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Carcinogenesis Advance Access originally published online on February 20, 2006
Carcinogenesis 2006 27(6):1169-1179; doi:10.1093/carcin/bgi363
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Underexpression of transcriptional regulators is common in metastatic breast cancer cells overexpressing Bcl-xL

Olga Méndez, Berta Martín, Rebeca Sanz, Ramón Aragüés 1, Victor Moreno 2, Baldo Oliva 1, Verena Stresing and Angels Sierra *

Centre d'Oncologia Molecular, Institut de Recerca Oncológica, IDIBELL, Hospital Duran i Reynals, Gran Via s/n, Km 2,7, E-08907 L'Hospitalet Ll., Barcelona 1 Grup de Bioinformàtica Estructural (GRIB-IMIM). Universitat Pompeu Fabra. C/Doctor Aiguader, 80, Barcelona 08003, Catalonia and 2 Servei d'Epidemiologia Institut Català d'Oncologia and Laboratorio de Bioestadística y Epidemiologia, Universitat Autónoma de Barcelona, Hospital Duran i Reynals, Gran Via s/n, Km 2,7, L'Hospitalet Ll. 08907, Spain

* To whom correspondence should be addressed. Tel: +34 93 2607429; Fax: +34 93 260 7426; Email: asierra{at}iro.es


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 References
 
Bcl-xL gene induces metastasis in the lung, lymph nodes and bone when breast cancer cells are inoculated in Nude Balb/c mice. In an attempt to identify the molecules required for diverse metastatic foci, we compared gene expression levels in tumor cells and metastatic variants with a cDNA GeneFilter containing 4000 known genes. The transcriptional regulators of {alpha}1-fetoprotein transcription factor, TBP-associated factor 172 (TAF-172) and the human zinc finger protein 5 (ZFP5) were downregulated. The expression of TAF-172 was inversely proportional to Bcl-xL expression (ANOVA P < 0.0001) and metastatic activity (ANOVA P < 0.0001). A protein interaction program allowed us to functionally associate Bcl-xL and TAF through TATA-binding protein (TBP), suggesting that Bcl-xL connects metabolic pathways with transcriptional machinery. The prediction included proteins involved in apoptosis, electron transfer, kinases and transcription factors. These results indicate that the selection of diverse metastatic cells from the broad spectrum of tumor cell leads to the underexpression of certain transcriptional regulators that might act as adaptor molecules to different microenvironments, and indicate that the synergistic activity of several genes is needed for the selection process in several metastatic foci.

Abbreviations: CP, crossing point; ZFP5, human zinc finger protein 5; i.m.f.p., into the mammary fat pad; PAGE, polyacrylamide gel electrophoresis; STRING, Search Tool for the Retrieval of Interacting Genes/Proteins; SPSS, Statistical Package for the Social Sciences; TBP, TATA-binding protein; TAF-172, TBP-associated factor 172


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussion
 References
 
Metastasis, the principal cause of treatment failure in cancer patients (1), is a multi-step process characterized by multiple genetic alterations (2–4). In breast carcinomas, the molecular and cellular mechanisms responsible for the growth of the metastatic phenotype are poorly understood. However, the phenotype has been described as resulting from interactions with the genetic background and from the relationship between the cells and their microenvironments (5). The eventual outcome of metastasis is dependent on the ‘cross-talk’ between the metastatic cells and the homeostatic factors that promote tumor growth (6). Different hormone concentrations in individual organs, differentially expressed local factors and paracrine growth factors may all influence the growth of malignant cells at particular sites (7). Indeed, the microenvironment pressure determines the survival of those cells with mutations that render the gene active or inactive under specific conditions, thereby defining the cellular behavior at the metastatic foci (8,9).

The selective nature of the metastatic process, along with the rapid evolution and phenotypic diversification of clonal tumor growth, is the result of the inherent genetic and phenotypic instability of many clonal populations of tumor cells (10). In addition to tumor heterogeneity and cell diversity, genetic changes driven by dynamic and stochastic evolutionary forces may occur, but such changes tend to mirror their somatic environment (11). Thus, the ubiquity of metastatic cells might result from the genetic alterations that metastatic cells show in different somatic environments subject to varying constraints (12).

Genetic instability has been linked to significant alterations in apoptosis control (13,14). Moreover, apoptosis loss may be instrumental in the tolerance to the progressive accumulation of genomic damage, which facilitates the appearance of variants of increased malignant potential (15,16).

Several studies have reported a role for apoptosis resistance in metastasis, linking the development of the metastatic phenotype to the loss of apoptosis in cells (17,18). The anti-apoptotic protein, Bcl-xL, increases genetic instability in cells, leading to biological cell diversification and tumor heterogeneity. Moreover, different genetic changes in metastases from lung, bone and lymph node foci might allow the selection of the most adaptable organ-specific phenotype, which contributes to the observation that excellent responses to treatment do not necessarily result in cures (19). Indeed, Bcl-xL mediates a phenotype in which redox pathways and glycolysis are coupled functions sheltering breast cancer metastatic cells from the primary tumor to the metastatic state (20).

Since metastasis selection is a consequence of the evolution of the tumor history, many phenotypes induced by paracrine or autocrine signals, which differ in each metastasis and in each microenvironment, might be mediated by molecules acting as adaptors. The aim of this work is to provide insight into the phenotype induced by Bcl-xL in metastatic cells, which results in a short dormancy period in several organs.

MDA-MB 435 cells, previously transfected with the anti-apoptotic Bcl-xL gene, induce metastasis in the lung, lymph node and bone when they are inoculated into the mammary fat pad (i.m.f.p) in Nude Balb/c mice (21). The difference in gene expression levels in metastatic variants was analyzed by macro-arrays, thereby allowing several genes, which were either over- or underexpressed in relation to the tumor, to be identified. Specifically, we found a lower expression of transcriptional suppressors, whose underexpression might lead to the upregulation of other genes.


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 References
 
Human breast carcinoma cell cultures and transfections
MDA-MB 435 cell cultures (435 cells) were maintained in a 1 : 1 (v/v) mixture of DMEM and Ham F12 medium (DMEM/F12) supplemented with 10% fetal bovine serum (FBS) and 2 mM L-glutamine in 5% CO2–95% air at 37°C in a humidified incubator. Transfections were carried out with Lipofectin (Inc., Gibco BRL, Gaithersburg, MD). Selection of 435/Bcl-xL and 435/Neo cells started 48 h after transfection of pSFFV-Neo Bcl-xL and pSFFV-Neo, using 500 µg/ml of neomycin G418 (Life Technologies).

As reported (18), orthotopic primary tumors were generated on Day 45 by inoculating 1 x 106 cells in 0.05 ml of medium without serum in the right inguinal mammary gland (i.m.f.p.) of seven-week-old athymic Nude Balb/c females. Animals were put down on Day 110 and their organs were removed, weighed and examined for metastasis. Metastases were identified by microscopic examination of hematoxylin-eosin-stained paraffin sections. Lung metastases from tumors overexpressing Bcl-xL were over 3 mm in diameter, whereas only small metastases (≤3 mm) were found in the lungs of 435/Neo-injected mice. Lymph node and bone metastases appeared in mice from Bcl-xL clones, but did not appear in mice from 435/Neo control cells.

Metastatic variants were generated by primary culture from bone, lung and lymph node metastases induced in mice with 435/Bcl-xL tumors. Monolayers of metastatic cells were obtained from trypsin-treated histocultures (tumor fragments of ~1 mm3) and fitted until growth in medium supplemented with 20% FBS, in the presence of 500 µg/ml of neomycin G418.

In a number of the experiments, we used MDA-MB 231, MDA-MB 468 and MCF-7 human breast cancer cells, supplied by American Type Culture Collection (Rockville, MD), and 435 metastatic variants that were obtained by primary culture from lung (435/Lung) and brain (435/Brain).

We also included 435 cells transfected with a pSFFV-Neo Bcl-2 construction, obtained as described (18), and 435 cells in which Bcl-xL anti-sense had been cloned in a ZeoSV2 construction (22).

cDNA arrays
Total RNA was isolated from 1 x 106 cells of each variant using Trizol®Reagent (Life Technologies). RNA purity was measured using the spectrum, and RNA quality was examined in an agarose gel that was visualized with ethidium bromide.

The cDNA array GeneFilter GF211 Human Named Genes from Research Genetics (Huntsville, AL) contained 3964 spots of 0.5 ng of cDNA, each corresponding to a known human gene; 192 positive controls, corresponding to spots of total genomic DNA; and 168 housekeeping genes whose signal was the same in all tissues when two different fluorescent dyes were used.

The array was hybridized with [33P]dCTP-labeled cDNA, which had been obtained as indicated by the manufacturer using oligodT (Research Genetics) by a reverse-transcriptase reaction and left overnight for hybridization at 42°C in MicroHyb hybridization solution (Research Genetics). The membrane was then washed and exposed to a high-resolution screen for 17 h (Molecular Dynamics, Sunnyvale, CA). After that, the image was scanned on a Storm Scanner at 50 micron resolution and transferred to Pathways software (Research Genetics), according to the internal software protocol.

Array data analyses
Data analysis was performed as described (23,24), by transferring all the information from the Pathways software to the SPSS program (Statistical Package for the Social Sciences) 8.0 version.

The background of each spot was subtracted from the raw intensity. Since each hybridization presents a different value, the process did not affect the general value, but it allowed us to eliminate those spots that had an intensity below 0.

In order to compare two images, Pathways software normalizes their hybridizations on the basis of the average total intensity of each filter. The formula used was as follows: * data points/positive controls (Normalization = raw intensity/* raw intensity x 2000).

We calculated ratios dividing the normalized data point values of each metastasis by the normalized data point values of the tumor to detect genes that are differentially expressed in metastasis.

LightCycler real-time PCR
To validate the expression profiles of some of the genes discovered by cDNA array, we used real-time PCR in a fluorescence thermocycler (LightCycler, Roche Molecular Biochemicals, Germany). To analyze these genes further, 900 ng of total RNA from each cell line was retro-transcribed. Inverse transcription was performed in a final total volume of 20 µl, and for each reaction the following components were added: 5% M-MLV RT, 20% RT buffer 5X (Promega Corporation, WI), 0.5 mM of each dNTP (Amersham Biosciences, Buckinghamshire, UK), 0.1 M of DTT, 0.5 µM of poly-T and 0.5 µl of ribonuclease inhibitor RNasin® (Promega Corporation, Madison, WI). The reaction was performed under the following conditions: 1 h at 37°C and 5 min at 95°C.

Experimental protocols for LightCycler real-time PCRs were optimized for each primer reaction. The specificity of RT–PCR products was tested using a high-resolution gel electrophoresis, and a single product with the desired length was obtained.

LightCycler FastStart DNA Master SYBR Green I (Roche Biochemicals, Mannheim, Germany) reaction was prepared in line with the manufacturer's recommended conditions: 2 µl sample (300ng) plus 18 µl MIX (dNTPs, Fast start Taq DNA polymerase, reaction buffer, Sybr Green I dye, and 10 mM MgCl2), 0.3–1 µM of each primer, and MgCl2 (2 mM to 4 mM).

The primers used were as follows: Cyclophiline, 5'-CTC CTT TGA GCT GTT TGC AG-3', 5'- CAC CAC ATG CTT GCC ATC C-3', (reference to evaluate the expression level of other genes).

Integrin ß1, 5'-AGC AGT AAT GCA AGG CCA AT-3', 5'-GTC CCA ACC TGA TCC TGT GT-3'.

TAF-172, 5'-TCC AAA AAG TGT CGC TCC TT-3', 5'-TTG GAG AAG GTT CTT CCG TG -3'.

Zinc Finger Protein 5, 5'-TAC CAG CTG CGA TTT GTG AG -3',5'-GGA TAA ATC ATG TGC CCC AC -3'.

The amounts of target and endogenous references were determined by drawing a standard curve for each sample. The standard was constructed from a pool of 5-fold serial dilutions of cDNA (375, 75, 15, 3, 0 ng). Each reaction was performed three times within one LightCycler run to confirm the intra-assay reproducibility. Inter-assay variation was assessed performing three different experiments.

In order to quantify gene expression, we used a mathematical model that determines the relative quantification of a target gene with that of the reference gene by using the crossing point (CP) and transcript efficiencies (E). The relative expression was expressed as Ratio = (Etarget) {Delta}CPtarget (mean control–mean sample)/ (Eref) {Delta}CPref (mean control–mean sample). CP is the point at which there is a significant increase in fluorescence, compared with that of the background. To calculate the E of a cycle in the exponential phase, we considered the graph slope given by the CP cycles and the cDNA input, according to the equation E = 10–1/slope.

This method was used for CP determination, using LightCycler Software 3.5 (Roche Molecular Biochemicals) as described elsewhere (25).

Western-blot analysis
Cells from exponential cultures were lysed in 200 µl RIPA buffer (50 mM Tris, 150 mM NaCl, 0.1% SDS, 1% NP-40, 0.5% sodium deoxycholate). Sample volumes were adjusted to contain 50 µg of protein as determined by the BCA protein assay reagent (Pierce, Rockford, IL) and electrophoresed in a 12% polyacrylamide gel. The separated proteins were transferred to PVDF membranes (Immobilon-p, Millipore Corporation, Bedford, MA) and non-specific protein-binding sites blocked by a 5% solution of non-fat dried milk with 0.01% Tween-20 in PBS. The following antibodies (Ab) were used: polyclonal rabbit Ab specific for human Bcl-xS/L (S-18) at 1/1000 dilution (Santa Cruz Biotechnology, Santa Cruz, CA); policlonal rabbit Ab anti-stathmin 1 (OP18) at 1/1500 dilution (Sigma, St Louis, MO); and monoclonal Ab anti-peroxiredoxin 3 (AOP-1) at 1/8000 dilution (Sigma).

Peroxidase-conjugated goat anti-rabbit secondary antibody 1/2000 (Amersham), or anti-mouse secondary antibody 1/2000 (Pierce, Perbio Science Ltd., Cheshire, UK), or anti-goat secondary antibody 1/3000 (Santa Cruz) were used as corresponded in each case.

An anti-human actin monoclonal antibody 1/2000 (Sigma) was also used as an internal standard for densitometric analysis of X-ray film. Densitometry analysis with the Quantity One program was carried out using the quantity of a band, that it is the sum of the intensities of all the pixels within the band boundary multiplied by the area of each pixel.

Bioinformatics
We used the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) (26) to find interactors of the proteins identified by mass spectrometry in a previous work (20) plus Blc-xL and TAF-172. We included TAF-172 and Bcl-xL in the network because we are interested in finding the relationship between these two proteins and the rest. STRING contains protein–protein interactions that have been predicted using different methods such as gene fusion (27), phylogenetic profiles (28) or gene neighborhood (29). For each of these predictions, STRING gives a confidence score, which measures how likely it is that the prediction is a true interaction.

Furthermore, we have developed an algorithm that predicts interactions between proteins by propagating known interactions in one species to ortholog proteins in another species (i.e. assuming protein–protein interologs) (28).

Using interactions from STRING (at a confidence level of 400) and the protein–protein interologs found by our algorithm, we built a protein–protein interaction network. Taking as root proteins of the network those identified proteins over- or underexpressed in metastatic cells (20), plus Bcl-xL and TAF-172, we obtained networks that attempt to describe the relationship between proteins relevant to metastasis that intermediate with TAF-172 and Bcl-xL. We clustered proteins by their cellular function and extracted those functions that were connecting at least two root proteins. These cellular functions and the root proteins that they connected provided us with clues about processes that are potentially involved to link transcription by TAF-172 and Bcl-xL.

Data analysis
The statistical analysis of the data was performed using SPSS for Windows. Two-way analysis of variance (ANOVA) was used to compare the mean expression levels. The non-parametric Kruskal–Wallis and Mann–Whitney U-test were also used for comparisons. In all the analyses, differences were considered significant when P < 0.05. Microsoft Excel was used to plot graphs.


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussion
 References
 
Genes differentially expressed in metastasis
In these experiments, we used MDA-MB 435 cells that had been transfected with the anti-apoptotic gene Bcl-xL, and 435/Neo cells as control (Figure 1). A pool of 435/Bcl-xL cells, with a transgene protein expression that was 2-fold higher than that of the control cells, was used to avoid clonal variations.


Figure 1
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  		Fig. 1. Western blot showing the expression of Bcl-xL gene in 435/Neo and 435/Bcl-xL cells. Whole cell lysates containing 50 µg of total protein were loaded, fractionated by PAGE and blotted on nitrocellulose membranes. The proteins indicated were detected using specific primary antibodies and were later visualized using HRP-conjugated secondary antibodies.

 
We compared gene expression levels of 435/Bcl-xLTumor cells and metastasis variants from lung (435/Bcl-xLLung), bone (435/Bcl-xLBone) and two different lymph nodes (435/Bcl-xLNode1 and 435/Bcl-xLNode3).

In order to detect the genes that were differentially expressed in the metastases, we only considered spots whose intensity was 10-fold higher than that of the background. In this way we eliminated spots that were more susceptible to error, because of their low intensity (Figure 2A and B).


Figure 2
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  		Fig. 2. Genes differentially expressed: metastasis versus tumor: (A) We compared the gene expression profile of the tumor cell line (435/Bcl-xL Tumor) versus four metastatic variants from lung (435/Bcl-xL Lung), bone (435/Bcl-xL Bone) and two different lymph nodes (435/Bcl-xL Node1 and 435/Bcl-xL Node3). These graphs show the genes with significant changes in expression. Cut-offs for 2-fold induction (log2 ≥ 1) and repression (log2 ≤ –1), as well as 10-fold background of membrane hybridization are indicated. Linear scatter plots of different gene expressions were generated by comparing the log2 metastasis/tumor ratios versus the tumor value. We only considered a gene expression change for those values with a log2 ratio over 1 or under –1, and greater than 10 times the background. (B) We compared the gene expression profile of the two different lymph node metastasis studied (435/Bcl-xL Node1 and 435/Bcl-xL Node3). This graph shows genes that are differentially expressed when comparing log2 metastasis/tumor ratios with the tumor value, showing the overexpressed (log2 ≥ 1) and when we compare them with underexpressed (log2 ≤ –1) genes, whose expression level is 10 times greater than the background.

 
All values were transformed to log2 in order to calculate each metastasis/tumor ratio, having established that a significant ratio would be one that was higher than 1 (log2 ratio ≥ 1)—overexpression—or lower than –1 (log2 ratio ≤ –1)—underexpression.

Gene expression in lymph node metastases and in bone metastasis differed moderately from tumor gene expression (Figure 2A): In 435/Bcl-xLNode1 cells, 36 genes were overexpressed (log2 ratio ≥ 1) and 28 genes were underexpressed (log2 ratio ≤ –1); whereas in 435/Bcl-xLNode3 cells, 49 genes were overexpressed and 25 genes were underexpressed; in 435/Bcl-x Lbone cells, 58 genes were overexpressed and 18 genes were underexpressed. In contrast, in lung metastasis, 778 genes were overexpressed and 1091 genes were underexpressed. This result could not be attributed to technical factors, since sequence membrane hybridizations were 435/Bcl-xLTumor, 435/Bcl-xLNode3, 435/Bcl-xLLung, 435/Bcl-xLBone, and 435/Bcl-xLNode1.

Assuming that genes will be selected in all the metastatic variants in the broad spectrum of the metastatic process, we sought to identify changes that had occurred redundantly in lymph node, bone and lung. We found only eight genes whose expression had changed in all the metastases (Table I). Two of these were overexpressed: monocarboxylate transporter (MCT3), which is a membrane protein, and collagen VI{alpha}2, which is an extracellular matrix protein. The six genes that were underexpressed were a prostate membrane antigen and a receptor for the G protein, both of which were located in the cellular membrane; the LECT2 precursor, which is a neutrophil migration chemokine receptor; and three genes related to transcription: {alpha}1-fetoprotein transcription factor (hFTF), zinc finger protein 5 (ZFP5), and TBP-associated factor 172 (TAF-172).


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  		Table I. Genes associated with the ubiquity of metastatic cells, their functional adscription, and references in which these genes are associated with breast cancer

 
We found 11 genes that were differentially expressed in the two lymph node metastases compared with that of the tumor (Figure 2B). These were the eight common metastatic genes and three new overexpressed genes: integrin ß1, the testis specific lactate dehydrogenase (LDHC4) and the apolipoprotein D.

Comparing these results with the information available in the literature and in public databases, we found that some genes had been previously described as aberrantly expressed in carcinomas, including breast cancer, which is consistent with our results (Table I).

Underexpression of transcriptional regulators
We adopted two approaches for the independent confirmation of the array data: in silico analysis and quantitative real-time PCR. Our results from the in silico analyses showed the underexpression of transcriptional regulators, which constitute an interesting phenotype for in-depth analysis. To assess the reliability of the array data further, experimental verification of ZFP5 and TAF-172 gene expression was carried out using real-time PCR.

As is shown in Figure 3A, the TAF-172 expression level was lower in the metastatic variants than in the tumor cells (ANOVA, P < 0.0001): 435/Bcl-xLTumor, 5.96 ± 0.67 ng; 435/Bcl-xLLung, 4.62 ± 0.30 ng; 435/Bcl-xLBone, 3.47 ± 0.31 ng; 435/Bcl-xLNode1, 3.11 ± 0.23 ng; and 435/Bcl-xLNode3, 4.42 ± 0.67 ng. Similar results were found in ZFP5 expression analyses (ANOVA, P < 0.0001), since expression levels obtained were 435/Bcl-xLTumor, 4.58 ± 0.27 ng; 435/Bcl-xLLung, 2.47 ± 0.06 ng; 435/Bcl-xLBone, 1.83 ± 0.01 ng; 435/Bcl-xLNode1, 1.53 ± 0.21 ng; and 435/Bcl-xLNode3, 2.73 ± 0.03 ng.


Figure 3
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  		Fig. 3. Experimental verification of gene expression by LightCycler real-time PCR to validate the reliability of the array data: (A) Underexpression of TAF-172 (ANOVA, P < 0.0001) and ZFP5 (ANOVA, P < 0.0001) in metastatic variants (435/Bcl-xL Lung, 435/Bcl-xL Bone, 435/Bcl-xL Node1 and 435/Bcl-xL Node3) compared with 435/Bcl-xL Tumor cells. (B) Analysis of several experimental tumors and metastasis induced in Nude Balb/c female mice by inoculating 435/Bcl-xL or 435/Neo cells i.m.f.p. Expression of TAF-172 in 435/Bcl-xL tumors compared with 435/Neo tumors (ANOVA, P = 0.024) and in 435/Bcl-xL lung metastasis (ANOVA, P = 0.175) and 435/Bcl-xL lymph node metastasis (ANOVA, P = 0.052) compared with 435/Bcl-xL tumors. (C) Analysis of several experimental tumors and metastasis induced in Nude Balb/c female mice by inoculating 435/Bcl-xL or 435/Neo cells i.m.f.p. Expression of ZFP5 in 435/Bcl-xL tumors compared with 435/Neo tumors (ANOVA, P = 0.063) and in 435/Bcl-xL lung metastasis (ANOVA, P = 0.950) and 435/Bcl-xL lymph node metastasis (ANOVA, P = 0.122) compared with 435/Bcl-xL tumors.

 
Moreover, we analyzed tumors and metastasis from different mice, including 435/Bcl-xL tumors (n = 3), metastasis in lung (n = 2) and in lymph nodes (n = 2); and controls, 435/Neo tumors (n = 2) and metastasis in lung (n = 2). As is shown in Figure 3B, TAF-172 is underexpressed in 435/Bcl-xL tumors compared with 435/Neo tumors (ANOVA, P = 0.024) and in 435/Bcl-xL lung metastasis (ANOVA, P = 0.175) and 435/Bcl-xL lymph node metastasis (ANOVA, P = 0.052) compared with 435/Bcl-xL tumors.

Expression of ZFP5 in 435/Bcl-xL tumors compared with 435/Neo tumors (ANOVA, P = 0.063) decrease (Figure 3C). In contrast, in 435/Bcl-xL lung metastasis (ANOVA, P = 0.950) and 435/Bcl-xL lymph node metastasis (ANOVA, P = 0.122) compared with 435/Bcl-xL tumors, the different expression was not significant.

As these results were consistent with those obtained from the array data, in order to establish the relationship between the underexpression of transcriptional regulators and anti-apoptotic proteins, we analyzed TAF-172 and ZFP5 in 435 transfected cells with Bcl-xL anti-sense (435/Bcl-xL-AS) and in cells overexpressing the anti-apoptotic gene Bcl-2 (435/Bcl-2), which had been previously analyzed for their biological and metastatic activity. The rates of expression of Bcl-2 and Bcl-xL (1.8-fold higher) in 435 transfectant cells were 1.5-fold and 1.8-fold higher, respectively; and rate of downregulation of Bcl-xL protein obtained by anti-sense transfection of cells was 3.2-fold (22,30).

As is shown in Figure 4A, 435 parental cells and 435/Neo cells expressed more TAF-172 (2.44 ± 0.27 and 2.54 ± 0.06 ng, respectively) than 435 transfected cells of either of the anti-apoptotic genes: Bcl-2, 1.91 ± 0.01, or Bcl-xL, 2.00 ± 0.21 ng. These differences were statistically significant (ANOVA, P < 0.001). Moreover, the expression level of TAF-172 was restored in 435/Bcl-xL-AS cells, 2.41 ± 0.03 ng.


Figure 4
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  		Fig. 4. Expression of TAF-172 and ZFP5 in breast cancer cells. (A) Relationship between the underexpression of transcriptional regulators and Bcl-xL expression. Control cells (parental MDA-MB 435 and 435/Neo) are compared with cells transfected with anti-apoptotic genes (435/Bcl-2 and 435/Bcl-xL) and cells transfected with the anti-sense of Bcl-xL gene (435/Bcl-xL-AS). The expression of TAF-172 is inversely related to the expression of Bcl-xL and Bcl-2 (ANOVA, P < 0.001). (B) Expression of transcription suppressors in metastatic (MDA-MB 435, 435/Lung and 435/Brain, and MDA-MB 231) versus non-metastatic (MCF-7 and MDA-MB 468) cells.

 
ZFP5 expression was found to be similar in 435 parental cells (1.32 ± 0.01 ng), 435/Neo cells, (1.43 ± 0.01) and 435/Bcl-2 cells (1.36 ± 0.05 ng), and higher in 435/Bcl-xL and 435/Bcl-xL-AS cells (1.88 ± 0.06 and 1.70 ± 0.06 ng, respectively). Three independent experiments showed that ZFP5 expression is not related with anti-apoptotic proteins in the same way as it is with TAF-172.

We then examined whether the expression of transcription suppressors was consistent with their metastatic activity (Figure 4B). TAF-172 expression in non-metastatic MDA-MB 468 cells, 2.49 ± 0.88 ng, and MCF-7 cells, 3.80 ± 0.40 ng, was higher than it was in metastatic MDA-MB 435 parental cells, 2.44 ± 0.27 ng, and MDA-MB 231 cells, 2.39 ± 0.17 ng, and in 435 metastatic variants that were obtained by primary culture from lung (435/Lung), 1.95 ± 0.04 ng, and brain (435/Brain), 1.78 ± 0.04 ng, (ANOVA, P = 0.003685).

ZFP5 expression was also associated with cell metastatic activity (ANOVA P < 0.0001), since it was underexpressed in metastatic cells: MDA-MB 231, 1.28 ± 0.04, MDA-MB 435, 1.32 ± 0.01 ng; and metastatic variants 435/Lung, 1.32 ± 0.02 ng, and 435/Brain, 1.25 ± 0.04 ng; compared with non-metastatic variants MDA-MB 468, 1.93 ± 0.05 ng, and MCF-7, 3.62 ± 0.05 ng.

In silico proposal of Bcl-xL interacting proteins that connect with transcription
We use the bioinformatic analysis taking as root proteins of the network the 17 proteins previously identified over- or underexpressed in metastatic cells overexpressing Bcl-xL (20) plus Bcl-xL and TAF-172. With this, we obtained a network that attempts to describe the relationship between the transcription factor TAF-172 and Bcl-xL. (Figure 4A). We found nine human proteins potentially related with Bcl-xL through an indirect association: ATP synthase D and beta chain, tubulin-specific chaperone A, peroxiredoxine 3 and 2, nucleoside diphosphate kinase A, aminoacylase 1, stathmin 1 and 14.3.3 epsilon (Table II).


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  		Table II. Predicted graph of interactions that includes Bcl-xLand TAF-172 plus proteins identified by 2DE and MALDI-TOF in a previous work with differential expression in metastasis (España et al., 2005).

 
Since lung metastasis overexpress peroxiredoxin 3 and stathmin 1 (20), we have chosen them to validate the in silico interacting proteins with Bcl-xL. In Figure 5B and C we show experimentally the association between Bcl-xL, REDOX and the transcription factor stathmin 1. Stathmin 1 and peroxiredoxin 3 were overexpressed in all metastasis from 435/Bcl-xL tumors. Moreover, peroxiredoxin 3 was overexpressed in lung metastasis from 435/Neo cells.


Figure 5
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  		Fig. 5. Protein–protein interaction network: (A) Taking as root proteins (yellow) of the network those identified proteins over- or underexpressed in metastatic cells (20), plus Bcl-xL and TAF-172 (in red, TAF-172 and TBP subunits, with which TAF-172 interact), we attempt to describe the relationship between proteins relevant to metastasis that intermediate with TAF-172 and Bcl-xL. We used interactions from STRING (at a confidence level of 400) and the protein–protein interologs found by our algorithm (blue). (B) Western blot showing experimentally the relationship between Bcl-xL, peroxiredoxin 3 and stathmin 1 in metastatic variants overexpressing Bcl-xL (435/Bcl-xL Node 1, 435/Bcl-xL Lung, 435/Bcl-xL Bone) with regard to metastatic variant from control (435/Neo Lung) and tumors (435/Bcl-xL Tumor, 435/Neo Tumor). Whole cell lysates containing 50 µg of total protein were loaded, separated by PAGE and blotted to nitrocellulose membranes. The indicated proteins were detected using specific primary antibodies and visualized using HRP-conjugated secondary antibodies. (C) Ratio of protein expression using the Quantity One program to analyze the western-blot (B). Differentially expressed proteins in 435/Bcl-xL metastasis as regards 435/Bcl-xL tumor and in 435/Neo tumor and metastasis as regards 435/Bcl-xL tumor.

 
The proteins that intermediate in these relationships are clustered according to their functions, of which the noteworthy ones are apoptosis, cell cycle and transcription, with more than one intermediate. In addition, all protein connectors that intermediate between any root nodes of the studied network were clustered too according to a functional criteria: (i) apoptosis, (ii) DNA-repair, synthesis and translation, (iii) cell division, (iv) extracellular matrix, (v) tumor suppressor, (vi) kinase, (vii) transcription, (viii) ubiquitination, (ix) immune system, (x) electron transfer, (xi) tumor suppressor, (xii) proteolysis, (xiii) intracellular transport and (xiv) nitric oxide synthesis. Proteins for which no function or human homolog was found are ignored.

On the other hand, TAF-172 interacts with a TATA-binding protein (TBP) subunit that connects TAF-172 and the beta-tubulin cofactor A, one of the members of the network (Table II). Consequently, TAF-172 gets related to Bcl-xL through this network by means of several proteins for which their differential expression in metastasis has been experimentally proved. The pathway linking Bcl-xL and TAF-172 involves the root nodes of the network, tubulin-specific chaperone A (also named beta-tubulin cofactor A) and ATP synthase D chain, plus the connectors involved between them. In particular, proteins with functions such as ‘acetylation’, ‘electron transfer’, ‘protein transport’ and ‘extracellular matrix’ appear to be involved in connecting ATP synthase D chain and the tubulin-specific chaperone A, whereas Bcl-xL is connected with the ATP synthase D chain through an apoptotic precursor.

In Table III we show the gene expression list of connectors arrayed in the membrane. The expression of TFIID TBP subunit that links TAF-172 with beta-tubulin cofactor A was not different between metastasis: higher than 1 (log2 ratio ≥ 1)—overexpression—or lower than –1 (log2 ratio ≤ –1)—underexpression. The NADH-ubiquinone oxireductase, which links ATP synthase D chain with tubulin-specific chaperone A was overexpressed in bone and lung metastasis. The prothymosin alpha, which links Bcl-xL with peroxiredoxin 3, was underexpressed in bone and lung metastasis. The IAP homolog, which links Bcl-xL with stathmin 1, was underexpressed in lung metastsis. The human GTP cyclohydrolase I, which links Bcl-xL with 14.3.3 epsilon, was overexpressed in bone and lung metastasis.


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  		Table III. Expression of connectors (with a predicted interaction between two root nodes) arrayed in the membrane in 435/Bcl-xL metastatic variantsa

 

   Discussion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
References
 
Metastasis is the main cause of treatment failure and death in breast cancer patients. Some carcinomas present a large number of secondary tumors, others present just a few, while others do not develop any metastases. Bcl-xL, a gene frequently overexpressed in human carcinomas, might mediate a phenotype that harbors metastatic cells in metastatic foci, and appears to be useful for shortening dormancy with the result of clinical metastasis in many organs (21). It remains unclear as to why a breast cancer cell supports the selective pressure that ensures its survival for a long time, leading to favorable interactions of metastatic cells with different host homeostatic mechanisms. We suspect that in metastasis many phenotypes redundantly express genes that are indispensable for the metastatic process, and that metastasis diversity might be mediated by the activation of some of these genes acting as adaptors to organ-specific growth.

Here, we highlight the underexpression of transcriptional regulators, such as TAF-172 and ZFP5, which are present in all metastatic variants overexpressing Bcl-xL. It appears that they might belong to a dominant phenotype related to diversity and short dormancy periods, which acts as a putative adaptor molecule during metastasis.

Transcription initiation is a key step in the control of gene expression and one that involves the interplay of many transcription factors. TAF-172 gene is a TATA-binding protein (TBP) associated factor that acts as an initiator of transcription by RNA polymerase II. TAF-172 regulates transcription together with TBP by removing TBP from the TATA box in an ATP-dependent manner (31,32).

The eukaryotic TFIID complex, composed of the TBP and TAFs, conform a highly versatile transcription apparatus responsible for core promoter recognition, coactivator function, catalysis of protein modification and targeting to specifically acetylated nucleosomes that may serve as a key integrator of molecular signals to and from the central transcription machinery (33). Despite the central role of TBP in transcription, changes in cellular TBP concentrations have selective effects on gene expression that may be critical in deregulated signaling that occurs downstream of genetic lesion and which causes tumors (34).

TAF-172 underexpression might imply an evolutionary survival force for cells that have a metastatic potential. Since TAF-172 expression in 435/Bcl-xL-AS cells and 435/Neo control cells reach similar levels, it would appear that during metastasis Bcl-xL has a role in the selection of the cells that present a low expression of this transcriptional regulator. The way in which Bcl-xL controls this expression, or the way in which Bcl-xL selects the prevalence of cells that underexpress TAF-172 leading to metastasis ubiquity, are interesting questions that merit further research. A recent study has shown that Mot-1, a yeast homolog of human TAF-172, can regulate the expression of 3% of all yeast genes (35). About 77% of the Mot1-repressed genes are involved in diauxic shift, stress response or sporulation (36).

We have previously reported that the Bcl-xL gene might have a role in shortening breast cancer dormancy and promoting cell survival in metastatic foci, because metastatic 435/Bcl-xL and control cells reached the target organs in similar numbers but 435/Bcl-xL tumors developed more metastases than controls (21). Therefore, in addition to the action of Bcl-xL that enables cells to adapt to changes in cellular metabolisms (37), downregulation of TAF-172 might be a useful mechanism for assessing the adaptive metabolic conditions of cells under stress (38).

Probably we should expect a more direct relationship between Bcl-xL and TAF-172; however, there are no experimental evidences to prove an additional and closer intermediate. Perhaps it may be a clue to the fact that the complex relationship we have found involves a large set of proteins from the redox system, whereas a close homolog of TAF-172 in Saccharomyces cerevisiae, Mot1, is a probable helicase also affected by oxidative stress.

In addition, the largest set of putative interactors of Bcl-xL included proteins involved in apoptosis: caspase-2, caspase-6 and caspase-7 precursors; Bcl-2/adenovirus E1B protein; and baculoviral IAP repeat-containing protein 2 and 3. It is also noteworthy that proteins with functions such as ‘cell-division’, ‘apoptosis’, ‘transcription’ and ‘DNA-repair’ appear significantly involved in connecting Bcl-xL with REDOX proteins (peroxiredoxins 3 and 2).

We did not find any association between ZFP5 expression and the expression of anti-apoptotic proteins. ZFP5 is a nuclear DNA binding protein that functions as a transcriptional repressor of c-Myc oncogene (39) and thymidine kinase promoters (40). Several reports associate c-Myc overexpression and/or amplification with the prognosis of breast cancer (41). Moreover, a synergistic interaction between c-Myc and Bcl-2 oncogenes in the lymph node progression of human breast carcinomas has been described (42). Therefore, we believe that ZFP5 underexpression might be a phenotype that is selected early on in the metastatic process.

Since the expression levels of TAF-172 and ZFP5 genes in all the metastases were found to be lower than those in tumor cells, and the expression level was lower in metastatic variants than in breast cancer cells without metastatic ability, we conclude that these genes are directly associated with the metastatic process with putative metastatic suppressor activity. The underexpression of TAF-172 in a panel of human breast cell lines has been reported previously (43). Neither gene type, however, is included in the group of genes associated with metastasis (44) or breast cancer prognosis (45).

We found that the majority of genes that are either overexpressed or underexpressed compared with tumor genes differed in each metastasis. These data imply that each metastasis has diverse taxonomies related to lung, lymph node and bone metastatic-specific growth, induced under the microenvironment pressure at each foci. Therefore, each metastasis may present different features because of its diverging history (46,47). A comprehensive genomic analysis of primary breast tumors and matched single cytokeratin-positive epithelial cells from bone morrow showed that genomic aberrations were more prevalent in patients who had developed metastasis (48). This suggests an independent evolution model of metastatic cells after their early separation from the primary tumor. Indeed, if the expression of linking proteins were different between metastatic variants, the nodes that are connecting might function with a difference. In agreement, Montel et al. (49) have found that metastasis to different organs occurs through similar genetic mechanisms, indicating that the host microenvironment is an active participant in tumor progression.

Cancer cell movement is not random, and different types of cancer have different destinations (50). This specificity seems to be accounted for the ‘soil and seed’ theory, which states that different organs provide optimal growth conditions for specific cancers (10). Moreover, if metastatic cells progress independently from the primary tumor (48,51), biological heterogeneity is found both within a single metastasis and among different metastases. Bcl-xL overexpression might increase cell autonomy outside their original microenvironment. The relationship between the anti-apoptotic protein Bcl-xL and transcriptional regulators could functionally associate genes that participate in molecular and cellular events and result in the acquisition of diverse metastasis growth by shortening the dormancy of metastatic cells in several organs. Thus, the identification of nodal points on these multiple survival-signal pathways might represent a useful target for anti-tumor drugs.


   Acknowledgments
 
We thank Mr R. Rycroft for expert language advice. This study was supported by grants from ‘Ministerio de Sanidad y Consumo’ FIS 01/1469, FIS/PI041937, and European Commission LSHC-CT-2004-506049. BO acknowledges grant BIO2005-00533 from MEC. RA acknowledges fellowship from grant BIO2002-0369 from MCyT. We are grateful to Timothy McDonnell from Dept of Molecular Pathology, UT MD Anderson Cancer Center, Houston, USA, for his advise, collaboration and stimulant criticism in cDNA arrays.

Conflict of Interest Statement: None declared.


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 Introduction
 Materials and methods
 Results
 Discussion
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Received August 19, 2005; revised December 19, 2005; accepted February 1, 2006.


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