTc concentration, we also found that overexpression of Whi5 in the haploid strain led to increased cell-to-cell variation in cell volume. In order to develop this aspect of the results further, we analysed DNA content of haploid cells grown in presence of high aTc and noticed an increase in the number of cells that had >2n DNA content. We believe this indicates a misregulation of the normal progression of the cell cycle, leading to endoreplication, which could then lead to the observed increase in cell-to-cell variation. We show this experiment in Figure 5—figure supplement 5. We also made the following addition to the Whi5 paragraph of the Results (6^th paragraph of the subsection titled “WTC₈₄₆ alleles allow precise control over protein dosage and cellular physiology”):

“Both diploid and haploid cells (especially haploids) expressing high levels of Whi5 showed increased variation in volume. […] We therefore believe that overexpression of Whi5 leads to endoreplication, and the increased variation in volume at high aTc concentrations in the haploid strain originates from these endoreplicated cells.”

It will be interesting to examine WTC₈₄₆ regulation of other cell size control genes, and we are attempting to recruit a student to address this work.

New figure legend reads:

“(C) DNA content of the haploid WTC_846-K1::WHI5 strain cells (Y2791, red) at mid-exponential phase in S Ethanol media, grown with 4 different aTc concentrations as marked on top of each plot. […] Without Whi5 there was a reduction of G1 cells with 1n DNA content in the Y2791 strain, and upon overexpression of Whi5 aneuploid cells with >2n DNA content are observed.”

7. In Figure 5E, it would be great to show, or at least explain clearly in the text, how the WTC₈₄₆::CDC20 strain compares to the standard cdc20-ts allele used in the field.

We did so. We added the following text together with the relevant references to our Results section to compare our Cdc20 allele to already existing ones:

“When compared to published data, arrest at G2/M using the WTC₈₄₆::CDC20 strain is more penetrant than that obtained using temperature sensitive (~25% unbudded cells) and transcriptionally controlled (~10% unbudded) alleles of cdc20. Release is at least just as fast as that observed for the temperature sensitive (~35 minutes) and the transcriptionally controlled allele (~40 minutes).”

8. In Discussion (pg. 10, bottom right) the authors need to show clearly that WTC₈₄₆ actually gives much lower cell-to-cell variation than all other current methods (at least where this has been measured).

We've done the best we can. When evaluating gene expression tools, cell-to- cell variation has not been routinely measured. This is possibly because all systems thus far have relied on a simple repression or activation configuration, and cell-to-cell variation has been quite high. Moreover, the best means to quantify the different contributions to cell-to-cell variation in expression require microscopic quantification of expression of two reporters in sets of hundreds of single cells tracked over time (Colman-Lerner et al. 2005) and even the best flow cytometric methods for achieving variation (Pesce 2018) involve dual reporter strains rather than the single reporter strains used here.

To address this point, we have included (in references 13, 19, 20), relevant instances in which variation in single reporter strains can be assessed. In these, cell-to-cell variation was not explicitly quantified, but induction resulted in highly variable (even bimodal) distributions of expression. One of those references is to a transcriptional controller induced by β-estradiol (LexA-hER-B112) we published previously. In response to the reviewer's direction, we quantified cell-to-cell variation in gene expression by this controller compared to expression by WTC₈₄₆. The β-estradiol induced system had high cell to cell variation (>0.4) throughout its entire dynamic range, whereas WTC₈₄₆ shows high variation only at a small portion of its dynamic range at low expression levels. Additionally, we constructed a strain where the commonly used, galactose inducible P_GAL1 promoter directed Citrine expression. We quantified cell-tocell variation in a dose response to galactose, where upon induction variability was always very high (>0.6). We included these comparisons as a supplementary figure.

We also included the following text in our Results section (last paragraph of the section titled “WTC₈₄₆ fulfills the criteria of an ideal transcriptional controller”):

“We quantified the cell-to-cell variability in Citrine expression using the single reporter (ViV) measure for the WTC₈₄₆::citrine strain (Y2759) grown in YPD, and compared it to variation in a β-estradiol (LexA-hER-B112) activation based transcriptional control system we previously described, and the commonly used galactose activated P_GAL1 promoter(Figure 4D, Figure 4—figure supplements 4, 6 and 9).”

9. In the Discussion (pg. 11 top right) the authors state that "WTC₈₄₆ alleles display no uninduced basal expression" (and earlier in Results they repeated speak of "abolished" basal expression). I think that it would be more correct to point out, at least in the

Discussion, that WTC₈₄₆ alleles display no uninduced basal expression as measured here, by the Citrine fluorescent read-out. Producing null phenotypes in cases where this was not possible with previous systems is a valuable demonstration of the utility of the new method, but not a demonstration of complete repression. The authors might try to measure mRNA steady-state levels by RT-qPCR to estimate copy number per cell, and together with any information on mRNA stability, to estimate transcription rate, if they really want to pin a number on the basal expression level.

The reviewer's qualification is of course correct. Since WTC₈₄₆ is intended as a tool for controlling gene expression, and our functional tests with low expressed genes did not show any problematic basal expression, we opted not to further quantify mRNA, but to change the wording so that it says what we did observe. Discussion now reads.

“It can set protein levels across a large input and output dynamic range. […] WTC₈₄₆ alleles also exhibit high maximum expression, low cell-to-cell variation, and operation in different media conditions without adverse effects on cell physiology.”

Instead of the old text which read:

“It can set protein levels across a large input and output dynamic range. WTC₈₄₆ alleles display no uninduced basal expression, high maximum expression, low cell-to-cell variation, and operate in different media conditions without adverse effects on cell physiology.”

10. Since one of the major potential applications of the WTC₈₄₆ system described here, as pointed out by the authors, is genome-wide screening, that authors should at least discuss how this might be efficiently implemented. The generation of a library of WTC₈₄₆ fusions to each protein-coding gene in yeast by standard methods would be extremely laborious and expensive. An alternative worth mentioning is the SWAp-Tag strategy described by Schuldiner and colleagues (Yofe et al. [2016] Nature Methods). There may very well be others.

We thank the reviewer for the suggested addition. As the reviewer suggested, one good way to generate a library of WTC₈₄₆ controlled endogenous genes would be to use existing whole genome collections such the SWAP-Tag as a starting point. SWAP-Tag allows insertion of N or C-terminal sequences to most ORFs in the yeast genome by creating standard sites for homologous recombination. Generating a whole genome collection of WTC₈₄₆ alleles would be accomplished crossing the existing SWAP-Tag N terminal collection with a WTC₈₄₆ donor strain, followed by sporulation on the appropriate selective medium. We’ve therefore added a short section and the necessary references in our discussion on this subject using the text below.

Last paragraph of the Discussion previously read:

“Taken together, our results show that WTC₈₄₆- controlled genes define a new type of conditional allele that allows precise control of gene dosage. […] We also expect them to find use in genome-wide gene-by-gene and gene-bychemical epistasis screens to detect protein dosage independent interactions,…”

New version reads:

“Taken together, our results show that WTC₈₄₆- controlled genes define a new type of conditional allele, one that allows precise control of gene dosage. S. cerevisiae […] We therefore suggest that growth rate-based assays using WTC₈₄₆ or any other inducible system pre-induce cells several generations before plating or pinning. WTC₈₄₆- alleles may find use in engineering applications, for example in directed evolution of higher affinity antibodies expressed in yeast surface display, where precise ability to lower surface concentration should aid selection of….”

11. Although the authors emphasize the utility of WTC₈₄₆ regulation for generating precisely graded expression levels of a given protein with minimal secondary effects growth or physiology, they devoted considerable effort towards achieving maximal shut-off without much discussion of the utility of this feature. In light of anchoring and induced degradation methods that operate at the protein level to generate loss-of-function effects, the authors should consider how WTC₈₄₆ regulation might be a useful complement to these methods, particularly when their efficiency or rapidity is in question.

We thank the reviewer for the suggestion. WTC₈₄₆ could certainly a useful complement to such methods, especially in cases where rapid shutoff of expression is required together with precise dosage control. We have added this to the last part of our discussion using the text below:

“WTC₈₄₆ can also be a useful complement boost the efficiency of methods that act at the protein level such as auxin induced degradation or AnchorAway techniques. […] For example, an experimenter might simultaneously induce depletion of the product of a controlled gene by such a method while adjusting aTc downward to rapidly reset the level of an expressed protein to a new, lower level.”

Additional comments and suggestions

1. Pg. 5 right column: "…and we developed a second measure (explained in SI) that normalized variation in dosage with respect to a key confounding variable, cell volume, to correct for its effect on protein concentration. In this, we measured the Residual Standard Deviation (RSD) in signal from cells after normalization of output for volume estimated by a vector of forward and side scatter signals…"

I think it would be a good idea to give the second measure a name here and make it clearer that Figure 2D reports the RSD of this value if I understand correctly. The sentence "In this, we measured the RSD in signal…" is awkward and unclear. Please re-word.

Yes. We named the method "Volume Independent Variation". We reworded the unclear sentence as shown below, and clarified in the legend that Figure 2D reports this measure. We modified all other figure legends in the manuscript and the supplementary material where RSD was reported to reflect the new name for the measure.

Old text read:

“In this, we measured RSD in signal from cells after normalization of output for volume estimated by a vector of forward and side scatter signals.”

New text reads:

“In ViV, we estimated cell volume by a vector of forward and side scatter signals, and measured the remaining (Residual) Standard Deviation of the single reporter output after normalization with this estimated volume.”

The new Figure 2 legend now reads:

“(D) Cell-to-cell variation of expression by these three architectures. We calculated single-reporter cell to cell variation (VIV) as described. […] Dot-dash line indicates VIV of the strain where Citrine is constitutively expressed from P_TDH3 and dashed line indicates VIV of autofluorescence in the parent strain without Citrine.”

2. In Figure 3A the fluorescence curves should be better aligned with the strain designations.

Yes, with thanks. We have made the necessary change to the alignment.

3. Why is there no curve shown for repressed P3tet in Figure 1B? (numbers should be aligned correctly also).

We thank the reviewer for pointing out the alignment issue and have fixed it in the figure.

There is no curve for P3tet because the curve is centred around fluorescence value ~20000 and thus lies outside the range of the xaxis we used in main Figure 1. If we extend the x-axis to include this curve, the difference between all the other curves becomes difficult to perceive. Therefore, we show the data for P3tet separately again in Supplementary Figure 1 (and we direct the reader to the Supplementary Figure at the relevant point in the text).

We have now added a note also in the figure legend (see below) directing the reader to Figure S1.

“Repressed activity of P_3tet is above the x axis depicted in this figure, but can be seen in Figure S1.”

4. Yeast gene names should be in italics throughout (including the figure legends). Deletions that a recessive should also by italics, and with the "Δ", essentially an allele designation, placed after the gene name.

Necessary changes made in text and the supplementary material.

5. Reference no. 34 refers to Rap1 and Grf2 binding sites (not Gcr1). Please clarify.

We thank the reviewer for pointing this out. Yes, the paper refers to Rap1 and Grf2 binding sites and finds them in the P_TDH3 UAS, but also includes in the last portion of its Results section the identification of the CATCC consensus binding sites. The authors of the paper suggest in their discussion that these are likely to be binding sites for the recently discovered Gcr1. This paper was the earliest reference we could find to the CATCC binding sites in the P_TDH3 UAS, and therefore we cited it in this context.

We added a more recent reference corroborating the GCR1 binding sites in the P _TDH3 UAS in the main text, next to the original reference 34. We also added a short note “Gcr1 binding sites were found in reference 13 and confirmed in reference 14“ in the legend of Figure S21.

6. Bottom pg. 11, right, I would suggest "…when inducer is absent, the measured basal expression of the controlled gene is abolished…"

We thank the reviewer and have made the suggested change.

7. References no. 28-30 and 35 are incomplete (no journal, volume, or page number).

We thank the reviewer for pointing this out. We fixed the references.

8. Bottom pg. 5, left should be >50,000.

We thank the reviewer for pointing this out. We fixed the symbol.

9. Page 8 right: "…according to,53 to enable…" could be replaced by "…to enable…" with the reference placed at the end of the sentence.

We thank the reviewer for the suggested change which we have implemented in the text.

10. Page 8, right: again, replace inverted "?" with ">" (?)

We thank the reviewer for catching these formatting errors. We have replaced the symbol.

Reviewer #1 (Significance (Required)):

The increasing interest in quantitative analysis of cell function emphasizes the need for systems that allow the graded and precise control of gene expression to address dosage effects on phenotype. Although systems of this sort have been developed in yeast, they all have specific drawbacks, as clearly described here. The WTC₈₄₆ system described here represents a genuine improvement on current methods and is likely to be an important addition to the yeast toolbox for gene function analysis. It may also motivate similar developments in mammalian cell culture systems, and eventually in whole animals.

We thank the reviewer for this assessment. Just as qualitative means to bring about conditional gene expression enabled experiments that produced important qualitative insights into cell function, so development of precise quantitative means to bring about conditional gene expression will enable better quantitative analysis of cell function. It will also enable new technical approaches such as the threshold phenotypes, epistasis approaches, and evolutionary methods mentioned above. We suspect that development of analogous methods in in vertebrate cells will likely prove even more important in making vertebrate models of human diseases.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

In this manuscript, Azizoglu and co-authors developed an autorepression-based transcriptional controller of gene expression for a given gene of interest in Saccharomyces cerevisiae. This system is very precise and allows for dynamic gene expression by adjusting the amount of inducer (aTc) and/or by changing the translation initiation sequences. In addition, it is equally good at being turned on and off in all the media tested and has low cellto-cell variation. The authors tested their transcriptional controller using different genes (including CDC20, IPL1, WHI5, TOR2, and others) and recapitulated various known phenotypes. They also identified a novel phenotype when overexpressing IPL1 in yeast which mimics a previously identified phenotype in mammalian cells when overexpressing Aurora B. This system will be very useful for the yeast community.

We also want to stress that now that we have proof of concept, we can do this in higher cells and multicellular organisms including vertebrates, which need it more.

Major comments

Are the key conclusions convincing?

Yes.

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

No. They do a good job backing up their claims in their supplementary files.

Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

No. See above.

Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

No suggested experiments.

Are the data and the methods presented in such a way that they can be reproduced?

Yes.

Are the experiments adequately replicated and statistical analysis adequate?

Yes.

Minor comments:

Specific experimental issues that are easily addressable. Could the authors add the pGAL1-10-IPL1 control to their WTC₈₄₆-driven IPL1 overexpression experiment (Figure S17)? It might be easier for the reader to understand why the exciting phenotype the authors found was not reported before.

We thank the reviewer for the suggested control. Since we believe that the WTC₈₄₆::IPL1 allele manifests an overexpression phenotype because it produces more protein than P_GAL1 would have, replacing the promoter of Ipl1 with P_GAL1 would be the best control. However, since IPL1 is an essential gene, and its misregulation affects chromosomal integrity, we weren't confident we could replace its promoter with P_GAL1 and still achieve the right expression level to keep the cells healthy. In fact, in previously published work on IPL1, overexpression of IPL1 was achieved by introducing a P_GAL1-IPL1 producing plasmid into an IPL1+ background.

To address the reviewer's concern we therefore took the same approach. We placed the P_GAL1 promoter upstream of Citrine in a centromeric plasmid, then grew the strain that carried the (P_GAL1Citrine) plasmid in the same (Gal Raf) media used by M. MunozBarrera and F. Monje-Casas 2014, to observe effects of Ipl1 overexpression on growth rate and colony formation. The authors did not observe detrimental effects of Ipl1 overexpression in either case.

Here, we used flow cytometry to measure single cell fluorescence from the strain bearing the P_GAL1-Citrine plasmid, as well as the WTC₈₄₆::citrine strain grown in YPD with 400ng/mL aTc (the same concentration we used in the Ipl1 overexpression experiment).

Our results showed that the median expression level of the centromeric P_GAL1-Citrine strain was approximately two fold lower than that in the integrated WTC₈₄₆::citrine strain, and that it displayed much higher cell to cell variation. As measured by the single reporter cell-to-cell variation measure (ViV), P_GAL1-Citrine variation was 0.9, whereas WTC₈₄₆::citrine variation was only 0.19. Ipl1 overexpression phenotype does not lead to a complete arrest of growth in the entire population- some cells are affected while others are not. With PGal1 driven overexpression, overall Ipl1 level is lower compared to WTC₈₄₆ driven overexpression. Additionally, due to high cell-to-cell variability only a handful of cells are actually expressing Ipl1 at this level, which would have made it difficult to observe the growth arrest/delay phenotype that is already not a uniform phenotype in the population. Therefore, we believe the combination of lower expression and higher cell-to-cell variation in the P_GAL1-Citrine strain likely accounts for the failure of Munoz-Barrera and Monje-Casas to observe a growth arrest phenotype.

We present the results in Figure 5—figure supplement 7. We explain the results in additional sentences at the end of the 4^th paragraph of the Results section titled “WTC₈₄₆ alleles allow precise control over protein dosage and cellular physiology“.

The paragraph in the main text now reads:

“At high aTc concentrations, the WTC₈₄₆-K2:IPL1 strain formed colonies with lower plating efficiency than the parent strain. […] Either the lower expression or the higher variation, or both, might account for the fact that P_GAL1 driven Ipl1 overexpression does not result in the mammalian Aurora B phenotype in S. cerevisiae."

Are prior studies referenced appropriately?

Yes.

Are the text and figures clear and accurate? The authors used the strain names in the text and it is hard to figure out what each strain contains. They could transmit this information by using the construction "strain Y### (contains [specify relevant details]).

We thank the reviewer for the suggestion. During the revision process, we took care to mention the relevant details as much as possible together with the yeast strain number within the text.

In Figure 3, specify that the sentence that explains the errors bars refers to section A and B. or move the sentence before C.

Right. There are actually error bars also around the data points in C, although they are very narrow and difficult to see. This sentence therefore refers to Figure 3C as well. In response to this comment, we adapted the figure to make it easier to see the error bars in Figure 3C.

In page 3, the authors mention ts and cs mutations. The authors should define what ts and cs are and italicize them.

We thank the reviewer for pointing this out. We have included the long format of these abbreviations where they were first mentioned, and have italicized the abbreviations.

Old text read:

“During the 20th century, workhorse methods to ensure the presence or absence of gene products have included use of ts and cs mutations within genes, for example to give insight into ordinality of cell biological events.”

New text reads:

“During the 20th century, workhorse methods to ensure the presence or absence of gene products have included use of temperature sensitive (ts) and cold sensitive (cs) mutations within genes, for example to give insight into ordinality of cell biological events.”

On page 4, the authors defined PIC and aTc, but both terms were previously defined.

Fixed.

On page 4, in the following sentence "We also combined the operators in these

constructs to generate P5tet (Figure 2B)", the figure is wrongly referenced, the reference should be to Figure 1B.

We thank the reviewer for catching this, and have fixed the reference.

On page 7, in the following sentence "The PACT1, PVPH1, PRNR2 strains showed no uninduced expression,.…" please add a reference to Figure 3B.

We thank the reviewer for the suggestion and have added the reference.

On page 10, in the following sentence "…at high aTc concentrations, cells bearing the higher translational efficiency TOR2 allele (WTC₈₄₆-K1::TOR2) grew more slowly than the otherwise-isogenic control parent strain with WT TOR2 (Figure S18C)" the figure is wrongly referenced, the reference should be to Figure S18D. I also suggest the authors add the following text: (Figure S18D, compare the 600ng/mL line to blue dashed line).

We thank the reviewer for pointing this out and have corrected the reference. We also added the suggested text for clarification.

On page 10, in the following sentence "…Whi5 is rapidly exported from the nucleus, and the cells enter START", I think they mean S-phase.

We thank the author for the comment and have made the correction to the text which now says S-phase.

On page 10, the Kozak sequence is missing in the following sentence: "Use of WTC₈₄₆::CDC20 to synchronize the cells…"

We thank the reviewer. We have added the correct Kozak sequence information to the strain name.

Throughout the paper, gene deletions should be written as follow: whi5Δ.

Throughout the paper, genes should be italicized (e.g. HIS3).

We thank the reviewer for the correction of the naming nomenclature. We have fixed the gene names throughout the paper and the supplementary material.

Throughout the paper, the authors have random question marks (which may be the result of converting a file in another format to a PDF) that may be removed.

We thank the reviewer for catching these formatting errors. We replaced the question marks with the correct symbols.

On Supplementary Text page 1, the following sentence: "However single reporter studies have a major, confounding contribution to measured variation in gene expression that multi-reporter studies don't:" is missing a comma after However and change don't to do not.

We thank the reviewer for both corrections which we have implemented as suggested.

In Supplemental Figure 7, the authors may want to add P2tet and P3tet for better main text that the promoter for essential genes was replaced with another promoter in the presence of the inducer (aTc) before referencing Figure 5, or in the legend of figure 5.

We believe the reviewer wanted to raise two separate points here. One about Figure S7, the other about clarifying the essential endogenous gene promoter replacement protocol we used. We address these two points separately below.

First. We thank the reviewer for the suggestion about Figure S7. In Figure S7, inclusion of the unrepressed promoter fluorescence curves would widen the x-axis range we need to show, and therefore would make it more difficult for the reader to perceive the small differences between the repressed expression of P_2tet and P_3tet compared to the autofluorescence. Therefore, given that the focus of this supplementary figure is narrowly on the repressed expression, we believe it is best to omit the unrepressed expression curves in this figure.

It is important to note that, although P_3tet is weaker than P_2tet, this fact is not the only reason that P_3tet shows a lower level of repressed expression. Unrepressed P_2tet is 1.14 times stronger than P_3tet (data from main figure 1). However, when repressed, P_2tet expression is 1.38 higher than that for P_3tet. This suggests that promoter strength is not the explanation for the lower repressed expression from P_3tet. To emphasize this point, in response to the reviewer direction, we added fold comparison numbers to the figure legend.

The new text in the figure legend of Supplementary Figure 7 reads as follows:

“For comparison, repressed expression in the P_2tet strain (Y2662) was 1.38 fold higher than in the P_3tet strain (Y2702), even though the expression in the unrepresssed P_2tet strain was only 1.14 fold more than P_3tet. This fact shows that, even given the difference in promoter strength, P_3tet is repressed more effectively than P_2tet.”

Second, we thank the reviewer for the suggested clarification to the strain generation protocol. We changed the main text to indicate that the strains were generated by replacing the promoter of the endogenous gene in cells growing in medium containing aTc., using the text presented below:

“We constructed strains in which WTC₈₄₆ controlled the expression of these genes by replacing the promoter of the endogenous gene. Before transformation the cells were grown in liquid medium containing aTc, and then plated on solid medium containing aTc (see Supplementary Materials for a detailed protocol).”

Reviewer #2 (Significance (Required)):

Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. The authors provide a new tool to dynamically control the expression of genes. The previous systems that have been developed are hard to modulate or are "leaky". The system developed in this work allows for precise control of gene dosage, has low cell-to-cell variation, and has a true OFF state.

Static steady state expression around an adjustable setpoint, i.e. clamped expression is good too.

Place the work in the context of the existing literature (provide references, where appropriate).

Regulating gene expression in a targeted way has been a common method in yeast research for decades. However, the current gene expression systems have deficiencies (e.g. leaky expression, not tunable expression, high cell-to-cell variation). The authors in this paper developed a new system that shore up these deficiencies by enabling tight on and off controlled gene expression.

State what audience might be interested in and influenced by the reported findings. The yeast community in general, as it is a transcriptional controller developed for S. cerevisiae.

We also believe the ability to make alleles of this type will be extended higher cells and multicellular organisms, particularly vertebrates.

Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Genetics, yeast, evolution, chromosome segregation.

Author details

1. Asli Azizoglu

   Computational Systems Biology and Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland

   Contribution

   Investigation, Visualization, Writing - original draft, Writing - review and editing

   For correspondence

   asli.azizoglu@bsse.ethz.ch

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2600-1322

2. Roger Brent

   Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing - review and editing

   Contributed equally with

   Fabian Rudolf

   For correspondence

   rbrent@fhcrc.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8398-3273

3. Fabian Rudolf

   Computational Systems Biology and Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland

   Contribution

   Conceptualization, Supervision, Writing - review and editing

   Contributed equally with

   Roger Brent

   For correspondence

   fabian.rudolf@bsse.ethz.ch

   Competing interests

   No competing interests declared

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