Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Induction of proto-oncogene BRF2 in breast cancer cells by the dietary soybean isoflavone daidzein

  • Jana Koo1,
  • Stephanie Cabarcas-Petroski2,
  • John L. Petrie3,
  • Nicole Diette1,
  • Robert J. White3 and
  • Laura Schramm1Email author
BMC Cancer201515:905

https://doi.org/10.1186/s12885-015-1914-5

Received: 28 August 2015

Accepted: 6 November 2015

Published: 16 November 2015

Abstract

Background

BRF2 is a transcription factor required for synthesis of a small group of non-coding RNAs by RNA polymerase III. Overexpression of BRF2 can transform human mammary epithelial cells. In both breast and lung cancers, the BRF2 gene is amplified and overexpressed and may serve as an oncogenic driver. Furthermore, elevated BRF2 can be independently prognostic of unfavorable survival. Dietary soy isoflavones increase metastasis to lungs in a model of breast cancer and a recent study reported significantly increased cell proliferation in breast cancer patients who used soy supplementation. The soy isoflavone daidzein is a major food-derived phytoestrogen that is structurally similar to estrogen. The putative estrogenic effect of soy raises concern that high consumption of soy foods by breast cancer patients may increase tumor growth.

Methods

Expression of BRF2 RNA and protein was assayed in ER-positive or –negative human breast cancer cells after exposure to daidzein. We also measured mRNA stability, promoter methylation and response to the demethylating agent 5-azacytidine. In addition, expression was compared between mice fed diets enriched or deprived of isoflavones.

Results

We demonstrate that the soy isoflavone daidzein specifically stimulates expression of BRF2 in ER-positive breast cancer cells, as well as the related factor BRF1. Induction is accompanied by increased levels of non-coding RNAs that are regulated by BRF2 and BRF1. Daidzein treatment stabilizes BRF2 and BRF1 mRNAs and selectively decreases methylation of the BRF2 promoter. Functional significance of demethylation is supported by induction of BRF2 by the methyltransferase inhibitor 5-azacytidine. None of these effects are observed in an ER-negative breast cancer line, when tested in parallel with ER-positive breast cancer cells. In vivo relevance is suggested by the significantly elevated levels of BRF2 mRNA detected in female mice fed a high-isoflavone commercial diet. In striking contrast, BRF2 and BRF1 mRNA levels are suppressed in matched male mice fed the same isoflavone-enriched diet.

Conclusions

The BRF2 gene that is implicated in cancer can be induced in human breast cancer cells by the isoflavone daidzein, through promoter demethylation and/or mRNA stabilization. Dietary isoflavones may also induce BRF2 in female mice, whereas the converse occurs in males.

Keywords

Breast CancerTFIIIBBRF2RNA polymerase IIISoyDaidzein

Background

RNA polymerase (pol) III has the responsibility of synthesizing a variety of short noncoding RNAs such as tRNAs and the spliceosomal U6 snRNA [1]. Initiation by pol III requires TFIIIB [1], a transcription factor complex with at least two forms in mammalian cells [2, 3]. Gene-internal pol III promoters, such as those found in tRNA genes, require TFIIIB composed of TBP, BDP1 and BRF1 subunits, whereas gene-external pol III promoters, as exemplified by U6 genes, require TFIIIB containing TBP, BDP1 and BRF2 [1]. Aberrant pol III transcription is a feature of many tumor types [4]. This reflects, in part, the fact that TFIIIB is strongly regulated by pathways involving oncogenes and tumor suppressors [4, 5]. For example, MYC [6] and the MAP kinase ERK [7] bind to TFIIIB and stimulate its activity, whereas an array of tumor suppressors inhibit TFIIIB activity [8], either directly or indirectly, including BRCA1 [9], PTEN [10, 11], p53 [12], and the RB family [13].

The BRF2 subunit of TFIIIB is encoded by an oncogene at 8p12 that is frequently amplified and overexpressed in breast cancers and lung squamous cell carcinomas (SqCC) [1420, 21]. BRF2 drives the 8p12 amplification in SqCC [22]. Its overexpression stimulates proliferation and saturation density of human bronchial epithelial cells, whereas its knockdown specifically suppresses proliferation and anchorage-independent growth of SqCC cells with 8p12 amplification [22]. Copy number increases and overexpression of BRF2 are apparent in most pre-invasive bronchial carcinomas in situ, with minimal staining in benign lesions [22]. BRF2 induction was therefore proposed as an early event in development of lung SqCC, that might serve as a marker and/or therapeutic target [22]. Subsequent independent studies reported elevated BRF2 protein in lung and esophageal SqCC, where high BRF2 was independently prognostic of unfavorable survival for both lung (P = 0.007) and esophageal (P = 0.009) SqCC [23, 24]. BRF2 overexpression may also be an oncogenic driver in some breast cancers and human mammary epithelial cells can be transformed by transfection of the BRF2 gene [15]. Analysis of published datasets, using the Web-based Oncomine platform, reveals that BRF2 was amongst the top 1 % of genes overexpressed in a study [25] of 154 invasive breast carcinomas (p = 3.53E-10), whilst a larger study of over two thousand breast samples [26] confirmed BRF2 overexpression in several tumor subgroups, with invasive ductal breast carcinomas the most significant (p = 2.17E-21). The cBioPortal cancer genomics database [2729] reveals amplification of the BRF2 gene in 12 % of 825 tumors in the Breast Invasive Carcinoma study (TCGA, Nature 2012) [30].

The chemopreventive polyphenol EGCG, enriched in green tea, specifically decreases TFIIIB activity in cervical cancer cells [31]. The polyphenols genistein and daidzein are isoflavone components of soybeans, a major crop in the United States and globally [32]. These soy isoflavones are major food-derived phytoestrogens that are structurally similar to estrogen with the capacity to weakly bind to estrogen receptors (ERs) [33]. The putative estrogenic effect of soy raises the concern that high consumption of soy foods by breast cancer patients and/or women at high risk for breast cancer may increase estrogen-dependent breast tumor growth [34]. A recent study reported a significant increase in cell proliferation in breast cancer patients who used soy supplementation [35]. Dietary soy isoflavones increase metastasis to lungs in an experimental model of breast cancer [36]. These data prompted us to investigate if the soy isoflavone daidzein regulates TFIIIB. We found that 10 uM daidzein stimulates expression of the TFIIIB subunits BRF1 and BRF2 in ER-positive breast cancer cells, as well as pol III products U6 snRNA and tRNAi Met. Daidzein treatment stabilizes BRF2 and BRF1 mRNAs and raises levels of their protein products. It also triggers selective demethylation of the BRF2 promoter. These effects are not seen in an ER-negative breast cancer line. An isoflavone-enriched diet also induces BRF2 in female mice, but has the opposite effect in males. These in vitro and in vivo data suggest that dietary isoflavones differentially regulate TFIIIB expression, an important observation given the evidence that BRF2 can drive tumorigenesis and is predictive of poor prognosis.

Methods

Cell lines and daidzein treatment

MCF-7 and MDA-MB-231 cells were obtained from the American Type Culture Collection (Rockville, MD). Cells were cultured in DMEM supplemented with FBS (5 % v/v), nonessential amino acids (100 mM), L-glutamine (5 mM), streptomycin (100 μg/ml), and penicillin (100 units/ml); all from BioWhittaker, Walkersville, MD. Cells were grown at 37 °C in a humidified atmosphere of 95 % air and 5 % CO2 as previously described [37, 38]. Daidzein (Sigma) treatments are as described in figure legends.

5-Azacytidine treatment

Asynchronous MCF-7 and MDA-MB-231 cells were plated at 1 × 104 cell/well in 6-well plates. After 24 h, cells were treated with 5 μM 5-azacytidine (Sigma) for 24, 48 and 72 h. At each time point, total RNA was collected using RNeasy total RNA isolation kit (Qiagen), according to the manufacturer's protocol and cDNA subsequently prepared to be used in qRT-PCRassays.

Quantitative reverse transcription PCR (qRT-PCR)

Total RNA was extracted from cancer cell lines using the RNeasy total RNA isolation kit (Qiagen), according to the manufacturer's protocol and qPCR was performed using diluted cDNA from treated breast cancer cells and SsoAdvanced™ Universal SYBR® Green Supermix (BioRad). Gene specific primers include: BRF2-forward, 5’-CAG AAG TGG AGA CCC GAG AG-3’; BRF2-reverse, 5’-CAG GGA GGG TTA GGG ACA CT-3’; BRF1-forward, 5’-GGC ATT GAT GAC CTG GAG AT-3’; BRF1-reverse, 5’-ACC AGA GGC CTC AAC CTT TT-3’; BDP1-forward, 5’-TGG AAG AAG CTG GAA GGA GA-3’; BDP1-reverse, 5’-TTC CTC AAT GGC ATC AAT CA-3’; TBP-forward, 5’-CGG CTG TTT AAC TTC GCT TC-3’; TBP reverse, 5’-CTG TTG TTG TTG CTG CTG CT-3’; U6-forward, 5’-GGT CGG GCA GGA AAG AGG GC-3’; U6-reverse, 5’- GCTAAT CTT CTC TGT ATC GTT CC-3’; tRNAi Met-forward, 5’- CTG GGC CCA TAA CCC AGA G-3’; tRNAi Met-reverse, 5’-TGG TAG CAG AGG ATG GTT TC-3’; GAPDH-forward, 5’- TCCACCACCCTGTTGCTGTA-3’; GAPDH-reverse, 5’- ACC ACA GTC CAT GCC ATC AC-3’; RPS13-forward, 5’-GTT GCT GTT CGA AAG CAT CTT G-3’; RPS13-reverse, 5’-AAT ATC GAG CCA AAC GGT GAA-3’; actin β-forward, 5’-TAG CGG GGT TCA CCC ACA CTG TGC CCC A-3’; actin β-reverse, 5’- CTA GAA GCA TTT GCG GTG GAC CGA TGG A-3’. Real time quantitative PCR reactions were carried out using the Bio-Rad CFX Connect System. The ΔΔCt method was employed for each gene tested as noted in figures using GAPDH and RPS13 expression levels for normalization. Meta-analysis of data using one-way ANOVA with a Tukey post-test with a 95 % confidence interval (GraphpadPrism3.03, San Diego California USA); * = p <0.05; ** = p < 0.01; *** = p < 0.001.

Western blot analysis

Total cellular protein was extracted using Cytobuster Protein Extraction Reagent (Merck Millipore; 71009). Proteins were fractionated by SDS-PAGE and transferred to nitrocellulose membranes, which were incubated overnight with antibodies against BRF1 (Bethyl Laboratories; A301-228A), BRF2 and actin (Santa Cruz Biotechnology; sc-390312 and sc-1615). Membranes were then incubated with HRP-conjugated anti-goat (Dako; P0449) anti-rabbit and anti-mouse (Cell Signaling; 7074 and 7076) IgG for 1 h. Bands were visualized using the enhanced chemiluminescence method.

Methylation analysis

Promoters were analyzed using MethPrimer [39] to identify potential CpG islands. NEBcutter V2.0 [40] was used to identify methylation sensitive restriction enzyme (MSRE) sites sensitive within promoter sequences. Genomic DNA was isolated from MCF-7 and MD-MB-231 cancer cells using DNeasy Blood & Tissue Kit (Qiagen) using the manufacturer’s protocol. Restriction enzymes AciI, AscI, BanI, BfuAI, BsrFI, BsrBI, BseYI, BfuAI, BspEI, Cac81, FspI, NciI, NruI (New England Biolabs) and HpyCH4III/Taal (Fermentas) were used. Restriction digestions were then analyzed by real time PCR using Universal SYBR Green (BioRad) and primers spanning the promoters regions and primers spanning the promoters regions (BRF2-forward, 5’-GGC CTC CAA AAG CGT T-3’; BRF2-reverse, 5’-AGC TGG CTC TGC GAA TAG T-3’; BRF1-forward, 5’-GGG GTT GGG TCC CAG GTC GC-3’; BRF1-reverse, 5’-GTC CTC CAG CAC TGA GCC GC-3’; U6-forward, 5’- AAG TAT TTC GAT TTC TTG GC-3’; U6-reverse, 5’- AAT ATG GAA CGC TTC ACG-3’; tRNAi Met-forward, 5’-TAG ATA GCA GAG TGG CGC A-3’; tRNAi Met-reverse, 5’-AAC TCC GAT AGC AGA GGA TG-3’). Results were quantified using the ΔΔCt method and normalized to RPS13 expression levels. Data presented are average of three independent experiments. Statistical analysis was performed using one-way ANOVA with a Tukey’s post-test with a 95 % confidence interval (Graphpad Prism 3.03); * = p <0.05; ** = p < 0.01; *** = p < 0.001.

mRNA stability of the BRF1 and BRF2 genes

The mRNA stability of BRF1 and BRF2 mRNA was determined following actinomycin D treatment. Actinomycin D was added to a final concentration of 5 μg/ml to block transcription [41]. At 0, 4, 8, 12 and 24 h post-actinomycin D treatment, the cells were harvested, and mRNA was quantified by qRT-PCRas described above. Data show relative expression values at time points indicated with control untreated sample set to 1 at 0 h post-actinomycin D treatment.

ELISA

BRF2 protein levels were measured from untreated and daidzein treated MCF-7 and MD-MB-231 cells using a human BRF2 ELISA kit (MyBioSource) as per the manufacturer’s protocol. Data presented are average of four independent experiments. Statistical analysis was performed using one-way ANOVA with a Tukey’s post-test with a 95 % confidence interval (Graphpad Prism 3.03); * = p <0.05; ** = p < 0.01; *** = p < 0.001.

Animals and isoflavone treatment

Female and male C57BL/6 J mice were purchased from Taconic Farms (Germantown, New York). Mice were housed 4 per cage in a temperature-controlled St. John’s University Animal Care Facility with alternating 12:12 h light–dark cycles, with ad libitum access to water and commercial Purina 5001 or 5 K96 chows (Fig. 7), as approved in its entirety by SJU IACUC (SJU Protocol number 1831.0). Mice were numbered with permanent marker identification, and acclimated to animal penthouse for 72 h prior to start of experiment. Mice were treated for a total of three weeks and monitored daily for signs of stress. Weekly tail bleeds and a terminal cardiac puncture bleed were collected and analyzed for TFIIIB levels via qRT-PCR.

Results

Daidzein induces BRF1, BRF2 and pol III transcript expression in MCF-7 breast cancer cells

Human breast cancer cell lines were treated for 48 h with daidzein and qRT-PCRwas used to assay expression of mRNAs encoding the subunits of TFIIIB. The mRNAs encoding BRF1 and BRF2 were both found to be induced significantly in ER-positive MCF-7 cells (p < 0.001 and p < 0.01, respectively; Fig. 1a). This response is selective and not shown by the mRNAs encoding TBP and BDP1, the other TFIIIB subunits. Furthermore, none of the TFIIIB mRNAs showed significant responses to daidzein in ER-negative MDA-MB-231 cells, under these conditions (Fig. 1b). The induction of BRF1 and BRF2 mRNAs in MCF-7 cells is translated into a corresponding increase in the protein products, as shown by western blotting (Fig. 2a). As with the mRNA, protein expression does not respond in MDA-MB-231 cells (Fig. 2b). Quantitative analysis by ELISA established that BRF2 protein induction in MCF-7 cells is 2.4-fold with 3 μM daidzein and 4.2-fold with 10 μM daidzein (Fig. 2c). As observed by western, no significant change in BRF2 levels was detected by ELISA with MDA-MB-231 cells (Fig. 2d). The uninduced level of BRF2 is higher in MDA-MB-231 than MCF-7 cells (1.4-fold elevated by quantitative ELISA), consistent with a previously published report [42], which showed that BRF2 mRNA is expressed at higher levels in MDA-MB-231 cells than in MCF-7 cells. However, it is unlikely that BRF2 has reached a saturation point in untreated MDA-MB-231 cells that would preclude further induction.
Figure 1
Fig. 1

Daidzein induces BRF1 and BRF2 mRNA specifically in MCF-7 breast cancer cells. MCF-7 (a) and MDA-MB-231 (b) cells were treated with 0, 3 or 10 μM daidzein for 48 h. BRF2, BRF1, BDP1 and TBP mRNA expression was then analysed by qRT-PCRusing the ΔΔCt method with RPS13 expression levels as a reference for normalization. Meta-analysis of three independent experiments performed in triplicate was completed using one-way ANOVA with a Tukey’s post-test with a 95 % confidence interval (Graphpad Prism 3.03); * = p <0.05; ** = p < 0.01; *** = p < 0.001

Figure 2
Fig. 2

Daidzein induces BRF1 and BRF2 protein specifically in MCF-7 cells. MCF-7 (a) and MDA-MB-231 (b) cells were treated with 0 or 3 μM daidzein for 48 h and then analysed for expression of BRF1, BRF2 and actin proteins by western blot. Quantification of BRF2 protein expression in (c) MCF-7 and (d) MDA-MB-231 cells treated with daidzein was achieved by ELISA. Data presented are average of four independent experiments. Statistical analysis was performed using one-way ANOVA with a Tukey’s post-test with a 95 % confidence interval (Graphpad Prism 3.03); * = p <0.05; ** = p < 0.01; *** = p < 0.001

If the observed induction of BRF1 and BRF2 is functionally significant, we would expect to see changes in expression of pol III products that depend upon these subunits for their transcription. Indeed, both U6 snRNA, dependent on BRF2, and tRNAi Met, requiring BRF1, show significant induction in MCF-7 cells by 10 μM daidzein (Fig. 3a). As with BRF1 and BRF2, neither of these pol III transcripts is induced when MDA-MB-231 cells are treated in the same way (Fig. 3b). As pol III activity is generally coupled to cell proliferation, we tested if daidzein has a mitogenic effect under our assay conditions. However, cell viability assays provided no evidence of enhanced proliferation when either cell line was exposed to 10 μM daidzein (Figs. 3c and d); indeed, significant suppression was observed after 72 h, in agreement with previous studies noting daidzein treatments greater than 1 μM inhibit breast cancer cell proliferation [43]. The selective induction of U6 snRNA and tRNAi Met therefore appears not to reflect a mitogenic response, but correlates with increases in BRF1 and BRF2.
Figure 3
Fig. 3

Daidzein induces U6 snRNA and tRNAi Met specifically in MCF-7 cells without inducing proliferation. MCF-7 (a) and MDA-MB-231 (b) cells were treated with 0, 3, 10 μM daidzein for 48 h. U6 snRNA and tRNAi Met expression was then analysed by qRT-PCRusing the ΔΔCt method with RPS13 expression levels as a reference for normalization. Meta-analysis of three independent experiments performed in triplicate was completed using one-way ANOVA with a Tukey’s post-test with a 95 % confidence interval (Graphpad Prism 3.03); * = p <0.05; ** = p < 0.01; *** = p < 0.001. CellTiter-Glo® (Promega) was used to count MCF- 7 (c) and MDA-MB-231 cells (d) after 24, 48 and 72 h treatment with 0, 3 or 10 μM daidzein, as indicated. Each dose and time point was performed in triplicate. MDA-MD-231 cell proliferation significantly decreased with 10 μM daidzein after 48 h (p < 0.05) and 72 h (p < 0.01) treatment. MCF-7 cell proliferation significantly decreased at 48 h (p < 0.01) with 3 μM daidzein treatment. At 10 μM daidzein treatment cell proliferation was significantly inhibited in MCF-7 cells at 24 h (p < 0.05), 48 h (p < 0.01) and 72 h (p < 0.01)

Daidzein decreases methylation of the BRF2 promoter specifically in MCF-7 cells

There are a number of published studies describing epigenetic regulation of gene expression by dietary polyphenols in prostate and breast cancer cell lines [44, 45]. We therefore investigated whether daidzein influences DNA methylation of the BRF1 and BRF2 promoter regions. Treatment of MCF-7 cells with 10 μM daidzein significantly decreased methylation of four restriction sites in the BRF2 promoter (Fig. 4a). The same treatment had minimal effect on the methylation status of several restriction sites within the BRF1 promoter (Fig. 4b). The minimal response of BRF1 indicates that the effect of daidzein on BRF2 promoter methylation is selective. Further evidence of specificity is provided by analysis of U6 and tRNAi Met genes, where DNA methylation is unchanged in response to daidzein (Figs. 4c and d). Localized demethylation of the BRF2 promoter was also observed when MDA-MB-231 cells were treated with daidzein, but the response was less marked than in MCF-7 cells and only one site showed a statistically-significant change at the 10 μM dose (Fig. 4e). In untreated cells, the BRF2 was much less heavily methylated in MDA-MB-231 than in MCF-7 (Fig. 4f), which may explain its higher expression in the former (Fig 2. and [42]).
Figure 4
Fig. 4

Daidzein changes DNA methylation at the BRF2 promoter specifically. CpG methylation- sensitive restriction enzymes cut sites are shown. Transcription start site (TSS) is indicated by +1. MethPrimer program was used to predict the location of CpG islands within the BRF2 (402–556 bp) and BRF1 (50–910 bp) promoters, as indicated by black arrow. Black bars denote the binding sites for primers used in the methylation profile analysis. a-d MCF-7 and (e) MDA-MB-231 cells were treated with 0, 3, 10 μM daidzein for 48 h. The genomic DNA was harvested and digested with methylation-sensitive restriction enzymes with cut sites within the promoter, noted on BRF2 and BRF1 promoter schematic, and as a negative control one methylation sensitive enzyme with no recognition sites in the promoter was used. BspEI and BsRFI do not have recognition sites in BRF2 and BRF1 promoters, respectively. The digestion profile was then analyzed by qPCR using primers spanning the BRF2 (a, e, f) and BRF1 (b) promoter regions, U6 (c) and tRNAi Met (d) genes. DNA methylation levels were calculated using ΔΔCt method with RPS13 expression levels used as a reference for normalization. Data presented are average of three independent experiments. Statistical analysis was performed using one-way ANOVA a Tukey’s post-test with a 95 % confidence interval (Graphpad Prism 3.03); * = p <0.05; ** = p < 0.01; *** = p < 0.001

If demethylation of promoter DNA can influence transcription of the BRF2 gene, then treatment with the methylation inhibitor 5-azacytidine might be predicted to induce expression. Indeed, BRF2 mRNA levels were elevated within 48 h of adding 5-azacytidine to MCF-7 cells, along with promoter demethylation (Figs. 5a and b). This response was specific, as it was not shown by BRF1 mRNA when assayed in parallel (Fig. 5c). Furthermore, the same treatment failed to induce either BRF1 or BRF2 in MDA-MB-231 cells (Figs. 5d and e). These data suggest that BRF2 promoter activity may be sensitive to DNA methylation, which responds to daidzein in MCF-7 cells.
Figure 5
Fig. 5

Azacytidine induces demethylation and expression of BRF2 selectively in MCF-7 cells. a, b, c MCF-7 and (d, e) MDA-MB-231 cells were treated with 5 μM 5-Azacytidine for 24, 48 and 72 h. Post-treatments, total RNA was isolated and analyzed by qRT-PCRfor expression of (a, d) BRF2 and (c, e) BRF1 mRNAs. The ΔΔCt method with GAPDH and RPS13 expression levels was used as a reference for normalization. Meta-analysis of two independent experiments performed in triplicate was completed using one-way ANOVA with a Tukey’s post-test with a 95 % confidence interval (Graphpad Prism 3.03); * = p <0.05; ** = p < 0.01; *** = p < 0.001. (b) Genomic methylation of BRF2 promoter DNA in MCF-7 cells was analyzed with methylation-sensitive restriction enzymes, as in Fig. 4a

Daidzein increases stability of BRF1 and BRF2 mRNAs in MCF-7 cells

The 3’-untranslated regions (3’-UTR) of BRF1 and BRF2 mRNAs are AU-rich, which may influence their stability [42]. Various classes of polyphenols have been shown to post-transcriptionally regulate levels of mRNA containing AU-rich elements by raising expression of RNA binding proteins [4649]. To test if daidzein stabilizes BRF mRNAs, as a potential mechanism of induction, we treated MCF-7 cells with the transcription inhibitor actinomycin D [41] and monitored mRNA decay using qRT-PCR. Although 10 μM daidzein was unable to prevent the turnover of BRF mRNAs by 24 h, it prevented degradation of both BRF2 (p < 0.01; Fig. 6a) and BRF1 (p < 0.05; Fig. 6b) for 12 h after transcription inhibition. This effect is specific, as mRNA encoding ribosomal protein S13 (RPS13) decayed with similar kinetics in the presence or absence of daidzein, when assayed in parallel (Fig. 6c). The selective stabilization of BRF mRNAs offers a potential explanation for the increased expression seen when MCF-7 cells are exposed to 10 μM daidzein. Additional mechanisms may also be involved, such as demethylation of the BRF2 promoter (Fig. 4b).
Figure 6
Fig. 6

Daidzein increases stability of BRF1 and BRF2 mRNAs selectively in MCF-7 cells. MCF-7 cells were treated with 5 μg/ml of actinomycin D, either alone (white bars) or with 10 μM daidzein (black bars) for 24 h. The cells were then harvested at 4, 8, 12 and 24 h. The RNA was extracted and analyzed by qRT-PCRfor levels of (a) BRF2, (b) BRF1, (c) RPS13, and actin β mRNAs. ΔΔCt method with RPS13 and actin β expression levels were calculated for assay normalization. Data presented are average of three independent experiments. Statistical analysis was performed using one-way ANOVA with a Tukey’s post-test with a 95 % confidence interval (Graphpad Prism 3.03); * = p <0.05; ** = p < 0.01; *** = p < 0.001

Commercial rodent chow differentially regulates BRF1 and BRF2 in a sex-dependent manner

Purina 5001 is a chow that is commonly fed to laboratory rodents and contains high levels of both daidzein and genistein, the two major isoflavone components of soybeans [50]. Our observations with daidzein in cultured cells prompted us to investigate if BRF expression is elevated in mice fed a diet enriched in these isoflavones. RNA was isolated from the blood of mice maintained on either Purine 5001 or the isoflavone-free casein-based Purina 5 K96 chow. Significantly elevated BRF2 expression was found in female mice with the high isoflavone Purina 5001 diet (Fig. 7a). In striking contrast, the opposite was seen in males, where BRF2 expression was significantly lower with high isoflavones relative to the isoflavone-free diet (Fig. 7b). BRF1 was also suppressed in males fed the high isoflavone Purina 5001 chow, but was unaffected in females. No statistically significant changes were found in the other TFIIIB subunits, TBP and BDP1. These in vivo data suggest that male and female C57BL/6 J mice respond differently to dietary sources of isoflavones and these differences are measurable in the blood. They emphasize the importance of taking into account both gender and diet in mouse models of cancer.
Figure 7
Fig. 7

Differential expression of TFIIIB subunits in female and male mice fed high or low isoflavone diets. Five week C57CBL/6 J (a) female and (b) male mice had free access to isoflavone-free, casein-based diet 5 K96 or regular Lab Diet 5001, with high isoflavone content. Post-treatments, total RNA was isolated from blood collected by cardiac puncture and analyzed by qRT-PCR for expression of BRF2, BRF1, BDP1, and TBP. The ΔΔCt method with GAPDH expression levels was used as a reference for normalization. Data presented are average of six female and six male mice from respective groups. Statistical analysis was performed using one-way ANOVA with a Tukey’s post-test with a 95 % confidence interval (Graphpad Prism 3.03); * = p <0.05; ** = p < 0.01; *** = p < 0.001

Discussion

Breast cancer is the leading cause of cancer-related deaths in females in developing countries and the second leading cause in developed countries [51, 52]. It has been estimated that there will be approximately 1.7 million new cases of breast cancer in 2020, a 26 % increase from current incidence rates, mostly due to the increase in new cases in the developing world [53]. As diet is known to influence the incidence of breast cancer [54], identifying the impact of specific foods is imperative. Soy has been studied extensively for its anti-cancer properties, but the epidemiological results have been contradictory, in part because soy can regulate gene expression via estrogen- dependent and independent pathways. We therefore investigated the molecular effects of the isoflavone daidzein in both ER-positive and ER-negative cancer cells. We observed clear effects in the former that were not seen in the latter. Specifically, daidzein raises expression of BRF2 and BRF1 in MCF-7 cells, as well as pol III transcripts that depend on these TFIIIB subunits for their synthesis. It is noteworthy that another polyphenol, EGCG that is enriched in green tea, also affects expression of BRF1 and BRF2 selectively, but in this case elicits an inhibitory effect, in keeping with its anti-cancer activity [31].

Induction of BRF2 and BRF1 by daidzein may be explained by stabilization of their respective mRNAs, although additional effects might also contribute. For example, we also observed demethylation of the BRF2 promoter that might result in a transcriptional response. How the polyphenol produces these effects remains to be established. A clear possibility is that the ER is involved, either directly or indirectly, as this can be weakly bound by daidzein [33]. Indeed, ER has been shown to bind directly to the BRF1 promoter in MCF-7 cells [55] and 17β-estradiol has been shown to induce tRNA synthesis [56]. Those studies did not investigate BRF2 or its target genes, such as U6. Circumstantial evidence in support of ER involvement is provided by the differential response in ER-positive MCF-7 and ER-negative MDA-MB-231 cells in culture, and the gender-specificity of induction in mice. However, further studies will be required to establish if ER mediates the observed effects. In terms of the current study, we consider the crucial finding to be that physiologically relevant doses of a common dietary compound can stimulate expression of a gene with established oncogenic properties. This raises concerns for women consuming soy.

It remains to be determined how BRF2 exerts oncogenic effects, although the obvious possibility is through transcriptomic changes, given its well-established function as a transcription factor. Genome-wide analyses showed that BRF2 has a highly restricted set of target genes [57], all encoding short pol III-dependent noncoding RNAs. In addition to U6 snRNA, these include 7SK RNA, which regulates transcription of protein-coding genes by inhibiting P-TEFb [5860], and hY RNAs, which promote DNA replication [61]. The hY RNAs are commonly overexpressed in tumors [62, 63]. For example, hY1 is overexpressed ~13-fold in lung, prostate and other cancers (P = 9.7 × 10−26) and its knockdown inhibits proliferation of lung and prostate cell lines [62]. As a key component of spliceosomes, U6 snRNA is an essential BRF2-dependent product. It was consistently detected at elevated levels in sera from 140 breast cancer patients, relative to 115 healthy age-matched controls [64]. U6 was also the most strongly upregulated (P < 0.0001; FDR <0.001) non-coding RNA in serum from 75 glioblastoma multiforme patients, relative to matched healthy controls [65]. MicroRNAs are widely believed to have considerable potential as biomarkers, but U6 outperformed all 381 miRNAs tested in this study [65]. It has also been suggested that BRF2 might serve as a biomarker, given its early induction in development of lung SqCC [22], where high BRF2 is independently prognostic (P = 0.007) of unfavourable survival [24], as also reported (P = 0.009) for oesophageal SqCC [23]. Our demonstration that BRF2 levels can be monitored in mouse serum provides initial evidence that a non-invasive test is practical for this potential biomarker.

Conclusions

This study demonstrates, both in vitro and in vivo, that a common soy isoflavone, at dietary concentrations, can influence expression of BRF2 and BRF1, essential components of the pol III-specific transcription factor TFIIIB. The response may involve an epigenetic component and gender-dependence in mice. Its importance is suggested by recent evidence that BRF2 can be oncogenic and prognostic of poor survival.

Ethics

Animal experiments presented were approved by SJU IACUC protocol 1831.0 in its entirety.

Availability of supporting data

Authors declare there is no additional supporting data to be made available. All DNA promoter sequences are readily available in PubMed database.

Declarations

Acknowledgements

We thank Helen Scaramell, Director Animal Center at St. John’s University, for helpful discussions.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Biological Sciences, St. John’s University, New York, USA
(2)
Pennsylvania State University, Monaca, USA
(3)
Department of Biology, University of York, York, UK

References

  1. Schramm L, Hernandez N. Recruitment of RNA polymerase III to its target promoters. Genes Dev. 2002;16(20):2593–620.View ArticlePubMedGoogle Scholar
  2. Schramm L, Pendergrast PS, Sun Y, Hernandez N. Different human TFIIIB activities direct RNA polymerase III transcription from TATA-containing and TATA-less promoters. Genes Dev. 2000;14(20):2650–63.PubMed CentralView ArticlePubMedGoogle Scholar
  3. Teichmann M, Wang Z, Roeder RG. A stable complex of a novel transcription factor IIB- related factor, human TFIIIB50, and associated proteins mediate selective transcription by RNA polymerase III of genes with upstream promoter elements. Proc Natl Acad Sci U S A. 2000;97(26):14200–5.PubMed CentralView ArticlePubMedGoogle Scholar
  4. White RJ. RNA polymerase III transcription and cancer. Oncogene. 2004;23(18):3208–16.View ArticlePubMedGoogle Scholar
  5. Grewal SS. Why should cancer biologists care about tRNAs? tRNA synthesis, mRNA translation and the control of growth. Biochem Biophys Acta. 2015;1849:898–907.PubMedGoogle Scholar
  6. Gomez-Roman N, Grandori C, Eisenman RN, White RJ. Direct activation of RNA polymerase III transcription by c-Myc. Nature. 2003;421(6920):290–4.View ArticlePubMedGoogle Scholar
  7. Felton-Edkins ZA, Fairley JA, Graham EL, Johnston IM, White RJ, Scott PH. The mitogenactivated protein (MAP) kinase ERK induces tRNA synthesis by phosphorylating TFIIIB. EMBO J. 2003;22(10):2422–32.PubMed CentralView ArticlePubMedGoogle Scholar
  8. White RJ. RNA polymerases I and III, non-coding RNAs and cancer. Trends Genet. 2008;24(12):622–9.View ArticlePubMedGoogle Scholar
  9. Veras I, Rosen EM, Schramm L. Inhibition of RNA polymerase III transcription by BRCA1. J Mol Biol. 2009;387(3):523–31.View ArticlePubMedGoogle Scholar
  10. Woiwode A, Johnson SA, Zhong S, Zhang C, Roeder RG, Teichmann M, et al. PTEN represses RNA polymerase III-dependent transcription by targeting the TFIIIB complex. Mol Cell Biol. 2008;28(12):4204–14.PubMed CentralView ArticlePubMedGoogle Scholar
  11. Cabarcas S, Watabe K, Schramm L. Inhibition of U6 snRNA Transcription by PTEN. Online J Biol Sci. 2010;10(3):114–25.PubMed CentralView ArticlePubMedGoogle Scholar
  12. Cairns CA, White RJ. p53 is a general repressor of RNA polymerase III transcription. EMBO J. 1998;17(11):3112–23.PubMed CentralView ArticlePubMedGoogle Scholar
  13. Sutcliffe JE, Cairns CA, McLees A, Allison SJ, Tosh K, White RJ. RNA polymerase III transcription factor IIIB is a target for repression by pocket proteins p107 and p130. Mol Cell Biol. 1999;19(6):4255–61.PubMed CentralView ArticlePubMedGoogle Scholar
  14. Lockwood WW, Chari R, Coe BP, Thu KL, Garnis C, Malloff CA, et al. Integrative genomic analyses identify BRF2 as a novel lineage-specific oncogene in lung squamous cell carcinoma. PLoS Med. 2010;7(7):e1000315.PubMed CentralView ArticlePubMedGoogle Scholar
  15. Tian Y, Lu M, Yue W, Li L, Li S, Gao C, et al. TFIIB-related factor 2 is associated with poor prognosis of nonsmall cell lung cancer patients through promoting tumor epithelial-mesenchymal transition. Biomed Res Int. 2014;2014:530786.PubMed CentralPubMedGoogle Scholar
  16. Lu M, Tian H, Yue W, Li L, Li S, Qi L, et al. TFIIB-related factor 2 over expression is a prognosis marker for early-stage non-small cell lung cancer correlated with tumor angiogenesis. PLoS One. 2014;9(2):e88032.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Lu M, Tian H, Yue W, Li L, Li S, Qi L, et al. Overexpression of TFIIB-related factor 2 is significantly correlated with tumor angiogenesis and poor survival in patients with esophageal squamous cell cancer. Med Oncol. 2013;30(2):553.View ArticlePubMedGoogle Scholar
  18. Cabarcas S, Schramm L. RNA polymerase III transcription in cancer: the BRF2 connection. Mol Cancer. 2011;10:47.PubMed CentralView ArticlePubMedGoogle Scholar
  19. Melchor L, Garcia MJ, Honrado E, Pole JC, Alvarez S, Edwards PA, et al. Genomic analysis of the 8p11-12 amplicon in familial breast cancer. Int J Cancer. 2007;120(3):714–7.View ArticlePubMedGoogle Scholar
  20. Garcia MJ, Pole JC, Chin SF, Teschendorff A, Naderi A, Ozdag H, et al. A 1 Mb minimal amplicon at 8p11-12 in breast cancer identifies new candidate oncogenes. Oncogene. 2005;24(33):5235–45.View ArticlePubMedGoogle Scholar
  21. Ng CK, Martelotto LG, Gauthier A, Wen HC, Piscuoglio S, Lim RS, et al. Intra-tumor genetic heterogeneity and alternative driver genetic alterations in breast cancers with heterogeneous HER2 gene amplification. Genome Biol. 2015;16(1):107.PubMed CentralView ArticlePubMedGoogle Scholar
  22. Lockwood WW, Chari R, Coe BP, Thu KL, Garnis C, Malloff CA, et al. Integrative genomic analyses identify BRF2 as a novel lineage-specific oncogene in lung squamous cell carcinoma. PLoS Med. 2010;7:e1000315.PubMed CentralView ArticlePubMedGoogle Scholar
  23. Lu M, Tian H, Yue W, Li L, Li S, Qi L, et al. Overexpression of TFIIB-related factor 2 is significantly correlated with tumour angiogenesis and poor survival in patients with esophageal squamous cell cancer. Med Oncol. 2013;30:553.View ArticlePubMedGoogle Scholar
  24. Lu M, Tian H, Yue W, Li L, Li S, Qi L, et al. TFIIB-related factor 2 over expression is a prognosis marker for early-stage non-small cell lung cancer correlated with tumor angiogenesis. PLoS One. 2014;9:e88032.PubMed CentralView ArticlePubMedGoogle Scholar
  25. Gluck S, Ross JS, Royce M, McKenna EF, Perou CM, Avisar E, et al. TP53 genomics predict higher clinical and pathologic tumour response in operable early-stage breast cancer treated with docetaxel-capecitabine +/− trastuzumab. Breast Cancer Res Treat. 2012;132:781–91.View ArticlePubMedGoogle Scholar
  26. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486:346–52.PubMed CentralPubMedGoogle Scholar
  27. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.View ArticlePubMedGoogle Scholar
  28. Eirew P, Steif A, Khattra J, Ha G, Yap D, Farahani H, et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature. 2015;518(7539):422–6.View ArticlePubMedGoogle Scholar
  29. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Network, C.G.A. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70.View ArticleGoogle Scholar
  31. Jacob J, Cabarcas S, Veras I, Zaveri N, Schramm L. The green tea component EGCG inhibits RNA polymerase III transcription. Biochem Biophys Res Commun. 2007;360(4):778–83.PubMed CentralView ArticlePubMedGoogle Scholar
  32. Bellaloui N, Bruns HA, Abbas HK, Mengistu A, Fisher DK, Reddy KN. Agricultural practices altered soybean seed protein, oil, fatty acids, sugars, and minerals in the Midsouth USA. Front Plant Sci. 2015;6:31.PubMed CentralPubMedGoogle Scholar
  33. Casanova M, You L, Gaido KW, Archibeque-Engle S, Janszen DB, Heck HA. Developmental effects of dietary phytoestrogens in Sprague–Dawley rats and interactions of genistein and daidzein with rat estrogen receptors alpha and beta in vitro. Toxicol Sci. 1999;51(2):236–44.View ArticlePubMedGoogle Scholar
  34. Rietjens IM, Sotoca AM, Vervoort J, Louisse J. Mechanisms underlying the dualistic mode of action of major soy isoflavones in relation to cell proliferation and cancer risks. Mol Nutr Food Res. 2013;57(1):100–13.View ArticlePubMedGoogle Scholar
  35. Shike, M., A.S. Doane, L. Russo, R. Cabal, J.S. Reis-Filho, W. Gerald, et al., The effects of soy supplementation on gene expression in breast cancer: a randomized placebo-controlled study. J Natl Cancer Inst, 2014. 106(9).Google Scholar
  36. Yang X, Belosay A, Hartman JA, Song H, Zhang Y, Wang W, et al. Dietary soy isoflavones increase metastasis to lungs in an experimental model of breast cancer with bone micro-tumors. Clin Exp Metastasis. 2015;32(4):323–33.View ArticlePubMedGoogle Scholar
  37. Wang P, Henning SM, Heber D. Limitations of MTT and MTS-based assays for measurement of antiproliferative activity of green tea polyphenols. PLoS One. 2010;5(4):e10202.PubMed CentralView ArticlePubMedGoogle Scholar
  38. Rollins J, Veras I, Cabarcas S, Willis I, Schramm L. Human Maf1 negatively regulates RNA polymerase III transcription via the TFIIB family members Brf1 and Brf2. Int J Biol Sci. 2007;3(5):292–302.PubMed CentralView ArticlePubMedGoogle Scholar
  39. Li LC, Dahiya R. MethPrimer: designing primers for methylation PCRs. Bioinformatics. 2002;18(11):1427–31.View ArticlePubMedGoogle Scholar
  40. Vincze T, Posfai J, Roberts RJ. NEBcutter: A program to cleave DNA with restriction enzymes. Nucleic Acids Res. 2003;31(13):3688–91.PubMed CentralView ArticlePubMedGoogle Scholar
  41. Bensaude O. Inhibiting eukaryotic transcription: Which compound to choose? How to evaluate its activity? Transcription. 2011;2(3):103–8.PubMed CentralView ArticlePubMedGoogle Scholar
  42. Cabarcas S, Jacob J, Veras I, Schramm L. Differential expression of the TFIIIB subunits Brf1 and Brf2 in cancer cells. BMC Mol Biol. 2008;9:74.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Choi EJ, Kim GH. Antiproliferative activity of daidzein and genistein may be related to ERalpha/cerbB-2 expression in human breast cancer cells. Mol Med Rep. 2013;7(3):781–4.PubMedGoogle Scholar
  44. Kikuno N, Shiina H, Urakami S, Kawamoto K, Hirata H, Tanaka Y, et al. Genistein mediated histone acetylation and demethylation activates tumor suppressor genes in prostate cancer cells. Int J Cancer. 2008;123(3):552–60.View ArticlePubMedGoogle Scholar
  45. Paluszczak J, Krajka-Kuzniak V, Baer-Dubowska W. The effect of dietary polyphenols on the epigenetic regulation of gene expression in MCF7 breast cancer cells. Toxicol Lett. 2010;192(2):119–25.View ArticlePubMedGoogle Scholar
  46. Katsanou V, Papadaki O, Milatos S, Blackshear PJ, Anderson P, Kollias G, et al. HuR as a negative posttranscriptional modulator in inflammation. Mol Cell. 2005;19(6):777–89.View ArticlePubMedGoogle Scholar
  47. Raghavan A, Robison RL, McNabb J, Miller CR, Williams DA, Bohjanen PR. HuA and tristetraprolin are induced following T cell activation and display distinct but overlapping RNA binding specificities. J Biol Chem. 2001;276(51):47958–65.PubMedGoogle Scholar
  48. Atasoy U, Watson J, Patel D, Keene JD. ELAV protein HuA (HuR) can redistribute between nucleus and cytoplasm and is upregulated during serum stimulation and T cell activation. J Cell Sci. 1998;111(Pt 21):3145–56.PubMedGoogle Scholar
  49. Kwak H, Jeong KC, Chae MJ, Kim SY, Park WY. Flavonoids inhibit the AU-rich element binding of HuC. BMB Rep. 2009;42(1):41–6.View ArticlePubMedGoogle Scholar
  50. Thigpen JE, Setchell KD, Kissling GE, Locklear J, Caviness GF, Whiteside T, et al. The estrogenic content of rodent diets, bedding, cages, and water bottles and its effect on bisphenol A studies. J Am Assoc Lab Anim Sci. 2013;52(2):130–41.PubMed CentralPubMedGoogle Scholar
  51. Torre, L.A., F. Bray, R.L. Siegel, J. Ferlay, J. Lortet-Tieulent, and A. Jemal, Global cancer statistics, 2012. CA Cancer J Clin, 2015.Google Scholar
  52. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5–29.View ArticlePubMedGoogle Scholar
  53. Breast cancer in developing countries. Lancet, 2009. 374(9701): p. 1567.Google Scholar
  54. Katzke VA, Kaaks R, Kuhn T. Lifestyle and cancer risk. Cancer J. 2015;21(2):104–10.View ArticlePubMedGoogle Scholar
  55. Zhang Q, Jin J, Zhong Q, Yu X, Levy D, Zhong S. ERa mediates alcohol-induced deregulation of Pol III genes in breast cancer cells. Carcinogenesis. 2013;34:28–37.PubMed CentralView ArticlePubMedGoogle Scholar
  56. Hah N, Danko CG, Core L, Waterfall JJ, Siepel A, Lis JT, et al. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell. 2011;145:622–34.PubMed CentralView ArticlePubMedGoogle Scholar
  57. Moqtaderi Z, Wang J, Raha D, White RJ, Snyder M, Weng Z, et al. Genomic binding profiles of functionally distinct RNA polymerase III transcription complexes in human cells. Nature Struct Mol Biol. 2010;17:635–40.View ArticleGoogle Scholar
  58. Nguyen VT, Kiss T, Michels AA, Bensaude O. 7SK small nuclear RNA binds to and inhibits theactivity of CDK9/cyclin T complexes. Nature. 2001;414:322–5.View ArticlePubMedGoogle Scholar
  59. Yang Z, Zhu Q, Luo K, Zhou Q. The 7SK small nuclear RNA inhibits the CDK9/cyclin T1 kinase to control transcription. Nature. 2001;414:317–22.View ArticlePubMedGoogle Scholar
  60. Peterlin BM, Price DH. Controlling the elongation phase of transcription with P-TEFb. Mol Cell. 2006;23:297–305.View ArticlePubMedGoogle Scholar
  61. Christov CP, Gardiner TJ, Szuts D, Krude T. Functional requirement of noncoding Y RNAs for human chromosomal DNA replication. Mol Cell Biol. 2006;26:6993–7004.PubMed CentralView ArticlePubMedGoogle Scholar
  62. Christov CP, Trivier E, Krude T. Noncoding human Y RNAs are overexpressed in tumours and required for cell proliferation. Br J Cancer. 2008;98:981–8.PubMed CentralView ArticlePubMedGoogle Scholar
  63. Meiri E, Levy A, Benjamin H, Ben-David M, Cohen L, Dov A, et al. Discovery of microRNAs and other small RNAs in solid tumors. Nucleic Acids Res. 2010;38:6234–46.PubMed CentralView ArticlePubMedGoogle Scholar
  64. Appaiah HN, Goswami CP, Mina LA, Badve S, Sledge GW, Liu Y, et al. Persistent upregulation of U6:SNORD44 small RNA ratio in the serum of breast cancer patients. Breast Cancer Res. 2011;13:R86.PubMed CentralView ArticlePubMedGoogle Scholar
  65. Manterola, L., E. Guruceaga, J.G. Perez-Larraya, M. Gonzalez-Huarriz, P. Jauregui, S. Tejada, et al., A small noncoding RNA signature found in exosomes of GBM patient serum as a diagnostic tool. Neuro-Oncology, 2014. doi:10.1093/neuonc/not218.

Copyright

© Koo et al. 2015

Advertisement