| INVESTIGATION

Comprehensive Analysis of the SUL1 Promoterof Saccharomyces cerevisiae
Matthew S. Rich,*,† Celia Payen,* Alan F. Rubin,*,‡,††,** Giang T. Ong,* Monica R. Sanchez,*Nozomu Yachie,†,‡‡,§§ Maitreya J. Dunham,* and Stanley Fields*,§,***,1*Department of Genome Sciences, §Department of Medicine, and ***Howard Hughes Medical Institute, University ofWashington, Seattle, Washington 98195, †Synthetic Biology Division, Research Center for Advanced Science and Technology, theUniversity of Tokyo, and §§PRESTO, Japan Science and Technology Agency, Meguro-ku, Tokyo 153-8904, Japan, ‡Bioinformatics
Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia, **Department ofMedical Biology, University of Melbourne, Victoria 3010, Australia, ††Bioinformatics and Cancer Genomics Laboratory, Peter
MacCallum Cancer Centre, Victoria 3002, Australia, and ‡‡Institute for Advanced Bioscience, Keio University, Tsuruoka, Yamagata
997-0035, Japan
ORCID ID: 0000-0001-9944-2666 (M.J.D.)

ABSTRACT In the yeast Saccharomyces cerevisiae, beneﬁcial mutations selected during sulfate-limited growth are typically ampliﬁcations of the SUL1 gene, which encodes the high-afﬁnity sulfate transporter, resulting in ﬁtness increases of .35% . Cis-regulatory
mutations have not been observed at this locus; however, it is not clear whether this absence is due to a low mutation rate such thatthese mutations do not arise, or they arise but have limited ﬁtness effects relative to those of ampliﬁcation. To address this question
directly, we assayed the ﬁtness effects of nearly all possible point mutations in a 493-base segment of the gene’s promoter throughmutagenesis and selection. While most mutations were either neutral or detrimental during sulfate-limited growth, eight mutations
increased ﬁtness .5% and as much as 9.4%. Combinations of these beneﬁcial mutations increased ﬁtness only up to 11%. Thus, inthe case of SUL1, promoter mutations could not induce a ﬁtness increase similar to that of gene ampliﬁcation. Using these data, we
identiﬁed functionally important regions of the SUL1 promoter and analyzed three sites that correspond to potential binding sites forthe transcription factors Met32 and Cbf1. Mutations that create new Met32- or Cbf1-binding sites also increased ﬁtness. Somemutations in the untranslated region of the SUL1 transcript decreased ﬁtness, likely due to the formation of inhibitory upstream open
reading frames. Our methodology—saturation mutagenesis, chemostat selection, and DNA sequencing to track variants—should be abroadly applicable approach.
KEYWORDS high-throughput sequencing; transcription; promoter mutagenesis; gene ampliﬁcation; chemostat; yeast; Saccharomyces cerevisiaeC

HANGES in the extent or timing of gene expression canhave profound effects on molecular and organismal phenotypes and thereby drive evolution (King and Wilson 1975;
Wray 2007). Heritable noncoding variation can alter gene
expression in cis or in trans (Skelly et al. 2009), and bothhave been shown to contribute signiﬁcantly to gene expression variation (Ronald et al. 2005; Tirosh et al. 2009; Skelly

Copyright © 2016 by the Genetics Society of America
doi: 10.1534/genetics.116.188037
Manuscript received January 27, 2016; accepted for publication February 21, 2016;
published Early Online March 1, 2016.
Supplemental material is available online at www.genetics.org/lookup/suppl/doi:10.
1534/genetics.116.188037/-/DC1.
1Corresponding author: Howard Hughes Medical Institute, Department of
Genome Sciences, University of Washington, Box 355065, Seattle, WA 98195.
E-mail: ﬁelds@uw.edu

et al. 2011). Two primary mechanisms by which cis variation
can increase gene expression are increases in gene copy number and point mutations in regulatory regions. However, therelative effect size of ampliﬁcation compared to point mutation is not known.
It has been proposed that ampliﬁcation is both quickly
achieved and then reverts after ﬁxation of ﬁtness-increasing
point mutations (Hendrickson et al. 2002; Yona et al. 2012).
This mechanism raises the question of whether ampliﬁcation
is simply a transitional state that provides an increased
chance for a beneﬁcial point mutation to occur. Alternatively,
gene ampliﬁcation may be so frequently observed because
the ﬁtness effects it confers are greater than those achievableby point mutations. Gene ampliﬁcations have been found tobe advantageous in many contexts, including phenotypic

Genetics, Vol. 203, 191–202 May 2016

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evolution (Hoekstra and Coyne 2007; Stern and Orgogozo
2008) and cancers (Brison 1993; Lengauer et al. 1998), but
few experiments have addressed whether cis-regulatory mutations could also lead to similar effects. In one study, random
mutations were introduced into yeast and assayed for theireffects on the expression of a ﬂuorescent reporter (Gruber
et al. 2012). Strains with altered reporter expression wereclassiﬁed as having either trans or cis mutations, the cis
mutations being either noncoding point mutations in thereporter construct or ampliﬁcations. As expected, ampliﬁcations increased reporter expression, but there was no difference in effect between these and ﬂuorescence-increasing
cis-regulatory point mutations (although only two suchmutations were isolated). No strains with a reporter copy
number greater than two were isolated, leaving open thepossibility that higher-copy-number gene ampliﬁcations
could enhance expression beyond that achieved by point
mutation.
To study these questions directly, we turned to another casein which gene ampliﬁcations are adaptive. When Saccharomyces cerevisiae is subjected to long-term growth under sulfate limitation, a region of chromosome II containing thesulfate transporter gene SUL1 is recurrently ampliﬁed after50 generations (Gresham et al. 2008; Payen et al. 2014).
The ampliﬁcation of this gene leads to a ﬁtness increase of
37–51% (Gresham et al. 2008; Payen et al. 2014), dependingon amplicon size and copy number. Ampliﬁcation appears tobe the preferred means by which ﬁtness can be increased, as
the appearance of coding or noncoding mutations at the SUL1
locus is very rare. However, this preference for ampliﬁcationof SUL1 could have other possible explanations in addition tosuperior ﬁtness, such as differences in the mutation rate between point mutations and ampliﬁcations. We sought to determine whether cis-regulatory mutations were capable ofcomparable effects by directly creating such mutations via
mutagenesis, an approach that allowed us to skirt the potential effect of mutation rate.
The speciﬁcs of SUL1’s transcriptional regulation arelargely unknown. SUL1 is one of the 45 genes composing
the core Met4 regulon of genes involved in yeast sulfur metabolism (Lee et al. 2010). Met4 is a transcriptional activator,
but has no DNA-binding activity. It is targeted to promoter
sequences by combinations of three transcription factors—
Cbf1, Met31, and Met32—that themselves lack transcriptional activation activity. Cbf1, Met31, and Met32 can induce
transcription individually and in combination (Lee et al.
2010; Petti et al. 2012). SUL1 is unannotated in many functional genomic studies on regulation, including RNA sequencing(Nagalakshmi et al. 2008), DNase I mapping (Hesselberth et al.
2009), and ChIP-Exo (Rhee and Pugh 2012), presumably due to
its low expression in the sulfate-rich conditions used by thesestudies.
Mutagenesis has long been applied to study the functionof noncoding sequence. Early work in yeast established thestructure of eukaryotic promoters, delineating upstream
activating sequences as the binding sites for transcriptional

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regulators. These experiments changed promoter sequencesby truncation, internal deletion, replacement of activating
sequences, rearrangement, and random mutagenesis (Struhl1989). However, these experiments were low throughput,
assaying promoter variants one at a time. More recent studies
that couple mutagenesis with high-throughput sequencing
have increased the resolution and throughput of the analysis
of eukaryotic cis-regulatory elements (Kwasnieski et al. 2012;
Melnikov et al. 2012; Patwardhan et al. 2012). These studies
assay thousands of variants simultaneously and provide detailed information on the relative mutational constraints oneach base of a promoter, identifying transcription factor-bindingsites and other regulatory elements.
Here, we use the methods of mutagenesis, selection, andsequencing to assay the ﬁtness of nearly all single mutations in
the SUL1 promoter. We show that, while point mutations canincrease ﬁtness in sulfate limitation by up to 10%, neither
single mutations nor combinations of these mutations canincrease ﬁtness to the extent that SUL1 ampliﬁcation can.
We also use these data to deﬁne potential transcription factorbinding sites that regulate SUL1 expression and to identify
point mutations that create new regulatory sites. Additionally, our assay is sensitive to post-transcriptional effects of cisregulatory mutations and identiﬁes detrimental mutations
that create new upstream open reading frames in the SUL1
59 untranslated region.

Materials and Methods
Oligonucleotides, yeast strains, and plasmids used inthis study

Oligonucleotides used in this study can be found in Supporting
Material, Table S1. The S. cerevisiae strain used in this study
was FY3, a MATa uracil auxotroph (ura3-52) of the S288c
background. The strain deleted for SUL1 was obtained bytransformation with a PCR fragment containing a NatMX cassette and two ﬂanking regions with homology to the SUL1
locus. The transformant was backcrossed three times to FY2
to select for a clone containing both the sul1 deletion and theura3-52 allele. A list of strains and plasmids used in this study
can be found in Table S2.
Fitness estimates of individual strains

Fitness measurements of individual clones were performed aspreviously described (Payen et al. 2014) in sulfate-limited
chemostats using a prototrophic FY strain where the HO locus
had been replaced with eGFP (MATa: YMD1214).
Promoter truncation and mutagenesis

Promoter truncations were created by amplifying the SUL1
locus from genomic DNA using oligos 314–319 as forward
primers and oligo 266 as the reverse primer and the following
PCR conditions: 98° for 1 min; then 25 cycles of 98° for 10
sec, 65° for 15 sec, and 72° for 15 sec; and then a ﬁnal incubation at 72° for 5 min. These PCR products were cloned

into an EcoRI- and SacI-digested pRS416 vector by Gibson
assembly, and their sequences were conﬁrmed by Sanger
sequencing. Yeast was transformed by lithium acetate transformation with each plasmid individually. At least two independent transformants were used for ﬁtness measurements.
The plasmid containing the 493-base promoter and remainder of SUL1 was named “pMR002.”
The template used for mutagenesis was ﬁrst ampliﬁed from
pMR002 using oligos 295 and 297. Promoter mutagenesis was
performed using the GeneMorph II Random Mutagenesis Kit(Agilent) according to manufacturer’s recommendations.
One and 10 nanograms of template were ampliﬁed in mutagenesis reactions using oligos 295 and 297 and the following
conditions: 95° for 2 min; then 25 cycles of 95° for 30 sec, 55°
for 30 sec, and 72° for 30 sec; and then a ﬁnal step of 72° for
10 min. These libraries were gel-extracted with a MinEluteGel Extraction kit (Qiagen), mixed one-to-one, and cloned
into a BamHI and SacI-digested pMR002 plasmid using Gibson
assembly. One microliter of this reaction was transformed
directly into ElectroMAX DH10B electrocompetent Escherichia coli (Life Technologies).
Barcodes were created by annealing oligos 283 and 296
and then performing a single cycle of extension using the
Klenow fragment of DNA polymerase I (NEB). These fragments were cleaned and concentrated using a DNA Clean andConcentrator-5 kit (Zymo Research). pMR002 library plasmids were digested overnight with HindIII, dephosphorylatedwith calf intestinal phosphatase (NEB), and gel-puriﬁed. Barcode fragments were mixed in a 10-fold molar excess with cut
library plasmids and cloned using Gibson assembly. ElectroMAX DH10B electrocompetent E. coli (Life Technologies)
was transformed with 1 ml of this reaction. In total, the librarycomprised 157,159 variants tagged by 634,639 barcodes.
Yeast strains FY3 and FY3 sul1 were transformed with barcoded plasmids using a high-efﬁciency protocol (Gietz andSchiestl 2007), resulting in .5 million transformed cells perlibrary.
Continuous culture in chemostats

Nutrient-limited media (sulfate-limited and glucose-limited)were prepared as described (Gresham et al. 2008). The
200-ml chemostat vessels were inoculated with 1 ml of eachpool (2 3 107 cells). The pools were grown in chemostats for
25 hr in batch and then switched to continuous culture at adilution rate of 0.17 6 0.01 vol/hr at 30°. The cultures reachedsteady state after 6 generations and were maintained for40 generations. A sample was taken immediately after the
initiation of pumping and was designated generation 0 (G0).
Samples (25–50 ml of culture at a density of 2 3 104 cells/ml)
for cell counting and DNA extraction were passively collectedonce or twice daily, every three to six generations on average.
Mapping barcodes to promoter sequences

To map barcodes to promoter sequences, 25 ng of pMR002
was ampliﬁed for nine cycles with KAPA HiFi Hotstart Readymix
using oligos 327 and 328 and the following cycling conditions:

98° for 20 sec and then nine cycles of 98° for 30 sec, 65° for
15 sec, and 72° for 25 sec. This reaction was cleaned with a
DNA Clean and Concentrator kit (Zymo Research) and quantiﬁed using a Qubit ﬂorometer (Life Technologies). The products were sequenced with 300-base paired-end reads and a
12-base index read on a MiSeq (Illumina). Oligo 325 wasused as the sequencing primer for both read 1 and read 2,
and oligo 326 was used as the sequencing primer for read 3.
Twelve-base barcode sequences were removed from read 1,
and read 1 was trimmed to 270 bases using Prinseq(Schmieder and Edwards 2011). The resulting forward readand read 3 were merged and mapped to their consensus
barcode (Hiatt et al. 2010; Starita et al. 2013). To remove
unbarcoded plasmids and truncations occurring during Gibson
assembly, barcode sequences were aligned to pMR002, andall barcodes that mapped were removed from the data set.
Barcode sequencing library preparation

Yeast samples (50 ml) were harvested and ﬂash-frozen after
growth in chemostats, and plasmids were extracted using the
Zymoprep Yeast Miniprep II kit (Zymo Research). Sequencing
libraries were created using two ampliﬁcation steps. First, 4 mlof each plasmid miniprep was ampliﬁed in a 25 ml reaction by
quantitative PCR with oligos 327 and 344 using KAPA HiFi
HotStart ReadyMix (KAPA) on a Bio-Rad MiniOpticon (BioRad). The following PCR conditions were used: 98° for 2 min
and then cycles of 98° for 10 sec, 65° for 15 sec, and 72° for
25 sec. Reactions were stopped after 10–13 cycles to avoid
overampliﬁcation. One microliter of each reaction was thendiluted into a 25 ml KAPA HiFi HotStart ReadyMix reaction
with oligos P5 and NexV2ad2_N to add sequencing indicesand amplify full-length products. As with the ﬁrst reaction,
quantitative PCR was used to avoid overampliﬁcation, andeach reaction was stopped after either seven or eight cycles.
The same cycling conditions were used as in the ﬁrst ampliﬁcation reaction. These reactions were cleaned with Ampure
XP beads (Agencourt) and sequenced with 25-base reads on a
MiSeq, NextSeq, and HiSeq 2000 (Illumina), using oligo 325
as the read 1 sequencing primer.
Barcode sequencing analysis

Twelve-base barcode sequences were extracted from reads
and analyzed using custom software implemented in Python(File S1). Barcode sequences that perfectly matched the consensus barcodes were counted, and counts were converted tofrequencies within each round of selection. Barcode frequencies were converted to log ratios between each round and the
input. The ﬁtness of each barcode was calculated as the slopeof the ordinary least-squares regression of these ratios, andthe number of generations elapsed for each sample. Variantﬁtnesses were normalized to wild-type ﬁtness by subtracting
the wild-type slope from the slope of each barcode. The ﬁtness of a mutant is the average ﬁtness of all barcodes that
map to that mutant. To create a set of high-conﬁdence variants, we compared the ﬁtness and input read counts of thebarcodes mapping to wild-type promoters. We qualitatively

SUL1 Promoter Mutagenesis

193set a minimum read count threshold at a point to maximize
the number of wild-type barcodes yet minimize the varianceof the wild-type ﬁtness scores and dependence of ﬁtness
scores on input count number. Using this heuristic, we set
the read count threshold for sulfate limitation experiments
at 50 input reads and for glucose-limitation experiments at
15 input reads (Figure S1).
Creation and selection of a combinatorial library ofhigh-ﬁtness mutations

A library of promoters containing only combinations of ﬁve
high-ﬁtness mutations and neutral variation at one position
was built using ﬁve sequential steps of PCR, each using the
product of the previous reaction as a primer. Inadvertently,
oligo 381 encoded either C or T, both neutral variants, at
position 2246. The primers and products used in each reaction were the following: (1) oligos 380 and 381; (2) the
product from (1) and 237; (3) the product from (2) and 382;
(4) oligos 237 and 382; (5) the product from (4) and M13F;(5) M13F and 237. All reactions were 25 ml KAPA HiFi HotStart ReadyMix reactions using the following PCR conditions:
98° for 2 min; then 25 cycles of 98° for 10 sec, 65° for 15 sec,
and 72° for 15 sec; and then 72° for 5 min. Only 15 cycles of
PCR were used in reaction 5. The ﬁnal product was gelextracted using a MinElute Gel Extraction kit (Qiagen),cloned into pMR002 using Gibson assembly, used to transform ElectroMAX DH10B electrocompetent E. coli (Life Technologies), and then used to transform YMD3017 using ahigh-efﬁciency yeast transformation (Gietz and Schiestl
2007). Selections and plasmid extractions were performed
as before. Fragments for sequencing were ampliﬁed using
400 nM each of a custom forward index primer (oligos
395–402, one per sample) and oligo 328, using the following
PCR conditions: 98° for 2 min and then cycles of 98° for 10
sec, 65° for 15 sec, and 72° for 25 sec. Quantitative PCR wasused to avoid overampliﬁcation, and reactions were stopped
after 8–16 cycles, depending on the sample.
Libraries were sequenced with overlapping 150-base reads
on a Miseq (Illumina) using oligo 442 as a forward primer andoligo 387 as a reverse primer. These reads covered ﬁve of thesix mutated sites in the library. We sequenced the ﬁnal mutatedsite with an 8-base index read (using oligo 443) and used thesecond index read to demultiplex samples. Reads were genotyped at each mutated site and analyzed similarly to our barcode
sequencing data (code for this analysis can be found in File S1).
Generation and genomic integration ofpromoter variants

We ﬁrst replaced the genomic SUL1 promoter with URA3.
URA3 was ampliﬁed from FY3 genomic DNA with oligos
678–681, and this PCR product was used to transform
YMD3107 via high-efﬁciency yeast transformation (Gietz
and Schiestl 2007). We created single mutants of the SUL1
promoter in two sequential PCR steps. We ampliﬁed the 39
end of the promoter with a primer containing each mutation(oligos 517–522 and 603–605) and oligo 237, using pMR002

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as a template. These products were gel-extracted with a FastGene PCR Extraction Kit (Nippon Genetics) and used as primers along with M13F to add the 59 end of the promoter tothe fragment (using pMR002 as template). These reactions
were gel-extracted and cloned into pMR002 by Gibson assembly. The promoter-SUL1 fragments of these plasmids were ampliﬁed with oligos 237 and 685 and used to transform YMR002,
creating strains YMR008-YMR012 and YMR017-YMR020.
These strains were made prototrophic (URA+) by backcrossing
to YMD3018, creating strains YMR022 and YMR024-YMR030.
Matching ﬁtness data to known transcription factor motifs

Single-mutant log-normalized frequencies at 0 and 40 generations were estimated using the slope and y-intercept ofthe linear ﬁt used to calculate variant ﬁtness, creating a loglikelihood of ﬁnding each variant in the data set. These ratios
were then ordered by position and identity (i.e., A, C, T, or G).
The estimated wild-type ratio at 40 generations was used forthe wild-type base at each position, and a value of 1.0 wasused for missing data. Any extrapolated values ,0.001 were
set to 0.001. Speciﬁc position ranges were extracted from thismatrix and compared to known transcription factor motifs(Zhu and Zhang 1999; MacIsaac et al. 2006) using Tomtom
version 4.10.0 (Gupta et al. 2007).
Finding newly created transcription factor-binding sites

We enumerated all possible single mutations in the SUL1
promoter in the context of a 25-mer (each mutation was
ﬂanked by 12 upstream and downstream wild-type bases),
as well as their wild-type alternatives. We searched this set ofsequences for occurrences of motifs with Find Individual Motif
Occurrences (FIMO) (Grant et al. 2011), relaxing the signiﬁcance threshold for reporting motif matches to P = 0.01. Thisrelaxed threshold allowed us to identify even weak matches to a
motif that could be improved by point mutations. We compared
the signiﬁcance of each mutant match to the signiﬁcance of that
motif’s match to the wild-type 25-mer for all motif matches thatoverlapped the middle position in the sequence and calculated alog-normalized ratio of these two scores.
Data availability

Scripts can be found at https://github.com/msr2009/Rich2016.
Raw data can be found in File S2. Raw sequencing reads can befound in the National Center for Biotechnology Information Sequence Reads Archive, Bioproject ID PRJNA273419. A ﬂowchart describing the experiments presented here can be foundin Figure S7. Variants are named in this manuscript using HGVS
format. For example, the mutation at position 2458 from T to A
is named 2458T.A.

Results
Extent of the SUL1 promoter

We ﬁrst sought to deﬁne the SUL1 regulatory region because
little functional annotation is available apart from the identiﬁcation of three putative TATA boxes at 2199, 293, and291 (Kellis et al. 2003; Basehoar et al. 2004). We created acentromeric plasmid-borne copy of SUL1 that included thecoding region, 687 bases upstream of the start codon and270 bases downstream of the termination codon. This region(chromosome II: 788548–792107) begins 118 bases downstream of the VBA2 termination codon and stops 57 bases
upstream of the ﬁrst base of ARS228. Deletion of this genomic region causes a 19% decrease in sulfate-limited ﬁtness(Payen et al. 2015). Expression of plasmid-borne SUL1 in thedeletion background increased ﬁtness by 20.4%. To identify
the functional extent of the SUL1 promoter, we measured theﬁtness in sulfate-limited media of sul1D yeast strains transformed with plasmids containing one of a set of truncatedpromoters, each progressively deleting 100 bases (Figure1). Truncation to a 592-base promoter caused a small ﬁtness
decrease (26.45%) compared to the full-length regulatory
sequence, although truncation further to 493 bases restoredﬁtness. All truncations that created a promoter shorter than493 bases were unable to complement the deletion of theendogenous SUL1, leading to substantial ﬁtness defects(233% to 253%). We deﬁned the 493 bases (chromosome
II: 788742–789234) as the minimal SUL1 promoter and used
this region for mutagenesis. Since the transcription start siteof SUL1 is also unannotated, mutations will be numbered
relative to the SUL1 start codon; for example, 2493 is chromosome II: 788742 and 21 is chromosome II: 789234.
SUL1 promoter mutagenesis and selection

We used error-prone PCR to create random mutations in the
SUL1 promoter and cloned these promoter variants scarlessly
by Gibson assembly into the SUL1 construct. After uniquely
barcoding plasmids and linking the plasmid barcodes to thesequence of the promoter variants, we obtained sequences of
152,723 variants uniquely tagged by 630,517 barcodes. Each
uniquely barcoded variant had on average 2.2 mutations. The
wild-type promoter was tagged by 92,753 barcodes (15.1%of the total barcodes) (Figure S2A). We transformed both
SUL+ and sul1D strains with this library. Transformants werecompeted as a pool during growth in sulfate-limited chemostats, and the activity of each barcoded promoter, as approximated by the relative ﬁtness of each strain in the pool, wascalculated as described in Materials and Methods. After stringent ﬁltering for high-conﬁdence variants (Materials andMethods), we calculated a wild-type-normalized ﬁtness value
for 29,906 promoter variants. The pooled transformants as awhole had a median ﬁtness decrease of 21.7%. Single-mutant
ﬁtnesses were speciﬁc to sulfate limitation, as ﬁtness values
after selection in glucose limitation, in which SUL1 activity
does not drive competitive ﬁtness, were neutral (Figure S4),
and the distribution of all ﬁtness scores in glucose limitation
was not statistically different from the ﬁtness score of the
94,912 wild-type promoter sequences in sulfate-limited
medium (P = 0.38, t-test).
Both the distribution of wild-type barcodes and the distribution of all variants were centered on zero and had a long
negative tail (Figure S2B). Unlike the distribution for wild-type

Figure 1 Determining the extent of the SUL1 promoter by truncation.
The ﬁtness of sul1D strains harboring plasmids with full-length and truncated SUL1 promoters was assayed by competition in sulfate-limited chemostats against a wild-type (SUL+) strain. Fitness values are normalized to
the full-length promoter (687 bases). Promoter lengths are measured fromthe SUL1 start codon. Error bars represent standard deviation (n = 2).

barcodes, the distribution of variant ﬁtness had a large shoulder corresponding to variants with ﬁtness decreases between
215 and 25%, consistent with many positions in the promoter
being sensitive to mutation.
We also examined the effect of mutations in a strain inwhich the endogenous copy of SUL1 was not deleted. The
ﬁtness effects of variants were generally highly correlated
between this SUL+ strain and the sul1D strain (Spearman’s
rho = 0.859) (Figure S3). Fitness values were correlated
between the two backgrounds for variants with wild-typenormalized ﬁtness . 215%. However, variants with wildtype-normalized ﬁtness , 215% in sul1D were not as unﬁt
in a SUL+ background (Figure S3).
Effect of point mutations in the SUL1 promoter

SUL1 ampliﬁcation is present in all populations at generation
100 during laboratory experimental evolution in sulfate limitation (Gresham et al. 2008; Miller et al. 2013; Payen et al.
2014). Accumulating more than one mutation in the 493
bases of the SUL1 promoter during those 100 generations isunlikely, so we ﬁrst limited the bulk of our analysis to theﬁtness effects from single mutations. We assayed 1400 of the1479 (94.6%) possible single mutants of the SUL1 promoter(Figure 2). Most mutations had little effect on ﬁtness. Basedon the ﬁtness distribution of uniquely barcoded wild-type
promoters, we established an empirical false discovery rateof 5% to be wild-type normalized ﬁtness less than 27.1% andgreater than 4.3%. Only 11 single mutations increased ﬁtness.4.3%, whereas 50 single mutations decreased ﬁtness by.7.1%. Overall, single mutations had a narrower distribution than the set of all variants, including a much shorter
negative tail. The effect size of ﬁtness-increasing mutations
was similar between singly and multiply mutated variants;

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195

only 46 variants with multiple mutations had ﬁtness increases
greater than the maximum single-mutant ﬁtness (9.4%), andthese variants increased ﬁtness up to 11.7%. All 46 of thesevariants contained at least one of 2353T.G, 2372T.C,
2404C.T, and 2458T.A, and 39 contained 2372T.C,
the second-most-ﬁt variant in the library. The effects ofﬁtness-decreasing variants appeared to be additive, as 1054
multiply mutated variants decreased ﬁtness more than themost detrimental single mutation (214.7%) (Figure S2B).
Of the 50 single mutations that decreased ﬁtness by.7.1%, 46 were found in four sites in the promoter (A–D
in Figure 2B). These sites appear as low-ﬁtness “stripes” on
the heatmap, with nearly every mutation that occurs in eachsite decreasing ﬁtness. These sites are short, between 8 and20 bases, and likely correspond to binding sites for the transcription factors that regulate SUL1 expression (see below).
Truncation of the SUL1 promoter to 393 bases, whichremoves two of these sites, resulted in the failure of theshortened promoter to complement a SUL1 deletion, presumably because it provides insufﬁcient expression.
Discovering SUL1 transcriptional regulatory sites

We used the single-mutation data to identify the likely transcription factors that regulate SUL1 expression. The wild-typesequence of site D (2200 to 2193) is TATAAATA, matching acanonical yeast TATA box (Basehoar et al. 2004). Using theslope and intercept data for each single mutant in the site,
we created a matrix of the log-likelihoods of ﬁnding eachsingle mutant in the site after 40 generations. We searchedtranscription factor motif data sets (Zhu and Zhang 1999;
MacIsaac et al. 2006) to ﬁnd signiﬁcant matches to the loglikelihood matrix. This analysis identiﬁed Spt15, the yeast
TATA-binding protein, as a match for this site (Figure 3D),
albeit one with a high false-discovery rate (q = 0.27), likely
driven by missing data in the ﬁrst three positions of thesite. Because we conservatively assigned missing values alog-likelihood of 1, i.e., no change in frequency during theselection, we decreased the signiﬁcance of our matches tothis site.
We then applied this methodology to the other three
sensitive sites (Figure 3, A–C). The log-likelihood matrix forsite A, spanning positions 2465 to 2448, weakly matched
the motif for Cbf1, a known regulator of sulfate metabolismthat recognizes CACGTG (Dowell et al. 1992), and Tye7, a
glycolytic activator that is implicated in Ty1-mediated gene
expression. A second site, from positions 2451 to 2449,
ﬂanking a hypothetical Cbf1 motif (CTCGTG), was also sensitive to mutation. These positions partially match the RYAAT
motif, which is necessary for full induction of genes during
growth in limited sulfate by enhancing binding of the Cbf1–
Met28–Met4 regulatory complex (Siggers et al. 2011).
The log-likelihood matrices for both site B and site C
contain the CCACA motif recognized by Met31 and Met32(Blaiseau et al. 1997). Site B matched Met32 (q = 0.03) andMet31 (q = 0.09) and appears to be an incomplete palindrome[GCCACA(CG)TGTGGC] centered on position 2407. Site C

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was the most signiﬁcant match of the analysis and matched
the Met32 motif (q = 0.005).
Although sites A, B, and C were generally sensitive to most
mutations, some mutations yielded large (.5%) increases inﬁtness. Comparison of the wild-type sequence to the highestﬁtness variant at these positions showed that each of thesebinding sites was one mutation away from the consensus
binding site for the implicated transcription factor. For example, mutation 2458T.A creates the consensus binding site ofCbf1 (CACGTG) and increased ﬁtness by 6.3% in our competition and increased Cbf1 binding strength 140-fold, as
measured in vitro by the MITOMI assay (Maerkl and Quake2007). Mutations 2413A.G and 2404C.T had similar effects in site B, creating a consensus binding site for Met32 andincreasing ﬁtness 6.5 and 7.1%, respectively. Mutation
2353T.G, which also creates a consensus Met32-binding
site, caused the largest ﬁtness increase in the data set (9.4%).
The importance of 2413A.G and 2356T.G distinguishes
these sites as Met32-binding sites, rather than similar Met31binding sites, as the importance of a 59 guanine is found only in
the Met32 site.
Other short sites in the promoter (e.g., 2329 to 2321)
showed similar trends of 8–20 contiguous positions being
sensitive to mutation, but with smaller ﬁtness effects. When
we performed a search for the log-likelihood of these sites
against known transcription factor-binding sites, we found no
signiﬁcant matches (data not shown).
Creation of new regulator-binding sites

The methodology that we used to ﬁnd endogenous regulators
depends on a signature of purifying selection throughout abinding site and so is not generally applicable to ﬁnding
mutations that create new transcription factor-binding sites.
Endogenous SUL1 regulatory sites are highly sensitive to mutation across the entire site, except for rare mutations that
optimize active sites (sites A, B, and C in Figure 2B). Thissignature of purifying selection would not be found in inactive binding sites, and point mutations that activate thesesites would be independent of the surrounding positions.
As such, we took a different approach to ﬁnd binding sitesthat occur or are lost upon single mutation.
To identify such sites, we ﬁrst enumerated all 25-mers andall possible single mutations centered in 25-mers in silico andthen searched these 25-mers for matches to transcription
factor-binding sites using relaxed parameters to allow for
weak matches. In total, 124,316 motifs matched at leastone 25-mer at a threshold of P , 0.01. An arbitrary P-valueof 0.01 was used for motifs that matched either the mutant or
wild-type 25-mer, but not both. We calculated the log ratio
between P-values for the wild-type and a mutant motif
match, log2(P_wt/P_mut) and used this ratio as a measureof motif strength.
Of the 588 motifs with a match signiﬁcance of P , 0.0001,
347 were either strengthened or weakened at least 10-fold by
mutations (Figure S5). The majority (52/56) of mutations
that decreased ﬁtness by .5% and altered the signiﬁcanceFigure 2 The effect of single mutations in the SUL1 promoter on sulfate-limited ﬁtness. (A) The ﬁtness change of each single mutation in our data set isplotted as a heatmap. At each position in the promoter along the x-axis, the base present in each single mutant is ordered on the y-axis, and every cell isshaded based on the ﬁtness change of that mutation. Missing data are white cells in the heatmap, and the wild-type base at each position is teal. (B)Mean ﬁtness change of all variants that are mutated at that position. Sites examined in Figure 3 are marked with pink backgrounds.

of a motif match by at least 10-fold occurred in either the
TATA box or sites A–C or created a new upstream open reading frame in the 59 UTR. Seven mutations matching 11 different motifs increased ﬁtness by .5% and had at least a
10-fold increase in motif signiﬁcance when compared to wild
type. We identiﬁed three of these (2353T.G, 2404C.T,and 2458T.A) in sites C, B, and A, respectively, as mutations
that optimize endogenous Met32- and Cbf1-binding sites.
One of the remaining mutations, 2372T.C, creates a Met32binding site (GCCACA), increasing the signiﬁcance of the motif
match by 23-fold and ﬁtness by 9.3%, the second-highest ﬁtness
increase from a single mutation in our data set. Another,
2246T.G, creates a one-off Cbf1 site (CATGTG), increasing
the signiﬁcance of the motif match by 32-fold and increasingﬁtness by 8.4% (Figure S5B).
Other mutations that increased ﬁtness .5% do not haveclear-cut biological explanations. For example, 2310A.T increased ﬁtness by 5.5% and the signiﬁcance of the match toHap2, a glycolytic activator, by 20-fold. The 2482A.G mutation might strengthen the binding of Aft1 or Aft2 (22.5- and
96-fold, respectively) or weaken the binding of Xbp1 by
43-fold. Aft1 and Aft2 regulate iron homeostasis, and Aft1
interacts physically with Cbf1 (Measday et al. 2005); the ﬁtness increase caused by 2482A.G may be dependent on Cbf1
binding to the neighboring binding site (site A). Alternatively, the ﬁtness increase could also be caused by decreased
Xbp1-mediated repression, as Xbp1 is known to repress

other Met4-responsive genes (Mai and Breeden 1997; Leeet al. 2010).
Effect of upstream open reading frames on SUL1 ﬁtness

Short open reading frames starting upstream of a gene’s coding sequence can post-transcriptionally regulate gene expression, as the ribosome creates unproductive polypeptides
instead of the correct protein (Morris and Geballe 2000;
Yun et al. 2012). No upstream open reading frames are present in the wild-type SUL1 59 untranslated region. Because
our assay selects on the amount of Sul1 protein in the cell, it
should identify mutations that cause a ﬁtness defect due topost-transcriptional regulation of SUL1 expression. Therefore, we searched for mutations that create an upstream open
reading frame (Figure 4), i.e., the mutation of a 3-mer to an
AUG start codon within the 59 untranslated region of the
SUL1 transcript (Smith et al. 1995). Our data contained 23of the 26 possible upstream ORFs, 8 of which decreased ﬁtness by at least 5%; none of the upstream ORF mutations
increased ﬁtness. Four upstream ORFs created a polypeptidethat was out of frame with the SUL1 coding sequence andread past the SUL1 start codon. These mutations were invariably deleterious (decreasing ﬁtness by $6%). Six upstream ORFs created an in-frame fusion to Sul1, withlengths ranging from 2 to 18 amino acids. The longest
fusion decreased ﬁtness by 7.2%, and as the fusions shortened, their effect on ﬁtness decreased, with fusions adding

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Figure 3 Determining SUL1 transcriptional regulatory sites through mutagenesis. To identify the transcription factors binding at the four sites highlighted in Figure 2B, we compared position weight matrices based on the ﬁtness values for single mutations at each site with a database of known yeasttranscription factor-binding motifs. (A–D) The wild-type sequence for each region is shown at the top of each panel. Below this is a barplot showing theﬁtness value for the single mutations at each site. Below the barplot is a set of sequence logos, the ﬁrst of which is based on the mutational data for theregion (calculated as described in Materials and Methods). Underneath this logo are the signiﬁcant (q , 0.2, except in D) matches between the ﬁtnessbased PWM and yeast transcription factor-binding sites. Panel labels are consistent with the labels in Figure 2B.

ﬁve or two amino acids being neutral (0.3 and 1.3% ﬁtness
increases, respectively).
Combinatorial analysis of high-ﬁtness mutations

Six mutations in our data set yielded a .6% increase in ﬁtness:
2353T.G, 2372T.C, 2246T.G, 2404C.T, 2413A.G,
and 2458T.A. Based on our analyses above, these mutations may increase the afﬁnity of a transcriptional activator.
Individually, these mutations conferred ,10% ﬁtness increases, much less than the increased ﬁtness (.37%) ofevolved strains with ampliﬁcations containing SUL1 (Gresham
et al. 2008; Payen et al. 2014). No variants that passed ﬁltration combined two or more of these mutations. To investigate the combinatorics of these mutations, we createdanother library in which ﬁve of these sites (excluding
2246T.G) were present as either the reference base or

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the high-ﬁtness mutation. Our library contained all 32 possible mutation combinations. As before, we transformed asul1D strain with this library, competed the population in
sulfate-limited chemostats, and calculated variant ﬁtness.
Fitness effects from single mutations were additive whencombined in pairs, although double mutants increasedﬁtness only 10.5%, a ﬁtness increase of 1.15% above themost-ﬁt single mutant (Figure 5). The addition of moremutations to each variant did not increase ﬁtness signiﬁcantly above the ﬁtness of the double mutants, with 11%
the maximum increase in ﬁtness reached by any mutation
combination.
Either 2353T.G or 2372T.C were necessary to reachthe ﬁtness plateau of an 10% increase. Four combinations without these mutations had a wild-type-normalizedﬁtness increase of 8.4 6 0.2% on average. All combinations

Figure 4 The effect of upstream open reading frames on SUL1 expression. We searched the 150 bases upstream of the SUL1 start codon for sites that
could mutate to form an upstream ATG. The heatmap of this region is shown with triangles marking the positions of mutations to upstream startcodons. Below each triangle is a cartoon representation of the upstream open reading frame created by each ATG. The position of the colored box ineach cartoon represents which base was mutated to create an ATG, and each box is colored based on the ﬁtness change of that mutation. Dashed lines
extending to right of each box show the extent of the hypothetical polypeptide encoded by each ORF. In the case in which the ORF is in-frame with the
SUL1-coding sequence, these polypeptides are drawn as colored boxes.

including 2353T.G or 2372T.C (n = 26) had a wildtype-normalized ﬁtness increase of 10.4 6 0.3%.
We tested a set of eight single mutants and the combinatorial variant in their native genomic context. Integration of
the 493-base promoter that deletes 200 bases of the intergenic sequence SUL1 and VBA2 led to strain ﬁtness of approximately that of a sul1D strain (data not shown). We therefore
integrated eight mutations and the combinatorial allele in thecontext of the entire 687-base promoter. The integrations
resulted in a scarless replacement of the SUL1 promoter.
The ﬁtnesses of integrated single mutants were highly correlated with pooled measurements of ﬁtness (Pearson coefﬁcient = 0.872, P = 0.002, Figure S6A). All variants except
2246T.G were contained within a 95% conﬁdence interval
based on the pooled ﬁtness values of the barcodes mapping toeach variant (Figure S6B). We could not calculate a conﬁdence interval for the ﬁve-mutation variant because it was
not found in the barcoded library and the combinatorial library did not employ barcodes.

Discussion
Ampliﬁcation has been proposed to be a “quick and dirty”
solution for adaptation, allowing more stable mutations tobe selected over a longer period of time (Hendrickson et al.
2002; Yona et al. 2012); indeed, ampliﬁcation of stress-speciﬁc
genes may be detrimental during nonstress growth. The ﬁtness
increase due to SUL1 ampliﬁcation [up to 51% (Payen et al.
2014)] is much higher than that due to any of the single mutations, the largest increase in ﬁtness of which was 9.35%.
Single mutations were not additive when combined, and theﬁtness increase of any combination of high-ﬁtness mutations
plateaued at 11%. This lack of additivity may imply that
there is a limit to the maximal rate of transcription achievablefrom the SUL1 promoter, limiting the effect of cis-regulatory
mutations. Thus, for the SUL1 promoter, ampliﬁcation and cisregulatory mutations are not equivalent, which is inconsistent
with the “quick and dirty” hypothesis of ampliﬁcation. Twocoding mutations in SUL1 have been isolated that increase
sulfate-limited ﬁtness by 23%, although it is not known how
they affect protein function. These mutants were isolated in astrain in which gene ampliﬁcation was prevented by deletionof the recombinase gene RAD51 (Payen et al. 2015). It remains
to be tested whether beneﬁcial mutations in the 39 UTR cansurpass gene ampliﬁcation.
In this study, we did not investigate the potential for transacting mutations to increase SUL1 expression, in part because
the transcription factors governing SUL1 expression werelargely unknown. Our data show that there are three sites
in the SUL1 promoter that match binding sites for sulfateregulatory transcription factors, two for Met32 and one for
Cbf1. Both these transcription factors provide DNA-binding
speciﬁcity for Met4, which recruits other transcriptional machinery. Met4 is a strong transcriptional activator (Titz et al.
2006), such that little Met4 occupancy in the promoter, perhaps only a single binding event, may be sufﬁcient to nearmaximally activate SUL1. Mutations that create new bindingsites for Met32 or Cbf1 presumably add an additional site forMet4 occupancy and conferred ﬁtness increases up to 9.3%,
which is a much smaller effect than that of ampliﬁcation.
Ampliﬁcation would increase the total copies of SUL1 available to be transcribed, leading to more protein than from asingle copy.
We determined that the Met32- and Cbf1-binding sites are
the primary regulators of SUL1 expression and that these sites
are not the consensus binding site for either factor. Consistent
with our analysis, met32 strains have decreased ﬁtness undersulfate limitation, whereas met31 strains do not (Payen et al.

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Figure 5 Combinatorial effects of highﬁtness mutations. The wild-type-normalized ﬁtness for each of 32 combinationsof ﬁve high-ﬁtness mutations is shown asa bar. The genotype of each variant is
identiﬁed on the x-axis: a black box ineach row indicates the presence of eachmutation in that variant.

2015). Our data also strengthen the observation that Met31
and Met32 are not fully redundant (Su et al. 2008; Cormieret al. 2010). Because each binding site in the SUL1 promoter
is at least one mismatch away from its consensus site, theirannotation by sequence-based motif-ﬁnding methods is difﬁcult. Studies showing combinatorial control by Cbf1 andMet31/32 of Met4-dependent genes failed to identify bystandard motif-ﬁnding approaches the Cbf1-binding site that
we identiﬁed in the SUL1 promoter (Lee et al. 2010; Carrillo
et al. 2012), although combinatorial regulation of SUL1 canbe parsed by analyzing transcription after inducing or repressing sulfate-metabolism regulators (McIsaac et al. 2012; Petti
et al. 2012). We searched the SUL1 promoter sequence using
two yeast transcription factor databases, YEASTRACT (Teixeira
et al. 2006; Monteiro et al. 2008; Abdulrehman et al. 2011;
Teixeira et al. 2014) and YeTFaSCo (de Boer and Hughes2012), to compare our results to the results from in silico
analyses. Our search yielded 99 possible motifs, only one ofwhich, a Met31 motif centered at 2410 (overlapping a sitethat we hypothesize to be a Met32 site), aligned with any ofour predictions.
Why are none of the transcription factor-binding sites in the
SUL1 promoter consensus sequences? A 5% increase in ﬁtness during sulfate limitation would be a strong evolutionary
force, and these mutations should therefore become ﬁxed in a
population. While the effects of these mutations were speciﬁc
to sulfate limitation, they do not appear to be detrimental
under permissive, sulfate-rich conditions, and thus theywould not be under purifying selection during nonstressful
growth. However, sulfate limitation, at least to the extent of
the experimental design here, may not be a selective pressure
frequently experienced by yeast in the wild. Alternatively,balancing selection may be working against maximal activation of SUL1; as another activity, Sul1 also transports toxic

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heavy metals (Cherest et al. 1997). SUL1 also appears to bedispensable under rich media conditions, as lager strains
carry loss-of-function mutations in the gene (Libkind et al.
2011). Our assay measures ﬁtness in steady state, so it ispossible that the wild-type promoter may have favorable kinetic qualities for fast adaptation to sulfate-limited environments; any temporally dependent aspects of SUL1 regulation
would go uninvestigated by our methodology.
The use of plasmids may confound some of our results. A
493-base SUL1 promoter on a centromeric plasmid complemented the genomic deletion of SUL1, but not when it was
genomically integrated. Centromeric plasmids have a low
copy number, but not necessarily a copy number of 1 (Karimet al. 2013). An increase in plasmid and SUL1 copy number
may have masked some of the effects of truncation and mutagenesis of the SUL1 promoter, with multiple copies of lowﬁtness promoters complementing a SUL1 deletion. We saw
this effect when measuring the ﬁtness of variants in the context of a SUL+ strain, where the endogenous copy of SUL1
masked variants that were detrimental in a sul1D background, and this could be the cause of the discrepancy inthe plasmid and integrated ﬁtness measurements for variants
like 2348A.T. Promoter strength has been shown to affectplasmid copy number (Karim et al. 2013). Our assay essentially counts the number of plasmids in a population, so biasing plasmid copy number dependent on promoter strength
would also bias our results.
Alternatively, background mutations in the strain may alterﬁtness independently of the SUL1 promoter genotype. Transformation is known to be mutagenic (Shortle et al. 1984),
and mutations can accumulate during strain construction(Wilkening et al. 2014). We advise that future studies carefully control for plasmid copy number. Copy number is not an
issue with integrations, but minor ﬁtness effects of variantsmay be difﬁcult to discern in integrants above the effects ofextraneous mutations in the strain background. This problem
may be circumvented by obtaining large numbers of barcoded integrants.
By using chemostats, we were able to perform sensitive
analyses of the ﬁtness effects of cis-regulatory mutations, ananalysis not accessible to other methodologies that assay
only transcriptional output. This approach should be applicable to studying transcriptional regulation more generally,as the SUL1 promoter can be replaced with other S. cerevisiae promoters, allowing for the same mutational analysis aslong as these other promoters are functional during growthunder limited sulfate. Finally, because this approach issensitive to post-transcriptional effects on protein expression, it could be adapted as a platform for studying posttranscriptional regulation in yeast, allowing, for example,
assays of the effects of 59 untranslated regions on protein
expression.

Acknowledgments
We thank Bill Noble and Charles Grant for help with Tomtom
and FIMO and for discussions about motif matching andKunihiro Ohta for the use of his tetrad dissection microscope. This work was supported by grants R01 GM094306(to M.J.D.) and P41 GM103533 (to M.J.D. and S.F.) fromthe National Institute of General Medical Sciences of the
National Institutes of Health. M.J.D. is a Rita Allen Foundation Scholar and a Senior Fellow in the Genetic Networks
program at the Canadian Institute for Advanced Research. S.F.
is an investigator of the Howard Hughes Medical Institute.
M.S.R. was partially supported by a Short-term Postdoctoral
Fellowship from the Japan Society for the Promotion of
Science.

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Communicating editor: A. Gasch

GENETICS
Supporting Information
www.genetics.org/lookup/suppl/doi:10.1534/genetics.116.188037/-/DC1

Comprehensive Analysis of the SUL1 Promoterof Saccharomyces cerevisiae
Matthew S. Rich, Celia Payen, Alan F. Rubin, Giang T. Ong, Monica R. Sanchez,
Nozomu Yachie, Maitreya J. Dunham, and Stanley Fields

Copyright © 2016 by the Genetics Society of America
DOI: 10.1534/genetics.116.188037

Fitness (%)

Sulfur Limitation
2
0
−2
−4
0

20

40

60

80

100

80

100

Generation 0 read counts

Glucose Limitation
Fitness (%)

2
0
−2
−4
0

20

40

60

Generation 0 read counts
Figure S1 Determining minimum read count threshold for variant filtration. Barcodes linked to wildtype SUL1 promoters wereused to set a minimum generation 0 read count for variant filtration. The fitness of each wildtype barcode is plotted againstthat barcode’s generation 0 read count. Filtration thresholds (red lines) were set at a point where the dependency between
read count and barcode fitness was not apparent.

Figure S2 Variant distributions. (A) Histogram showing number of mutations per variant. (B) Density plot of variant fitnesschange distributions.

Figure S3 Correlation between fitness changes of variants in FY3 and FY3sul1Δ. Only variants passing filtration in both datasets(n=29,763) are shown. The dashed line shows y=x.

Figure S4 The effect of single mutations in SUL1 promoter on glucose-limited fitness. (A) The fitness change of each single
mutation in our dataset is plotted as a heatmap. At each position in the promoter along the x-axis, the base present in eachsingle mutant is ordered on the y-axis, and every cell is shaded based on the fitness of that mutation. Missing data are whitecells in the heatmap and the wildtype base at each position is teal. (B) Mean fitness change of all variants that are mutated atthat position. Sites examined in Figure 3 are marked with pink backgrounds.

Figure S5 Identifying cryptic regulatory sites uncovered by mutations. (A) All motifs that strongly match either a wildtype ormutant 25-mer (P<0.001). Motifs are plotted by the change to match significance of each mutation (x-axis) and the fitness ofeach single mutant (y-axis). Points are colored based on their annotated function. (B) Inset showing motif matches that are
weakened by mutations that confer fitness increases. Unannotated mutations that match multiple motifs are grouped thetranscription factor motif matched is noted. Transcription factors that we hypothesize to be functionally important at that site
are in bold.

Figure S6 Correlation of fitness measurements between pooled plasmid assays and genomic Integrations. (A) Scatterplot
showing fitness measurements for strains based either on a pooled assay in which promoter variants are found on centromeric
plasmids (x-axis) or individual measurements in which promoter variants were integrated scarlessly at the SUL1 promoter (yaxis). Error bars represent standard deviations (n=2). The dashed line shows y=x. (B) Boxplots showing the distribution of fitness
values amongst redundantly barcoded variants tested in (A). Whiskers for boxplots span 95% of the total distribution. Larger
outlined points mark the fitness change of strains where each variant has been integrated at the SUL1 promoter. In bothpanels, fitness measurements are normalized to the wildtype fitness. The variant “5mut” comprises five mutations: -353T>G, 372T>C, -404C>T, -413A>G, and -458T>A.

Figure S7 Flow-chart of experiments presented in this study

Table S1 Oligonucleotides used in this study.
Oligo#

Oligo Name

Oligo Sequence

266

pRS416-SacI_chr2:792107_R

TCACTAAAGGGAACAAAAGCTGGATTCGGCTCAAGCTCAAGTTAACTTCTAGACC

283

pRS-Barcode@HindIII

GGCCCCCCCTCGAGGTCGACGGTATCGATANNNNNNNNNNNNAGCTTGATATCGGGATCC

294

pRS416-RI+HI_chr2:788548_F

ACGGTATCGATAAGCTTGATATCGGGATCCGACCGACAGTGCTGAACTGTGAG

295

pRS416-RI+HI_chr2:788742_F

ACGGTATCGATAAGCTTGATATCGGGATCCCGCCACCTCGAGTGCAC

296

fill-in_283_pMR002

CTCACAGTTCAGCACTGTCGGTCGGATCCCGATATCAAGCT297

SUL1p-mut_R

AGCATCCTCCTGATTATGCACATATTCAGTCGAGCTCTTACGTGACATATTCTTTCCGAG

314

pRS416-RI+HI_chr2:788643_F

GACGGTATCGATAAGCTTGATATCGGGATCCGTGAATAAGATGTGGCTGTGATAACC

315

pRS416-RI+HI_chr2:788845_F

GACGGTATCGATAAGCTTGATATCGGGATCCGCAAATCGGAATTTGAGTCACAGATC

316

pRS416-RI+HI_chr2:788943_F

GACGGTATCGATAAGCTTGATATCGGGATCCTTTGAACACTTCTACCTGTTCATGTC317

pRS416-RI+HI_chr2:789043_F

GACGGTATCGATAAGCTTGATATCGGGATCCGTGAAGTAAAATGTGTTGTAATGCACATGG

318

pRS416-RI+HI_chr2:789130_F

GACGGTATCGATAAGCTTGATATCGGGATCCGTGAAACCGATTATATAAAAGTATATTAGCTGAC

319

pRS416-RI+HI_chr2:789211_F

GACGGTATCGATAAGCTTGATATCGGGATCCCCCTGCAGAATACTCGGAAAGAAT325

pRS416-HindIIIBC-seqF

GGGCCCCCCCTCGAGGTCGACGGTATCGATA326

pMR002-seqR

TTCCCTATATGTGCGGTATTCTGATTCAAATACTTCGATATCAGCATCCTCCTGATTATGCACATATTCAGTCGAGCTCTTACGTGACAT327

P5-M13F

AATGATACGGCGACCACCGAGATCTACACTGTAAAACGACGGCCAGT

328

P7_SUL1-109_R

CAAGCAGAAGACGGCATACGAGATCCTCATCACCATTGTGAAGTCCGTCT

344

nexV2_SUL1-109_R

GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCCTCATCACCATTGTGAAGTCCGTCT380

SUL1p_6mut_F1

TCTCGCTGGARCCACAGTGYGGCTTTGCAATTTTGCAAATCGGAATTTGAGYCACAGATCCCAGAAAAACKCCACACCTTCCCCACGCAG

381

SUL1p_6mut_R1

CACAGTGCATAATTTCAKATGACAGTGTTCGAGAAAAGACATGAACAGG

382

SUL1p_6mut_F2

GTGCACTTTTTTTAATAAAGATCWCGTGTAATTGTCCAAATCTGACTTTT

387

SUL1p_6mut_seqR

GGGTCTAATTTGACAACTTGTTCGTTATCGCTTGCTGCGTGGGGAAGGTGTGG

395

P5_X_M13_F1

AATGATACGGCGACCACCGAGATCTACACNNNNNNNNNTGTAAAACGACGGCCAGT442

SUL1p_6mut_seqF2

CGATAAGCTTGATATCGGGATCCCGCCACCTCGAGTGCACTTTTTTTAATAAAGATC

443

SUL1p_6mut_seqI2

ATTAGACCCATAATAATTTTGAACACTTCTACCTGTTCATGTCTTTTCTCGAACACT

517

n.-353T>G

AGATCCCAGAAAAACGCCACACCTTCCCCAC

518

n.-372T>CTCGGAATTTGAGCCACAGATCCCAG

519

n.-246T>GGTCTTTTCTCGAACACTGTCATGTGAAATTATGCA

520

n.-404C>TGGAACCACAGTGTGGCTTTGCAATT

521

n.-413A>GAGTCTCGCTGGAGCCACAGTGCGGC

522

n.-458T>AGTGCACTTTTTTTAATAAAGATCACGTGTAATTGTC

603-33A>G

ggtcaatagctttaaaaataGaaataaatccctgcag

604-400T>C

cagtgcggcCttgcaattttgc

605-348A>T

gaaaaactccacTccttccccac

678

pSUL1::URA3_F

TCCCCCCACTTTTTTGTGTTACCGCCACCTCGAGTGCACgattcggtaatctccgaacag

679

pSUL1::URA3_R

ACATATTCAGTCGAGCTCTTACGTGACATgggtaataactgatataattaaattgaagct680

pSUL1::URA3_F2

CACAAACCAATGAAAAAAGGCTTCAGAAAACTTGCTGTAAAGTCCCCCCACTTTTTTGTG

681

pSUL1::URA3_R2

attcaaatacttcgatatcagcatcctcctgattatgcacatattcagtcgagctcttac

685

pSUL1_687bp_F

gaccgacagtgctgaactgtgag

PGO40

SUL1::NatMX4 Forward primer

TTGAAACTAGATATTCTACTTGACACTAAACTTTTTTTGTATTTCGTGGACAGATTGTACTGAGAGTGCACCATA

PGO41

SUL1::NatMX4 reverse primer

TGAAAACGGCCCTTTTCGTGTACTAATGCTTTCAGGAAATTATAAAATTTCCTTACGCATCTGTGCGGTATTTCANexV2ad2_N2

NexV2ad2_N2

CAAGCAGAAGACGGCATACGAGATNNNNNNNNNGTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG

M13F

M13F

TGTAAAACGACGGCCAGT

P5P5AATGATACGGCGACCACCGAGATCTACAC

1 – P5_N_M13_F is a set of indexed oligonucleotides used to demultiplex Illumina sequencing datasets (by the second index read.) Each oligonucleotide has a unique sequencein the region marked by Ns.
2 - NexV2ad2_N is a set of indexed oligonucleotides used to demultiplex Illumina sequencing datasets (by the first index read). Each oligonucleotide has a unique sequence inthe region marked by Ns.

Table S2 Yeast strains used in this study.
Name

Mating type

Genotype

Reference

FY2

alpha

ura3-52(Winston et al. 1995)

FY3

aura3-52(Winston et al. 1995)

FY4

aprototroph(Winston et al. 1995)

YMD3017a

SUL1::NatMX4, ura3-52 Gal+

This study

YMR002a

FY3 PSUL1::URA3 ura3-52

This study

YMD3018

alphasul1::NatMXThis study

YMR008a

PSUL1-246T>G ura3-52

This study

YMR009a

PSUL1-404C>T ura3-52

This study

YMR010a

PSUL1-458T>A ura3-52

This study

YMR011a

PSUL1-400T>C ura3-52

This study

YMR012a

PSUL1-348A>T ura3-52

This study

YMR017a

PSUL1-353T>G ura3-52

This study

YMR018a

PSUL1-372T>C ura3-52

This study

YMR019a

PSUL1-413A>G ura3-52

This study

YMR020a

PSUL1-5mut ura3-52

This study

YMR022a

PSUL1-246T>GThis study

YMR024a

PSUL1-458T>A

This study

YMR025a

PSUL1-400T>C

This study

YMR026a

PSUL1-348A>T

This study

YMR027a

PSUL1-353T>GThis study

YMR028a

PSUL1-372T>CThis study

YMR029a

PSUL1-413A>G

This study

YMR030a

PSUL1-5mut

This study

YMD1214aho::kanMX-eGFP(Payen et al. 2014)

YMD1139apFA6a-TEF2Pr-eGFP-ADH1-Primer-NATMX4

D. Breslow

File S1: Software used for data analysis. (.zip, 3 MB)

Available for download as a .zip file at:
http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.116.188037/-/DC1/FileS1.zipFile S2: Raw data files. (.zip, 44 MB)

Available for download as a .zip file at:
http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.116.188037/-/DC1/FileS2.zip