The miR-17-5p microRNA is a key regulator of the G1/S phase cell cycle transition
- Nicole Cloonan1,
- Mellissa K Brown1,
- Anita L Steptoe1,
- Shivangi Wani1,
- Wei Ling Chan^1,
- Alistair RR Forrest1, 3,
- Gabriel Kolle1,
- Brian Gabrielli2 and
- Sean M Grimmond1Email author
© Cloonan et al.; licensee BioMed Central Ltd. 2008
Received: 25 March 2008
Accepted: 14 August 2008
Published: 14 August 2008
MicroRNAs are modifiers of gene expression, acting to reduce translation through either translational repression or mRNA cleavage. Recently, it has been shown that some microRNAs can act to promote or suppress cell transformation, with miR-17-92 described as the first oncogenic microRNA. The association of miR-17-92 encoded microRNAs with a surprisingly broad range of cancers not only underlines the clinical significance of this locus, but also suggests that miR-17-92 may regulate fundamental biological processes, and for these reasons miR-17-92 has been considered as a therapeutic target.
In this study, we show that miR-17-92 is a cell cycle regulated locus, and ectopic expression of a single microRNA (miR-17-5p) is sufficient to drive a proliferative signal in HEK293T cells. For the first time, we reveal the mechanism behind this response - miR-17-5p acts specifically at the G1/S-phase cell cycle boundary, by targeting more than 20 genes involved in the transition between these phases. While both pro- and anti-proliferative genes are targeted by miR-17-5p, pro-proliferative mRNAs are specifically up-regulated by secondary and/or tertiary effects in HEK293T cells.
The miR-17-5p microRNA is able to act as both an oncogene and a tumor suppressor in different cellular contexts; our model of competing positive and negative signals can explain both of these activities. The coordinated suppression of proliferation-inhibitors allows miR-17-5p to efficiently de-couple negative regulators of the MAPK (mitogen activated protein kinase) signaling cascade, promoting growth in HEK293T cells. Additionally, we have demonstrated the utility of a systems biology approach as a unique and rapid approach to uncover microRNA function.
MicroRNAs (miRNAs) are short, non-coding, RNA regulators of gene expression that have been identified in a broad range of eukaryotes. In addition to regulating growth, development, differentiation, and metabolism in model organisms, some miRNAs have also been classified as tumor suppressors or oncogenes (reviewed in ).
The first reported and most well studied oncomiR is the human miR-17-92 polycistron: a cluster of seven miRNAs derived from the c-myc regulated c13orf25 locus at chromosome 13q31.3 . miRNA 17-5p is homologous with two other miRNAs within this cluster (miRs 18 and 20), while miR-19a differs by only one nucleotide from miR-19b-1 . The status of miR-17-3p as a functional miRNA is still controversial [4–6]. The entire cluster also has paralogues within the genome, at chromosome Xq26.2 (hsa-mir-106a, has-mir-18b, has-mir-20b, hsa-mir-19b-2, hsa-mir-92-2) and chromosome 7q22.1 (hsa-mir-106b, hsa-mir-93, hsa-mir-25) [2, 3, 5]. The former has been implicated in the progression of T-cell leukemia , while the latter has yet to be implicated in any disease state.
By contrast, over-expression of the mir-17-92 locus has been identified in lung cancers , chronic myeloid leukemias , B-cell and mantle cell lymphomas [2, 8], hepatocellular tumors , bladder cancers , and breast, colon, pancreas, prostate, and stomach solid tumors . Additionally, the mir-17-92 cluster appears to act as a tumor suppressor in some breast and ovarian cancer cell lines . The association of miR-17-92 with a broad range of cancers not only underlines the clinical significance of this locus, but also suggests that miR-17-92 may regulate fundamental biological processes.
Although miRNAs are generally predicted to target hundreds of genes [13, 14], experimental evidence of miRNA-mRNA interactions from the miR-17-92 cluster has been limited to a few key components. Previous work has confirmed that CDKN1B is regulated by the miR-17-92 cluster ; E2F1-3, NCOA3, and RBL2 are targets of hsa-mir-17-5p [5, 12, 16, 17]; PCAF, RUNX1, and TGFBR2 are targets of both miR-17-5p and miR-20a [11, 18–20]; CTGF is a target of miR-18a ; and PTEN and THBS1 are targets of miR-19a [21, 22]. Many of these targets are known cell cycle regulators, although none of these interactions are sufficient to explain the oncogenic potential of this locus. The specific mechanisms of either the tumor suppressor or oncogenic activities of the miR-17-92 miRNAs remain unknown.
In this study, we employ a systems biology approach to uncover a large network of interacting genes that are directly targeted by miR-17-5p. We show that ectopic expression of miR-17-5p leads to dysregulation of normal cell cycle progression and a pro-proliferative response in HEK293T cells. For the first time, we show how this miRNA can drive both pro- and anti-proliferative signals, allowing for the switch between oncogenic and tumor suppressor activities.
The mir-17-92 locus is cell cycle regulated
miR-17-5p is sufficient to drive a proliferative signal in HEK293T cells
Although the entire miR-17-92 locus has been implicated in the progression of tumor development, several groups have previously reported differences in absolute expression of the individual miRNAs from this cluster [2, 3, 11, 15], and non-coordinated dynamic expression of these same miRNAs [6, 16, 20]. Together, these data suggest that although these miRNAs are derived from the same transcript, they are differentially regulated into their mature (active) form. As differential regulation may allow for different functions, we wondered whether individual miRNAs could drive the proliferative response seen with miR-17-92 over-expression [3, 16, 19], or whether this phenotype was caused by synergistic action of the entire cluster. To address this, we undertook functional network analysis using Ingenuity Pathways Analysis (IPA). By using this web-based tool, findings presented in more than 200,000 peer-reviewed publications could be queried to determine the biological functions of genes predicted to be targets of the miR-17-92 polycistron.
For each miRNA in the miR-17-92 cluster, we reviewed its target genes, as previously predicted by PicTar (a miRNA-mRNA interaction predictor based on thermodynamic potential and evolutionarily conserved target sites ). We used IPA to screen for potentially enriched functional categories for these gene sets. To gauge the robustness of these annotations, we performed parallel analyses on similarly sized randomly selected gene sets. A functional category was deemed significantly enriched if its IPA score was more than four standard deviations above the mean score determined for the random gene lists.
The miRNAs 17-5p and 20a share extensive sequence similarity, reflected in the significant overlap between predicted targets, however Hayashita et al.  found that miR-20a could not produce the hyper-proliferative phenotype in A549 cells. We therefore chose to focus our study further on the miR-17-5p-target network. We examined the effect of ectopic expression of miR-17-5p on HEK293T cell proliferation using a double-stranded RNA (dsRNA) miR-17-5p precursor, or a dsRNA negative control miRNA precursor. HEK293T cells have low levels of endogenous miR-17-5p expression, and miR-17-5p treated HEK293Ts proliferated faster post-transfection than the control cells (Figure 2c). To confirm this phenotype, we created vector based constructs with expression of miR-17-5p and created independent stable HEK293T cell lines with puromycin selection. The miR-17-5p activity of these cells was confirmed by luciferase reporter activity (Additional data file 2). The proliferative rate of the stable miR-17-5p cell lines (hereafter HEK293T-17-5p) was also significantly faster than the parental vector sequence alone (HEK293T-control; Figure 2d). Taken together, these results demonstrate that over-expression of miR-17-5p is sufficient to drive a proliferative signal in HEK293T cells.
Over-expression of miR-17-5p alters the cell cycle profile of HEK293T cells
Validation of predicted binding sites by luciferase assays
MAPK9 translation is targeted by miR-17-5p
MAPK9 (more commonly known as JNK2) is an important member of the mitogen activated protein kinase (MAPK) family. MAPK9 is a negative regulator of cellular proliferation through a protein-protein interaction with its substrate JUN, targeting this transcription factor for protein-degradation. Knockout of MAPK9 stabilizes the JUN protein, resulting in increased CCND1 expression and rapid exit from G1 . Our finding that miR-17-5p is capable of interacting with sequence in the 3'UTR of MAPK9 mRNA suggests that MAPK9 could be an important contributor to the hyper-proliferative phenotype caused by miR-17-5p. To examine this further we assessed the level of endogenous MAPK9 and CCND1 proteins after transient transfection with the miR-17-5p plasmid. We used protein expression of RBL2 as a positive control for miR-17-5p activity, and ACTB levels as a control for loading (Figure 5a). We see RBL2 and MAPK9 protein levels reduced in cells transfected with the miR-17-5p plasmid, but not with the plasmid control. MAPK9 protein levels are also significantly decreased in stable HEK293T-17-5p cell lines (Figure 5b). Additionally, we see an increase in CCND1 protein expression, confirming that de-coupling of the MAPK pathway from G1/S transition could contribute to our hyper-proliferative phenotype.
miR-17-5p targets both suppressors and promoters of cellular proliferation
Amongst the confirmed targets of miR-17-5p are several inhibitors of cellular proliferation (such as TSG101, RBL1, and MAPK9), and their suppression is consistent with the pro-proliferative phenotype observed in HEK293T-17-5p cells. Conversely, several known promoters of cellular proliferation (such as MYCN, NCOA3, and NR4A3) were also found to be targets of miR17-5p, results that are not consistent with our pro-proliferative phenotype. In order to understand this apparent contradiction, we used IPA to examine known relationships between the targets of miR-17-5p (Additional data file 3) . We find that this is a highly interacting network, comprising of many known transcriptional regulators that have known protein-DNA interactions with other members of the network.
In mammalian cells, miRNAs generally affect the protein output of a gene by inhibiting the translation of the mRNA. However, by changing the levels of a transcriptional regulator, a miRNA can indirectly affect the levels of mRNAs from other genes, which may include other targets of the miRNA. If the mRNA levels of a miRNA target are increased sufficiently, then this target will be able to overcome the effects of translational suppression, and maintain or increase protein levels. In the case of miR-17-5p, if proliferation-inhibitors suppress the mRNA levels of proliferation-promoters, then the consequential reduction of inhibitor-protein would lead to an increased level of promoter-mRNA, stabilizing the pro-proliferative signal. An example of this exists in our network - STAT3 protein (proliferation-inhibitor) inhibits the transcription of IRF1 mRNA (proliferation-promoter) .
Understanding the mechanism through which the miR-17-92 locus is able to promote cellular proliferation and tumorigenesis in multiple cell lines and tissues is essential if miRNAs from this polycistron are to be seriously considered as therapeutic targets. Here we have demonstrated the ability of a single miRNA from this locus, miR-17-5p, to drive a hyper-proliferative phenotype, acting to suppress the G1/S cell cycle checkpoint and dramatically increase the proliferation rate of the cell. We reveal that miR-17-5p targets a large genetic network of interacting proteins that act co-ordinately to control the transition from G1 to S phase. Rather than "fine tuning" cell cycle progression as previously suggested , we propose that this coordinated targeting allows miR-17-5p to efficiently de-couple negative regulators of the MAPK signaling cascade, promoting growth in HEK293T cells (Figure 7a).
If the primary function of miR-17-5p is to interfere with cell cycle regulation, then we might expect: that the primary transcript encoding miR-17-5p and the mature miRNA are cell cycle regulated; and that its maximal expression will be at a time prior to the mature miRNAs maximal activity. For example, the G1 specific proteins CCND1 and CCND2 have their peak mRNA expression in G2/M , which allows time for transport and translation before the mature protein is required. Similarly, the process of miRNA maturation involves multiple processing and transportation steps, and non-coordinated dynamic expression of miRNAs from the miR-17-92 cluster suggests that this process is highly regulated [6, 16, 20]. Indeed, we find that the locus is cell cycle regulated, and the maximal expression of mature miR-17-5p is in the G2/M phase of HeLa cells. This timing allows translational suppression of proteins that affect the activity of proteins that start to accumulate in this phase, and confirms a likely functional action upon the G1/S transition boundary.
The miR-17-92 locus is known to be regulated by the MYC oncogene, and the E2F family of transcription factors [5, 17, 28]. Phase-enriched expression of miR-17-92 was not previously observed in serum stimulated primary fibroblasts ; however, the typical degree of synchrony achievable with this cell type (60-80%) may have prevented detection of phase-enrichment within this experiment [27, 29, 30]. In our study, we observed the lowest expression of this gene during S-phase. Interestingly, miR-17-92 expression also decreased (non-significantly) at 16 hours in synchronized fibroblasts , which is a time-point consistent with the induction of S-phase in this cell type . Although not tested here, it seems likely that any periodicity of the miR-17-92 locus would be driven by the cell-cycle regulated E2F family of transcription factors  rather than the transiently expressed MYC .
Regulation of the G1/S transition by miRNAs has previously been reported as essential for germ line stem cell division in Drosophila melanogaster, allowing stem cells to proliferate in an environment where most other cells are quiescent . Interestingly, the Drosophila bypass appears to be mediated through the Dap protein, an orthologue of human CDKN1A. In our study, CDKN1A was found to be a possible target of miR-17-5p directly, but more importantly was central to our genetic network, with at least five miR-17-5p targets acting to influence the levels of this protein (Figure 7). Consistent with a similar endogenous function in vertebrates, the miR-17-92 locus is highly expressed in mouse embryonic stem cells and chicken embryos, with expression levels decreasing during development and differentiation [34, 35]. Two recent studies also show that expression of this miRNA is reduced when cells exit the cell cycle. The miR-17-92 cluster of miRNAs is down-regulated in female primordial germ cells as they enter meiosis (and exit from their normal, rapidly proliferating state) . In B cells, expression of miR-17-5p is critical for early B cell development, but expression is greatly reduced upon B cell maturation, also marked by exit from the cell cycle .
Whilst miR-17-5p is capable of interacting with a number of known promoters of cellular proliferation, the mRNA levels of these genes in the stable system are greatly increased, leading to counteraction of the activity of miR-17-5p translational repression. This discrepancy cannot be explained by factors that interfere with miRNA binding, as the cells used to test miRNA-mRNA interactions were also used to assay endogenous mRNA and protein levels. Rather, the compensatory increase of mRNA levels is likely due to the combinatorial effect of withdrawing a number of important transcriptional regulators. This highlights the importance of considering biological phenotypes as the result of genetic networks subject to multiple layers of regulation, rather than the overly simplistic view of single molecular interactions driving phenotypes. This network model can also explain the ability of miR-17-5p to act as an oncogene or a tumor suppressor in different cellular contexts, dependant on the expression of other transcriptional regulators. In cell systems where the expression of the proliferation-promoters dominates, miR-17-5p would stabilize the pro-proliferative signal by removing proliferation-inhibitors, and increasing the mRNA levels of proliferation-promoters. Conversely, in systems where proliferation-inhibitors dominate, withdrawal of miR-17-5p would lead to increased proliferation-promoters and decreased mRNA levels of proliferation-inhibitors (Figure 7b).
We have uncovered a large genetic network in this study, although it is likely that this does not represent the complete story. Only genes known to be involved with the cell cycle were considered for this analysis, and as IPA interactions are based only on published data, little studied molecules, or molecules not previously associated with progression of the cell cycle are likely to be overlooked. Novel components of this network are likely to be identified by dual interactions with miR-17-5p and its target genes. The methods of pathway analysis presented here provide a unique and rapid approach to the discovery of miRNA function, regardless of how few miRNA-mRNA interactions have been previously described.
We find that miR-17-92 is a cell cycle regulated locus, and a single miRNA from this cluster, miR-17-5p, is sufficient to drive a hyper-proliferative phenotype in HEK293T cells. This miRNA acts to suppress the G1/S cell cycle checkpoint and dramatically increase the proliferation rate of the cell by targeting a large genetic network of interacting proteins. This coordinated targeting allows miR-17-5p to efficiently de-couple negative regulators of the MAPK signaling cascade, promoting growth in HEK293T cells. Targeting of both proliferation-promoters and proliferation-inhibitors allows this miRNA to act as both a tumor suppressor and an oncogene in different cellular contexts.
Materials and methods
Network and functional analyses
miRNA-mRNA interactions were predicted by PicTar . Sets of 1,000 random genes were generated using the random gene selection tool . Lists of GenBank gene identifiers were uploaded into IPA . Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. The Functional Analysis tool identified the biological functions that were most represented in data sets uploaded. Although IPA uses a Fischer's exact test to calculate a p-value, we did not use this to determine the significance of this enrichment. Instead, the mean and standard deviation of the negative log of the p-values derived from random gene sets was calculated for each biological function tested. A biological function was considered to be significantly enriched if the negative log of the p-value was more than four standard deviations away from the mean for that function.
Predicted target sites of miR-17-5p were cloned into the SpeI and HindIII sites of pMIR-REPORT Luciferase (Ambion, Austin, TX, USA). Synthetic oligos corresponding to 60 nucleotides surrounding the target sequence were annealed before ligation into the pMIR plasmid. To create plasmids expressing miR-17-5p, synthetic oligos were annealed and ligated into the BamH1 and HindIII sites of pSilencer 4.1 CMV-puro (Ambion). A list of all primers used is available in Additional data file 4. All constructs were verified by sequencing.
Selection of stable pSilencer cell lines
HEK293T cells were maintained in DMEM (Invitrogen, Mount Waverley, VIC, Australia) containing 10% (v/v) fetal calf serum, in a 5% CO2 atmosphere at 37°C. Cells were transfected with either pSilencer-17-5p (HEK293T-17-5p) or the parent pSilencer plasmid (HEK293T-control) using Effectene (Qiagen, Doncaster, VIC, Australia) according to manufacturer's instructions. After 24 h, puromycin selection began at 500 ng/ml. After one week, selection pressure was increased to 1 μg/ml puromycin. Individual colonies were selected two weeks post-transfection, and tested for miRNA activity (Additional data file 2).
MTT cell proliferation assays
HEK293T cells were transiently transfected with either 10 or 50 nM of the appropriate pre-miR miRNA precursor (Ambion), using HiPerfect (Qiagen) according to the manufacturer's instructions. Stable pSilencer cell lines were plated at 1 × 104 cells per well. MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide) activity was assayed using a Cell Growth Determination Kit (Sigma-Aldrich, Castle Hill, NSW, Australia) according to the manufacturer's instructions and detected on a PowerWave XS spectrophotometer (BioTek, Winooski, VT, USA). Doubling times were calculated from best-fit curves generated in GraphPad Prism 4 (Graphpad Software, La Jolla, CA, USA).
Cell cycle blocks and synchronization
HEK293T-17-5p and HEK293T-control cells were treated with 2.5 mM thymidine (Sigma-Aldrich) for 16 h, released into fresh media for 8 h, and treated again with 2.5 mM thymidine for another 16 h.
HEK293T-17-5p and HEK293T-control cells were incubated with 2 mM hydroxyurea (Sigma-Aldrich) for 16 h.
HEK293T-17-5p and HEK293T-control cells were incubated in DMEM with no fetal calf serum for 48 h. HeLa cells were synchronized by incubation for 18 h with 2.5 mM thymidine (Sigma-Aldrich), released into fresh media for 8 h, and treated again with 2.5 mM thymidine for another 18 h. To obtain synchronized populations, these cells were then released for 0 h (S phase), 8 h (G2/M), and 14 h (G1/G0). Chemically synchronized populations were verified by flow cytometry.
All cells were harvested and fixed in 70% ethanol at -20°C overnight, then resuspended in buffer (5 mM EDTA, PBS, pH 7.4) approximately 1 h prior to analysis. DNA was stained using 40 μg/ml propidium iodide (Sigma-Aldrich), and RNA was removed using 400 μg/ml RNase A (Sigma-Aldrich). Cells were filtered through 35 μm cell strainer mesh (Becton Dickinson, North Ryde, NSW, Australia) and analyzed on Becton Dickinson LSR II flow cytometer fitted with 488 nm laser. Cell data were gated using WinList v6.0 and analyzed in Modfit LT v3.0, both programs from Verity Software House (Topsham, ME, USA).
Luciferase assays of potential miRNA binding sites
HEK293T-17-5p#1 cells were co-transfected with 50 ng of a pMIR-REPORT Luciferase construct, 50 ng of pMIR-REPORT β-galactosidase (Ambion), and 10 pmol of 2'-O-Me ASOs. Anti-17-5p and control sequences were previously described . After transfection, cells were incubated for 22-24 h prior to assaying. For transient expression assays with dsRNA, HEK293T cells were transfected as above, substituting either 10 or 50 nM of the appropriate pre-miR miRNA precursor (Ambion) for ASOs. After transfection, cells were incubated for 42 h prior to harvesting. Luciferase activity was assayed using the Luciferase Assay System (Promega Corporation, Alexandria NSW, Australia), and detected on a Wallac 1420 luminometer (Perkin Elmer, Waltham, MA, USA). β-Galactosidase activity was determined using the β-Galactosidase Enzyme Assay System (Promega), and detected on a PowerWave XS spectrophotometer (BioTek). Luciferase activity was normalized to β-galactosidase activity in each well. Assays were conducted in triplicate, and independently repeated three times.
RNA purification and qRT-PCR analyses
Total RNA was purified from cell pellets using either an RNeasy Mini Kit (Qiagen), or a miRNeasy Mini Kit (Qiagen), and in both cases RNA integrity was assessed using an Agilent Bioanalyzer 2100. For mRNA, cDNA was synthesized using SuperScript III (Invitrogen), and qRT-PCR was performed using SYBR green PCR master-mix (Applied Biosystems, Scoresby, VIC, Australia). For mature miRNA, cDNA was synthesized using a Taqman MicroRNA RT Kit (Applied Biosystems), and qRT-PCR was performed using a miR-17-5p MicroRNA Taqman assay (Applied Biosystems). All RT-PCR was performed on an Applied Biosystems 7000 Sequence Detection System. Control reactions without reverse transcriptase were performed to check for DNA contamination. Details of all primers used are available in Additional data file 4.
Antibodies and immunoblots
Cells were washed twice with PBS and resuspended in sample buffer (50 mM Tris-Cl pH 6.8; 100 mM dithiothreitol; 2% (w/v) SDS; 10% (v/v) glycerol), and allowed to lyse on ice for 10 minutes. After lysis, samples were cleared by centrifugation at 10,000 × g for 10 minutes at 4°C. Samples were analyzed by immunoblot using standard procedures . Rabbit anti-actin (Sigma-Aldrich) was used at 1 in 200. Rabbit anti-cyclin D1 (SP4; Neomarkers Inc, Freemont, CA, USA), rabbit anti-SAPK/JNK (56G8, Cell Signaling Technology, Boston, MA, USA), and mouse anti-Rb2 (10/Rb2; Becton Dickinson) were all used at 1 in 500. Goat-anti-mouse-HRP and goat-anti-rabbit-HRP (Bio-Rad, Gladesville, NSW, Australia) were used at 1 in 2,000, and detected using the SuperSignal West Pico Chemiluminescent Substrate (Pierce Biotechnology, Murarrie, QLD, Australia).
Additional data files
The following additional data are available with the online version of this paper. Additional data file 1 is a table listing the G1/S associated mRNAs predicted to be targets of miR-17-5p. Additional data file 2 is a figure showing the validation of miR-17-5p activity in stable HEK293T cell lines over-expressing miR-17-5p. Additional data file 3 is a figure depicting the interactions between miR-17-5p targets and cell cycle components. An interactive version of this figure where literature support and gene/protein information can be viewed through IPA is available . Additional data file 4 is table listing all primers used in this study.
2'-O-Methyl antisense oligoribonucleotide
Dulbecco's modified Eagle's media
Ingenuity Pathways Analysis
mitogen activated protein kinase
3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide
quantitative real time PCR
NC is supported by a UQ postdoctoral research fellowship, MKB is a recipient of an Australian Postgraduate Award, and SMG is an Australian NHMRC Senior Research Fellow. We are also grateful for the excellent technical assistance provided by QBI flow cytometry staff, particularly Geoff Osbourne, and Virginia Nink. This work was funded in part by NHMRC project grant number 456140. We thank Casey Spiller for the gift of mouse anti-RB2 antibody.
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