International Journal of Biological Macromolecules 72 (2015) 145–150
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A feedback expression of microRNA-590 and activating transcription factor-3 in human breast cancer cells P.J. Miranda, S. Vimalraj, N. Selvamurugan ∗ Department of Biotechnology, School of Bioengineering, SRM University, Kattankulathur, Tamil Nadu, India
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Article history: Received 20 May 2014 Received in revised form 30 July 2014 Accepted 31 July 2014 Available online 20 August 2014 Keywords: MicroRNA-590 ATF-3 Breast cancer
a b s t r a c t MicroRNAs (miRNAs) are small non coding RNA molecules (∼23nt) that are capable of regulating several physiological and pathological processes by targeting mRNAs post transcriptionally, and miRNAs are also known to be regulated by their own target gene(s) in a feedback manner. In this study, we analysed the expression of miRNAs (pre-mir-93, pre-mir-20b, pre-mir-520c, pre-mir-143, pre-mir-154 and pre-mir590) by body map, an in silico method and by qRT-PCR in MDA-MB231 (highly invasive and metastatic in nature), and MCF-7 (poor invasive and metastatic in nature) cells. These miRNAs were down regulated in MDA-MB231 cells, and among these, miR-590 was found to putatively target activating transcription factor-3 (ATF-3), a stress response gene. ATF-3 expression level was significantly increased in MDAMB231 cells and inhibition of ATF-3 expression in these cells increased the expression of pre-mir-590. Thus, these results suggest that there is a negative feedback expression of pre-mir-590 and its putative target gene, ATF-3 in human breast cancer cells. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Breast cancer is one of the most common types of cancers that causes fatality every year and it was recently identified that the rate of breast cancer is increased greatly in South East Asia compared to 10% from America and Europe [1,2]. Breast cancer can be classified as luminal, basal-like, normal-like and erbB2 sub groups [3,4]. However, the enigma of the disease origination still remains unanswered. Moreover, the development of multidrug resistance by many tumour cells upon subsequent exposure to ‘non cross resistant’ chemotherapy regimens aggravated the disease [5]. The recent evidences for the causative factor identified microRNAs (miRNAs), the major regulator whose expression levels vary in various stages of breast cancer and they play a pivotal role in disease progression and metastasis [6–12]. These miRNAs are involved in suppression of gene expression at post transcriptional level thereby regulating varied cellular functions such as cell growth, differentiation, apoptosis and other metabolic process [10–12].
∗ Corresponding author at: Department of Biotechnology School of Bioengineering SRM University Kattankulathur 603 203. Tamil Nadu. India. Tel.: +91 9940632335(M). E-mail addresses: [email protected], [email protected] (N. Selvamurugan). http://dx.doi.org/10.1016/j.ijbiomac.2014.07.051 0141-8130/© 2014 Elsevier B.V. All rights reserved.
It is known that the level of miRNAs was modulated by various factors including hormones that render the miRNAs as a secondary post transcriptional regulation which further alter the pathways in breast cancer [13,14]. The pathway of a gene regulation could be mediated by the up or down regulation of miRNAs [15,16]. Transforming growth factor-beta (TGF-) is a multi cytokine, and its pathway and components are activated under both physiological and pathological processes. Other factors such as parathyroid hormone receptor protein (PTHRP), vascular epithelial growth factor (VEGF), C-X-C Motif (CXCR4), Jagged 1, Matrix Metallo Proteinases (MMPs) are secreted from breast cancer cells which promote them towards metastasis [17]. TGF-1 inhibits cell proliferation under physiological condition; whereas under pathological condition (breast cancer), it promotes cell growth [18]. TGF- family is known to influence cellular functions by Smad dependant and Smad independent pathways. In Smad dependant pathway, Smad(s) are activated by TGF- receptors and they translocate into the nucleus to involve in transcriptional regulation [10,16,17]. TGF-1, as a pro-metastatic gene also enhances cell migration in breast cancer through activation of a cascade of Smad(s). The action of TGF-1 could be due to its regulation of miRNAs in breast cancer [19]. We previously reported that TGF-1 stimulates expression of activating transcription factor3 (ATF-3) in sustained and prolonged manner in human breast cancer cells (MDA-MB231, highly invasive and bone metastatic in nature) [18]. Even though ATF-3 is a stress response gene
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product, it is involved in breast cancer metastasis [20] along with cell invasion and proliferation [18]. Based on available literature, we selected the miRNAs that were down regulated in breast cancer and by in silico analysis, the miRNAs that target ATF-3 was identified. Among these miRNAs, we selected miR-590 and it was found to putatively target ATF-3. There are no attempts made to study the mechanism of oncogenesis dictated by miR-590 through ATF-3 in breast cancer so far. Considering the role of miRNAs in breast cancer, it is proved that altering the miRNA functionalities might act as a lucrative area of therapeutics [21,22]. Not only does miRNAs regulate its target genes, but the in vivo system apparently results in various regulatory loops between the target genes and their regulators [23,24]. In this current study we performed the expression studies of the miRNAs in human breast cancer cells (MDA-MB231, highly invasive and metastatic cell line and MCF-7, poor invasive and metastatic cell line). The expression level of pre-mir-590 and its putative target gene, ATF-3 was determined in these cells. To study a feedback or dependent expression of miR-590 and ATF-3, inhibition of ATF-3 protein expression in MDA-MB231 cells was carried out by RNA short hairpin (sh) technique, and cells stably transfected with scrambled shRNA or ATF-3 shRNA were used for determination of pre-mir-590 expression.
Table 1 The primers used for real time RT-PCR in this study. 5 →3 sequence
Gene Pre-mir-93 Pre-mir-520c Pre-mir-20b Pre-mir-154 Pre-mir-143 Pre-mir-590 Pre-mir-130a Pre-mir-30c U6 ATF3 GAPDH
Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse
CTCCTGGGTCACTGGTGTTT GTCGCCTCTTTTTCCTTTCA CCTGCAAACTGCAAAGTTGA AAAGGCAGCTCCAGCAATAA GAGATGTACCCTCCAGAA CCATGTCGTAGTCCAGCA GGGAGACCTTTTACTTGGC GGCAGTCAACATCCAGGAC CTGCACCTGACGCCCTTCACC CACATGACCCCACCGAACTCAAAGA GGCTATCCTCTCAGAGTGACATTT GCTTTATCAGGTTATGTTGCATGGT GCTGGCCAGAGCTCTTTTCACA CACTACACGGCCAATGCCCTTT TGTGTAAACATCCTACACTCTCAG GAGTAAACAACCCTCTCCCA CTCGCTTCGGCAGCACA AACGCTTCACGAATTTGCGT CTCCTGGGTCACTGGTGTTT GTCGCCTCTTTTTCCTTTCA TTGATGTCATCATACTTGGCAGGT CAG TCAAGGCTGAGAATGGGA
2. Materials and methods 2.1. Cell culture MDA-MB231, a highly invasive and metastatic cell line and MCF-7, a poor invasive and metastatic cell line were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplementing with 10% fetal bovine serum (FBS) (Gibco, Oklahoma,USA) and 1% penicillin/streptomycin (Gibco, Oklahoma, USA) at 37 ◦ C in a 5% CO2 incubator. 2.2. Real-time RT-PCR analysis Total RNA was isolated from the cells using Tri-reagent (Invitrogen, California, USA) according to the manufacturer’s instructions, and the quantitative and qualitative analyses were carried out using Qubit 2.0 Flurometer (Invitrogen, California, USA), and 1.5% agarose electrophoresis respectively. The cDNA was synthesised using reverse transcriptase kit from Invitrogen California, USA, as per manufacturer’s protocol. The real time-PCR was carried out using SYBR green reagents (Invitrogen, California, USA). Oligonucleotides primers were designed for the precursor miRNAs and their predicted target genes (Table 1). The expression of miRNAs and target genes were normalised using internal controls U6 and GAPDH, respectively. The Ct (threshold cycle) values were calculated from amplification curve. The 2 power of Ct method was used to determine the relative quantification of miRNAs and target gene expression [6]. The experiment for miRNA and target gene expression was carried out under the following conditions: 95 ◦ C for 3 min as initial denaturation, followed by 39 cycles of 95 ◦ C for 10 s, 58 ◦ C for 30 s, with a final melting curve between 58 ◦ C and 95 ◦ C for 5 s. 2.3. In silico analysis MiRNA body map (http://www.miRNAbodymap.org/tutorial.php) was used to identify the differential expression of various miRNAs among normal and cancer tissue samples [25]. It was performed according to the database instructions. The target prediction for the selected miRNAs using miRanda (http://www.microrna.org/ microrna/home.do), TargetScan 6.2 (http://www.targetscan.org/) and PicTar (http://pictar.mdc-berlin.de/) were performed.
miRmap (http://mirmap.ezlab.org/app/) was used to quantify the thermodynamic stability between miR-590 and ATF3 mRNA.
2.4. Stable transfection The scrambled shRNA plasmid and ATF-3 shRNA plasmid was obtained from Santa Cruz Biotechnology, Dallas, USA. They were stably transfected into MDA-MB231 cells using X-treme Gene siRNA transfection reagent (Roche Applied Science, Indianapolis,USA). The cells were plated at 0.5–1 × 106 cells per plate in 10 cm plates in DMEM-F12 containing 10% FBS. The cells were transfected with 1 g DNA using 5 l X-treme Gene siRNA transfection reagent per well in 1 ml of serum-free DMEM-F12 according to the protocol given by the company. The cells were supplemented with 1 ml of DMEM-F12 containing 10% FBS after 6 h. After 24 h, the cells were incubated with media containing different concentrations of puromycin (0.1–10 g/ml) for clonal selection.
2.5. Western blot analysis From the transfected clones, whole cell lysates were prepared and subjected to 12% SDS-PAGE analysis. The gel was blotted electrophoretically onto polyvinylidene difluoride membrane (PVDF) (Bio-Rad, CA, USA). The membrane was blocked using 5% non fat dry milk, and left for overnight incubation with ATF-3 primary antibody at 4 ◦ C overnight. The membrane was then incubated with horse radish peroxidase conjugated anti-rabbit secondary antibody after thorough washing. The immunoreactive signals were visualized using an enhanced chemiluminescence detection kit (Thermo Scientific, IL USA).
2.6. Statistical analysis All the expression analysis data obtained from the RT-PCR were from triplicate samples and they were expressed as mean ± SD. The significant difference (p < 0.05) between groups was determined by the student’s t-test.
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Fig. 1. Heat map of differential expression of miRNA in human breast cancer and normal tissue sample. The differential expression of miR-143, miR-10b, miR-148a, miR-99a, miR-30c, let-7i, miR-130b, miR-130a, miR-15b, miR-106a, miR-20b, miR-93, miR-24, miR-21, miR-424, miR-520c-3p, and miR-154 were analysed using miRNA body map along with their hierarchical cluster correlation. The expression pattern was verified from the previously validated experimental data among various breast cancer tissue samples and in normal breast samples. The expression is depicted by the following colors, Red - up regulation, blue - down regulation, white - neutral basal or no data available. Here, N represents breast normal cells and T represents breast tumor cells.
3. Results and discussion 3.1. Expression of miRNAs in MCF-7 and MDA-MB231 cells MiRNAs show a differential expression pattern for each subclass of breast cancer. For instance, 10 miRNAs (miR-19b, miR-29a, miR-93, miR-181a, miR-182, miR-223, miR-301a, miR-423-5p, miR-486-5 and miR-652) were identified from a set of microarray data for their differential expression in Luminal A-like breast cancer [26]. In the process of evaluating the correlation between miRNAs and their target genes in breast cancer, we initially selected a list of miRNAs based on previous literatures in breast cancer microarray profiling of miRNA expression in normal and breast cancer [27–34]. Few of the miRNAs for which previous wet lab works available were selected and subjected to miRNA body map analysis (http://www.mirnabodymap.org/) to assess their differential expression in normal and breast cancer cells. It was performed based on database instructions [32]. This database holds expression of miRNAs from various tissues by RT-qPCR analysis, and the database allows the analysis of differential expression of miRNAs between/among specific samples. The results emphasise the differential expression of miR-143, miR-10b, miR-148a, miR-99a, miR-30c, let-7i, miR-130b, miR-130a, miR-15b, miR-106a, miR20b, miR-93, miR-24, miR-21, miR-424, miR-520c-3p, and miR-154 in 51 breast cancer (yellow) and 4 normal breast tissue (green) of human (Fig. 1). However, to our knowledge, no work was carried out with miR-590 in breast cancer and hence there was no data fetched from the body map. The body map is based on color (blue: down regulation and red: up regulation) and cluster clearly depicts the differential expression of selected miRNAs in cancer and normal breast tissues. The expression pattern of miRNAs could be varied from cell line to cell line because of the differences in their phenotypes and genotypes [35,36]. Since this study was focussed to identify the miRNAs that target ATF-3 and ATF-3 is overexpressed in MDA-MB231 cells, the analysis of down regulated miRNAs in
MDA-MB231 cells were of our interest. Real time RT-PCR was carried out to evaluate the selected miRNAs’ expression in MCF-7 and MDA-MB231 (Fig. 2). The result showed that pre-mir-93, premir-20b, pre-mir-520c, pre-mir-143, pre-mir-154 and pre-mir-590 were significantly down regulated in MDA-MB231 compared to MCF-7 cells. 3.2. Expression of ATF-3 in MCF-7 and MDA-MB231 cells A functional role of ATF-3 in cell proliferation, cell invasion and metastasis in vitro has been previously reported from our laboratory [18]. Its role in breast cancer progression in vivo has also
Fig. 2. Expression of pre-mir-93, pre-mir-20b, pre-mir-520c, pre-mir-143, pre-mir154, pre-mir-590 in MCF-7 and MDA-MB231 cells. Total RNA was isolated and real time RT-PCR was carried out using the primers for the selected miRNAs and the expression was normalized with U6. The data is depicted in logarithmic scale for the ease of depiction. The expression of all these miRNAs was significantly decreased (*) in MDA-MB231 cells compared to MCF-7 cells. P value was found to be ≤0.05.
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Fig. 3. Expression of miR-590 putative target gene, ATF-3 in MCF-7 and MDA-MB231 cells. (a) The putative target region analysis was performed for ATF-3 mRNA 3 UTR by miR-590 seed sequence. (b) Depicts the thermodynamic stability of ATF3 and miR-590 interaction. (c) Total RNA was isolated from cells and real time RT-PCR was carried out using the primers for ATF-3 gene. Relative expression of mRNAs was calculated after normalization with GAPDH. Asterisk (*) indicates significant up regulation compared to MCF-7 cells. P value was found to be ≤0.05.
been reported [20,37]. Since miRNAs regulate cancer progression by altering their target gene expression and thereby enhancing the oncogenic property of a cell, in this study, our aim was to identify the expression of miRNAs involved in breast cancer and correlate them with the expression of their putative target genes. ATF-3 was found to be one of the putative target genes of all six miRNAs analyzed as above (Fig. 2). Fig. 3A and B depict the miR-590s’ target region at 3 UTR of ATF-3 mRNA and its thermodynamic stability, respectively. FZD3, TOB1, MAP3K2, ACVR1 C/2A, SMAD4/5, FGF7, etc., were also identified as major putative targets genes of miR590. The expression of putative target gene, ATF-3 of pre-miR-590 in MCF-7 and MDA-MB231 cells showed that mRNA expression of ATF-3 was significantly increased in MDA-MB231 cells compared to MCF-7 cells (Fig. 3C). There are evidences indicating that the putative target genes also influence the expression of the miRNAs [38–41]. From our results (Figs. 2 and 3C), it appears that the expression of up regulation of ATF-3 and down regulation of pre-mir-590 in MDA-MB231 cells indicate a negative feedback regulation between them.
three clones (1, 2, 3) were diluted and replated with medium containing puromycin (1 g/ml). Stable MDA-MB231 cells expressing scrambled shRNA or ATF-3 shRNA (clones 1, 2, 3) were used in this study. Since TGF-1 stimulates ATF-3 expression in MDA-MB231 cells [18] and these cells secrete TGF-1 due to their autocrine nature [17], we also checked these clones for their responsiveness in stimulation of ATF-3 expression. Stable MDA-MB231 cells expressing scrambled shRNA or ATF-3 shRNA were treated with TGF-1 for 4 h. Whole cell lysates were prepared and subjected to Western blot analysis. In clone 2, it was found that there was inhibition of ATF-3 protein expression under control and TGF-1treated conditions (Fig. 4). The inhibition of a target gene by RNA interference technique is much powerful for studying the role of that target gene in breast cancer. During colon cancer, shRNA for glucosylceramide synthase (GCS) reduced the multidrug resistance
3.3. Inhibition of ATF-3 protein expression in stable MDA-MB231 cells In order to correlate the expression of pre-mir-590 with its putative target gene ATF-3, inhibition of ATF-3 expression in MDAMB231 cells was done by shRNA technique and this technique reduces the protein expression of a particular gene. MDA-MB231 cells were stably transfected with scrambled shRNA plasmid or ATF-3 shRNA plasmid. The selection of clonal cells was carried out using puromycin. At a high concentration of puromycin, no cells were survived while at a low concentration of puromycin, many colonies were grown. At a fixed minimum inhibitory concentration of 1 g/ml of puromycin, clones were selected and among these,
Fig. 4. Expression of ATF-3 in stable MDA-MB231 cells. Stable MDA-MB231 cells expressing scrambled shRNA or ATF-3 shRNA were treated with control or TGF-1containing media for 4 h. In this study, 3 clonal cells (1, 2, 3) expressing ATF-3 shRNA were used. Whole cell lysates were prepared and subjected to Western blot analysis using the antibodies as indicated. ␣-tubulin was used as internal loading control.
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miRNA-452 is known to regulate the expression of thyroid hormone receptor (TR1) and miR-224 regulates the expression of triiodothyronine (T3) signalling. During a regulatory loop, T3 gene is known to regulate miR-224/miR-452/GABRE cluster [48]. Similarly, in this work, though we expect some feedback loop between ATF-3 and its regulatory miRNA-590, there could be more than one interlinked pathway resulting in dependence between their expressions. Thus, our study demonstrates that the expression of pre-mir590 was down regulated in human breast cancer and this could be regulated by its own target ATF-3 in human breast cancer cells. To our knowledge this is the first study to report a negative feedback regulation of expression between pre-mir-590 and ATF-3 in human breast cancer cells. More study on the expression and regulation patterns of the miR-590 and its target gene, ATF-3 would further advance our knowledge on utilization of miRNAs for therapeutic targeting therapy. Fig. 5. Expression of pre-mir-130a, pre-mir-590, pre-mir-154, pre-mir-143, premir-30c and pre-mir-20b in stable MDA-MB231 cellsl. Total RNA was isolated from stable MDA-MB231 cells expressing scrambled shRNA or ATF-3 shRNA (clone 2). Real time RT-PCR analysis was carried out for the selected miRNAs. Asterisk (*) represents significant up regulation compared to stable MDA-MD231 cells expressing scrambled shRNA. P value was found to be ≤0.05.
of the cells caused due to high GCS protein [42]. In the pancreatic cancer cells, silencing of signal transducers and activators of transcription-3 (STAT3) by RNA interference technique decreased the expression of its downstream targets such as MMP-2 and VEGF [43]. 3.4. Analysis of the feedback or dependent expression of ATF-3 and pre-mir-590 To explore the feedback or dependent expression of pre-miR590 and ATF-3, total RNA was isolated from stable MDA-MB231 cells expressing scrambled shRNA or ATF-3 shRNA (clone 2). Real time RT-PCR was carried out to determine the expression of the six miRNAs in these cells. There was no significant change in the expression of pre-mir-130a, pre-mir-154, pre-mir-143, premir-30c and pre-mir-20b in stable MDA-MB231 cells expressing either scrambled shRNA or ATF-3 shRNA; whereas pre-mir-590 expression was found to be increased in stable MDA-MB231 cells expressing ATF-3 shRNA (Fig. 5). It appears that there is dependent expression of pre-mir-590 (Figs. 2 and 5) and its putative target gene ATF-3 (Fig. 4). A number of reports are now available to support a feedback loop expression of miRNAs and their target genes. miR-146a-5p targets CXCL12 in meschenchymal stem cells (MSC). miR-146a-5p regulates the I-kappa-B kinase epsilon (IKK”) suppressor chemokine (C-X-C motif) ligand 12 (CXCL12), by acting in coordination with SIKE1 via the nuclear factor kappa-B (NF-iB) and conversely, NF-B is known to activate the miR-146a-5p promoter forming a negative feedback loop between miR-146a-5p and CXCL12 in MSCs [44]. Similarly, miR-137 targets CAR during MDR neuroblastoma and is known to be negatively regulated by CAR [45]. In addition, during cancer progression miR-21 forms a feedback loop with its targets ERK/NF-B and JNK/c-Jun via its target genes Pdcd4 and Spry1 in the HELF cells [46]. Not restricting with a direct regulatory loop, miRNAs are also identified to vary with its function from a normal cell and a carcinoma. For instance, miR101 is known to target Polycomb Repressive Complexes 2 (PRC2) in a normal cell while in hepatocellular carcinoma (HCC) miR101 forms a double negative feedback loop with its own target gene PRC2 in a MYC dependent manner [47]. While we see many direct feedback loops between a miRNA and its target, certain target genes regulate the miRNA expression by targeting the miRNA while it forms a complex. In two different simultaneous processes,
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International Journal of Biological Macromolecules journal homepage: www.elsevier.com/locate/ijbiomac
A feedback expression of microRNA-590 and activating transcription factor-3 in human breast cancer cells P.J. Miranda, S. Vimalraj, N. Selvamurugan ∗ Department of Biotechnology, School of Bioengineering, SRM University, Kattankulathur, Tamil Nadu, India
a r t i c l e
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Article history: Received 20 May 2014 Received in revised form 30 July 2014 Accepted 31 July 2014 Available online 20 August 2014 Keywords: MicroRNA-590 ATF-3 Breast cancer
a b s t r a c t MicroRNAs (miRNAs) are small non coding RNA molecules (∼23nt) that are capable of regulating several physiological and pathological processes by targeting mRNAs post transcriptionally, and miRNAs are also known to be regulated by their own target gene(s) in a feedback manner. In this study, we analysed the expression of miRNAs (pre-mir-93, pre-mir-20b, pre-mir-520c, pre-mir-143, pre-mir-154 and pre-mir590) by body map, an in silico method and by qRT-PCR in MDA-MB231 (highly invasive and metastatic in nature), and MCF-7 (poor invasive and metastatic in nature) cells. These miRNAs were down regulated in MDA-MB231 cells, and among these, miR-590 was found to putatively target activating transcription factor-3 (ATF-3), a stress response gene. ATF-3 expression level was significantly increased in MDAMB231 cells and inhibition of ATF-3 expression in these cells increased the expression of pre-mir-590. Thus, these results suggest that there is a negative feedback expression of pre-mir-590 and its putative target gene, ATF-3 in human breast cancer cells. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Breast cancer is one of the most common types of cancers that causes fatality every year and it was recently identified that the rate of breast cancer is increased greatly in South East Asia compared to 10% from America and Europe [1,2]. Breast cancer can be classified as luminal, basal-like, normal-like and erbB2 sub groups [3,4]. However, the enigma of the disease origination still remains unanswered. Moreover, the development of multidrug resistance by many tumour cells upon subsequent exposure to ‘non cross resistant’ chemotherapy regimens aggravated the disease [5]. The recent evidences for the causative factor identified microRNAs (miRNAs), the major regulator whose expression levels vary in various stages of breast cancer and they play a pivotal role in disease progression and metastasis [6–12]. These miRNAs are involved in suppression of gene expression at post transcriptional level thereby regulating varied cellular functions such as cell growth, differentiation, apoptosis and other metabolic process [10–12].
∗ Corresponding author at: Department of Biotechnology School of Bioengineering SRM University Kattankulathur 603 203. Tamil Nadu. India. Tel.: +91 9940632335(M). E-mail addresses: [email protected], [email protected] (N. Selvamurugan). http://dx.doi.org/10.1016/j.ijbiomac.2014.07.051 0141-8130/© 2014 Elsevier B.V. All rights reserved.
It is known that the level of miRNAs was modulated by various factors including hormones that render the miRNAs as a secondary post transcriptional regulation which further alter the pathways in breast cancer [13,14]. The pathway of a gene regulation could be mediated by the up or down regulation of miRNAs [15,16]. Transforming growth factor-beta (TGF-) is a multi cytokine, and its pathway and components are activated under both physiological and pathological processes. Other factors such as parathyroid hormone receptor protein (PTHRP), vascular epithelial growth factor (VEGF), C-X-C Motif (CXCR4), Jagged 1, Matrix Metallo Proteinases (MMPs) are secreted from breast cancer cells which promote them towards metastasis [17]. TGF-1 inhibits cell proliferation under physiological condition; whereas under pathological condition (breast cancer), it promotes cell growth [18]. TGF- family is known to influence cellular functions by Smad dependant and Smad independent pathways. In Smad dependant pathway, Smad(s) are activated by TGF- receptors and they translocate into the nucleus to involve in transcriptional regulation [10,16,17]. TGF-1, as a pro-metastatic gene also enhances cell migration in breast cancer through activation of a cascade of Smad(s). The action of TGF-1 could be due to its regulation of miRNAs in breast cancer [19]. We previously reported that TGF-1 stimulates expression of activating transcription factor3 (ATF-3) in sustained and prolonged manner in human breast cancer cells (MDA-MB231, highly invasive and bone metastatic in nature) [18]. Even though ATF-3 is a stress response gene
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product, it is involved in breast cancer metastasis [20] along with cell invasion and proliferation [18]. Based on available literature, we selected the miRNAs that were down regulated in breast cancer and by in silico analysis, the miRNAs that target ATF-3 was identified. Among these miRNAs, we selected miR-590 and it was found to putatively target ATF-3. There are no attempts made to study the mechanism of oncogenesis dictated by miR-590 through ATF-3 in breast cancer so far. Considering the role of miRNAs in breast cancer, it is proved that altering the miRNA functionalities might act as a lucrative area of therapeutics [21,22]. Not only does miRNAs regulate its target genes, but the in vivo system apparently results in various regulatory loops between the target genes and their regulators [23,24]. In this current study we performed the expression studies of the miRNAs in human breast cancer cells (MDA-MB231, highly invasive and metastatic cell line and MCF-7, poor invasive and metastatic cell line). The expression level of pre-mir-590 and its putative target gene, ATF-3 was determined in these cells. To study a feedback or dependent expression of miR-590 and ATF-3, inhibition of ATF-3 protein expression in MDA-MB231 cells was carried out by RNA short hairpin (sh) technique, and cells stably transfected with scrambled shRNA or ATF-3 shRNA were used for determination of pre-mir-590 expression.
Table 1 The primers used for real time RT-PCR in this study. 5 →3 sequence
Gene Pre-mir-93 Pre-mir-520c Pre-mir-20b Pre-mir-154 Pre-mir-143 Pre-mir-590 Pre-mir-130a Pre-mir-30c U6 ATF3 GAPDH
Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse
CTCCTGGGTCACTGGTGTTT GTCGCCTCTTTTTCCTTTCA CCTGCAAACTGCAAAGTTGA AAAGGCAGCTCCAGCAATAA GAGATGTACCCTCCAGAA CCATGTCGTAGTCCAGCA GGGAGACCTTTTACTTGGC GGCAGTCAACATCCAGGAC CTGCACCTGACGCCCTTCACC CACATGACCCCACCGAACTCAAAGA GGCTATCCTCTCAGAGTGACATTT GCTTTATCAGGTTATGTTGCATGGT GCTGGCCAGAGCTCTTTTCACA CACTACACGGCCAATGCCCTTT TGTGTAAACATCCTACACTCTCAG GAGTAAACAACCCTCTCCCA CTCGCTTCGGCAGCACA AACGCTTCACGAATTTGCGT CTCCTGGGTCACTGGTGTTT GTCGCCTCTTTTTCCTTTCA TTGATGTCATCATACTTGGCAGGT CAG TCAAGGCTGAGAATGGGA
2. Materials and methods 2.1. Cell culture MDA-MB231, a highly invasive and metastatic cell line and MCF-7, a poor invasive and metastatic cell line were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplementing with 10% fetal bovine serum (FBS) (Gibco, Oklahoma,USA) and 1% penicillin/streptomycin (Gibco, Oklahoma, USA) at 37 ◦ C in a 5% CO2 incubator. 2.2. Real-time RT-PCR analysis Total RNA was isolated from the cells using Tri-reagent (Invitrogen, California, USA) according to the manufacturer’s instructions, and the quantitative and qualitative analyses were carried out using Qubit 2.0 Flurometer (Invitrogen, California, USA), and 1.5% agarose electrophoresis respectively. The cDNA was synthesised using reverse transcriptase kit from Invitrogen California, USA, as per manufacturer’s protocol. The real time-PCR was carried out using SYBR green reagents (Invitrogen, California, USA). Oligonucleotides primers were designed for the precursor miRNAs and their predicted target genes (Table 1). The expression of miRNAs and target genes were normalised using internal controls U6 and GAPDH, respectively. The Ct (threshold cycle) values were calculated from amplification curve. The 2 power of Ct method was used to determine the relative quantification of miRNAs and target gene expression [6]. The experiment for miRNA and target gene expression was carried out under the following conditions: 95 ◦ C for 3 min as initial denaturation, followed by 39 cycles of 95 ◦ C for 10 s, 58 ◦ C for 30 s, with a final melting curve between 58 ◦ C and 95 ◦ C for 5 s. 2.3. In silico analysis MiRNA body map (http://www.miRNAbodymap.org/tutorial.php) was used to identify the differential expression of various miRNAs among normal and cancer tissue samples [25]. It was performed according to the database instructions. The target prediction for the selected miRNAs using miRanda (http://www.microrna.org/ microrna/home.do), TargetScan 6.2 (http://www.targetscan.org/) and PicTar (http://pictar.mdc-berlin.de/) were performed.
miRmap (http://mirmap.ezlab.org/app/) was used to quantify the thermodynamic stability between miR-590 and ATF3 mRNA.
2.4. Stable transfection The scrambled shRNA plasmid and ATF-3 shRNA plasmid was obtained from Santa Cruz Biotechnology, Dallas, USA. They were stably transfected into MDA-MB231 cells using X-treme Gene siRNA transfection reagent (Roche Applied Science, Indianapolis,USA). The cells were plated at 0.5–1 × 106 cells per plate in 10 cm plates in DMEM-F12 containing 10% FBS. The cells were transfected with 1 g DNA using 5 l X-treme Gene siRNA transfection reagent per well in 1 ml of serum-free DMEM-F12 according to the protocol given by the company. The cells were supplemented with 1 ml of DMEM-F12 containing 10% FBS after 6 h. After 24 h, the cells were incubated with media containing different concentrations of puromycin (0.1–10 g/ml) for clonal selection.
2.5. Western blot analysis From the transfected clones, whole cell lysates were prepared and subjected to 12% SDS-PAGE analysis. The gel was blotted electrophoretically onto polyvinylidene difluoride membrane (PVDF) (Bio-Rad, CA, USA). The membrane was blocked using 5% non fat dry milk, and left for overnight incubation with ATF-3 primary antibody at 4 ◦ C overnight. The membrane was then incubated with horse radish peroxidase conjugated anti-rabbit secondary antibody after thorough washing. The immunoreactive signals were visualized using an enhanced chemiluminescence detection kit (Thermo Scientific, IL USA).
2.6. Statistical analysis All the expression analysis data obtained from the RT-PCR were from triplicate samples and they were expressed as mean ± SD. The significant difference (p < 0.05) between groups was determined by the student’s t-test.
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Fig. 1. Heat map of differential expression of miRNA in human breast cancer and normal tissue sample. The differential expression of miR-143, miR-10b, miR-148a, miR-99a, miR-30c, let-7i, miR-130b, miR-130a, miR-15b, miR-106a, miR-20b, miR-93, miR-24, miR-21, miR-424, miR-520c-3p, and miR-154 were analysed using miRNA body map along with their hierarchical cluster correlation. The expression pattern was verified from the previously validated experimental data among various breast cancer tissue samples and in normal breast samples. The expression is depicted by the following colors, Red - up regulation, blue - down regulation, white - neutral basal or no data available. Here, N represents breast normal cells and T represents breast tumor cells.
3. Results and discussion 3.1. Expression of miRNAs in MCF-7 and MDA-MB231 cells MiRNAs show a differential expression pattern for each subclass of breast cancer. For instance, 10 miRNAs (miR-19b, miR-29a, miR-93, miR-181a, miR-182, miR-223, miR-301a, miR-423-5p, miR-486-5 and miR-652) were identified from a set of microarray data for their differential expression in Luminal A-like breast cancer [26]. In the process of evaluating the correlation between miRNAs and their target genes in breast cancer, we initially selected a list of miRNAs based on previous literatures in breast cancer microarray profiling of miRNA expression in normal and breast cancer [27–34]. Few of the miRNAs for which previous wet lab works available were selected and subjected to miRNA body map analysis (http://www.mirnabodymap.org/) to assess their differential expression in normal and breast cancer cells. It was performed based on database instructions [32]. This database holds expression of miRNAs from various tissues by RT-qPCR analysis, and the database allows the analysis of differential expression of miRNAs between/among specific samples. The results emphasise the differential expression of miR-143, miR-10b, miR-148a, miR-99a, miR-30c, let-7i, miR-130b, miR-130a, miR-15b, miR-106a, miR20b, miR-93, miR-24, miR-21, miR-424, miR-520c-3p, and miR-154 in 51 breast cancer (yellow) and 4 normal breast tissue (green) of human (Fig. 1). However, to our knowledge, no work was carried out with miR-590 in breast cancer and hence there was no data fetched from the body map. The body map is based on color (blue: down regulation and red: up regulation) and cluster clearly depicts the differential expression of selected miRNAs in cancer and normal breast tissues. The expression pattern of miRNAs could be varied from cell line to cell line because of the differences in their phenotypes and genotypes [35,36]. Since this study was focussed to identify the miRNAs that target ATF-3 and ATF-3 is overexpressed in MDA-MB231 cells, the analysis of down regulated miRNAs in
MDA-MB231 cells were of our interest. Real time RT-PCR was carried out to evaluate the selected miRNAs’ expression in MCF-7 and MDA-MB231 (Fig. 2). The result showed that pre-mir-93, premir-20b, pre-mir-520c, pre-mir-143, pre-mir-154 and pre-mir-590 were significantly down regulated in MDA-MB231 compared to MCF-7 cells. 3.2. Expression of ATF-3 in MCF-7 and MDA-MB231 cells A functional role of ATF-3 in cell proliferation, cell invasion and metastasis in vitro has been previously reported from our laboratory [18]. Its role in breast cancer progression in vivo has also
Fig. 2. Expression of pre-mir-93, pre-mir-20b, pre-mir-520c, pre-mir-143, pre-mir154, pre-mir-590 in MCF-7 and MDA-MB231 cells. Total RNA was isolated and real time RT-PCR was carried out using the primers for the selected miRNAs and the expression was normalized with U6. The data is depicted in logarithmic scale for the ease of depiction. The expression of all these miRNAs was significantly decreased (*) in MDA-MB231 cells compared to MCF-7 cells. P value was found to be ≤0.05.
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Fig. 3. Expression of miR-590 putative target gene, ATF-3 in MCF-7 and MDA-MB231 cells. (a) The putative target region analysis was performed for ATF-3 mRNA 3 UTR by miR-590 seed sequence. (b) Depicts the thermodynamic stability of ATF3 and miR-590 interaction. (c) Total RNA was isolated from cells and real time RT-PCR was carried out using the primers for ATF-3 gene. Relative expression of mRNAs was calculated after normalization with GAPDH. Asterisk (*) indicates significant up regulation compared to MCF-7 cells. P value was found to be ≤0.05.
been reported [20,37]. Since miRNAs regulate cancer progression by altering their target gene expression and thereby enhancing the oncogenic property of a cell, in this study, our aim was to identify the expression of miRNAs involved in breast cancer and correlate them with the expression of their putative target genes. ATF-3 was found to be one of the putative target genes of all six miRNAs analyzed as above (Fig. 2). Fig. 3A and B depict the miR-590s’ target region at 3 UTR of ATF-3 mRNA and its thermodynamic stability, respectively. FZD3, TOB1, MAP3K2, ACVR1 C/2A, SMAD4/5, FGF7, etc., were also identified as major putative targets genes of miR590. The expression of putative target gene, ATF-3 of pre-miR-590 in MCF-7 and MDA-MB231 cells showed that mRNA expression of ATF-3 was significantly increased in MDA-MB231 cells compared to MCF-7 cells (Fig. 3C). There are evidences indicating that the putative target genes also influence the expression of the miRNAs [38–41]. From our results (Figs. 2 and 3C), it appears that the expression of up regulation of ATF-3 and down regulation of pre-mir-590 in MDA-MB231 cells indicate a negative feedback regulation between them.
three clones (1, 2, 3) were diluted and replated with medium containing puromycin (1 g/ml). Stable MDA-MB231 cells expressing scrambled shRNA or ATF-3 shRNA (clones 1, 2, 3) were used in this study. Since TGF-1 stimulates ATF-3 expression in MDA-MB231 cells [18] and these cells secrete TGF-1 due to their autocrine nature [17], we also checked these clones for their responsiveness in stimulation of ATF-3 expression. Stable MDA-MB231 cells expressing scrambled shRNA or ATF-3 shRNA were treated with TGF-1 for 4 h. Whole cell lysates were prepared and subjected to Western blot analysis. In clone 2, it was found that there was inhibition of ATF-3 protein expression under control and TGF-1treated conditions (Fig. 4). The inhibition of a target gene by RNA interference technique is much powerful for studying the role of that target gene in breast cancer. During colon cancer, shRNA for glucosylceramide synthase (GCS) reduced the multidrug resistance
3.3. Inhibition of ATF-3 protein expression in stable MDA-MB231 cells In order to correlate the expression of pre-mir-590 with its putative target gene ATF-3, inhibition of ATF-3 expression in MDAMB231 cells was done by shRNA technique and this technique reduces the protein expression of a particular gene. MDA-MB231 cells were stably transfected with scrambled shRNA plasmid or ATF-3 shRNA plasmid. The selection of clonal cells was carried out using puromycin. At a high concentration of puromycin, no cells were survived while at a low concentration of puromycin, many colonies were grown. At a fixed minimum inhibitory concentration of 1 g/ml of puromycin, clones were selected and among these,
Fig. 4. Expression of ATF-3 in stable MDA-MB231 cells. Stable MDA-MB231 cells expressing scrambled shRNA or ATF-3 shRNA were treated with control or TGF-1containing media for 4 h. In this study, 3 clonal cells (1, 2, 3) expressing ATF-3 shRNA were used. Whole cell lysates were prepared and subjected to Western blot analysis using the antibodies as indicated. ␣-tubulin was used as internal loading control.
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miRNA-452 is known to regulate the expression of thyroid hormone receptor (TR1) and miR-224 regulates the expression of triiodothyronine (T3) signalling. During a regulatory loop, T3 gene is known to regulate miR-224/miR-452/GABRE cluster [48]. Similarly, in this work, though we expect some feedback loop between ATF-3 and its regulatory miRNA-590, there could be more than one interlinked pathway resulting in dependence between their expressions. Thus, our study demonstrates that the expression of pre-mir590 was down regulated in human breast cancer and this could be regulated by its own target ATF-3 in human breast cancer cells. To our knowledge this is the first study to report a negative feedback regulation of expression between pre-mir-590 and ATF-3 in human breast cancer cells. More study on the expression and regulation patterns of the miR-590 and its target gene, ATF-3 would further advance our knowledge on utilization of miRNAs for therapeutic targeting therapy. Fig. 5. Expression of pre-mir-130a, pre-mir-590, pre-mir-154, pre-mir-143, premir-30c and pre-mir-20b in stable MDA-MB231 cellsl. Total RNA was isolated from stable MDA-MB231 cells expressing scrambled shRNA or ATF-3 shRNA (clone 2). Real time RT-PCR analysis was carried out for the selected miRNAs. Asterisk (*) represents significant up regulation compared to stable MDA-MD231 cells expressing scrambled shRNA. P value was found to be ≤0.05.
of the cells caused due to high GCS protein [42]. In the pancreatic cancer cells, silencing of signal transducers and activators of transcription-3 (STAT3) by RNA interference technique decreased the expression of its downstream targets such as MMP-2 and VEGF [43]. 3.4. Analysis of the feedback or dependent expression of ATF-3 and pre-mir-590 To explore the feedback or dependent expression of pre-miR590 and ATF-3, total RNA was isolated from stable MDA-MB231 cells expressing scrambled shRNA or ATF-3 shRNA (clone 2). Real time RT-PCR was carried out to determine the expression of the six miRNAs in these cells. There was no significant change in the expression of pre-mir-130a, pre-mir-154, pre-mir-143, premir-30c and pre-mir-20b in stable MDA-MB231 cells expressing either scrambled shRNA or ATF-3 shRNA; whereas pre-mir-590 expression was found to be increased in stable MDA-MB231 cells expressing ATF-3 shRNA (Fig. 5). It appears that there is dependent expression of pre-mir-590 (Figs. 2 and 5) and its putative target gene ATF-3 (Fig. 4). A number of reports are now available to support a feedback loop expression of miRNAs and their target genes. miR-146a-5p targets CXCL12 in meschenchymal stem cells (MSC). miR-146a-5p regulates the I-kappa-B kinase epsilon (IKK”) suppressor chemokine (C-X-C motif) ligand 12 (CXCL12), by acting in coordination with SIKE1 via the nuclear factor kappa-B (NF-iB) and conversely, NF-B is known to activate the miR-146a-5p promoter forming a negative feedback loop between miR-146a-5p and CXCL12 in MSCs [44]. Similarly, miR-137 targets CAR during MDR neuroblastoma and is known to be negatively regulated by CAR [45]. In addition, during cancer progression miR-21 forms a feedback loop with its targets ERK/NF-B and JNK/c-Jun via its target genes Pdcd4 and Spry1 in the HELF cells [46]. Not restricting with a direct regulatory loop, miRNAs are also identified to vary with its function from a normal cell and a carcinoma. For instance, miR101 is known to target Polycomb Repressive Complexes 2 (PRC2) in a normal cell while in hepatocellular carcinoma (HCC) miR101 forms a double negative feedback loop with its own target gene PRC2 in a MYC dependent manner [47]. While we see many direct feedback loops between a miRNA and its target, certain target genes regulate the miRNA expression by targeting the miRNA while it forms a complex. In two different simultaneous processes,
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