Proteomic approaches to the study of papillomavirus–host interactions
Elizabeth A. White, Peter M. Howleyn
Department of Microbiology and Immunobiology, Harvard Medical School, NRB Room 950, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
a r t i c l e i n f o
Yeast two hybrid
a b s t r a c t
The identification of interactions between viral and host cellular proteins has provided major insights
into papillomavirus research, and these interactions are especially relevant to the role of papilloma-
viruses in the cancers with which they are associated. Recent advances in mass spectrometry
technology and data processing now allow the systematic identification of such interactions. This has
led to an improved understanding of the different pathologies associated with the many papillomavirus
types, and the diverse nature of these viruses is reflected in the spectrum of interactions with host
proteins. Here we review a history of proteomic approaches, particularly as applied to the papilloma-
viruses, and summarize current techniques. Current proteomic studies on the papillomaviruses use
yeast-two-hybrid or affinity purification-mass spectrometry approaches. We detail the advantages and
disadvantages of each and describe current examples of papillomavirus proteomic studies, with a
particular focus on the HPV E6 and E7 oncoproteins.
& 2012 Elsevier Inc. All rights reserved.
Papillomavirus diversity and disease associations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Proteomic approaches and tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Papillomaviruses as candidates for proteomic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Designing systems for proteomic analysis of papillomaviruses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Discovery of novel protein–protein interactions through papillomavirus proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Identification of PV E7 interacting proteins by large-scale proteomics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
pRB1 and UBR4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
PTPN14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
ZER1 and ENC1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Proteomic identification of interactions between HPV E6 and host cellular proteins and characterization of genus-, species-, and type-specific binding
patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
E6AP, p53, and the proteasome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
MAML1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
CBP/p300 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
PDZ proteins and p53 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Ccr4-Not. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Centrosome and keratinocyte-specific proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Comparison of HPV affinity purification-mass spectrometry and yeast-two-hybrid studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Mutant HPV proteins are valuable proteomic tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Proteomic approaches to the study of other viruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Coupling systematic proteomic analysis to global gene expression analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Conclusions and future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
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Virology 435 (2013) 57–69
Papillomavirus diversity and disease associations
The papillomaviruses (PVs) are a family of non-enveloped
viruses containing double stranded, circular DNA genomes of
approximately 8000 bp. PVs infect squamous epithelial cells and
have a restricted host range. Although papillomaviruses have
been identified that infect organisms from reptiles to humans,
and PVs likely infect all amniotes, each virus can in general infect
only a single species (Gottschling et al., 2011; Mengual-Chulia
et al., 2012). The viral genomes share a conserved structure, and
each PV identified to date encodes the core proteins E1, E2, L1,
and L2 (Garcia-Vallve et al., 2005). Many papillomaviruses also
encode the accessory proteins E6 and E7; smaller numbers of
papillomaviruses encode additional proteins and splice variants of
the various viral genes. Most of the PV genomes contain 7–9 open
reading frames. While the virus’ genome structure is conserved,
the genome sequences differ from one another significantly. Two
papillomaviruses are different ‘types’ if their L1 genes differ by
10% or more at the DNA sequence level (Bernard et al., 2010; de
Villiers et al., 2004). L1 sequences were used to group the 189 PV
types listed in the most recent classification into species and
further into genera (Bernard et al., 2010). The number of PV types
that have been identified and sequenced continues to increase
The majority of the human papillomaviruses (HPVs) fall into
genus alpha and genus beta, although a few of the HPVs identified
so far are members of the gamma, mu, and nu genera (Bernard
et al., 2010). A phylogenetic tree representing HPV types from
these five genera is shown in Fig. 1. Most of the alpha genus HPVs
infect mucosal tissues, and a subset of the alpha HPVs are the 15
‘high-risk’ types that cause cervical cancer and other anogenital
cancers (Lowy et al., 2008; Schiffman et al., 2007). Additionally
HPV16 and HPV 18 (two of the alpha genus HPVs) have been now
associated with a significant fraction of head and neck cancers,
most notably oropharyngeal cancers. So far these have been
associated with as many as 30% of these cancers worldwide and
60% in the United States (Gillison et al., 2008; Kreimer et al., 2005;
Watson et al., 2008). Some of the other ‘low-risk’ HPVs from
genus alpha cause genital warts and are infrequently associated
with cancer. In contrast, the HPVs from genus beta infect the
cutaneous epithelium [reviewed in (Howley and Lowy, 2006)].
Several of the beta HPVs are found in hyperproliferative lesions in
lesions in patients with EV may progress to squamous cell
carcinoma (SCC), often in sun-exposed regions of patients’ skin.
The beta HPVs have also been potentially implicated in non-
melanoma skin cancers (NMSCs) in non-EV individuals (Akgul
et al., 2006; Feltkamp et al., 2008). However, transcription of PV
RNA was not detected in SCCs in one recent study (Arron et al.,
2011). Infection by the beta HPVs is in general quite frequent but
typically asymptomatic, either because of efficient clearance
of infected cells or because of persistent infections that go
undetected by the immune system and do not cause abnormal
pathology (Doorbar, 2006).
Proteomic approaches and tools
The importance of protein-protein interactions to biological
processes was not always appreciated. In the 1950s and 1960s, it
was determined that proteins consisted of a polypeptide chain
that could fold into a defined structure, and this led to the idea
Fig. 1. Sequence relatedness among HPV types. A phylogenetic tree based on the sequences of the L1 genes of the 143 human papillomavirus types currently listed in the
Papillomavirus Episteme (PaVE) (van Doorslaer et al., in press) was generated as described by Dereeper et al. (2008, 2010). The HPVs are grouped into five genera (alpha,
beta, gamma, mu, and nu), as shown. Genera are further subdivided into species, which are indicated (grey text) for viruses in genus alpha and genus beta. Virus types
discussed in the text are indicated in red.
E.A. White, P.M. Howley / Virology 435 (2013) 57–69
that these structures could form defined surfaces that might
mediate protein-protein interactions (Braun and Gingras, 2012).
In these early years, only a very few protein-protein interactions
(PPIs) were understood and were studied individually. Many of
the key early determinations of protein-protein interactions came
from the study of oncogenic viruses. These include the discovery
in the late 1970s of SV40 large T antigen bound to p53 (Lane and
Crawford, 1979; Linzer and Levine, 1979), the finding that pRB1
bound to adenovirus E1A in the 1980s (Whyte et al., 1988), and
Early analysis of the composition of proteins in a mixture and
of potential PPIs was possible using two-dimensional gel electro-
phoresis, but this technology was quite limited. Several technical
advances allowed the study of proteins and their interactions to
begin to progress (Patterson and Aebersold, 2003). Techniques
including Edman degradation and mass spectrometry began to
allow the determination of a protein or peptide sequence. When
the peptide sequence could be matched to a known gene, this
allowed the study of that specific protein. Researchers could
advance the study of individual PPIs further using new tools
including the generation of gene fusions, the development of
protein and epitope tags either singly or in tandem, and the
pairing of matched antibodies and epitope tags (Patterson and
Aebersold, 2003). Gene fusions in particular paved the way for the
development of yeast-two-hybrid technology, which was origin-
ally proposed by Fields and Song as a proof-of-principle experi-
ment (Fields and Song, 1989) but went on to become a powerful
and widely used technique. Two more recent key advances are
the sequencing of the complete genomes of an ever-increasing
number of organisms, and advances in mass spectrometric
technology. In particular, identification of peptides from more
and more complex mixtures became possible with the develop-
ment and refinement of LC-MS/MS technology, in which a peptide
mixture is injected by electrospray into a mass spectrometer
without any previous separation (Coon et al., 2005; Wilm et al.,
These developments now leave the proteomics community at
a key crossroad. With the ability to generate increasing amounts of
data on more and more protein-protein interactions, there is a need
to implement a range of different computational solutions for the
analysis of these data. Current and ongoing challenges include
refining the ability to remove background or false-positive interac-
tions, managing and sharing proteomic data, integrating proteomic
data with gene expression data, and comparing data across proteomic
Today, yeast-two-hybrid and affinity purification-mass spec-
trometry are the two basic strategies typically used to define PPIs
on a large scale (Sardiu and Washburn, 2011). Each has advan-
tages and disadvantages, and below we will compare the types of
data generated by the two approaches with the PVs. In both
methods the goal is to determine interactors of the ‘bait’ protein,
and these interactors are often referred to as the ‘prey’.
The first high-throughput method for the detection of protein–
protein interactions was the yeast-two-hybrid (Y2H) system
(Fields and Song, 1989). It is based on the separable nature of
the yeast GAL4 transcription factor and the ability to make
protein fusions. The bait protein of interest is fused to the GAL4
DNA-binding domain (DBD) and potential prey proteins are fused
to GAL4’s transactivation domain. Interactions in a yeast cell
between bait and prey thus reconstitute GAL4 function and lead
to the activation of a reporter gene whose promoter is decorated
with GAL4 binding sites. Genome sequencing efforts and the
availability of large open reading frame (ORF) collections have
made Y2H approaches more powerful. It became possible using
these tools to rapidly screen large numbers of potential bait and/
or prey proteins, and the first large-scale yeast-two hybrid studies
were published in 2000–2001 (Ito et al., 2000, 2001; Uetz et al.,
2000; Walhout et al., 2000).
One advantage of Y2H screening is that it is potentially a high-
throughput, low cost experiment. Researchers can screen a cloned
genome library generated from any cell type of interest, helping
to increase the number of potential prey interactions that might
be of physiologic significance. The yeast-two-hybrid system does
however have significant limitations. In particular, there are many
opportunities to detect false-positive or false-negative interac-
tions, either because yeast proteins promote or inhibit the
interaction between a bait and prey or because a bait or prey
may itself contribute to transcriptional activation or repression of
the reporter gene. In addition, the binding of two proteins may
require post-translational modifications that are not present in
yeast. Nonetheless, yeast-two-hybrid analysis is widely used
today, and data analysis approaches will need to handle compar-
isons that include Y2H data.
To circumvent some of these limitations, the yeast-two-hybrid
approach has been adapted to a so-called mammalian two-hybrid
system. Using mammalian cells allows researchers to consider
protein modifications that may impact binding, to use a relevant
cell type, and to manipulate the cells with drugs or other
treatments during an experiment. The reconstituted transcription
factor typically is comprised of the herpes simplex virus VP16
activation domain and the GAL4-DBD. The mammalian system is
less scalable than the yeast system, and thus far has been used
mostly for smaller scale studies or validation of other experi-
ments. Mammalian-two-hybrid systems have been comprehen-
sively reviewed in Lievens et al. (2009).
Affinity purification coupled with mass spectrometry (AP-MS)
is the second major approach to detecting protein-protein inter-
actions, and for a more detailed review of AP-MS we direct the
reader to Dunham et al. (2012). A bait protein of interest is
purified from cells using an epitope tag coupled with antibody or
other affinity purification, or less often using an antibody to the
endogenous protein. Mass spectrometry is then used to identify
bound prey proteins. Early implementations of this approach
relied on separating proteins on a gel, then identifying proteins
present in individual bands. More recently, high-throughput
sequencing of complex protein mixtures has become possible
using LC-MS/MS technology. Now hundreds of potential interact-
ing proteins can be identified from a single sample.
The advantages of AP-MS are that it takes place in mammalian
cells where native protein modifications are present and cells can
be treated or manipulated as desired. It is unbiased in the sense
that any protein present in a cell that can be detected by mass
spectrometry is a potential prey. Disadvantages include the
potentially laborious task of tagging and expressing each poten-
tial bait protein of interest, either transiently or in a stable cell
line, and the relatively low throughput compared to a Y2H
experiment. An interaction needs to be preserved through many
purification steps to be detected, so transient interactions can be
difficult to detect, and when multiple protein isoforms exist for a
prey protein it can be difficult to distinguish them by MS.
Post-collection analysis is required for both Y2H and AP-MS
data sets. These analyses generate a large amount of data to be
compiled and visualized, and both types of data sets may contain
a large number of false-positive or background/nonspecific inter-
actions. Early global yeast-two-hybrid studies included little
computational analysis. More recent, larger scale yeast-two-
hybrid analyses have begun to detect thousands of interactions,
leading to the idea that these must be filtered or assigned a
confidence score in some way. For example, Giot et al. (2003)
mapped Drosophila binary interactions, generated a statistical
model based on a training set, and used this to refine the 20,439
interactions they detected to a high-confidence list of 4780
E.A. White, P.M. Howley / Virology 435 (2013) 57–69
interactions. Most of the computational analysis of Y2H data sets
so far has been in the form of post-publication comparison and
analysis of several data sets to one another or to literature curated
or other reference data sets (Braun et al., 2009; Yu et al., 2008).
The analysis is generally not integrated into the initial Y2H data
collection and processing protocol.
More methods have been developed for the analysis of AP-MS
data, and these are discussed in greater detail in Nesvizhskii
(2012). This was less important in early low-throughput experi-
ments and in some tandem affinity purification (TAP) strategies
still used today, as these experiments relied on dual purifications,
often followed by separation on an acrylamide gel and analysis of
individual protein bands. Researchers have moved to higher-
throughput techniques, often using purification with a single tag
and analysis of the resulting complex sample, and so separating
real data from background interactions has become paramount.
One estimate is that 10% of all proteins identified after AP-MS are
bona fide interactors of a given bait (Trinkle-Mulcahy et al., 2008).
The methods that attempt to eliminate nonspecific interac-
tions range from the very simple to the very complex. The first
and easiest analysis method was to subtract all negative-control
interactors from the list of interactors identified for the bait of
interest. Early genome-wide AP-MS studies in yeast advanced this
analytical approach by integrating varying amounts of data
processing into their workflows, but in general considered only
the presence or absence of an interaction, not the quantity of the
protein detected. These approaches ranged from performing
replicate experiments to some statistical analysis and machine
learning methods (Gavin et al., 2006; Ho et al., 2002; Krogan et al.,
2006). Collins et al. (2007) next combined these two approaches
and refined the yeast genomic interaction data set.
Current analytical methods use the quantitative or semi-
quantitative information present in a mass spectrometry data
set to generate confidence scores for a given PPI, and some of the
key methods are highlighted here. These use the spectral count
that is generated for each protein identified by MS as the basis for
quantification (Breitkreutz et al., 2010; Sardiu et al., 2008, 2009;
Sardiu and Washburn, 2011; Sowa et al., 2009). The AP-MS
studies covered in this review have used several of these meth-
ods, although some of the studies have adapted an approach that
was originally developed to analyze a different large data set.
CompPASS was first used to define the interactomes of the human
deubiquitinating enzymes (Sowa et al., 2009) and has subse-
quently been used by our group to analyze HPV protein-human
host cell protein interactions. MiST was developed and has been
used for the analysis of interactions between HIV-1 proteins and
cellular proteins (Jager et al., 2012a). A survey of immune
regulatory proteins from a number of viruses used an adaptation
of an earlier normalized spectral abundance factor (NSAF) method
(Pichlmair et al., 2012; Zybailov et al., 2006). In addition to our
studies with the HPV proteins described below, the other recent
HPV protein/host cell protein interaction study used tandem
affinity purification and considered interactions to be significant
when they were detected in duplicate experiments (Rozenblatt-
Rosen et al., 2012; Zhou et al., 2010). CompPASS, MiST, and a third
computational platform, SAINT (Choi et al., 2011), determine
confidence scores for each interaction differently, but each has
been shown to be a powerful tool in separating bona fide interactors
Papillomaviruses as candidates for proteomic analysis
The PV replication is invariably linked to and dependent upon
the differentiation of squamous epithelial cells that the virus
infects (Doorbar, 2006). HPV particles infect cells in the basal
layer of a stratified epithelium, either mucosal or cutaneous, and
progress through the subsequent stages of their life cycle in the
differentiating cells. Virus particle production and release occurs
at the top layer of the desquamating epithelium. This connection
between differentiation and viral replication has limited the
ability to recapitulate the viral life cycle in tissue culture. Several
systems have been implemented to model this life cycle in
differentiating cells, but on a much smaller scale than would be
required for proteomic experiments. Nonetheless, papilloma-
viruses are excellent candidates for proteomic analysis. Many
PV genomes have been cloned and completely sequenced, and
their small open reading frames are easily subcloned and epitope
tagged. More importantly, it appears that nearly all of the HPV-
mediated changes in the host cellular environment must occur
through virus-host PPIs, since only one PV protein is known to
encode an enzymatic activity: the helicase function of the E1
Protein–protein interactions have revealed the functions of the
papillomavirus proteins since the earliest days that these viruses
were studied. The E6 and E7 oncoproteins engage in key PPIs. E7
proteins bind to and promote the degradation of pRB, releasing
E2F transcription factors thus promoting S phase entry and DNA
replication (Dyson et al., 1992, 1989; Munger et al., 1989). High-
risk E6 proteins bind the cellular ubiquitin ligase E6AP to form a
complex that targets p53 for degradation, thereby blocking
signaling through the apoptotic pathways that would otherwise
be triggered by E7 (Huibregtse et al., 1991; Scheffner et al., 1990;
Werness et al., 1990). When the normal regulation of E6 and E7
expression is lost, these protein-protein interactions can then
trigger cellular immortalization and genomic instability that may
ultimately result in cellular transformation and cancer. Some of
these functions, such as E7 binding to Rb, are conserved across all
HPV types examined to date (White et al., 2012). Others, like the
targeted degradation of p53 by E6, are restricted to a subset of the
HPVs. Bovine papillomavirus type 1 (BPV1) is often used as a
model virus, but its oncoproteins function slightly differently.
BPV1 E7 does not bind to pRB1 or the other RB related pocket
proteins (DeMasi et al., 2005), and BPV1 E6 neither binds p53 nor
targets it for degradation.
Proteomic studies on a limited number of PV proteins in the
early 2000s revealed several important interactions. An AP-MS
experiment using BPV1 E2 as the bait revealed that BPV1 E2 binds
to and uses Brd4 to tether its genomes to host mitotic chromo-
somes in dividing cells (You et al., 2004), and the E2/Brd4
interaction has since been characterized for all PVs that have
been studied (McPhillips et al., 2006). Other early AP-MS studies
on a few HPV and BPV E6 and E7 proteins detected other PPIs
(DeMasi et al., 2005; Huh et al., 2007, 2005; McLaughlin-Drubin
et al., 2008; Vos et al., 2009), but relied on an abundant cellular
binding partner and a stable interaction, since the bound proteins
were detected after one-dimensional SDS-PAGE and excision and
sequencing of an individual protein band. Perhaps most impor-
tantly, these studies reinforced the idea that many PV functions
are mediated through protein-protein interactions and indicated
that larger-scale proteomic experiments would reveal new infor-
mation about PV biology, virus-host cell interactions, and possibly
links to disease.
Designing systems for proteomic analysis of papillomaviruses
The first choice in designing a proteomic study is whether to
use a Y2H system, an AP-MS approach, or both. In the case of
an AP-MS experiment, several parameters are more flexible
than in the two-hybrid case. An AP-MS experiment begins
with the choice of analysis platform, from mass spectrometry
E.A. White, P.M. Howley / Virology 435 (2013) 57–69
instrumentation to data analysis and processing. Next, the cell
line should be chosen early in the AP-MS design. A cell line might
be chosen for ease of culture, for biological relevance, for ready
comparison of newly generated data to large existing data sets,
for cost considerations, or for other study-specific reasons. The
investigators must decide whether the tagged bait proteins of
interest will be expressed in cells stably or transiently, as some
cell lines may be compatible with one method but not the other.
The type of vector and cloning system required will be based on
the cell line and expression method chosen. Epitope tag(s) can
then be chosen based on the available vectors and perhaps the
need to compare to existing data sets. Finally, individual bait
proteins may require other adaptations. For example, some
protein functionality may only be compatible with N-terminal,
C-terminal, or even internal epitope tags. Other proteins may be
toxic when maintained in cells, indicating that they may require
transient or inducible expression prior to an experiment. Finally,
different analysis platforms require different numbers and types
of controls, including negative controls, replicates, unrelated
protein baits, or others. A proteomic experiment will have the
maximum chance of success when as many of these factors as
possible are considered before the experiment even begins.
Discovery of novel protein–protein interactions through
Several recent publications have reported proteomic analyses
of the papillomaviruses, and these have used different approaches
to generate data sets with some overlap and some unique
features. We will concentrate on two large-scale AP-MS studies,
with references to related work and with mention of yeast-two-
hybrid approaches ongoing in other groups. These two are the
work of a large multi-lab consortium (Gulbahce et al., 2012;
Rozenblatt-Rosen et al., 2012) and our own studies (Martinez-
Noel et al., 2012; Tan et al., 2012; White et al., in press, 2012).
Similarities and differences both in implementation and in results
are highlighted below and are summarized in Table 1.
The Rozenblatt-Rosen study of PV interacting proteins was
part of a larger effort to study proteins encoded by four different
families of tumor viruses (the papillomaviruses, the polyoma-
viruses, the herpesviruses, and the adenoviruses), with the overall
goal of identifying genes that drive cancer (Rozenblatt-Rosen
et al., 2012). The authors analyzed interactions for proteins from
the four virus families by both Y2H and AP-MS approaches, but in
this review we will discuss only their HPV interaction results. The
AP-MS component of their study included 31 ORFs from 6 differ-
ent PVs (two high-risk: HPV16 and 18, two low-risk: HPV6b and
11, and two genus beta, species 1: HPV5 and 8). The E6 and E7
oncoproteins and E6 mutants account for about half of these
ORFs, and this is the PV interaction data that the authors highlight
in their analysis. E6s were tagged with Flag and HA at their
N-termini, and the high-risk HPV E6s were additionally analyzed
with a C-terminal Flag-HA tag to block the association of PDZ
proteins that bind to the C-terminus of high-risk HPV E6s. HPV E7
proteins were tagged at their C-termini with Flag and HA. Each of
these choices is appropriate, as N-terminal tags on E6 proteins
allow interactions with PDZ domain proteins, and C-terminal tags
on E7 have been show to allow the interaction of E7 with cellular
proteins including the large cellular protein UBR4 (originally
named p600). Adding amino acids (aa) at the N-terminus of E7
blocks the E7/UBR4 interaction (DeMasi et al., 2005).
These tagged ORFs were incorporated into retroviral vectors
and used to create stable cell lines in IMR-90 human diploid
fibroblasts. These cells have the advantage of being ‘normal’, i.e.,
they have a limited lifespan and are not transformed, but they are
fetal lung fibroblasts, which are mesenchymal in origin. Thus they
are relatively un-related to the squamous epithelial cells that are
the natural host of PV infection, and epithelial-specific interactors
of the HPV proteins will not be detected in this assay. Presumably,
the authors chose to use IMR-90 cells for ease of culture and to
use one cell type for proteins from all four tumor virus families
they examined. Viral proteins were purified by a two-step affinity
purification using both Flag and HA tags and only proteins that
were detected in both of two independent experiments were
considered in subsequent analyses.
Our studies took a similar approach but with several key
differences. The goal of our studies is to better understand the
biological differences among various HPV types through their
PPIs. To this end we cloned 160 ORFs from 20 different viral
genomes, about 80% coverage. We planned to perform our
analysis using CompPASS software developed by Sowa and
colleagues (Sowa et al., 2009). As in the Rozenblatt-Rosen study
we began with the analysis of HPV E6 and E7 interactors,
hypothesizing that virus-specific interactions would be most
apparent for the oncoproteins. Our HPV E7 study included
seventeen different HPV types and our HPV E6 study included
sixteen different E6 baits, both listed below. We made similar
tagging decisions as above. E7 proteins were tagged at the
C-terminus with Flag and HA, and E6 proteins were tagged at
Recent virus–host interaction studies discussed in the review.
Virus System Analytical approachReferencesSummary
HPV AP-MS CompPASS (Sowa et al.,
(Tan et al., 2012;
White et al., in
press; White et al.,
Seventeen different HPV E7 baits and sixteen different HPV E6 baits were
analyzed by AP-MS and CompPASS with the goal of understanding HPV
diversity though PPIs. The E7 study was conducted in keratinocytes and
identified largely conserved interactions, with some species- and type-
specific PPIs. The E6 study identified genus-specific interactions and a
higher proportion of species- and type-specific PPIs than for the E7 baits.
HPV TAP-MS and
duplicate runs Y2H:
analytical platform as
in (Dreze et al., 2010)
et al., 2012)
E6, E7, and several other ORFs from six (TAP-MS) or seven (Y2H) HPV
types were analyzed for binding to host cellular proteins. The TAP-MS
study was performed in IMR-90 fibroblasts stably expressing tagged HPV
ORFs. This work is part of a larger study aimed at identifying pathways
that are perturbed by four different tumor viruses and as such, are likely
to drive cancer development when pathway function is lost
HIV-1AP-MS MiST (Jager et al.,
(Jager et al., 2012a,
All 18 HIV-1 proteins were analyzed by AP-MS in Jurkat and 293 cells and
data was processed using MiST. This study provides a global survey of
E.A. White, P.M. Howley / Virology 435 (2013) 57–69
the N-terminus with HA. Our protocol used a single anti-HA AP
step, so the Flag tag was extraneous and was removed from the
next-generation E6 expression vector.
In our studies, we have used N/Tert-1 human keratinocytes, a
cell line derived from normal human foreskin keratinocytes that
was immortalized by the addition of hTert, the catalytic subunit
of telomerase (Dickson et al., 2000). N/Tert-1 cells do not express
p16INK4a, a common feature of immortalized epithelial cells
(Kiyono et al., 1998). Thus, our analysis was conducted in
squamous epithelial cells, the normal host cell of a PV infection.
One requirement of the CompPASS system is that each IP-MS/MS
experiment is compared to a library of interaction data generated
from a large number of comparable IP-MS/MS experiments, as
introduced above. The first step in establishing this system was to
generate approximately 40 different N/Tert-1 cell lines each
expressing an HA-tagged cellular protein. Each cell line was
processed for an IP-MS/MS experiment and used to generate the
CompPASS ‘stats table’ as the basis for comparison for the
individual viral baits.
Identification of PV E7 interacting proteins by large-scale
We have published the results of the proteomic analysis of
HPV E7 proteins, conducted in N/Tert-1 cells (White et al., 2012).
We tested seventeen different E7 baits, ten HPV E7s from genus
alpha (HPV18 and 45 from species 7; HPV16, 31, and 33 from
species 9; HPV6b, 55 (a subtype of HPV44), and 74 from species
10; and HPV2a and 57 from species 4) and seven HPV E7s from
genus beta (HPV8, 25, and 98 from species 1; HPV17a and 38 from
species 2; HPV76 from species 3; and HPV92 from species 4).
Rozenblatt-Rosen and colleagues tested six E7s in their TAP-MS
study, as described above. We highlight several of our findings
and compare them to those of Rozenblatt-Rosen and colleagues in
the following sections. Some of the interactions detected in one or
both studies are summarized in Fig. 2.
pRB1 and UBR4
Our experiments identified several known interactors of HPV
E7s, including pRB1 and UBR4. E7 proteins share an LXCXE motif
responsible for binding to pRB1 and related ‘pocket proteins’
including pRBL1 (p107) and pRBL2 (p130). This motif is conserved
among the seventeen different HPV E7s in our study, and binding
to pRB1 therefore served as a positive control in these experi-
ments. Each E7 tested bound to pRB1. pRBL1 and pRBL2 each
bound to 10 of the 17 E7 baits, although not the same 10 E7s in
each case. A more revealing result was that UBR4 bound to each
HPV E7 tested. Prior to this report, UBR4 was known to interact
with HPV16, 6b, and 11 E7s and with bovine papillomavirus (BPV)
E7 (DeMasi et al., 2005; Huh et al., 2005). UBR4 contains a UBR
box, a motif that is common to proteins involved in the N-end
rule-mediated degradation of proteins (Tasaki et al., 2005, 2009),
but UBR4 itself has not yet been definitively shown to be a
functional E3 ubiquitin ligase or to act in the N-end rule pathway.
Based on the conservation of the UBR4-E7 interaction across several
PV genera, it seems that this critical interaction has PV-related
functions that are yet to be revealed. Other newly identified cellular
proteins also interact with all E7s tested. KCMF1, another putative E3
ubiquitin ligase, bound to all seventeen E7s. Preliminary IP-MS/MS
and CompPASS experiments in our laboratory suggest that KCMF1
interacts with UBR4 in the absence of E7; this binding may or may
not be related to the function of the UBR4-E7 interaction.
The non-receptor tyrosine phosphatase PTPN14 also bound to
many of the E7 baits. It was detected with 12 of 17 E7s as a HCIP,
and the PPI confidence scores were higher for alpha than beta E7s.
Fig. 2. Selected binding partners of HPV E7 proteins. HPV types used in the studies of Rozenblatt-Rosen et al. and White et al. on E7 interacting proteins (Rozenblatt-Rosen
et al., 2012; White et al., 2012) were arranged into a phylogenetic tree based on the sequences of their L1 genes. Selected conserved, species-specific, or type-specific PPIs
involving E7 proteins are indicated.
E.A. White, P.M. Howley / Virology 435 (2013) 57–69
PTPN14 has recently been implicated in density-dependent cell
growth (Huang et al., 2012; Liu et al., 2012; Wang et al., 2012).
It binds to YES1, a regulator of Hippo signaling, and negatively
regulates YES1 when cells are at high density. The interaction of
PTPN14 with E7 therefore raises the question of how E7 might
control cell density, possibly promoting proliferation, through the
The six E7s in the Rozenblatt-Rosen TAP-MS study also bound
to pRB1, p600/UBR4, KCMF1, and PTPN14. Five E7s bound to
RBL1, three bound to RBL2, four bound to KCMF1, and four bound
to PTPN14, indicating similar results as our own study. From their
data, this group concludes that HPV E7s do not target groups of
interacting proteins in a ‘class-specific’ manner, that is, there are
not significant differences among E7 interactors between the
high-risk, low-risk, and cutaneous E7 proteins. Our own results
are consistent with this idea insofar as the predominant and most
easily detected cellular binding partners of the E7s are those
proteins whose interactions are conserved across virus types.
ZER1 and ENC1
Nonetheless, there are specific interactions exhibited by the E7
proteins from viruses of different types. In both our study,
which tested 17 E7s, and that of Rozenblatt-Rosen and colleagues,
which tested six E7s, Zer1 bound only to HPV16 E7. We analyzed
the function of Zer1 (also known as Zyg11BL), the substrate
specificity component of a CUL2-based cullin-RING ligase (CRL).
Zer1 contains a BC-box and a CUL2-box and interacts with Elongin
B, Elongin C, and CUL2 (Mahrour et al., 2008). In cells that express
HPV16 E7, we demonstrated that Zer1 contributes to the
E7-mediated degradation of pRB1. siRNA-mediated knockdown
of Zer1 resulted in an increased level of pRB1, particularly the
hypophosphorylated form that is bound by E7. We thus refined
the understanding of the mechanism by which HPV16 E7 targets
pRB1 for degradation (White et al., 2012). Curiously, although
degradation of hypophosphorylated pRB1 is observed in cells
expressing other high-risk E7s, the Cul2-Zer1 ligase is specific
for HPV16, and our data does not suggest other CRL complexes in
association with other E7s. All E7s bind to CUL3, but our
preliminary evidence has not suggested that this interaction
contributes to pRB1 degradation. In our studies HPV genus alpha,
species 7 E7s bind to the BTB protein ENC1. BTB proteins bind to
CUL3, but since the E7/ENC1 interaction is not universal, we do
not believe that all E7s bind to CUL3 using ENC1. Further
experiments will be required to understand how other E7s target
pRB1 for degradation and why E7s bind to CUL3.
Proteomic identification of interactions between HPV E6 and
host cellular proteins and characterization of genus-, species-,
and type-specific binding patterns
The publication by Rozenblatt-Rosen and colleagues also
reported TAP-MS data for six different E6 proteins, from the same
HPV types as used in their analysis of E7s. Our group has also
recently reported the AP-MS based identification of host cellular
proteins that bind to E6 proteins from sixteen different HPV types
(White et al., in press). The sixteen E6 baits in our study are nine
E6s from genus alpha: HPV16, 31, 33, and 52 (high-risk, species
9); HPV18 and 45 (high-risk, species 7); HPV6b (low-risk, species
10); HPV2a, 57 (species 4) and eight E6s from genus beta: HPV8,
20, 25, and 98 (species 1); HPV17a and 38 (species 2); HPV76
(species 3); and HPV92 (species 4). Thus, we can again compare
the two studies and the E6 interactions they identified. The
Rozenblatt-Rosen study was conducted in fibroblasts whereas
our experiments were performed using keratinocytes. In our
study, in addition to the analysis of E6 interactions from
untreated E6-expressing cells, we also analyzed each E6 bait after
the cell lines were treated with the proteasome inhibitor MG132.
We hypothesized that it might be easier to detect E6 interactors
that are targeted for degradation by E6, such as p53 or PDZ
domain proteins, when the proteasome was not active. Selected
interactions detected in one or both of the studies are summar-
ized in Fig. 3.
988 256b3316 45182a 57 115 HPV Type
Species 7, 9:
HPV5, 8, 20, 25:
Fig. 3. Selected binding partners of HPV E6 proteins. HPV types used in the studies of Rozenblatt-Rosen et al. (2012) and White et al. (in press) on E6 interacting proteins
were arranged into a phylogenetic tree based on the sequences of their L1 genes. Selected conserved, species-specific, or type-specific PPIs involving E6 proteins are
E.A. White, P.M. Howley / Virology 435 (2013) 57–69
E6AP, p53, and the proteasome
Both studies detected known E6 binding partners. All genus
alpha E6s bound to E6AP, although in our study the interactions
with the non-cancer types (species 4) were not scored as HCIPs by
the CompPASS software. This reinforces the idea that the inter-
action between E6 and E6AP has different consequences for
different genus alpha viruses, since only for the viruses from
high-risk species does the complex also contain p53. Rozenblatt-
Rosen and colleagues detect p53 in complex only with HPV18 E6,
highlighting the difficulty of detecting this interaction when the
proteasome is active. We found p53 with all five of our high-risk
genus alpha E6s, but only in one case could we detect this
interaction in cells with an active proteasome. Other known
interacting partners of E6AP including HERC2 (Kuhnle et al.,
2011) were identified in these studies. HERC2 bound to HPV18
E6 in the Rozenblatt-Rosen paper and bound to all five of
the high-risk alpha E6s in our study. We also detected HERC2
in complex with the low-risk HPV6b E6, although not as an
E6AP is known to interact with the proteasome (Besche et al.,
2009; Kleijnen et al., 2000; Martinez-Noel et al., 2012; Scanlon
et al., 2009; Tai et al., 2010; Wang et al., 2007), and consistent
with that data we observed that E6 immunoprecipitated some
proteasome subunits. We concluded that E6AP mediates the
interaction of E6 with the proteasome, that this interaction is
better detected after MG132 treatment, and that an E6AP
binding-deficient mutant of E6 (HPV16 E6 I128T) no longer binds
to the proteasome.Theseideas
Rozenblatt-Rosen publication, which detected proteasome sub-
units in complex with HPV16, 8, and 6b E6s.
Recently, our group and that of Scott Vande Pol have shown
that beta genus E6 proteins as well as the BPV E6 bind to the
cellular protein MAML1, a transcriptional regulator that is
involved in several cell signaling pathways (Brimer et al., 2012;
Tan et al., 2012). This interaction is also noted in both proteomic
studies, and the downstream effect of the beta E6-MAML inter-
action is inhibition of the Notch signaling pathway (Brimer et al.,
2007; Rozenblatt-Rosen et al., 2012; Tan et al., 2012). Notch acts
as an oncogene in some settings such as T-cell leukemia but as a
(McElhinny et al., 2008; Wu and Griffin, 2004). Two recent studies
have shown that Notch is frequently mutated in head and neck
cancers, highlighting the importance of this pathway in the
epithelial cells infected by beta HPVs (Agrawal et al., 2011;
Stransky et al., 2011). Notch is a determinant of keratinocyte
differentiation, and Notch activation leads to induction of the cell
cycle inhibitor p21 and the expression of differentiation markers
(Devgan et al., 2006; Nguyen et al., 2006; Rangarajan et al., 2001;
Restivo et al., 2011). Now that the two E6 proteomic studies have
extended the binding observations to a larger number of E6s, we
can generalize to say that nearly all of the genus beta E6s that
were tested in either study bound to MAML1. Our study was also
able to detect MAML1 binding partners including Notch1 and
RBPJ for most of the beta E6s tested.
These two genus-specific interactions involve the same bind-
ing motif. Genus alpha E6s bind to E6AP, and this interaction has
been demonstrated to occur through the LXXLL motif in E6AP
(Chen et al., 1995). The Tan and Brimer studies demonstrated that
MAML1 contains an LXXLL motif that is responsible for the
interaction with beta E6s (Brimer et al., 2012; Tan et al., 2012).
The LXXLL motif in either case is thought to be bound by the
flexible linker region between the two globular domains in the
N- and C-terminal halves of E6s (Nomine et al., 2006). It is
interesting to speculate, then, that the ability of E6 to bind to
either an LXXLL motif like the one in E6AP or like the one in
MAML1 arose early in the evolution of genus alpha versus genus
beta HPVs, and that this relates to their respective tropisms for
mucosal or cutaneous epithelium.
Both our data and those of Rozenblatt-Rosen indicate that
HPV5 and HPV8 E6 proteins (both from genus beta, species 1)
bind to the acetyltransferases CBP and p300. The Rozenblatt-
Rosen study proposes that this interaction is related to the impact
of beta E6 proteins on Notch signaling, and this is consistent with
their data since CBP/p300 contribute to the MAML1-dependent
transactivation of Notch responsive genes (Oswald et al., 2001). In
contrast, our results show that nearly all beta HPV E6 proteins
bind to MAML1, but only three of four beta E6 proteins from
species 1 and no other E6s from genus beta bound to CBP/p300.
Furthermore, our proteomic data and previous studies indicate
that HPV16 E6 binds to CBP/p300 (Patel et al., 1999; Zimmermann
et al., 1999). Thus, if a subset of beta E6s bind to CBP and p300 as
an additional way to affect Notch signaling, other beta E6s must
not require CBP/p300 to impact the Notch pathway and HPV16
must use CBP/p300 for other purposes. Studies on HPV16 E6
binding to p300/CBP suggest that it blocks p53 and NFkB
transcription and inhibits cellular differentiation (Patel et al.,
1999) or that it downregulates p53 activity and p53-dependent
transcription (Thomas and Chiang, 2005; Zimmermann et al.,
1999). p300 binding to HPV5 and 8 E6s has been proposed to
result in the degradation of p300 and the loss of Akt binding to
p300 (Howie et al., 2011).
PDZ proteins and p53
Through examining additional HPV types, our study of E6
interactions identified a number of new binding partners of genus
beta HPV E6s. Some of these are proteins that were previously
thought to bind only to alpha type E6s; others are proteins not
previously shown to bind to any E6. For example, p53 binds to
HPV38 and HPV92 E6; cells that express these E6s appear to
stabilize p53 rather than target it for degradation. We also
observed that several genus beta E6s bound to proteins that
contain PDZ domains. This is a surprise in light of the fact that it is
the C-terminal PDZ binding motif present in high-risk HPV E6
proteins that mediates their interaction with PDZ proteins. Our
study validates one of these interactions, that of the beta genus
HPV38 E6 with the PDZ protein PTPN13. The HPV8 E6/PDZD11
interaction was detected in both our proteomic study and that of
Rozenblatt-Rosen and colleagues. Beta papillomavirus E6 proteins
do not contain the classical C-terminal PDZ binding motif, so this
interaction may be a result of binding through a protein inter-
mediate or because the PDZ protein is binding to a different
sequence in E6. Even non-oncogenic viruses encode factors that
bind to PDZ proteins, and these interactions can contribute to
virus replication in ways unrelated to transformation (Javier and
Rice, 2011). Beta HPVs have been shown to alter PDZ functions,
with HPV8 E6 able to reduce transcription of the gene encoding
the PDZ protein Syntenin 2 (Lazic et al., 2012).
Finally, in our broad survey of beta E6 proteins we identified
cellular proteins that bound only to E6s of a single HPV species.
Our study includes two genus beta, species 2 HPV E6s, and these
bind to a unique subset of cellular proteins including all 10
E.A. White, P.M. Howley / Virology 435 (2013) 57–69
subunits of the 1 MDa form of the Ccr4-Not complex. Ccr4-Not is
conserved from yeast to humans and has various functions, the
most well-characterized being its function as a deadenylase. This
deadenylase activity is a focus of study for many labs, and
recently Ccr4-Not has been established as a major factor in the
removal of polyA tails from messenger RNAs that are being
targeted by a cognate microRNA (Behm-Ansmant et al., 2006;
Braun et al., 2011; Chekulaeva et al., 2011; Fabian et al., 2011,
2009). Other functions, including a ubiquitin ligase activity
associated with the CNOT4 subunit (Albert et al., 2002), are less
well understood. We are not aware of any other reports of a viral
protein that binds to or affects the function or expression of Ccr4-
Not, and we are continuing to study the downstream effects of
this E6 interaction.
Centrosome and keratinocyte-specific proteins
Other species-specific interactions exist; for example HPV 92
is the only virus type in genus beta, species 4 that has yet been
identified, and correspondingly it was the only HPV E6 in our
study that bound to another distinct subset of cellular proteins.
These interactors include HIF1a and ARNT (HIF1b), which together
comprise the HIF1 heterodimer (Wang et al., 1995). Protein compo-
nents of the centrosome including AZI1 (CEP131), CEP152, CEP63,
and KIAA1712 (CEP44) bound to HPV92 E6, and perhaps this is
related to the microtubule binding protein KIAA1543 (CAMSAP3)
that bound as well. Cell adhesion proteins such as JUB, AAMP, and
the bovine papillomavirus type 1 (BPV1) E6-interacting protein
Paxillin bound to HPV92 E6, as did a cation transporter that
regulates keratinocyte proliferation (SLC12A8), and DUSP3, a nega-
tive regulator of MAPK. Our group has recently shown that MAPK6
and HIF1AN bind to E6AP (Martinez-Noel et al., 2012), so the
presence of DUSP3 and HIF1 here raise the question of whether
HPV92 E6 uses other binding events to target pathways impacted by
a high-risk HPV E6.
Comparison of HPV affinity purification-mass spectrometry
and yeast-two-hybrid studies
As described above, there is significant overlap between the
two large HPV AP-MS studies completed to date. In contrast, there
is less overlap between the AP-MS data and the yeast-two-hybrid
data included in the Rozenblatt-Rosen et al. manuscript. Their
Y2H study detected 454 binary interactions involving 123 viral
baits. 28 of the 123 baits were HPV proteins from 7 virus types
(the six types used in the TAP-MS study plus HPV33), and at least
one cellular interactor was detected for 16 of these 28 HPV
proteins. This resulted in a total of 267 interactions between
HPV and cellular proteins detected by Y2H. Although interactions
were detected for all seven E7 proteins and five of the E6 proteins
tested, there was no overlap between the Y2H and TAP-MS
data sets, at least for the HPV component of the study. In the
study overall, only 6 interactions were in common between
the 454 Y2H and 3787 TAP-MS interactions that were identified.
This means that for the HPV baits, none of the known
control interactors detected in the TAP-MS experiments (e.g.,
pRB1, p600, KCMF1, Zer1, or PTPN14 for E7; or p53, p300,
CBP, or MAML1 for E6) were detected in the yeast-two-hybrid
Some groups continue to conduct Y2H studies only, and a
recent report described binding partners of the E2 proteins from
12 HPV types (eight from genus alpha: types 16 and 33 from high-
risk species 9, types 18 and 39 from high-risk species 7, low-risk
types 6 and 11 from species 10, type 32 from species 1 (associated
with the oral mucosa), and type 3 from species 2; three from
genus beta: types 5 and 8 from species 1 and type 9 from species
2; and one virus, HPV1, from genus mu) (Muller et al., 2012).
Although their experiments began with an unbiased yeast-two-
hybrid study in which they screened a cDNA library generated
from HaCaT keratinocytes, the authors quickly note that only five
of 53 known E2 binding partners were detected in their screen. In
light of that potentially low discovery rate, they continued by
using a complementary assay in mammalian cells. They chose
about 25% (48) of their 202 potential interactors identified from
the Y2H on the basis of having interacted with multiple E2s, then
rescued another ?25% on the basis of having a potential func-
tional relationship to E2 or its binding partners. These plus 19
‘Gold Standard’ or known E2 interactors not identified in the
screen resulted in a total of 121 proteins to be re-screened in a
High-Throughput Gaussia princeps luciferase-based complemen-
tation assay (HT-GPCA). This assay is similar to a mammalian
two-hybrid. The E2 bait was fused to one half of a luciferase
reporter, while the prey was fused to the second half. A bait–prey
interaction thus reconstitutes measurable luciferase activity.
A cutoff value derived from a separate study was used to determine
significant vs. non-significant interactions.
The HT-GPCA detected 72% of the gold standard interactions,
and based on that result the authors continue to test their larger
set of potential interactors in this assay. Here 42% of the potential
interactions, representing 98 of the 121 potential cellular pro-
teins, bound to one or more E2s. The authors compare this to a set
of 10 cellular proteins chosen randomly and tested for interaction
with E2, in which they detect ?6% of the possible interactions.
They therefore establish this as their false negative rate, but it is
important to note that the two sets of cellular proteins to be
tested were chosen in very different ways: the potential E2
interactors as proteins involved in transcription, DNA replication,
or as relatives of an initial E2 interactor; and the negative controls
randomly. Thus the representation by cellular compartment,
association with chromatin, and other factors are potentially
quite different between the two sets, meaning that they are
biased in different ways for association with E2. Since many
potential interactors are associated with chromatin, proteomic
experiments on the E2s will require special considerations to
address the relative insolubility of chromatin-associated proteins
and the transient nature of their interactions (Lambert et al.,
2012), and these experiments will be more powerful when they
are conducted in a truly unbiased way.
Mutant HPV proteins are valuable proteomic tools
Additional experimental design steps can help to eliminate issues
related to background or contaminant proteins. In one study from
our laboratory, the use of mutant ORFs allowed Powell and
colleagues to demonstrate that the HPV E84E2C protein uses the
NCoR/HDAC3 complex to repress the HPV long control region (LCR)
E6/E7 promoter (Powell et al., 2010). E84E2C is a protein that is
encoded by at least a few of the alpha genus HPV types and by BPV1
(Choe et al., 1989; Doorbar et al., 1990; Rotenberg et al., 1989;
Stubenrauch et al., 2000). A 12 aa E8 ORF is joined through splicing
to the E2 hinge and C-terminal DNA binding domains of E2; E8
replaces the full-length E2 transactivation domain. Before this study
began several groups including that of Stubenrauch and colleagues
had observed that E84E2C repressed transcription from the LCR and
had characterized two repression-incompetent mutant forms of
E84E2C (Choe et al., 1989; Doorbar et al., 1990; Lace et al., 2008;
Rotenberg et al., 1989; Stubenrauch et al., 2000, 2007, 2001; Zobel
et al., 2003). One is a 3 amino acid substitution or ‘KWK’ mutant
(aa 5–7 changed from KWK to AEA); the other is a deletion of E8 aa
3–12. The Powell study used a proteomic approach in which C33A
E.A. White, P.M. Howley / Virology 435 (2013) 57–69
cells stably expressed wild type (wt), KWK, or deletion mutant
E84E2C proteins. These proteins were tagged with an internal HA
epitope tag located in the E2 flexible hinge region. This tag had
previously been shown not to interfere with E84E2C repression
functions and is a reminder that tag choice and location is a critical
feature of a proteomic experiment design. Here, C-terminal tags are
known to interfere with the function of the E2 DNA binding domain;
an N-terminal tag on E8 interfered with the repression function.
The C33A cells were processed for anti-HA immunoprecipita-
tion, mass spectrometry, and CompPASS analysis using an exist-
ing stats table consisting of data from 293T cells. The wt and KWK
mutant E84E2C baits recovered 12 HCIPs each, but the deletion
mutant immunoprecipitated 144 HCIPs. Comparison of the wt
to mutant forms established that six of the wt interacting
proteins (NCOR1, TBLR1, ARG1, BLMH, TGM3, and CASP14) were
not found in either of the repression-deficient mutants. Further
validation experiments showed that NCOR1 and HDAC3 are
critical mediators of E84E2C repression. They bind to wild type,
but not mutant E84E2C proteins in IP-western blot experiments.
When NCOR1 and/or HDAC3 were depleted from E84E2C-
expressing cells by siRNA knockdown, repression of the LCR was
relieved. NCOR1 and HDAC3 did not affect LCR repression in cells
expressing full-length E2. Even though the experiments were
conducted in a different cell type than the 293T cells in which
the comparison data were generated, this experimental design
based on known biological functions allowed the authors to
determine the molecular basis of E84E2C-mediated repression
of the HPV LCR.
The two E6 studies introduced above also take advantage of
some existing E6 mutants. E6s consist of two globular domains
bridged by a flexible linker, and this limits the availability of
exposed sites that can be mutated without affecting the overall
structure of E6 (Nomine et al., 2006). A few useful mutants have
been characterized and are widely used, including mutants that
alter the predominant splice donor site in E6 (forcing the
production of only full-length E6 and not spliced E6*) (Sedman
et al., 1991), an HPV16 E6 mutant that is impaired in its binding
to E6AP (I128T) (Liu et al., 1999), and a mutant lacking the flexible
C-terminal PDZ binding domain (in HPV16 E6, this is D146–151)
(Kiyono et al., 1997). Both our study and that of Rozenblatt-Rosen
use the I128T mutation; in the Rozenblatt-Rosen study the I128T
mutant is studied in the context of the splice donor mutation,
while in our study it is compared to the wild-type E6 ORF.
The data from both studies suggest that introduction of the I128T
mutation both eliminates E6AP binding and the binding of nearly all
other E6 interactors. In the Rozenblatt-Rosen study, 16E6 nonsplice
bound to 23 cellular proteins and 16E6 nonsplice with I128T bound
to seven; of the three proteins in common to the two E6s, two are
E6AP and PSMD3, a subunit of the proteasome. The number of
peptides detected for E6AP and PSMD3 in the presence of the I128T
mutation is just 10–15% of the peptides detected for the nonsplice
mutant alone. Similarly, introducing the I128T mutation into the E6
in our study resulted in loss of binding to E6AP and no proteins
detected as HCIPs in the absence of MG132.
These results indicate that I128 is a critical residue for HVP16
E6 interactions. Nearly all other protein interactions are lost when
E6 does not bind to E6AP. While we do not believe that every
interaction with E6 is mediated through E6AP (e.g., PDZ binding
proteins), it is possible that high-risk HPV E6s binding to E6AP
could either mediate interactions with other proteins or could
stabilize E6 in a conformation that allows these interactions, and
this is consistent with recent data from the Vande Pol laboratory
(Ansari et al., 2012). It will be interesting to characterize a similar
mutant for a beta E6 that interacts with the LXXLL motif in
MAML1, and to see if binding to MAML1 or another LXXLL protein
is also critical for interactions with beta E6s.
Proteomic approaches to the study of other viruses
HPV researchers are certainly not alone in applying powerful
proteomic techniques to better understand virus-host PPIs.
Although beyond the scope of this review, we direct readers to
several other studies that have used this technology successfully.
Nevan Krogan’s group (Jager et al., 2012a, 2011) conducted a
comprehensive AP-MS study using all ORFs of HIV-1 expressed in
293 and Jurkat cells. In a companion paper, they reported that
HPV-1 Vif allows the recruitment of the transcription factor CBP-b
to a ubiquitin ligase complex that is specific for APOBEC3G but
not APOBEC3A (Jager et al., 2012b). Pichlmair et al. (2012)
recently reported a proteomic study using 70 different viral ORFs
from 30 viruses. The ORFs were chosen for their ability to
modulate the host innate immune response, and the analysis
revealed a large number of host cellular pathways, some pre-
viously unreported, that are involved in the innate immune
response to viral infection. Other more targeted studies have
answered specific questions about herpesvirus biology (Kramer
et al., 2011; Loret et al., 2008; Salsman et al., 2012) or measles
biology (Komarova et al., 2011). Still other groups have applied
yeast-two-hybrid techniques to the large-scale study of influenza
and EBV (Calderwood et al., 2007; Shapira et al., 2009). However,
the diversity of the HPVs and the direct comparisons between
HPV types that is allowed by AP-MS does make them exception-
ally well-suited to comparative proteomic studies.
Coupling systematic proteomic analysis to global gene
As high-throughput mass spectrometry approaches become
more common and as the cost and availability of high-throughput
sequencing improves, we anticipate that more and more studies
will combine proteomics and gene expression studies into a
single systematic investigation of cellular changes mediated by
virus infection and viral proteins. Rozenblatt-Rosen and collea-
gues demonstrate the potential of this approach by coupling
gene expression data to their proteomic results. They use the
same viral ORF-expressing fibroblast cell lines to generate RNA
which is analyzed on microarrays. In keeping with the discovery
that beta HPV E6s modulate signaling through the Notch path-
way, they show that Notch transcriptional targets are decreased
both in beta-E6 expressing fibroblasts and in cells depleted of
MAML1. The study of influenza interactors by Shapira and cow-
orkers also used gene expression profiling data and was able
to identify several pathways (including NFkappaB signaling,
MAPK signaling, and p53-dependent apoptosis) that are regulated
both through PPIs and altered gene expression (Shapira et al.,
Conclusions and future directions
The advent of large-scale proteomic techniques has broadened
and deepened progress in the study of papillomavirus–host cell
interactions. The field has progressed from the initial discoveries
describing single protein-protein interactions, to later studies
identifying several host proteins in complex with a single
HPV bait, and now to the studies examining multiple baits from
many HPV types. Mass spectrometry advances have allowed the
detection of more and more interactors with higher confidence
than ever before. We are confident that the continued develop-
ment of such powerful technologies and the in-depth study of the
interactions they define will propel research on the papilloma-
viruses and papillomavirus-associated diseases.
E.A. White, P.M. Howley / Virology 435 (2013) 57–69
Studies related to the papillomaviruses conducted in the
Howley laboratory referred to in this review were supported by
grants from the NIH to PMH: 1RC1 CA145188 and P01 CA50661.
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