Mol. Cells 2014; 37(9): 691-698
Published online September 18, 2014
https://doi.org/10.14348/molcells.2014.0184
© The Korean Society for Molecular and Cellular Biology
Correspondence to : *Correspondence: cqlw1975@126.com (QC); kkhuo@fudan.edu.cn (KH)
SCYL1-BP1 is thought to function in the p53 pathway through Mdm2 and hPirh2, and mutations in SCYL1-BP1 are associated with premature aging syndromes such as
Keywords HEK293T cells, knockdown, microRNA, network, SCYL1-BP1
The cDNA encoding SCY1-like 1 binding protein 1 (SCYL1-BP1; accession number: NM_152281) was originally cloned in our laboratory from a human fetal liver cDNA library. SCYL1-BP1 is soluble, highly conserved and widely expressed in many tissues. SCYL1-BP1 can interact with SCY1-like protein 1 (SCYL1)(Di et al., 2003) and RING finger and CHY zinc finger domain-containing protein 1 (hPirh2) (Yan et al., 2010a; Zhang et al., 2005). SCYL1-BP1 co-localizes with SCYL1 and may be involved in mitosis-related cellular function via binding SCYL1 (Di et al., 2003). In addition, SCYL1-BP1 was reported to be a ubiquitination substrate of hPirh2 and can induce Double minute 2 protein(Mdm2) self-ubiquitination by interacting with Mdm2 (Yan et al., 2010a). hPirh2 and Mdm2 are important E3 ubiquitin ligases of p53 (Brooks and Gu, 2006; Leng et al., 2003); therefore, SCYL1-BP1 also acts as a co-factor to affect the p53 pathway(Yan et al., 2010b). SCYL1-BP1 gene mutations are associated with premature aging syndromes such as
MicroRNAs (miRNAs) are a class of endogenous, 19-23 nucleotide, non-coding, single-stranded small RNAs, which are widespread and highly conserved in animals and plants (Bartel, 2009; Esquela-Kerscher and Slack, 2006). Through perfect or partial base-pairing, mainly at the 3′-untranslated region (3′-UTR) of the target gene mRNA, miRNAs can guide the degradation of the target mRNA silencing complex (RISC) and negatively regulate the expression of target mRNA at translational and even the transcriptional level (Forman et al., 2008; Hou et al., 2011; Lytle et al., 2007). MiRNAs can regulate more than 30% of genes in eukaryotic cells, and participate in multiple regulatory pathways, such as cell proliferation, differentiation, development, aging and apoptosis. Moreover, certain miRNAs suppress or promote tumors in tumorigenesis, making them potential biomarkers for the diagnosis and prognosis oftumors.
Here, using HEK293T cells as an experimental paradigm of transfection, we report that miRNA expression levels were significantly altered when the expression of the SCYL1-BP1 gene was knocked down using RNA interference (RNAi). We used network analysis to functionally characterize the significant impact of potential kernel miRNA-target genes. Importantly, we showed that the expression levels of
Human embryonic kidney cells 293T (HEK293T) were cultured in Dulbecco’s Modified Eagle Medium (DMEM; Mediatech Inc., USA) supplemented with 10% fetal bovine serum (FBS; Biochrom, Germany), 2 mM L-glutamine, 100 U/ml penicillin, and 100 μg/ml streptomycin at 37°C in a 5% CO2 humidified atmosphere. All transfection experiments were performed using Lipofectamine 2000 (Invitrogen, USA), following the manufacturer’s instructions.
The sequences of the siRNA oligonucleotides numbers 1, 2, and 3, corresponding to the SCYL1-BP1 mRNA, were GGAGACUAAAGCAGACUAATT, GGCCAGCUUAGACUAUUCATT, and CCCAAAUCAAGAAGGUAAUTT, respectively. A disordered se-quence of the oligonucleotides that has no interfering function in the human genome, UUCUCCGAACGUGUCACGUTT, served as the negative control. Western blotting was used to detect the knockdown efficiency in all cells.
The HEK293T cells of SCYL1-BP1 knockdown and control groups were collected and a miRVana PARIS kit (Ambion, USA) was used to extract the total RNA, according to the manufacturer’s protocol, and based on the RNase-free DNase I (Promega, USA) to eliminate DNA contamination. The concentration of the RNAs isolated from cells ranged from 1.5?12 ng/μl.
MiRNA profiles of knockdown and control cells were generated using an Agilent Human miRNA microarray V3 (Agilent Technologies Inc., USA); 60 ng of RNA was labeled and hybridized for each array. Hybridization signals were detected with the Agilent microarray scanner, and the data were extracted using Feature Extraction V10.7 (Agilent Technologies). All raw data were transformed to log2 and their expressions were normalized each by zero mean and unit sample variance.
Using the random variance model
Three databases were used to predict miRNA-target genes: TarBase (v6.0) (Vergoulis et al., 2012), miRecords (2013) (Xiao et al., 2009) and miRTarBase (2013) (Hsu et al., 2011), which host the largest collection of manually curated experimental data. In this work, we mainly focused on validated target genes; we plan to study the unverified miRNA-target genes in the future.
The weighted miRNA-target network was constructed using miRNAs and experimentally validated target genes. In the process of network building, miRNAs were weighted by their expression-fold changes (|log2|), and target genes were weighted based on the degree distributions. To validate the veracity of a network, all nodes of the network were ranked according to their weights and their similarity was tested (Chen et al., 2013a; Zheng et al., 2011). Thereafter, we obtained deregulated nodes for mapping the consecutive networks. In the weighted miRNA-target network, the nodes represent miRNAs or genes and the edges represent the connection strength. Then, the simplified miRNA-target network was reconstructed using optimized nodes, which were defined as Degree (D) ≥ 2Average (d) from the degree distribution of the network.
The MINT database was used to construct PPI networks related to target genes. We first calculated the topological profiles of a PPI network using the ClusterONE algorithm, which was defined as
Of the inferred miRNA-target genes, those showing a significant (
Quantitative real-time PCR (q-PCR) was performed with SYBR Green PCR Master Mixture (TOYOBO, LTD, Japan) according to the manufacturer’s instructions, using a Rotor-Gene 6000 Real-time PCR machine (Corbett Life Science, Australia). Melting curve analysis at the end of the PCR cycles validated the specificity of each PCR product. All mRNAs were analyzed in triplicate, and the cycle threshold (Ct) was defined as the number of cycles required for the fluorescent signal to reach the threshold. The levels of mRNAs in cells were calculated using the formula 2ΔCt where ΔCt = Ct of internal reference ?Ct of target mRNA.
Three small interfering RNA sequences (siRNA1-, siRNA2-, and siRNA3-SCYL1-BP1) and a control sequence (siNC, siGAPDH) were used to perform RNA interference (RNAi) in HEK293T cells. Western blotting validated the knockdown efficiency (Fig. 1A). Compared with the control group, the SCYL1-BP1 expression level was significantly decreased in cells treated with the siRNAs.
The miRNA profiles were calculated using the random module
To investigate the biological functions of these differentially expressed miRNAs, miRPath (v2.0) analysis was performed to identify miRNA pathways (Vlachos et al., 2012), and significant pathways were determined when
Comparing the miRNA-related pathway terms, seven pathways overlapped between the up- and downregulated miRNAs, which are mainly associated with cell cycle, viral carcinogenesis, chronic myeloid leukemia, and cancer-related pathways. This suggests that these pathways are indispensable for both SCYL1-BP1 knockdown and control HEK293T cells. Strikingly, 10 pathways of upregulated miRNAs and three pathways of downregulated miRNAs were dramatically different. These data suggested that the regulatory mechanism of the upregulated miRNAs may be more complicated, because high expression levels of miRNAs are more likely to inhibit translation or induce target mRNA degradation (Hou et al., 2011).
Validated target genes of differentially expressed miRNAs were selected based on TarBase (v6.0), miRecords (2013) and miRTarBase (2013). After redundancy analysis, the final database of miRNA-target genes was built, which included 2887 target genes regulated by 94 upregulated miRNAs and 3288 target genes regulated by 38 downregulated miRNAs.
Using the miRNAs and relative target genes, we built an upregulated miRNA-target network and a downregulated miRNA-target network. As shown in Figs. 3A and 3B, the topological structure of the network is similar to a ‘Medusa’ model (Guo et al., 2011), which comprises a regulatory core with kernel nodes. It indicates that the hub nodes (miRNAs or target genes) of the network are determinants of the observed gene expression profiles, but the peripheral nodes should be regulated, but are not regulating (Chen et al., 2013b).
To understand these networks holistically, we conducted functional enrichment analysis for the target genes of the networks using DAVID analysis (Huang da et al., 2009a; 2009b). GO terms (10% top terms) analysis revealed that the targets of the upregulated miRNA are mainly associated with intracellular, nucleus, protein binding, membrane-bounded organelle, cellular macromolecule metabolic process, cell cycle, enzyme-linked receptor protein signaling pathway, regulation of transcription and cell proliferation (Fig. 3C). Similarly, the targets of the downregulated miRNAs are mainly associated with intracellular part, protein binding, intracellular membrane-bounded organelle, cytoplasm and cellular process (Fig. 3D). Importantly, KEGG pathway analysis showed a functional association of upregulated miRNA targets with various cancer-related pathways, such as Colorectal cancer (
Thus, these bioinformatics analyses suggested that ablation of SCYL1-BP1 expression significantly increased the levels of cancer-related miRNAs. The SCYL1-BP1 landscape in the miRNA maps of HEK293T cells may subsequently affect several biological processes, such as cancer progression.
The simplified miRNA-target network was reconstructed using screened nodes, which were defined as Degree (D) ≥ 2Average (d) from the degree distribution of the network. As shown in Fig. 4A, 594 target genes overlapped in both simplified networks, which suggest that the common targets play important roles in HEK293T cells. For this reason, we constructed a PPI network for common targets using the MINT database. The PPI network profile is shown in
To screen the potential kernel nodes from the cluster-related PPI networks, we calculated the BC, CC, (d) and TC for each node in the network. The selected degree was defined as d ≥2. The other parameters were defined as BC ≥ Average (BC), CC ≥Average (CC), and TC ≥Average (TC), respectively. Finally, 19 kernel nodes were screened from the common target-related PPI network and 11 kernel nodes were selected from unique target-related PPI network (Table 1).
To explore whether these hub nodes are potential kernel genes in predicting the progression of network, we performed qPCR to measure the expression levels of 30 genes in SCYL1-BP1 knockdown and control HEK293T cells (Table 1).
As shown in Fig. 5A, the total targets set (n = 30) included common targets (n =19) and upregulated miRNA unique targets (n = 11). At the transcript expression level, statistical significance was noted for the total targets (
The SCYL1-BP1 interacts with several proteins (Yan et al., 2010a; Zhang et al., 2005). However, the specific function and mechanism of SCYL1-BP1 remain unclear. As a class of gene regulators, miRNAs have an important combinatorial hallmark in the gene regulation process (Chen et al., 2013a). Generally, a given miRNA may have multiple different mRNA targets; a given target might also be targeted by multiple miRNAs (Krek et al., 2005; van Iterson et al., 2013). In this study, we generated miRNA expression maps of SCYL1-BP1 knockdown and control in HEK293T cells using an miRNA microarray, and observed the miRNAs levels were significantly changed by comparison. Subsequently, the networks of up- and downregulated miRNA-target were constructed. The topological structures were similar to the ‘Medusa’ model, which implied that the hub nodes are determinants of the observed gene expression profiles (Chen et al., 2012; Zheng et al., 2011).
GO terms and pathway analysis revealed that the upregulated miRNAs and target genes are mainly associated with the cell cycle, p53 Signaling Pathway and cancer-related pathways. The imbalance of G1/S and G2/M phases of the cell cycle is associated with dysfunction in hepatocarcinoma (Spaziani et al., 2006), while the p53 signaling pathway is highly correlated with the pathogenesis of numerous cancers (Stegh, 2013). This implies that ablation of SCYL1-BP1 expression significantly increases the levels of cancer-related miRNAs, which then affect the expressions of their related target genes and transform the original progressions of the cell cycle and p53 signaling pathway in cells.
To determine the biological consequence of ablating SCYL1-BP1 expression in cells, we first simplified the miRNA-target network and obtained 594 overlapped target genes between up-and downregulated miRNA-target networks. Next, we constructed the common target-related PPI network and upregulated miRNAs’ unique target-related PPI network, respectively. Finally, 30 nodes representing potential kernel genes were screened from the networks, which might play important roles in biological progression in cells. The qPCR results showed that the expressions of the kernel genes were significantly downregulated in SCYL1-BP1 knockdown cells. Importantly, the mRNA expression levels of
EEA1 is a membrane-tethering factor required for the fusion and maturation of early endosomes in endocytosis, which plays a crucial role in the tethering process leading to homotypic endosome fusion (Bergeland et al., 2008). EEA1 is considered a new substrate of p97, and p97 may regulate the oligomeric state of EEA1 to influence its membrane-tethering function, which in turn affects the size of early endosomes (Ramanathan and Ye, 2012). Beas et al. demonstrated that the heterotrimeric G protein Gαs facilitates dissociation of EEA1 from membranes. This is a key step in EEA1 endosome maturation, which limits EGFR signaling from EEA1 endosomes, and then inhibits cell proliferation (Beas et al., 2012). In cells, Rab5 and its effector EEA1 associate with membranes of EEA1 endosomes, which is associated with the fusion of endosomes (Poteryaev et al., 2012). Moreover, endogenous EEA1 is a ubiquitin-regulated factor, and may be modified via an E3 independent ubiquitin conjugation mechanism in cells. Furthermore, ubiquitination may significantly alter EEA1 activity to influence endocytosis (Ramanathan et al., 2013).
BMPR2, a member of the bone morphogenetic protein (BMP) receptor family of transmembrane serine/threonine kinases, plays a critical role in the regulation of genomic integrity in pulmonary endothelial cells (Li et al., 2014). Decreased BMPR2 expression can enhance GM-CSF (granulocyte macrophage colony-stimulating factor) mRNA translation, which increases inflammatory cell recruitment and exacerbates idiopathic pulmonary arterial hypertension (PAH)(Sawada et al., 2014). BMPR2 downregulation is also likely to promote vascular smooth muscle cell proliferation and restenosis, which represent a new clue for the early diagnosis of carotid restenosis (Luo et al., 2013). In human adipose-derived mesenchymal stem cells, BMPR2 is targeted by miR-100 and may play a negative role in osteogenic differentiation, which is important for improving the treatment of bone-related diseases (Zeng et al., 2012).
BRCA2, breast cancer susceptibility 2, is a well-known tumor suppressor gene that has been linked to defined human cancer syndromes (Karami and Mehdipour, 2013). Previous studies demonstrated that BRCA2 mutation or deficiency is linked to many human cancers (Birkbak et al., 2013; King et al., 2003; Norquist et al., 2010; Venkitaraman, 2002; Yang et al., 2011). In particular, p53 mutations may have a synergistic effect on tumorigenesis in BRCA2-associated cancers (Shive et al., 2014). In cells, BRCA2 overexpression is associated with poor outcome and correlates with a high proliferation rate (Magwood et al., 2012). Furthermore, BRCA2 suppresses homologous recombination, reduces RAD51 foci formation and inactivates p53 function (Magwood et al., 2012). On the other hand, downregulation of BRCA2 expression has been suggested as a biomarker for docetaxel treatment in breast cancer patients (Egawa et al., 2001). Interestingly, BRCA2 knockdown has been proposed to have a potential therapeutic benefit in multidrug treatment of human tumors (Rytelewski et al., 2013).
In conclusion, diminished expression of SCYL1-BP1 dramatically reduced the expression levels of
. The distribution of potential kernel nodes from the PPI network and relative qPCR-validated results
Gene symbol | Closeness centrality | Degree | Betweenness centrality | Topological coefficient | Expression value (Knockdown) | Expression value (Control) | |
---|---|---|---|---|---|---|---|
Common targets | APPL1 | 0.1554 | 6 | 0.0144 | 0.2361 | 4.6539 | 6.1072 |
BCL2 | 0.1770 | 13 | 0.0402 | 0.0905 | 4.4642 | 5.5385 | |
C8orf33 | 0.1385 | 22 | 0.0605 | 0.0454 | 5.5917 | 7.0576 | |
CCT3 | 0.2171 | 3 | 0.0186 | 0.5191 | 8.5699 | 10.7811 | |
EEA1 | 0.1555 | 9 | 0.0231 | 0.1717 | 1.7756 | 3.3968 | |
EZH2 | 0.2235 | 14 | 0.1635 | 0.0855 | 6.6229 | 8.4216 | |
GRB2 | 0.1965 | 37 | 0.1682 | 0.0327 | 6.1013 | 7.4184 | |
HIPK3 | 0.1346 | 13 | 0.0434 | 0.1025 | 2.6519 | 3.7642 | |
ITGB1 | 0.1862 | 11 | 0.0687 | 0.1034 | 5.7594 | 7.1643 | |
MAPK14 | 0.1986 | 18 | 0.2012 | 0.0784 | 4.7795 | 5.7354 | |
MCL1 | 0.1744 | 10 | 0.0370 | 0.1333 | 5.5614 | 6.8035 | |
MCM3 | 0.1975 | 7 | 0.0374 | 0.2258 | 7.7238 | 9.7779 | |
PCNA | 0.1644 | 20 | 0.052 | 0.0636 | 6.6981 | 8.8594 | |
PDLIM7 | 0.1858 | 3 | 0.0494 | 0.4973 | 4.4889 | 5.3724 | |
PPP1R12A | 0.1968 | 4 | 0.0188 | 0.2500 | 4.2588 | 5.5308 | |
PRMT5 | 0.2369 | 36 | 0.2195 | 0.0286 | 5.8914 | 7.3711 | |
SMAD4 | 0.1900 | 35 | 0.0953 | 0.0285 | 3.4742 | 4.7586 | |
SPRY2 | 0.1977 | 17 | 0.0704 | 0.0617 | 5.3249 | 6.4559 | |
VCL | 0.1965 | 3 | 0.0102 | 0.3333 | 5.6283 | 6.7397 | |
Unique targets | BMPR2 | 0.1661 | 3 | 0.0559 | 0.3333 | 2.4658 | 4.2482 |
BNIP2 | 0.1820 | 3 | 0.0334 | 0.3333 | 4.4202 | 5.8564 | |
BRCA1 | 0.1519 | 14 | 0.0481 | 0.0904 | 4.8327 | 6.4537 | |
BRCA2 | 0.1517 | 11 | 0.0436 | 0.1111 | 1.0285 | 3.0866 | |
DDX5 | 0.1914 | 16 | 0.0643 | 0.0984 | 6.875 | 9.5271 | |
GNB5 | 0.1513 | 4 | 0.0283 | 0.3125 | 3.7189 | 4.6893 | |
IRAK1 | 0.6400 | 7 | 0.4277 | 0.2527 | 6.5358 | 8.2885 | |
KIF23 | 0.1826 | 5 | 0.0477 | 0.2000 | 6.3873 | 8.151 | |
RBL2 | 0.1874 | 5 | 0.0754 | 0.2303 | 3.6676 | 4.9137 | |
STAU1 | 0.1293 | 3 | 0.0231 | 0.3333 | 6.2764 | 7.9761 | |
UBE2D1 | 0.1886 | 11 | 0.1947 | 0.1157 | 4.5769 | 5.9081 |
Mol. Cells 2014; 37(9): 691-698
Published online September 30, 2014 https://doi.org/10.14348/molcells.2014.0184
Copyright © The Korean Society for Molecular and Cellular Biology.
Yang Wang1, Xiaomei Chen1, Xiaojing Chen1, Qilong Chen2,*, and Keke Huo1,*
1State Key Laboratory of Genetic Engineering, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China, 2Research Center for TCM Complexity System, Shanghai University of TCM, Shanghai 201203, China
Correspondence to:*Correspondence: cqlw1975@126.com (QC); kkhuo@fudan.edu.cn (KH)
SCYL1-BP1 is thought to function in the p53 pathway through Mdm2 and hPirh2, and mutations in SCYL1-BP1 are associated with premature aging syndromes such as
Keywords: HEK293T cells, knockdown, microRNA, network, SCYL1-BP1
The cDNA encoding SCY1-like 1 binding protein 1 (SCYL1-BP1; accession number: NM_152281) was originally cloned in our laboratory from a human fetal liver cDNA library. SCYL1-BP1 is soluble, highly conserved and widely expressed in many tissues. SCYL1-BP1 can interact with SCY1-like protein 1 (SCYL1)(Di et al., 2003) and RING finger and CHY zinc finger domain-containing protein 1 (hPirh2) (Yan et al., 2010a; Zhang et al., 2005). SCYL1-BP1 co-localizes with SCYL1 and may be involved in mitosis-related cellular function via binding SCYL1 (Di et al., 2003). In addition, SCYL1-BP1 was reported to be a ubiquitination substrate of hPirh2 and can induce Double minute 2 protein(Mdm2) self-ubiquitination by interacting with Mdm2 (Yan et al., 2010a). hPirh2 and Mdm2 are important E3 ubiquitin ligases of p53 (Brooks and Gu, 2006; Leng et al., 2003); therefore, SCYL1-BP1 also acts as a co-factor to affect the p53 pathway(Yan et al., 2010b). SCYL1-BP1 gene mutations are associated with premature aging syndromes such as
MicroRNAs (miRNAs) are a class of endogenous, 19-23 nucleotide, non-coding, single-stranded small RNAs, which are widespread and highly conserved in animals and plants (Bartel, 2009; Esquela-Kerscher and Slack, 2006). Through perfect or partial base-pairing, mainly at the 3′-untranslated region (3′-UTR) of the target gene mRNA, miRNAs can guide the degradation of the target mRNA silencing complex (RISC) and negatively regulate the expression of target mRNA at translational and even the transcriptional level (Forman et al., 2008; Hou et al., 2011; Lytle et al., 2007). MiRNAs can regulate more than 30% of genes in eukaryotic cells, and participate in multiple regulatory pathways, such as cell proliferation, differentiation, development, aging and apoptosis. Moreover, certain miRNAs suppress or promote tumors in tumorigenesis, making them potential biomarkers for the diagnosis and prognosis oftumors.
Here, using HEK293T cells as an experimental paradigm of transfection, we report that miRNA expression levels were significantly altered when the expression of the SCYL1-BP1 gene was knocked down using RNA interference (RNAi). We used network analysis to functionally characterize the significant impact of potential kernel miRNA-target genes. Importantly, we showed that the expression levels of
Human embryonic kidney cells 293T (HEK293T) were cultured in Dulbecco’s Modified Eagle Medium (DMEM; Mediatech Inc., USA) supplemented with 10% fetal bovine serum (FBS; Biochrom, Germany), 2 mM L-glutamine, 100 U/ml penicillin, and 100 μg/ml streptomycin at 37°C in a 5% CO2 humidified atmosphere. All transfection experiments were performed using Lipofectamine 2000 (Invitrogen, USA), following the manufacturer’s instructions.
The sequences of the siRNA oligonucleotides numbers 1, 2, and 3, corresponding to the SCYL1-BP1 mRNA, were GGAGACUAAAGCAGACUAATT, GGCCAGCUUAGACUAUUCATT, and CCCAAAUCAAGAAGGUAAUTT, respectively. A disordered se-quence of the oligonucleotides that has no interfering function in the human genome, UUCUCCGAACGUGUCACGUTT, served as the negative control. Western blotting was used to detect the knockdown efficiency in all cells.
The HEK293T cells of SCYL1-BP1 knockdown and control groups were collected and a miRVana PARIS kit (Ambion, USA) was used to extract the total RNA, according to the manufacturer’s protocol, and based on the RNase-free DNase I (Promega, USA) to eliminate DNA contamination. The concentration of the RNAs isolated from cells ranged from 1.5?12 ng/μl.
MiRNA profiles of knockdown and control cells were generated using an Agilent Human miRNA microarray V3 (Agilent Technologies Inc., USA); 60 ng of RNA was labeled and hybridized for each array. Hybridization signals were detected with the Agilent microarray scanner, and the data were extracted using Feature Extraction V10.7 (Agilent Technologies). All raw data were transformed to log2 and their expressions were normalized each by zero mean and unit sample variance.
Using the random variance model
Three databases were used to predict miRNA-target genes: TarBase (v6.0) (Vergoulis et al., 2012), miRecords (2013) (Xiao et al., 2009) and miRTarBase (2013) (Hsu et al., 2011), which host the largest collection of manually curated experimental data. In this work, we mainly focused on validated target genes; we plan to study the unverified miRNA-target genes in the future.
The weighted miRNA-target network was constructed using miRNAs and experimentally validated target genes. In the process of network building, miRNAs were weighted by their expression-fold changes (|log2|), and target genes were weighted based on the degree distributions. To validate the veracity of a network, all nodes of the network were ranked according to their weights and their similarity was tested (Chen et al., 2013a; Zheng et al., 2011). Thereafter, we obtained deregulated nodes for mapping the consecutive networks. In the weighted miRNA-target network, the nodes represent miRNAs or genes and the edges represent the connection strength. Then, the simplified miRNA-target network was reconstructed using optimized nodes, which were defined as Degree (D) ≥ 2Average (d) from the degree distribution of the network.
The MINT database was used to construct PPI networks related to target genes. We first calculated the topological profiles of a PPI network using the ClusterONE algorithm, which was defined as
Of the inferred miRNA-target genes, those showing a significant (
Quantitative real-time PCR (q-PCR) was performed with SYBR Green PCR Master Mixture (TOYOBO, LTD, Japan) according to the manufacturer’s instructions, using a Rotor-Gene 6000 Real-time PCR machine (Corbett Life Science, Australia). Melting curve analysis at the end of the PCR cycles validated the specificity of each PCR product. All mRNAs were analyzed in triplicate, and the cycle threshold (Ct) was defined as the number of cycles required for the fluorescent signal to reach the threshold. The levels of mRNAs in cells were calculated using the formula 2ΔCt where ΔCt = Ct of internal reference ?Ct of target mRNA.
Three small interfering RNA sequences (siRNA1-, siRNA2-, and siRNA3-SCYL1-BP1) and a control sequence (siNC, siGAPDH) were used to perform RNA interference (RNAi) in HEK293T cells. Western blotting validated the knockdown efficiency (Fig. 1A). Compared with the control group, the SCYL1-BP1 expression level was significantly decreased in cells treated with the siRNAs.
The miRNA profiles were calculated using the random module
To investigate the biological functions of these differentially expressed miRNAs, miRPath (v2.0) analysis was performed to identify miRNA pathways (Vlachos et al., 2012), and significant pathways were determined when
Comparing the miRNA-related pathway terms, seven pathways overlapped between the up- and downregulated miRNAs, which are mainly associated with cell cycle, viral carcinogenesis, chronic myeloid leukemia, and cancer-related pathways. This suggests that these pathways are indispensable for both SCYL1-BP1 knockdown and control HEK293T cells. Strikingly, 10 pathways of upregulated miRNAs and three pathways of downregulated miRNAs were dramatically different. These data suggested that the regulatory mechanism of the upregulated miRNAs may be more complicated, because high expression levels of miRNAs are more likely to inhibit translation or induce target mRNA degradation (Hou et al., 2011).
Validated target genes of differentially expressed miRNAs were selected based on TarBase (v6.0), miRecords (2013) and miRTarBase (2013). After redundancy analysis, the final database of miRNA-target genes was built, which included 2887 target genes regulated by 94 upregulated miRNAs and 3288 target genes regulated by 38 downregulated miRNAs.
Using the miRNAs and relative target genes, we built an upregulated miRNA-target network and a downregulated miRNA-target network. As shown in Figs. 3A and 3B, the topological structure of the network is similar to a ‘Medusa’ model (Guo et al., 2011), which comprises a regulatory core with kernel nodes. It indicates that the hub nodes (miRNAs or target genes) of the network are determinants of the observed gene expression profiles, but the peripheral nodes should be regulated, but are not regulating (Chen et al., 2013b).
To understand these networks holistically, we conducted functional enrichment analysis for the target genes of the networks using DAVID analysis (Huang da et al., 2009a; 2009b). GO terms (10% top terms) analysis revealed that the targets of the upregulated miRNA are mainly associated with intracellular, nucleus, protein binding, membrane-bounded organelle, cellular macromolecule metabolic process, cell cycle, enzyme-linked receptor protein signaling pathway, regulation of transcription and cell proliferation (Fig. 3C). Similarly, the targets of the downregulated miRNAs are mainly associated with intracellular part, protein binding, intracellular membrane-bounded organelle, cytoplasm and cellular process (Fig. 3D). Importantly, KEGG pathway analysis showed a functional association of upregulated miRNA targets with various cancer-related pathways, such as Colorectal cancer (
Thus, these bioinformatics analyses suggested that ablation of SCYL1-BP1 expression significantly increased the levels of cancer-related miRNAs. The SCYL1-BP1 landscape in the miRNA maps of HEK293T cells may subsequently affect several biological processes, such as cancer progression.
The simplified miRNA-target network was reconstructed using screened nodes, which were defined as Degree (D) ≥ 2Average (d) from the degree distribution of the network. As shown in Fig. 4A, 594 target genes overlapped in both simplified networks, which suggest that the common targets play important roles in HEK293T cells. For this reason, we constructed a PPI network for common targets using the MINT database. The PPI network profile is shown in
To screen the potential kernel nodes from the cluster-related PPI networks, we calculated the BC, CC, (d) and TC for each node in the network. The selected degree was defined as d ≥2. The other parameters were defined as BC ≥ Average (BC), CC ≥Average (CC), and TC ≥Average (TC), respectively. Finally, 19 kernel nodes were screened from the common target-related PPI network and 11 kernel nodes were selected from unique target-related PPI network (Table 1).
To explore whether these hub nodes are potential kernel genes in predicting the progression of network, we performed qPCR to measure the expression levels of 30 genes in SCYL1-BP1 knockdown and control HEK293T cells (Table 1).
As shown in Fig. 5A, the total targets set (n = 30) included common targets (n =19) and upregulated miRNA unique targets (n = 11). At the transcript expression level, statistical significance was noted for the total targets (
The SCYL1-BP1 interacts with several proteins (Yan et al., 2010a; Zhang et al., 2005). However, the specific function and mechanism of SCYL1-BP1 remain unclear. As a class of gene regulators, miRNAs have an important combinatorial hallmark in the gene regulation process (Chen et al., 2013a). Generally, a given miRNA may have multiple different mRNA targets; a given target might also be targeted by multiple miRNAs (Krek et al., 2005; van Iterson et al., 2013). In this study, we generated miRNA expression maps of SCYL1-BP1 knockdown and control in HEK293T cells using an miRNA microarray, and observed the miRNAs levels were significantly changed by comparison. Subsequently, the networks of up- and downregulated miRNA-target were constructed. The topological structures were similar to the ‘Medusa’ model, which implied that the hub nodes are determinants of the observed gene expression profiles (Chen et al., 2012; Zheng et al., 2011).
GO terms and pathway analysis revealed that the upregulated miRNAs and target genes are mainly associated with the cell cycle, p53 Signaling Pathway and cancer-related pathways. The imbalance of G1/S and G2/M phases of the cell cycle is associated with dysfunction in hepatocarcinoma (Spaziani et al., 2006), while the p53 signaling pathway is highly correlated with the pathogenesis of numerous cancers (Stegh, 2013). This implies that ablation of SCYL1-BP1 expression significantly increases the levels of cancer-related miRNAs, which then affect the expressions of their related target genes and transform the original progressions of the cell cycle and p53 signaling pathway in cells.
To determine the biological consequence of ablating SCYL1-BP1 expression in cells, we first simplified the miRNA-target network and obtained 594 overlapped target genes between up-and downregulated miRNA-target networks. Next, we constructed the common target-related PPI network and upregulated miRNAs’ unique target-related PPI network, respectively. Finally, 30 nodes representing potential kernel genes were screened from the networks, which might play important roles in biological progression in cells. The qPCR results showed that the expressions of the kernel genes were significantly downregulated in SCYL1-BP1 knockdown cells. Importantly, the mRNA expression levels of
EEA1 is a membrane-tethering factor required for the fusion and maturation of early endosomes in endocytosis, which plays a crucial role in the tethering process leading to homotypic endosome fusion (Bergeland et al., 2008). EEA1 is considered a new substrate of p97, and p97 may regulate the oligomeric state of EEA1 to influence its membrane-tethering function, which in turn affects the size of early endosomes (Ramanathan and Ye, 2012). Beas et al. demonstrated that the heterotrimeric G protein Gαs facilitates dissociation of EEA1 from membranes. This is a key step in EEA1 endosome maturation, which limits EGFR signaling from EEA1 endosomes, and then inhibits cell proliferation (Beas et al., 2012). In cells, Rab5 and its effector EEA1 associate with membranes of EEA1 endosomes, which is associated with the fusion of endosomes (Poteryaev et al., 2012). Moreover, endogenous EEA1 is a ubiquitin-regulated factor, and may be modified via an E3 independent ubiquitin conjugation mechanism in cells. Furthermore, ubiquitination may significantly alter EEA1 activity to influence endocytosis (Ramanathan et al., 2013).
BMPR2, a member of the bone morphogenetic protein (BMP) receptor family of transmembrane serine/threonine kinases, plays a critical role in the regulation of genomic integrity in pulmonary endothelial cells (Li et al., 2014). Decreased BMPR2 expression can enhance GM-CSF (granulocyte macrophage colony-stimulating factor) mRNA translation, which increases inflammatory cell recruitment and exacerbates idiopathic pulmonary arterial hypertension (PAH)(Sawada et al., 2014). BMPR2 downregulation is also likely to promote vascular smooth muscle cell proliferation and restenosis, which represent a new clue for the early diagnosis of carotid restenosis (Luo et al., 2013). In human adipose-derived mesenchymal stem cells, BMPR2 is targeted by miR-100 and may play a negative role in osteogenic differentiation, which is important for improving the treatment of bone-related diseases (Zeng et al., 2012).
BRCA2, breast cancer susceptibility 2, is a well-known tumor suppressor gene that has been linked to defined human cancer syndromes (Karami and Mehdipour, 2013). Previous studies demonstrated that BRCA2 mutation or deficiency is linked to many human cancers (Birkbak et al., 2013; King et al., 2003; Norquist et al., 2010; Venkitaraman, 2002; Yang et al., 2011). In particular, p53 mutations may have a synergistic effect on tumorigenesis in BRCA2-associated cancers (Shive et al., 2014). In cells, BRCA2 overexpression is associated with poor outcome and correlates with a high proliferation rate (Magwood et al., 2012). Furthermore, BRCA2 suppresses homologous recombination, reduces RAD51 foci formation and inactivates p53 function (Magwood et al., 2012). On the other hand, downregulation of BRCA2 expression has been suggested as a biomarker for docetaxel treatment in breast cancer patients (Egawa et al., 2001). Interestingly, BRCA2 knockdown has been proposed to have a potential therapeutic benefit in multidrug treatment of human tumors (Rytelewski et al., 2013).
In conclusion, diminished expression of SCYL1-BP1 dramatically reduced the expression levels of
. The distribution of potential kernel nodes from the PPI network and relative qPCR-validated results.
Gene symbol | Closeness centrality | Degree | Betweenness centrality | Topological coefficient | Expression value (Knockdown) | Expression value (Control) | |
---|---|---|---|---|---|---|---|
Common targets | APPL1 | 0.1554 | 6 | 0.0144 | 0.2361 | 4.6539 | 6.1072 |
BCL2 | 0.1770 | 13 | 0.0402 | 0.0905 | 4.4642 | 5.5385 | |
C8orf33 | 0.1385 | 22 | 0.0605 | 0.0454 | 5.5917 | 7.0576 | |
CCT3 | 0.2171 | 3 | 0.0186 | 0.5191 | 8.5699 | 10.7811 | |
EEA1 | 0.1555 | 9 | 0.0231 | 0.1717 | 1.7756 | 3.3968 | |
EZH2 | 0.2235 | 14 | 0.1635 | 0.0855 | 6.6229 | 8.4216 | |
GRB2 | 0.1965 | 37 | 0.1682 | 0.0327 | 6.1013 | 7.4184 | |
HIPK3 | 0.1346 | 13 | 0.0434 | 0.1025 | 2.6519 | 3.7642 | |
ITGB1 | 0.1862 | 11 | 0.0687 | 0.1034 | 5.7594 | 7.1643 | |
MAPK14 | 0.1986 | 18 | 0.2012 | 0.0784 | 4.7795 | 5.7354 | |
MCL1 | 0.1744 | 10 | 0.0370 | 0.1333 | 5.5614 | 6.8035 | |
MCM3 | 0.1975 | 7 | 0.0374 | 0.2258 | 7.7238 | 9.7779 | |
PCNA | 0.1644 | 20 | 0.052 | 0.0636 | 6.6981 | 8.8594 | |
PDLIM7 | 0.1858 | 3 | 0.0494 | 0.4973 | 4.4889 | 5.3724 | |
PPP1R12A | 0.1968 | 4 | 0.0188 | 0.2500 | 4.2588 | 5.5308 | |
PRMT5 | 0.2369 | 36 | 0.2195 | 0.0286 | 5.8914 | 7.3711 | |
SMAD4 | 0.1900 | 35 | 0.0953 | 0.0285 | 3.4742 | 4.7586 | |
SPRY2 | 0.1977 | 17 | 0.0704 | 0.0617 | 5.3249 | 6.4559 | |
VCL | 0.1965 | 3 | 0.0102 | 0.3333 | 5.6283 | 6.7397 | |
Unique targets | BMPR2 | 0.1661 | 3 | 0.0559 | 0.3333 | 2.4658 | 4.2482 |
BNIP2 | 0.1820 | 3 | 0.0334 | 0.3333 | 4.4202 | 5.8564 | |
BRCA1 | 0.1519 | 14 | 0.0481 | 0.0904 | 4.8327 | 6.4537 | |
BRCA2 | 0.1517 | 11 | 0.0436 | 0.1111 | 1.0285 | 3.0866 | |
DDX5 | 0.1914 | 16 | 0.0643 | 0.0984 | 6.875 | 9.5271 | |
GNB5 | 0.1513 | 4 | 0.0283 | 0.3125 | 3.7189 | 4.6893 | |
IRAK1 | 0.6400 | 7 | 0.4277 | 0.2527 | 6.5358 | 8.2885 | |
KIF23 | 0.1826 | 5 | 0.0477 | 0.2000 | 6.3873 | 8.151 | |
RBL2 | 0.1874 | 5 | 0.0754 | 0.2303 | 3.6676 | 4.9137 | |
STAU1 | 0.1293 | 3 | 0.0231 | 0.3333 | 6.2764 | 7.9761 | |
UBE2D1 | 0.1886 | 11 | 0.1947 | 0.1157 | 4.5769 | 5.9081 |
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