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Year : 2015  |  Volume : 11  |  Issue : 4  |  Page : 887-892

Construction and analysis of the regulatory network disturbed by the silenced Sp1 transcription factor in HeLa cells

Department of Gynaecology, First Hospital of Shanxi Medical University, Yingze, Taiyuan, Shanxi Province, China

Date of Web Publication15-Feb-2016

Correspondence Address:
Lijun Yu
Department of Gynaecology, First Hospital of Shanxi Medical University, No. 85, Jiefang South Road, Yingze District, Taiyuan City, Shanxi Province, 030001
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0973-1482.140811

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 > Abstract 

Background: The objective of our study was to explore the characteristics of the regulatory network after siRNA-Sp1 (Specificity Protein 1) treatment in HeLa cells through the regulation network construction with bioinformatics methods.
Materials and Methods: Using GSE37935 datasets downloaded from Gene Expression Omnibus data, the differentially expressed genes (DEGs) were screened out by the limma package in R software. Combining the DEGs with the data from the microRNA (miRNA) databases and transcription factor databases, an integrated regulatory network was established with Cytoscape. Then the motifs in the network were examined by FANMOD.
Results: A total of 708 DEGs were screened, and a regulatory network consisting of 585 nodes and 1070 edges was constructed. By analyzing the two modules extracted from the network, we found that the most significant biological processes were cell cycle and apoptosis, some significant DEGs among them were CDKN1A, CUL5, and EGFR. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis discovered that DEGs, including EGFR, CDKN1A, RRM2B, and GADD45B, were significantly enriched in glioma pathway and p53 signaling pathway.
Conclusion: While Sp1 was silenced by siRNA, the regulatory network in HeLa cells changed a lot. Genes related to cell cycle and apoptosis in the cell nucleus were dysregulated and the p53 signaling pathway was disturbed.

Keywords: Gene expression profiling analyses, HeLa cells, integrated regulatory network, module, Sp1

How to cite this article:
Li F, Yu L, Li L, Wei M. Construction and analysis of the regulatory network disturbed by the silenced Sp1 transcription factor in HeLa cells. J Can Res Ther 2015;11:887-92

How to cite this URL:
Li F, Yu L, Li L, Wei M. Construction and analysis of the regulatory network disturbed by the silenced Sp1 transcription factor in HeLa cells. J Can Res Ther [serial online] 2015 [cited 2021 Jan 23];11:887-92. Available from: https://www.cancerjournal.net/text.asp?2015/11/4/887/140811

 > Introduction Top

The ubiquitous Sp1 transcription factor (TF) is the first identified member of a family of nuclear proteins, called Sp/KLF (specificity protein/Krόppel-like factor). With a DNA binding domain containing three zinc fingers, it binds GC-rich elements that are common regulatory elements in promoters of numerous genes. [1] By conducting a unbiased mapping of Sp1 binding sites in vivo, scientists reported that the human genome may contains at least 12,000 Sp1 binding sites. [2] Therefore it is not surprising that Sp1 has been reported to participate in many biological processes, such as cell growth, differentiation, proliferation, metabolism, apoptosis, and cell cycle. In addition, there are also some researches that claimed that it correlates to the tumorigenesis and development of various cancers. [3],[4],[5],[6],[7]

HeLa cells is an immortal cell line first obtained from the cervical cancer cells of Henrietta Lacks, and now it is the most widely used cell line for scientific research around the world. At present, there are several researches concerning the changes of Sp1 in HeLa cells. For example, Seve et al. reported that the conformation of Sp1 was changed by the Tat protein and its ability to bind to its cognate DNA sequence and to retain its zinc was affected too. [8] In another research, the regulation of Sp1 was reported to inhibit the Topo II alpha expression and therefore affect the HeLa cell apoptosis induced by genistein. [9] Interacting with p53, Sp1 inhibited the activity of Ki-67, which was associated with cell proliferation. [10] In a word, as a cell line derived from the cancer cells, HeLa cells were also influenced by the dysregulation of Sp1.

While all the biological processes consist of many components including DNA, RNA, proteins, and some micromolecules and their interplay, researches may be limited if we study individual gene alone. MicroRNAs (miRNAs) are a class of small noncoding RNA molecules. They play important roles in several biological processes (e.g. cell signaling, cell development, and cell death). [11] Up to now, most efforts have been made to find miRNAs and their targets. Sharing a similar regulatory logic with miRNA, TFs also triggered many biological events, dependent on the binding sites present on the target gene. In cells, these two important regulators were usually found cooperated in the gene regulatory network. Hence studies combining miRNAs and TFs with genes may prompt a more clear regulatory mechanism in cells.

In this study, we used the gene expression profile of GSE37935 downloaded from Gene Expression Omnibus (GEO) to investigate the changes of the regulatory network in HeLa cells in which Sp1 was silenced. With the miRNA and TF databases, we tried to find the most remarkable variation in the regulatory mechanism.

 > Materials and methods Top

Affymetrix microarray data

The expression profile of GSE37935 [12] was downloaded from GEO database (http://www.ncbi.nlm.nih.gov/geo/), which consisted of three samples of normal HeLa cell lines and three samples of HeLa cells disturbed by Sp1. Platform information was GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array. The original files (.raw) and annotation files were also downloaded from the platform.

Identification of differentially expressed genes

The R software version 2.15.3 [13] was used to analyze the microarray data. The raw expression datasets from all conditions were normalized using the Robust Multichip Averaging (RMA) method. [14] Then the limma package [15] in R, a linear regression model, was applied to analyze the significance of differential expression, and the Bayesian method [16],[17] was used to proceed multiple testing. Finally, the differentially expressed genes (DEGs) only with the cutoff criterion |logFC| larger than 0.5 and P less than 0.05 were selected.

Construction of the integrated regulatory network

The integrated regulatory network was constructed based on systematic integration of various high-throughput datasets. The miRNAs associated with DEGs were selected from miRNA regulation database (the integration database of miRecords, TarBase, starbase, and miR2Disease), [18],[19],[20],[21] and TFs associated with DEGs were selected from Transcriptional Regulatory Element Database (TRED). [22] The integrated miRNA-DEG-TF regulatory network was constructed by using Cytoscape software (http://cytoscape.org/), [23] which was an open source software for visualizing complex networks and integrating these networks with any type of attribute data.

Integrated Regulatory Network Motif Analysis

Motif is a small set of recurring regulation patterns in network, which possesses most of the functional features of the original network. [24] The motif analysis of the original integrated regulatory network was performed by FANMOD (http://theinf1.informatik.uni-jena.de/~wernicke/motifs/), [25] a tool for fast network motif detection. The significant motifs were screened by setting the motif size M = 4, P < 0.05 and Z-score > 2.

Functional modules analysis of network

The integrated regulatory network modules were constructed based on the motif analyses of original integrated regulatory network. The top-two motifs with P less than 0.05 and Z-score lager than 2 were screened. After that, the DEGs meeting with motif analyses were obtained from the original network by using the Perl script program (http://www.perl.org/). The two new modules corresponding to the original integrated network were constructed by integrated the motifs and DEGs finally.

The Gene Ontology (GO) [26] functional annotation and the following Kyoto Encyclopedia of Genes and Genomes (KEGG) [27] pathway enrichment analysis of module-related genes were done by tools of the DAVID (the Database for Annotation, Visualization and Integrated Discovery) software. [28] In both analyses, P < 0.05 was considered statistically significant.

 > Results Top

Identification of DEGs and construction of the network

A total of 708 DEGs with |logFC|>0.5 and P < 0.05 were considered statistically significant. With the derived regulatory relationships of miRNA-DEG and TF-DEG, we obtained a network with 587 nodes and 1070 edges [Figure 1].
Figure 1: Integrated miRNA-DEG-TF regulatory network Yellow nodes represent DEGs; green nodes represent TFs related to DEGs; blue nodes represent miRNAs related to DEGs; the green edges represent the regulatory of TFs; the amaranth edges represent the regulatory of miRNAs

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Analysis of motifs and the corresponding modules

The top-five motif analysis results of the integrated regulatory network were chosen by setting motif-size M = 4, P less than 0.05, and Z-score lager than 2 [Figure 2], [Table 1]. All of the five motif categories not only had different geometrical structures, but also had different signs of regulation and represented several significant kinds of regulatory interactions, such as TF-Gene (motif 1 in [Figure 2]), TF-Gene and miRNA-Gene (motif 2 in [Figure 2]), and miRNA-Gene (motif 5 in [Figure 2]).
Figure 2: Motif analysis of the regulatory network Yellow nodes represent DEGs; green nodes represent TFs related to DEGs; blue nodes represent miRNAs related to DEGs. The motifs are motif-1 to motif-5 from left to right, respectively

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Table 1: Motif analysis of regulatory network, the green nodes represent micro-RNA; the blue nodes represent TFs; the yellow nodes represent DEGs

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GO analysis of the regulatory network modules

The top-two modules were constructed based on the motif analyses and primary integrated regulatory network, as shown in [Figure 3] (A and B). The GO functional annotation results revealed that module 1 was associated with 19 biological processes, and the two most notable processes were cell cycle (P = 0.001662) and regulation of apoptosis (P = 0.002121). Some salient genes enriched in these terms were CDKN1A (cyclin-dependent kinase inhibitor 1A) and CUL5 (cullin 5). Meanwhile, module 2 was also found mostly correlated to cell cycle process (P = 6.21E-04) and regulation of apoptosis (P = 0.003406) [Table 2]. Epidermal growth factor receptor (EGFR) and CDKN1A were found enriched in both terms.
Figure 3: Corresponding modules of the regulation network motifs A: blue nodes represent miRNAs, yellow nodes represent DEGs; green nodes represent TFs. B: blue nodes represent miRNAs, yellow nodes represent DEGs, green nodes represent TFs

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Table 2:

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Pathway analysis of modules

With the online biological classification tool DAVID, we observed that the glioma pathway was significantly enriched by DEGs in module 2, with P = 0.504202. Two genes involved in it were EGFR and CDKN1A. Interestingly, p53 signaling pathway was discovered enriched by DEGs in both modules, including CDKN1A, RRM2B, and GADD45B, with P = 0.457317 and 0.504202, respectively [Table 3].
Table 3: Pathways in module 1 and 2

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 > Discussion Top

Providing the microarray experiments data in GSE37935, Oleaga et al. compared the gene expression in the Sp1 silenced HeLa cells with the controls, and then screened several target genes of Sp1. [12] However, as an important TF, the inactivation of Sp1 must induce variation in the related genes and even the corresponding regulatory network. In this work, by analyzing the gene expression data and their regulatory relationships with TFs and miRNAs, we obtained two functional modules from an original integrated regulatory network and identified some functional genes and pathways, which may play significant roles in the regulatory network of HeLa cells.

In our study, we found that most of the DEGs in the two modules were components in nucleus, which were involved in cell cycle and cell apoptosis. CDKN1A encodes a cyclin-dependent kinase inhibitor. It had been demonstrated that CDKN1A is a potent and reversible inhibitor of cell cycle progression and upregulated CDKN1A induces irreversible G1 arrest and apoptosis. [29] While the downregulation of CDKN1A was found involved in the inhibition of proliferation induced by Sp1 in vascular smooth muscle cells, [30] Deniaud et al. reported that they did not find a significant modification of CDKN1A gene expression upon Sp1 induction in Baf-3 cells. [31] CUL5 is a component of E3 ubiquitin ligase complexes and is involved in the regulation of the stability of p53. [32],[33] It has been reported as a cell cycle and apoptosis regulator in various kind of cells. [34] EGFR is an important signaling mediator, it plays a critical role in cell cycle arrest, apoptosis, or dedifferentiation. [35] In former researches, Sp1 has been reported to regulate apoptosis in a DNA binding-dependent manner in some cells [36],[37] and to induce an inhibition of cell cycle progression that precedes apoptosis. [38] These results suggest that Sp1 may participate in cell apoptosis and cell cycle by interacting with CDKN1A, CUL5, and EGFR in HeLa cells.

By proceeding with pathway analysis, we found that both the DEGs in two modules were enriched in p53 signaling pathway and three genes, including CDKN1A, RRM2B, and GADD45B, were enriched in this pathway. P53 is a vital tumor suppressor gene, it is activated by a number of stress signals, including DNA damage, oxidative stress, and activated oncogenes. With various genes regulated by p53, the p53 signaling pathway mainly includes three biological processes: Cell cycle arrest, cellular senescence, and apoptosis. [39] In a previous study, it has been reported that p53 regulates the activity of CDC25B (Cell Division Cycle 25 B, a gene involved in the cell cycle) via Sp1, [40] and the DNA binding domain of Sp1 is required for its interaction with p53. [41] These results suggest that Sp1 may play a critical role in cellular functions correlated by interacting with p53 signaling pathway.

As a cell cycle regulator, the expression of CDKN1A has been reported associated to the growth arrest of HeLa cells upon human papillomavirus (HPV) oncoproteins E2 overexpression, [42] which is consistent with our results. CDKN1A is also a direct target of miR-22. Regulated by p53, miR-22 then target CDKN1A CIP-1 RNA to facilitate the p53-dependent apoptosis, forming a p53-miR-22-CDKN1A axis. [43] Considering that Sp1 interacts with CDKN1A in HeLa cells, we believe that the relationship between Sp1 and CDKN1A may affect the whole p53 pathway.

Ribonucleotide reductase M2B (RRM2B) gene encodes one of the small subunit of a p53-inducible ribonucleotide reductase, which is an important enzyme in DNA synthesis. By supplying deoxyribonucleotides for DNA repair in cells arrested at G1 or G2, it plays a pivotal role in cell survival by repairing damaged DNA in a p53/TP53-dependent manner. [44] RRM1 is the other subunit of the ribonucleotide reductase, which interacts with RRM2B in DNA repair. In a previous study, Parker et al. reported that Sp1 was involved in the transcription of RRM1. [45] Hence we suggest that Sp1 may participate in the p53 signaling pathway by interacting with RRM2B.

Growth arrest and DNA-damage-inducible, beta (GADD45B) is a DNA-methylation-silenced tumor suppressor gene. It regulates cell growth, differentiation, and cell death following cellular exposure to diverse stimuli. [46] In prostate cancer cells, it was found upregulated by CG-5 by transcriptional repression of DNA (cytosine-5)-methyltransferase 1 (DNMT1), which was associated with reduced expression of Sp1 and E2F1 (E2F transcription factor 1). [47] The evidence confirmed that Sp1 indeed has regulated the expression of GADD45B, an important gene in p53 signaling pathway.

To our surprise, we found that the glioma pathway was present in the analysis and EGFR was one of the DEGs. Glioma is an aggressive primary brain tumor, in which the EGFR gene is frequently amplified or mutated. [48] For example, EGFR activation was reported to enhance the cyclooxygenase-2 expression through activation of Sp1/Sp3 in glioma. [49] This result demonstrates that the same process may also happen in the HeLa cells.

In conclusion, by combining the GEO expression profile data with the miRNA and TF databases, we got an integrated regulatory network of HeLa cells in which Sp1 was silenced and found that the cell nucleus components were saliently influenced by the inactivation of Sp1. Considering the pathway analysis results, we suggest that the cell cycle and cell apoptosis may be affected by the dysfunction of Sp1 and the remarkable DEGs in our modules, such as CDKN1A, CUL5, EGFR, RRM2B, and GADD45B, may play important roles in further study. However, further experiments are still indispensable to confirm our results.

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  [Figure 1], [Figure 2], [Figure 3]

  [Table 1], [Table 2], [Table 3]


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