Abstract
Background
The endomembrane system, known as secretory pathway, is responsible for
the synthesis and transport of protein molecules in cells. Therefore,
genes involved in the secretory pathway are essential for the cellular
development and function. Recent scientific investigations show that ER
and Golgi apparatus may provide a convenient drug target for cancer
therapy. On the other hand, it is known that abundantly expressed genes
in different cellular organelles share interconnected pathways and
co-regulate each other activities. The cross-talks among these genes
play an important role in signaling pathways, associated to the
regulation of intracellular protein transport.
Results
In the present study, we device an integrated approach to understand
these complex interactions. We analyze gene perturbation expression
profiles, reconstruct a directed gene interaction network and decipher
the regulatory interactions among genes involved in protein transport
signaling. In particular, we focus on expression signatures of genes
involved in the secretory pathway of MCF7 breast cancer cell line.
Furthermore, network biology analysis delineates these gene-centric
cross-talks at the level of specific modules/sub-networks,
corresponding to different signaling pathways.
Conclusions
We elucidate the regulatory connections between genes constituting
signaling pathways such as PI3K-Akt, Ras, Rap1, calcium, JAK-STAT, EFGR
and FGFR signaling. Interestingly, we determine some key regulatory
cross-talks between signaling pathways (PI3K-Akt signaling and Ras
signaling pathway) and intracellular protein transport.
Electronic supplementary material
The online version of this article (10.1186/s12859-018-2036-2) contains
supplementary material, which is available to authorized users.
Keywords: Perturbation, Genetic Network, Secretory Pathway
Background
The secretory pathway is composed of different organelles suspended in
the cytoplasm. It includes rough endoplasmic reticulum (rough ER), ER
exit sites (ERESs), the ER-to-Golgi intermediate compartment (ERGIC)
and the Golgi complex cellular organelles, which have distinct
functions in the transport of proteins to their final destination in
the cell. Not only does the secretory pathway play an important role in
proteins synthesis and delivery, but it also facilitates the proper
folding and post-translational modifications of protein [[29]1]. At
present, we know that these organelles are able to interact dynamically
with each other and play an important role in the establishment of
cellular homeostasis; furthermore, the cross-talks between these inter
cellular compartments are also required to maintain the structure and
shape of the cell and for its survival [[30]2]. Recent studies show all
these cellular organelles within the secretory pathway are sensitive to
stress conditions and capable to propagate the signaling for cell death
[[31]2]. Basically, signaling implies the conversion of mechanical or
chemical stimuli directed towards the cell into a specific cellular
response. In a general signaling pathway, a signal is received by the
receptor molecules, which leads to a change in functioning and
modulation of the cellular response driven by series of molecular
interactions within the cellular boundary. These interactions include
the activation and inhibition of numerous kinases and signaling
molecules producing a complex inter dependent molecular cross-talks. To
understand the complex relationship between signaling and secretory
pathway in a broader perspective, it is important to study the genetic
interactions within the cell and determine the gene regulatory network.
Previously, researchers have been using correlation and gene
co-expression based networks, to infer a genome wide representation of
the complex functional organization of gene interaction networks
[[32]3]. These networks are predicted on the similarity of the gene
expression profiles. However, these reconstructed gene networks are
undirected, and therefore it is difficult to infer the causality
relationship between two connected genes. The other caveat associated
with co-expression network analysis regards the handling of large data
sets, which limits the biological interpretation of the data [[33]4,
[34]5]. Though regression methods have been used to determine directed
edges and to identify the set of genes having regulatory effects on
their target, these methods are generally computational demanding and
often limited to predict the set of genes regulated by transcription
factors [[35]6, [36]7]. Recently, gene perturbation studies have
started playing an important role in directed gene networks
reconstruction and in determining their reciprocal influence
[[37]8–[38]11]. In the present work, we study the gene-gene
interactions in MCF7 breast cancer cell line using an integrated
approach (shown in Fig. [39]1) based on functional genomics and network
analysis, derived from expression profiles of knocked-down or
over-expressed genes within the secretory pathway. Signaling associated
to and from protein transport machinery provides convenient therapeutic
targets for drug development in cancer therapy [[40]12]. Therefore, we
try to decipher the direct and indirect genetic regulatory components
of secretory pathway and their corresponding cross-talk with cellular
signaling within the cell. Our goal is to understand the complex
interactions among genes, constituting important signaling pathways
with respect to protein transport in a cancer cells. Furthermore, we
investigate the cause and disturbance in the delicate balance of
cross-talks among these genes, which can lead to cancer progression. We
try to highlight the interesting aspects of gene-gene interactions,
which they could be as potential drug target for cancer therapies.
Fig. 2.
Fig. 2
[41]Open in a new tab
Regulatory interaction network. a Regulatory interaction network
obtained from 591 perturbation experiments. The node color represents
the degree of nodes; hub nodes are represented in red and yellow color.
b out-degree distribution of nodes (perturbations). c in-degree
distribution of nodes (effected genes). d Gene ontology enrichment
analysis of perturbed genes. e Gene ontology enrichment analysis of
effected genes
Methods
Data retrieval
To reconstruct the regulatory networks of secretory pathway we use the
library of integrated network-based cellular signatures (LINCS) L1000,
which contains more than a million gene expression profiles of
perturbed human cancer cell lines ([42]http://lincs.hms.harvard.edu).
In more detail, it consists of 1328098 expression profiles of 22268
genes (Fig. [43]1a). A set of 978 genes, named landmark genes, were
directly measured using a microarray technology. The remaining 21290
are target genes, whose expression has been inferred by a deep learning
algorithm (D-GEX) trained on GEO data. Perturbation experiments were
executed on different cell lines, at different time points, silencing
or over-expressing genes, or treating cells with chemical compounds.
For our work, we studied 3647 experiments in which genes have been
knocked-down/over-expressed in MCF7 breast cancer cell line. Among the
2552 genes of the secretory pathway, only 591 were perturbed in L1000
dataset. For each perturbation experiment, we collect data for all the
biological and technical replicates at two different time points (96H,
144H). We used the so called level 3 dataset, which includes
standardized gene expression profiles of directly measured landmark
transcripts plus imputed genes (Fig. [44]1b). Finally, to map
transcription factors and gene interactions information on the 591
perturbation experiments, we use the human transcription factors from
AnimalTFDB 2.0 [[45]13] and manually curated human signaling network
data from Edwin Wang and associated group
([46]http://www.cancer-systemsbiology.org/data-software). This
signaling network dataset contains more than 6000 proteins and 63,000
relations. These relations represent activation, inhibition and
physical interactions, which in turn describe complexes that play
crucial roles in cell signaling.
Fig. 1.
Fig. 1
[47]Open in a new tab
Schematic representation of the pipeline. a Perturbation experiments. b
Perturbation expression profiles matrix. c Co-regulated gene-gene
interaction network. d Identification and clustering of functional
modules. e Identification of expression activated seb network (hotspot
identification). f pathways and GO term enrichment analysis to infer
cross-talk between different clusters. g Interaction database analysis
along with functional annotation to infer regulatory patterns
Reconstruction of Regulatory Interactions
To obtain gene regulatory interactions and reconstruct gene network
(Fig. [48]1c) from gene perturbation experiments, we developed a
computational pipeline, which is divided into several steps. The
z-scored perturbation expression profiles are represented as Z[j]∈Z
with j = 1,…,n, in our case n=591. Each profile Z[j] comprises m[j]
biological replicates (usually between 2 and 4), which are repeated
measurements of biologically distinct samples and capture random
biological variation [[49]14]. Each biological replicate is the average
of q[k] technical replicates, which are repeated measurements of the
same sample (usually between 4 and 6). A given perturbation experiment
is represented as:
[MATH: Pj,k,lwherej=1,…
,n;k=1,…
,mj;l=1,…
,qk :MATH]
1
where m[j] is the number of biological replicates in j-th profile, and
q[k] is the number of technical replicates for the k-th biological
sample in the perturbation experiment. The mean biological sample
[MATH: P¯j,k
:MATH]
from the q[k] technical replicates is calculated as:
[MATH: P¯j,k
=∑l=1qkPj,k,l/qk :MATH]
2
In the second step, we create a matrix with all biological replicates
(averages), for which we calculate the first principal component, that
is the linear combination of the biological replicates pointing in the
direction of maximum variance. Before performing the Principal
Component Analysis, we pre-processed the data to normalize their mean
as follow:
[MATH: P¯¯j=<
/mo>∑k=1mjP¯j,k
/mj :MATH]
3
[MATH: Zj
=P¯j,1
−P¯¯jσj,…,P¯j,mj−P¯¯jσjj=1,…
,n, :MATH]
4
where σ[j] is the standard deviation of
[MATH: P¯j,1
,…,P¯j,mj :MATH]
.
For each perturbation j, we select one biological replicate
[MATH: Z^j :MATH]
, with maximum correlation with first principal component of Z[j]. Each
column of the matrix
[MATH: A=Z^1,…,Z^n :MATH]
represents the influence of perturbation on the expression values of
all the genes in the experiments. Finally, for each perturbation, we
selected only those genes, for which we observed a differential fold
change ≥ 4 or ≤−4, in case of over expressed or under expressed genes
respectively, in at least one biological replicate. Generally, any
selection based solely on fold change is arbitrary and there is no
right nor wrong threshold; but fold change (FC) cut-off of ≥ 2 or ≤−2,
leads to look only at genes which vary widely among the other genes.
For this reason in our work, to reduce the number of genes, we decided
to use a most stringent FC, finally obtaining a list of 576 perturbed
genes. The construction of the network is now straightforward, because
the perturbation of a gene directly or indirectly affects the
regulation of the others that have been detected as differentially
expressed in that experiment. We applied a simplification algorithm to
get rid of direct regulations introduced by the described
reconstruction method [[50]9].
Network analysis
In this work we consider only gene interaction networks in which
directed edges connecting two genes represent a biochemical process
such as a reaction, transformation, interaction, activation or
inhibition. We have not considered gene co-expression networks (GCN) in
which the direction and type of co-expression relationships are not
determined, because they are an undirected graphs where each node
corresponds to a gene, and a pair of nodes is connected with an edge if
they show a similar expression pattern. Therefore with the help of
Cytoscape network visualization tool [[51]15], we visualize the
gene-gene interaction network from obtained regulatory interactions
(Fig. [52]1c) and carry out directed network analysis using Network
Analyzer [[53]16]. We computed the topological parameters, such as
number of nodes, edges and connected components for directed regulatory
network. Further, we also computed the network diameter, radius,
clustering coefficient, characteristic path length, betweenness and
closeness, as well as the distributions of degrees, neighborhood
connectivity and number of shared neighbors.
Network hot-spot identification
In Cytoscape, the j-Active Modules plugin identifies expression
activated sub-networks (Fig. [54]1e) from previously obtained molecular
interaction network [[55]17]. These sub-networks are highly connected
components of the existing network, where the genes show similar
significant expression changes in response to particular subsets of
conditions (perturbations). The method uses a statistical approach to
score sub-networks with a search algorithm for finding sub-networks
with high score. The idea of finding these sub-networks is to determine
functional modules represented by highly connected network regions with
similar responses to experimental conditions. We run j-Active Modules
on our gene interaction network in default mode, taking betweenness
centrality and neighborhood connectivity as node attributes. In
advanced parameter section, we set number of modules to 5 and overlap
threshold to 0.8. Further, we employ search strategy to obtain
high-scoring modules using local and greedy search.
Reactome functional interaction (FI) network analysis
To study the pathways enrichment and network patterns in the
sub-network with respect to signaling and intracellular protein
transport, we use ReactomeFI-plugin [[56]18] in Cytoscape, to integrate
the Reactome database [[57]19], and other tools such as Transcriptator
[[58]20] and Metabox library [[59]21]. Taking a FDR cut-off value
≤0.05, we carry out pathway enrichment analysis for a set of genes in a
given sub network, and investigate the functional relationships among
genes in enriched pathways. With the help of this plugin, we first
access the Reactome Functional Interaction (FI) network, and fetch FI
indexing for all the nodes (genes) present in sub-network. Later, we
build a FI sub-network based on a set of genes, query the FI data
source for the underlying evidence for the interaction to construct
modules by running a network clustering algorithm (spectral partition
based network clustering) [[60]22] and analyze these network modules of
highly interacting groups of genes (Fig. [61]1d). Finally, we carried
out functional enrichment analysis to annotate the modules, and expand
the network by finding genes related to the experimental data set.
Inferring gene regulatory interactions with respect to protein transport
signaling
To understand the functional and regulatory relationship among genes in
expression activated sub-networks, we use GeneMania plugin [[62]23]
(Fig. [63]1g). It extends the sub-networks by searching publicly
available biological datasets to find related genes. These include
protein-protein, protein-DNA and genetic interactions, pathways,
reactions, gene and protein expression data, protein domains and
phenotype screening profiles. Integration of physical interaction,
genetic interaction, co-localization and pathway information related to
the nodes present in the sub-networks, helps to show regulatory
interactions among specific genes involved in signaling of protein
transport.
Extending regulatory interaction network through external resources to
determine genetic cross-talks
By analyzing manually curated human signaling network data, we obtain
further signaling interactions and introduce them to extend the
regulatory interaction network already obtained from ReactomeFI network
analysis. The signaling interaction data contain activation, inhibition
and physical interactions. The physical relations represent complexes
that play a role in cell signaling. Furthermore, we also map the
transcription factors, gene ontology, and gene specific enriched
reactome and KEGG pathways information on the reconstructed direct
sub-network to infer direct physical and genetic interactions between
perturbed genes and their effected components (Fig. [64]1f). We
primarily focus on Ras and PI3K-Akt signaling pathways, to study and
infer cross-talks among genetic components between them.
Results and Discussion
Biological function enrichment analysis of perturbed genes
We carried out Gene Ontology and pathway enrichment analysis of the
complete list of perturbed genes, for which corresponding expression
profiles are utilized in this study. It includes 576 unique perturbed
genes which have regulatory effects on the other genes. DAVID and
Transcriptator functional annotation tools are used to carry out
enrichment analysis taking a multiple correction p-value cutoff ≤ 0.05.
The complete list of perturbed genes is provided as Additional
file [65]1. The functional term enrichment analysis suggests the role
of these genes in intracellular protein transport such as exocytosis
and endocytosis. As expected, the cellular components enrichment
analysis suggests that most of these genes functions are localized in
ER, Golgi apparatus, Golgi membrane, ER lumen, extracellular exosome,
plasma membrane, trans-Golgi network, ER to Golgi transport vesicle
membrane, endosome, endocytic vesicle membrane and lysosome etc.
Similarly, biological process enrichment analysis shows biological
processes related to regulation of intracellular protein transport such
as exocytic and endocytic cellular mechanism, protein folding and
modification process, as highly enriched terms. Some of these enriched
biological functional terms are: positive regulation of protein
phosphorylation, protein phosphorylation, protein glycosylation, ER to
Golgi vesicle-mediated transport, retrograde vesicle-mediated
transport, Golgi to ER, endocytosis, autophagy, Golgi organization,
vesicle-mediated transport, sphingolipid biosynthetic process, ER
unfolded protein response, ER calcium ion homeostasis, response to ER
stress, IRE1-mediated unfolded protein response, lipoprotein
biosynthetic process, protein autophosphorylation, chaperone-mediated
protein folding, intrinsic apoptotic signaling pathway in response to
ER stress and positive regulation of ERK1 and ERK2 cascade. The
complete results of enrichment analysis is provided in Additional
file [66]2.
Regulatory interaction network from 591 gene perturbation expression data
profile
Using our pipeline we reconstructed a network based on regulatory
interactions consisting of 4467 nodes and 12871 edges (Fig. [67]2a).
The edges between the nodes in this network represent the 4 folds up or
down regulation of affected genes in response to perturbation
experiments. The characteristic path length of this network is 5.24 and
average number of neighbors is 5.76. We calculate the following network
statistics for all the constituting nodes in the network: topological
coefficients, betweenness, closeness, distributions of degrees,
neighborhood connectivity, average clustering coefficient and stress
centrality. The table with all topological parameters for each node in
the network is provided in Additional file [68]3. VHL shows a maximum
out-degree of 923, which implies that its perturbation has a regulatory
effect on the whole network. From the analysis of the network, we
obtained 349 nodes having out-degree greater or equal to 10
(Fig. [69]2b). Among these 349 high out-degree nodes (represented in
Fig. [70]2a, in darker color), there are 15 nodes which represent the
maximum (≥ 100) of out-degrees, in other words, the perturbations in
these genes have a significant effect on the transcriptional response.
These nodes are represented in Table [71]1. Enriched Gene ontology
terms associated with these genes are protein transport, localization,
protein and lipid metabolism. Nodes (effected genes) with high
in-degree’s (Fig. [72]2c) are highly enriched in kinase activity,
transcriptional regulation, cell adhesion, RNA binding, protein
binding, purine nucleotide binding, signal transduction, catalytic
activities. The perturbed genes with high out-degrees in this directed
network, are enriched in protein transport, localization and protein
and lipid metabolism (Fig. [73]2d) and perturbation of these genes have
direct and indirect regulatory effects on the elements of signal
transduction, binding, transcriptional regulation and kinase activities
(Fig. [74]2e). The complete functional enrichments are provided in the
Additional file [75]4.
Table 1.
15 nodes with the maximum (≥ 100) of out-degrees
VHL TOR1A CSNK1D
CHEK2 STK16 POR
SP3 USP32 SRPRB
GPR107 RAF1 FEZ1
CFTR PPAP2B CRTAP
[76]Open in a new tab
Inferring gene regulatory components through the identification and analysis
of expression activated (hotspot) sub-network
With the help of j-Active plugin, we disaggregate the larger
perturbation network into 5 smaller expression activated sub-network
modules represented in Table [77]2. In the sub-networks, all the nodes
are directly connected to the hub node/perturbed gene, and show similar
significant changes (up or down-regulations) in expression in response
to particular subsets of conditions (perturbations). For the sake of
understanding the underlying gene-regulatory interaction within these
sub networks, we selected the smallest module 5 for our study
(Fig. [78]3). Firstly, we obtained the pathways, molecular function and
biological processes enrichment analysis of module 5 sub-network using
Reactome FI analysis. The genes are divided into three functional
modules and are regulated by the perturbations of FAM3C, PPAP2B and
CLTA respectively. Enrichment analysis shows that initiation,
elongation and termination of translation processes, along with
nonsense mediated decay are significantly enriched in genes regulated
by FAM3C and perturbations of PPAP2B and CLTA do not have any effect on
the regulation of these pathways. The perturbation of FAM3C may plays
an important role in the eukaryotic translation pathways as well as
nonsense mediated decay. Further, integration of co-expression,
physical interaction, genetic interaction, co-localization and pathway
information related to the nodes present in the sub-network, strengthen
the relationship between FAM3C and its regulatory effects on the genes
with respect to nonsense mediated decay. While many biological process,
such as translation, RNA-metabolic process, cellular protein metabolic
process, mesenchymal to epithelial transition, positive regulation of
transcription, meiosis, negative regulation of interferon-gamma
production, rRNA processing are not directly effected by the
perturbation of FAM3C, they are regulated by the PPAP2B perturbation.
Table 2.
The 5 modules obtained with j-Active Modules
Module n of nodes n of edges
1 3136 4424
2 375 573
3 334 364
4 225 231
5 159 162
[79]Open in a new tab
Fig. 3.
Fig. 3
[80]Open in a new tab
Network hot-spot Network hot-spot identified as gene regulatory network
with respect to their regulatory interaction. Analysis of this network
hot-spot through GeneMania analysis. Gene ontology (biological process)
enrichment analysis for the network regulated by FAM3C perturbation.
The edges with arrow signs represent the 4x increase in the expression
of connected genes, while edges with dot represent the
down-regulations. We map the information of perturbed genes
(represented by green color) and transcription factor (in red color) on
the network
Cross-talks in signaling pathway
To understand the complex gene-gene interaction network of cell
signaling in the context of cellular protein transport and cancer
progression, we carried out functional gene ontology and reactome
pathways enrichment analysis for the constructed gene regulatory
network. Based on the pathways enrichment, we extracted the sub-network
consisting of gene/nodes involved in prominent signaling such as
PI3K-Akt, RAP1, Ras pathway, calcium, P53 and MAPK signaling.
Furthermore, we carried out the indexing of nodes with the help of
ReactomeFI plugin and we clustered the sub-network into 5 functional
modules based on gene ontology terms in biological process, molecular
function, cellular components and reactome pathways enrichment
analysis. The results are provided in the Additional file [81]5. Taking
into account highly enriched signaling pathways (p-value ≤ 0.05) and
cut-off for constituent nodes ≥ 12 in each module, we observed high
intensity of cross-talks between signaling involved in protein
transport (such as Ras signaling) and cancer progression (PI3K-Akt).
Genes associated with PI3K-Akt signaling interacts with calcium
signaling pathway. Considering module wise study, we notice that
PI3K-Akt signaling is the most prominent and enriched signaling
pathway, forming the core of each cluster (Fig. [82]4).
Fig. 4.
Fig. 4
[83]Open in a new tab
Clustering of regulatory network into 5 functional modules. Signaling
pathway enrichment analysis in each functional module obtained from
clustering of gene-gene interaction network. The color represents the
functional module in clustered network. The blue color represents the
module 0, light green represents module 1, khaki color represents
module 2, bright green represents module 3 and in the last, module 4 is
represented by violet color. The bar plot for each functional module
shows the number of genes enriched in signaling pathways, by taking the
cut-off for corrected p-value ≤ 0.05
In cluster/module 0, PI3K-Akt interacts with Ras and Rap1 signaling
pathways, while Ras pathway forms the major component of protein
transport. Ras is a part of well described mitogen activated protein
(MAP) kinase-Ras-Raf-MEK-ERK- pathway downstream initiated by receptor
tyrosine kinase and integrins, leading to several cellular processes
such proliferation, differentiation of cell, membrane genesis, protein
synthesis and secretion and also has an intermediate effect on gene
expression [[84]1].
In cluster/module1, cluster/module2 and cluster/module3, we observe the
interactions between PI3K-Akt and kinase signaling such as ERBB2, EGFR
and ERBB4. In the last module/cluster 4, enrichment of PI3K-Akt
signaling, JAK-STAT and calcium signaling suggests activation of
PI3K-Akt signaling through both calcium and stress-activated protein
Jun kinases and vice versa. In the past, researchers portrays the role
of intracellular Ca2+ and disturbances in its cellular concentration,
with respect to tumor initiation, angiogenesis, progression and
metastasis in the normal cells [[85]24]. To delve further into the
relationship between Ras and PI3K-Akt signaling, we extracted the
constituent genes/nodes from regulatory interactions within module 0,
and we observed interesting relationship among them, as shown in
Table [86]3. The regulatory interactions among these genes (Fig. [87]5)
show that important constituent genes in PI3K-Akt signaling are
regulated by the genetic components of Ras signaling pathway. In some
cases, common genes regulate both pathways, exhibiting higher level of
cross-talk between them. It is worth noting that perturbation of CDH1
leads to the 4 fold decrease in the expression of YWHAZ gene, which is
a member of the 14-3-3 protein family and a central hub protein for
many signal transduction pathways. YWHAZ gene regulates apoptotic
pathways critical to cell survival and plays a key role in a number of
cancers and neuro-degenerative diseases [[88]25]. This gene is a
well-known target for cancer therapy (14-3-3 zeta as novel molecular
target for cancer therapy). Hence CDH1 could be a potential gene as a
molecular target for cancer therapy.
Table 3.
Genes enriched in PI3K-Akt and Ras signaling
Signaling pathway Genes
PI3K-Akt YWHAZ,COL4A5,SGK1,ITGB5,TSC2,LAMA2,
PTEN,GRB2,PP2R3A
Ras signaling pathway CDH1,HRAS,CALM1,FGFR3,KDR,AKT1,FGFR4,
Rap1A,PRKC1
PI3k-AKT and Ras signaling HRAS,KDR,FGFR3,AKT1,FGFR2,FGFR4
[89]Open in a new tab
Fig. 5.
Fig. 5
[90]Open in a new tab
Interesting gene regulatory interactions with respect to PI3K-Akt
signaling and Ras signaling pathway. PI3K-Akt signaling components are
represented by oval shape. Genes involved in Ras signaling are
represented by rectangular shape. Yellow color represent the perturbed
genes involved in both signaling pathway. In this figure, we have shown
the PI3K-Akt enriched genes in red color, while the genes enriched in
Ras pathway are represented by rectangular shape. The yellow color
represents the genes which undergoes perturbation experiments to obtain
the gene-gene regulatory network. The edges with arrow signs represent
the four fold increase in the expression of target/effected genes,
while edges with dot represent the four fold down-regulation
All these genetic regulatory interactions might provide a viable target
for cancer drug therapy. From our results, we observed that PTEN is
heavily down-regulated by the perturbation of TSC2 gene. Both the genes
play an important role in PI3K-Akt signaling. PTEN gene is a tumor
suppressor [[91]26], and mutation in this gene leads to cancer
developments. TSC2 mutations lead to tuberous sclerosis, and its gene
products is supposedly a tumor suppressor [[92]27]. From this
information, we can infer that perturbation of TSC2 gene plays an
important role in increasing cancer risk in muscular dystrophy, as it
regulates LAMA2 gene. Genetic mutations in LAMA2 genes have their
implication in a severe form of muscular dystrophy [[93]28]. TSC2,
LAMA2 and PTEN interactions could be useful to study a potential drug
therapy for cancer as well as muscular dystrophy. Similarly CCND1
amplification and its protein expression is strongly correlated with
breast cancer [[94]29], the perturbation of KDR gene, which is a type
III receptor tyrosine kinase involved in Ras pathway, down-regulates
the CCND1 expression and controls its amplification with respect to
cancer.
From the regulatory interaction network analysis, we infer that genetic
perturbations involved in protein transport have profound effects on
the signal transduction, and transcriptional regulation activities of
the cell. We also carried out functional analysis of the nodes with
high value of betweenness centrality, as these nodes do play an
important role in bridging between the sub-networks and hub nodes. The
results show several enriched pathway: ER-nucleus signaling pathway
(GO:0006984), cellular response to topologically incorrect protein
(GO:0035967), response to topologically incorrect protein (GO:0035966),
response to unfolded protein (GO:0006986) with significant p-value ≤
0.05. Furthermore, functional enrichment analysis of the nodes with
respect to out-degree, in-degree and betweenness centrality, helps us
to understand the underlying cross-talk between protein transport,
localization on the signal transduction and transcriptional rewiring
and their mutual effects on protein folding. In our regulatory network,
we find some interesting and significantly enriched signaling pathways,
such as PI3K-Akt, Ras, Rap1, calcium, JAK-STAT, EFGR and FGFR
signaling. In recent years, researchers observed the role of PI3K-Akt
signaling in cancer progression, which is basically a disturbance in
the balance of cell division and growth with respect to programmed cell
death. This particular signaling pathway is disturbed in many human
cancer and not only does it play a major role in tumor development, but
also in its potential response to the treatment [[95]30]. In our
results, we observe that PI3K-Akt signaling interacts with several
kinases, such as ERBB2, EGFR and ERBB4. These kinases are known to play
an important role in a very aggressive form of breast cancer [[96]31].
This kind of signaling leads to a characteristic behavior of cancer
cells such as uncontrolled proliferation, resistance to apoptosis and
increased motility. Apart from this, PI3K-Akt signaling shares
interactions with platelets and fibroblast growth factors signaling
pathways, which play a very important part in cell growth regulation,
proliferation, survival, differentiation and angiogenesis [[97]31].
Most of these pathways are involved in the normal deployment of protein
transport but also have a potential role in activating both upstream
and downstream important signaling pathways. Some of these functions
are cell proliferation, differentiation, membrane biogenesis,
inflammation protein syntheses, cell migration and gene expression
regulation. In a broader sense, all these signaling networks comprise a
fine tuning balance for cellular function. Any disturbance in such a
balance leads to negative signaling cascades and has a deteriorating
effect on cell functioning, possible leading to cancer progression.
Cross-talks between intracellular protein transport and signaling pathways
In addition to study the cross-talks between different signaling
pathways in intracellular protein transport, we also infer the
regulatory effects of signaling pathways on intracellular protein
trafficking mechanism related to exocytic and endocytic pathways
[[98]32]. Through exocytic pathway, protein cargo moves from ER, via
Golgi apparatus, to the plasma membrane. During this movement, it also
undergoes to a modification by the addition of sugar and lipids. On the
other hand, moving through this forward exocytic pathway via
ER-Golgi-plasma membrane compartments, the protein cargo has to be
retrieved back to its original compartment in a reverse direction, to
maintain the compartment identity. This backward movement of protein
cargo from plasma membrane to Golgi to ER compartment is known as
retrograde protein transport. There is also an endocytic pathway,
through which cargo is internalized from the cell milieu. The best
characterized endocytic pathway proceeds from clathrin coated vesicles
through early and late endosomes to lysosomes. The lysosomes is a major
degradation site for internalized cargo and cellular membrane proteins
[[99]32]. In our results, we observed regulatory interactions among
genes involved in intracellular protein transport and PI3K-Akt, Ras,
MAPK, interferon and calcium signaling. In the modules study, we
specifically focus on PI3K-Akt and Ras signaling pathways and their
regulatory interactions with intracellular protein transport components
in MCF-7 cell line. In the previously described module 0, as shown in
Fig. [100]6, genes enriched in PI3K-Akt signaling pathway such as
IL7R,TNN, ITGB4, CSF1R, STK11, ITGB1, ITGB7, FGF17, COL5A3, PDGFRA,
PP2R3A, AKT1, HSP90AA, CCND1 and PTEN and YWHAZ are regulated by the
perturbations in CSNK1D, TSC2, PML, SHH, KDR and SGK1 genes. Out of
these perturbed genes, KDR,SGK1 and TSC2 also play an important role in
PI3K-Akt signaling [[101]33, [102]34]. The CSNK1D gene is involved in
protein phosphorylation process, endocytosis and Golgi organization
[[103]35]. The result agrees with the fact that PI3K-Akt is a signal
transduction pathway, which helps in cell survival, growth,
proliferation, cell migration and angiogenesis. The key proteins are
PI3K (phosphatidylinositol 3-kinase) and AKT (Protein Kinase B). It is
interesting to observe that CSNK1D perturbation positively regulates
most of the components of PI3K-Akt signaling except it down-regulates
the PTEN gene. The PTEN (phosphatase and tensin homolog) gene is a
major antagonist of PI3K activity [[104]36]. It is a tumor suppressor
gene and often mutated or lost in cancer cells. Additionally, CSNK1D
perturbation also down-regulate YWHAZ gene, which is also a major
regulator of apoptotic pathways and plays an important role in cell
survival [[105]25, [106]37, [107]38]. Being a constituent of PI3K-Akt
signaling, TSC2 gene also contributes to endocytosis, and when
perturbed, down-regulates the PTEN gene. PML and SHH genes, which are
associated with ER calcium ion homeostasis and endocytosis process
respectively, along with CSNK1D gene, down-regulate HSP90AA2 (Heat
shock protein 90kDa alpha (cytosolic), class A member 2) gene. HSP90AA2
is a heat shock gene, generally expressed to combat a stressful
situation and whose protein product functions as chaperon by
stabilizing new proteins to ensure correct folding [[108]39]. In
summary, we observe that the perturbation in genes involved in protein
phosphorylation, endocytosis, Golgi organization and calcium ion
homeostasis in ER, have a stronger effect in the activation of PI3K-Akt
signaling, and in the down regulation of PTEN, YWHAZ and HSP90AA2 genes
which are important for the normal functioning of the cell.
Fig. 6.
Fig. 6
[109]Open in a new tab
Regulatory interactions between PI3K-Akt signaling and intracellular
protein transport. Genes enriched in PI3K-Akt signaling pathway are
represented by diamond shape in pink color such as IL7R,TNN, ITGB4,
CSF1R, STK11, ITGB1, ITGB7, FGF17, COL5A3, PDGFRA, PP2R3A, AKT1,
HSP90AA, CCND1 and PTEN and YWHAZ. These genes are regulated by the
perturbation in CSNK1D, TSC2, PML, SHH, KDR and SGK1 gene represented
by oval shape in red color. Out of these perturbed genes, KDR, SGK1 and
TSC2 also plays an important role in PI3K-Akt signaling; and they are
represented by diamond shape and in red color. The edges with arrow
signs represent the four fold increase in the expression of
target/effected genes, while edges with dot represent the four fold
down-regulation
Similarly, we also observe the cross-talks between Ras signaling
pathways and intracellular protein transport mechanism. The cross
regulatory interactions between the components of Ras and intracellular
protein transport pathways are depicted in resultant (Fig. [110]7). We
observe constituent genes of Ras pathway such as CSF1R, PDGFRA, FGF17
[[111]40], FGFR2, FGFR4, RAP1A [[112]41], GRB2 [[113]42] and KDR are
either regulated by the components of intracellular protein transport
or they also regulate each other. CSF1R (Colony stimulating factor 1
receptor (CSF1R)) is a receptor for a cytokine called colony
stimulating factor 1. PDGFRA (platelet-derived growth factor receptor
A) encodes a typical receptor tyrosine kinase, which binds to platelets
derived growth factors and plays an active role in initiating cell
signaling pathways responsible for cellular growth and differentiation
[[114]43–[115]45]. Both of them are positively regulated by the
perturbation in CSNK1D gene [[116]46]. The CSNK1D gene is involved in
endocytosis, Golgi organization,positive regulation of protein
phosphorylation,protein phosphorylation. Some of the components of Ras
pathway such as KDR [[117]47], FGFR4 [[118]48], FGFR2 [[119]48,
[120]49] are also involved in intracellular protein transport mechanism
[[121]50]. KDR which is a type III receptor tyrosine kinase, also known
as vascular endothelial growth factor receptor 2 (VEGFR-2), is also
involved in positive regulation of protein phosphorylation [[122]50].
Perturbation in KDR, affects the ETS2 gene in Ras pathway. Fibroblast
growth factor receptor 2 (FGFR2) and Fibroblast growth factor receptor
4 (FGFR4) are members of the fibroblast growth factor receptor family.
These receptors signal by binding to their ligand and dimerisation and
initiate a cascade of intracellular signals. These signals are involved
in cell division, growth and differentiation [[123]51, [124]52].
Perturbation in FGFR2, down-regulates TMED10 and AKT1. TMED10 is
involved in ER to Golgi vesicle-mediated transport, retrograde
vesicle-mediated transport, Golgi to ER, Golgi organization. While AKT1
is a serine-threonine protein kinase, activation of this gene
phosphorylates and inactivates components of the apoptotic machinery
[[125]53–[126]55]. Perturbation in FGFR4 gene, down-regulates VAMP7
[[127]56, [128]57] and RAP1A expression [[129]58], which are involved
in ER to Golgi vesicle-mediated transport, endocytosis,
vesicle-mediated transport and Ras Pathway respectively. The results of
all the regulatory interactions and subsequent enriched pathways are
provided in the Additional file [130]6.
Fig. 7.
Fig. 7
[131]Open in a new tab
Ras signaling cross-talk protein transport. The cross regulatory
interactions between the components of Ras (represented by diamond
shape and in pink color) and intracellular pathways represented by oval
shape and in red color. Constituent genes of Ras pathway such as CSF1R,
PDGFRA, FGF17, FGFR2, FGFR4, RAP1A, GRB2 and KDR are either regulated
by the components of intracellular protein transport or they also
regulate each other variably. Some of the components of Ras pathway
such as KDR, FGFR4, FGFR2 are also involved in intracellular protein
transport mechanism. The edges with arrow signs represent the four fold
increase in the expression of target/effected genes, while edges with
dot represent the four fold down-regulation
Conclusions
In this work, we develop a computational integrated pipeline to analyze
genes perturbation experimental data and uncover the regulatory
interactions among genes. We use functional genomics and network
biology approach to create a directed network, where nodes represent
the perturbed and impacted genes, while direct edges represents the
positive and negative regulatory effects of perturbation on its
neighboring elements. We implemented this approach to infer regulatory
cross-talk between signaling pathways and intracellular protein
transport in MCF7 cell line. Our aim is to elucidate the regulatory
connection between genes constituting signaling pathways such as
PI3K-Akt, Ras, Rap1, calcium, JAK-STAT, EFGR and FGFR signaling and
intracellular protein transport mechanism in MCF7 cell line. We focus
on PI3k-Akt signaling and Ras pathway, to highlight some of their
mutual key regulatory features. In our results, we find some
interesting regulatory components of PI3k-AKT signaling with respect to
Ras pathway as well as intracellular protein transport mechanism. From
the literature, it is known that development of resistance to cancer
therapy is an important clinical problem [[132]30]. Inactivation of
apoptotic programme leads to drug resistance in tumor cells. This
resistance is mainly supported by PI3K-Akt signaling and hence this
signaling contributes to the resistance of cancer cell [[133]59,
[134]60]. As it is known that Ras and calcium signaling activate the
PI3K-Akt signaling in a cell, targeting the upstream and downstream
signaling pathways with respect PI3K-Akt signaling is a feasible
approach to procrastinate resistance in cancer cells. In future, we
will hopefully extend this work and develop a methodology as well as
computational integrated platform to construct an interaction network
from perturbation data not only from one cell line but simultaneously
from multiple tissue samples/cell lines, for the comparative analysis
of putative regulatory interactions among genes in different
experimental conditions.
Additional files
[135]Additional file 1^ (11.6KB, zip)
Perturbed gene list. Complete list of perturbed genes. (ZIP 11 kb)
[136]Additional file 2^ (203.8KB, zip)
DAVID Enrichment analysis 591 genes. Gene ontology and pathway
enrichment analysis of the complete list of perturbed genes. (ZIP 203
kb)
[137]Additional file 3^ (49.6KB, zip)
Node statistics table. Topological parameters of each nodes in the
network. (ZIP 50 kb)
[138]Additional file 4^ (40.9KB, zip)
Network enrichment. Gene Ontology and Pathways enrichment analysis
results for the complete regulatory interaction network from 591 gene
perturbation expression data. (ZIP 41 kb)
[139]Additional file 5^ (131.6KB, zip)
Network functional modules enrichment. Module wise functional and
pathways enrichment analysis of the network. (ZIP 131 kb)
[140]Additional file 6^ (99.9KB, zip)
Enrichment terms for gene cross-talks. All regulatory interactions and
enriched pathways for genes involved in cross-talk between
intracellular protein transport and signaling pathway. (ZIP 100 kb)
Acknowledgements