HIF1α and p53 Regulated MED30, a Mediator Complex Subunit, is Involved in Regulation of Glioblastoma Pathogenesis and Temozolomide Resistance

Abstract

Glioblastoma (GBM) is the most common, malignant, and aggressive form of glial cell cancer with unfavorable clinical outcomes. It is believed that a better understanding of the mechanisms of gene deregulation may lead to novel therapeutic approaches for this yet incurable cancer. Mediator complex is a crucial component of enhancer-based gene expression and works as a transcriptional co-activator. Many of the mediator complex subunits are found to be deregulated/mutated in various malignancies; however, their status and role in GBM remains little studied. We report that MED30, a core subunit of the head module, is overexpressed in GBM tissues and cell lines. MED30 was found to be induced by conditions present in the tumor microenvironment such as hypoxia, serum, and glucose deprivation. MED30 harbors hypoxia response elements (HREs) and p53 binding site in its promoter and is induced in a HIF1α and p53 dependent manner. Further, MED30 levels also significantly positively correlated with p53 and HIF1α levels in GBM tissues. Using both MED30 overexpression and knockdown approach, we show that MED30 promotes cell proliferation while reduces the migration capabilities in GBM cell lines. Notably, MED30 was also found to confer sensitivity to chemodrug, temozolomide, in GBM cells and modulate the level of p53 in vitro. Overall, this is the first report showing MED30 overexpression in GBM and its involvement in GBM pathogenesis suggesting its diagnostic and therapeutic potential urging the need for further systematic exploration of MED30 interactome and target networks.

Electronic supplementary material

The online version of this article (10.1007/s10571-020-00920-4) contains supplementary material, which is available to authorized users.

Introduction

Glioblastoma (GBM) is the fastest growing and most devastating type of glial cell cancer with poor prognosis (Thakkar et al. 2014). It is the most aggressive and infiltrative form of glioma. GBM is reported to have three different sources of origin namely, neural stem cells, neural stem cell derived astrocytes, and oligodendrocyte precursor cells (Yao et al. 2018). The mainstay treatment of GBM includes surgery followed by radiation and chemotherapy (Stupp et al. 2005). However, it is difficult to remove the tumor entirely as the tumor is diffused in nature with high degree of infiltration, and the tumor cells often develop resistance to chemo-drug (Temozolomide) and radiation; hence, these methods prove inefficient in their aim to achieve prolonged remission of GBM tumor. Therefore, innovative new methods of tumor suppression are desperately required for GBM treatment. Investigating molecular elements involved in GBM pathogenesis and understanding their mechanism of action can provide novel methods for cancer treatment.

Malignant transformation of cells is linked to a loss of the regulatory network of gene expression. Recent studies suggest that altered expression of the genes coding for various components of the transcriptional machinery, which include transcription factors, mediator complex and RNA pol II may bear a strong effect on the cellular transcriptome (Yin and Wang 2014, Allen et al. 2015, Lambert et al. 2018). Mediator complex is a large, multi-subunit complex that is essential for transcription by RNA polymerase II by functioning as a transcriptional co-activator (Poss et al. 2013). It acts as a bridge, transmitting signal from transcription factors to RNA Pol II. Mediator complex is highly conserved in all the eukaryotes ranging from yeast to humans. The mediator complex consists of 4 distinct modules—head, middle and tail along with a kinase module (also known as CDK8 module) (Conaway et al. 2013). The head, middle and tail form the core complex and are always found in association, to which the kinase module binds reversibly. The binding of the kinase module to the core subunits may have a positive or negative effect on transcription regulation.

There are emerging reports on alterations of mediator complex subunits and their effects on cancer progression. The first link of alteration between mediator subunits and cancer was shown by Zhu et al. in 1999, between MED1, a middle domain subunit, and breast cancer. MED1 was found to be overexpressed and was shown to induce various oncogenic genes and miRNAs in breast cancer (Yang et al. 2018; Nagpal et al. 2018). MED1 was also found to be deregulated in lung cancer, prostate cancer and melanoma. Similarly, MED17 (a head module subunit) was found to be upregulated in prostate cancer, knockdown of which led to a reduction in proliferation accompanied with an increase in apoptosis (Vijayvargia et al. 2007). MED15, a tail module subunit, was also found to be associated with breast cancer, bladder cancer and prostate cancer where it was found to be epigenetically regulated. Among the kinase module subunits, MED12 is known to be associated with multiple cancers that include, breast, ovarian, colon, lung, and prostate cancer where it is found to play a role in tumor progression(Schiano et al. 2014). Increased expression of CDK19 is co-related with aggressiveness in prostate cancer(Becker et al. 2020). There are several other reports of the involvement of various subunits of mediator complex in carcinogenesis. However, the exact cause and mechanism of the alteration of the Mediator complex subunits have not been elucidated yet. Thus, more work on the mediator complex is required to understand their function and regulation in malignant transformation.

MED30 also known as Thyroid Hormone Receptor-Associated Protein Complex 25 KDa Component (TRAP25) or Thyroid Hormone Receptor-Associated Protein 6 (THRAP6) is a core subunit in the head module of the mediator complex and is a metazoan-specific subunit present on the human chromosome 8q24.11 (Weber et al. 2018). The head module binds to the C-Terminal Domain of Pol II. As a result of alternate splicing, two isoforms have been reported for MED30 subunits – one is of 178 amino acids in length and has a mass of 20,277 Da while the other is 143 amino acids long and has a mass of 16,279 Da (Rienzo et al. 2014). In the study by Baek et al. in 2002, it was demonstrated that immuno-depletion of MED30 simultaneously depleted other MED subunits which further compromised its ability to support high level of transcriptional activation.

The first report of MED30 link with cancer was made by Lee et al. in 2015. They showed that MED30 shows enhanced expression in patient samples and functions as an oncogene in gastric cancer by conferring proliferative and invasive properties. In another report, enhanced level of MED30 was correlated with favorable patient survival and reduction in proliferation, invasion in bladder cancer cell lines (Syring et al. 2017). It was also reported to be overexpressed in various breast cancer cell lines (Hasegawa et al. 2012).

However, the status and role of MED30 in GBM remains unstudied so far. Based on TCGA data analyses we report here that MED30 is overexpressed in GBM tissues and cell lines. MED30 is a stress-induced gene, which is regulated by p53 and HIF1α in GBM. Further, MED30 plays a role in inhibiting the cellular migration while enhancing proliferation and affects chemo-drug temozolomide (TMZ) mediated cell death. Overall, this stands as first study highlighting the status and role of MED30 in GBM.

Materials and Method

Cell Lines and Culture Condition

GBM cell line A172 was obtained from National Brain Research Centre (NBRC, Manesar, India), and U87MG was purchased from National Centre of Cell Sciences (NCCS, Pune, India) and both were cultured in Dulbecco′s Modified Eagle Medium(DMEM) supplemented with 10% Fetal Bovine serum (FBS) and 100U/mL Penicillin–Streptomycin (Pen-Strep) antibiotic. The culture was maintained in 5% CO₂ incubator at 37 °C.

For hypoxia treatment, the cells were placed in a hypoxia workstation (Invivo200, Ruskinn, UK) maintained at 0.2% oxygen, 5% CO₂ and 37 °C for given time periods.

For MED30 expression analysis in GBM cell lines, cDNA synthesized from RNA isolated from epileptic brain samples (non-cancerous tissue) was used as control.

MED30 Overexpression and Knockdown

MED30 coding sequence (CDS) region was retrieved from NCBI database (RefSeq Accession number NM_080651.4). Primers were generated for this sequence along with unique restriction site overhangs at extreme ends of both the primers to facilitate directional cloning. 6X His-tag sequence was also incorporated on reverse primer so that the tag could be fused at the C-terminal of the protein generated. The primer sequence is mentioned in Supplementary Table S1. Mammalian expression vector pcDNA3.1 + was chosen to develop the construct. Restriction mapping with unique restriction endonuclease followed by sequencing of the plasmid was used for cloning confirmation.

MED30 siRNA, along with its negative control, were obtained from commercially available source (Sigma-Aldrich). Increasing concentrations of siRNA (10–50 nM) or their control were transfected in GBM cells. qRT-PCR was performed to identify the change in MED30 transcript level. 50 nM of control or MED30 siRNA concentration was standardized to be used for further experiments.

Transient Transfection

Cells were seeded (10⁵ cells per well) in a 12-well plate. Using Lipofectamine 2000 (Invitrogen), the cells were transfected with siRNA (50 nM) for knockdown or plasmid (1 μg) for overexpression studies, along with their respective controls. Manufacturer’s protocol was followed for the transfection. After 5 h, the media was changed, and further experiments were set up to 48 h post-transfection.

Quantitative Real-Time PCR (qRT-PCR)

Whole cell lysate was prepared after 48 h of transfection and RNA was extracted using the RNeasy mini kit (Qiagen) by following the manufacturer’s protocol. RNA quantity and quality were analyzed using Nanodrop 2000 (Thermoscientific) spectrophotometer. The cDNA was synthesized from 500 ng RNA using iScript cDNA synthesis kit (Bio-Rad). Quantification was performed using SsoFast EvaGreen Supermix (Bio-Rad) on the CFX96TM real-time system (Bio-Rad). Data was analyzed using β-Actin as a reference, applying the 2^−ΔΔCT algorithm. The list of primer sequences used is provided in Supplementary Table S1.

Cell Proliferation Assay

MTT assay was used to determine cell proliferation. 5 × 10³ cells were seeded in triplicate in 48-well plate 24 h post-transfection. Cells were treated with 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) and were kept at 37 °C for 2 h to allow conversion of MTT to formazan. The formazan crystals were dissolved using DMSO and were quantified at 595 nm using the iMark microplate reader (Bio-Rad).

Wound Healing Assay

Wound healing assay was used to investigate the effect of MED30 on migration. Cells were seeded in a 12-well plate at a density of 10⁵ cells/well. These were transfected with MED30 clone or MED30 siRNA along with their control using Lipofectamine 2000. After 48-h of transfection, growth medium was replaced with serum-free DMEM, and a scratch was made using a microtip. The distance of the wound was measured immediately after scratch was made and after a specific time period.

Boyden Chamber Assay

Boyden chamber assay was used to measure cellular migration. After 48-h of transfection, 2.5 × 10⁴ cells were placed in the Boyden chamber (BD Biocoat chambers, BD Biosciences Discovery Labware, Bedford, MA) along with serum-free medium. The chamber was then placed in a growth medium containing well of a 24-well plate. The cells were allowed to migrate for 8–10 h after which the migrated cells were stained with crystal violet and imaged under bright field microscope.

Colony Formation Assay

A172 and U87MG cells transiently transfected with MED30 clone or MED30 siRNA were seeded in a six-well plate at a density of 1000 cells/well. These were allowed to grow for 8 days to 2 weeks or unless colonies with approximately 50 cells each were formed. The cells were then stained with crystal violet postfixation with 10% formalin. The colonies were counted manually and imaged.

Soft Agar Assay

Transient MED30 transfected cells were placed at a density of 500 cells/well in medium containing agar such that final agar concentration comes up to be 0.35%. The cell suspension was then placed over a layer of 0.5% agar supplemented with growth medium. A film of the medium was placed over the agar layer after solidification, and the plate was kept at 37 °C in a 5% CO₂ incubator with media change after every 3–4 days or as required. After 2–3 weeks, colonies were stained with crystal violet followed by imaging and counting under a microscope.

Western Blot

Whole cell protein was isolated from MED30 overexpressing cells after 48 h of transfection. Cell lysate was prepared by using NP40 protein extraction buffer followed by centrifugation at 15,000 rpm for 25 min at 4 °C. The supernatant was collected, and protein quantification was performed by Bradford assay. Loading mix was prepared by mixing 30 µg protein and 4 X laemmli buffer. The mix was kept at 95 °C for ten mins to denature proteins followed by SDS-PAGE. The proteins were then transferred to a nitrocellulose membrane by a 2 h long transfer procedure followed by 2 h of blocking in 5% BSA. The membrane was washed three times with Tris-buffered saline-Tween-20 (TBST) followed by overnight incubation with primary antibody at 4 °C. Again, the membrane was washed using TBST followed by incubation with secondary antibody for 2 h. Detection was done using a chemiluminescent substrate.

Promoter Luciferase Assay

Promoter luciferase assay was used to study the regulation of MED30 by specific transcription factors- p53 and HIF1α. Transcription factor binding sites for selected Transcription factors were identified in 4 kb upstream region of transcription start site of MED30 gene using ALGGEN PROMO3 (Farre et al. 2003; Messeguer et al. 2002) prediction tool. The chosen transcription factors were cloned in a mammalian expression vector and were transiently transfected into GBM cells. MED30 levels were detected 48 h post-transfection using qRT-PCR.

To confirm the regulation by identified transcription factors, the binding site of each transcription factor was cloned upstream of the Firefly Luciferase gene in pGL3-TK basic vector. The construct with the binding site and its respective transcription factor overexpressing clone were transiently co-transfected in GBM cells. Cell lysate was prepared, and the luciferase activity was detected using Dual-Luciferase® Reporter Assay System (PROMEGA) by following Manufacturer’s protocol.

Temozolomide Treatment

All the experiments were conducted at the IC₅₀ values of temozolomide in GBM cell lines. The IC₅₀ values of TMZ were first determined in the GBM cell lines as 400 µM in U87MG and 100 µM in A172 as also reported by other groups (Couer et al. 2015, Ryu et al. 2012, Agnihotri et al. 2012). U87MG and A172 cell were first transiently transfected with MED30 overexpression construct or its siRNA along with their respective controls. For cell viability assay, the cells were re-seeded at a density of 5 × 10³ cells per well in triplicate in a 48-well plate 24 h post-transfection. After 12 h, the cells were treated with IC₅₀ concentration of TMZ or its control (DMSO). Cell viability was determined 48 h post-treatment using MTT assay.

For apoptosis detection and RNA isolation, transfected cells were treated with IC₅₀ concentration of TMZ or its control (DMSO) 24 h post-transfection. Caspase 3/7 detection assay or RNA isolation was performed 48 h post-treatment.

Caspase 3/7 Detection Assay

The Caspase-Glo® 3/7 Assay kit (Promega) was used to determine Caspase 3/7 activity and apoptosis in cells. Cells were transfected with MED30 overexpressing clone or MED30 siRNA, along with their respective controls and were trypsinized and centrifuged 48 h post-transfection. The pellet was resuspended in serum-free medium, and the cells were counted. A 20 μl suspension consisting of 8 × 10³ cells was mixed with an equal volume of Caspase-Glo® 3/7 Reagent and incubated in the dark at room temperature for 1.5 h after which the luminescence was measured. The luminescence is directly proportional to the amount of substrate reduced and is a direct measurement of Caspase3/7 activity.

Patient Data Analysis

The GBM patient data was analyzed using various public datasets such as GEPIA (Tang et al. 2017) (https://gepia.cancerpku.cn/), GlioVis (Bowman et al. 2017) (https://gliovis.bioinfo.cnio.es) and Prognoscan (Mizuno et al. 2009) (https://gibk21.bse.kyutech.ac.jp/PrognoScan/index.html). GlioVis was used to analyze CGGA and TCGA-GBMLGG cohort.

Statistical Analysis

Experiments were performed at least thrice in triplicates unless specified. Two-tailed Student’s t-test and one-way or two-way analysis of variance followed by Bonferroni correction was applied for calculation of p values when appropriated using Microsoft Excel. Data with p value < 0.05 was considered significant. Wherever applicable, error bars were used to denote standard deviation.

Results

MED30 Subunit is Overexpressed in GBM Tissues and Cell Lines

To identify the MED30 levels in GBM tissues, we used different GBM patient data analysis platforms. First, we analyzed TCGA LGG (low-grade glioma) and GBM patient cohort using GEPIA, a web server tool for analyses of RNA sequencing data from TCGA and GTex. MED30 transcript levels were found to be upregulated in LGG and GBM tissue as compared to the control (Fig. 1a). Next, we analyzed TCGA-GBM data using GlioVis tool and found that MED30 showed significant overexpression in GBM tissues as compared to non-tumor control (Fig. 1b). However, analyses of grade dependent expression of MED30 across gliomas yielded varying results depending upon the cohort used for analysis. In case of TCGA-GBMLGG data analysis by Gliovis, it was revealed that MED30 had the highest expression in GBM across different types of gliomas (Fig. 1b). However, analysis of CGGA cohort by Gliovis showed significant difference in expression of MED30 in GBM when compared to oligoastrocytoma and anaplastic oligoastrocytoma but no significant difference in expression in GBM as compared to astrocytoma, anaplastic astrocytoma, anaplastic oligodendroglioma and oligodendroglioma were seen (Fig. 1c).We next checked MED30 levels in GBM cell lines (U87MG and A172) using qRT-PCR and found that MED30 levels were significantly overexpressed in GBM cell lines as compared to the non-tumorous epileptic brain tissue (Fig. 1d). We also checked for MED30 levels in various human brain cell types using Genvestigator tool (Hruz, T. et al. 2008). It appeared that MED30 is expressed in low to medium levels in different human normal brain cell types (Supplementary table S2).

Fig. 1.

MED30 is upregulated in GBM tissues and cell lines a Graph showing fold change in MED30 transcript levels in GBM (n = 163) and GBM-LGG patient (n = 518) cohort as compared to control (n = 207) analyzed using GEPIA web tool. b (i) Graph of TCGA-GBM data showing significant overexpression of MED30 in GBM (n = 156) vs non-tumor control (n = 4). (ii) Graph showing analyses of TCGA-GBMLGG data using GlioVis indicating highest expression of MED30 in GBM, across various subtypes of glioma (oligodendroglioma (OD) n = 191, oligoastrocytoma (OA) n = 130, astrocytoma (A) n = 194, GBM n = 152). c Graph generated using Gliovis tool for the CGGA patient data showing MED30 transcript levels in various glioma subtypes (oligodendroglioma (OD) n = 93, oligoastrocytoma (OA) n = 9, astrocytoma (A) n = 175, anaplastic oligodendroglioma (AOD) n = 94, anaplastic oligoastrocytoma (AOA) n = 21, anaplastic astrocytoma (AA) n = 214 and GBM n = 388.d qRT-PCR data showing MED30 transcript levels in GBM cell lines. β-actin was used as the normalization control (e and f)Kaplan–Meier curves from PrognoScan database displaying correlation of MED30 levels with the overall survival pattern of e GBM and f glioma tissues. g and h Kaplan–Meier curve from GlioVis, data analysis of g TCGA-GBM cohort and h CGGA-GBM cohort i Kaplan–Meier curve from GEPIA tool showing correlation of MED30 expression with survival in GBM tissues. The graphical data points in (d) represent mean ± S.D of at least three independent experiments (** represents p value < 0.001). Error bars denote ± SD

We next performed correlation of MED30 levels with GBM patient overall survival using various patient data analysis platforms; however, the results were conflicting. Kaplan–Meier curves from PrognoScan database displayed correlation of high MED30 levels with improved overall survival pattern in GBM or glioma tissues in two different datasets (Fig. 1e, f); however, survival analyses using GlioVis(TCGA-GBM), GlioVis (CGGA-GBM) or GEPIA tools revealed no significant correlation of GBM patient survival with MED30 expression (Fig. 1g-i). Interestingly, high-MED30 levels were correlated with poor survival in TCGA-LGG patient data (GEPIA) or CGGA (Grade II) patient dataset (Supplementary Figure S1a, b). Overall, it seems that while high MED30 levels are correlated to poor prognosis in low-grade glioma tissues, its association with GBM patient survival remains inconclusive based on the data obtained from various datasets.

MED30 is p53 and HIF1α Regulated

We found that the frequency of chromosomal alteration in MED30 genomic locus was close to negligible in GBM. TCGA-LGG/GBM patient data analyses using cBioportal revealed that MED30 levels were amplified in only 1.96% of LGG and 0.51% of GBM tissues suggesting that the MED30 loci is not much affected at the genomic level indicating that MED30 overexpression results from transcriptional or post-transcriptional regulation (Fig. 2a).

Fig. 2.

MED30 is transcriptionally regulated by p53 and HIF1α. a Graph showing percentage of alternation frequency in MED30 genomic locus in TCGA-GBM patient dataset analyzed using cBioportal tool b Schematic representation of p53 and HIF1α binding sites in MED30 promoter region. c qRT-PCR data showing induction in MED30 transcript levels upon p53 and HIF1α overexpression in U87MG and A172 cell lines. β-actin was used as the normalization control. d Binding site for p53 present in the promoter region of MED30 was cloned in PGL3-tk-Luciferase vector and co-transfected with p53 overexpressing plasmid to check effect of p53 on MED30 promoter region. The graph shows relative luciferase activity. e Three independent clones (P1-P3) were generated in PGL3-tk-Luciferase vector containing binding sites for HIF1α present in the promoter region of MED30 and co-transfected with HIF1α overexpressing plasmid to check effect of HIF1α on MED30 promoter region. The graph shows relative luciferase activity. f Graphs showing positive correlation of expression between MED30 and TP53 in CGGA-GBM, TCGA-GBMLGG and Rembrandt datasets. g Graphs showing positive correlation of expression between MED30 and HIF1α in CGGA-GBM and Rembrandt datasets. The graphical data points represent mean ± S.D of at least three independent experiments (*represents p value < 0.05 and ** represents p value < 0.001). Error bars denote ± SD

To identify the underlying mechanism of MED30 regulation at transcript levels, we tried to find the transcription factors that could be regulating MED30 expression by binding to its promoter region. We found several transcription factors that had binding sites in the promoter region of MED30 using AlGGEN PROMO3 software. We focused on two transcriptional factors HIF1α and p53, both of which had binding sites with zero dissimilarity in the MED30 promoter region. The p53 pathway is deregulated in 84% of GBM tissues and 94% of GBM cell lines while intratumoral hypoxia is a characteristic feature of glial tumors (Zhang et al. 2018, Colwell et al. 2017). Thus, we selected p53 and HIF1α for further analyses. Four binding sites for HIF1α and one binding site for p53 were found in MED30 promoter region (Fig. 2b).

The GBM cell lines (U87MG and A172) were transfected with p53, and HIF1α overexpression clones and MED30 levels were detected 48 h post-transfection by qRT-PCR (Fig. 2c). In both the GBM cell lines, the transcript level of MED30 was found to be significantly upregulated by both, p53 and HIF1α overexpression. To further confirm the binding of these factors on the MED30 promoter region, the selected binding sites were cloned in pGL3-tk-luciferase reporter vector and dual promoter luciferase assay was performed in U87MG cell line. Three independent clones (P1-P3) were generated in PGL3-tk-Luciferase vector containing binding sites for HIF1α present in the promoter region of MED30. An increase in luciferase activity was observed upon overexpression of p53 or HIF1α in U87MG, suggesting that MED30 is regulated by p53 and HIF1α in GBM (Fig. 2d and 2e).

We next tried to correlate MED30 levels with TP53/HIF1α in GBM tissues using online databases. Notably, we found a positive correlation between MED30 and TP53 in CGGA-GBM, TCGA-GBMLGG and Rembrandt datasets (Fig. 2f). Similarly, MED30 and HIF1α levels showed significant positive correlation in GBM tissues from CGGA-GBM and Rembrandt datasets except in TCGA-GBMLGG dataset it did not show any correlation suggesting variability in MED30 and HIF1α correlation in patient dataset (Fig. 2g). Based on these observations, MED30 seems to be a regulated by p53 and HIF1α in GBM cells with p53 and MED30 showing positive correlation in GBM tissues as well.

Serum, Glucose Deprivation, and Hypoxia bring about Induction in MED30 Levels

HIF1α is a master regulator of hypoxia signaling in cancer cells, while p53 regulates cellular response to external stress. Having established transcriptional regulation of MED30 by p53 and HIF1α, we wanted to see whether hypoxia and nutrient depletion would lead to similar induction in MED30 transcript levels. Thus, we checked for the levels of MED30 in response to oxygen and nutrition stress (conditions often present in the core region of the tumor mass). To check the effect of oxygen deprivation on MED30 regulation, U87MG and A172 cell lines were exposed to 0.2% oxygen for 48 h and MED30 levels were detected by performing qRT-PCR on the total RNA extracted from these samples. We observed that MED30 transcript levels go up significantly in both the cell lines (Fig. 3a). Next, we checked for the effect of serum/glucose deprivation on MED30 levels. For this, U87MG and A172 cells were kept in serum-free or glucose-free medium for 48 h after which qRT-PCR was performed. It was observed that MED30 transcript levels are induced under conditions of serum/nutrient stress (Fig. 3b, c). Altogether, these data suggested that conditions present in the tumor microenvironment such as hypoxia and serum/nutrient deprivation bring about induction in MED30 levels in GBM.

Fig. 3.

MED30 is induced by hypoxia and nutrient deprivation in GBM cells. qRT-PCR data showing induction in MED30 transcript levels upon exposure to a hypoxia (0.2% O₂) b serum starvation and c glucose starvation in U87MG and A172 cell lines. β-actin was used as the normalization control. The graphical data points represent mean ± S.D of at least three independent experiments (*represents p value < 0.05 and **represents p value < 0.001). Error bars denote ± SD

MED30 Promotes Cell Proliferation in GBM

We next studied the effect of MED30 on cell proliferation. For this, we performed MED30 overexpression and knockdown in U87MG and A172 cell lines followed by various assays on the transfected cells. The MED30 overexpression construct was prepared in the mammalian expression vector pcDNA3.1 (+) with 6X His-tag at its C-terminal end to detect its expression. A MED30-specific siRNA was used for the knockdown studies. U87MG and A172 cell lines were transfected with the MED30 construct or MED30-specific siRNA (50 nM) along with their respective controls (pcDNA3.1 ( +) empty vector and siRNA negative control). The transcript levels of MED30 was found to be highly induced upon overexpression while the levels diminished significantly upon its knockdown (Supplementary Figure S2a and S2b). Western Blot using Anti-His antibody confirmed the expression of MED30 at the protein level (Supplementary Figure S2c).

First, MTT assay was performed in the U87MG and A172 cells, which had modulated MED30 expression, to determine the effect of the gene on GBM cell viability. A significant increase in the proliferative potential of both the cell lines was observed in the case of MED30 overexpression as opposed to the reduction in proliferation upon its knockdown (Fig. 4a, b). A similar trend was observed in the colony formation potential of the MED30 modulated cells. The MED30 overexpressing U87MG and A172 cells showed an increase in the colony forming capacity with higher number of colonies as compared to control while contrasting results were observed upon MED30 knockdown (Fig. 4c, d). The effect of MED30 was also observed on the anchorage-independent growth potential of the GBM cell lines by performing the soft agar assay. A greater number of colonies were able to survive without anchorage upon the overexpression of MED30 in U87MG and A172. The number of colonies that could survive anchorage independence diminished upon MED30 knockdown in both the cell lines (Fig. 4e, f). From our data, we show that MED30 promotes cell proliferation and enhance the colony formation potential of GBM cells.

Fig. 4.

MED30 promotes cellular proliferation in GBM cells. a, b Graph showing relative proliferation using MTT assay in cells transfected with a MED30 overexpression and b MED30 siRNA with their respective controls in (i) U87MG and (ii) A172 cells. c, d Colony formation assay results in U87MG and A172 cells upon c MED30 overexpression and d MED30 knockdown [(i) images of colonies (ii) graphical representation of fold change in number of colonies] e, f Soft agar assay results in U87MG and A172 cells upon e MED30 overexpression and f MED30 knockdown [(i) images of colonies (ii) graphical representation of fold change in number of colonies formed]. The graphical data points represent mean ± S.D of at least three independent experiments (*represents p value < 0.05 and ** represents p value < 0.001). Error bars denote ± SD

MED30 Promotes TMZ Sensitivity Through Increased Apoptosis and p53 Levels

We showed that MED30 promotes cellular proliferation in GBM cells. Rapidly dividing cells are most responsive to chemotherapy. Thus, we checked whether MED30 affects response to chemo-drug (TMZ) in GBM. To examine this, U87MG and A172 cells with altered expression of MED30 (overexpression and knockdown) were treated with TMZ (IC₅₀- 400 µM for U87MG and 100 µM for A712) and its control (DMSO), respectively. After 48-h of TMZ treatment, MTT assay was performed and cell viability was calculated between TMZ and DMSO treated cells. The results were plotted by calculating the fold change in the viabilities between MED30 modulated cells and their control. The results showed that cells treated with TMZ alone showed poor survival than cells treated with TMZ along with MED30 knockdown, while MED30 overexpression significantly reduced survival of TMZ-treated cells (Fig. 5a, b and Supplementary Figure S3a, S3b). We next checked for the effect of MED30 on cellular apoptosis using Caspase 3/7 assay. We also measured p53 levels since it is known as a key player in apoptosis and TMZ sensitivity. We found that cells treated with TMZ combined with Ctrl siRNA showed higher caspase activity and p53 transcript levels as compared to cells receiving TMZ and MED30 siRNA while MED30 overexpression increased apoptosis along with an increase in p53 levels in TMZ treated cells (Fig. 5c–f and Supplementary Figure S3c-f). From our data, we can state that MED30 influences sensitivity towards TMZ chemo-drug by promoting apoptosis and inducing the levels of p53 upon TMZ treatment. Interestingly, we also noted that MED30 shows opposing effect in control cells (not treated with the drug). We found that MED30 knockdown brought about an increase in apoptosis and p53 levels while MED30 overexpression inhibited it in GBM cells not treated with the TMZ suggesting that MED30 works in a highly context dependent manner (Fig. 5c–f and Supplementary Figure S3c-f).

Fig. 5.

MED30 enhances TMZ sensitivity in GBM cells. Graph showing relative cell viability using MTT assay upon TMZ treatment post-siRNA mediated knockdown in a U87MG cells b A172 cells. Effect on apoptosis was studied by detecting the relative caspase 3/7 activity in TMZ treated GBM cell lines pre-transfected with MED30-specific siRNA and universal negative control. Graph represents relative caspase 3/7 activity with respect to control in c U87MG cells d A172 cells. qRT-PCR data showing p53 transcript levels upon TMZ treatment in GBM cell lines pre transfected with MED30-specific siRNA and universal negative control in e U87MG cells. f A172 cells. β-actin was used as the normalization control. The data shows that MED30 induces p53 transcript levels in TMZ treated GBM cells. The graphical data points represent mean ± S.D of at least two independent experiments (*represents p value < 0.05 and **represents p value < 0.001). Error bars denote ± SD

MED30 Inhibits Cellular Migration

We next studied the effect of MED30 on cell migration using wound healing or scratch assay in both MED30 overexpressing and knockdown A172 and U87MG cells. It was observed that upon MED30 overexpression, the migration rate decreased while upon MED30 knockdown, the rate increased in U87MG and A172 cells (Fig. 6a-d). To further confirm the results, trans-well migration assay (Boyden chamber Assay) was performed in both the sets of A172 and U87MG cells. We found a similar result with a decrease in migration rate in MED30 overexpressing U87MG and A172 (Fig. 6e, f). Contrasting results were obtained in MED30 knockdown cells (Fig. 6g, h). Thus, MED30 was shown to inhibit cell migration in GBM.

Fig. 6.

MED30 inhibits cellular migration in GBM cell lines. a Microscopic images of U87MG and A172 cells showing wound healing assay post-MED30 overexpression at different time points b graphical representation of the data showing relative migration. c Microscopic images of U87MG and A172 cells showing wound healing assay post-MED30 knockdown at different time points d Graphical representation of the data showing relative migration. e Microscopic Images showing trans-well migration assay post-MED30 overexpression in U87MG and A172 cells f Graphical representation of the data. g Microscopic Images showing trans-well migration assay post-MED30 knockdown in U87MG and A172 cells h Graphical representation of the quantification of trans-well migration assay using ImageJ. The graphical data points represent mean ± S.D of at least three independent experiments (*represents p value < 0.05 and **represents p value < 0.001). Error bars denote ± SD

Discussion

MED30 gene is located on chromosome 8 and codes for a 20 kDa protein which is a part of head module of the core assembly of mediator complex subunit. Thus, it is an imperative part of the machinery of regulation of gene expression in cells. Its association with malignant transformation has been studied across various malignancies such as gastric cancer, bladder cancer and renal cell carcinoma. In this study, we show for the first-time clinical status, regulatory and functional analyses of MED30 in GBM. GBM patient data analysis using several public databases revealed MED30 to be overexpressed in GBM tissues. The MED30 overexpression does not seem to be due to genomic abnormalities since MED30 locus was amplified in only less than 2% of the GBM tissues. Further, correlation of MED30 to patient survival suggested that while MED30 overexpression is correlated to poor prognosis in low grade gliomas, in GBM tissues the data is inconsistent. The majority datasets showed no correlation of MED30 expression with GBM patient survival except two datasets (Prognoscan) that showed that MED30 overexpression promotes patient survival.

We show that MED30 is a stress-regulated gene, and its transcript levels go up in GBM cell lines upon hypoxic and nutrient-depletion stress, two major factors present in the tumor microenvironment. Hypoxia mediated MED30 induction could be one of the reasons for high level of MED30 observed in GBM. We further found that MED30 is transcriptionally regulated by p53 and HIF1α. A previous study reported HIF1α mediated recruitment of CDK8 kinase mediator to stimulate elongation of RNA polymerase II in hypoxia (Galbraith et al. 2013). Another mediator subunit, MED15, is also reported to be positively correlated with HIF1α in hepatocellular carcinoma (Wang et al. 2018). However, no report on the regulation of any MED subunit by p53 is known.

Our study deciphered the role of MED30 on cell proliferation and migration in GBM cells. MED30 has been shown to be overexpressed in gastric and bladder cancer and promote both cell proliferation and migration. Here, we found that MED30 promoted cell proliferation in GBM cells. We showed that MED30 promotes colony formation potential and anchorage independence in GBM cells in vitro. Both these assays take almost 2–3 weeks to conclude post-transient transfection. Though effects of transient transfections generally last for few days but it is likely that short-term manipulation of MED30 was enough to bring about considerable effect on cell proliferation and consequently on colony formation abilities. We obtained contrasting results using MED30 overexpression or knockdown, thus confirming the role of MED30 in colony formation and anchorage independence. Generation of stable cell lines overexpressing MED30 or knockdown of MED30 using shRNA/Crispr-Cas9 based constructs should be done to obtain more reliable results. Interestingly, we found that while MED30 promotes cell proliferation in GBM, it inhibited cellular migration. There has been a body of literature that supports that proliferation and invasion are not parallel events and that cells can switch between proliferative and invasive phenotype. As for example miR-146b is reported to promote cell proliferation while at the same time attenuating migration and invasion in ovarian cancer. Similar results were obtained for p16(INK4A) in basal cell carcinoma wherein p16(INK4A) was found to be associated with increase infiltration but ceased proliferation (Yan et al. 2018, Svensson et al. 2003, Hoek et al. 2008). Certain reports also link reduced proliferation with infiltrative EMT-induced pro-migratory phenotype (Evdokimova et al. 2009). Thus, our findings also state contrasting effects on proliferation and migration by MED30. This is interesting as we observed MED30 to have a negative correlation with patient survival in low grade gliomas; however, as tumor progresses, we see no significant correlation of MED30 with prognosis. These results are in line with the hypothesis that hyper proliferative phenotype is essential for initiation of tumors; however, growth inhibition is necessary for their circulation and dissemination during metastases.

A few MED subunits such as MED19, MED12, and MED1 have been shown to be associated with chemo-drug resistance in cancer. MED19 enhances chemoresistance in breast cancer and non-small cell lung carcinoma (Liu et al. 2019, Wei et al. 2015). Med1 promotes chemoresistance towards tamoxifen in HER2 overexpressing breast cancer cells (Nagalingam et al. 2012; Cui et al. 2012). MED12 suppression confers multi drug resistance in lung cancer; however, its methylation enhances chemosensitivity in breast cancer (Huang et al. 2012, Wang et al. 2015). Hence, we were interested to know if MED30 has any effect on the cell sensitivity to Temozolomide (TMZ) in GBM, the therapeutic drug used for GBM treatment. Our result suggested that MED30 reduces the cell survival upon TMZ treatment along with an increment in both apoptosis and p53 transcript level. Similar results were also reported for miR-141 and miR-200a in ovarian cancer wherein both these pro-proliferative miRNAs increased chemosensitivity of tumors (Mateescu et al. 2011). From our data, we state that MED30 indeed sensitizes the GBM cells towards TMZ treatment partly by regulating p53 levels and hence enhancing apoptosis. It is to be noted that these effects are limited to TMZ treatment since in the absence of TMZ, MED30 decreases p53 transcript levels and apoptosis in GBM. Such opposing effects are not new with several transcription factors been shown to display opposing effects in a context dependent manner (Rowland and Peeper 2006). A study in 2011 reported that ATF3 promotes apoptosis upon treatment by an alkylating reagent such as methyl methanesulfonate while it inhibits apoptosis when it is overexpressed in cancer (Tanaka et al. 2011). Similarly, Ras oncogene has opposing effects on p53. It promotes p53 inhibition by enhancing MDM2 levels on one hand while on the other hand stabilizes it by promoting p¹⁴ARF expression. The net effect on p53 is the outcome of balance between the two (Ries et al. 2000). In our case as well we see that MED30 has contrasting effects on p53 on TMZ treatment and otherwise, in other words the effects of MED30 on p53 are context dependent. The conditions prevalent in tumor microenvironment (hypoxia, nutrient derivation) promote MED30 expression, wherein it inhibits p53 to prevent apoptosis and enhance tumor growth. Upon TMZ treatment MED30 enhance p53 expression to promote TMZ mediated apoptotic cell death. Our work here also highlights a feedback loop between MED30 and p53 in GBM. We also found a positive correlation of expression of p53 and MED30 in GBM tissues which again might be due to the fact that most of the patients in the dataset received chemotherapy, having been exposed to TMZ. It is also known that chemo-drugs are more effective in rapidly proliferating cells, and since we have established that MED30 escalate cell proliferation, this might also be another method of conferring TMZ sensitivity by MED30.

In summary, this stands as the first study that gives an insight into the association of MED30 and GBM pathogenesis; however, an in-depth investigation is needed to better understand MED30 targeted pathways and to study its potential as therapeutic target or predictive biomarker in GBM.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Abbreviations

cDNA

Complimentary DNA

DMEM

Dulbecco’s Modified Eagle Medium

DMSO

Dimethyl Sulfoxide

FBS

Fetal Bovine Serum

MED

Mediator

MTT

3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide

TCGA

The Cancer Genome Atlas

THRAP/TRAP

Thyroid Hormone Receptor-Associated Protein

TMZ

Temozolomide

GBM

Glioblastoma

siRNA

Small Interfering Ribonucleic Acid

qPCR

Quantitative Polymerase Chain Reaction

CGGA

Chinese Glioma Genome Atlas

Author Contribution

RK conceptualized and coordinated the whole study. AS and SS performed the in vitro analysis of MED30 in GBM cell lines. AS performed functional assays with MED30 overexpression. AS performed MED30 regulation studies. SS, AS and JD performed functional assays with MED30 knockdown in GBM cell lines. AS, SS and RK wrote the manuscript.

Funding

This work was supported by internal IIT Delhi funds to RK.

Compliance with Ethical Standards

Conflict of interest

The authors declare no competing financial or other interest in relation to this work.