Genome Biology 2007, 8:R46
2007Huisinga and PughVolume 8, Issue 4, Article R46
A TATA binding protein regulatory network that governs
transcription complex assembly
Kathryn L Huisinga*† and B Franklin Pugh*
Addresses: *Center for Gene Regulation, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University
Park, PA 16802, USA. †Department of Biology, Washington University, Saint Louis, MO 63130, USA.
Correspondence: B Franklin Pugh. Email: firstname.lastname@example.org
© 2007 Huisinga and Pugh; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A TATA binding protein regulatory network <p>A portion of the assembly process involving the regulation of the TATA binding protein (TBP) throughout the yeast genome is modeled and experimentally tested.</p>
Background: Eukaryotic genes are controlled by proteins that assemble stepwise into a
transcription complex. How the individual biochemically defined assembly steps are coordinated
and applied throughout a genome is largely unknown. Here, we model and experimentally test a
portion of the assembly process involving the regulation of the TATA binding protein (TBP)
throughout the yeast genome.
Results: Biochemical knowledge was used to formulate a series of coupled TBP regulatory
reactions involving TFIID, SAGA, NC2, Mot1, and promoter DNA. The reactions were then linked
to basic segments of the transcription cycle and modeled computationally. A single framework was
employed, allowing the contribution of specific steps to vary from gene to gene. Promoter binding
and transcriptional output were measured genome-wide using ChIP-chip and expression
microarray assays. Mutagenesis was used to test the framework by shutting down specific parts of
Conclusion: The model accounts for the regulation of TBP at most transcriptionally active
promoters and provides a conceptual tool for interpreting genome-wide data sets. The findings
further demonstrate the interconnections of TBP regulation on a genome-wide scale.
The model eukaryotic cell Saccharomyces cerevisiae runs its
life with approximately 5,700 genes [1,2]. In any given envi-
ronment, each gene is expressed at a level that allows the cell
to function optimally in that environment. Most genes are
lowly expressed and relatively few are highly expressed,
which characterizes two ends of an expression continuum .
Several general regulatory features dictate the expression lev-
els of every gene [4-6]. First, promoter regions are packaged
into chromatin, which regulates promoter accessibility. Sec-
ond, sequence-specific DNA binding proteins orchestrate the
remodeling of chromatin and the recruitment of the tran-
scription machinery. Third, general transcription initiation
factors (GTFs) such as TFIIA, -B, -D, -E, -F, -H and RNA
polymerase II (pol II) assemble into a transcription pre-initi-
ation complex (PIC). Fourth, pol II and associated elongation
factors produce an RNA transcript. Each level of regulation
involves many proteins.
Published: 2 April 2007
Genome Biology 2007, 8:R46 (doi:10.1186/gb-2007-8-4-r46)
Received: 4 September 2006
Revised: 22 December 2006
Accepted: 2 April 2007
The electronic version of this article is the complete one and can be
found online at http://genomebiology.com/2007/8/4/R46
R46.2 Genome Biology 2007, Volume 8, Issue 4, Article R46 Huisinga and Pugh http://genomebiology.com/2007/8/4/R46
Genome Biology 2007, 8:R46
Since cells follow the laws of chemistry, the hundreds of pro-
teins regulating RNA production at thousands of genes will
require millions of coupled reaction steps. Defining these
steps has been a longstanding and continuing effort in tran-
scription biochemistry. A major challenge is piecing together
individual steps, defined in isolation, into a biochemical gene
regulatory network that describes the totality of a gene
expression program in vivo. Such a network allows aspects of
gene regulatory programs to be modeled computationally,
providing a guide for conceptualizing and predicting the com-
plex interplay of regulatory proteins.
The biochemical networks described here differ from previ-
ously described genetic networks [7,8]. The latter are typi-
cally DNA-centered and describe the spatial and temporal
aspects of organismal development as a consequence of an
unfolding cascade of chronological gene expression events
that control downstream events. Here, biochemical networks
are protein-centered, describing transcriptional control in
terms of differential equations governing the dynamic inter-
play among proteins and promoter DNA. The two are related
in that biochemical networks drive genetic networks. How-
ever, no biochemical model of a global gene regulation net-
work currently exists, and no paradigm exists by which a
biochemical model can be tested on a genome-wide scale.
Towards this goal, we have constructed a prototype model
that describes one section of the global network in terms of a
composite of well-defined biochemical interactions that regu-
late the function of the TATA binding protein (TBP). From
this model, we formally define a reaction mechanism involv-
ing interactions among TBP, its regulators, and promoter
DNA, ultimately culminating in PIC assembly and RNA pro-
duction. This formulation is analogous to a mechanism
describing a series of coupled enzymatic reactions and, thus,
can be computed using a software simulator of enzymatic
reactions. The simulator reports steady-state levels of inter-
mediates in PIC assembly and the amount of RNA produced.
Perturbations to the network are modeled computationally,
and tested experimentally via genetic mutations in the net-
work. This work provides an initial framework for developing
and testing biochemically based transcriptional regulatory
networks that govern PIC assembly and RNA output, and a
means for understanding their design logic.
Results and discussion
A model of the TBP regulatory network
The building block for modeling our global biochemical gene
regulatory network is an elementary reaction step such as:
Px + Dy → PxDy
where a protein (P) whose identity is 'x' (for example, TBP)
binds to DNA (D) located in the promoter of gene 'y' to form
a protein-DNA complex (PxDy), as demonstrated previously
[9-11]. The forward flux of P and D through the reaction is
governed by a gene-specific flux constant k1. PxDy may be cou-
pled to a second reaction step such as:
PxDy + Pz → PxDyPz
exemplified by a TBP·DNA complex binding the TBP regula-
tor called negative cofactor 2 (NC2). Examples of other types
of reaction steps include protein-protein assembly, protein-
DNA disassembly, rearrangements within a complex, and
In principle, hundreds of different transcription regulatory
proteins can act upon each other and upon thousands of
genes, giving a nearly infinite combination of potential reac-
tion steps. In reality, biologically relevant interactions have
specificity, which keeps the number of reaction steps to a
finite but nevertheless large number. In constructing a bio-
chemical gene regulatory network, we employed only reaction
steps that have strong experimental support. TBP regulatory
mechanisms are among the best characterized eukaryotic
gene regulatory systems [12-14], and thus are ideally suited
for integrative modeling studies.
Figure 1 illustrates the prototype TBP regulatory network
upon which our studies are based. The model attempts to
assimilate a variety of individual TBP regulatory mechanisms
into a common regulatory network that is potentially applica-
ble to all genes. This represents the first description of an
integrated TBP regulatory mechanism. The model serves as a
visual framework for interpreting in vivo promoter occu-
pancy and gene expression data. While parts of the model
could be wrong or incomplete, it serves as a useful starting
point to evaluate whether potential gene regulatory mecha-
nisms defined in vitro with purified components can be inte-
grated into a network of coupled reactions that account for
transcription factor occupancy and gene expression profiles
in vivo on a genome-wide scale. The goal here is to take a step
towards bridging a myriad of in vitro biochemical mecha-
nisms with genome-scale in vivo regulatory processes, rather
than to establish a rigorous mathematical model for regula-
In this model, TBP resides as a self-inhibited dimer when not
bound to DNA (segments 2 and 3 in Figure 1) [9,15]. Dissoci-
ation into monomers, as directed by promoter-specific regu-
lators, is required for DNA binding. When TBP resides in the
multisubunit TFIID complex, it may also engage in interac-
tions with other TFIID subunits such as the TBP-associated
Factor 1 (TAF1) amino-teminal domain (TAND) (segment 1 in
Figure 1) [16,17]. TAND has the potential to act negatively by
blocking TBP's DNA binding surface, and positively by tether-
ing TBP to TFIID. Although not shown in the model, pro-
moter-bound regulators modulate the assembly of TFIID-
TBP at promoters [18,19], giving rise to promoter-specific
control of the network.
Genome Biology 2007, Volume 8, Issue 4, Article R46 Huisinga and Pugh R46.3
Genome Biology 2007, 8:R46
The model shows TBP assembling onto promoter regions via
three possible pathways. One pathway involves TFIID (seg-
ment 4) and a second involves the Spt-Ada-Gcn5-Acetyl-
transferase complex termed SAGA (segments 5,6). Both lead
to formation of a PIC containing TBP, pol II, and many other
transcription proteins (segments 12 and14). The PIC pro-
duces a transcribing pol II from which RNA is made (seg-
ments 13 and 15). The third pathway, outlined in more detail
below, is a nonproductive one.
TFIID and SAGA are compositionally and functionally related
complexes [20,21]. In principle, a given promoter can utilize
either the TFIID or SAGA pathway . The SAGA pathway
is tailored towards TATA-containing promoters, whereas the
TFIID pathway plays a greater role at TATA-less promoters
Inhibiting the SAGA pathway (but not the TFIID pathway)
are two negative regulators of TBP, termed NC2 and Mot1
(Figure 1, segments 8, 9, and 10) [22,24]. NC2 binds to a TBP-
DNA complex and blocks PIC assembly [25,26]. Mot1 uses the
energy of ATP hydrolysis to dissociate TBP from DNA
[11,27,28]. Mot1 can also dissociate TBP from DNA in the
presence of NC2 , and NC2 stimulates TBP-Mot1 interac-
tions . Since the genome-wide gene expression profile of
an NC2 mutant is very similar to the expression profiles of
Mot1 mutants (Figure S1 of Additional data file 1) [30,31], we
make the simplifying assumption that the two largely work
together. Consistent with the notion that Mot1 dissociates
TBP-NC2 complexes, Mot1 mutants result in the accumula-
tion of TBP-NC2 complexes in vivo . However, we do not
exclude the possibility that Mot1 might act in the absence of
NC2 at some promoters.
An integrated prototype of the TBP regulatory network
An integrated prototype of the TBP regulatory network. Three parallel assembly pathways proceed temporally from left to right along line segments that
represent promoter DNA. Two pathways, directed by the multisubunit TFIID complex ('D', in cyan) and the compositionally related SAGA complex ('S', in
green) lead to productive pre-initiation complex (PIC) assembly, which goes on to produce RNA. The RNA ultimately is degraded leading to a steady-state
balance between production and degradation. In this model, TBP ('T', in yellow) resides as a self-inhibited dimer when not bound to DNA. Dissociation
into monomers is required for DNA binding. The TAND domain of TFIID's TAF1 subunit further inhibits TBP binding to DNA along the TFIID pathway.
The third pathway loads TBP onto promoter DNA in a nonproductive manner, and interferes with PIC assembly unless dissociated by the combined
action of NC2 and Mot1 ('N' and 'M' in red) . NC2 and Mot1 also dissociate TBP loaded via the SAGA pathway. While Mot1 dissociates TBP in the
absence of NC2 in vitro, it appears to be linked to NC2 function in vivo (Figure S1 in Additional data file 1). Numbers correspond to steps defined in Figure
2a. The presence of chromatin and general transcription regulators is implicit and not shown because their contributions to PIC assembly are not being
Genome Biology 2007, Volume 8, Issue 4, Article R46 Huisinga and Pugh R46.15
Genome Biology 2007, 8:R46
Genome-wide occupancy data for SAGA (Spt3), NC2 (Bur6),
Mot1, TFIID (TAF1, TAF5, TAF6, TAF9), and pol II (Rpb1,
Rpb2, Rpb7) were obtained from [50,52]. Occupancy values
are defined as the ratio of the chIP signal/control signal,
where the control represents signal generated from nonspe-
cific contamination of genomic DNA during immunoprecipi-
tation using the method described in reference .
The relationships to the top and bottom 10% of the expression
and ChIP-chip distributions (Additional data file 4) were cal-
culated in Excel with the data downloaded from the refer-
enced lab or journal's websites. The percent rank of the
distribution was calculated with the PERCENTRANK
function. Then the number of genes that appear in the top
10% (>0.9 in PERCENTRANK) or the bottom 10% (<0.1 in
PERCENTRANK) and appear in each cluster were calculated.
The CHITEST function of Excel was then used to calculate p
values from the observed and expected values. P values for
rows 5-11 in Table 1 were calculated by GO term finder at the
Saccharomyces Genome Database .
Additional data files
The following additional data are available with the online
version of this paper. Additional data file 1 is a PDF contain-
ing supporting text and figures. Additional data file 2 is an
Excel workbook that contains the log2 ratios of fold changes in
gene expression for the data shown in Figure 3 (63 × 6,227
expression ratios), the standard deviation of each cluster, and
ChIP-chip log2 ratios of occupancy relative to wild-type TBP
for experiments that were not published elsewhere (that is,
Figure 4c). Additional data file 3 is an Excel workbook con-
taining the flux constant values for each cluster and the prod-
ucts of the simulation. Additional data file 4 is an Excel
workbook containing p values for overlapping relationships
between gene clusters described here and a large amount of
published genomic data.
Additional data file 1 Supporting text and figuresSupporting text and figures. Click here for fileAdditional data file 2Log2 ratios of fold changes in gene expression for the data shown in Figure 3, standard deviation of each cluster and ChIP-chip log2
ratios of occupancy relative to wild-type TBP for experiments that were not published elsewhere Log2 ratios of fold changes in gene expression for the data shown in Figure 3 (63 × 6,227 expression ratios), the standard deviation of each cluster, and ChIP-chip log2 ratios of occupancy relative to wild-type TBP for experiments that were not published elsewhere (that is, Figure 4c). Click here for file Additional data file 3 Flux constant values for each cluster and the products of the simulationFlux constant values for each cluster and the products of the simulation. Click here for file Additional data file 4 P values for overlapping relationships between gene clusters described here and a large amount of published genomic data P values for overlapping relationships between gene clusters described here and a large amount of published genomic data.Click here for file
We thank N Altman, D Gilmour, J Reese, and S Tan and members of the
Pugh laboratory for many helpful discussions. This work was supported by
NIH grant GM59055 and NSF grant BES-0425662.
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