We compared a non-metastasising sarcoma cell population with three related populations of increasing metastatic potential. The metastatic cells in vitro exhibited a significantly reduced incidence of actin stress fibres but enhanced motility and chemotaxis. We also investigated gene expression underlying progression to a metastatic phenotype by performing a microarray analysis of the four sarcoma populations. We identified a subset of genes with significantly altered expression levels between non-metastasising and metastasising cells in tissue culture and in primary tumours. One such gene, encoding protein 4.1B, is downregulated in the metastatic sarcoma populations. To investigate possible roles of 4.1B in the mechanisms of metastasis, we used RNA interference (RNAi) to reduce its expression in the non-metastatic cells. Cells with reduced 4.1B expression displayed an altered F-actin morphology, with significantly fewer stress fibres. We also found that the 4.1B RNAi cells migrated at twice the speed of the untreated cells. Metastatic cells exogenously expressing 4.1B migrated at half the speed of control metastatic cells and displayed suppressed chemotaxis. Therefore, we propose that the reduction of 4.1B in the metastatic cells promotes the metastatic phenotype as a result of inducing a loss of actin stress fibres and a concomitant increase in cell motility.


Metastasis, the spread of cancer from its place of origin to a secondary site, is the major cause of death in cancer patients. Therefore, identification of the genes underlying the mechanisms of metastasis is of great interest. To metastasise, tumour cells need to progress through a series of critical steps known as the metastatic cascade (Ahmad and Hart, 1997). The metastatic cascade involves the detachment of cells from the primary tumour into the surrounding tissue, intravasation into the blood or lymphatic system where they are transported, arrest in a capillary bed, extravasation and, finally, the survival and division of the tumour cells at the secondary site. Just as the interplay of tumour suppressor and promoter genes makes a cell become cancerous, so the development of metastasis requires a combination of genes whose altered expression levels allow its progress through the metastatic cascade. The development of microarray technology has enabled investigation of the changes in gene expression required for a tumour cell to metastasise. van't Veer et al. undertook a microarray analysis of breast tumours from 117 patients and identified a gene expression signature of poor prognosis (van't Veer et al., 2002). When this signature was used to predict the clinical outcome of another cohort of breast cancer patients, it outperformed all other methods of predicting the likelihood of distant metastases within five years (van de Vijver et al., 2002). A similar study with 279 diverse primary human tumours also resulted in a more accurate prognosis associated with metastasis (Ramaswamy et al., 2003). Such microarray analyses are useful to predict clinical outcomes, but there are drawbacks. For example, microarray data from patients is of limited value for identifying the expression changes mediating the progression from a benign to a metastatic tumour, because in most cases, only one sample per patient is available. Furthermore, Ein-Dor et al. recently reported that thousands, rather than hundreds, of samples are required to produce robust gene expression signatures from patients (Ein-Dor et al., 2006).

Animal models can circumvent some of the issues which complicate patient-based experiments, and have been used to implicate RhoA (Clark et al., 2000), RhoGDI2 (Gildea et al., 2002), and Ezrin (Khanna et al., 2004) as metastasis-associated proteins. Here, we used a rat sarcoma model derived by tumour progression from spontaneously transformed cells (Vesely et al., 1987). This model has specific advantages for microarray analysis of metastasis. First, it is composed of a panel of cell populations that are related but have different abilities to shed metastases in inbred animals. Therefore, the differences in gene expression between the cell populations are likely to reflect mainly the differences in their metastatic potential. Second, the use of inbred rats for the model provides both a uniform genetic background, reducing the `noise' in the microarray data and readily the available tumour tissue. Third, the cells exhibit in vitro behaviours that can be related to their in vivo metastatic potential and offer opportunities for assaying in vitro the effects of genes revealed by the microarray. For example, in this model, changed morphology and increased speed of migration in vitro is associated with higher metastatic potential.

Our microarray analysis of the rat sarcoma model indicated that 23 genes were differentially expressed between metastatic and non-metastatic cells in a significant manner. One of these genes, Epb41l3, is significantly downregulated in metastasising sarcoma cells. Epb41l3 was originally isolated as the cDNA DAL-1 from a screen of lung adenocarcinomas (Tran et al., 1999), which was later found to be a fragment of full-length Epb41l3. Its protein product 4.1B has an N-terminal FERM (4.1, ezrin, radixin, moesin) domain, a spectrin binding domain, a C-terminal domain, and the three unique domains U1, U2 and U3 (Parra et al., 1998). Like other members of the 4.1 family, 4.1B is thought to be involved in tethering the F-actin cytoskeleton to membrane proteins (Conboy, 1993). Its tumour suppressor activity has been demonstrated in lung adenocarcinoma, meningioma and breast carcinoma cell lines (Charboneau et al., 2002; Gutmann et al., 2001; Tran et al., 1999), and also as being lost in about 60% of lung adenocarcinomas (Tran et al., 1999) and meningiomas (Gutmann et al., 2000). Here, we show that 4.1B may also function as a metastasis suppressor by supporting orderly arrangements of actin stress fibres and suppressing the enhanced cell motility and chemotaxis associated with increased metastatic potential.


Sarcoma model of metastasis developed in inbred LEWIS rats and the characterisation of four cell populations

We developed a rat sarcoma model of metastasis and analysed four related cell populations: K2, T15, A297 and A311 cells. To assess their metastatic potential, one-million cells were subcutaneously implanted into the back of inbred LEWIS rats. Primary tumours developed in all cases. Four to six weeks after the inoculation, metastases were found in the lungs of rats implanted with T15, A297 and A311 cells – with incidence shown in Fig. 1. The incidence of rats with metastases was significantly increased compared with the experimental group using K2 cells.

Since cell motility is thought to be critical at various points during the metastatic cascade, we studied the motile responses of the cells to a gradient of platelet-derived growth factor (PDGF) and insulin-like growth factor (IGF). The circular histograms in Fig. 2A present the distributions of the cell track directions and illustrate the differences in chemotaxis. K2 cells did not show a significant chemotactic response in this analysis. By contrast, the T15, A297 and A311 cells showed significant chemotactic responses (ANOVA P<0.05). There were also differences in the distribution of speed of cell migration (Fig. 2B). The non-metastatic K2 cells migrated at 11.1±0.5 μm/hour (mean ± s.e.m.), and the T15, A297 and A311 cells at 17.1±0.7 μm/hour, 19.1±0.9 μm/hour and 14.1±0.9 μm/hour respectively. The difference between K2 and T15 cells was statistically significant (ANOVA P<0.05). Similarly, the difference between A297 and K2 cells was statistically significant (ANOVA P<0.01). The difference in the speed of cell migration between A311 and K2 cells was not significant; however the fraction of cells migrating faster than 20 μm/hour was significantly larger (χ2P<0.01) in A311 cells (20%) compared with K2 cells (8%). We conclude that the speed of in vitro migration for the metastatic cells is increased, at least in a subpopulation of the cells. In the following experiments testing motility of treated cells in vitro we focused on K2 and T15 or A297 cells whose speed of cell migration was significantly changed between the whole populations.

We have shown previously that three cell populations (K2, T15 and A239/870N) from this model have distinct F-actin arrangements, and postulated that these were related to metastatic potential (Pokorna et al., 1994). Here, we have added two new cell populations with higher metastatic potential and re-evaluated the cell behaviour, in parallel to the microarray analysis, because cell characteristics may change through time. Indeed the A239/870N cells had reduced metastatic potential and were excluded from further study. Differences in F-actin organisation for the four populations included in this study – and presented in Fig. 3A – are consistent with the previous findings. The non-metastatic cells tend to have F-actin arranged into thick stress fibres that traverse the cell, whereas the metastatic cells are usually polarised, with their F-actin concentrated into ruffles at the leading edge, and elsewhere arranged cortically and in a disordered manner. Quantification of randomly acquired fields revealed that 89±8% of the non-metastatic K2 cells contained at least one stress fibre, in comparison to significantly (P<0.001) reduced levels in the metastatic T15, A297 and A311 cells, where the incidences of cells with stress fibres were 7±2%, 3±8% and 7±8%, respectively (Fig. 3B).

Microarray analysis of the sarcoma cell populations reveals genes with a potential role in metastasis

To investigate the gene expression differences underlying the differences in metastatic potential, the F-actin cytoskeleton and motility, we performed microarray analysis of the cultured cells and the tumours that arose when the cells were implanted into rats. We subjected the cultured cells to three treatments: (1) starvation in 0.5% serum for 5 hours, (2) 5-hour starvation followed by 30-minute treatment with PDGF-IGF and (3) 5-hour starvation followed by a 3-hour treatment with PDGF-IGF. These regimens reflect the times in the Dunn chemotaxis assay at which cell motility is low, commencing, and at its peak. Our analysis showed that treatment with PDGF-IGF did not have any significant effect on gene expression (P>0.08 for all individual changes). Therefore, the gene expression values of the treatments were averaged to obtain the gene expression value for each cell population. We then looked for significant (P<0.05) differences in gene expression, more than 2.5 times in the non-metastatic and metastatic cell populations. Additionally, we rejected genes whose expression did not correlate between the cultured cells and primary tumours, thus restricting our investigations to genes whose expression is regulated in vitro in the same way as in vivo. This procedure generated a list of twenty-three candidate genes (Table 1). To validate the microarray data, we performed reverse transcriptase (RT)-PCR for ten of the candidate genes and observed the same expression patterns as those seen in the microarray (Fig. 4). We then started analysing the functional effects of identified genes. Cell cytoskeleton-related proteins were given a priority when we were choosing the candidates. We selected Bk, Cask, Epb41l3 and Ril, and found that overexpression or knockdown of Bk, Cask and Ril had no noticeable effect on the motility, cytoskeleton or morphology of the sarcoma cells in vitro. However, our studies indicated a role for Epb41l3 in F-actin organisation and cell motility.

Expression and localisation of 4.1B protein, the product of Epb41l3 gene

The expression of Epb41l3, which encodes the protein 4.1B, is significantly reduced 36 times in the metastatic sarcoma cells (Fig. 5A). The similarity of expression patterns in the cultured cells and the primary tumours excludes the possibility that the differential expression of Epb41l3 is owing to cell culture conditions alone. The transcription data were confirmed at the protein level by western blotting (Fig. 5B) by which 4.1B protein was undetectable in the metastatic cells. While cloning rat Epb41l3, we unexpectedly found a third splice variant (GenBank accession number DQ462202, shown in supplementary material Fig. S2). The localisation of 4.1B was investigated using a GFP fusion protein. In sarcoma and HeLa cells, GFP-4.1B is cytoplasmic and excluded from the nucleus (Fig. 6). There is colocalisation with F-actin, particularly in areas of apparent protrusive activity (Fig. 6G,H). It is also enriched at the plasma membrane (Fig. 6I,L). This localisation pattern matches closely immunocytochemical analysis of NCI-460 cells (Tran et al., 1999) and MCF-7 cells (Charboneau et al., 2002). Therefore we conclude that the GFP-4.1B fusion protein adequately reflects the true localisation of the endogenous 4.1B protein. We also identified enrichments of GFP-4.1B localised in many but not all focal adhesions, visualised using interference reflection microscopy, in T15 sarcoma cells (Fig. 7).

Downregulation of 4.1B causes a loss of actin stress fibres

We observed that the depletion of 4.1B from K2 cells by treatment with siRNA targeting Epb41l3 leads to an altered F-actin cytoskeleton (Fig. 8A). The control cells and uninjected cells are characterised by thick stress fibres, whereas the Epb41l3 siRNA-treated cells have fewer stress fibres and less total F-actin. Such an arrangement of F-actin is typical of the metastatic cell populations (see Fig. 3). To quantify the effect of Epb41l3 siRNA on stress fibres, it was necessary to analyse a large number of cells. Therefore, we performed these experiments in HeLa cells, which are more amenable to chemical transfection, rather than the sarcoma cells. Fig. 8B shows that siRNA targeting human EPB41L3 causes a loss of actin stress fibres, and an overall reduction of F-actin in HeLa cells. Western blotting (Fig. 8C) confirmed that 4.1B is expressed in HeLa cells and that its expression can be reduced by treatment with EPB41L3 siRNA. To be certain that the loss of stress fibres is due to the silencing of EPB41L3 and not because of an unspecific effect of the siRNA, we performed an RNA interference (RNAi) rescue experiment. Fig. 8D shows confocal images of cells that were transfected with EPB41L3 siRNA followed by RNAi-resistent 4.1B cDNA or GFP cDNA 48 hours later. The cells expressing RNAi-resistent 4.1B recovered their typical F-actin morphology, whereas the cells transfected with GFP as a control maintained the phenotype of a disrupted F-actin cytoskeleton. Quantification of the confocal images (Fig. 8E) revealed that, when transfected with control siRNA, stress-fibre-containing HeLa cells typically comprised just over 50% of the population. However, when the cells were transfected with EPB41L3 siRNA, the percentage of actin stress-fibre-containing cells significantly decreased to under 25%, irrespective of absence or presence of subsequent GFP expression. In the RNAi-rescue cells, the percentage of cells with F-stress fibres recovers to a level that is not significantly different from the cells transfected with control siRNA. Therefore, we are confident that the effect on the F-actin cytoskeleton is specifically due to the decrease in 4.1B. Having controlled for potential adverse effects of microinjection in K2 cells by including a GFP control and having performed rescue experiments to test for possible adverse consequences of chemical transfection in HeLa cells, we conclude that the 4.1B siRNA treatment causes loss of stress fibres in HeLa cells as well as in the sarcoma cells.

The effect of 4.1B expression on cell motility and chemotaxis

We investigated the function of 4.1B in cell motility by using siRNA to reduce its expression in non-metastatic K2 cells and cDNA to express it in metastatic T15 cells. K2 cells treated with Epb41l3 siRNA significantly increased speed, migrating about twice as fast (Fig. 9A) compared with those injected with control siRNA (ANOVA P<0.01). The same experiment was performed as a control in A297 cells, which do not express 4.1B. There was no change in cell migration (no significance in ANOVA, mean ± s.e.m. of normalised speed of cell migration 1.3±0.1 and 1.0±0.1 for control siRNA and Epb41l3 siRNA, respectively; a total of 20 cells from five time-lapse experiments was analysed) which supported the specificity of the siRNA effect on speed of cell migration. Having seen an increase in speed of cell migration in 4.1B-depleted K2 cells, we performed the complementary experiment by overexpressing 4.1B cDNA in T15 cells, which do not endogenously express detectable levels of 4.1B. Cells were microinjected – with GFP cDNA alone or a mixture of 4.1B and GFP cDNA – and analysis of their movements revealed significant differences in speed of cell migration. The expression of 4.1B caused a reduction in cell motility; cells injected with 4.1B and GFP cDNA migrated at approximately half the speed (Fig. 9B) of those injected with GFP cDNA (ANOVA P<0.01). We also evaluated chemotaxis of the metastatic T15 cells exogenously expressing 4.1B and found that the treatment significantly (ANOVA P<0.05) reduced the chemotactic response (Fig. 10); the speed of cell migration was also significantly reduced (ANOVA P<0.02).


Here, we have described the use of a rat sarcoma cell model to identify a potential metastasis suppressor protein, 4.1B. These rat sarcoma cells exhibited in vitro behaviour that can be related to their metastatic potential. First, the cells had significantly different arrangements of F-actin, which could influence their metastatic potential because cytoskeletal regulation is a key determinant of invasion. Second, the metastatic cells showed increased chemotaxis towards PDGF-IGF; chemotaxis towards blood vessels might be important during intravasation (Wyckoff et al., 2000). Last, the metastatic cell populations contained a larger subpopulation of fast-migrating cells. Enhanced cell motility is a common feature of metastatic cells and has been described by several groups. A recent in vivo study has shown that metastatic tumour cells move 4.5 times as frequently as non-metastatic tumour cells in the same tumour microenvironment (Sahai et al., 2005). It has been suggested that only a subpopulation of cells from a tumour, rather than every cell in the tumour, becomes metastatic (Fidler and Hart, 1982). Therefore, it is reasonable to suppose that the increased speed of migration of these subpopulations contributes to the increased metastatic potentials observed in T15, A297 and A311 cells.

Our model has advantages for the microarray study of metastasis, because the cell populations originated from a common, spontaneous tumour transformation, and were unlikely to show the genetic heterogeneity that can complicate the interpretation of microarray data. The use of inbred rats further helped to reduce heterogeneity. Thus, we were able to focus on the genetic differences contributing to the different abilities of cell populations and tumours to shed metastases. Indeed, we found relatively few differentially expressed genes.

We had included a growth factor treatment in the microarray experiment because, in our model, progression to the invasive phenotype correlated with enhanced chemotaxis to a gradient of PDGF-IGF. We found that an up to 3-hour treatment with PDGF-IGF had no significant effect on gene expression. A possible explanation for this is that a short treatment with PDGF-IGF may exert its effect via molecular switches, whose activation state cannot be determined by microarray analysis. We took advantage of the unchanged gene expression by averaging the mean expression values for the three growth factor treatments, which improved the statistical significance of the data.

The reliability of our microarray data is demonstrated in two ways. First, RT-PCR of ten candidate genes confirmed the expression patterns found by the microarray analysis. Second, some of the genes identified, for example, Cspg4, Ankyrin 3, Grem1 and Neo1, have already been implicated in metastasis by other groups. Moreover, we have shown functional effects for one of the candidate genes. Of the 23 most differentially expressed genes, 15 are downregulated and eight are upregulated in metastasis. Cspg4, coding for the proteoglycan NG2, is overexpressed approximately 65 times in metastatic cells. Burg et al. reported that expression of NG2 in melanoma cells enhanced their metastatic properties (Burg et al., 1998). Ankyrin 3, which binds the Rho guanine-nucleotide exchange factor Tiam1, is upregulated approximately 12 times in the metastatic cells. Bourguignon et al. report that the Tiam1/Ankyrin interaction promotes Rac1 signalling and migration in SP1 mouse breast tumour cells (Bourguignon et al., 2000). The function of Tiam1 is likely to be affected by an increased availability of ankyrin, such as in the metastatic cells of this model. Reversion-induced LIM protein (RIL) is overexpressed with metastasis. RIL may be a regulator of actin stress fibre turnover because Vallenius et al. (Vallenius et al., 2004) found that expression of RIL caused the rapid formation of new stress fibres and frequent collapse of thick stress fibres. A dynamic actin cytoskeleton is essential for cell motility and metastasis and, therefore, the increased availability of a regulator of actin turnover such as RIL would be an advantage to a metastasising cell. Grem1, encoding gremlin, a negative regulator of monocyte chemotaxis (Chen et al., 2004), is downregulated with metastasis. This could explain the strong chemotaxis to PDGF-IGF and enhanced motility in our metastasising cells. Gremlin can also suppress transformation and tumorigenesis (Topol et al., 1997) so its upregulation in the non-metastatic cells might prevent their successful growth in a new site and account in part for their inability to cause metastases. The metalloprotease ADAMTS-1 is downregulated with metastasis in our model which agrees with Kuno et al., who reported that overexpression of ADAMTS-1 in CHO cells causes the inhibition of tumour growth and metastasis (Kuno et al., 2004). Plk2 was significantly downregulated in our metastasising cells and a similar downregulation mediated by transcriptional silencing in malignant lymphomas was reported by Syed et al. (Syed et al., 2006). They also showed that the Plk2 expression promoted apoptosis, which might also be important in our sarcomas. Although there is currently no clear role for RNase 4 in cancer, there are instances where unusual RNase levels have been detected in cancer patients (Peracaula et al., 2000) and RNase 4 was downregulated in our metastasising sarcoma cells. Neogenin, which is downregulated in our metastatic cells, might play a role in mammary carcinogenesis, because its expression is inversely correlated with mammary carcinogenicity (Lee et al., 2005), which again supports the pattern of expression we report here.

The main focus of this study is the protein 4.1B. Our data suggest that 4.1B functions as a metastasis suppressor because its loss supports a reorganisation of the F-actin cytoskeleton and concomitant enhanced cell motility, both of which are likely to be important in metastasis. 4.1B has not previously been related to the development of metastasis, but the tumour suppressor activity of 4.1B has been demonstrated in several cell lines and 4.1B loss reported in various cancers (Charboneau et al., 2002; Gutmann et al., 2001; Kikuchi et al., 2006; Tran et al., 1999). We found that the Epb41l3 transcript in the sarcoma cells is a variant of the 4.1B minor isoform type II, and therefore represents a third splice variant of Epb41l3. Different splice variants of Epb41l3 are thought to contribute to its tissue specificity (Gascard et al., 2004).

Depletion of 4.1B in K2 sarcoma cells by a specific siRNA resulting in a loss of stress fibres was also observed in HeLa cells treated with three independent siRNAs, which demonstrates that this response is more general and not restricted to our sarcoma model. A reduction in stress fibres that accompany the metastatic phenotype has been also observed by others (Khanna et al., 2001). The relationship between 4.1B and the F-actin cytoskeleton was further explored by expressing 4.1B in the metastatic cells. The exogenous expression of 4.1B in metastatic T15 cells, which have no endogenous expression, was without an apparent effect on F-actin organisation (data not shown). We conclude that 4.1B in the non-metastatic K2 cells is necessary for the presence of actin stress fibres, whereas the exogenous expression of 4.1B in the metastatic cells is not sufficient for the reappearance of stress fibres; most likely, additional changes in expression are required.

We also found that reduction of 4.1B expression in non-metastatic K2 cells by siRNA treatment caused speed of cell migration to double. Correspondingly, exogenous expression of 4.1B in the metastatic T15 cells reduced their speed approximately by half and, additionally, significantly suppressed chemotaxis. The fact that overexpression of 4.1B, and also its depletion, can lead to changes in motility in different cell types, strongly suggests that 4.1B is important for cell migration in this model. Furthermore, F-actin rearrangements that accompany changes in speed of cell migration have been observed by others. For example, Clark et al. reported that metastatic cells migrate more and have more filopodial protrusions than cells from primary tumours (Clark et al., 2000). We conclude that the experimental reduction of 4.1B expression by RNAi in non-metastasising cells, which mimicks the loss of 4.1B in the metastatic cells, induces a reduction of stress fibre occurrence, and enhanced cell motility and chemotaxis thus introducing important aspects of the metastatic phenotype in the non-metastatic cells. Although the metastasising sarcoma cells show an increasing trend in the metastatic potential, we did not see a similar tendency in changes of stress fibre morphology, cell motility or 4.1B expression. It is likely that these changes represent an important step in tumour progression towards a metastatic phenotype common to all three metastasising populations, and additional changes are required to increase the metastatic potential further.

We found that GFP-4.1B was expressed at cell-cell contacts in the sarcoma cells and at the plasma membrane, which agrees with data from other groups (Tran et al., 1999; Charboneau et al., 2002). 4.1 proteins are known to tether the cytoskeleton to the membrane by crosslinking spectrin and/or actin dimers to integral membrane proteins (Conboy, 1993). Therefore, we propose that the loss of stress fibres and enhanced motility is mediated by the role of 4.1B in linking F-actin to focal adhesions and plasma membrane. Our finding of GFP-4.1B enrichment in many focal adhesions supports this possibility.

Recent clinical data provide further support for the relevance of our findings to metastasis. The profiling database Oncomine™ (www.oncomine.org) presents an example of significant downregulation of 4.1B protein in human metastatic, infiltrating lobular carcinoma of breast, using all secondary sites in comparison to the primary tumour. Three probes of EPB41L3 expression were used and the changes in expression levels were between 0.32 and 0.60 times, with P values between 0.007 and 0.031. Another example, the loss of heterozygosity of the region of chromosome 18, where EPB41L3 is localized, has been observed in more than 55% of invasive ductal carcinoma in situ (DCIS) tumours (Kittiniyom et al., 2001). The epigenetic inactivation of EPB41L3 by promoter methylation was found to be an indicator of poor prognosis in patients with lung adenocarcinoma (Kikuchi et al., 2005). Similarly, epigenetic inactivation of EPB41L3 by promoter methylation was found to correlate with a shorter survival in patients with renal clear cell carcinoma (Kikuchi et al., 2006). These observations support our data, which suggest that the loss of 4.1B can enhance cell behaviours that are associated with metastatic potential.

In summary, we used a sarcoma model of metastasis to identify genes potentially associated with metastasis. We found some genes whose role in metastatic disease was already described, and others whose association in metastasis is as yet unconfirmed. Having studied one such gene, Epb41l3, we conclude that the loss of 4.1B protein in metastatic cells causes a loss of actin stress fibres and a significant increase in cell motility and, thus, plays a role in the progression to the metastatic phenotype.

Materials and Methods

Cell culture

K2 cells (full name LW13K2), are spontaneously transformed rat embryonic fibroblasts (Vesely and Weiss, 1973). T15 cells (full name RPSL4T15) were developed from K2 cells by neoplastic progression in vivo and in vitro (Vesely et al., 1987). A297 cells (full name A297Nb) were taken from a metastasis that formed in a rat injected with T15 cells. A311 cells (full name A337/311RP) were obtained from a metastasis that arose in a rat injected with the A297 cells. K2, T15, A297 and A311 cells were maintained in MEM with Hank's salts supplemented with 10% bovine serum (SML, Germany), 0.09% sodium bicarbonate, 0.12 g/l Na-pyruvate (Sigma) and 1 mM glutamine, at 37°C with 5% CO2. HeLa cells were maintained in E4 medium supplemented with 10% foetal calf serum, at 37°C with 10% CO2.

Assessment of metastatic potential

Cell populations were implanted subcutaneously into the back of Lewis rats at one-million cells per transplant. Animals were sacrificed 4-6 weeks later and dissected. All organs in the thoracic and abdominal cavities were carefully inspected for metastases. The metastatic potential is the percentage of rats developing metastases.

Low light level digital imaging microscopy, and analysis of cell motility and chemotaxis

Chemotaxis in a concentration gradient of 60 ng/ml PDGF and 80 ng/ml insulin-like growth factor (IGF) was assessed using the Dunn chemotaxis chamber as described previously (Zicha et al., 1991). To assess serum-stimulated random motility of cells with reduced 4.1B expression or exogenous 4.1B expression, we microinjected K2 or T15 cells that had been seeded on coverslips, with 2 μM Cy3-labelled siRNA targeting Epb41l3, or 0.075 μg/μl 4.1B cDNA construct mixed with GFP cDNA construct. Controls were 2 μM Cy3-labelled control siRNA or 0.05 μg/μl GFP cDNA construct alone. Cells treated with siRNA were incubated for 48 hours. Cells microinjected with the mixture of expression constructs were incubated for 5 hours. In both cases, the cells were then starved in medium containing reduced (0.5%) bovine serum for 5 hours, and assembled into chambers for live imaging in medium containing 10% bovine serum. In experiments assessing chemotaxis of T15 cells exogenously expressing 4.1B, we microinjected T15 cells on coverslips with a mixture of 4.1B and GFP contructs, starved the cells in 0.5% bovine serum for five hours and started time-lapse recording in the Dunn chamber. Movies were acquired using a 10× NA 0.32 phase-contrast objective on an Axiovert 135TV microscope (Zeiss) equipped with a Hamamatsu Orca ER CCD camera. AQM software (Kinetic Imaging) was used to capture one frame every 5 minutes for 16 hours. Cell translocations were interactively tracked using Motion Analysis software (Kinetic Imaging). Speed of cell migration and chemotaxis of the cells was evaluated using an algorithm implemented in Mathematica® (Wolfram Research, Inc.), as previously described (Zicha and Dunn, 1995), except that the statistical comparison of the chemotactic responses was based on ANOVA applied to normalised displacement in the direction of the gradient. Furthermore, in RNAi or cDNA expression experiments, the speeds of cell migration were normalised using the average speed of uninjected cells from the same recording.

RNA extraction

Subconfluent, early passaged cells were starved in medium containing reduced (0.5%) serum for 5 hours, followed by (1) no further treatment, (2) stimulation for 30 minutes with 30 ng/ml PDGF and 40 ng/ml IGF or, (3) stimulation for 180 minutes with 30 ng/ml PDGF and 40 ng/ml IGF. Three biological replicates were performed for each condition, and samples were checked for mycoplasma by agar colony growth and found to be free of contamination. Cells were harvested by detachment with trypsin:versene 1:4 and homogenised in Qiashredder columns (Qiagen). To obtain primary tumours, one-million cells per transplant were subcutaneously implanted in rats. After 4-6 weeks the rats were sacrificed, and 1 cm3 of primary tumour was excised and stored at –20°C in RNAlater (Qiagen). Tumour samples were homogenised for 40 seconds with a rotor-stator homogeniser, then sonicated for 20 seconds, and lysates were cleared of cell debris by centrifugation. Total RNA from cells and tumours was extracted using the RNeasy mini-kit (Qiagen) according to the manufacturer's instructions. Quality of RNA was confirmed by agarose gel electrophoresis.

Microarray analysis

All experiments were performed using Affymetrix Rat 230A GeneChip® oligonucleotide arrays. Briefly, 10 μg of RNA primed with T7-linked oligo(dT) was used to generate first-strand cDNA. After second-strand synthesis, in vitro transcription was performed in the presence of biotinylated UTP and CTP (Enzo Diagnostics), resulting in approximately 65 μg biotinylated cRNA. A complete description of procedures is available upon request. The cRNA was processed as per manufacturer's recommendation using an Affymetrix GeneChip® Instrument System. Briefly, spike controls were added to 10 μg fragmented cDNA before overnight hybridisation. Arrays were then washed and stained with streptavidin-phycoerythrin, before being scanned on an Affymetrix GeneChip® scanner. A complete description of these procedures is available upon request. After scanning, array images were assessed by eye to confirm scanner alignment and the absence of significant bubbles or scratches on the chip surface. 3′-end:5′-end ratios for GAPDH and β-actin were within acceptable limits (from 0.97 to 1.72), and BioB spike controls were found to be present on all chips, with BioC, BioD and CreX also present in increasing intensity. When scaled to a target intensity of 100 (using Affymetrix MAS 5.0 array analysis software), scaling factors for all arrays were within acceptable limits (0.19-0.405), as were background, Q values and mean intensities.

Fluorescence intensity data from The GeneChip® arrays were processed using an algorithm developed in Mathematica® (supplementary material Fig. S1). Genes which the Affymetrix software had classed as `absent' on each GeneChip® were excluded from the analysis. The fluorescence intensity data were normalised using the median intensity of each chip. The median value of three replicates was calculated, and gene expression was considered in terms of relative changes between cell populations and between different treatments with PDGF-IGF. The t test was applied to each relative expression level, and the P-value corrected with the Benjamini-Hochberg algorithm (Benjamini and Hochberg, 1995) to avoid excess false-positive calls. There being no significant differences in gene expression in response to PDGF-IGF, the three time-points were averaged and used to calculate the relative expression levels between the four cell populations, with the ANOVA and Benjamini-Hochberg algorithm applied as above. The criteria used to select candidate genes were a relative expression level change of more than 2.5 times (P<0.05), with no significant difference in the pattern of gene expression between the cultured cells and the primary tumours. Logged normalised expression values for these genes were calculated and plotted.


RT-PCR was performed to confirm the gene expression levels indicated by the microarray analysis. Total RNA was extracted from each cell population and reverse-transcribed into cDNA as described above. Fragments of each gene of interest were amplified by PCR using Taq Polymerase (Qiagen). The sequences of the RT-PCR primers are, Adamts1 forward 5′-GATGGTTTACAGGCTGCCTTC-3′, reverse 5′-TTGTTTGGCACACCAGTAAGC-3′; Ank3 forward 5′-CCGACTCCCTCAGACACTACA-3′, reverse 5′-GTGTTCCTTCCAGGTCTCTCC-3′; Bk forward 5′-GACCTGATCGCCATAAGGAAG-3′, reverse 5′-GGTTTTGAAGTGGGGAATCAA-3′; Cask forward 5′-ACCATTCGGAAAATCCATGAG-3′, reverse 5′-CTGACACAAGGCCGATAACAA-3′; Cspg4 forward 5′-ACCATCCAGAGAGCCACAGTA-3′, reverse 5′-AGCAGGACGTTAGTGAGGACA-3′; Epb41l3 forward 5′-CATCCAGCAGCAAACTCTCAC-3′, reverse 5′-GTCACGAAGGAACAGGGTAGG-3′; Gapdh forward 5′-TGCTGAGTATGTCGTGGAGTCT-3′, reverse 5′-CCCTGTTGCTGTAGCCATATTC-3′; Grem1 forward 5′-GATGACTGAGAGCGTTGTTCG-3′, reverse 5′-GACCCAGTCACCTTTCTCTGG-3′; Plk2 forward 5′-AACTTGGCCAATGCTCTGTTT-3′, reverse 5′-AAGAGCATGTTCAGGGCGTAT-3′; Ril forward 5′-AGCAGGCCTGAGAACAAGAAC-3′, reverse 5′-TAGCGGAAGGATCCAGACTGT-3′; Tsnax forward 5′-AACGCTTGCTATGCCCTTAAA-3′, reverse 5′-CCTTCCACCCAAAATGTCACT-3′. PCR products were resolved by gel electrophoresis, and visualised by UV transillumination.

Western blotting

Cells were lysed in RIPA buffer containing protease inhibitors for 15 minutes on ice. Lysates were cleared by centrifuging at 10,000 g for 10 minutes. Protein concentration was measured using the Coomassie assay (Pierce). Equal amounts of protein were resolved by electrophoresis on a 4-12% Novex gel (Invitrogen), transferred to PVDF membrane (Immobilon-P, Millipore) and probed with an antibody to 4.1B (kindly provided by Narla Mohandas, New York Blood Center, NY) using standard immunoblotting and enhanced chemiluminescence protocols. Blots were re-probed with mouse anti-GAPDH or anti-β actin antibody (Abcam) to confirm equal protein loading.

Cloning and expression of 4.1B

Total RNA was extracted from each cell population and reverse-transcribed into cDNA as described above. The full-length cDNA of rat Epb41l3 was amplified by PCR using a forward primer containing a HindIII site, 5′-TAAATCAAGCTTGCAGCAATGACAACCGAATCAGGATCAGACTCAGAA-3′, and a reverse primer containing a KpnI site, 5′-TTAATTGGTACCTGCTGCTCAATCCTCTCCATCTTCTGGTGTGATTTC-3′, and cloned into pEGFP-C3 (Clontech). To generate an untagged 4.1B construct, the GFP sequence was excised using BsrGI and AgeI restriction endonucleases, and the ends of the vector made blunt by treatment with mung bean exonuclease, and re-ligated so that untagged 4.1B could be expressed. All nucleases were from New England BioLabs. Cells were transfected with 4.1B or GFP-4.1B construct using Effectene Reagent (Qiagen) according to the manufacturer's instructions and used in experiments after 24 hours. Cells microinjected with the GFP-4.1B construct were used in experiments after 5 hours.

RNA interference

Annealed and purified siRNA oligonucleotides were from Ambion, Inc. and were resuspended according to their protocol. Rat Epb41l3 gene was targeted by R-epb41l3 siRNA oligonucleotide with the sequence GCAUGCAGUGCAAAGUGAC. Human EPB41L3 gene was targeted by H-epb41l3 siRNA1 oligonucleotide with the sequence GCAUCACUAAACCGAUAAU, H-epb41l3 siRNA2 oligonucleotide with the sequence GCUCGAAUAUCAGCAAUUA and H-epb41l3 siRNA3 oligonucleotide with the sequence GCGAUUACAUUAGUGAGUU. The control siRNA was Silencer Negative Control no.1 siRNA (Ambion, Inc.). Chemical transfections of 100 nM siRNA oligonucleotides were carried out using Effectene Reagent (Qiagen) according to the manufacturer's protocol. For microinjection studies, oligonucleotides were labelled with Cy3 using Silencer siRNA Labeling Kit (Ambion, Inc.) and injected at 2 μM. Cells were used in experiments 48 hours after transfection. For RNAi rescue experiments, HeLa cells were transfected with rat 4.1B construct 48 hours after siRNA transfection, and the effects of the rescue assessed after 24 hours (72 hours after the first transfection).

Cytochemistry and laser scanning confocal microscopy

Intact cells, cells transfected with GFP-4.1B construct, and cells microinjected with Cy3-labelled siRNA were fixed in 4% formaldehyde in PBS for 10 minutes, permeabilised in 0.1% Triton X-100 for 10 minutes, stained with Rhodamine-phalloidin or Alexa Fluor-488–phalloidin at 1:2000 dilution for 30 minutes, and then mounted in Mowiol. Randomly selected images were acquired on a Zeiss LSM 510 laser scanning confocal microscope with a 63× NA 1.4 phase-contrast objective lens. Quantitative analysis of the incidence of actin stress fibres was conducted using a blind protocol. T15 cells microinjected with a mixture of GFP and 4.1B expression constructs were fixed after 5 hours in 4% formaldehyde in PBS for 10 minutes and fluorescence images were acquired as above. Simultaneously with GFP fluorescence we visualised interference reflection images using the 488 nm line of an argon laser with no emission filter in front of a second detector.


We are grateful to Peter Parker, Anne Ridley and Michael Way for helpful discussions and support throughout the project, to Trevor Duhig and Alastair Nicol for comments on the manuscript, and to Deborah Aubyn and Peter Jordan for help with microscopy.


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