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BIOINFORMATICS
Vol. 27 ISMB 2011, pages i7–i14
doi:10.1093/bioinformatics/btr217
Sorting the nuclear proteome
Denis C. Bauer1,2, Kai Willadsen2,3, Fabian A. Buske2, Kim-Anh Lê Cao4,
Timothy L. Bailey2, Graham Dellaire5 and Mikael Bodén2,3,6,∗
1Queensland Brain Institute, 2Institute for Molecular Bioscience, 3School of Chemistry and Molecular Biosciences,
4Queensland Facility for Advanced Bioinformatics, The University of Queensland, St Lucia, Australia, 5Departments
of Pathology and Biochemistry & Molecular Biology, Dalhousie University, Halifax, Nova Scotia, Canada and 6School
of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Australia
ABSTRACT
Motivation: Quantitative experimental analyses of the nuclear
interior reveal a morphologically structured yet dynamic mix of
membraneless compartments. Major nuclear events depend on
the functional integrity and timely assembly of these intra-nuclear
compartments. Yet, unknown drivers of protein mobility ensure that
they are in the right place at the time when they are needed.
Results: This study investigates determinants of associations
between eight intra-nuclear compartments and their proteins in
heterogeneous genome-wide data. We develop a model based
on a range of candidate determinants, capable of mapping the
intra-nuclear organization of proteins. The model integrates protein
interactions, protein domains, post-translational modification sites
and protein sequence data. The predictions of our model are
accurate with a mean AUC (over all compartments) of 0.71.
We present a complete map of the association of 3567 mouse
nuclear proteins with intra-nuclear compartments. Each decision
is explained in terms of essential interactions and domains, and
qualified with a false discovery assessment. Using this resource,
we uncover the collective role of transcription factors in each of the
compartments. We create diagrams illustrating the outcomes of a
Gene Ontology enrichment analysis. Associated with an extensive
range of transcription factors, the analysis suggests that PML bodies
coordinate regulatory immune responses.
Contact: m.boden@uq.edu.au
Supplementary information: Supplementary data are available at
Bioinformatics online.
1 INTRODUCTION
The cell nucleus has morphologically distinct sub-compartments.
However, unlike cytoplasmic organelles, the sub-compartments
within the nucleus are not membrane-enclosed, but rather are formed
via recruitment of proteins, RNA and DNA.
The translocation of proteins from the cytosol into the nucleus, and
their subsequent association with its sub-compartments represent
two distinct levels of cellular regulation. At the first level, nuclear
import and export of proteins is largely governed by the nuclearmembrane-associated
nuclear pore complex (NPC) along with a set
of proteins that recognize and actively assist cargo proteins (Stewart,
2007). The import signals used by cargo proteins to bind with carrier
proteins were recently extensively explored and modelled (Kosugi
et al., 2009). In contrast, at the second level of regulation, the
∗To whom correspondence should be addressed.
mechanisms underpinning the sorting of proteins into intra-nuclear
compartments are not well understood.
The continuous flux of proteins into and out of the nucleus
as well as among intra-nuclear compartments underpins central
events such as DNA replication, mRNA processing and ribosome
biogenesis (Gorski et al., 2006; Misteli, 2007). Hence, to understand
these events we need to fully appreciate the mechanisms of
intra-nuclear trafficking.
We are now at a stage when we have access to experimental
evidence of abundance, localization and modification on a proteomic
scale. However, the information offered by many high-throughput
techniques does not illustrate the functional purpose of nuclear
proteins and compartment structures. Computational modelling, on
the other hand, may be able to elucidate functional roles not captured
by any individual existing experimental technology (Gorski and
Misteli, 2005).
When attempting to gain insight into the underlying mechanisms
of translocation, the ability of a model to provide explanations
for its predictions is as important as the predictions’ accuracy.
Several predictors have been reported that evaluate the tendency of a
protein to associate with a nuclear compartment (Lei and Dai, 2005;
Shen and Chou, 2007). However, these predictors do not provide
clear information as to what factors influence these predictions.
Additionally, most models are not designed to incorporate any of the
constraints that are fundamental to translocation; existing predictors
do not recognize that proteins can associate transiently with several
compartments at different stages, or that molecular interaction is a
prime means of establishing and retaining such associations. Instead,
the output of these predictors may be influenced by broad sequence
similarity, lacking specific detail of any underlying cause.
This study explores determinants of intra-nuclear compartment
association in heterogeneous genome-wide data, and develops
a model capable of mapping the intra-nuclear organization of
proteins. We use this model to predict the association of the
mouse proteome with different intra-nuclear compartments. We also
portray transcription factors in terms of how they associate with
nuclear architecture to impart novel insights about their ‘spatial’
synergy. We assign functional roles to individual compartments via
their constituent regulatory proteins, using over-represented Gene
Ontology (GO) terms in shared target genes.
2 BACKGROUND
We distinguish between eight different locations in the nucleus,
each morphologically defined and known to associate with at least
20 different proteins. To select appropriate features for a model,
© The Author(s) 2011. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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we first discuss what is known about the structural and functional
role of compartments, their molecular composition and the
properties that may modulate protein association.
Association with compartments primarily relies on different posttranslational
modifications, binding and interaction with core protein
members, RNA and regions of DNA, prompting us to peruse a range
of different data resources, as discussed in Section 3.
Nucleolus: the largest and best-studied nuclear compartment is the
nucleolus. It is highly dynamic and forms at the end of mitosis
around a cluster of ribosomal genes, reflecting its primary role
of supporting ribosomal biogenesis. Beyond producing ribosomal
subunits, nucleoli are involved in cell-cycle control via protein
sequestration, and in stress response. Mammalian cells contain one
to four nucleoli (see Boisvert et al., 2007 for a review).
Recent large-scale mass spectrometry studies have identified over
700 proteins that stably co-purify with isolated human nucleoli
(Andersen et al., 2002, 2005). Several studies have established
amino acid motifs that appear to target individual proteins to the
nucleolus (Sirri et al., 2008). However, it is unclear whether there
is a common sorting principle. Instead it is likely that a multitude
of molecular interactions retain proteins within the compartment
(Bodén andTeasdale, 2008), which explains the prevalence ofWD40
motifs (i.e. scaffolds for protein interactions) in nucleolar proteins
(Bickmore and Sutherland, 2002).
Perinucleolar compartment: the perinucleolar compartment (PNC)
has been suggested as a pan-cancer marker (Pollock and Huang,
2009) since it appears primarily in transformed and cancer cells,
forming a meshwork on the nucleolar surface. PNCs are dynamic
structures, assembled in late telophase, and disassembled at the
beginning of mitosis (Pollock and Huang, 2009).
Polymerase III transcribed RNA, and proteins involved in RNA
metabolism, are known to associate with the PNC. RNA binding
domains seem to be critical for the association of some proteins
(e.g. Ptb) with the PNC and the compartment integrity is sensitive
to the presence of RNase. PNCs appear to be linked to an as yet
undefined DNA locus (Pollock and Huang, 2009). Most of the 20 or
so proteins that are known to localize to the PNC are also known to
associate with other nuclear sites.
Promyelocytic leukaemia body: the Promyelocytic Leukaemia
(PML) protein is a core constituent of the nuclear compartment
known as the PMLnuclear body, or nuclear domain 10 (Bernardi and
Pandolfi, 2007). The PML gene was first discovered as the fusion
partner of the retinoic acid receptor alpha in a common translocation
found in the promyelocytes of patients with acute promyelocytic
leukaemia (APL; de Thé et al., 1991). In APL, the PML bodies
are absent from leukaemia cells, and PML protein expression is
frequently reduced in several forms of cancer, including brain, breast
and prostate (Gurrieri et al., 2004). PML bodies are composed
primarily of protein, containing little or no DNAor RNA, while they
make extensive contacts with chromatin at their periphery (Boisvert
et al., 2000). More than 75 proteins have been demonstrated
to associate with PML bodies (Dellaire and Bazett-Jones, 2004;
Dellaire et al., 2003). Through these protein interactions, PML
bodies are thought to function in a host of cellular processes
including the anti-viral response, gene regulation, DNA repair,
tumour suppression and apoptosis (Bernardi and Pandolfi, 2007).
The formation requires the PML protein and is regulated in part by
modification of PML by the Small Ubiquitin-like Modifier (SUMO).
Many PML body proteins are SUMOylated and/or contain SUMOinteraction
motifs (SIMs). In this way PML bodies are thought
to form by SUMO-mediated intra- and intermolecular interactions
among their components (Shen et al., 2006).
Nuclear speckle: nuclear speckle domains (or Interchromatin
Granule Clusters) are believed to be involved in the pre-mRNA
processing machinery and regulating factors that are needed
for transcription (Lamond and Spector, 2003). As such, these
compartments are transit-zones for many RNA binding proteins;
a significant portion of speckle proteins exhibit RNA recognition
motifs (Bickmore and Sutherland, 2002).
Sometimes referred to as a nuclear speckle targeting signal, many
proteins are rich in Arginine and Serine. Indeed, Bickmore and
Sutherland (2002) observe that 14 of 18 splicing proteins with an
isoelectric point exceeding 10 have an RS domain.
Cajal body: Cajal bodies appear to be sites where proteins
associated with a variety of nuclear processes concentrate to increase
their functional efficiency, e.g. pre-assembly and modification of
spliceosome components (Morris, 2008). Cajal bodies are relatively
small and do not occur in all tissue types. Spliceosome formation
still occurs, though with lower efficiency, when Cajal bodies are
absent, supporting the view that they are non-essential assemblies
of cooperating proteins (Morris, 2008).
Coilin is a core Cajal body protein and necessary for recruiting
small nuclear ribonucleoproteins, though ‘residual’bodies still form
in its absence (Morris, 2008). Cajal bodies respond dynamically to
changes in transcription, and rapid movement of proteins into and
out of the body has been observed. They disassemble during mitosis
but do not regularly assemble at interphase (Morris, 2008).
Chromatin: chromatin packages DNA and as such is responsible
for regulating access of DNA-binding proteins. DNA binding motifs
(e.g. so-called AT-hooks) are prevalent in chromatin-associated
proteins (Bickmore and Sutherland, 2002). Chromatin consists
of many structural proteins, including histones and non-histone
proteins, many of which either post-translationally modify histones
or remodel chromatin in an ATP-dependent fashion (Becker and
Hörz, 2002; Jenuwein and Allis, 2001). Lysines are common
in chromatin proteins, and are often modified e.g. acetylated or
methylated (Bickmore and Sutherland, 2002). Protein interaction
motifs are prevalent in chromatin resident proteins, reflecting the
range of interactions required to form and utilize this structure for
transcription and replication (Bickmore and Sutherland, 2002).
Nuclear pore complex: the nuclear pore complex (NPC) is a
highly structured assembly of approximately 30 proteins that forms
a channel through the nuclear membrane (Hetzer et al., 2005).
The constituent proteins, nucleoporins, interact in various ways to
assist in transporting cargo into and out of the nucleus. Specifically,
importins and exportins bind to cargo target sequences via nuclear
localization signals and nuclear export sequences, allowing the
complex to actively translocate the cargo. This translocation occurs
in a multi-stage process involving Ran, which cycles between a
GTP- and GDP-bound state (Stewart, 2007).
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In contrast to other internal compartments, NPCs are relatively
static (though some nucleoporins bind and disassociate rapidly),
occur in large (though highly variable) numbers, and are found in
all cells with a nucleus. They form at the end of mitosis from newly
synthesized nucleoporins (Hetzer and Wente, 2009).
NPCs are believed to offer a permissive environment for
transcription via DNA, RNA and/or protein interaction, e.g. direct
gene interaction or chromatin binding and subsequent modification
(Zhao et al., 2009).
Nuclear lamina: in metazoan cells, the nuclear lamina is the inner
protein scaffold of the membranous nuclear envelope and is of
structural importance (Dechat et al., 2008). The lamina is largely
composed of protein filaments (lamins that establish a protein–
protein network), is perforated by NPCs, and makes contact with
chromatin (Dechat et al., 2008). The lamina is reformed when the
nuclear envelope breaks down at cell division (Hetzer et al., 2005).
The lamina is implicated in regulatory processes involving
chromatin. Regions of lamina associated with heterochromatin are
believed to be transcriptionally repressive, and lamins may play
a role in chromatin remodelling and DNA binding. Additionally,
abnormalities in the lamina have been linked to specific histone
methylations (Dechat et al., 2008).
3 MATERIAL AND METHODS
3.1 Data
We annotated the mouse nuclear proteome with intra-nuclear compartment
associations, as described in Mohamad and Bodén (2010). We combined
data from multiple data sources including the experimentally determined
mouse nuclear proteome (Fink et al., 2008), the Nuclear Protein Database
(Dellaire et al., 2003), NOPdb (Leung et al., 2006), and many smaller
datasets from the literature. Data from generic (and sometimes automatically
annotated) databases such as Uniprot and HPRD was included only when
supported by other sources. In addition, we used Ensembl’s orthology map to
assign annotations for human proteins to the mouse nuclear proteome when
necessary.
A total of 3567 proteins are annotated as nuclear, of which 2281 proteins
are lacking in any compartment association. The set of 1286 proteins that
associate non-exclusively with compartments (see Table 1) are used to train
models and establish their test accuracy on held-out subsets (see Section 4.2).
We use these models to predict the intra-nuclear compartment association for
the set of 2281 proteins lacking this information (see Section 4.3).
Sequence and protein interaction data were sourced from Uniprot and
BioGrid (Breitkreutz et al., 2008), respectively. Protein domains were
identified using InterProScan and InterPro (Hunter et al., 2009). Sequence
motifs and protein post-translational modification sites were identified using
the Eukaryotic Linear Motif (ELM) resource (Gould et al., 2010). Combining
the information available from these datasets with literature evidence, we
determined correlations of protein features grouped into three sets: protein
interactions, protein domains and sequence motifs. From this exploration we
identify specific features that are used as input features for our model.
3.2 Model
Bayesian networks (BNs) offer a flexible and practical modelling framework,
in which nodes represent random variables and directed edges specify their
dependencies. Dependencies can be obtained from domain expertise, the
incorporation of which results in a graphical representation of the collection
of variables, reflecting (potentially causal) relationships between ‘parent’
and ‘child’ nodes (where a ‘child’ variable is conditionally dependent on its
‘parent’ variables).
Fig. 1. Template for BN architecture. Nodes are depicted as circles. Edges
are directed top-to-bottom, indicating that nodes above are parents to nodes
below.
We explored protein features to be used as inputs to a model that is
able to identify the compartment(s) to which the protein belongs. In our
BN features are random variables. Datasets are thus cast as collections
of specific instantiations of random variables, e.g. ‘Protein-interacts-withPml’=False,
‘Protein-sequence-has-SUMO-site’=True and ‘Protein-
associates-with-PML-bodies’=False.
Each compartment is represented by a set of (non-exclusive) random
variables. The nodes for these variables are linked according to a template
(see Fig. 1), with one instance of this template for each compartment.
The random variables are divided into groups, namely ‘PPI’, ‘Domain’ and
‘Motif’, which are sourced from protein interaction data, InterPro domains
and ELM entries, respectively. We expect there to be some dependencies
within each group. However, to reduce the number of parameters and increase
interpretability, each group of variables is joined via a latent (unobserved)
Boolean variable. As a result of training, the latent variable for a group
will take a probability that maximizes the likelihood of the data. The
latent variables are direct parents of the compartment variable. We thus
interpret each group variable as indicating the importance of the values of
its features, e.g. if the PPI latent variable is True, one or more PPI feature
values contribute positively with compartment-specific support. The Boolean
compartment variable in the template (in each compartment instance) is a
parent of a SVM output variable.
For specific query proteins, variables have known values and can be
instantiated. However, the values of some variables are not known; these
are left unspecified, and their probabilities are inferred. The joint probability
of all variables, X1,...,XN , in the BN is given by,
P(X1 =x1,...,XN =xN )=
N
i=1
P(Xi =xi |pa(Xi))
where pa(Xi) is the set of parents of the i-th variable.
Inference of P(X |e) where X is the (uninstantiated) query variable, and
e is the available evidence, is based on the full joint probability. In order to
obtain this value, we marginalize over the set of unobserved variables Y,
P(X |e)=η
y∈Y
P(X,e,y)
where η is a normalizing constant that ensures that conditional probabilities
of X’s possible values sum to 1. We use the model to predict the posterior
probability of association with a compartment given evidence of the protein
(see Section 4.2).
The parameters of the BN are thus the conditional probabilities associated
with each node. In this study they are learned from observations in the
datasets. The structure of the BN is pre-specified to reflect domain knowledge
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(see Section 4.1). We use standard Expectation–Maximization to cope with
the absence of explicit evidence for variables.
All but one feature in our datasets can be naturally cast as a Boolean
random variable. Based on our previous work, to capture similarity
between amino acid sequences we train support-vector machines (SVMs)
to distinguish between classes of proteins (Bodén et al., 2010). Specifically,
for each compartment we train a SVM to distinguish between members and
non-members of the compartment. The raw output of the SVM is represented
as a continuous random variable, with a Boolean parent variable that is True
when the protein is a member, and False otherwise. This is achieved by
parameterizing two Gaussian densities with the means and variances of SVM
scores for True proteins, and False proteins.
4 RESULTS
4.1 Model features
Each compartment is represented by a set of (non-exclusive) random
variables incorporated into a Bayesian network, recognizing their
dependencies. The nodes for these variables are linked according
to a template (see Fig. 1), with one instance of this template for
each compartment. The random variables are divided into groups,
namely ‘PPI’, ‘Domain’and ‘Motif’, which are sourced from protein
interaction data, InterPro domains and ELM entries, respectively.
By consulting the literature and as discussed above, we identified
core members of each compartment as candidates to be used within
the PPI group of each compartment module in the BN. For instance,
PML bodies use Pml protein as a scaffold for recruiting other
members to the compartment (see Section 2). Hence, ‘Proteininteracts-with-Pml’
is used as a PPI variable in the PML body
module. When we failed to establish a set of four candidates, we
determined the correlation between their interaction with all other
compartment members to nominate additional PPI variables. While
not as widely recognized as the manually assigned set, many of the
features identified by correlation also have literature support.
Domains from InterPro and motifs from ELM were also identified
by consulting the literature. When a domain or motif is known to
play an important role for establishing the association of a query
protein with the compartment, we use it as a variable. In addition,
we observed the correlation between the occurrence of domains in
proteins and compartment. We added the domains and motifs with
the strongest compartment correlations to the BN, until four InterPro
domains and four ELM motifs had been identified for each. The
complete set of PPI, domain and motif variables, used in the model
experimented within Section 4.2, is provided in Supplementary
Table S2 with literature support (when available).
We note that the exploration of candidate features described
above may introduce a selection bias. We therefore tried alternative
variable selections—randomly choosing what variables to include—
and established the overall accuracy of such models. The accuracies
changed very little under such perturbation, offering re-assurance
that any selection bias is negligible.
4.2 Quantifying predictive accuracy of compartment
model
In order to assess the accuracy of our compartment association
predictions, we use the area under the ROC curve (AUC) for each
compartment classification variable (one versus all). To understand
how useful the model is for guiding experimentation, we also
Table 1. Cross-validated prediction accuracy on proteins with known
compartment associations
Compartment Proteins AUC50 (SD) AUC (SD)
Cajal body 51 0.22 (0.02) 0.60 (0.03)
Chromatin 323 0.17 (0.02) 0.71 (0.01)
Nuclear lamina 77 0.17 (0.04) 0.70 (0.01)
Nuclear pore 51 0.41 (0.07) 0.79 (0.05)
Nuclear speckle 404 0.24 (0.01) 0.71 (0.01)
Nucleolus 596 0.14 (0.01) 0.60 (0.01)
Perinucleolar 24 0.41 (0.09) 0.80 (0.05)
PML body 91 0.23 (0.06) 0.77 (0.03)
Mean (compartment) 0.25 0.71
provide the AUC50—the AUC measured up until 50 false positives
(see Gribskov and Robinson, 1996).
Table 1 shows the prediction accuracy (using 5-fold crossvalidation
averaged over five independent repeats) for a BN that
uses protein interactions, domains (from InterPro), post-translational
modification sites/sequence motifs (from ELM) and sequence data
via SVMs. The BN has a Boolean node for each compartment
that is interpreted as the model’s prediction (see Section 3). The
mean AUC (over all eight compartments) is 0.71. This result
is substantially better than random (0.50) and highlights how
incomplete annotations are; many compartments have not been
subjected to high-throughput screening, and 64% of nuclear proteins
have no compartment annotation at all.
We investigated variations to the template module, with and
without interactions, domains and motifs (see Supplementary
Table S1). We explored the use of gene expression data in the form
of DNA microarray data collected over large numbers of tissues
(Su et al., 2004) as additional continuous variables to the template.
Predictive accuracy varied, and in some cases exceeded that of the
standard configuration. Generally, gene expression data contributed
very little so we removed it to increase model interpretability. The
accuracy using other groups of variables varied by compartment.
However, to reduce selection biases we use the standard template
configuration consistently in all simulations reported here. This BN
performed robustly for all compartments.
4.3 Predicting novel compartment associations
There are many proteins that are known to be imported into the
nucleus, but which have no known intra-nuclear compartment
association. In this section, we use the model to predict novel
compartment associations for the 2281 un-annotated proteins in our
assembled dataset.
We create an ensemble model (specified with variables identified
in Supplementary Table S2) from the five independently trained
models (with different dataset splits). Specifically, we average the
posterior probability each model estimates for each compartment,
for each protein and also report the standard deviation. We estimate
the rate of false discovery (FDR) using the set of proteins with
known compartments (samples held out from training).
For each intra-nuclear compartment we report the predictions
made by our model up until the FDR exceeds 0.2, given
the probability of the prediction (see Supplementary Table S3).
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Table 2. Strongest compartment association prediction of nuclear proteins
Protein Compartment Probability Est. FDR
ZN593 (Q9DB42) Nucleolus 1.00 (0.00) 0.00
NFAT5 (Q9WV30) PML body 0.94 (0.00) 0.00
IRF9 (Q61179) Cajal body 0.94 (0.01) 0.00
PHF12 (Q5SPL2) Chromatin 0.93 (0.00) 0.00
RUXF (P62307) Nuclear speckle 0.92 (0.00) 0.12
SMG1 (Q8BKX6) Nuclear pore 0.90 (0.00) 0.20
ZFHX3 (Q61329) Nuclear lamina 0.78 (0.01) 0.80
MINT (Q62504) Perinucleolar 0.40 (0.01) 0.62
In each case, we provide the evidence used by the model to make its
prediction; this ability to inspect and interpret predictions is one of
the advantages arising from the use of a Bayesian network model.
At a confident FDR of 0.2, we predict in addition to those already
known, 13 Nucleolar proteins, 13 PML body proteins, 21 Nuclear
speckle proteins, 6 Cajal body proteins, 6 Chromatin proteins and
1 Nuclear pore protein.
To first narrow the focus of our discussion, we list the highestconfidence
prediction for each compartment in Table 2. For all
but one compartment, at least one protein was predicted with a
probability higher than 50%; the exception was the Perinucleolar
compartment, though due to the small number of perinucleolar
proteins in our dataset, the associated prior probability is very small.
We also note that the FDR (at the probability of the top prediction)
is small for most compartments.
For each of the top predictions, we discuss below details of
the evidence used by the model to make the prediction. We use
the posterior probability of latent nodes (specific to each group
of features in the template module) to indicate the importance of
protein interaction, presence of domain and occurrence of motifs
for compartment association. In many cases, we also make reference
to a specific variable by its name (e.g. a protein name, an InterPro
domain name or an ELM name, see Supplementary Table S3 for
a complete list with probabilities assigned to each variable). With
confident predictions in hand, we conducted a targeted literature
search to establish any recent compartment association evidence
that has not yet been included in datasets.
Nucleolus: the model predicts with probability 1.0 an association
between SSF1 and the nucleolus, confirmed by Kim and Hirsch
(1998). With the same probability, the model also predicts ZN593,
a zinc finger protein, to be associated with the nucleolus. This
prediction is firmly based on protein interactions (the latent variable
for the group of protein interactions has a probability of 0.99)
with the nucleolar GTP-binding protein 1 (NOG1) and the FHA
domain-interacting nucleolar phosphoprotein MKI67.
PML body: the model predicts that NFAT5, a nuclear factor of
activated T-cells, is associated with PML bodies due to it containing
a p53-like transcription factor (TF) domain (SSF49417) as well as
a SUMO-motif, a targeting motif found in a USP7 binding protein,
docking to the NTD domain (LIG_USP7_2), and the leucine-rich
export signal that binds to CRM1 exportin (TRG_NES_CRM1_1).
The latent variable for the domain group of variables has a
probability 0.99. This implication of PMLbodies in antiviral defence
is supported in the literature by (Everett and Chelbi-Alix, 2007).
Recently, the related activated T-Cell factor NFAT1 (also known
as NFATC2) has been shown to associate with PML NBs, and this
interaction appears to enhance the ability of NFAT1 to transactivate
its target genes (Lo et al., 2008).
Cajal body: the model predicts IRF9 (0.96), an interferon
regulatory factor, to be associated with the Cajal body. The
decision was based on it containing the domain of an interferon
regulatory factor (PF00605). Furthermore, the decision was greatly
influenced by the presence of motifs (0.60), including the major
TRAF2-binding consensus motif (LIG_TRAF2_1), the exposed
glycosaminoglycan attachment site (MOD_GlcNHglycan) and a
subtilisin/kexin isozyme-1 cleavage site (CLV_PCSK_SKI1_1).
Chromatin: both of the top ranking proteins NSD1 and HRX
(0.93), are methyltransferases and are confirmed to associate with
chromatin (Berdasco et al., 2009; Guenther et al., 2005). The model
also predicts PHF12 (0.93), a PHD finger protein, to be associated
with chromatin. This is based on interaction with paired amphipathic
helix protein Sin3a, on it containing a FYVE/PHD zinc finger
domain (SSF57903), the motif recognized by class I SH3 domains
(LIG_SH3_1), and on a site for attachment of a fucose residue to
serin (MOD_OFUCOSY).
Nuclear speckle: RUXF, a small nuclear ribonucleoprotein,
is predicted to be associated with the nuclear speckle based
on interaction with survival of motor neuron protein-interacting
protein 1 (GEMI2), survival motor neuron protein (SMN) and
splicing factor 3Asubunit 3 (SF3A3). Additionally, the model draws
on the protein containing a GRB2-like Src Homology 2 (SH2)
domains binding motif (LIG_SH2_GRB2).
Nuclear pore complex: the Serine/threonine–protein kinase SMG1
is predicted with a probability 0.90 to associate with the nuclear
pore. This decision is based on the atypical motif for N-glycosylation
site (MOD_N-GLC_2) and the nuclear receptor box motif, which
confers binding to nuclear receptors (LIG_NRBOX). Additionally,
the high score is partially attributed to the SVM, indicating the
presence of currently unknown sequence features.
Nuclear lamina: ZFHX3, a zinc finger homeobox protein, is
predicted to associate with the nuclear lamina with a probability
0.78. This is supported by the presence of a motif recognized
by class II PDZ domains (LIG_PDZ_2), the atypical motif for
N-glycosylation site (MOD_N-GLC_2 ) as well as a SUMO-motif.
Predicting associations for the full nuclear proteome: only a
fraction of all nuclear proteins are so far associated with one or more
intra-nuclear location. In response, and to broaden the focus of the
discussion, this section estimates the full protein complement of each
compartment by identifying the predicted compartment associations
(including the possibility of predicting no association) for each of
the 2281 unannotated proteins.
In Supplementary Table S4, we publish a comprehensive map of
associations with intra-nuclear compartments. We set the probability
threshold to be exceeded for a positive prediction for each
compartment variable in the Bayesian network using the annotated
data. Specifically, to balance sensitivity and specificity, we fix each
compartment threshold such that the model renders the correct
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D.C.Bauer et al.
Table 3. Predicted compartment associations for 2281 unannotated nuclear
proteins
Compartment Additional Probability FDR at
proteins threshold threshold
Cajal body 23 0.24 0.76
Chromatin 509 0.43 0.52
Nuclear lamina 17 0.38 0.68
Nuclear pore 12 0.31 0.43
Nuclear speckle 229 0.41 0.45
Nucleolus 1266 0.44 0.47
Perinucleolar 1 0.29 0.58
PML body 96 0.34 0.64
number of positives on the annotated set. Note that these thresholds
render both false negatives and false positives and we use this to
estimate the FDR of each model at these permissive thresholds.
Table 3 shows the number of proteins that are predicted as
associating with each compartment. The FDR is relatively high
at these low probability thresholds, especially for the smaller
compartments, which matches the larger number of negatives in the
datasets. For reference, Supplementary Table S4 lists all predicted
proteins for each compartment.
4.4 Transcription factor analysis
Not unlike how ‘transcription factories’ (Sutherland and Bickmore,
2009) are hypothesized to operate, we seek to establish the
collective, regulatory role of individual intra-nuclear compartments.
To do so, we first evaluate the prevalence of TFs in each of the
compartments. Importantly, we leverage the more comprehensive
picture of nuclear organization offered by the model herein and
use both known and predicted intra-nuclear associations of nuclear
proteins (recall that 2281 of 3567 lack such observations).
We label proteins as TFs by first consulting two sources. RIKEN’s
dataset (Kanamori et al., 2004) annotates 1675 mouse proteins
to be either TFs or co-regulators of a TF, based on homology to
known human TFs and GO annotations. In our data, 977 of these
proteins are annotated to be nuclear proteins and 213 are known
to associate with at least one compartment. DBD (Wilson et al.,
2008) focuses on DNAbinding proteins and requires a known DNAbinding/transcriptional
regulation domains in the protein sequence.
DBD contains 2549 predicted mouse TFs, of which 775 are
annotated to be nuclear and only 95 have a known compartment.
Around 542 nuclear proteins are annotated by both datasets to be
TFs, and 66 of these have a known compartment.
Table 4 lists the numbers of TFs among proteins in the
different compartments when using RIKEN’s and DBD’s TF
definitions. About 17% and 7% of the proteins with known
compartment association are identified as TFs (for RIKEN and
DBD, respectively). Using this as a background, we can measure
TF statistical enrichment for each compartment by applying the
Fisher Exact test to the counts of TFs and non-TFs in a compartment
and not in the compartment. According to this analysis, chromatin
and PML body are significantly enriched in TFs (discussed
below), whereas nuclear lamina and nucleolus are under-enriched
(P 0.05). Reassuringly, no TF was found to be associated with
the nuclear pore. The same trend is observed with both the RIKEN
Table 4. Transcription factor enrichment in compartments, with significant
over-representation within compartments marked
Compartment RIKEN TFs DBD TFs
Count Percentage Count Percentage
Cajal body 10 19.6 4 7.8
Chromatin 100 31.0* 47 14.6*
Nuclear lamina 4 5.2 4 5.2
Nuclear pore 0 0.0 0 0.0
Nuclear speckle 54 13.4 19 4.7
Nucleolus 71 11.9 18 3.0
Perinucleolar 6 25.0 0 0.0
PML body 27 29.7* 17 18.7*
All 213 16.9 95 7.4
∗P<0.05.
and DBD definitions. It is worth noting that nuclear speckles appear
to be close to average and are thus not significantly enriched in
TFs—contrary to expectations (see Section 2).
Previous work indicates a potential gene regulatory role of
PML bodies (Block et al., 2006; Lallemand-Breitenbach and
de Thé, 2010), beyond that of acting as a site for post-translational
modifications (Gupta et al., 2008; Song et al., 2008). For instance,
Wang et al. (2004) observe that PML bodies localize to sites of high
transcriptional activity. Also, PML bodies are known to sequester
TFs for timely release (Lin et al., 2003). Our analysis lends support
to such theories by identifying a large set of TFs that we predict or
experimentally observe to associate with PML bodies.
Intra-nuclear co-localization of transcription factors may indicate
that the factors co-operate, or that the localization site itself has a
regulatory role. To investigate if a nuclear compartment contributes
to transcriptional regulation, we identified the set of TFs with a DNA
binding site in TRANSFAC (Matys et al., 2006) that are known
or predicted to associate with each compartment. By scoring the
presence of binding sites in all promoters in mouse, we determined
the statistical enrichment of GO terms for the putative targets of the
applicable group of TFs; note that the set of enriched GO terms are
those associated with the putative targets, not those associated with
the TFs themselves. Enrichment is established through the same
statistically rigorous method as developed in our previous work
(Buske et al., 2010). Importantly, this test does not require that sites
are co-bound, but instead only considers whether the set of potential
gene products are annotated in ways that cannot be explained by
chance alone.
For each compartment c, in Supplementary Figures S1–S7, we
present all GO terms that are statistically over-represented in gene
targets of member TFs (see Supplementary Table S5 for a list of
TF binding matrices used to establish putative targets for each
compartment). Diagrams contain terms taken from the Biological
Process and Molecular Function ontologies. We note that many
essential and specific terms are only supported when TFs that are
predicted to belong to the compartment are included. With PML
bodies particularly enriched in TFs, to illustrate the utility of our
method, we discuss in more detail the GO terms identified for this
compartment below.
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Sorting the nuclear proteome
The TFs that localize to PML bodies target genes that are
associated (in a statistically significant sense) with a variety of
biological processes. In particular, we note that TFs in PML bodies
target immune genes. Several terms relate broadly to positive
regulation of immune response, cytokine production, leukocyte and
lymphocyte mediated immunity. These are observations identified
via predicted PML body members, that are well supported in the
literature. For instance, the morphology of PML bodies changes
markedly in response to interferon, a protein with viral defence
and immunomodulatory activities (Regad and Chelbi-Alix, 2001).
In fact, interferon directly induces transcription of core PML
body proteins (Everett and Chelbi-Alix, 2007). We also note that
targets involved in the regulation of apoptosis/cell death and
cytotoxicity figure strongly in this analysis of PML body TFs.
The role of PML bodies in apoptosis is well-established (Bernardi
et al., 2008). The analysis suggests that PML bodies play a
signal transduction role involving G-protein coupled receptors,
e.g. cytokines. There are some indications in the literature that
several PML body proteins are targets of cytokines (Salomoni,
2009).
5 CONCLUSION
We introduce a computational model that integrates evidence
garnered from protein interaction, domain and post-translational
modification data, to systematically address fundamental questions
of nuclear compartment localization. We use a Bayesian Network, a
probabilistic and transparent modelling framework, whose decisions
can clearly be traced back to the influence of the provided biological
features.
After carefully designing and training our model, we identify
and make available the determinants of intra-nuclear compartment
association of the full mouse nuclear proteome. The model predicts
intra-nuclear compartment associations of each protein with an
accuracy substantially above chance (AUC is 0.71) and indicates
the individual factors that influence its decisions. The mobility of a
nuclear protein is typically determined by its interaction with other
nuclear components and is often modulated by post-translational
modifications, including sumoylation. We also note that specific
functional domains are sometimes enriched in compartments and
can be used to determine likely associations.
This work presents a unique intra-nuclear protein association
map involving all the main compartments for 3567 mouse proteins,
and provides detailed justifications for each individual prediction.
This resource will enable cell biologists to effectively investigate
what compartments a protein of interest is likely to associate with,
and identify the factors including interactions and domain features
that modulate this sorting or at least unify proteins from the same
compartment.
Whether nuclear compartments in general fill a regulatory role
remains an open question—but our analyses lend significant support
to the idea that PML bodies and chromatin are implicated in
regulatory processes. We offer an analysis that is not simply based on
individual TFs, but one that groups them according to compartment
association to identify their downstream targets. We publish detailed
Gene Ontology diagrams for each compartment that will allow
biologists to investigate the statistical support for any regulatory
function and to guide targeted experimentation.
6 SUPPLEMENTARY MATERIAL
Supplementary Table S1 shows the prediction accuracy for different
BN architectures. Supplementary Table S2 lists the PPI, domain and
motif variables provided to the model from biological evidence.
Supplementary Table S3 contains the top-25 predictions for each
compartment, the basis for the predictions and supporting evidence
from the literature where available. Supplementary Table S4
lists compartment predictions for all proteins. Supplementary
Table S5 provides the list of compartment-associated TFs with their
TRANSFAC binding motifs.
Supplementary Figures S1–S7 show trees of GO terms that are
over-represented in the set of target genes of TFs associated with
a given intra-nuclear compartment. Terms are coloured green if
they can be considered over-represented using only the known
set of compartment-associated TFs. Terms coloured blue are only
discovered when using both known and predicted TFs; thus, blue
terms constitute novel functional predictions made on the basis of
results from our model. Uncoloured terms have been included only
to provide context based on the GO term hierarchy.
ACKNOWLEDGEMENT
We thank Nurul Mohamad for collecting the datasets that enabled
the development of the model.
Funding: This study received support from the Australian Research
Council (ARC) Centre of Excellence in Bioinformatics. G.D. is a
Canadian Institutes of Health Research (CIHR) New Investigator
and is funded by an operating grant from the CIHR (MOP-84260).
Conflict of Interest: none declared.
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