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DNA methylation-induced ablation of miR-133a accelerates cancer aggressiveness in glioma through upregulating peroxisome proliferator-activated receptor γ
Emerging evidences suggest that miRNAs can be used as theranostic biomarkers for multiple cancers, including glioma. Thus, identification of novel miRNAs for glioma treatment and prognosis becomes necessary and urgent. Here, by analyzing miRNA expression profiles in the glioma and para-cancer tissues by miRNA microarray and verified by RT-PCR, we found that miR-133a was significantly downregulated in the cancerous tissues, and patients with low-expressed miR-133a levels predicted an unfavorable prognosis. The following functional experiments confirmed that overexpression of miR-133a restrained cell proliferation and colony formation abilities, and induced cell cycle arrest to restrain cancer progression in glioma cells. Then, the underlying mechanisms were uncovered, and the peroxisome proliferator-activated receptor γ (PPARγ, PPARG) was verified as the downstream target of miR-133a. Mechanistically, miR-133a negatively regulated PPARG expressions by binding to its 3’ untranslated regions (3’UTR). The following rescuing experiments evidenced that miR-133a overexpression-induced anti-cancer effects in glioma cells were abrogated by upregulating PPARγ. Interestingly, we noticed that the promoter region of miR-133a was hypermethylated, and removal of DNA methylation by 5-Azacytidine (AZA) significantly increased the expression levels of miR-133a in glioma cells. Taken together, we concluded that DNA-methylation-induced miR-133a silence contributed to cancer progression in glioma through upregulating PPARγ, and firstly identified the DNA-methylation-regulated miR-133a/PPARG axis as the novel indicators for glioma treatment and prognosis.
]. Although the diagnosis and treatment strategies of glioma have been significantly improved in recent years with the advancement of science and technology, the clinical outcome of this disease is still very poor. According to recent data, the five-year survival rate of patients with medium to low-grade glioma is about 30%-70%, among which the patients with high-grade gliomas have a dismal median survival duration of 14 months and a five-year survival rate of less than 3% [
]. However, the current treatment strategies for this disease are still ineffective due to a lack of knowledge regarding to the pathogenesis of glioma. Therefore, in order to reduce the incidence and mortality, and improve the prognosis of glioma patients, it becomes necessary and urgent to uncover the underlying mechanisms that drive the aggressiveness of this disease.
MicroRNAs (miRNAs) are defined as a class of endogenous single-stranded non-coding small RNAs that can bind to the 3′ untranslated region (UTR) of the downstream target mRNAs through complementary base-pairing theory, which further promotes the degradation of the target mRNAs and inhibits its translation [
]. Recent studies have established a key role of miRNAs in regulating the gene expression networks in cell cycle progression, proliferation, differentiation and apoptosis, as well as inflammation, stress responses and various pathological conditions [
]. In addition, analyses of the aberrant expression profiles of miRNAs in specific diseases have identified the involvement of miRNAs in regulating those diseases. For instance, aberrant high expression levels of miR-191 and miR-193a are indicators of poor prognosis for melanoma patients [
]. Similarly, the downregulation of miR-1247 in osteosarcoma inhibits tumor progression through MAP3K9 regulation. In glioma tissues and cells, multiple miRNAs, including miR-21 [
PI3K inhibitor combined with miR-125b inhibitor sensitize TMZ-induced anti-glioma stem cancer effects through inactivation of Wnt/β-catenin signaling pathway.
] are all abnormally expressed and associated with tumorigenesis and cancer progression.
Based on the existed information, it is reported that the expression levels of miRNAs can be modulated by various upstream regulators. Especially, studies show that epigenetic modifications, including DNA methylation, that play an important role in regulating miRNA transcription and expression [
]. In fact, hypermethylation of the CpG islands in the promoter regions of tumor-suppressing miRNAs is one of the most common phenomena that regulates the development of cancer aggressiveness [
], which indicates that DNA methylation-mediated miRNA downregulation miRNAs may be crucial for regulating the development of glioma. Moreover, peroxisome proliferator-activated receptor γ (PPARγ, PPARG) is a peroxisome proliferator-activated receptor that belongs to type II nuclear hormone receptor family, and elicits numerous biological effects upon ligand binding and activation [
The pan-cancer mutational landscape of the PPAR pathway reveals universal patterns of dysregulated metabolism and interactions with tumor immunity and hypoxia.
]. For example, in pancreatic cancer cells, activated PPARG can induce cell differentiation, regulate cell cycle and modulate the expression levels of apoptotic and anti-apoptotic genes, resulting in the progression of cancer [
Inhibition of COX-2 and activation of peroxisome proliferator-activated receptor gamma synergistically inhibits proliferation and induces apoptosis of human pancreatic carcinoma cells.
]. Also, researchers verify that rosiglitazone-mediated activation of PPARG in the liver cancer cell line SMMC7221 lead to cell cycle arrest at the G1-phase, and also affect cancer metabolism by regulating the levels of γ-glutamyltransferase and alpha-fetoprotein [
Taken together the existed information, in this study, we compared the miRNA expression profiles of glioma and para-cancer tissues, and screened out that miR-133a was significant downregulated in the cancerous tissues. Meanwhile, bioinformatics analysis found that PPARG was the downstream target gene of miR-133a, and we carried out the following exploration to further research on the related regulatory mechanisms to obtain novel insights for treating glioma and improving its prognosis.
Materials and Methods
Tissue samples
Fifteen surgically excised glioma tissue samples were collected at our hospital from August 2018 to August 2019, and glioma was confirmed by clinical and pathological examination. Eight of the 15 samples were obtained from male patients and 7 from female patients, with an average age of 52.3 ± 6.9 years. The histological diagnosis of glioma was based on the Central Nervous System Tumor Grading Criteria established by WHO in 2016 [
]. Three cases were grade I tumors, 5 grade II, 3 grade III and 4 grade IV. Furthermore, brain tissues were removed from 15 age-matched healthy controls with brain trauma (9 males and 6 females, average age 52.8 ± 7.4 years) during intracranial decompression. The inclusion criteria for the patients were: 1) primary tumor occurrence, and 2) lack of radiotherapy, chemotherapy or any treatment before surgery. The tissue specimens were flash frozen in liquid nitrogen, and stored at -80°C. The inclusion criteria of control brain injury were as follows: 1) all patients were suffering from craniocerebral trauma; 2) all patients had no intracranial tumor, cerebral hemorrhage, cerebral infarction and other intracranial related diseases before injury. The study was approved by the ethics committee of our hospital. All samples and data were collected after obtaining written informed consent from the patients.
Cell lines and main reagents
The human normal glial cell line HEB and glioma cell lines U251, U87, T98-G and A172 were purchased from the Cell Resource Center of Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. DMEM/HG, fetal bovine serum (FBS), Opti-MEM and 0.25% trypsin containing 0.02% EDTA were purchased from Gibco, MTT kit, 5-Azacytidine (AZA) and histone deacetylase inhibitor (TSA) from Sigma, Trizol, reverse transcription kit from Thermo, SYBR Green Real-time PCR kit from Shanghai Solarbio Bioscience & Technology, QIAamp DNA Mini kit and EpiTect Bisulfite kit from Qiagen, Lipofectamine 3000 from Invitrogen, and the cell cycle assay kit from BD. The pcDNA3.1-miR-133a mimic, scrambled miRNA (miR-NC) and the pcDNA3.1-PPARG overexpression plasmid were obtained from Guangzhou Ruibo Bio. Antibodies against PPARG, cyclin D1, cyclin D2, CDK4 and β-actin were from Abcam, and the horseradish peroxidase (HRP)-labeled IgG secondary antibody from Guangzhou Jingcai. PPARG wild type (WT) and mutant (MUT) luciferase reporter plasmids were purchased from Shanghai Jima and the luciferase assay kit from Promega. The PCR primers were designed and synthesized by Shanghai Sangon Biotechnology. Other reagents were from our laboratory and of analytical grade.
Cell culture and transfection
The cell lines were thawed, and cultured at 37°C under 5% CO2 in DMEM/HG containing 10% FBS. The cells were harvested by trypsin digestion once they were 70%-80% confluent, centrifuged at 800 rpm for 5 min at room temperature, and resuspended in DMEM/HG for further passaging. For the transfection of A172 and U251 cells, they were harvested at 80% confluency, re-suspended in Opti-MEM, and seeded in a 6-well plate at the density of 2 × 105 per well. Following overnight incubation, the cells were transfected with 100 ng pcDNA3.1-PPARG or 50 nM pcDNA3.1-miR-133a mimic and pcDNA3.1-miR-NC (using Lipofectamine 3000 according to the manufacturer's instructions. Six hours later, the medium was replaced with DMEM/HG containing 10% FBS, and the cells were cultured for another 48 h. The medium was replaced with complete DMEM/HG containing 2 µg/mL puromycin, and the cells were cultured for 3 days. The transfected cells were re-plated and after 1-2 weeks, the resulting clones were expanded to establish stable miR-133a mimic and miR-NC cell lines.
miRNA microarray analysis
Three tissue samples each from glioma patients and healthy controls were sent to Shanghai Kangcheng Biotechnology Co. Ltd., using the miRCURY LAN Array system (Exiqon, Vedbaek, Denmark) for miRNA microarray analysis. Briefly, total RNA was extracted from the tissues using Trizol reagent, and purified with a miRNA Mini kit (Qiagen, #217004, Germany) according to the instructions. The purity and concentration of the RNA samples were analyzed using a spectrophotometer, and 1µg RNA per sample was labeled using a Hy3/Hy5 Power Labeling kit (Exiqon, Vedbaek, Denmark) according to the instructions. The labeled RNA was then hybridized with a miRCURYtm LNA Array (v.19.0, Exiqon) according to array manual. After hybridization, the sliced were obtained and washed using Wash buffer kit (Exiqon). The original signal intensity of the chip was tested using a GenePix 4000B chip scanner (MD). Using intensity (int) > 50 as the normalization factor, the differences in miRNA expression levels between the glioma and para-cancer tissues were analyzed by inter-chip standardization, intra-chip standardization, expression difference comparison, statistical significance test, and cluster analysis. Following normalization, the miRNA with significant differential espression between two groups were determined by Folding changes (>2) and P values (<0.05).
Cell proliferation assay
The miR-133a mimic and miR-NC-transfected cells were seeded into 96-well plates at the density of 2 × 104 per well in 200 µL complete DMEM/HG medium. After culturing for 12, 24, 36 and 48 h, 20 µL MTT reagent (5 mg/mL) was added to each well, and the cells were incubated further for 4 h. The culture medium was removed, and 150 µL dimethyl sulfoxide (DMSO) was added into per well to solubilize the formazan crystals. After shaking for 10 min at room temperature, the absorbance value (OD490) of each well was measured at 490 nm. Each time point per group was tested in five replicate wells, and the mean values were calculated.
Colony formation assay
The miR-133a and miR-NC-transfected cells were seeded into 6-well plates at the density of 1 × 103 cells/well. The cells were cultured for 8 days, and the medium was changed every 3 days. The resulting colonies were fixed with 4% paraformaldehyde for 5 min, stained with 0.05% crystal violet for 20 min at room temperature, and gently washed with double distilled water five times. The number of colonies were counted under a white light microscope.
Real-time quantitative PCR (RT-PCR)
Total RNA was extracted from tissues and the cell lines using Trizol reagent, checked for purity and quantified as described, and reversely transcribed into cDNA using a reverse transcription kit. Real-time quantitative PCR was performed using the SYBR Green Real-time PCR kit according to the instructions. The reaction conditions were: pre-denaturation at 95°C (10 min), followed by 40 cycles of denaturation at 95°C (7 s), annealing at 60°C (20 s) and extension at 72°C (38 s). The primer sequences were as follows: miR-133a - forward 5′-GCC AAG CTG GTA AAA TGG AA-3′ (5′-sequence of miR-133a) and reverse 5′-TAT GGT TTT GAC GAC TGT GTG AT-3′ (3′-sequence of miR-133a); PPARG- forward 5′-TTG ATT TCT CCA GCA TTT C-3′ (5′-sequence of PPARG) and reverse 5′-TTC AAT CGG ATG GTT CTT-3 (3′-sequence of PPARG); U6 (internal control) - forward 5′-CTC GCT TCG GCA GCA CA-3′ (5′-sequence of U6) and reverse 5′-AAC GCT TCA CGA ATT TGC GT-3′ (3′-sequence of PPARG). The expression levels of miR-133a and PPARG mRNA were analyzed using the 2−∆∆Ct method.
Western blotting
The stably transfected cells were washed thrice with cold PBS at 4°C, lysed with RIPA cell lysis buffer supplemented with a protease inhibitor, and centrifuged at 4°C and 12,000 rpm for 20 min. The supernatants were aspirated and the protein concentration was determined by the BCA method. Equal amounts of protein per sample (30 µg) were mixed with 5 × loading buffer at the ratio of 4:1, and denatured by boiling for 10 min. The protein samples were resolved by SDS-PAGE, and the bands were transferred to PVDF membranes by the wet transfer method. The membranes were blocked with 5% skim milk at room temperature for 2 h, and incubated overnight with primary antibodies against PPARG (1:500), cyclin D1 (1:500), cyclin D2 (1:500) or CDK4 (1:500), and β-actin (1:1000) at 4°C on a shaker. After washing thrice with TBST buffer, the membranes were incubated with a horseradish-labeled secondary antibody (1:2,000) for 1 h at room temperature, followed by three more washes with TBST. The blots were then developed using an ECL solution and photographed on a gel imager. The Image J software was used to measure the gray value of each band, and the ratio of the intensities of the target proteins to that of the internal control β-actin was calculated. The experiment was repeated thrice.
Dual luciferase gene reporter assay
The online database TargetScan (http://www.targetscan.org) was screened for the putative target genes of miR-133a. To validate PPARG as a target, the dual luciferase reporter assay was performed. Briefly, A172 and U251 cells were harvested in the logarithmic growth phase and seeded in 96-well plates at the density of 2 × 104 cells/well. Following overnight culture, the cells were co-transfected with luciferase reporter plasmids harboring wild type (WT) or mutated PPARG promoter sequences, and miR-133a mimic or miR-NC using Lipofectamine 3000. Each group was tested in five replicates. After 48 h, according to the instructions of dual luciferase assay kit (Solarbio, #D0010, China) treatment transfected cell, and the fluorescence intensity of firefly luciferin and Renilla fluorescein was detected by chemiluminescence detector (GloMax 20/20, Promega, Madison, Wisconsin, USA).
Propidium iodide (PI) staining
The suitably transfected A172 and U251 cells were gently washed with cold PBS, harvested, and centrifuged at 300 rpm for 5 min at room temperature. The supernatant was removed, and the cells were re-suspended in 500 µL PBS. Ice-cold 70% alcohol (3.5 mL) was added immediately, and the cells were thoroughly pipetted and fixed overnight at 4°C. After washing thrice with PBS, the cells were stained with 500 μL PI/RNase staining solution provided in the cell cycle flow detection kit for 30 min at 4°C in the dark. The cell cycle distribution was analyzed by flow cytometry. The experiment was repeated thrice.
Drug treatment
HEB, A172 and U251 cells were harvested and seeded in a 6-well plate at the density of 1 × 105 cells/well, and cultured till 70%-80% confluency. The medium was replaced with DMEM/HG containing 1 μM AZA or 300 nM TSA, and the cells were cultured for 72 h. The control cells were cultured in DMEM/HG containing 1 μM DMSO.
Methylation-specific quantitative PCR (MSP)
The CpG islands in the miR-133a gene were predicted using the website http://cpgislands.usc.edu, and one CpG island was detected in its promoter region. Genomic DNA was extracted from the A172 and U251 cells using a DNA extraction kit as per the manufacturer's instructions, and the purity and concentration were determined using an ultraviolet spectrophotometer. The DNA was modified with bisulfite using the EpiTect Bisulfite kit (Qiagen, #59104, Germany) according to the manufacturer's instructions, and the methylated and unmethylated miR-133a were amplified using the following primers: methylated - forward 5′-GGT TGT TTG TTT TTT GGT TCG-3′ (5′-sequence of methylated miR-133a) and reverse 5′-ATC CTA AAA CTA CCC AAA ATC GTA-3′ (3′-sequence of methylated miR-133a); unmethylated - forward 5′-GGG ATG AGG ATT AGG ATT TT-3′ (5′-sequence of unmethylated miR-133a) and reverse 5′-CAA ACA AAA CAC AAT AAA AAC AAA CA-3′ (3′-sequence of unmethylated miR-133a). The PCR cycling conditions were: pre-denaturation at 94°C (3 min), followed by 35 cycles of denaturation at 94°C (30 s), demethylation at 53°C (30 s) and extension at 72°C (90 s), and final extension at 94°C for 5 min. Generation of an amplified product with either methylated or unmethylated primers respectively indicated presence and absence of methylated sequences in the genome. Generation of amplified products with both primer pairs implied partial methylation. The methylation level of miR-133a gene was calculated by the ΔΔCt method. The experiment was repeated thrice.
Establishment of tumor-bearing mice models
The BALB/c-nu nude mice (age 5 weeks) were housed in the specific-pathogen-free (SPF) conditions, and the A172 cells were implanted into the dorsal flanks of the mice at the density of 1 × 106 cells diluting in the PBS buffer. The tumor volumes were monitored every 5 days, and at 25 days post-injection, the mice were anesthetized and sacrificed, and the mice tumors were obtained by surgical resection, which were further weighed to evaluate the tumorigenesis of the A172 cells in vivo. The volumes of the tumors were calculated by using the following formula: Tumor volume = (length × width2)/2. The animal experiments were approved by the ethics committee of our hospital.
Statistical analysis
Statistical analysis was performed using SPSS 19.0 (SPSS, Inc., Chicago, Illinois, USA) and GraphPad Prism 5.0. The data were expressed as (X±S). The normality of data and homogeneity of variance were assessed firstly. Data conformed to normal distribution and homogeneity of variance were was tested by parameters (t-test); data not conformed to normal distribution and homogeneity of variance was tested by nonparametric test (Mann-Whitney U test). One-way ANOVA was used for inter-group comparison, and independent-sample t-test and 2-tailed for comparing two groups. P values< 0.05 were considered statistically significant. Survival curves analysis, were constructed using the Kaplan-Meier method and log-rank test was used for comparison between groups (*P < 0.05, **P < 0.01, and ***P < 0.001 were defined as statistically significant.)
Results
Aberrant expression of miR-133a is related with patients’ prognosis in glioma
The miRNA microarrays of glioma and para-cancer tissues revealed 81 differentially-expressed miRNAs, of which 28 were up-regulated and the remaining were down-regulated (Fig. 1A). In addition, 15 miRNAs were consistently up- or down-regulated in all three glioma tissue samples, of which miR-133a showed the most significant down-regulation (Fig. 1A). Subsequent RT-qPCR validation on all tissue samples confirmed that miR-133a levels were significantly lower in the gliomas compared to para-cancer tissues (p<0.01; Fig. 1B). Based on our knowledge that the tumor microenvironment of glioma is very complicated, which includes tumor cells, immune cells, stromal cells and so on, which is deemed as tumor heterogeneity. To validate whether miR-133a is abnormally expressed in glioma cells, we performed the in vitro experiments to exclude the interferences of other types of cells. As expected, the results showed that miR-133a was significantly downregulated in the glioma cell lines U251, U87, T98-G and A172 compared to that in the normal glial cell line HEB (p<0.01; Fig. 1C), which were consistent with the clinical data. In addition, by analyzing the data from TCGA database, we verified that miR-133a was downregulated in the cancerous tissues of glioma (p<0.01; Fig. 1D), and patients with high-expressed miR-133a levels tended to have a favorable prognosis (p<0.05; Fig. 1E). The above data suggested that miR-133a was significantly downregulated in glioma and high-expressed miR-133a predicted a favorable prognosis in glioma patients.
Figure 1MiR-133a levels are aberrantly downregulated in glioma. A. Heat map of 30 differentially expressed miRNAs (p<0.01) in the microarray analysis (red indicates up-regulation and green indicates down-regulation). B. RT-PCR results showing miR-133a levels in 15 glioma and para-cancer tissues samples. C. RT-PCR results showing miR-133a levels in various cell lines; ⁎⁎p<0.01. D. TCGA database results showing miR-133a relative expression in cancerous and non-tumor tissue of glioma; ** p<0.01. E. Kaplan–Meier OS analysis shows the correlations of miR-133a levels with glioma patients’ prognosis, ** p<0.01. Individual experiment repeated at least for three times.
DNA methylation-induced downregulation of miR-133a in glioma cells
We next considered the upstream mechanisms that regulated the expression status of miR-133a, and trough searching for the putative CpG islands upstream of miR-133a gene (-1200) using the prediction website http://cpgislands.usc.edu, we identified one CpG island in the promoter region (Fig. 2A). To determine whether DNA methylation affected the expression of miR-133a in glioma cells, we analyzed its levels after treating the cells with the methylation inhibitor AZA or the acetylase inhibitor TSA. The results showed that AZA significantly upregulated miR-133a in the glioma cells lines (p<0.01), but TSA had no significant effect (Fig. 2B), indicating that DNA methylation rather than acetylation is the epigenetic mechanism regulating miR-133a expression status. Furthermore, MSP showed significantly greater methylation in the miR-133a CpG islands in the glioma cell lines compared to the normal glial cells (p<0.01), which was decreased to normal levels in the presence of AZA (Fig. 2C). Taken together, hypermethylation of the miR-133a promoter in glioma cells significantly downregulates its levels, and reducing methylation at this site can restore miR-133a expression.
Figure 2DNA methylation inhibits the expression of miR-133a in glioma cells. A. CpG island location in miR-133a gene promoter region. B. MiR-133a expression levels in control and AZA-treated glioma cells were detected by RT-PCR assay, **p< 0.01. C. Extent of DNA methylation in miR-133a promoter region were examined in the normal HEB cells and glioma cells (A172 and U251 cells), **p< 0.01. Individual experiment repeated at least for three times.
Overexpression of miR-133a inhibits the proliferation and tumorigenesis of glioma cells
The miR-133ahigh A172 cells and miR-133alow U251 cells were respectively transfected with the miR-133a NC and mimic, which significantly increased the expression levels of miR-133a in miR-133a mimic group compared to that in the un-transfected controls (p<0.01, Fig. 3A). However, miR-NC had no effect on miR-133a expression levels (p>0.05, Fig. 3A), which validated the specificity of the miRNA constructs in the present experiments. Further results suggested that miR-133a overexpression significantly decreased the proliferative capacity of both A127 cells and U251 cells, compared to the respective controls (p<0.01; Fig. 3B). Consistent with this, the colony formation ability of A127 cells and U251 cells transfected with miR-133a also decreased significantly compared to the controls (p<0.01; Fig. 3C). Further in vivo experiments verified that overexpression of miR-133a also suppressed tumorigenesis of A127 cells in tumor-bearing mice models (Supplementary Figure S1A, B). Taken together, miR-133a inhibits the proliferation and tumorigenesis of glioma cells.
Figure 3Overexpression of miR-133a inhibits the proliferation of glioma cells. A. Expression levels of miR-133a in the A172 and U251 cells transfected with the miR-133a mimic, as it is determined by RT-PCR, **p<0.01. B. Proliferation rate of control and miR-133a-overexpressing glioma cells was determined by performing MTT assay, **p<0.01. C. Number of colonies formed by glioma cells transfected with miR-133a mimic or miR-NC was determined by colony formation assay, **p<0.01. Individual experiment repeated at least for three times.
MiR-133a overexpression induces cell cycle arrest in glioma cells
Next, we investigated the regulating effects of miR-133a on cell cycle in the glioma cells, and our flow cytometer assay results showed that overexpression of miR-133a significantly increased the proportion of glioma cells in the G1 phase compared to miR-NC (p<0.05; Fig. 4A), suggesting that miR-133a-upregulation induced G1-S phase arrest in the glioma cells. Moreover, our Western Blot analysis results confirmed that miR-133a overexpression decreased the expression levels of Cyclin D1, Cyclin D2 and CDK4 to block cell cycle in the glioma cells (p<0.05; Fig. 4B). Taken together, those data supported the notion that miR-133a promoted cell cycle arrest at G1-S phase to inhibit the proliferation of glioma cells.
Figure 4MiR-133a induces cell cycle arrest in glioma cells. A. Flow cytometry plots showing the cell cycle distribution of miR-133a-overexpressing and control glioma cellsm, *p< 0.05. B. Western Blot analysis was performed to examine the expression levels of CyclinD1, Cyclin D2 and CDK4 in glioma cells following miR-133a overexpression; **p< 0.01. Individual experiment repeated at least for three times.
MiR-133a targets PPARG for its degradation in glioma cells
The downstream targets of miR-133a were then predicted by performing the Targetscan software (http://www.targetscan.org/vert_61/.), and we found that there existed targeting sites between miR-133a and the 3’UTR regions of PPARG gene (Fig. 5A, Supplementary Table 1), suggesting that PPARG was the potential target of miR-133a. The following dual-luciferase gene reporter assay showed that a significant decrease of luciferase activity in cells co-transfected with the miR-133a mimic and PPARG WT plasmids (Fig. 5B), confirming that PPARG was a target gene of miR-133a. Furthermore, PPARG was significantly upregulated in the cancerous tissues compared to the adjacent healthy tissues in glioma (p<0.01; Fig. 5C), and upregulation of miR-133a suppressed PPARG protein levels in the glioma cells (p<0.01; Fig. 5D). However, the pan-cancer analysis results indicated that the expression levels of PPARG had noting to do with patients’ prognosis in brain lower grade glioma (LGG) (Supplementary Figure S2). The above data hinted that miR-133a negatively regulated PPARG expressions in glioma cells by targeting its 3’UTR.
Figure 5MiR-133a inhibits PPARG expression in glioma cells. A. TargetScan prediction results indicate the targeting sites of miR-133a and PPARG mRNA, **p<0.01. B. The predicted targeting sites were validated by performing the dual-luciferase reporter gene system assay, **p<0.01. C. The expression status of PPARG in glioma and para-cancer tissues was examined by performing RT-PCR analysis, **p<0.01. D. The protein levels of PPARG in A172 and U251 cells with miR-133a mimic or miR-NC transfection were detected by Western Blot analysis, **p<0.01. Individual experiment repeated at least for three times.
MiR-133a inhibits proliferation of glioma cells by degrading PPARG
To determine whether downregulation of PPARG was the mechanistic basis of the inhibitory effect of miR-133a on glioma cells, we transfected the control and miR-133a-overexpressing glioma cells with PPARG expression or empty vector. While the empty vector had no effect on the inhibitory action of miR-133a on the proliferation and colony forming ability of glioma cells, exogenous expression of PPARG rescued the cells from growth inhibition (p<0.01 for both; Fig. 6A-B, Supplementary Figure S3A, B). Taken together, miR-133a exerts its inhibitory effects in glioma cells by suppressing PPARG expression, and restoring the latter can abrogate miR-133a-mediated inhibition.
Figure 6MiR-133a inhibits the proliferation of glioma cells by down-regulating PPARG. A. MTT assay results verify that upregulation of miR-133a suppresses glioma cell proliferation in a PPARG-dependent manner, **p<0.01. B. The suppressing effects of miR-133a overexpression on colony formation abilities of glioma cells were reversed by upregulating PPARG, **p<0.01. Individual experiment repeated at least for three times.
Glioma is the most common primary malignant tumor of the central nervous system, and up until now, there are still not effective treatment strategies for this disease due to its highly-invasive nature, high mortality rate, poor therapeutics and unknown underlying mechanisms. Thus, it is urgent and necessary to identify the hub genes that play critical role in regulating glioma development, and providing novel therapeutic agents for this disease [
]. As previously described, miRNAs are defined as non-coding RNAs containing about 20-25 nucleotides in length. Since its first discovery by Lee et al. in nematodes in 1993, over 2,000 mature miRNAs have been identified in humans that regulate the expression levels of approximately 30% of the total genes in eukaryotes. Based on the existed knowledge, miRNAs regulate various cellular functions, such as cell proliferation, cell cycle, apoptosis and differentiation [
]. Among all the miRNAs, mR-133a is downregulated in various cancers and acts as a tumor suppressor. Specifically, miR-133a is expressed at low levels in renal cancer tissues and cell lines, and its forced expression inhibited the proliferation and invasion of renal cancer cells, induced apoptosis and arrested cell cycle progression by targeting TAGLN2 [
]. Similarly, Cheng et al. find that miR-133a is ablated in gastric cancer tissues, and inhibited the malignant phenotypes in gastric cancer cells by targeting its downstream CDC42/PAKs signaling pathway, and low-expressed miR-133a predicted a worse prognosis in gastric cancer patients [
In consistent with the previous publications, this study screened the differential miRNA expression profiles of glioma and para-cancer tissues, and confirmed that miR-133a was aberrantly downregulated in the cancerous tissues, and patients with high-expressed miR-133a tended to have a favorable prognosis. In addition, miR-133a was also ablated in the glioma cells, compared to the normal cells, and forced expression of miR-133a in the glioma cells significantly inhibited proliferative and colony forming abilities, indicating that it exerted its anti-tumor effects in glioma. In addition, we identified that PPARG as the downstream target of miR-133a, and miR-133a promoted PPARG degradation through binding to its 3’UTR. Consistent with this, PPARG was significantly upregulated in the glioma tissues and cells, in contrast to the normal counterparts, and miR-133a negatively regulated its expression levels in the glioma cells. Of note, although we had evidenced that miR-133a was downregulated, whereas PPARG was aberrantly overexpressed in the cancerous tissues, compared to the non-cancerous healthy tissues, it was still hard for us to conclude that those two genes were specifically aberrant-expressed in glioma cells due to the heterogeneity of the glioma, which included tumor cells, stromal cells, immune cells and other types of cells in its tumor microenvironment. Interestingly, our in vitro experiments evidenced that miR-133a was significantly downregulated in the glioma cell lines (U251, T-98G, A172 and U87 cells), in contrast with the normal HEB cells, which indirectly reflected the fact that miR-133a was ablated in glioma cells. To exclude the interferences of other types of cells, we were going to conduct single cell RNA-seq analysis in our future work, which will help us to fully elucidate this important academic issue.
Forced expression of PPARG in the miR-133a-overexpressing cells rescued them from the inhibitory effects of the latter, which further indicated that the targeted suppression of PPARG by miR-133a is the mechanistic basis of its action. Studies show that PPARG activation upregulate Cyclin Ds, Cyclin E and CDK4, which accelerates cell cycle progression and cell proliferation [
]. Cyclin D1 is a key regulatory protein of the mammalian cell cycle which binds to the downstream Cyclin D2 and CDK4 to form a complex that phosphorylates the retinoblastoma protein (Rb). This releases the nuclear transcription factor E2F from Rb, and promotes its nuclear translocation to transcriptionally activate genes involved in G1 to S phase transition, finally enabling the cells to enter a proliferative state [
]. Consistent with the above findings, this study verified that miR-133a-upregulation suppressed the expression levels of Cyclin D1, Cyclin D2 and CDK4 to induce cell cycle arrest at G1-S phase. Furthermore, studies show that various epigenetic modifications, including DNA methylation and histone acetylation, play key roles in regulating miRNA expression [
], we next analyzed the levels of miR-133a in glioma cells after specifically inhibiting DNA methyltransferase or histone deacetylase.
The expression levels of miRNAs can be modulated by various upstream regulators, including DNA methylation and histone deacetylation. To explore whether miR-133a is influenced by those epigenetic modifications, we respectively treated the glioma cells with AZA and TSA, and we surprisingly found that the levels of miR-133a were significantly elevated by AZA instead of TSA, suggesting that DNA methylation but not histone deacetylation that played critical role in regulating the expression levels of miR-133a in glioma cells. Consistently, the following MSP assay verified that the methylation levels of miR-133a was significantly higher in glioma cells than that in normal glial cells. The above findings supported the notion that the CpG islands in the miR-133a gene promoter region are hypermethylated in glioma cells, which lead to its downregulation. Therefore, removal of methylation at this site can restore miR-133a expression in glioma cells, and targeting DNA methylation was considered as novel treatment strategy for this disease. Collectively, we summarized the novel findings of the present study as follows: (1) We firstly identified that PPARG was the downstream target of miR-133a, and miR-133a negatively regulated PPARG to hamper glioma progression. (2) We firstly considered DNA methylation as the main mechanisms that regulated the expression status of miR-133a in glioma cells.
Although we had provided evidences to uncover the underlying mechanisms by which the DNA methylation/miR-133a/PPARG signal pathway played critical role in regulating glioma progression, there still existed several limitations in this study: (1) The clinical samples in the present study were limited, and more clinical data was still needed to validate our current findings. (2) The detailed molecular mechanisms and molecules that regulated miR-133a DNA methylation were still needed to be uncovered. (3) From the academic perspectives, it was better to use patient-derived cell models but not serum-cultured cell lines in the present study. However, as the results technical reasons and some academic concerns, we failed to obtain the patient-derived cell models to finish this experiment. For the technical reasons: since the glioma tumor tissues were characterized with heterogenous characteristics, and the tumor microenvironment contained various types of cells, including stromal cells, immune cells, tumor cells and so on. Given that it was a lack of tumor-specific markers for glioma cells, it was hard for us to isolate the pure glioma cells from the patient-derived tissues. Although we had tried to use some of the published biomarkers to isolate glioma cells, we still failed to obtain the pure glioma cells due to the non-specificity of those markers. For the academic concerns: given the fact that glioma cells from different patients had different biological properties due to individual differences of genes, the primary patient-derived glioma cells isolated glioma cells will inevitably not representative, and the data from those cells will definitely unconvincing. We are now trying to improve the cell isolating procedures and searching for the collaboration among different labs worldwide, and the results of patient-derived primary cell models will be published in our future work.
Conclusions
In conclusion, this study for the first time evidenced that DNA-methylation-mediated miR-133a-ablation contributed to the progression of glioma by degrading the tumor-initiating PPARG, and providing evidences to validate the notion that the DNA methylation/miR-133a/PPARG signaling pathway could be used as ideal biomarkers for glioma diagnosis, treatment and prognosis. Also, those cancer-related genes might be used as novel molecular targets for drug development.
Declarations
Funding information
This study was financially supported by the Second Affiliated Hospital of Shenzhen University (Grant No. 7369345).
Data availability statement
All the data had been included in the manuscript, and the original raw data could be obtained from the corresponding author with reasonable request.
Author contributions
Liang Liu designed and conducted the investigations, and drafted the manuscript. Zhengquan Zhu and Xu Li provided technical supports and analyzed the data. Yong Zheng provided guidance and proofread the final version of the manuscript for publication.
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The authors regret for the duplicated images in the Figure 3C and 6b, and the reasons why this mistake happened had also been investigated. Actually, the duplicated images presented in the Figure 3C and 6B were due to the inappropriate manipulation of the Adobe Illustrator (AI) software, which were used to combine the small images together as a whole Figure. The small images were inserted into the AI software according to their position in the computer, and the AI software will remind the users whether to replace the original images once they were removed or the names of their folders were changed.
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