Systematic measurement of transcription factor-DNA
interactions by targeted mass spectrometry identifies
candidate gene regulatory proteins
Hamid Mirzaeia,b, Theo A. Knijnenburga,c, Bong Kima, Max Robinsona, Paola Picottid,e, Gregory W. Cartera,f, Song Lia,
David J. Dilwortha, Jimmy K. Enga,g, John D. Aitchisona, Ilya Shmulevicha, Timothy Galitskia,h, Ruedi Aebersoldd,e,1,
and Jeffrey Ranisha,1
aInstitute for Systems Biology, Seattle, WA 98109;bDepartment of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX 75390;
cBioinformatics and Statistics, Division of Molecular Biology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands;dDepartment of Biology,
Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, CH-8093 Zürich, Switzerland;eFaculty of Science, University of Zürich,
CH-8006 Zürich, Switzerland;fThe Jackson Laboratory, Bar Harbor, ME 04609;gDepartment of Genome Sciences, University of Washington, Seattle, WA
98195; andhEMD Millipore Corporation, Billerica, MA 01821
Edited by Mark Groudine, Fred Hutchinson Cancer Research Center, Seattle, WA, and approved January 4, 2013 (received for review October 12, 2012)
Regulation of gene expression involves the orchestrated interaction
of a large number of proteins with transcriptional regulatory
elements in the context of chromatin. Our understanding of gene
regulation is limited by the lack of a protein measurement technol-
ogy that can systematically detect and quantify the ensemble of
proteins associated with the transcriptional regulatory elements
of specific genes. Here, we introduce a set of selected reaction mon-
itoring (SRM)assays forthesystematic measurementof464proteins
with known or suspected roles in transcriptional regulation at RNA
polymerase II transcribed promoters in Saccharomyces cerevisiae.
the reproducible quantification of 42% of the proteins over a wide
range of abundances. By deploying the assay to systematically iden-
tify DNA binding transcriptional regulators that interact with the
environmentally regulated FLO11 promoter in cell extracts, we iden-
tified 15 regulators that bound specifically to distinct regions along
∼600 bp of the regulatory sequence. Importantly, the dataset
includes a number of regulators that have been shown to either
control FLO11 expression or localize to these regulatory regions in
ing that two of the SRM-identified factors, Mot3 and Azf1, are re-
quired for proper FLO11 expression. These results demonstrate the
utility of SRM-based targeted proteomics to guide the identification
of gene-specific transcriptional regulators.
termine the composition of the regulatory complexes that as-
semble at specific genes and to determine how the composition of
these complexes change in response to cellular, genetic, and envi-
ronmental signals. Despite considerableefforts to address these key
questions, the lack of methods for routine analysis of the ensemble
regulatory elements (TREs) remains a significant limitation.
Current approaches for studying TF–TRE interactions include
the EMSA (1, 2), protein binding microarrays (PBMs) (3), the
yeast one-hybrid method (4), and chromatin immunoprecipitation
(ChIP)-based methods (5, 6). Although each of these methods can
provide information about TF–DNA interactions, their utility for
at TREs is limited by the need for genetic engineering, protein
detection reagents, and/or purified proteins. Notably, ChIP-chip
has been used to systematically study the localization of most
transcriptional regulators (TRs) across the yeast genome (7), but
this tour de force required the creation and independent assay of
203 TR-specific, epitope-tagged strains.
require genetic engineering or protein detection reagents, and can
readily provide information about the ensemble of TFs associated
ritical to understanding gene regulation is the ability to de-
of the ability of MS to identify large numbers of proteins in com-
plex mixtures, and, when performed in a quantitative manner, can
identify specific TF–DNA binding events even in the presence of
a high background of nonspecifically copurifying proteins. Al-
though this is a powerful method to study TF–DNA interactions,
canlimit sampling tothe most abundant peptides eluting from the
the reproducibility of the method can be limited due to the sto-
chastic nature of precursor ion selection before collision-induced
Although significant effort has been focused on developing
methods for the identification of TF–TRE interactions, less effort
has been exerted toward improving the reproducibility, limit of de-
tection, and dynamic range of the platforms used to analyze the
samples. However, selected reaction monitoring (SRM) permits
highly reproducible measurements of a target set of peptides, with
of quadrupole mass analyzers to detect and quantify specific, pre-
determined peptides in complex mixtures (11, 12). The reproduc-
ibility and wide dynamic range of SRM originate from its focus on
targeted signals over extended periods of time. Additionally, more
time istypicallyallocatedtoacquisitionof datacorrespondingtothe
targeted analyte in SRM compared with shotgun MS experiments.
Shorter duty cycles also contribute to SRM quantification accuracy
by permitting acquisition of sufficient data points over the chro-
matographic elution profile of a specific peptide to support accurate
peak reconstruction (13, 14). The favorable sensitivity, dynamic
range, reproducibility, quantification accuracy, and relative insensi-
tivity to chemical noise make SRM an attractive protein analysis
platform for characterization of systems such as the TF–TRE inter-
actome where most of the potential interacting proteins are known.
To improve our ability to systematically study the ensemble of
TFs associated with TREs, we developed an array of SRM assays
that targets most known and putative proteins that function at
RNA polymerase II (Pol II) TREs in Saccharomyces cerevisiae.
Author contributions: H.M., T.A.K., M.R., P.P., J.D.A., I.S., T.G., R.A., and J.R. designed
research; H.M., T.A.K., B.K., M.R., P.P., G.W.C., S.L., and D.J.D. performed research;
H.M., T.A.K., M.R., P.P., J.K.E., T.G., and R.A. contributed new reagents/analytic tools;
H.M., T.A.K., M.R., G.W.C., T.G., and J.R. analyzed data; and H.M., T.A.K., B.K., M.R.,
G.W.C., T.G., R.A., and J.R. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: The SRM coordinates are provided in Dataset S1.
1To whom correspondence may be addressed. E-mail:firstname.lastname@example.org or rudolf.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
| February 26, 2013
| vol. 110
| no. 9
We selected three to five proteotypic peptides for each targeted
protein and developed optimized LC and MS settings for sen-
sitive and reliable detection of each peptide in a complex sample.
The final set of assays, which includes 464 proteins, 1,639 pep-
tides, and 9,834 SRM transitions (mass spectral coordinates to
trace a peptide via its fragment ions), is the largest set of SRM
assays deployed to measure a subproteome to date. Moreover,
because the SRM assays are transferrable to other laboratories,
and even to other instruments with minimal optimization (15),
they will serve as a valuable resource for future systematic studies
of the TF proteome.
We first used the assays to probe a yeast nuclear extract, where
we reproduciblydetected and accuratelyquantified more than 42%
of alltarget proteins without any fractionation. To demonstrate the
utility of the assays to identify TF–TRE interactions, we applied
them to systematically assess the ability of 222 known and putative
TRs to specifically bind to 642 bp of the FLO11 gene regulatory
region in cell extracts. FLO11 encodes a cell surface glycoprotein
that is required for yeast to execute important developmental
decisions, such as the transition from round cell growth to pseu-
dohyphal or invasive growth in response to diverse environmental
and internal signals (16, 17). The FLO11 gene regulatory region is
one of the most complex in yeast, containing at least four upstream
activation sequences (UASs) and nine repression elements that
together span ∼2.8 kb (17). In addition, a broad array of regulators
has been implicated in the control of FLO11 expression (16–23).
Because of its central role in integrating cellular and environ-
mental signals, the complexity of its promoter, and a large body of
research on its regulation, the FLO11 gene was an attractive
target for testing the utility of the SRM assays. The set of SRM
assays identified 17 FLO11-specific binding events involving 15
TRs including the known FLO11 regulator Msn1. Comparison of
the SRM dataset with results from motif scanning, PBMs (3),
a large-scale ChIP-chip study (7), and functional data revealed
prior supporting evidence for 11 of the binding events. Based on
this analysis, as well as our network analysis of filamentation (20),
we selected two candidates for follow-up studies: Mot3 and Azf1.
These studies established activating and repressing roles for Azf1
and Mot3, respectively, in the control of FLO11 expression, and
they validated the localization of Azf1 to a previously charac-
terized UAS in the FLO11 promoter. The results highlight the
effectiveness of the SRM-based approach to systematically mea-
sure the TF proteome, and, when used in combination with
a DNA affinity purification step, to systematically screen for
TF SRM Assay: Array of Validated SRM Assays Targeting the Yeast TF
Proteome. To develop a system to systematically study TF–TRE
interactions, we first generated an array of definitive and quanti-
tative SRM assays to unambiguously detect and quantify most
known and putative proteins that function at or near RNA Pol II
literature to include known and putative DNA binding TRs, cor-
egulator complexes, chromatin remodeling and modifying com-
plexes, and the general transcription machinery (Dataset S1). We
then searched the PeptideAtlas database (www.peptideatlas.org)
(24, 25) for mass spectral evidence for each target protein. For
proteins with existing mass spectral evidence, target peptides were
selected from the list of previously detected peptides using pre-
viously described selection criteria (12, 25). For proteins without
existing mass spectral evidence, we either generated the data by
enriching target proteins using protein or promoter DNA affinity
purification followed by trypsin digestion and shotgun MS analysis,
or we used the PeptideSieve algorithm to predict the best target
peptides (26). We then chemically synthesized the peptides se-
lected from PeptideAtlas and PeptideSieve and analyzed them by
in our assays. For enriched proteins, peptides derived from tryptic
digestion of the samples were used to generate the assays. This
analysis resulted in validated SRM assays for 464 proteins and
1,639 peptides (Fig. S1). Eighty-three percent of the proteins can
be detected by monitoring transitions from more than one peptide,
and ∼40% can be detected by monitoring transitions from five
peptides. We then calculated the SRM transitions for all peptides
containing isotopically heavy lysine (6C13, 2N15) or arginine
(6C13, 4N15) at their C termini. Our final list contained 9,834
heavy and light SRM transitions (Dataset S1).
Due to the large number of SRM assays, it was necessary to use
a scheduled mode of measurement to increase the capacity for si-
multaneous monitoring of peptides (27). In addition, halogenated
peptides as internal standards (H-PINS) technology was used to
ensure reliable detection of targeted peptides during SRM analysis
(28). H-PINS consist of a set of halogenated peptides which are
usedas standardstocalibratethe retention times(RTs) oftargeted
peptides throughout the analysis. The use of H-PINS is important
becauseduringscheduledSRM,a change inpeptideRTcouldlead
to complete loss of signal if the peptide elutes outside of the win-
dow of time when it is expected to elute. All mass spectral coor-
dinates required to use the assays are provided in Dataset S1.
Quantifying the TF Proteome in Nuclear Extracts. We assessed the
reproducibility, limit of detection, and false discovery rate (FDR)
oftheTFSRM assay (SRMA), as well its accuracyand precision of
quantification, by monitoring all 9,834 target transitions (1,639
peptides, 464 proteins) as well as 3,720 decoy transitions (620
“decoy peptides”; decoy peptides were generated by reversing the
sequences of randomly selected target peptides) in yeast nuclear
extracts. Extracts prepared from cells grown in media containing
either isotopically heavy or light lysine and arginine were mixed in
a 2:1 ratio (light:heavy), digested, and analyzed in triplicate by
LC-SRM in scheduled mode. SRM peptide identification was
based on detecting three coeluting transitions for both heavy and
light peptides with expected relative transition ratios within a pro-
jected elution window. We reproducibly identified 327 peptides
representing 196 of the 464 (42%) target proteins, but only1 decoy
peptide, yielding an estimated FDR of <1% (Fig. 1A and Dataset
S2). Reproducibility was excellent as 92% (327 of 355) of the
the assay, yeast nuclear extract was monitored for the entire set of target
proteins as described in the text. (A) Assay reproducibility. Venn diagrams
depicting the number of peptides and proteins that were detected in trip-
licate analyses of the samples are shown. (B) Detection limit. A plot depicting
the range of abundances for detected proteins is shown. (C) Quantification
precision. The coefficient of variation for protein light:heavy (L:H) ratios was
used as the measure of protein quantification precision. (D) Quantification
accuracy was determined by fitting the protein L:H ratios to a Gaussian
standard curve; see also Dataset S2.
Application of TF SRMA to detect proteins in nuclear extracts. To test
| www.pnas.org/cgi/doi/10.1073/pnas.1216918110Mirzaei et al.
identified peptides and 94% (196 of 209) of the identified proteins
were detected in all three analyses.
Interestingly, seven of the detected proteins are not represented
in PeptideAtlas (24), and four of these proteins were not visualized
in a global protein quantification study by Western blotting (29). It
is likely that the target proteins that were not detected either were
not expressed under the growth conditions used, were below the
limit of detection, or were not detected due to the presence of
unanticipated modifications to their representative peptides. To
assess our limit of detection, we checked the concentration of
detected proteins in terms of copy per cell as determined in
a Western blotting study (29). TF SRMA is capable of quantifying
proteins that are expressed at a wide range of abundance levels,
from <50 to 213,000 copies per cell (Fig. 1B). The precision of the
analysis is reflected by the mean coefficient of variation (CV) of
relative quantifications of all proteins measured by triplicate
analyses, which was 27% (Fig. 1C). The relative abundance for
ratios of 2.15 (Fig. 1D). The measurement was therefore accurate
to 7.5% [2.15 − 2)/2]. TF SRMA therefore provided reproducible,
accurate, and precise quantification for 42% of the measured TF
proteome in unfractionated nuclear extracts.
Identification of FLO11 Promoter Binding Factors Using TF SRMA. We
next used TF SRMA to systematically measure the propensity
of 222 known or putative DNA binding TRs (Datasets S3 and S4)
to interact with specific segments of the FLO11 promoter. As de-
scribed above, the FLO11 promoter is an attractive target for
testing the utility of the SRM assays because it contains a large
number of cis-regulatory elements and numerous trans-acting fac-
identified FLO11 regulatory elements by construction of FLO11::
lacZ reporter constructs containing either serial ∼200-bp deletions
in the promoter or overlapping 400-bp promoter segments (17).
We focused on the 642 bp contained in segments 5, 6, and 7 be-
cause it contains several cis-regulatory elements that are required
forproper FLO11 expression (17), and a number of TRs, including
Flo8, Sfl1, Ste12, Tec1, Msn1, and Gcn4 have been implicated in
controlling FLO11 expression by acting on sequences within this
region (17, 18, 23).However, ofthese TRs, only Flo8 and Sfl1 have
been shown to directly bind to this region of the FLO11 promoter
(18). Thus, we expected to identify some of these known TRs as
well as previously unrecognized FLO11 regulators.
To identify FLO11 promoter binding factors, we used quanti-
tative TF SRMA to compare the abundance levels of TRs isolated
by either FLO11 promoter affinity purifications or by a control
located between convergently transcribed genes. Proteins specifi-
cally enriched in the FLO11 promoter purifications could be
identified based on their relative abundances from the two purifi-
cations during quantitative SRM analysis. Quantification was
achieved by preparing two SILAC-labeled extracts (30), one each
from cells grown to postdiauxic conditions on media containing
isotopically heavy or light lysine and arginine. These are conditions
that activate FLO11 expression (17). We performed two experi-
ments for each FLO11/control comparison (Fig. S2). In the “for-
ward” experiment, the FLO11 promoter segment was incubated
with the heavy labeled extract and the control DNA was incubated
with the light labeled extract, whereas in the “reverse” experiment
the extracts were reversed. After combining the eluate from each
FLO11 promoter purification with eluate from the control purifi-
cation, the mixtures were digested and prepared for SRM analysis.
Protein ratios and corresponding P values were computed sep-
arately for the forward and reverse experiments by combining all
heavy/light transition pair ratios corresponding to the protein
(Table 1, Datasets S3 and S4, and SI Text). The P values from the
forward and reverse experiment for each protein were combined
using Fisher’s method, and proteins with an FDR of <1% were
called significant binding events. Of 666 potential TR–DNA
interactions (222 proteins, 3 promoter segments) assayed, we
detected 17 instances of preferential binding to a FLO11
promoter segment, involving 15 unique proteins (Table 1). Al-
though most of the enriched TRs were not known to regulate the
FLO11 promoter, the known FLO11 regulator Msn1 was sig-
nificantly enriched in the segment 6 purification. This is direct,
physical evidence for an Msn1–DNA interaction in this region;
MSN1 responsive elements (19, 23). Taken together, two in-
dependent lines of evidence suggest that Msn1 regulates FLO11
expression by acting eitherdirectly or indirectly on segment 6.The
results show that the systematic SRM assay is capable of identi-
fying a known FLO11 TR as well as potential TR–FLO11
The known FLO11 regulators Flo8, Ste12, and Gcn4 were
quantifiedby TFSRMA,buttheywere notsignificantlyenrichedin
the FLO11 promoter purification (Dataset S3). Potential reasons
for not detecting enrichment of TRs that have been implicated in
regulating FLO11 via segments 5, 6, or 7 could be that the proteins
do not bind efficiently to the immobilized promoter segments un-
FLO11 promoter, or the peptides chosen for studying these pro-
teins were not detected due to the presence of unanticipated
modifications. We note that this is a proof-of-principle experiment
designed to demonstrate the feasibility of using SRM to system-
to bind to specific TREs. Further optimization of the cell growth
additional specific binding events.
Comparison of SRM Results with Other Systematic TR–DNA Interaction
Studies. To guide our selection of TRs for functional validation
that supported the SRM results. Specifically, we compared the
SRM TR–DNA binding data for each FLO11 promoter segment
with TR–DNA interaction data from motif scanning, PBMs (3),
and in vivo techniques, respectively (Figs. S3 and S4, Dataset S5,
and SI Text). Eleven of 17 interactions identified by SRM had
independent support. Seven are supported by all available data
[Azf1::5, Azf1::6, Mot3::6, Mot3::7, War1::6, Yap6::7, Hmo1::7
(numbers after colon indicate the FLO11 DNA segment)].
Proteins showing preferential binding to FLO11
n Log ratioCVn Log ratio CVFDR
6 11 11
Shown are all proteins with FDR of <1% and a binding preference [log2
(ratio) > 0] for a FLO11 promoter segment relative to control in both bi-
ological replicates, along with the number of transitions (n) contributing to
each protein measurement and the coefficient of variation (CV) of the ratios.
FWD, forward; REV, reverse.
Mirzaei et al.PNAS
| February 26, 2013
| vol. 110
| no. 9
Mcm1::6 is supported by PBM, and motif scanning but not ChIP;
Crz1::5, Rtg3::5, Msn1::6, Yap1::7 are supported by only one other
source. Among the TRs involved in binding events supported by all
available data, Mot3 and Azf1 are especially interesting, because
they are found on two segments (5 and 6 for Azf1, and 6 and 7 for
Mot3). Based on the results of this comparative analysis, and our
previous network analysis of filamentation (20), Mot3 and Azf1
were selected for follow-up studies to determine whether they reg-
ulate FLO11 expression.
To assess how TF SRMA compares with these other systematic
approaches for detection of TR–DNA interactions, we performed
an enrichment test for all pairs of data from our SRM study, motif
scanning, PBM, and ChIP-chip (SI Text). Although we find a sig-
each of the other methods (P < 0.05), we did not find a significant
analysis indicates that TF SRMA, PBM, and ChIP-chip provide
largely distinct TR–DNA interaction information for the FLO11
regulatory regions studied here. However, integration of this in-
formation, as done in this study, can help guide identification of
Mot3 Is a Repressor of FLO11 Expression. Mot3 is an attenuator of
signaling responses (32) that negatively regulates filamentous and
invasive growth (20, 33). Our SRM evidence indicates that Mot3
binds segments 6 and 7 of the FLO11 promoter (Table 1 and Fig.
S5). These findings are supported, in part, by motif scanning
(segments 5–7) and ChIP-chip data (7) (the probe used in this
study covers segments 5–7) (Fig. S6 and Datasets S5 and S6).
Segment 7 has also been functionally identified as a promoter se-
quence that can repress FLO11 expression (17). Based on these
results, we hypothesized that Mot3 functions as a transcriptional
repressor of FLO11 upon binding to its promoter.
We initially tested this hypothesis by assaying the expression of
green fluorescent protein (GFP) driven by a copy of the FLO11
promoter, inserted into the genome adjacent to the intact FLO11
gene, in the presence and absence of a galactose-inducible MOT3
expression construct. Results are shown in Fig. 2A. We found that
FLO11 expression is increased after growth on low-nitrogen solid
media (SLAD), and this increase in expression is suppressed upon
galactose induction of MOT3 expression. Furthermore, by micro-
array analysis, deletion of MOT3 leads to a threefold increase in
FLO11 gene expression on SLAD media (Fig. 2B). These results
confirm that Mot3 is a repressor of FLO11 expression.
We next assayed the filamentation phenotype of a MOT3 gene
deletion strainand a straininducibly overexpressing MOT3 (GAL1-
MOT3). Because FLO11 is the prototype filamentation gene, we
predicted that filamentation competence would correlate with
effects on GFP expression. We found that deletion of MOT3 in
cells leads to hyperfilamentation, as well as hyperinvasion on
SLAD agar (Fig. S7), and hyperadhesion on YPD agar (Fig. 2C).
We also found that galactose-induced MOT3 overexpression sup-
pressed cell elongation and hyperfilamentation (Fig. S7). These
data further support our SRM-derived hypothesis that Mot3
binds the FLO11 promoter, either directly or indirectly, and
negatively regulates its expression. Together with the SRM and
ChIP-chip data, the results suggest that Mot3 is a repressor of
FLO11 expression that functions by localizing to promoter seg-
ments 6 and 7.
Azf1 Is an Activator of FLO11 Expression.Azf1isazincfingerTRthat
can affect expression of different classes ofgenes depending on the
bind to FLO11 promoter segments 5 and 6 (Table 1 and Fig. S5),
and these findings are supported, in part, by motif scanning (seg-
segment-specific ChIP-qPCR studies on chromatin isolated from
cells expressing a myc-tagged Azf1 protein after growing cells to
postdiauxic conditions (Fig. 3A). Our ChIP study revealed that
Azf1 localizes to segment 6 in vivo (P = 0.0001). It is possible that
Azf1 does not localize to segment 5 in vivo under postdiauxic
conditions, perhaps due to the chromatin environment, or that the
ChIP assay is not able to detect Azf1 localization at segment 5.
Because segment 6 activates FLO11 expression under both post-
diauxic and exponential growth conditions (17), we hypothesized
that Azf1 controls FLO11 expression via binding to segment 6. We
initially tested this hypothesis by assessing the expression levels of
FLO11 inwild-typecellsandanAZF1 deletionstrainafter growing
cells under postdiauxic conditions. We were unable to reliably
detect a difference in FLO11 expression levels between the strains
under these conditions. Subsequently, we assessed the expression
levels of an integrated GFP reporter driven by the FLO11 pro-
moter in either a wild-type strain or an AZF1 deletion mutant after
transferring stationary phase cells to glucose-rich media. These are
conditions that also induce FLO11 transcription due to a transient
increase in cAMP levels and subsequent activation of the protein
kinase A (PKA) pathway (36). Under these conditions, we found
that induction of GFP expression was severely compromised in the
AZF1 deletion strain compared with a wild-type strain and ex-
pression of Azf1 from a plasmid restored GFP expression to near
wild-type levels (Fig. 3B). Together with our SRM and ChIP
results, the data indicates that Azf1 is an activator of FLO11 ex-
pression that functions, at least in part, by binding to segment 6.
Furthermore, the results highlight the utility of the SRM-based in
vitro promoter binding assay combined with ChIP-qPCR as a way
to pinpoint TR interaction sites in TREs.
TREs and their dynamics is critical to understanding gene regu-
lation. To address this issue, we have developed a set of SRM-
based assays for the systematic measurement of the yeast TF pro-
teome. SRM is an attractive technology for this purpose because it
unfractionated nuclear extracts with high reproducibility and
quantitative accuracy and precision (Fig. 1). Next, we applied the
assay to identify potential regulators and their binding locations
and an attenuator of adhesive growth. (A) MOT3
overexpression suppresses GFP expression driven
by the FLO11 promoter. Data are shown for yeast
cells (S288c background) containing either an empty
vector, grown on synthetic low-ammonium solid
media containing either glucose (SLAD, black line)
or galactose (SLAG, red line), or a GAL1-driven
MOT3 expression construct, grown on SLAD (blue
line) or SLAG (violet line). (B) FLO11 gene expres-
sion intensity relative to wild-type (WT) yeast-form
growth. Three biological replicates are shown for
WT and mot3Δ. The mean expression ratio of WT
SLAD is 4.6, and mean expression ratio of mot3Δ SLAD is 12.7. (C) MOT3 deletion strains are hyperadhesive on YPD (Σ1278b background for B and C).
Mot3 is a repressor of FLO11 expression
| www.pnas.org/cgi/doi/10.1073/pnas.1216918110Mirzaei et al.
along ∼600 bp of the FLO11 promoter. TF SRMA identified 15
unique regulators involved in 17 TR binding events that were sig-
nificantly enriched in FLO11 promoter purifications. In support of
the effectiveness of the approach, one of these TRs, Msn1, was
previously shown to regulate FLO11 expression, and the segment
where Msn1 was enriched overlaps precisely with previously de-
scribed MSN1 responsive elements (19, 23). Furthermore, there is
supporting evidence from either ChIP-chip, motif scanning, or
PBM data for 10 of the 16 additional binding events that we
detected. We note that this is likely an underestimate of the ef-
fectiveness of the SRM-based approach because we only analyzed
∼20% of the known regulatory region and cell extracts were pre-
pared from only one growth condition.
Although we found a significant overlap of binding events
determined by motif scanning and SRM, PBM, or ChIP-chip, we
did not find a significant overlap of binding events identified
among the other approaches. This result is perhaps not surprising
given the numerous differences between the techniques—ChIP is
an in vivo technique, whereas TF SRMA and PBM are in vitro
techniques; PBM relies on the use of purified proteins, whereas
TF SRMA and ChIP use crude extracts or whole cells, respec-
tively; and each technique relies on different protein detection
technologies. Furthermore, the ChIP probe used to detect
binding events on FLO11 segment 5 in the Harbison et al. study
(7), only overlapped with <50% of the segment. This could also
account for the lack of overlap between some of the TF–DNA
interactions detected by SRM and ChIP on segment 5. Overall,
this analysis indicates that TF SRMA, PBM, and ChIP-chip
provide largely distinct TR–DNA interaction information for the
regulatory regions studied here, and it highlights the utility of
having multiple approaches for detecting these interactions. It is
worthwhile to point out that, whereas ChIP-chip and PBM can
measure the binding of a single TF to all promoters in a single
experiment, TF SRMA permits binding measurements of all TFs
at a single promoter in an experiment. As such, the approaches
TF SRMA required 11 MS analyses to systematically assay the
TR proteome enrichment at each of the FLO11 TREs. The re-
quirement for multiple runs is dictated by a need to balance sen-
sitivity with the analytical capacity of the QQQ instrument. We
expect even further improvements in throughput and sensitivity
due to recent software and hardware advancements in QQQ in-
strumentation. Thus, TF SRMA provides an attractive method to
efficiently assay the TF proteome without the need for antibodies
orgeneticengineering.Wenote that a limitationoftheapproachis
its inability to detect proteins/peptides that are not targeted. This
concern will be alleviated as improvements in instrumentation and
continued SRM assay development will permit monitoring of in-
creasing numbers of proteins. Also, SRM complements shotgun
MS approaches that canbe usedto discoverunanticipated proteins
with the caveat that undersampling may limit the sensitivity and
reproducibility of shotgun experiments (11).
We prioritized the list of SRM identified factors for follow-up
studies by looking for independent evidence of TF–DNA binding
to the FLO11 promoter segments. Mot3 was a particularly attrac-
tive candidate for functional studies based on this analysis com-
bined with previous studies that revealed Mot3 to be a repressor of
both filamentation and invasive growth (20, 33). Consistent with
our prediction that Mot3 functions as a repressor of FLO11 ex-
pression, we found that overexpression of Mot3 represses FLO11
expression when cells are grown on low-nitrogen, SLAD medium
(Fig. 2A) and deletion of MOT3 leads to a threefold increase in
been shown to function as a repressor of a diverse set of genes (32,
37). Although the mechanism is unclear, Mot3 appears to function
in a chromatin-dependent manner, which can involve cooperation
with the repressor Rox1 to facilitate recruitment of the Ssn6-Tup1
corepressor complex (38), or by imposing a requirement for the
Rpd3L deacetylase complex (39). Similar mechanisms may exist at
FLO11 given that Tup1 and components of the Rpd3L complex
have been shown to affect FLO11 expression (21, 40). In addition,
ChIP-chip results suggest that Rox1 can localize to segments 5–7,
which overlap with the Mot3 binding segments that we detected
and, like Mot3, Rox1 is a repressor of filamentation and haploid
invasive growth (41).
We also functionally validated the SRM binding results for Azf1
by showing that it localizes to the FLO11 promoter (Fig. 3A) and is
required for proper expression of FLO11 when cells are exposed to
freshglucose-containingmedia(Fig.3B).These conditions result in
activation of the cAMP–PKA signal transduction pathway, which
has been shown to impinge on the FLO11 promoter via the Flo8
activator and the Sfl1 repressor (17, 18, 23). Interestingly, the Flo8
and Sfl1 binding region in the FLO11 promoter overlaps with the
Azf1 binding region that we identified. This raises the intriguing
possibility that Azf1 may cooperate with Flo8 during glucose-
dependent activationofFLO11.Ourobservation that Azf1binds to
the FLO11 promoter and regulates its expression upon exposure to
fresh glucose-containingmediaexpands our knowledgeof the array
of regulators that integrate environmental signals at the FLO11
promoter. It will be interesting to determine whether Azf1 regula-
tion of FLO11 expression is mediated by the cAMP–PKA pathway
and whether Azf1 cooperates with Flo8 during glucose-dependent
activation of FLO11. We note that whereas Azf1 binding to the
FLO11 promoter was detected by both SRM and ChIP under
postdiauxic conditions, AZF1 was not required for FLO11 expres-
sion under these conditions. Together, the results suggest that the
functional requirement for the Azf1–FLO11 promoter interaction
is condition dependent.
Our identification of unique and potential FLO11 regulators,
and localization of their DNA interactions, will permit refinement
of models of the molecular interaction network controlling FLO11
expression. Future studies will be directed at testing predictions
based on these models to clarify the mechanisms involved in the
regulation of FLO11 expression. Furthermore, the methods
promoter. (A) ChIP-qPCR analysis on chromatin derived from cells grown to
postdiauxic conditions using myc-tagged Azf1 and PCR primers that amplify the
indicated FLO11 promoter segments. (B) Azf1 induces GFP expression driven
by the FLO11 promoter. FLO11-GFP expression was measured by FACS in a
wild-type strain, an azf1 deletion strain, and an azf1 deletion strain trans-
formed with an AZF1 expression plasmid after transferring stationary-phase
cells to fresh glucose-rich media for the indicated times (S288c background).
Azf1 is an activator of FLO11 expression and localizes to the FLO11
Mirzaei et al.PNAS
| February 26, 2013
| vol. 110
| no. 9
presented here, and applied to the FLO11 locus in yeast, can be Download full-text
readily applied to other TREs to identify key regulators of
Materials and Methods
Yeast Strains. Yeast strains used in this study are listed in Dataset S7, and
a discussion of the rationale for the use of the strains is presented in SI Text.
Immobilized FLO11 Promoter DNA Affinity Chromatography. Immobilized pro-
moter DNA affinity chromatography was performed essentially as described
previously (8) with exceptions described in SI Text.
SRM Assay Development. SRM assay development and implementation of
H-PINS technology was performed essentially as described previously (12, 28)
with exceptions described in SI Text. Decoy peptides were generated using
the mProphet algorithm (42).
SRM Analysis of Yeast Nuclear Extracts. Nuclear extracts from cells grown in
light and heavy SILAC media were mixed in a 2:1 ratio, respectively, digested
with trypsin, and purified. Peptides (2 μg) were analyzed in triplicate by
LC-SRM using a 4000QTrap in 18 batches (Dataset S8) as described in SI Text.
SRM data were processed using ABI’s Multiquant software. Relative quanti-
fications of peptides were based on the average ratio between the peak
heights from the heavy and light transitions. Relative quantification of pro-
teins, where multiple peptides were detected per protein, was based on the
average light to heavy peptide ratios.
SRM Analysis of FLO11 Promoter DNA Binding Purified Samples. SRM analysis
was performed as described above with exceptions described in SI Text and
ACKNOWLEDGMENTS. This work was funded with federal (US) funds from
the National Heart, Lung, and Blood Institute via Seattle Proteome Center
Contract N01-HV-8179 (to R.A.); National Institute of General Medical Sciences
Grant P50 GMO76547/Center for Systems Biology (to J.D.A., T.G., J.R., and I.S.);
and National Institute of General Medical Sciences Grant K25 GM079404 (to
G.W.C.). The study was also supported in part by SystemsX.ch, the Swiss initia-
tive for systems biology via the projects, by the European Research Council
advanced grant “Proteomics v3.0” (Grant 233226), European Union Seventh
Framework Programme grant “Unicellsys” (Grant 201142), and a contract from
the University of Luxembourg.
1. Garner MM, Revzin A (1981) A gel electrophoresis method for quantifying the
binding of proteins to specific DNA regions: Application to components of the Es-
cherichia coli lactose operon regulatory system. Nucleic Acids Res 9(13):3047–3060.
2. Fried M, Crothers DM (1981) Equilibria and kinetics of lac repressor-operator inter-
actions by polyacrylamide gel electrophoresis. Nucleic Acids Res 9(23):6505–6525.
3. Zhu C, et al. (2009) High-resolution DNA-binding specificity analysis of yeast tran-
scription factors. Genome Res 19(4):556–566.
4. Li JJ, Herskowitz I (1993) Isolation of ORC6, a component of the yeast origin recog-
nition complex by a one-hybrid system. Science 262(5141):1870–1874.
5. Ren B, et al. (2000) Genome-wide location and function of DNA binding proteins.
6. Johnson DS, Mortazavi A, Myers RM, Wold B (2007) Genome-wide mapping of in vivo
protein-DNA interactions. Science 316(5830):1497–1502.
7. Harbison CT, et al. (2004) Transcriptional regulatory code of a eukaryotic genome.
8. Ranish JA, et al. (2003) The study of macromolecular complexes by quantitative
proteomics. Nat Genet 33(3):349–355.
9. Mittler G, Butter F, Mann M (2009) A SILAC-based DNA protein interaction screen that
identifies candidate binding proteins to functional DNA elements. Genome Res 19(2):
10. Himeda CL, Ranish JA, Hauschka SD (2008) Quantitative proteomic identification of
MAZ as a transcriptional regulator of muscle-specific genes in skeletal and cardiac
myocytes. Mol Cell Biol 28(20):6521–6535.
11. Domon B, Aebersold R (2010) Options and considerations when selecting a quanti-
tative proteomics strategy. Nat Biotechnol 28(7):710–721.
12. Picotti P, et al. (2010) High-throughput generation of selected reaction-monitoring
assays for proteins and proteomes. Nat Methods 7(1):43–46.
13. Lange V, et al. (2008) Targeted quantitative analysis of Streptococcus pyogenes vir-
ulence factors by multiple reaction monitoring. Mol Cell Proteomics 7(8):1489–1500.
14. Wolf-Yadlin A, Hautaniemi S, Lauffenburger DA, White FM (2007) Multiple reaction
monitoring for robust quantitative proteomic analysis of cellular signaling networks.
Proc Natl Acad Sci USA 104(14):5860–5865.
15. Addona TA, et al. (2009) Multi-site assessment of the precision and reproducibility of
multiple reaction monitoring-based measurements of proteins in plasma. Nat Bio-
16. Lo WS, Dranginis AM (1998) The cell surface flocculin Flo11 is required for pseudohy-
phae formation and invasion by Saccharomyces cerevisiae. Mol Biol Cell 9(1):161–171.
17. Rupp S, Summers E, Lo HJ, Madhani H, Fink G (1999) MAP kinase and cAMP fila-
mentation signaling pathways converge on the unusually large promoter of the yeast
FLO11 gene. EMBO J 18(5):1257–1269.
18. Pan X, Heitman J (2002) Protein kinase A operates a molecular switch that governs
yeast pseudohyphal differentiation. Mol Cell Biol 22(12):3981–3993.
19. Braus GH, Grundmann O, Brückner S, Mösch HU (2003) Amino acid starvation and
Gcn4p regulate adhesive growth and FLO11 gene expression in Saccharomyces cer-
evisiae. Mol Biol Cell 14(10):4272–4284.
20. Carter GW, et al. (2007) Prediction of phenotype and gene expression for combina-
tions of mutations. Mol Syst Biol 3(96):96.
21. Barrales RR, Jimenez J, Ibeas JI (2008) Identification of novel activation mechanisms
for FLO11 regulation in Saccharomyces cerevisiae. Genetics 178(1):145–156.
22. Lambrechts MG, Bauer FF, Marmur J, Pretorius IS (1996) Muc1, a mucin-like protein
that is regulated by Mss10, is critical for pseudohyphal differentiation in yeast. Proc
Natl Acad Sci USA 93(16):8419–8424.
23. Gagiano M, Van Dyk D, Bauer FF, Lambrechts MG, Pretorius IS (1999) Divergent
regulation of the evolutionarily closely related promoters of the Saccharomyces
cerevisiae STA2 and MUC1 genes. J Bacteriol 181(20):6497–6508.
24. Deutsch EW, Lam H, Aebersold R (2008) PeptideAtlas: A resource for target selection
for emerging targeted proteomics workflows. EMBO Rep 9(5):429–434.
25. Picotti P, et al. (2008) A database of mass spectrometric assays for the yeast proteome.
Nat Methods 5(11):913–914.
26. Mallick P, et al. (2007) Computational prediction of proteotypic peptides for quan-
titative proteomics. Nat Biotechnol 25(1):125–131.
27. Stahl-Zeng J, et al. (2007) High sensitivity detection of plasma proteins by multiple
reaction monitoring of N-glycosites. Mol Cell Proteomics 6(10):1809–1817.
28. Mirzaei H, et al. (2009) Halogenated peptides as internal standards (H-PINS): In-
troduction of an MS-based internal standard set for liquid chromatography-mass
spectrometry. Mol Cell Proteomics 8(8):1934–1946.
29. Ghaemmaghami S, et al. (2003) Global analysis of protein expression in yeast. Nature
30. Ong SE, et al. (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as
a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1(5):
31. MacIsaac KD, et al. (2006) An improved map of conserved regulatory sites for Sac-
charomyces cerevisiae. BMC Bioinformatics 7(113):113.
32. Grishin AV, Rothenberg M, Downs MA, Blumer KJ (1998) Mot3, a Zn finger tran-
scription factor that modulates gene expression and attenuates mating pheromone
signaling in Saccharomyces cerevisiae. Genetics 149(2):879–892.
33. Jin R, Dobry CJ, McCown PJ, Kumar A (2008) Large-scale analysis of yeast filamentous
growth by systematic gene disruption and overexpression. Mol Biol Cell 19(1):284–296.
34. Newcomb LL, Hall DD, Heideman W (2002) AZF1 is a glucose-dependent positive regu-
lator of CLN3 transcription in Saccharomyces cerevisiae. Mol Cell Biol 22(5):1607–1614.
35. Slattery MG, Liko D, Heideman W (2006) The function and properties of the Azf1
transcriptional regulator change with growth conditions in Saccharomyces cerevisiae.
Eukaryot Cell 5(2):313–320.
36. Gagiano M, Bauer FF, Pretorius IS (2002) The sensing of nutritional status and the
relationship to filamentous growth in Saccharomyces cerevisiae. FEMS Yeast Res 2(4):
37. Hongay C, Jia N, Bard M, Winston F (2002) Mot3 is a transcriptional repressor of er-
gosterol biosynthetic genes and is required for normal vacuolar function in Saccha-
romyces cerevisiae. EMBO J 21(15):4114–4124.
38. Mennella TA, Klinkenberg LG, Zitomer RS (2003) Recruitment of Tup1-Ssn6 by yeast
hypoxic genes and chromatin-independent exclusion of TATA binding protein. Eu-
karyot Cell 2(6):1288–1303.
39. Sertil O, Vemula A, Salmon SL, Morse RH, Lowry CV (2007) Direct role for the Rpd3
complex in transcriptional induction of the anaerobic DAN/TIR genes in yeast. Mol
Cell Biol 27(6):2037–2047.
40. Conlan RS, Tzamarias D (2001) Sfl1 functions via the co-repressor Ssn6-Tup1 and the
cAMP-dependent protein kinase Tpk2. J Mol Biol 309(5):1007–1015.
41. Kadosh D, Johnson AD (2001) Rfg1, a protein related to the Saccharomyces cerevisiae
hypoxic regulator Rox1, controls filamentous growth and virulence in Candida albi-
cans. Mol Cell Biol 21(7):2496–2505.
42. Reiter L, et al. (2011) mProphet: Automated data processing and statistical validation
for large-scale SRM experiments. Nat Methods 8(5):430–435.
| www.pnas.org/cgi/doi/10.1073/pnas.1216918110 Mirzaei et al.