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Cancer Research 67, 6814, July 15, 2007. doi: 10.1158/0008-5472.CAN-07-0752
© 2007 American Association for Cancer Research
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Cell, Tumor, and Stem Cell Biology

Eukaryotic Translation Initiation Factor 4E–Induced Progression of Primary Human Mammary Epithelial Cells along the Cancer Pathway Is Associated with Targeted Translational Deregulation of Oncogenic Drivers and Inhibitors

Ola Larsson1, Shunan Li1, Olga A. Issaenko1,2, Svetlana Avdulov1, Mark Peterson1, Karen Smith1, Peter B. Bitterman1 and Vitaly A. Polunovsky1

Departments of 1 Medicine and 2 Surgery, University of Minnesota, Minneapolis, Minnesota

Requests for reprints: Vitaly A. Polunovsky, Department of Medicine, University of Minnesota, 420 Delaware Street, Minneapolis, MN 55455. Phone: 612-626-2112; Fax: 612-625-2174; E-mail: polun001{at}umn.edu or Peter B. Bitterman, Department of Medicine, University of Minnesota, 420 Delaware Street, Minneapolis, MN 55455. Phone: 612-624-5175; Fax: 612-625-2174; E-mail: bitte001{at}umn.edu.


   Abstract
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 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Pathologic redirection of translational control by constitutive activation of eukaryotic translation initiation factor 4F (eIF4F), the cap-dependent translation initiation apparatus, is an obligatory step in oncogenesis; however, its mechanism remains undefined. Here, we simulate this pro-oncogenic state by overexpressing eIF4E, the rate-limiting component of eIF4F, in primary human mammary epithelial cells (HMECs) and examine the resultant changes in cell biology and gene expression profiles of total and polyribosome-bound mRNA genome wide. Overexpressed eIF4E rescues primary HMECs from telomere-independent growth arrest and disables checkpoints governing S-phase entry as well as apoptosis in HMECs immortalized by telomerase, imparting cells with proliferative and survival autonomy. Although the transcriptional response to increased eIF4E was modest, the translational response was large, selective, and bidirectional. In addition to translational activation of known and novel eIF4E-responsive oncogenic drivers regulating cell growth and survival, our data unveil previously unrecognized cellular defenses including translational activation of tumor suppressors, translational repression of transcripts enriched with miRNA target sites, and translational modulation of genes governing translation itself. These findings provide insight into the proneoplastic and compensatory mechanisms embedded in the oncogenic translational program. They support a model whereby deregulated eIF4E moves human epithelial cells along the cancer pathway by profoundly altering ribosomal recruitment to cancer-related transcripts, and eIF4E-modified cells counter these potentially oncogenic alterations with a compensatory translational mechanism that mitigates acquisition of malignancy. [Cancer Res 2007;67(14):6814–24]


   Introduction
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 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
The cap-dependent initiation step of protein synthesis is a key regulatory hub in the flow of genetic information from the genome to the proteome (1), and its disruption is an ubiquitous property of cancer (2). Breast carcinoma exemplifies this principle (3). Increased expression of the mRNA cap-binding protein eIF4E is an early event in breast tumorigenesis (4); increased abundance of eIF4E in breast tumor cells confers an increased risk of cancer-related death (5); and sustained activation of the cap-dependent translation initiation apparatus is required for breast cancer cells to maintain a malignant phenotype (6).

Despite the direct connection between aberrant eIF4E-mediated translation and human breast carcinogenesis, the mechanisms remain unknown. The oncogenic function of deregulated cap-dependent translation is thought to arise through a redirection of translational control; however, neither the global repertoire of translationally regulated mRNAs in eIF4E-transformed human cells nor the molecular mechanism governing their recruitment to ribosomes in response to oncogenic signals have been elucidated.

To date, human cell systems used for analysis of eIF4E-mediated carcinogenesis have been of unknown genetic constitution, resulting in a phenotype driven by undefined interactions. Here, we approached this problem by developing a genetically tractable model, which integrates transformation of primary human mammary epithelial cells (HMEC) by eIF4E with genome-wide analysis of transcriptional and translational profiles. We show that sustained activation of eIF4E allows primary HMECs to bypass telomere-independent growth arrest in vitro (M0) and accelerates progression of rescued cells toward replicative senescence. Introduction of eIF4E into HMECs that have bypassed the senescence barrier imparted cells with in vitro hallmarks of malignancy, and profoundly altered the global pattern of polyribosome-associated mRNA encoding regulators of cell fate. Our data support the concept that deregulation of eIF4E-mediated translational control differentially alters the expression of cancer-related genes in a manner that promotes malignant conversion, and reveals the existence of a complex regulatory network that defends the cell from these changes.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Cell culture conditions and reagents. We isolated primary HMECs from reduction mammoplasty tissue from two premenopausal women following established protocols. Dr. Weinberg (Whitehead Institute, Cambridge, MA) provided us with primary HMECs constitutively expressing human telomerase reverse transcriptase (hTERT). We cultured primary and immortalized HMECs in mammary epithelium basal medium (MEBM, Cambrex) supplemented with 10 ng/mL human epidermal growth factor (EGF), 5 µg/mL insulin, 0.5 µg/mL hydrocortisone, 52 µg/mL bovine pituitary extract, 50 µg/mL gentamicin, and 50 ng/mL amphotericin-B (Cambrex), plus 10–5 mol/L isoproterenol and 5 µg/mL transferrin. We obtained breast carcinoma cell lines from American Type Culture Collection and maintained these cells in high-glucose (4.5 g/L) DMEM (Sigma) supplemented with 10% FCS (Sigma), 100 units/mL penicillin, 100 units/mL streptomycin, and 125 ng/mL amphotericin (Life Technologies). For growth factor starvation, we incubated exponentially growing HMECs in MEBM lacking insulin, bovine pituitary extract, and EGF.

Plasmids, retroviral infection, and stable transfection. We previously described the methods for generation of MSCV-M1GR1 retroviral constructs expressing HA-tagged translational regulators eIF4E and 4E-BP1 linked to a green fluorescent protein (GFP) reporter, and the retroviral infection procedure (6). Infection frequencies based on GFP expression were typically 75% to 85% for both primary and hTERT-expressing HMECs. We sorted GFP-positive cells at 488 nm laser emission using a Becton Dickinson fluorescence-activated cell sorting (FACS) DiVa (BD Biosciences) with a 530/528 optical filter based on identical GFP expression levels in GFP/vector and GFP/gene-of-interest expressing cells. To assess expression of the gene of interest, we subjected the sorted cell populations to immunoblot analysis within 5 days postsorting. We cotransfected HMECs coexpressing hTERT and hemagglutinin-tagged eIF4E (HA-eIF4E) with a pACTAG-2 plasmid encoding the neomycin resistance gene cassette and 3HA-tagged 4E-BP1 (either pACTAG neo/HA-4E-BP1wt or pACTAG neo/HA-4E-BP1{Delta}), with subsequent selection of the neomycin-resistant cell population as previously described (6).

Immunoblot and cap-binding (7-Me-GTP pull-down) analysis. We have previously described the experimental procedures used in this report (7). We probed blots using the following primary antibodies: anti-Rb (total and phospho-Ser780, Ser795, Ser807/81; the Rb antibody kit, Cell Signaling); anti-cdk4, anti-p21Cip1, anti-eIF4E (phospho-Ser209) and anti-4E-BP1 (phospho-Ser65; Cell Signaling); anti-eIF4E (total), anti-cdk2, and anti-p27Kip1 (Transduction Laboratories); anti-cyclin A, anti-cyclin E, anti-p16Ink4A and anti-cyclin D1 (BD PharMingen); anti-actin (Sigma); anti-4E-BP1 (total, Abcam); anti-eIF4G from Dr. Sonenberg (McGill University). We analyzed samples from at least two independent experiments for each protein tested.

Analysis of cell proliferation and apoptosis. For analysis of cell cycle progression and apoptosis, we determined DNA content using flow cytometric analysis of ethanol fixed, propidium iodide–stained cells on a FACSCalibur flow cytometer (Becton Dickinson) with the CellQuest program as previously described (7). For analysis of cell cycle entry, we incubated growth factor–deprived cells with 1 µmol/L bromodeoxyuridine (BrdUrd) in MEBM medium supplemented with growth factors. As a function of time, we analyzed cells for total DNA content and BrdUrd incorporation using the BrdUrd flow kit (BD, PharMingen) according to the manufacturers' instructions.

Senescence-ß-galactosidase assay. We fixed and stained cells using the senescence-associated ß-galactosidase (SA-ß-gal) staining kit (Cell Signaling) in accord with instructions provided by the manufacturer.

Anchorage-dependent colony formation. We followed our previously published procedure (6). Cultures were continued for 7 days (37°C, 5% CO2) and photographed.

Anchorage-independent growth. We conducted this assay as previously reported (6) and examined cell growth patterns by microscopic observation after 2 weeks.

Three-dimensional cell culture. We followed published procedures without modification (8).

RNA and polyribosome preparations. We fractionated and prepared polyribosome stratified RNA as previously described (9). We collected 10 fractions of 0.5 mL each into tubes containing 50 µL of 10% SDS. RNA in the fractions was purified using Tri-reagent (Sigma) and fractions 7 to 10 were combined into the "heavy" fraction (>2 ribosome per transcript). We also obtained an unstratified total RNA sample using the standard Tri-reagent protocol.

Microarray assays and analysis. We used the single round labeling procedure (Affymetrix) to label both polyribosome (>2 ribosome per transcript—"heavy" fraction) and total RNA samples. Each experiment was done twice or thrice and labeled independently (see Supplementary Fig. S2 for detailed information about the number of replications per group). We hybridized the samples to the HG-U133plus2 microarrays from Affymetrix using the standard protocol. We analyzed data in R and bioconductor (10) unless otherwise stated, and used the Bioconductor library "gcrma" to perform gcrma normalization with updated probe set definitions "HS133P_HS_REFSEQ_6" as defined in ref. (11) as these improve both precision and accuracy (12). We used the "mas5calls" function within the "affy" library to extract present, marginal or absent calls for each gene on each chip. To generate a translational efficiency measure, we used the geometric (log 2) mean estimates obtained from total RNA from either the HMEC/hTERT or the HMEC/hTERT/4E cells and expressed the estimates obtained from each of the polyribosomal heavy fraction replicates as a ratio between the polyribosomal RNA signal and the total RNA signal for each cell type. We used standard methods, including scaling factor and 3' to 5' ratio, to confirm high technical quality.

Further, we looked at overall reproducibility of the estimates derived from the total RNA and the polyribosomal/total RNA ratios using Pearson correlation (Supplementary Fig. S2) and found experimental group clusters. Although correlation is a robust measure to assess similarities (13), we also used principal components analysis to look at biological significance. As shown in Supplementary Fig. S3, the translational activity measures (translation/transcription) form clusters according to sample class. These analyses indicate good technical quality, good reproducibility and biological significance.

To find differentially expressed genes, we used significance analysis of microarrays (14) and included only those genes that had more than three "present" or "marginal" calls across all arrays, as this provided substantial signal to noise improvement (15). We collected all genes that showed a false discovery rate (FDR) <10%. We looked for overrepresentation of genes assigned certain functions using the PERL package GO::TermFinder (16) based on the annotation from the Gene Ontology Consortium (17). We extended the annotation to include known miRNA targets (TargetScan v3.0; ref. 18). All categories that were enriched >1.5-fold showed a P value < 0.05 (Fisher's exact test) and included <1,000 genes were considered significant and of interest. The full data set has been deposited at Gene Expression Omnibus accession GSE6043, including raw data, as this is the basis for future integrative analysis (19, 20).

Statistical analysis. We did statistical analyses using one-way ANOVA with Dunnett's multiple comparison test (S-PLUS Guide to Statistical and Mathematical Analysis, Version 4.0, Seattle, WA).


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
eIF4E Rescues Primary HMECs from Telomere-Independent Growth Arrest
Primary explant cultures of HMECs undergo growth arrest after 15 to 20 population doublings in vitro unless supported by a stromal feeder layer, a telomere-independent withdrawal from the cell cycle designated mortality stage 0 (M0; ref. 21). M0 cells acquire a large, flat morphology, and activate acidic senescence-associated ß galactosidase (SA-ß-gal). A small, preexisting subpopulation of HMECs evades the M0 barrier (22), and after a number of additional cell divisions, these M0-resistant cells reach the telomere-dependent mortality stage 1 (M1), followed by crisis (M2) and cell death (23).

To examine the effect of eIF4E on life span, we transduced HMECs with a retroviral vector encoding GFP and hemagglutinin (HA) epitope-tagged mouse eIF4E around the time of M0 (30 days after isolation, 17 population doublings). Introduction of ectopic eIF4E significantly increased the HMEC population size, extended proliferative life span, and reduced the percentage of SA-ß-gal–positive cells (Fig. 1 ). HMECs transduced with eIF4E did not form large colonies at low density on tissue culture plastic, collagen, or soft agar (not shown); and after an interval of active proliferation, these cells proceeded toward M1, with the majority becoming SA-ß-gal positive by day 25. Thus, although ectopic eIF4E significantly increased the cohort of HMECs evading the M0 barrier, it did not confer archetypal characteristics of neoplastic growth.


Figure 1
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  		Figure 1. eIF4E rescues primary HMECs from M0 growth arrest. A, immunoblot analysis. Expression of endogenous eIF4E and exogenous HA-eIF4E in primary nontransfected (NT) HMECs or from HMECs transfected with an empty retroviral vector or a vector encoding HA-eIF4E. Actin is shown as a loading control. Number (B) and SA-ß-gal staining (C) of HMECs transfected with HA-eIF4E or empty vector as a function of time in culture. Points, mean of two independent experiments in triplicates; bars, SD.

 
eIF4E Confers HMECs Immortalized by hTERT with Clonogenicity and Anchorage Independence
To determine whether interventions breaching the M1 barrier might cooperate with eIF4E to promote neoplastic conversion, we introduced HA-eIF4E/GFP into HMECs immortalized by constitutive expression of the gene encoding hTERT (24). We sorted HMEC/hTERT transduced with a retroviral vector encoding GFP and eIF4E, or GFP alone. The cells containing ectopic eIF4E showed an approximate doubling of their eIF4E content (Fig. 2A ). When plated at low density, HMEC/hTERT did not form large colonies on plastic, collagen, or in soft agar. In marked contrast, the combined expression of eIF4E and hTERT enabled cells to efficiently form colonies on plastic, transformed foci on collagen, and to grow exuberantly in soft agar in a glandular pattern that profoundly differed from the large, amorphous multilayer spheroids formed by breast carcinoma cells (Fig. 2B). When injected into nude mice, HMECs coexpressing eIF4E and hTERT failed to form tumors, whereas authentic breast carcinoma cells were tumorigenic (not shown). Thus, although concurrent activation of hTERT and eIF4E in primary HMECs is not tumorigenic, it results in neoplastic growth properties in vitro.


Figure 2
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  		Figure 2. eIF4E cooperates with telomerase to enable focus formation and anchorage-independent growth. A, immunoblot analysis of eIF4E expression. Results for NT HMEC/hTERT and FACS-sorted GFP-bright HMEC/hTERT (Vector) and HMEC/hTERT/4E (HA-eIF4E). B, effect of eIF4E overexpression on anchorage-dependent (tissue culture plastic and type I collagen) and anchorage-independent (soft agar) growth. Colonies on plastic were stained with Coomassie blue and photographed after 14 d. The growth pattern on collagen was analyzed after 7 d and the growth pattern on soft agar was analyzed after 14 d. As a positive control, the growth pattern of breast carcinoma cells (MDA-MB-468) is shown. Representative examples from three independent assays.

 
eIF4E Promotes the G0-S Transition
Because the proliferation of HMECs and HMEC/hTERT strictly depends on the presence of EGF and insulin (24, 25), we examined whether eIF4E could reduce or replace this requirement. Introduction of eIF4E increased the number of cycling HMEC/hTERT in growth factor–free medium and in the presence of individual growth factors (Fig. 3A ). The ability of eIF4E to promote escape from G0 arrest was dependent on its cap binding function, as mutating amino acid 56 from W to A, which ablates cap binding (26), eliminated the positive proliferative effect of eIF4E (Fig. 3B).


Figure 3
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  		Figure 3. Activated eIF4E reduces the requirement for growth factors by disrupting Rb checkpoint control. A to C, frequency of proliferating cells as quantified by flow cytometric analysis. Columns, mean for triplicate samples of two independent experiments; bars, SD. A, eIF4E enhances the kinetics of cell cycle entry. HMEC/hTERT infected with empty vector, or vector encoding wild-type HA-eIF4E, was cultured in growth factor–free medium for 48 h, and either continued in growth factor–free medium for 18 h or shifted for the same time interval to medium containing EGF or insulin. *, P < 0.005 versus vector. B, functionally active eIF4E decreases entry into quiescence. HMEC/hTERT harboring a vector encoding wild-type or a disabled mutant (eIF4E W56A) of eIF4E were deprived of growth factors for 72 h. *, P < 0.001 versus vector. C, 4E-BP1 attenuates the positive cell cycle effect of eIF4E in a manner dependent on its ability to antagonize eIF4E. HMECs coexpressing hTERT and eIF4E were stably transfected with control vector or with a vector encoding either wild-type 4E-BP1 or a highly active 4E-BP1 mutant (BP1 A37/A46, which is most potent for binding to eIF4E), or an inactive 4E-BP1 mutant (BP1{delta}, which lacks the eIF4E binding site). Cells were subjected to 72 h of growth factor withdrawal and cell proliferation was assessed. D, eIF4E accelerates S-phase entry and alters the expression of cell cycle regulators. Cells deprived of growth factors for 48 h were shifted into medium supplemented with growth factors and 1 µmol/L BrdUrd (top). Incorporation of BrdUrd quantified by flow cytometry. *, P < 0.001 versus vector control. Points, mean for triplicate samples; bars, SD. Cells deprived of growth factors for 48 h (time 0) were supplemented with growth factors (bottom) and examined by immunoblot for steady-state levels of cell cycle regulators as a function of time after growth factor addition. Data are from three independent experiments.

 
To determine whether the proliferative activity conferred by eIF4E requires its participation in the eIF4F translation initiation complex, HMEC/hTERT/4E were cotransfected with members of the eIF4E-binding protein (4E-BP) family (27, 28). We used vectors encoding 4E-BP1, either wild-type or a double phosphorylation site mutant 4E-BP1A36/A47 that resists inactivation through the phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin phosphorylation cascade (29, 30). 4E-BP1wt attenuated—and 4E-BP1A36/A47 completely abrogated—the growth-promoting effect of eIF4E (Fig. 3C). In contrast, introduction of the 4E-BP1 54-63 deletion mutant (4E-BP1{Delta}), which lacks the eIF4E binding site, failed to antagonize the cell cycle promoting activity of eIF4E. Thus, eIF4E reduces the dependence of HMEC/hTERT on growth factors to evade G0 arrest.

To determine how these changes in the translational machinery affected cell cycle dynamics, we monitored the effect of HA-eIF4E on the kinetics of S-phase entry after growth factor stimulation of growth factor–deprived cells. Both vector and HA-eIF4E–expressing cells revealed the same 3 to 4 h lag period. In contrast, expression of HA-eIF4E led to a sharp rise in the percentage of S-phase cells between 4 and 16 h poststimulation (Fig. 3D). These results show that activation of eIF4E significantly augmented the ability of HMEC/hTERT to traverse the G1-S boundary in response to growth factors.

Cell cycle entry and transit through the G1-S boundary are both regulated by events that target members of the Rb protein family and the p53 pathway (31). To examine mechanisms that might account for the growth promoting activity of coexpressed hTERT and eIF4E, we analyzed the abundance of key promoters and inhibitors of prereplicative events (Fig. 3D). Cyclins D1 and E were not detectable in growth factor–deprived HMEC/hTERT, but were easily detected in cells ectopically expressing HA-eIF4E. In addition, HA-eIF4E–transduced cells revealed a durably increased pattern of cyclin D1 expression after growth factor stimulation, whereas growth factor stimulation of control cells only resulted in transient activation of cyclin D1. Introduction of HA-eIF4E also increased the abundance of cyclin A and CDK4. The expression of major negative regulators of the G1-S checkpoint—p16Ink4A, p21Cip1, p27Kip1, p53, and Rb—in HA-eIF4E–transduced HMEC/hTERT was either unchanged or up-regulated (p16, p21, Rb, p53; Fig. 3D). Of note, ectopic eIF4E increased the abundance of the phosphorylated forms of Rb in both growth factor–deprived cells (p-Rb S795) and in the early prereplicative period after growth factor stimulation (p-Rb S780 and p-Rb S807/811). Thus, activation of eIF4E leads to changes in multiple cell cycle signals, both positive and negative. The net outcome of sustained eIF4E activation reduces the requirement for exogenous growth stimulation by disrupting the Rb-regulated G0-G1 and G1-S checkpoints.

Activated eIF4E Liberates hTERT/HMEC from Dependence on Environmental Survival Signals
Activation of the eIF4E pathway suppresses apoptosis in some cancer cell systems (6, 32, 33). We therefore explored the possibility that eIF4E collaborates with hTERT to rescue HMECs from apoptosis. The presence of EGF and insulin in growth medium suffices to prevent intrinsic apoptosis HMEC/hTERT (Fig. 4A ). HA-eIF4E attenuated apoptosis in growth factor–deprived HMEC/hTERT. The antiapoptotic activity was not observed with the cap-binding defective W56A mutant. When either 4E-BP1wt or 4E-BP1A36/A47 was expressed in HMEC/hTERT bearing HA-eIF4E, apoptosis sensitivity was restored (Fig. 4B). 4E-BP1{Delta}, which lacks the eIF4E binding site, did not display this activity. Thus, up-regulated eIF4E attenuates intrinsic apoptosis in growth factor–deprived HMEC/hTERT in a manner that requires integration of eIF4E into a translationally active complex.


Figure 4
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  		Figure 4. Ectopic expression of eIF4E decreases cellular dependence on exogenous antiapoptotic signals. A and B, eIF4E suppresses apoptosis in growth factor–deprived cells in a 4E-BP1–inhibitable manner. HMEC/hTERT harboring a retroviral vector encoding the genes indicated were incubated in complete growth medium or in medium lacking insulin, bovine pituitary extract, and EGF for 48 h. Gene product designations are as described in the legend to Fig. 3. Percentage of hypodiploid cells was determined by flow cytometry. Columns, mean of three independent experiments in triplicate; bars, SD. *, P < 0.001 versus vector control. C, acinar morphogenesis in three-dimensional culture. These representative phase images from two experiments show acinus-like structures formed by HMEC/hTERT/4E or control vector after incubation for 10 d in Matrigel. Arrows, hollow lumens in the acini of vector-infected HMEC/hTERT and cell-filled lumens in the structures formed by HMEC/hTERT/4E. Structures formed by MDA-MB-468 breast carcinoma cells are shown as a control for malignant growth.

 
Apoptosis is an essential component of mammary gland morphogenesis (34). The acquisition of apoptosis resistance at early preinvasive stages of breast carcinogenesis, such as ductal carcinoma in situ, is associated with luminal filling and loss of normal acinar architecture (35, 36). Using a three-dimensional in vitro mammary morphogenesis model, lumen formation and maintenance of a hollow lumen are associated with apoptosis of center cells deprived of contact with components of the basement membrane (8, 37). We examined the role of eIF4E on apoptosis-dependent acinar morphogenesis. HMEC/hTERT harboring HA-eIF4E or vector controls were cultured on a reconstituted basement membrane gel derived from Engelbreth-Holm-Swarm tumor (Matrigel). After 10 days in culture, cells harboring empty vector formed spheroids composed of a single layer of cells surrounding a hollow lumen (Fig. 4C). In contrast, breast carcinoma cells formed completely disorganized sphere-like clusters. Ectopic expression of eIF4E resulted in an intermediate phenotype with distorted acinus-like structures that contained a completely or partially filled luminal space. Thus, sustained activation of eIF4E disrupts HMEC/hTERT morphogenesis in three-dimensional cultures, presumably by permitting cells to avert apoptosis triggered by loss of contact with constituents of the basement membrane.

Genome-Wide Analysis of Translational Activity Reveals Both Translational Activation of Genes Governing Cancer-Related Functions and Mechanisms that Negatively Regulate Oncogenesis
Ectopic overexpression of eIF4E in HMECs harboring hTERT conferred these cells with autonomy, fundamentally altering control of the cell cycle and apoptosis. The most direct explanation of this result follows from prior publications examining one gene product at a time (33, 38). These studies indicate that when eIF4E is overexpressed, it increases the translational efficiency of a subset of transcripts encoding regulators of the cell cycle and viability. To examine this phenomenon on a genome-wide scale and to elucidate the underlying mechanism, we combined translational efficiency estimates derived from polyribosome preparations of RNA—which stratifies RNA based on the number of bound ribosomes—with a quantitative abundance measure obtained from microarray analysis of polyribosome-associated mRNA (measuring 23535 RefSeqs). To optimize detection of genes that confer the autonomous phenotype, HMEC/hTERT and HMEC/hTERT/eIF4E were studied 24 h after growth factor deprivation. We used both polyribosome bound RNA (>2 ribosome) and total cellular RNA to generate a ratio describing the relative activity of each transcript (polyribosome estimate/total RNA estimate) for each cell type.

To determine whether overexpression of eIF4E leads primarily to changes in the transcriptional or translational stage of the gene expression pathway, we compared the number of genes identified as differentially expressed when comparing total with polyribosome-associated RNA profiles in HMEC/hTERT and HMEC/hTERT/4E. At the selected significance levels (significance analysis of microarray FDR <0.1) and after adjusting for the number of replicates, we identified 188 RefSeqs (141 unique known genes) as being transcriptionally regulated in cells overexpressing eIF4E and 1518 RefSeqs (1108 unique known genes) as showing changes in translational efficiency (Supplementary Table S1). Thus, introduction of eIF4E predominantly changes the translational step of gene expression.

When all replicates from the translational activity measures were included in the analysis, we identified 3,086 RefSeqs (2,247 unique known genes) that were subject to translational control (Supplementary Table S2). We examined the validity of this set of genes on several levels. First, we compared the result to one previous study of ectopic overexpression of eIF4E in NIH/3T3 cells (9). A gene designated FAH, one of the targets in the NIH/3T3 study that was extensively validated—including reverse transcription-PCR across the polysomal fractions—was identified in the present study as well. Second, we compared the global result from the present study and the study of NIH/3T3 cells by collecting those genes that showed a difference between parental cells and eIF4E-expressing cells. We found an overlap of 248 genes (Supplementary data set 1). Third, a recent report using an inducible eIF4E model has identified ribosomal genes to be one of the major targets of increased eIF4E (39). Compared with this study, the validated ribosomal genes (using Northern blotting across the polysome fractions or measurement of protein level) identified as direct targets of eIF4E (RPL13, RPL26, and RPL35) as well as the validated ribosomal genes that were not targets of eIF4E (RPL9 and RPS17) showed an identical regulation pattern in the present study. Fourth, we identified one target that we previously validated extensively (BCL-XL) in a previous report (40). Finally, the translational profiling data is corroborated by Western blots for p16, p21, CDK4, and 4EBP1 (Fig. 3D). Thus, the present study is validated by existing knowledge and agrees well with measured protein levels reported here.

We used Gene Ontology annotations to look for overrepresentation of biological themes among the 3,086 RefSeqs identified as eIF4E targets. We identified a significant bias toward genes governing several cancer-related functions, including proliferation, apoptosis, and motility (Fig. 5 ; Supplementary Table S3). Gene Ontology annotation is derived from a variety of sources that are not equally reliable. We therefore looked in detail at three key gene groups we viewed as directly relevant to the preneoplastic changes in HMEC/hTERT/4E:


Figure 5
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  		Figure 5. Ectopic expression of eIF4E alters translational control of cancer-related genes. A, selected cancer-related and life cycle gene categories enriched among genes translationally regulated in HMEC/hTERT/4E are displayed. Both the selected categories and the corresponding genes are shown. Genes that showed an identical gene to function association were grouped in the same column and separated by "/". Both functions and genes were subjected to hierarchical clustering. Black indicates that a certain gene was found in the corresponding functional category. B, all genes that were annotated by Gene Ontology to various functions were collected and represented as percentages of all genes annotated.

 
Proliferation-related genes. Members of several growth factor families have previously been identified as targets of eIF4E-mediated translational control (3, 41). Our analysis revealed that 13 of the 14 growth factor–related genes were translationally activated, including IGFL1, HDGF, BMP2, and PGF (Supplementary Table S4). Thus, activation of eIF4E leads to strong autocrine/paracrine up-regulation of extracellular signals capable of stimulating cell proliferation and suppressing apoptosis.

To detect eIF4E-mediated effects downstream of growth factors, we reduced the cell cycle set to 71 genes by excluding genes that were annotated automatically and assigned a direction to each gene, designating it as positive (promoting cell cycle transit) or negative (inhibiting cell cycle transit; see Table 1 for those showing large fold changes and Supplementary Table S5 for all 71 genes). By combining this functional direction with the direction of translational regulation (activation or repression), we assigned a cumulative effect of increased eIF4E on cell proliferation. We found 44 genes with positive effects and 27 with negative effects. Notably, eIF4E not only activated cell cycle promoters such as CDK2, CDK4, CDC25B, RAS, PGF, and SKP2, but also activated cell cycle inhibitors p21 and p16, and repressed EGF receptor (EGFR). Another group of genes that often affect proliferation are proto-oncogenes. We identified RAS, RAF, and AKT1 to be translationally activated, and FOS and MAFB as translationally repressed (Supplementary Table S6). These data reveal that overexpressed eIF4E alters a spectrum of cell cycle regulators, some of which restrain proliferation, indicating the activation of compensatory mechanisms.


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  		Table 1. Translationally regulated genes related to the cell cycle and apoptosis

 
Apoptosis-related genes. Based on the apoptosis-resistant phenotype observed and our previous publication identifying BCL-XL as one mediator of eIF4E-dependent apoptosis resistance (33), we traced changes in translational activation of genes encoding proteins of the BCL family. Applying the same approach that we used to analyze translational control of proliferation, we identified BCL2L1 (BCL-XL and BCL-XS) and BCL2L12 as translationally activated negative regulators of apoptosis (Table 1; Supplementary Table S6). As was seen for proliferation, all translational effects were not in a single direction; we observed translational activation of apoptosis inducers, including BAD, BAK1, and BIK.

Genes related to regulation of translation. We examined how overexpression of eIF4E—the rate-limiting component of translation initiation—affects other components of the translational machinery and its regulators. Having already found activation of AKT1 (Supplementary Table S6), this additional analysis identified RHEB, another upstream activator of eIF4E, to be translationally activated. There was translational activation of all 76 RefSeq identified as being components of the ribosome (Supplementary Table S7) along with several subunits of the eIF3 multiprotein complex, eIF5A and S6K (Supplementary Table S8). However, similar to the pattern unveiled in our analysis of proliferation and apoptosis, we also found evidence that the cell was actively attempting to counter the effects of up-regulated eIF4E. We identified translational activation of the eIF4E repressor 4E-BP1, the translational repressor PAIP2, the eIF4E antagonist eIF4E2 (eIF4EHP), and translational repression of eIF4G3 (eIF4GII; Supplementary Table S8). In accord with our other analyses, these findings indicate that there are negative regulators restricting the effects of eIF4E overexpression.

In concert with prior reports (2, 42), we found that translation of some mRNAs are more affected by changes in eIF4E abundance than others. However, our results did not provide an explanation for the large number of transcripts that were translationally repressed in cells with overexpressed eIF4E. One class of molecules that restrains translation is micro-RNA (miRNA). Using miRNAs catalogued in the TargetScan database (18), we looked for overrepresentation of known miRNA target sites within the 3' untranslated regions (UTR) of genes that were translationally repressed in HMEC/hTERT/4E. Remarkably, the subset of translationally repressed genes was highly enriched with target sites for 33 miRNAs (Supplementary Table S9), and 51% of all annotated repressed genes were targeted by at least one of the enriched miRNA (Supplementary Fig. S1). The same analysis of translationally activated genes did not reveal a single miRNA target site. Of note, the two translationally repressed proto-oncogenes, MAFB and FOS, and the translationally repressed growth factor receptor, EGFR, had binding sites for the enriched miRNAs. These findings indicate that there might be a compensatory global up-regulation of miRNA-mediated translational repression upon ectopic activation of eIF4E.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Activation of the cap-dependent translation machinery is causally linked to the genesis of cancer (6, 43, 44); however, studies addressing mechanism have been constrained by the undefined genetic status of the cells used for study. Here, we use a genetically defined experimental system to analyze how translationally regulated events promote oncogenic alterations in primary human cells. We show that deregulation of eIF4E—an intervention that recapitulates a central event in many naturally incident human cancers—can liberate cells from the requirement for paracrine growth maintenance signals, and profoundly alters the translational control of genes regulating cancer-related functions.

Growth arrest of early passage primary cells in response to oncogene activation or restrictive growth conditions is thought to be a manifestation of the tumor defense system that prevents expansion of cells with deregulated proliferation. Cultured HMECs require signals from cocultivated fibroblasts to evade telomere-independent M0 growth arrest (21). We show that ectopic expression of eIF4E significantly increases the proportion of primary HMECs that evade M0, indicating that sustained activation of eIF4E—as a single genetic intervention—enables primary cells to bypass the M0 barrier to uncontrolled proliferation. However, these cells progressed to telomere-dependent senescence (M1) showing that activation of eIF4E on its own is insufficient to transform primary HMECs. We further show that when the M1 barrier has been eliminated by introduction of hTERT, activation of eIF4E resulted in cells that display disruption of Rb-dependent G0-G1 and G1-S checkpoint control, autonomous growth, and apoptosis resistance but not tumorigenesis. We interpret these data to show that hTERT and eIF4E cooperate to move primary human epithelial cells to an intermediate stage along the cancer pathway.

We have previously examined the effect of eIF4E on specific target genes (7, 32, 33, 40). However, our genome-wide approach to examining the mechanism of eIF4E-mediated neoplasia provided several new important insights. As might be expected, we found that many potentially oncogenic transcripts enjoy a selective increase in translational efficiency compared with the transcriptome as a whole. These included transcripts encoding proliferation-related proteins such as growth factors, signaling intermediates, and cell cycle machinery components, and those interdicting apoptosis. These findings are compatible with the idea that genes positively regulating proliferation and suppressing apoptosis undergo coordinated translational control.

However, our data provide evidence that the cell is actively attempting to counter the proneoplastic changes promoted by ectopic eIF4E expression. One mechanism comports with the known function of eIF4E and involves an increase in the translational efficiency of negative regulators. In the case of proliferation, we found activation of several cell cycle inhibitors. With regard to apoptosis, proapoptotic members of the Bcl-2 family were activated. When examining translational control, transcripts encoding three negative regulators—4E-BP1, PAIP2, and eIF4E2 (eIF4E-HP1)—showed increased translational efficiency. Even among growth factor–related transcripts that showed the strongest positive unidirectional effect, we observed that EGFR was translationally repressed, revealing evidence of homeostatic regulation.

The second mechanism the cell used to counter ectopic eIF4E was a complete surprise and paradoxically involved decreased recruitment of ribosomes to certain mRNAs. Remarkably, we found a dramatic overrepresentation of known miRNA target sites within the 3' UTRs of genes that were translationally repressed. The potential significance of this finding is highlighted by the complete absence of miRNA target site enrichment in the translationally activated genes. This would represent a previously unrecognized component of the miRNA-mediated mammalian tumor-suppressor systems (45).

Our findings indicate that primary and hTERT immortalized human cells ectopically expressing eIF4E have advanced along the cancer pathway to acquire neoplastic properties. However, these cells are not tumorigenic. Thus, our data can be interpreted to suggest that in response to ectopic activation of eIF4E, there are at least two compensatory mechanisms: (a) translational activation of transcripts encoding counterregulators of neoplastic conversion and (b) activation of miRNA-mediated translational repression. These countermeasures must be finely tuned to permit the physiologic activation of translation essential for development, growth, and tissue repair, while preventing acquisition of autonomy.


   Acknowledgments
 
Grant support: NIH grant CA111338-01 and Susan G. Komen Breast Cancer Foundation grant BCTR0402671 (V.A. Polunovsky, S. Li, O.A. Issaenko, and S. Avdulov), NIH grants HL073719 and HL076779 (P.B. Bitterman, M. Peterson, and K. Smith), and a postdoctoral fellowship from the Swedish Research Council (O. Larsson).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank N. Sonenberg, D.Yee, R. Kratzke, A. Benyumov, and P. Dahlberg for their insightful comments; R. Weinberg for providing the hTERT retroviral construct and HMECs expressing hTERT; Yong Kim for help with design of retroviral constructs; Rachel McMullen for technical assistance with cell culture; Hong Xia for assistance with the BrdUrd experiments; Sarah Bowell from the University of Minnesota Cancer Center Tissue Procurement Facility for the provision of human mammary tissue; Joel Sederstrom from the Flow Cytometry Core for cell sorting assistance; and the University of Minnesota Supercomputing Institute for computer resources.


   Footnotes
 
Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).

O. Larsson, S. Li, and O.A. Issaenko contributed equally to this work.

Received 2/23/07. Revised 4/12/07. Accepted 5/ 8/07.


   References
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 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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