Gene Network Inference and Biochemical Assessment
Delineates GPCR Pathways and CREB Targets in Small
Intestinal Neuroendocrine Neoplasia
Ignat Drozdov1,2, Bernhard Svejda3, Bjorn I. Gustafsson4, Shrikant Mane5, Roswitha Pfragner6, Mark
Kidd3*, Irvin M. Modlin3*
1 Cardiovascular Division, King’s College London BHF Centre of Research Excellence, James Black Centre, London, United Kingdom, 2 Centre for Bioinformatics, School of
Physical Sciences and Engineering, King’s College London, London, United Kingdom, 3 Gastrointestinal Pathobiology Research Group, Yale University School of Medicine,
New Haven, Connecticut, United States of America, 4 Department of Gastroenterology, St Olavs Hospital, and Department of Cancer Research and Molecular Medicine,
NTNU, Trondheim, Norway, 5 Keck Affymetrix Facility, Yale University School of Medicine, New Haven, Connecticut, United States of America, 6 Institute of
Pathophysiology and Immunology, Centre for Molecular Medicine, Medical University of Graz, Austria
Abstract
Small intestinal (SI) neuroendocrine tumors (NET) are increasing in incidence, however little is known about their biology.
High throughput techniques such as inference of gene regulatory networks from microarray experiments can objectively
define signaling machinery in this disease. Genome-wide co-expression analysis was used to infer gene relevance network in
SI-NETs. The network was confirmed to be non-random, scale-free, and highly modular. Functional analysis of gene coexpression modules revealed processes including ‘Nervous system development’, ‘Immune response’, and ‘Cell-cycle’.
Importantly, gene network topology and differential expression analysis identified over-expression of the GPCR signaling
regulators, the cAMP synthetase, ADCY2, and the protein kinase A, PRKAR1A. Seven CREB response element (CRE) transcripts
associated with proliferation and secretion: BEX1, BICD1, CHGB, CPE, GABRB3, SCG2 and SCG3 as well as ADCY2 and PRKAR1A
were measured in an independent SI dataset (n = 10 NETs; n = 8 normal preparations). All were up-regulated (p,0.035) with
the exception of SCG3 which was not differently expressed. Forskolin (a direct cAMP activator, 1025 M) significantly
stimulated transcription of pCREB and 3/7 CREB targets, isoproterenol (a selective ß-adrenergic receptor agonist and cAMP
activator, 1025 M) stimulated pCREB and 4/7 targets while BIM-53061 (a dopamine D2 and Serotonin [5-HT2] receptor
agonist, 1026 M) stimulated 100% of targets as well as pCREB; CRE transcription correlated with the levels of cAMP
accumulation and PKA activity; BIM-53061 stimulated the highest levels of cAMP and PKA (2.8-fold and 2.5-fold vs. 1.8–2fold for isoproterenol and forskolin). Gene network inference and graph topology analysis in SI NETs suggests that SI NETs
express neural GPCRs that activate different CRE targets associated with proliferation and secretion. In vitro studies, in a
model NET cell system, confirmed that transcriptional effects are signaled through the cAMP/PKA/pCREB signaling pathway
and that a SI NET cell line was most sensitive to a D2 and 5-HT2 receptor agonist BIM-53061.
Citation: Drozdov I, Svejda B, Gustafsson BI, Mane S, Pfragner R, et al. (2011) Gene Network Inference and Biochemical Assessment Delineates GPCR Pathways
and CREB Targets in Small Intestinal Neuroendocrine Neoplasia. PLoS ONE 6(8): e22457. doi:10.1371/journal.pone.0022457
Editor: Hava Karsenty Avraham, Beth Israel Deaconess Medical Center, United States of America
Received February 28, 2011; Accepted June 24, 2011; Published August 11, 2011
Copyright: ß 2011 Drozdov et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Parts of this work were supported by the British Heart Foundation (BHF) through a PhD studentship for ID, and by the National Institutes of Health:
CA097050 (IMM) and DK080871 (MK). No additional external funding was received for this study. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: imodlin@optonline.net (IMM); mark.kidd@yale.edu (MK)
inactivation, and engender a ‘carcinoid syndrome’. This consists
of a variety of symptoms including episodic skin flushing, diarrhea,
bronchoconstriction, sweating and abdominal cramping, and as
many as 30–50% of individuals may have cardiac valvular disease
[5].
Although the cell of origin of SI NET has been identified as the
enterochromaffin (EC) cell, the secretory and proliferative
regulation of these cells is poorly defined and, as a result, progress
in the development of effective therapeutic strategies for diseases
associated with the cell, e.g. NETs or Crohns disease [6], has been
limited. The principal secretory product of the EC cell is serotonin
(5-HT), although substance P (motility regulator) and guanylin
(secretory regulator) have also been identified [7–9]. The most
successful therapy, to date, has been somatostatin analogs which
activate inhibitory G-protein coupled receptors (GPCRs) and
Introduction
Neuroendocrine or ‘‘carcinoid’’ tumors of the gut, usually
misperceived as a rare, indolent neoplasia, have not rigorously
been studied, are poorly understood and often misdiagnosed [1].
The perception that these tumors are rare has been altered by
introduction of diagnostic strategies including endoscopy, the
measurement of plasma biochemical markers such as Chromogranin A, and nuclear medicine techniques, including somatostatin receptor scintigraphy (SRS) [2]. A review of the current
Surveillance Epidemiology and End Results (SEER) database
indicates that small intestinal (SI) neuroendocrine tumors (NET)
comprise 24.3% of all NETs with the overall 5-year survival rate of
64.1% [3,4]. In the event of liver metastases, bioactive tumor
products enter the systemic circulation, bypassing hepatic
PLoS ONE | www.plosone.org
1
August 2011 | Volume 6 | Issue 8 | e22457
CREB Targets in Carcinoid Disease
result in decreased secretion of bioactive products with concomitant amelioration of symptoms [10–12].
GPCRs represent the largest family of cell-surface molecules
involved in environment sensing and signal transmission, accounting for .2% of the total genes encoded by the human genome
[13]. Mutations in GPCRs and Ga subunits have been identified in
endocrine tumors and are often associated with symptoms caused
by unregulated hormonal secretion. For example, activating
mutations of the thyroid stimulating hormone receptor (TSHR)
are found in some thyroid carcinomas and approximately 80% of
thyroid adenomas, while germline mutations in TSHR cause
familial non-autoimmune hyperthyroidism [14]. In the GPCRmediated downstream signal transduction system, cyclic AMP
responsive element-binding (CREB) protein has been shown to be
an important transcription factor that is involved in the
progression of hepatocellular carcinoma, leukemia, pituitary
tumor, and lung cancer through control of cell function (secretion,
proliferation, angiogenesis and apoptosis) [15–17]. To date, the
cAMP/CREB mechanism in SI NETs has not been demonstrated.
In our previous evaluation of transcriptome analyses (Affymetrix
U133 Plus chips) of the normal human EC cell and GI NET cell
line KRJ-I, we identified candidate luminal GPCRs and neural/
hormonal GPCRs including b1 adrenergic and dopamine D
receptors (DR) [18,19]. Further investigation demonstrated that
isoproterenol, a ß-adrenergic GPCR agonist, stimulated 5-HT
secretion through increased intracellular cAMP [20]. Others have
shown, in PC12 (rat pheochromocytoma cells), HEK293T
(Human Embryonic Kidney cells) and the pancreatic beta cell
line, MIN6, that activation of the cAMP pathway stimulates gene
expression through protein kinase A (PKA)-mediated phosphorylation of CREB at Ser-133 [21,22]. Since little is known about
neoplastic EC cell transcription and proliferation or secretion, we
considered that delineation of the molecular basis of GPCRmediated transcription through cAMP/PKA/CREB would provide novel information regarding the mechanistic basis of these
processes and facilitate the identification of new therapeutic targets
that might be used to inhibit NET function. As these tumors
autoregulate their own growth through amine production [23] and
regulate the local microenvironment (e.g. stimulate fibroblast
proliferation and secretion) [24], delineating GPCR-pathways may
identify novel targets to inhibit tumor cell proliferation.
We used gene network analysis and identified in silico the
cAMP/CREB-mediated mechanisms of transcription in SI NETs.
Using an established SI NET model, the human EC cell line
(KRJ-I) [25], we validated GPCR-mediated transcription of
CREB targets through cAMP/PKA/pCREB-activation in SI
NETs. Our results provide novel information regarding the
transcription of CREB response elements (CREs) known to be
relevant to tumor proliferation and secretion that are activated by
GPCR regulation of intracellular cAMP. Furthermore, we offer
the first formal network topology analysis of this disease.
with PCC over 0.60 are known to be more biologically relevant
[26], ii) at PCC,0.94 the network was excessively large (most of
the nodes were present), suggesting evidence of false-positive
edges, iii) at PCC$0.94, a large number of connected components
emerged, while the overall network density remained high,
suggesting that genes were organized into tightly interconnected
modules that may be functionally relevant (Figure S1). The final
network contained 3470 genes and 4549 links (average node
degree = 2.6). Of these, 788 (23%) genes have known tumorigenic
somatic mutations (obtained from the Catalogue of Somatic
Mutations in Cancer [COSMIC] database).
It has been suggested that, in a co-expression network,
functionally related genes tend to organize into tightly linked
communities [27]. The Louvain algorithm was used to identify
these functional modules in the SI NET network in an unbiased
manner (see Methods). The network was partitioned into 882
clusters, of which 10 contained .20 genes. Network modularity
was 0.86, confirming that the SI NET interactome is embedded
with highly interconnected modules, reinforcing the complex
nature of signaling cascades in this disease (Figure 1A). The top
10 clusters were enriched for Gene Ontology (GO) Biological
Process (BP) terms. The most enriched terms included ‘Nervous
system development’ (BEX1, SYN1, GRIA2), ‘Immune response’
(CD38, IGKC, SLAMF8), and ‘Cell cycle’ (ASPM, MKI67,
TOP2A).
To determine the overall architecture of the SI NET
interactome, the node degree frequency distribution was calculated and established to be ‘‘scale-free’’ (Figure 1B). Generally, a
scale-free architecture implies that most of the connections are
confined to a few highly interconnected nodes (hubs) – a hallmark
of most biological networks. However, it is possible to reconstruct
similar connectivity patterns using random edge rewiring. To
confirm that the SI NET network was non-random, we compared
it to two models of random networks (see Methods): the MaslovSneppen model (scale-free architecture, preserved node degrees,
randomly rewired edges) and the Erdős–Rényi model (preserved
number of nodes, edges are constructed using a random Gaussian
probability distribution). For each model, 200 random networks
were generated and intersected with the original SI NET graph.
On average, the Maslov-Sneppen and the Erdős–Rényi networks
shared 78.3 (standard deviation = 7.7) and 229.1 (standard
deviation = 15.3) links with the SI NET interactome respectively
(Figure 1C). This substantiates the hypothesis that the original
network is significantly non-random (minimal z-score = 282.3).
To assess the stability of the SI NET network, we measured the
effects of random (error) and targeted (attack) node removal on the
network diameter. Removal of random nodes had no effect on the
diameter, suggesting that the SI NET interactome was robust
against random mutations. However, targeted removal of the most
connected hubs, as predicted, caused the network to collapse
(Figure 1D).
Results
2. In Silico Prediction of CREB Targets
Interestingly, 940/3470 genes (27%) in the SI NET network
were differentially expressed (student’s t-test, p#0.05). Of these,
539 were up-regulated and 401 were down-regulated compared to
normal SI mucosa. Eight genes (CHGA, CPE, ENO2, INSM1,
PTPRN2, SERPINA10, and SLC18A1/2) that have been
previously identified as markers of neuroendocrine tumors [28–
31] were confirmed in this study to be altered. Automated KEGG
pathway analysis (using the DAVID Functional Annotation
Database) of differentially expressed genes, identified an overrepresented (p = 0.03) GPCR signaling pathway characterized by
an over-expression of cyclic AMP synthetase and adenylate cyclase
1. Systems-wide properties of a SI NET gene
co-expression network
Gene co-expression patterns reflecting the pathogenesis of SI
NETs were represented as undirected weighted network where
nodes correspond to genes and edges correspond to co-expressions
between them. We examined the network by systematically testing
the Pearson correlation coefficient (PCC) cut-off in the range from
0.5 to 1 (Figure S1). Only gene pairs with an absolute PCC$0.94
were included in the network. The reasons for selecting such
stringent cut-off (0.94) were three-fold: i) gene correlation profiles
PLoS ONE | www.plosone.org
2
August 2011 | Volume 6 | Issue 8 | e22457
CREB Targets in Carcinoid Disease
Figure 1. Network analysis of the SI NET interactome. 1A) The SI NET network and top 10 functional gene clusters identified using the Louvain
algorithm and enriched for Gene Ontology (GO) Biological Process (BP) terms. 1B) Node degree frequency distribution for the SI NET gene coexpression network. 1C) Comparison of the SI NET network to random graph models generated by using the Erdős–Rényi and Maslov-Sneppen
algorithms. 1D) Change in stability of the SI NET network following the removal of highly connected genes (attack) and random genes (error).
doi:10.1371/journal.pone.0022457.g001
2 (ADCY2, t-value = 6.0) and ADCY9 (t-value = -3.8), and a PKA
responsible for phosphorylation of the CREB transcription factor,
PRKAR1A (t-value = 2.8) (Figure S2). The Wnt signaling
pathway was also identified. These findings are consistent with
previous studies in vitro of small intestinal and pituitary NET cell
lines investigating cAMP recruitment through a GPCR complex
and downstream elements of CREB phosphorylation [20,32–36].
We next compared up-regulated genes to a CREB Target Gene
database (http://natural.salk.edu/CREB/) to identify potential
CREs in SI NETs. Using a confidence level for the binding value
(BV),0.001 and a binding ratio (BR).1.5, which are considered
to be significant for the identification of CREs [22], a list of 123
genes representing putative CREB binding targets and cAMP
response elements was compiled. Interestingly, the putative CREs
localized only to the ‘Synaptic transmission’ and ‘Nervous system
development’ clusters of the SI NET interactome, which is
consistent with the biological function of known CREB targets as
well as the nature of the neuroendocrine system [37] (Figure 2A).
We specifically selected for further investigation the CREs (BEX1,
BICD1, CHGB, CPE, GABRB3, SCG2, and SCG3) given their
PLoS ONE | www.plosone.org
known association with the
[16,29,30,38–40] (Table 1).
regulation
of
cell
function
3. Real-time PCR Validation of the Gene Expression
Analysis
To confirm the over-expression of CRE transcripts in SI NETs,
we measured transcript expression by real-time PCR (RT-PCR) of
n = 7 CREB targets (Table 1) in the SI NET cell line KRJ-I
(n = 10) and in normal EC cell preparations (n = 8) (Figure 2B).
Transcript levels were normalized to expression of housekeeping
genes ALG9, TFCP2, and ZNF410 as described [41] using
GeNorm [42]. Levels of BEX1, BICD1, CHGB, CPE, GABRB3
and SCG2 were up-regulated in SI NETs (p,0.05) while
Secretogranin III (SCG3) was not differently expressed, p = 0.24
(Figure 2B).
Additionally, we measured transcription of ADCY2 and
PRKAR1A. Levels of these upstream CRE pathway regulators
were elevated 700% and 722% respectively, in KRJ-I (p = 0.02)
compared to normal EC cells (Figure 2B).
3
August 2011 | Volume 6 | Issue 8 | e22457
CREB Targets in Carcinoid Disease
Figure 2. ADCY2, PRKAR1A, and CREB response elements expression examined by Real-time PCR. 2A) Identification of putative CREB
response elements in the SI NET interactome. 2B) Transcripts of ADCY2, cAMP synthetase, and PRKAR1A, a key member of the PKA, were up-regulated
in KRJ-I, (700% and 722% respectively, p,0.02 compared to normal). Six of 7 CRE transcripts were confirmed to be over-expressed in SI NETs (p,0.05)
with the exception of SCG3 (p = 0.24). MEAN6SEM (nTumor = 10, nNormal = 8).
doi:10.1371/journal.pone.0022457.g002
measured cAMP in response to each of the ligands in the KRJ-I
cell line. Twenty minute incubation with forskolin (1025 M),
isoproterenol (1025 M) or BIM-53061 (1026 M) all increased
intracellular cAMP accumulation 1.72-fold, 1.67-fold, and 2.78fold respectively compared to control (p,0.05). Lower concentrations of forskolin (1026 M) had no effect on cAMP accumulation
(Figure 4A). PKA activity was similarly stimulated by these agents
in the order of BIM-53061 = forskolin (1025 M) (2.2–2.4-fold).isoproterenol (1.85-fold) (Figure 4B), as was pCREB (BIM53061 = forskolin (1025 M) (1.75–1.9-fold).isoproterenol (1.2fold) (Figure 4C).
4. In vitro model of CRE transcription
To evaluate whether these genes were regulated through the
cAMP signaling pathway in vitro, we investigated their expression
in KRJ-I. CRE transcription was measured by stimulating KRJ-I
cells with the cAMP activator forskolin (1026 M and 1025 M), the
selective b-adrenergic receptor agonist isoproterenol (1025 M),
and the dopamine D2 (D2R) and Serotonin (5-HT) receptor
agonist BIM-53061 (1026 M) (Figure 3) for two hours. While
ADCY2 was significantly upregulated only by forskolin (1025 M,
220%, p,0.03), PRKAR1A transcripts were up-regulated both by
forskolin and BIM-53061 (188%, p = 0.0004, and 153%, p = 0.021
respectively). Lower concentrations of forskolin (1026 M) did not
stimulate transcription of either ADCY2 or PRKAR1A. Overall,
BIM-53061 was a universal CRE activator (100% of target genes
transcriptionally activated), while the effects of forskolin (1025 M,
,42% activated) and isoproterenol (,57% activated) were less
pronounced.
Discussion
The role of the cAMP signaling pathway in the regulation of
tumor CREB-mediated transcription has not previously been
investigated in gastrointestinal NETs. In the current study, using a
transcript database of SI NETs and normal SI mucosa we
demonstrated: 1) transcripts of ADCY2, a member of the adenylate
cyclase family, are up-regulated in SI NETs and KRJ-I; 2)
intracellular accumulation of cAMP is stimulated by forskolin,
5. cAMP/PKA and pCREB activation in vitro
Next, to confirm that the mechanisms regulating CRE
transcription occurred through the cAMP signaling pathway, we
PLoS ONE | www.plosone.org
4
August 2011 | Volume 6 | Issue 8 | e22457
CREB Targets in Carcinoid Disease
Table 1. 7 CREB targets assessed by Real-time PCR in a SI NET database and the KRJ-I cell line.
Gene Symbol
Gene Title
CREB p-value
CREB binding
ratio
GO Biological Process
Chromosomal
Location
BEX1
brain expressed, X-linked 1
5.00E-05
2.1
multicellular organismal development
Xq21-q23
nervous system development
cell differentiation
BICD1
bicaudal D homolog 1 (Drosophila)
1.30E-03
1.7
RNA processing
12p11.2-p11.1
intracellular mRNA localization
anatomical structure morphogenesis
CHGB
chromogranin B (secretogranin 1)
7.50E-08
3
—
20pter-p12
CPE
carboxypeptidase E
1.10E-12
7.4
protein modification process
4q32.3
proteolysis
neuropeptide signaling pathway
metabolic process
insulin processing
GABRB3
gamma-amino[10]butyric acid
(GABA) A receptor, beta 3
1.20E-02
1.5
Transport
15q11.2-q12
SCG2
secretogranin II (chromogranin C)
1.70E-04
1.9
MAPKKK cascade
signal transduction
2q35-q36
Angiogenesis
regulation of endothelial cell proliferation
cell motility
inflammatory response
intracellular signaling cascade
protein secretion
SCG3
secretogranin III
2.50E-05
2.2
—
15q21
doi:10.1371/journal.pone.0022457.t001
isoproterenol and BIM-53061; 2) intracellular PKA activity and
pCREB is stimulated by these agents; 4) cAMP-dependent protein
kinase, PRKAR1A, is over-expressed in SI NETs and KRJ-I cells; 5)
CREs are differentially transcribed when subject to a classic cAMP
activator, a selective ß-adrenergic receptor agonist, or a selective
D2R and 5-HT receptor agonist. Additionally, we performed the
first formal large scale network topology assessment of SI NET
disease.
Initially, using gene network inference, we reconstructed the SI
NET co-expression network from genome wide expression levels
obtained from microarray profiling. The network (Table S1) was
determined to be scale-free, non-random, and topologically stable.
This suggests a system that is dominated by a few highly connected
biologically relevant hubs and that is ‘‘protective’’ against random
perturbations (e.g. mutations). Indeed, this is consistent with the
behavior of most cellular networks [43] and confirms that the
biology of SI NETs can be further probed using graph-theory
approaches. The advantage of this approach is that it allows the
study of significant genes in relation to the entire system rather
than by merits of up- or down-regulation alone. For example,
node betweenness centrality as well as node degree have been
previously reported as possible indicators of gene essentiality [44].
We computed these statistics for every gene in the SI NET
network. Thus our dataset can further be explored using
functional assays utilizing network topology as well as differential
expression.
Highly modular structure of the SI NET network was explored
using an unbiased graph clustering technique (the Louvain
algorithm). The method is a greedy optimization method that
PLoS ONE | www.plosone.org
attempts to optimize the modularity [45] of a partition of the
network. We identified 10 modules (.20 genes) in the SI NET
that were subsequently enriched for GO-BP terms including
‘Nervous system development’ and ‘Cell cycle’. The functional
cluster heterogeneity suggests that the SI NET disease is a multimodal entity with complex metabolic, hormonal, and proliferative
cascades that call for a systems-wide assessment as well as
traditional approaches.
We used differential expression analysis to map significantly
changed genes onto the SI NET network to increase the biological
utility of the analysis. Most of the significantly changed genes
formed tight networks involved in transcription, secretion, cell
proliferation, tissue development, embryonic development and
extracellular matrix regulation. Using the DAVID functional
annotation tool [46], it was determined that cAMP/CREB
signaling cascade was highly upregulated in SI NETs (Figure
S2). It was of interest to note that the statistical enrichment also
identified that the Wnt signaling pathway was similarly altered. It
was previously suggested that the cAMP/CREB signaling may
also contribute to Wnt-regulated processes in cancer [47]. It
appears that our network analysis reiterates this concept; however,
further implications of this finding in NET biology need to be
investigated.
We further explored the CREB mechanism in silico by
identifying possible CREB binding targets and cAMP response
elements among the significantly altered genes using the Salk
CREB Target Gene database [22]. These CRE targets encoded
genes responsible for nucleosome assembly (NAP1L3, TSPYL4),
regulation of transcription (TERF2IP), organism development
5
August 2011 | Volume 6 | Issue 8 | e22457
CREB Targets in Carcinoid Disease
Figure 3. In vitro assessment of ADCY2, PRKAR1A, and CREB response elements transcripts. ADCY2 responded to cAMP activator forskolin
(1025 M: 220%) (3A), while PRKAR1A was stimulated by forskolin and dopamine D2 and 5-HT2 receptor agonist BIM-53061 (1026 M) (188% and 153%
respectively) (3B). BIM-53061 was a universal CRE activator, while forskolin had a less pronounced effect (3C–I). The selective ß-adrenergic receptor
agonist isoproterenol (1025 M) stimulated transcription of BEX1, BICD1, SCG2 and SCG3. Forskolin (1026 M) had no effect. *p,0.05 vs. CON.
MEAN6SEM (n = 6).
doi:10.1371/journal.pone.0022457.g003
(BEX1, INA), secretion (CHGB, SCG2, SCG3, SYN1) and adhesion
(TRO). Because we were specifically interested in genes involved in
cAMP-mediated secretory processes, we examined this subset
further.
The accumulation of cAMP in response to activation of GPCRs
induces a wide range of cellular processes including transcription,
metabolism, cell cycle progression and apoptosis through the PKA
pathway [48]. In this study, transcript of PRKAR1A, the type
1alpha regulatory subunit (RIalpha) of PKA, was up-regulated in
KRJ-I and stimulated by forskolin and BIM-53061. This is
consistent with the function of these compounds as inducers of the
cAMP pathway. The selective D2R and 5-HT receptor agonist,
BIM-53061 appears to be at least as potent a cAMP recruiter as
either forskolin or isoproterenol particularly for secretory gene
transcription. This suggests the involvement of the dopamine/5HT-mediated pathway in the recruitment of intracellular cAMP/
PKA activation. In addition, PRKAR1A transcript levels stimulated
by BIM-53061 were consistent with the accumulation of
intracellular cAMP suggesting a direct involvement of neural
GPCR receptor activation with ADCY2 and PRKAR1A recruitment
and subsequent PKA-induced CREB phosphorylation.
PLoS ONE | www.plosone.org
We have demonstrated that elevation in cAMP is associated
with normal and neoplastic EC cell secretion [18,49]. In the
present study, we identified elevated levels of ADCY2 and
PRKAR1A transcripts in a database of SI NETs compared to
normal SI mucosa, suggesting that cAMP signaling may indeed be
activated in tumor cells [32,50]. ADCY2 is a class B member of
the Adenylate Cyclase (ADCY) which is calcium insensitive but is
stimulated by Gbc subunits of heterotrimeric G-proteins and is
therefore directly coupled with GPCRs [35]. In the KRJ-I cell line,
transcript levels of ADCY2 were sensitive to forskolin and BIM53061, which suggests that cAMP-induced transcription may
occur through activation of this cyclase regulator in the KRJ-I cell
line.
Cellular gene expression is regulated following CREB protein
phosphorylation at serine residue 133 [21,51]. This occurs as a
consequence of cAMP accumulation which liberates the C
subunits of PKA that passively diffuse into the nucleus and induce
CREB phosphorylation. CREB is an important transcription
factor activated by multiple signal transduction pathways in
response to external stimuli, including synaptic activity, hormones,
growth factors, cytokines, and stress [15]. It affects cellular
6
August 2011 | Volume 6 | Issue 8 | e22457
CREB Targets in Carcinoid Disease
Figure 4. Functional assessment of the cAMP pathway in the KRJ-I cell line. 4A) Intracellular cAMP accumulation in KRJ-I cell line.
Stimulation with forskolin (1025 M), isoproterenol (1025 M) or BIM-53061 (1026 M) increased cAMP accumulation in vitro 72%, 67%, and 178%
respectively. Forskolin at lower concentrations (1026 M) had no effect. *p,0.05, **p = 0.006 vs CON. MEAN6SEM (n = 3). 4B) PKA activity in the KRJ-I
cell line. Stimulation with forskolin (1025 M), isoproterenol (1025 M) or BIM-53061 (1026 M) increased PKA activity in vitro 142%, 85%, and 122%
respectively. Forskolin at lower concentrations (1026 M) had no effect. *p,0.05, **p,0.01 vs. CON. MEAN6SEM (n = 4). 4C) Phospho-CREB(Ser133)
activation in KRJ-I cell line. Stimulation with isoproterenol (1025), BIM-53061 (1026 M) or forskolin (1025 M) increased CREB phosphorylation at the
Ser133 site after 15 mins by 122%, 175% and 192% respectively. *p,0.05 vs. CON. MEAN6SEM (n = 3).
doi:10.1371/journal.pone.0022457.g004
functions and enhances growth, increases angiogenesis, and
decreases apoptosis. We identified in silico and demonstrated in
vitro cAMP-mediated regulation of seven putative CREB targets –
BEX1 (modulates nerve growth factor [NGF] signaling through
nuclear factor-kappaB [NFkB] to regulate cell cycle, apoptosis,
and differentiation in neural tissues [38]), BICD1 (structural
constituent of cytoskeleton [39]), CHGB (neuroendocrine cellspecific gene, which may play a role in early tumor development
but is also a NET secretory product [16,52], CPE (pulmonary
NET marker [29]), GABRB3 (characteristic GABA receptor of
normal and neoplastic human EC cells and controlled through
CREB [20,36,40]), SCG2 (secreted neuroendocrine marker
observed in prostatic small-cell neuroendocrine carcinoma [30])
and SCG3 (secretory product commonly expressed in pituitary
adenomas [53]). In vitro investigations of these CREs indicate that
cAMP activation through either adenylate cyclase activation
(forskolin) or through neural GPCR activation (isoproterenol and
dopamine/5-HT) resulted in gene expression. CRE transcription
correlated with cAMP levels: BIM-53061, which was associated
with the highest cAMP (2.8-fold) accumulation, was also associated
with the majority of genes transcribed (100%). Both forskolin and
isoproterenol stimulated cAMP levels 1.8-fold and were associated
with 42–57% of target genes being transcribed. The suggestion
that gene transcription was cAMP concentration-dependent was
reinforced by the observation that forskolin (1026 M), which did
not significantly elevate cAMP, was not associated with CRE
target transcription. Similar investigations in the rat pituitary cell
line GH4 have shown that forskolin-induced cAMP accumulation
results in an increase of prolactin and growth hormone gene
transcription [54], suggesting that a single intracellular mediator
can simultaneously regulate the transcription of different sets of
responsive genes by stimulating independent biochemical events.
The study provides an illustration of how genome-wide network
inference can be used to infer CRE-mediated transcription in
neoplastic cell lines and has implications for defining the
mechanisms of NET proliferation and secretion. Similar studies
examining the progression of cancer [55,56], heart disease [57],
neuropsychiatric disorders [58,59], asthma pathogenesis [60], and
PLoS ONE | www.plosone.org
the analysis of factors associated with infertility [61] have provided
information in regard to these disease processes. We propose that
the application of this methodology to the investigation of NETs or
other diseases associated with abnormal EC cell secretion, like
Crohn’s disease [6] or IBS [62], will provide significant
mechanistic information on the cell regulatory phenomena. Our
current data demonstrates that neoplastic EC cells over-express
regulators in the cAMP signaling pathway and that activation of
neural GPCRs results in proliferative and secretory gene
transcription thus providing novel information regarding the
neural activation of tumor behavior. This investigative strategy,
that emphasizes co-expression network inference, provides a useful
tool to define and delineate the mechanisms involved in the
mechanistic cellular basis of the clinical manifestations of NET
disease. It is likely that the application of this technique will
facilitate the identification of specific regulatory elements that can
be targeted for therapeutic gain.
Materials and Methods
Statistical analyses of Affymetrix GeneChip data
Raw expression data for each of the 13 microarray experiments
(Affymetrix U133A; normal mucosa: n = 4; primary SI NETs:
n = 9) was normalized using the MAS5.0 algorithm available
through the Bioconductor suit [63] for the R statistical language
[64]. Affymetrix probe identifiers (IDs) were mapped to their
corresponding Ensembl (September 26, 2010) gene IDs [65]. In
cases where multiple probesets mapped to a single gene, only
median signal intensity was retained. Data is deposited in the
ArrayExpress database (accession number: E-GEOD-6272).
Gene Network Inference
Pairwise similarity in gene expression vectors was expressed by
the PCC. Gene pairs that correlated above a predefined PCC
threshold value were represented in the form of an undirected
weighted network, where nodes (vertices) correspond to genes and
links (edges) correspond to co-expression between genes. The
Maslov-Sneppen randomized network model was generated by
7
August 2011 | Volume 6 | Issue 8 | e22457
CREB Targets in Carcinoid Disease
from 16106 cells in log phase growth (TRIZOLH, Invitrogen,
USA). Real time RT-PCR analysis was performed using Assayson-DemandTM products and the ABI 7900 Sequence Detection
System according to the manufacturer’s suggestions. Cycling was
performed under standard conditions (TaqManH Universal PCR
Master Mix Protocol) and data normalized using GeNorm [42]
and expression of the novel house-keeping genes, ALG9, TFCP2
and ZNF410 [41].
cAMP and PKA Activation. To test whether KRJ-I cells
were physiologically responsive to neural GPCR agonists,
intracellular cAMP accumulation in response to the three
stimulants after 20 mins was assayed using a cAMP ELISA
assay (R&D Research, Minneapolis, MN). PKA activity was
quantitated in the same samples (Enzo Life Sciences, Butler Pike,
PA). Cells (56104 cells/well, in triplicate) were stimulated with
forskolin (1025 M, 1026 M), isoproterenol (1025 M), and BIM53061 (1026 M) after which cells were lysed with 0.1 N HCL and
freezing. All samples and controls were acetylated prior to
performing the cAMP ELISA (R&D cAMP ELISA handbook).
PKA activity was determined according to the manufacturer’s
recommendations. Lysed samples were incubated with 20 ml PKA
reaction mixture at 30uC for 30 min. The reaction was terminated
and activity quantitated versus levels of a highly specific substrate
using an ELISA protocol. Absorbance readings for either cAMP or
PKA were measured at 450 nm on a microplate reader (Bio-Rad
3500).
pCREB quantitation - western Blotting. KRJ-I cells
(46105 cells/ml) were seeded in 6 well plates (Falcon, BD,
Franklin Lakes, NJ) and treated with each of the agents for 15 and
60 mins. After cells were harvested, whole-cell lysates were
prepared by adding 200 ml of ice-cold cell lysis buffer (106
RIPA lysis buffer (Millipore, Billerica, MA), complete protease
inhibitor [Roche, Indianapolis, IN], phosphatase inhibitor set 1&2
(Calbiochem, Gibbstown, NJ), 100 mM PMSF (Roche), 200 mM
Na3VO4 (Acros Organics), 12.5 mg/ml SDS (American
Bioanalytical, Natick, MA). Tubes were centrifuged at 12,000 g
for 20 min and protein amount in the supernatant was quantified
using the BCA protein assay kit (Thermo Fisher Scientific,
Rockford, IL). For western blot, total protein lysates (20 mg)
were denaturated in SDS sample buffer, separated on an SDSPAGE gel (4, 10%) and transferred to a PVDF membrane (BioRad, Hercules, CA, pore size 0.45 mm). After blocking (5% BSA
for 60 min at room temperature) the membrane was incubated
with the phospho-CREB (Ser133) primary antibody (Cell
Signaling Technology, Danvers, MA) in 5% BSA/PBS/Tween
20 overnight at 4uC. The membranes were incubated with the
horseradish peroxidase-conjugated secondary antibodies (Cell
Signaling Technology) for 60 min at room temperature and
immunodetection was performed using the Western LightningTM
Plus-ECL (PerkinElmer, MA). Blots were exposed on X-OMATAR films. The optical density of the appropriately sized bands was
measured using ImageJ software (NIH, USA). The ratio between
phospho-protein expression was reported relative to that of b-actin
(Sigma-Aldrich, MO).
rewiring edges in the original network while preserving the degrees
of the respective nodes [66]. The number of rewiring steps taken
for each model was 46 (number of edges). This method ensures
that the topological structure of the network is retained during
randomization. The Erdős–Rényi random model was generated
by retaining the nodes of the original network and building edges
using a uniform probability [67].
Network Topology Concepts
Topological properties examined were node degree, network
diameter, betweenness centrality, connected components, clustering coefficient, and modularity [68]. Node degree is defined as the
total number of edges that connect to a given node. Network
diameter is defined as the average shortest path between any pair
of nodes in the network. Betweenness centrality is the measure of
node importance within a graph, where nodes that occur on many
shortest paths between nodes have higher betweenness. Connected
components are maximal connected subgraphs of an undirected
graph in which any two vertices are connected to each other by
edges. Clustering coefficient is the degree to which nodes tend to
cluster together. Modularity quantifies the capacity of a network to
divide into clusters or communities. Higher modularity indicates a
favorable partition.
Network clustering and functional enrichment
Clusters of genes in a co-expression network were identified
using the Louvain method, a fast algorithm for community
detection in graphs [69]. The Louvain method is a greedy
algorithm for iterative grouping of nodes into communities based
on optimization of modularity [45]. A distinct advantage of this
method is its parameter-free architecture that allows unbiased
exploration of network structure. Because clusters of co-expressed
genes are known to be functionally related [27], functional
enrichment for GO-BP terms was performed. For a cluster with n
genes and an a priori defined functional category with K genes, the
hypergeometric test was used to evaluate the significance of the
overlap k between the cluster and a functional category [70]. All
genes in a network were used as reference.
Pathway Analysis
Over-represented pathway analysis was performed using the
DAVID functional annotation tool [46] and prediction of CREB
target phosphorylation was assessed using CREB target gene
database (http://natural.salk.edu/CREB/) [22] with a confidence
level of the binding value (BV)#0.001 and a binding ratio
(BR)$1.5.
Validation and in vitro experiments
Culture Conditions. KRJ-I cells, derived from a ‘‘typical’’ SI
NET [18,71], were cultured as floating aggregates at 37uC with
5% CO2. Cells were kept in Ham’s F12 medium (GibcoTM)
containing 10% fetal bovine serum (FBS) (Sigma-Aldrich),
penicillin 100 U/ml, and streptomycin 100 mg/ml [18,25].
Real-Time PCR. To validate the presence of genes involved
in cAMP-mediated transcription pathway, two approaches were
undertaken. In the first approach, transcripts for selected CREs,
ADCY2, and PRKAR1A were measured in an independent data set
of neoplastic EC cell line KRJ-I (n = 10) and normal EC cell
preparations (n = 8) using real-time PCR. In the second approach,
the effect of forskolin (1025 M and 1026 M), isoproterenol
(1025 M), and BIM-53061 (1026 M) was measured on target
transcription in KRJ-I cells. KRJ-I cells (56104 cells/well, in
triplicate) were stimulated for 2 hours and RNA was extracted
PLoS ONE | www.plosone.org
Supporting Information
Figure S1 SI NET network properties as functions of
Pearson correlation coefficient (PCC). For each PCC cutoff,
the number of nodes, number of edges, number of connected
components, and network density were measured. It was noted
that at PCC$0.94, the SI NET network was most modular while
retaining a reasonable number of genes and links.
(TIF)
8
August 2011 | Volume 6 | Issue 8 | e22457
CREB Targets in Carcinoid Disease
Figure S2 cAMP/CREB signaling cascade. Differentially
Author Contributions
expressed elements identified using gene network inference are
highlighted in red and annotated.
(TIF)
Conceived and designed the experiments: ID BS BIG SM RP MK IMM.
Performed the experiments: ID BS BIG SM MK. Analyzed the data: ID
BS SM MK. Contributed reagents/materials/analysis tools: ID SM RP
MK IMM. Wrote the paper: ID BS BIG SM RP MK IMM.
Table S1 SI NET interactome.
(DOC)
References
1. Modlin IM, Champaneria MC, Chan AK, Kidd M (2007) A three-decade
analysis of 3,911 small intestinal neuroendocrine tumors: the rapid pace of no
progress. Am J Gastroenterol 102: 1464–1473.
2. Boushey RP, Dackiw AP (2002) Carcinoid tumors. Curr Treat Options Oncol 3:
319–326.
3. US National Cancer Institute. Surveillance Epidemiology and End Results
(SEER) data base, 1973–2004. http://seer.cancer.gov/. pp. One of the most
authoritative sources of information on cancer incidence, survival, and mortality.
The SEER database is sponsored by the National Cancer Institute in the United
States. Established in 1973, the SEER database compiles data that cover about
1910% of the US population.
4. Gustafsson BI, Kidd M, Modlin IM (2008) Neuroendocrine tumors of the diffuse
neuroendocrine system. Curr Opin Oncol 20: 1–12.
5. Modlin IM, Kidd M, Latich I, Zikusoka MN, Shapiro MD (2005) Current status
of gastrointestinal carcinoids. Gastroenterology 128: 1717–1751.
6. Kidd M, Gustafsson BI, Drozdov I, Modlin IM (2009) IL1beta- and LPSinduced serotonin secretion is increased in EC cells derived from Crohn’s
disease. Neurogastroenterol Motil 21: 439–450.
7. Thomson AB, Keelan M, Thiesen A, Clandinin MT, Ropeleski M, et al. (2001)
Small bowel review: normal physiology part 2. Dig Dis Sci 46: 2588–2607.
8. Cetin Y, Kuhn M, Kulaksiz H, Adermann K, Bargsten G, et al. (1994)
Enterochromaffin cells of the digestive system: cellular source of guanylin, a
guanylate cyclase-activating peptide. Proc Natl Acad Sci U S A 91: 2935–2939.
9. Roth KA, Gordon JI (1990) Spatial differentiation of the intestinal epithelium:
analysis of enteroendocrine cells containing immunoreactive serotonin, secretin,
and substance P in normal and transgenic mice. Proc Natl Acad Sci U S A 87:
6408–6412.
10. Modlin IM, Latich I, Kidd M, Zikusoka M, Eick G (2006) Therapeutic options
for gastrointestinal carcinoids. Clin Gastroenterol Hepatol 4: 526–547.
11. van der Hiel B, Stokkel MP, Chiti A, Lucignani G, Bajetta E, et al. (2003)
Effective treatment of bone metastases from a neuroendocrine tumour of the
pancreas with high activities of Indium-111-pentetreotide. Eur J Endocrinol 149:
479–483.
12. Florio T (2008) Molecular mechanisms of the antiproliferative activity of
somatostatin receptors (SSTRs) in neuroendocrine tumors. Front Biosci 13:
822–840.
13. Dorsam RT, Gutkind JS (2007) G-protein-coupled receptors and cancer. Nat
Rev Cancer 7: 79–94.
14. Rodien P, Ho SC, Vlaeminck V, Vassart G, Costagliola S (2003) Activating
mutations of TSH receptor. Ann Endocrinol (Paris) 64: 12–16.
15. Abramovitch R, Tavor E, Jacob-Hirsch J, Zeira E, Amariglio N, et al. (2004) A
pivotal role of cyclic AMP-responsive element binding protein in tumor
progression. Cancer Res 64: 1338–1346.
16. Mahapatra NR, Mahata M, Ghosh S, Gayen JR, O’Connor DT, et al. (2006)
Molecular basis of neuroendocrine cell type-specific expression of the
chromogranin B gene: Crucial role of the transcription factors CREB, AP-2,
Egr-1 and Sp1. J Neurochem 99: 119–133.
17. Pigazzi M, Ricotti E, Germano G, Faggian D, Arico M, et al. (2007) cAMP
response element binding protein (CREB) overexpression CREB has been
described as critical for leukemia progression. Haematologica 92: 1435–1437.
18. Kidd M, Eick GN, Modlin IM, Pfragner R, Champaneria MC, et al. (2007)
Further delineation of the continuous human neoplastic enterochromaffin cell
line, KRJ-I, and the inhibitory effects of lanreotide and rapamycin. J Mol
Endocrinol 38: 181–192.
19. Kidd M, Modlin IM, Mane SM, Camp RL, Eick G, et al. (2006) The role of
genetic markers–NAP1L1, MAGE-D2, and MTA1–in defining small-intestinal
carcinoid neoplasia. Ann Surg Oncol 13: 253–262.
20. Modlin IM, Kidd M, Pfragner R, Eick GN, Champaneria MC (2006) The
functional characterization of normal and neoplastic human enterochromaffin
cells. J Clin Endocrinol Metab 91: 2340–2348.
21. Mayr B, Montminy M (2001) Transcriptional regulation by the phosphorylation-dependent factor CREB. Nat Rev Mol Cell Biol 2: 599–609.
22. Zhang X, Odom DT, Koo SH, Conkright MD, Canettieri G, et al. (2005)
Genome-wide analysis of cAMP-response element binding protein occupancy,
phosphorylation, and target gene activation in human tissues. Proc Natl Acad
Sci U S A 102: 4459–4464.
23. Drozdov I, Kidd M, Gustafsson BI, Svejda B, Joseph R, et al. (2009) Autoregulatory Effects of Serotonin on Proliferation and Signaling Pathways In Lung
and Small Intestine Neuroendocrine Tumor Cell Lines. Cancer 115:
4934–4945.
24. Svejda B, Kidd M, Giovinazzo F, Eltawil K, Gustafsson B, et al. (2010) The 5HT2B receptor plays a key regulatory role in both neuroendocrine tumor cell
PLoS ONE | www.plosone.org
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
9
proliferation and the modulation of the fibroblast component of the neoplastic
microenvironment. Cancer 116: 2902–2912.
Pfragner R, Wirnsberger G, Niederle B, Behmel A, Rinner I, et al. (1996)
Establishment of a continuous cell line from a human carcinoid of the small
intestine (KRJ-I): Characterization and effects of 5-azacytidine on proliferation.
International Journal of Oncology 8: 513–520.
Elo LL, Jarvenpaa H, Oresic M, Lahesmaa R, Aittokallio T (2007) Systematic
construction of gene coexpression networks with applications to human T helper
cell differentiation process. Bioinformatics 23: 2096–2103.
Stuart JM, Segal E, Koller D, Kim SK (2003) A gene-coexpression network for
global discovery of conserved genetic modules. Science 302: 249–255.
Kidd M, Modlin IM, Mane SM, Camp RL, Shapiro MD (2006) Q RT-PCR
detection of chromogranin A: a new standard in the identification of
neuroendocrine tumor disease. Ann Surg 243: 273–280.
He P, Varticovski L, Bowman ED, Fukuoka J, Welsh JA, et al. (2004)
Identification of carboxypeptidase E and gamma-glutamyl hydrolase as
biomarkers for pulmonary neuroendocrine tumors by cDNA microarray.
Hum Pathol 35: 1196–1209.
Clegg N, Ferguson C, True LD, Arnold H, Moorman A, et al. (2003) Molecular
characterization of prostatic small-cell neuroendocrine carcinoma. Prostate 55:
55–64.
Taniwaki M, Daigo Y, Ishikawa N, Takano A, Tsunoda T, et al. (2006) Gene
expression profiles of small-cell lung cancers: molecular signatures of lung
cancer. Int J Oncol 29: 567–575.
Boikos SA, Stratakis CA (2007) Molecular genetics of the cAMP-dependent
protein kinase pathway and of sporadic pituitary tumorigenesis. Hum Mol Genet
16 Spec No 1: R80–87.
Bossis I, Stratakis CA (2004) Minireview: PRKAR1A: normal and abnormal
functions. Endocrinology 145: 5452–5458.
Barber DL, Buchan AM, Walsh JH, Soll AH (1986) Regulation of neurotensin
release from canine enteric primary cell cultures. Am J Physiol 250: G385–390.
Feinstein PG, Schrader KA, Bakalyar HA, Tang WJ, Krupinski J, et al. (1991)
Molecular cloning and characterization of a Ca2+/calmodulin-insensitive
adenylyl cyclase from rat brain. Proc Natl Acad Sci U S A 88: 10173–10177.
Kidd M, Modlin IM, Eick GN, Champaneria MC (2006) Isolation, functional
characterization, and transcriptome of Mastomys ileal enterochromaffin cells.
Am J Physiol Gastrointest Liver Physiol 291: G778–791.
Watanabe H, Smith MJ, Heilig E, Beglopoulos V, Kelleher RJ, 3rd, et al. (2009)
Indirect regulation of presenilins in CREB-mediated transcription. J Biol Chem
284: 13705–13713.
Naderi A, Teschendorff AE, Beigel J, Cariati M, Ellis IO, et al. (2007) BEX2 is
overexpressed in a subset of primary breast cancers and mediates nerve growth
factor/nuclear factor-kappaB inhibition of apoptosis in breast cancer cell lines.
Cancer Res 67: 6725–6736.
Claussen M, Suter B (2005) BicD-dependent localization processes: from
Drosophilia development to human cell biology. Ann Anat 187: 539–553.
Hu Y, Lund IV, Gravielle MC, Farb DH, Brooks-Kayal AR, et al. (2008)
Surface expression of GABA (A) receptors is transcriptionally controlled by the
interplay of CREB and its binding partner ICER. J Biol Chem 283: 9328–9340.
Kidd M, Nadler B, Mane S, Eick G, Malfertheiner M, et al. (2007) GeneChip,
geNorm, and gastrointestinal tumors: novel reference genes for real-time PCR.
Physiol Genomics 30: 363–370.
Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, et al. (2002)
Accurate normalization of real-time quantitative RT-PCR data by geometric
averaging of multiple internal control genes. Genome Biol 3: RESEARCH0034.
Albert R (2005) Scale-free networks in cell biology. J Cell Sci 118: 4947–4957.
Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M (2007) The importance of
bottlenecks in protein networks: correlation with gene essentiality and expression
dynamics. PLoS Comput Biol 3: e59.
Newman ME (2006) Modularity and community structure in networks. Proc
Natl Acad Sci U S A 103: 8577–8582.
Huang da W, Sherman BT, Lempicki RA (2009) Systematic and integrative
analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:
44–57.
Chen AE, Ginty DD, Fan CM (2005) Protein kinase A signalling via CREB
controls myogenesis induced by Wnt proteins. Nature 433: 317–322.
Sands WA, Palmer TM (2008) Regulating gene transcription in response to
cyclic AMP elevation. Cell Signal 20: 460–466.
Kidd M, Modlin IM, Pfragner R, Eick GN, Champaneria MC, et al. (2007)
Small bowel carcinoid (enterochromaffin cell) neoplasia exhibits transforming
growth factor-beta1-mediated regulatory abnormalities including up-regulation
of C-Myc and MTA1. Cancer 109: 2420–2431.
August 2011 | Volume 6 | Issue 8 | e22457
CREB Targets in Carcinoid Disease
60. Rolph MS, Sisavanh M, Liu SM, Mackay CR (2006) Clues to asthma
pathogenesis from microarray expression studies. Pharmacol Ther 109:
284–294.
61. Zhang X, Jafari N, Barnes RB, Confino E, Milad M, et al. (2005) Studies of gene
expression in human cumulus cells indicate pentraxin 3 as a possible marker for
oocyte quality. Fertil Steril 83 Suppl 1: 1169–1179.
62. Faure C, Patey N, Gauthier C, Brooks EM, Mawe GM (2010) Serotonin
signaling is altered in irritable bowel syndrome with diarrhea but not in
functional dyspepsia in pediatric age patients. Gastroenterology 139: 249–258.
63. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, et al. (2004)
Bioconductor: open software development for computational biology and
bioinformatics. Genome Biol 5: R80.
64. (2010) R Development Core Team. R: A Language and Environment for
Statistical Computing.
65. Birney E, Andrews TD, Bevan P, Caccamo M, Chen Y, et al. (2004) An
overview of Ensembl. Genome Res 14: 925–928.
66. Maslov S, Sneppen K (2002) Specificity and stability in topology of protein
networks. Science 296: 910–913.
67. Erdős P, Rényi A (1959) On Random Graphs. I. Publicationes Mathematicae 6:
290–297.
68. Freeman TC, Goldovsky L, Brosch M, van Dongen S, Maziere P, et al. (2007)
Construction, visualisation, and clustering of transcription networks from
microarray expression data. PLoS Comput Biol 3: 2032–2042.
69. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of
communities in large network. J Stat Mech P10008.
70. Xu D, Matsuo Y, Ma J, Koide S, Ochi N, et al. (2010) Cancer cell-derived IL1alpha promotes HGF secretion by stromal cells and enhances metastatic
potential in pancreatic cancer cells. J Surg Oncol 102: 469–477.
71. Pfragner R, Wirnsberger G, Niederle B, Behmel A, Rinner I, et al. (1996)
Establishment of a continuous cell line from a human carcinoid of the small
intestine (KRJ-I): Characterization and effects of 5-azacytidine on proliferation.
Internation Journal of Oncology 8: 513–520.
50. Taylor SS, Buechler JA, Yonemoto W (1990) cAMP-dependent protein kinase:
framework for a diverse family of regulatory enzymes. Annu Rev Biochem 59:
971–1005.
51. Gonzalez GA, Montminy MR (1989) Cyclic AMP stimulates somatostatin gene
transcription by phosphorylation of CREB at serine 133. Cell 59: 675–680.
52. Kimura N, Yoshida R, Shiraishi S, Pilichowska M, Ohuchi N (2002)
Chromogranin A and chromogranin B in noninvasive and invasive breast
carcinoma. Endocr Pathol 13: 117–122.
53. Jin L, Chandler WF, Smart JB, England BG, Lloyd RV (1993) Differentiation of
human pituitary adenomas determines the pattern of chromogranin/secretogranin messenger ribonucleic acid expression. J Clin Endocrinol Metab 76:
728–735.
54. Waterman M, Murdoch GH, Evans RM, Rosenfeld MG (1985) Cyclic AMP
regulation of eukaryotic gene transcription by two discrete molecular
mechanisms. Science 229: 267–269.
55. DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, et al. (1996) Use of a
cDNA microarray to analyse gene expression patterns in human cancer. Nat
Genet 14: 457–460.
56. Welford SM, Gregg J, Chen E, Garrison D, Sorensen PH, et al. (1998) Detection
of differentially expressed genes in primary tumor tissues using representational
differences analysis coupled to microarray hybridization. Nucleic Acids Res 26:
3059–3065.
57. Heymans S, Schroen B, Vermeersch P, Milting H, Gao F, et al. (2005) Increased
cardiac expression of tissue inhibitor of metalloproteinase-1 and tissue inhibitor
of metalloproteinase-2 is related to cardiac fibrosis and dysfunction in the
chronic pressure-overloaded human heart. Circulation 112: 1136–1144.
58. Loring JF, Wen X, Lee JM, Seilhamer J, Somogyi R (2001) A gene expression
profile of Alzheimer’s disease. DNA Cell Biol 20: 683–695.
59. Sutton R, Doran HE, Williams EM, Vora J, Vinjamuri S, et al. (2003) Surgery
for midgut carcinoid. Endocr Relat Cancer 10: 469–481.
PLoS ONE | www.plosone.org
10
August 2011 | Volume 6 | Issue 8 | e22457