Toll-Like Receptor 6 and Connective Tissue Growth Factor Are Significantly Upregulated in Mitomycin-C-Treated Urothelial Carcinoma Cells Under Hydrostatic Pressure Stimulation

Abstract

Background: Urothelial carcinoma (UC) is the most common histologic subtype of bladder cancer. The administration of mitomycin C (MMC) into the bladder after transurethral resection of the bladder tumor (TURBT) is a common treatment strategy for preventing recurrence after surgery. We previously applied hydrostatic pressure combined with MMC in UC cells and found that hydrostatic pressure synergistically enhanced MMC-induced UC cell apoptosis through the Fas/FasL pathways. To understand the alteration of gene expressions in UC cells caused by hydrostatic pressure and MMC, oligonucleotide microarray was used to explore all the differentially expressed genes. Results: After bioinformatics analysis and gene annotation, Toll-like receptor 6 (TLR6) and connective tissue growth factor (CTGF) showed significant upregulation among altered genes, and their gene and protein expressions with each treatment of UC cells were validated by quantitative real-time PCR and immunoblotting. Conclusion: Under treatment with MMC and hydrostatic pressure, UC cells showed increasing apoptosis using extrinsic pathways through upregulation of TLR6 and CTGF.

Introduction

Bladder cancer is the ninth most common cancer worldwide, accounting for 2.1% of all cancer deaths (Parkin et al., 2005). Urothelial carcinoma (UC) is the most common histologic subtype of bladder cancer, accounting for more than 90% of cases (Blaveri et al., 2005). In this organ-confined disease, surgery with neoadjuvant chemotherapy remains the standard of care to provide a survival benefit and to reduce tumor recurrence significantly (Kamat and Mathew, 2011; Castillo et al., 2012). Effective chemotherapy has therefore been widely used in adjuvant settings. Both mitomycin C (MMC) and bacillus Calmette-Guérin (BCG) have been frequently selected in monotherapy or alternating therapy in a variety of bladder cancer cases (Cho et al., 2012; Jarvinen et al., 2012).

MMC also decreased tumor recurrence rates by up to 50% after one immediate postoperative instillation of chemotherapy (Tyritzis et al., 2009; Wu et al., 2010). However, exposure of cancer cells to high-dose treatment with MMC can elicit multidrug resistance (Speers et al., 2007; Birare et al., 2009). Therefore, it is necessary to improve the efficacy of MMC treatment without inducing drug resistance.

Many approaches, such as intravesical electromotive drug administration (Di Stasi et al., 2008; Di Stasi et al., 2011; Ho, 2011) and thermochemotherapy (van der Heijden et al., 2007; Nativ et al., 2009), have been reported as promising treatments. In addition, we also previously demonstrated that the decreasing viability of UC cells was encountered with MMC chemotherapy under stress challenges by hydrostatic pressure, which is a certain type of mechanobiological stress (Chen et al., 2013). So far, however, there have been no studies exploring genomewide gene expression changes in stress-treated UC cells. To understand the global gene expressions caused by imposing different pressures on UC cells, oligonucleotide microarray was used to explore all the differentially expressed genes. In this study, the significant genes with similar expressions were selected by bioinformatics analysis and annotated by gene ontology (GO) (Zhang et al., 2010). Finally, the most typical genes were validated by quantitative real-time PCR (qPCR) and immunoblotting.

Materials and Methods

Total RNA extraction from UC cells

BFTC905 and HT-1376 (ATCC CRL-1472) cells, the two UC cell lines were, respectively, cultured in the presence of 10 μg/mL of MMC for 2.5 h. The MMC-treated cells were then incubated in a well-designed hydrostatic bioreactor for 10 kPa pressure treatment in duplicate or were cultured as usual to be used as control sets, as per our previous description (Zhang et al., 2010). Total RNA from each culture was independently extracted using the RNeasy Mini Kit, according to the manufacturer's instructions (Qiagen, Valencia, CA).

Microarray hybridization

Before microarray hybridization, each extract of total RNA from BFTC905 cells was quantified using a NanoDrop ND-1000 spectrophotometer (Nanodrop, Wilmington, DE) and a Bioanalyzer RNA Nano 6000 chip (Agilent Technologies, Palo Alto, CA). Then, 0.2 μg of total RNA (two samples from BFTC905 cells treated with MMC and hydrostatic pressure and two samples from controls) was amplified into cRNA using a Low Input Quick-Amp Labeling kit (Agilent Technologies) and was labeled with Cy3-deoxycytidine triphosphate during the in vitro transcription process. To acquire differentially expressed genes in response to the hydrostatic pressure, 0.6 μg of Cy3-labeled cRNA was fragmented to an average size of approximately 50-100 nucleotides by incubation with a fragmentation buffer at 60°C for 30 min, and then, differently fragmented cRNA was independently hybridized with Agilent SurePrint G3 Human GE 8x60K oligonucleotide microarray (Agilent Technologies) at 65°C for 17 h (Huang et al., 2008; Xie et al., 2013; Xu et al., 2013). The array is content sourced from the RefSeq, Ensembl, UniGene, and GenBank databases and provides full coverage of the human transcriptome. After washing and drying by nitrogen gun blowing, the microarrays were scanned with an Agilent microarray scanner (Agilent Technologies) at 535 nm, according to Agilent's standard protocols. The original microarray data from this study are available at the NCBI GEO database (www.ncbi.nlm.nih.gov/geo/) under the accession number GSE45385.

Array data analysis

The scanned images were analyzed with Agilent Feature Extraction software (Agilent Technologies), version 10.5.1.1. Image analysis and normalization software were used to quantify the signal and background intensity for each feature, substantially normalizing the data using the rank-consistency-filtering LOWESS method (Chang et al., 2008). After global normalization, probes with intensity<70 were filtered out for further analysis. The relative level (RL) was obtained from the ratio of each filtered gene's signal in MMC-treated UC cells under hydrostatic pressure to its signal in those control cells. Differently expressed genes in response to hydrostatic pressure were selected to represent the same expression trends (RL>1.50- or<0.67-fold) by independent duplication (Chen et al., 2001; Marvanova et al., 2004). Furthermore, the interested genes were generated by annotating and cross-matching candidates with the three GO terms (response to chemical stimulus, response to stress, and response to external stimulus) that are involved in the external cell environment. To explore possible pathways and the significance of interested genes, the Kyoto Encyclopedia of Genes and Genomes (KEGG) was used, particularly, its pathway diagrams and protein-protein interaction predictions (PIPs), to understand the gene's key functions (Klukas and Schreiber, 2007) for further analyses (Scott and Barton, 2007; McDowall et al., 2009).

Reverse transcription and validation of gene expressions

To confirm the gene expressions in response to hydrostatic pressure on UC cells, 1 μg of total RNA was reverse transcribed to single-stranded cDNA using the ABI Reverse Transcriptase kit (Applied Biosystems, Carlsbad, CA). Candidates were validated by qPCR through either the universal probe library system (Roche Diagnostics, Mannheim, Germany) or QuantiTest SYBR Green PCR kits (Qiagen, Hilden, Germany) in a LightCycler 1.5 Real-Time PCR system (Roche Applied Science), as previously described (Yang et al., 2009). The data were analyzed using the 2^−ΔΔCt method (Zhang et al., 2012). The relative quantitation of target mRNA levels in various samples was determined by normalizing their expression to that of glyceraldehyde 3-phosphate dehydrogenase (GAPDH, NM002046).

Immunoblotting of TLR6 and connective tissue growth factor protein

The concentration of protein in the cell lysates was determined using the Bradford assay (Bio-Rad Laboratory, Hercules, CA). Twenty microgram of each sample was mixed with the same volume of 2×SDS sample buffer (63 mM Tris-HCl (pH 6.8), 10% glycerol, 2% SDS, 0.0025% bromophenol blue) and boiled for 10 min. An aliquot of each sample was subjected to SDS/PAGE and transferred onto a PVDF membrane (25 mM Tris-HCl (pH 8.3), 192 mM glycine, 0.1% SDS, and 15% methanol) using a Semidry Transphor unit (Nihon Eido, Tokyo, Japan) at a constant current of 60 mA for 1 h. After the transfer, the membrane was blocked with 5% nonfat milk in the TBST buffer (20 mM Tris (pH 7.4), 150 mM NaCl, and 0.05% Tween 20) for 1 h. For immunoblotting of TLR6, the membrane was probed with the rabbit polyclonal antibody against human TLR6 (1:1000, # ab62569; Abcam, Cambridge, United Kingdom) in the TBST buffer for 1 h. For immunoblotting of the connective tissue growth factor (CTGF), the membrane was probed with the rabbit polyclonal antibody specific to human CTGF (1:1000, # ab6992; Abcam). The membranes were then washed three times with TBST and incubated with the horseradish peroxidase (HRP)-conjugated anti-rabbit antibody (1:5000; Sigma-Aldrich, St. Louis, MO) for 1 h. The specific band images were visualized after adding the HRP substrate Western Lightning Chemiluminescence Reagent Plus (PerkinElmer, Waltham, MA). The images were captured and quantified using Alpha FluroChem FC2 with Alpha View Software (Version 2.0.1.1; Alpha Innotech, Santa Clara, CA).

Statistical analysis

Quantitative data are expressed as the means±SEMs. Differences in gene expressions were compared using the Student's-t test. Statistical analyses were performed using SPSS software (SPSS, Somers, NY), version 13.0. The data shown are representative of at least three experiments with similar results. Values of p<0.05 were considered to indicate statistical significance.

Results

Candidates of MMC-treated UC cells in response to hydrostatic pressure

The effective genes that responded to MMC treatment and/or hydrostatic pressure challenge of UC cells were assessed by expression microarray. Following bioinformatics analyses and gene annotating, a total of 12 genes (8 upregulated and 4 downregulated) were identified in response to hydrostatic pressure by three GO terms, that is, response to chemical stimulus, response to stress, and response to external stimulus (Fig. 1). A summary of differently expressed genes, along with their relative fold changes, is provided in Table 1.

FIG. 1.

Genes with significant upregulation or downregulation in microarray analysis. mRNA was harvested from urothelial carcinoma (UC) cells treated with mitomycin C (MMC) (10 μg/mL) with or without hydrostatic pressure (10 kPa) stimulation. mRNA from each preparation was converted into cRNA and was labeled with Cy3-deoxycytidine triphosphate during the in vitro transcription process and then was independently hybridized to Agilent SurePrint G3 Human GE 8x60K oligonucleotide microarrays. Both conditions were created in duplicate. After bioinformatics and gene ontology analysis, alterations in gene expression with significance were selected. The details of those genes are listed in Table 1.

Table 1.

Summary of Genes with Significantly Different mRNA Expression

Gene symbol	Accession	Fold change, [10kPa-M10]/[0kPa-M10]), experiment 1 ^a	Fold change, ([10kPa-M10]/[0kPa-M10]), experiment 2 ^a	Gene ontology ^b
OR51\|1	NM_001005288	2.290	1.752	1
GPRC6A	NM_148963	1.649	2.013	1
F2R	BG026194	1.946	1.537	2, 3
CTGF	NM_001901	1.924	1.542	1, 2, 3
LATS1	NM_004690	1.807	1.597	1
TLR6	NM_006068	1.780	1.533	1, 2, 3
KLF6	NM_001160124	1.673	1.618	2, 3
MAP3K9	NM_033141	1.616	1.660	2
UCN	NM_003353	0.425	0.650	1, 2, 3
LCAT	NM_000229	0.333	0.631	1
MTL5	NM_004923	0.585	0.373	1
AOC3	NM_003734	0.343	0.613	1, 2, 3

The fold change of these genes was listed from two independent replicates.

1. Response to chemical stimulus; 2. response to stress; 3. response to external stimulus.

Pathway analysis and protein-PIP of differentially expressed genes

Among those upregulated genes, we found that Toll-like receptor 6 (TLR6) and CTGF showed significant differences. In addition, TLR6 of the four annotated genes could be imported into a specific pathway through the KEGG database, as illustrated in Figure 2, demonstrating that TLR6 would interact with Toll-like receptor 2 (TLR2), forming a heterodimer complex in the cell membrane once the cell was challenged with an extracellular stress, such as hydrostatic pressure. As a result, the subsequent signal transduction led to cell apoptosis through myeloid differentiation primary response gene 88 (MyD88), Fas-associated protein with death domain (FADD), and caspase 8 (CASP8). Searching the possible protein-protein interactions (Scott and Barton, 2007; McDowall et al., 2009), TLR6 and CTGF individually showed significance in protein-protein interactions (PIP score≥25). Briefly, two (Table 2) and five different genes (Table 3) were predicted to interact with TLR6 and CTGF, respectively.

FIG. 2.

Toll-like receptor 6-related pathway. Toll-like receptor 6 (TLR6) was used to search for its related pathways at the KEGG Web site. TLR6 forms a heterodimer with Toll-like receptor 2 (TLR2) in the cell membrane and acquires extracellular stress, such as with hydrostatic pressure. The signal is delivered into the cytosol and induces cell apoptosis through myeloid differentiation primary response gene 88 (MyD88), Fas-associated protein with death domain (FADD), and caspase 8 (CASP8).

Table 2.

TRL6-Interacting Proteins

Protein symbol	Full name	PPI scores
TLR1	Toll-like receptor 1	40.0
MYD88	Myeloid differentiation primary response protein	32.8

Table 3.

Connective Tissue Growth Factor-Interacting Proteins

Protein symbol	Full name	PPI scores
COL1A1	Collagen alpha-1(I) chain precursor	496.0
COL5A1	Collagen alpha-1(V) chain	174.0
COL3A1	Collagen alpha-1(III) chain precursor	165.0
CYR61	CCN family member 1	51.2
SPARC	Osteonectin	42.0

Gene expressions were validated by qPCR and immunoblotting

To validate the microarray data and to analyze further the results from bioinformatics analysis, the expressions of two upregulated genes (TLR6 and CTGF) from MMC-treated UC cells in response to hydrostatic pressure were quantified by qPCR. As the results show in Figure 3A, the qPCR data revealed good concordance with the microarray data in terms of the direction of upregulated expressions. The BFTC905 cells treated with 10 μg/mL of MMC treatment had increased mRNA levels of TLR6 and CTGF. Interestingly, the hydrostatic pressure (10 kPa) alone significantly resulted in the increasing of both gene expressions (TLR6: 14.65-fold, p=0.003 and CTGF: 10.34-fold, p<0.001). Moreover, this hydrostatic pressure synergistically promoted the expressions of TLR6 (15.26-fold, p<0.001) and CTGF (16.51-fold, p≤0.001) in MMC-treated BFTC905 cells. To eliminate the concern that these gene expressions may reflect the characteristics of the particular cell line and not a general phenomenon, we enrolled another well-described UC cell line, HT-1376 to perform qPCR analysis of TRL6 and CTGF (Fig. 3B). The results also showed the comparable upregulated TLR6 and CTGF gene expressions with statistical significance in each experimental condition. The protein expressions of TLR6 and CTGF in both UC cell lines were validated by immunoblotting and quantified (Fig. 3C, D). It showed accordance with the mRNA expressions in each experimental condition.

FIG. 3.

Verification of microarray results by real-time PCR and immunoblotting. The real-time PCR was used for verification of the TLR6 and connective tissue growth factor (CTGF) mRNA expression profiles in BFTC905 (A) and HT-1376 (B) UC cell lines treated with hydrostatic pressure (10 kPa), MMC (10 μg/mL), with or without hydrostatic pressure (10 kPa). The TLR6 and CTGF protein expressions were verified by immunoblotting under each experimental condition in both cell lines (C) and the protein expression levels were quantified (D). The GAPDH protein expression was used as control.

Discussion

Using a microarray approach, we screened out two possible candidate genes, TLR6 and CTGF, in response to MMC and hydrostatic pressure stimulation. TLR6, also known as CD 286, participates in the innate immune response to gram-positive bacteria and fungi (Takeda and Akira, 2005). We also noted that TLR6 cooperated with TLR2 in the cell membrane (Farhat et al., 2008), activating MyD88 and Toll-interleukin 1 receptor (TIR) domain containing adaptor protein (TIRAP) (Lin et al., 2010) and then interacting with FADD (Loiarro et al., 2009) and caspase 8, leading to cellular apoptosis (Aliprantis et al., 2000; Sprick et al., 2000). According to our previous results, we monitored the UC cells that underwent apoptosis if we treated UC cells with MMC, and the apoptosis phenomenon became severe if we challenged the MMC-treated UC cells with hydrostatic pressure through FasL/Fas-dependent pathways (Chen et al., 2013). From our results, TLR6 mRNA expression was significantly elevated, by nearly twofold, in UC cells under MMC and hydrostatic pressure stimulation, compared to the group of cells that underwent MMC treatment only. We believe that the synchronized effects of MMC and hydrostatic pressure stimulation resulted in TLR6 expression, and this increased TLR6 expression might have resulted in apoptosis of the UC cells through the TLR2, MyD88, and caspase 8 pathways.

Actually, CTGF belongs to the CCN gene family and is considered to be involved in fibroblast proliferation, migration, and adhesion as well as in extracellular matrix formation (Abreu et al., 2002). Overexpression of CTGF, cooperating with TGF-beta, is thought to play a crucial role in the fibrosis pathway, which participates in the fibrosis of major organs, fibroproliferative diseases, and scarring (Grotendorst, 1997). In addition, CTGF expression is also correlated with cell death though activation of endopeptidase, which is involved in apoptotic processes (Eguchi et al., 2008). Moreover, it was reported that CTGF could induce apoptosis in the human breast cancer cell line MCF-7 (Hishikawa et al., 1999), and it had negative effects on human rhabdomyosarcoma cell growth (Croci et al., 2004). Additionally, in sarcoma, CTGF modulated apoptosis through the Fas pathways (Joyner et al., 2011). Matika et al. (2012) demonstrated that the antiproliferative factor (APF), which is a sialoglycopeptide, inhibited the proliferation of T24 bladder carcinoma cells in vitro, and there was 7.5-fold upregulation, in a dose-dependent manner, of CTGF expression in T24 bladder carcinoma cells treated with APF. CTGF overexpression enhanced APF's antiproliferative activity, whereas CTGF knockdown diminished APF-induced p53 expression. All of these results demonstrate the importance of CTGF in UC cells.

Protein complexes are biochemical entities with functions in cells; therefore, the quantities of interacting partners can reflect the physiological significance of a protein. From our survey, the TLR6-interacting proteins with scores greater than 25 using the PIPS approach were TLR1 and Myd88, and the CTGF-interacting proteins were collagen alpha-1 type 1, type 5, and type 3, CCN family member 1, and osteonectin (also called SPARC). One of the TLR6-interacting proteins, TLR1, was reported to interact with MyD88 through TLR2 (Brown et al., 2006; Jin et al., 2007), and we already know that TLR2 will interact with TLR6 and form a heterodimer in the cell membrane as a receptor, triggering the subsequent apoptosis signal through MyD88; therefore, TLR6, TLR2, MyD88, and TLR1 very possibly constitute a large protein complex that responds to MMC and hydrostatic pressure and, hence, results synergistically in the programmed cell death of UC cells. Similarly, this synergistic effect on the apoptotic activity is also revealed in HEK 293 cells, a human kidney cell line, triggered by mycoplasmal lipoproteins (Into et al., 2004). Thus, we assume that the same cellular effect will be produced by different extracellular stimuli through the same pathway in different cells. In contrast, all of the CTGF-interacting proteins are constituents of or interact with the cell matrix. Among them, SPARC appears to regulate cell growth through interactions with the extracellular matrix and cytokines (Lane and Sage, 1994; Mok et al., 1996), and SPARC has been considered to be a human bladder cancer marker because the downregulated SPARC protein has been found in malignant urothelial cells (Mitra and Cote, 2009; Larson et al., 2010).

In conclusion, we propose here an in vitro study that, under treatment with MMC and hydrostatic pressure, UC cells showed a phenomenon of increasing apoptosis by means of extrinsic pathways through upregulation of TLR6 and CTGF in response to hydrostatic pressure. However, we do not rule out the possibility that the apoptosis of the treated UC cells with MMC and hydrostatic pressure may not only be induced by this mechanism. These results may translate to the treatment of UC clinically by modulating TLR6 and CTGF expression in the future.

Footnotes

Acknowledgments

This study was supported, in part, by research grants from the Cathay General Hospital (CGH-MR-9917, CGH-MR-10120 and CGH-MR-10220) and a joint grant from the National Central University and Cathay General Hospital (101CGH-NCU-A1), Taiwan.

Author Disclosure Statement

No competing financial interests exist.

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