Systematic identification of an integrative network module during senescence from time-series gene expression
© The Author(s). 2017
Received: 29 March 2016
Accepted: 2 March 2017
Published: 15 March 2017
Cellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have indicated that senescence is a multi-step evolving process related to important complex biological processes. Most studies analyzed only the genes and their functions representing each senescence phase without considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process.
We suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result, it was confirmed that the difference of average perturbation scores of common networks at both two time points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator.
Heretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches.
KeywordsSenescence Time-series gene expression Data integration Network analysis
Cellular senescence is irreversible exit from the cell cycle resulting from the limited replicative capacity  caused by telomere shortening [2, 3], DNA damage, and the epigenetic derepression of several genes such as the IKK4a/ARF locus . Senescence and aging are complex processes with multiple causal mechanisms . Recently, due to increase of understanding for the senescence mechanism, it was shown to be a heterogeneous phenotype driven by multiple casual mechanisms instead of a singular state . To understand senescence, an analytical approach based on systems biology is needed to reveal the interactions among several multiple effector programs of the senescence phenotype . Genome-wide profiling of molecular-level changes during senescence such as changes in gene expression is particularly important.
A recent study analyzed genome-wide gene expression at various time points during the establishment of replicative senescence and revealed senescence stage-specific gene perturbations . Analysis of the functional enrichment for each stage indicated initial perturbation of cell cycle-related genes and subsequent perturbation of metabolic, inflammatory, and immune-related genes at the middle stage. At the final stage, genes related to cell death and cell growth regulation were perturbed. Thus, genome-wide time-series analysis can reveal the genotypic signature underlying senescence.
Time-series gene expression data have been widely used to explore the molecular-level events during a phase change such as the senescence process described above, despite of difficulty of culturing cells to full senescence. Typical analytical methods use co-expression patterns to identify functional modules or compare pairwise time points to capture features of the transition or to identify temporally regulated gene expression versus one control sample . Consequently, these approaches yield results based on individual genes or gene sets without considering the connectivity between them. However, cellular processes involve the interactions among several molecules, and these processes can be represented as a biological network with genes or proteins as nodes and their relationships as edges. Thus, interactome data of biophysically interacting proteins are especially useful for analyzing biological processes, but few attempts have been made to integrate time-series gene expression and protein-protein interaction (PPI) data, particularly in senescence and aging.
Recently, a study reported the construction of an age-specific integrative gene network with PPI and topological analysis of the network to reveal the key modules in aging . To construct a network, differentially expressed age-specific genes were selected by following methodology of , which had used criterion as follows; 1.5-fold change, 0.01 FDR. The protein interactions mapped with the selected genes become edges of the network. As a result, 37 age-specific networks were obtained and ‘ground truth’ gene set collected by analyzing brain gene expression data was used to demonstrate whether the networks were significantly related with the aging process or not. The authors revealed that the global topology of the age-specific networks was similar to each other, whereas the local topologies of several genes were significantly different. For the topological comparison among age-specific networks, the similarity measure called graphlet degree distribution agreement  was used. It was revealed that the local topologies were significantly changed with age and those genes were associated with age.
We proposed a novel approach to investigate the core modules of a genetic network highly correlated with phenotypic changes from time-series data. To construct the network, we integrated a perturbed gene set with biophysically validated PPIs recently published  and identified and analyzed the core sub-gene network. Based on the evidence that continuously perturbed interactions can be used to interpret a gradual phenotypic change with abrupt changes at the beginning and end points, we hypothesized that a sub-gene network with a constant topological structure but gradually altered context (e.g., expression pattern) plays an important role in phenotypic change. This concept can be applied to study cellular senescence because several senescence-related functions, such as the cell cycle, are not enriched at the abrupt phase; actually, a previous study revealed that cell cycle remodeling was nearly continuous during senescence . The proposed approach is distinguished from the previous senescence studies. The previously mentioned study  did not consider the interactions among the perturbed genes during senescence and  analyzed the network modules topologically changed during aging.
To test our hypothesis, we applied the proposed method to two replicative senescence datasets from human diploid fibroblasts (HDFs) and mesenchymal stem cells (MSCs) and one independent cancer progression dataset from human tissue neoplasia. We performed functional enrichment of the identified core sub-gene network and simple significance tests to confirm whether our findings reflect changes in gene expression that account for the observed phenotypic change.
Data description and system overview
As a proof of concept for our approach, we intensively used recently published time-series gene expression data (GSE41714) measured during replicative senescence in HDFs . This time-series microarray data (GSE41714) was composed of twelve sequential order of senescence stages according to the population doubling time including young and old phenotypes as the first and last time points, respectively. The senescence phenotype for each stage was identified and confirmed by typical determinants such as increased or decreased reactive oxygen species (ROS) levels and high or low level of senescence-associated β-galactosidase activity. We also used time-series microarray data (GSE9593) collected during replicative senescence in MSCs . These data included nine passages from young to old status. Finally, we employed time-series microarray data (GSE15299) for elucidating epithelial cancer progression . This dataset included four time points and was selected to test whether the proposed approach was applicable to processes besides senescence. The series matrix files of these microarray based data were downloaded and if there was no obvious description about normalization process in GEO, the normalization was performed. The array platform of these three datasets was different each other. GSE41714, GSE9593 and GSE15299 used Illumina HumanHT-12 V4.0 expression beadchip, Illumina HumanHT-12 V4.0 expression beadchip and Affymetrix Human Genome U133 Plus 2.0 Array, respectively.
To identify the connectivity between genes, we downloaded a recently published human PPI dataset  with 23,124 high-confidence PPIs compiled from systematic screening with high-throughput yeast two-hybrid and literature studies and validated using biological assays. Although the high-confidence PPI set is smaller than the typical PPI set from the I2D database [17–19], biological validation allows greater confidence in conclusions based on these PPIs. The proteins in these PPIs were mapped into gene symbols using UniPROT .
Identification of time-specific networks
Identification and analysis of common network
From the identified time-specific networks which have different size and include different interactions, we detect topologically conserved sub-network across time-point. As described in the Introduction, we assumed that sub-networks with constant topological structures play important roles in phenotype change. Topological conservation for a sub-network refers to its continuous perturbation with changing phenotype. As mentioned in the Introduction, we assumed that these network modules and the transition of their information such as perturbation scores were related to phenotypic changes. To prove our assumption, we calculate the average difference of perturbation scores between two time points after identification of common network. For example, in Fig. 2, if gene 2 and gene 4 are selected as a member of common network, the average difference of perturbation scores between time-point1and2 and time-point4 will be 1.1 which is calculated by (|0.5–(−0.45)| + |-0.775–0.475|)/2. From this calculation, we can determine whether the variation of perturbation scores and the state of phenotypic changes are associated or not. To investigate that perturbation scores were related to phenotypic changes, the statistical tests for all possible pairs of adjacent time points and one additional pair composed of the first and the last time points was performed.
Statistical support for significance of common network
To demonstrate the effectiveness of the proposed method, we applied it to three time-series gene expression datasets: two senescence datasets and a cancer progression dataset. Although we focused on senescence, we included the cancer progression dataset to test the applicability of our method to other types of datasets. We detected a common network from whole time point-specific networks and demonstrated how the common network can explain the phenotypic change for the three experimental datasets by performing statistical tests and functional enrichment with KEGG pathway database and gene ontology database.
To determine a optimal thresholds for identifying perturbed genes, we used dataset-specific cut-off values. As described in Method section, we calculated the mean and standard deviation for the perturbation scores in given dataset and found that the perturbation scores were not normally distributed: the mean was near zero, and the standard deviation was relatively small. However, we assumed that the distribution was Gaussian because the number of perturbation scores was large enough to apply the central limit theorem. Supporting Fig. 6 shows the distribution about perturbation scores. We used μ ± 1σ as a threshold: perturbation scores above μ + 1σ or below μ–1σ were chosen to construct each network. The number of genes selected by using μ ± 2σ or μ ± 3σ as a threshold was too small to construct networks. The experimental results while changing the cut-off threshold were shown in supporting Table 4 and 5 of the additional file.
Significance test in HDF senescence and cancer progression dataset
Network information about the stage-specific network
No. of node
No. of edge
Ratio of the used interaction (%)
Very advanced stage
The result of statistical significance test to compare perturbation scores between two time points on senescence dataset
Comparing time points
△P from our approach (mean)
△P from random sampling
Early – Middle
Middle – Advanced
Advanced – Very advanced
Early – Very advanced
Network information for cancer progression dataset
No. of node
No. of edge
Ratio of the used interaction (%)
The result of statistical significance test to compare perturbation scores between two time points on cancer progression dataset
Comparing time points
△P from our approach (mean)
△P from random sampling
Day 0–Day 5
Day 5–Day 20
Day 20–Day 35
Day 0–Day 35
Functional enrichment in HDF senescence and cancer progression dataset
Through abovc experiments, we computationally analyzed common network in time-dependent gene expression profile. In addition to the computational validation, we performed two types of functional enrichment test on the common network. Fisrt, we performed gene ontology based enrichment using BiNGO  plugin in Cytoscape . Secondly, we carried out pathway enrichment test using KEGG database. Because the identified common network had small size and simple topology, we used the entire common network as an input of functional enrichment test.
On the common network from HDF senescence dataset, the list of top 10 terms of functional enrichment test with KEGG pathway and Gene Ontology database. (P-value < 0.01)
Pathways in cancer
Small cell lung cancer
p53 signaling pathway
Arrhythmogenic right ventricular cardiomyopathy (ARVC)
Hypertrophic cardiomyopathy (HCM)
response to endogenous stimulus
regulation of cell proliferation
negative regulation of cell proliferation
response to hormone stimulus
response to organic cyclic substance
negative regulation of epithelial cell proliferation
response to steroid hormone stimulus
response to organic substance
On the common network from cancer progression dataset, the list of top 10 terms of functional enrichment test with KEGG pathway and Gene Ontology database. (P-value < 0.01)
anatomical structure development
anatomical structure morphogenesis
regulation of glucan biosynthetic process
regulation of polysaccharide biosynthetic process
regulation of glycogen biosynthetic process
response to chemical stimulus
response to endogenous stimulus
regulation of polysaccharide metabolic process
Additional analysis with MSC senescence dataset
We performed statistical test and functional enrichment for MSC senescence dataset. The detailed results were described in the Additional file 1 with supporting Table 1, 2 and 3 and supporting Figs. 1 and 2. The distribution of average difference between all pairs of two time point used in our test was shown in Additional file 1 (supporting Figs. 3, 4 and 5). The information of the identified common networks from three datasets was listed in Additional file 2.
Identification of regulatory module from common network
Time point-specific gene networks can be more important than the common network in most analyses. However, our approach aids in elucidating temporal changes in biological functions. Thus, our approach is appropriate for investigation of continuously affected molecular effectors such a cell cycle in replicative senescence. To demonstrate this hypothesis, we performed additional experiments and described the result herein.
As has been demonstrated by several previous studies [23–25], variation of the perturbation score values in the identified common network was generally consistent with expectations for cellular senescence. Interestingly, the gene expressions at the young and senescence stage were completely opposed. Based on this result, we propose that the identified common network can switch cell cycle activity between young and senescence status. Alternatively, transcription factors (TFs) that regulate genes in the identified common network could act as the switch.
Interestingly, we observed a feedback loop composed of the replicative senescence related genes: CDK6, CCND1, CDKN1A, and CDKN1B (Fig. 9a). In the observed regulatory network, including the feedback loop, CDK6 was the most important node because it acted as a hub and could be the genesis of the loop. It has been known that CDK6 regulates DNA replication in G1 and reported to switch cell cycle status from G1 phase to S phase . Furthermore, high expression of CDK6 can form a transcription complex and induce the expression of tumor suppressor p16 . It has been reported that p16 is significantly related with molecular mechanism of senescence .
In our feedback loop (Fig. 9b), the subunit regulators of CDK6 (CCND1, CCND2, and CCND3) were down-regulated in young status; thus, these four proteins were simultaneously down-regulated in young status. However, expression of CDK6 was increased upon senescence. Along with this expression alteration of CDK6, CDK6 could inhibit CDKN1B by negatively regulating its phosphorylation. CDKN1B translation is also reduced during G1 arrest , and CDKN1B down-regulation could inhibit CCND1, which is also positively influenced by CDKN1A. Thus, CDK6 may be consistently up-regulated by CCND1 during senescence, allowing maintenance of full senescence status. Much of this process has been previously reported [30–32], but our method allowed identification of the regulatory relationship among them with a more integrated view, and we note that our approach yielded a result reflecting the senescence process.
In addition, we investigated TFs which can control this regulatory relationship. We used recently published computational method, iRegulon . Among the results which have high enrichment score (NES > 0.3), we selected TFs inferred by TRANSFAC database widely used to search TFs. Then, we filtered TFs targeting on CDK6. As a result, we can identified seven TFs; STAT5A, ARID3A, POU4F3, DLX5, ZNF35, LMX1A, PAX2. Among them, STAT5A and ARID3A have been revealed to be related with cell cycle process [34, 35]. PAX2 has been reported to be mechanistically associated cancer cell proliferation . Through this result, it is possible that the identified regulatory relationship and the related TFs can be regarded as strong candidate which controls cell cycle phase replicative senescence.
In this study, we proposed a novel approach to identify gene networks that are significantly correlated with phenotypic changes from time-series data. In this process, we integrated a recently published PPI dataset with time-series gene expression data to produce informative interactions among genes. Networks were validated with statistical tests and functional enrichment. To demonstrate the suitability of the proposed method, we used three different real datasets for cellular senescence and cancer progression. The identified networks were appropriate to explain the phenotypic changes. In our future work, we plan to carry out perturbing experiments with the identified TFs to demonstrate whether they can contribute to changing phenotype by affecting expression level of CDK6 and its looping member or not.
False discovery rate
Human diploid fibroblasts
Mesenchymal stem cells
Reactive oxygen species
This research was supported by the DGIST R&D Program of the Ministry of Science, ICT and Technology of KOREA (20160165 and 20160172), Samsung Advanced Institute of Technology, and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2015R1A2A2A03004088).
Availability of data and materials
The results in this paper are based upon the publicly available data from Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/gds) under the accession numbers GSE41714, GSE9593, GSE15299 and GSE49860.
The work presented here was carried out in collaboration between all authors. Conceived and designed the study: CP and SJY designed and developed the proposed method. CP and YY performed computational experiments to validate our approach. SL, SJR, YL intensively participated in analysis for the experimental results. SJY and YY participated in writing discussion section and helped to write a manuscript. SCP entirely coordinated and supported the study. All authors provided valuable advises in developing the proposed method and modifying the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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