RET is a transmembrane receptor required for the development of neuroendocrine and urogenital cell types. Activation of RET has roles in cell growth, migration, or differentiation, yet little is known about the gene expression patterns through which these processes are mediated. We have generated cell lines stably expressing either the RET9 or RET51 protein isoforms and have used these to investigate RET-mediated gene expression patterns by cDNA microarray analyses. As seen for many oncogenes, we identified altered expression of genes associated generally with cell–cell or cell-substrate interactions and up-regulation of tumor-specific transcripts. We also saw increased expression of transcripts normally associated with neural crest or other RET-expressing cell types, suggesting these genes may lie downstream of RET activation in development. The most striking pattern of expression was up-regulation of stress response genes. We showed that RET expression significantly up-regulated the genes for heat shock protein (HSP) 70 family members, HSPA1A, HSPA1B, and HSPA1L. Other members of several HSP families and HSP70-interacting molecules that were associated with stress response protein complexes involved in protein maturation were also specifically up-regulated by RET, whereas those associated with the roles of HSP70 in protein degradation were down-regulated or unaffected. The major mechanism of stress response induction is activation of the heat shock transcription factor HSF1. We showed that RET expression leads to increased HSF1 activation, which correlates with increased expression of stress response genes. Together, our data suggest that RET may be directly responsible for expression of stress response proteins and the initiation of stress response.

The RET proto-oncogene encodes a receptor tyrosine kinase required for normal development of the kidney, peripheral, and central nervous systems, and of spermatogonia (1, 2, 3). RET has been implicated in cell type-specific processes including cell proliferation, migration, and differentiation, and likely plays each of these roles in specific cells and at specific developmental time points. In neural cell lineages, RET also plays an important role in cell survival, particularly in response to environmental stresses (4, 5, 6, 7, 8, 9, 10, 11).

In normal cells, the RET receptor is activated by the binding of both a circulating ligand and a cell surface-bound coreceptor. RET ligands are members of the glial cell line-derived neurotrophic factor (GDNF) family (reviewed in Ref. 12). These molecules interact directly with coreceptors of the GDNF family receptors α (GFRα) proteins, which are linked to the cell surface by glycosylphosphatidyl-inositol linkage, and the resultant complexes bind to the RET receptor to activate downstream signaling events. In addition, activating point mutations of RET have been identified in patients with multiple endocrine neoplasia type 2, an inherited cancer syndrome characterized by medullary thyroid carcinoma and pheochromocytoma and are found in a large proportion of sporadic medullary thyroid carcinoma (reviewed in Ref. 13). Furthermore, rearrangements of RET, resulting in juxtaposition of the RET kinase domain with a dimerization domain from any of several other proteins, occur somatically in papillary thyroid carcinoma (14). In each case, these mutations result in activation of the RET receptor, leading to inappropriate and/or increased RET-mediated signal transduction and resultant cell proliferation and tumorigenesis.

Activation of RET, either by ligand or through specific mutations, results in phosphorylation of multiple tyrosine residues that, in turn, interact with specific adaptor molecules to trigger downstream signaling. Four of the tyrosines phosphorylated on RET activation, tyrosines 905, 1015, 1062, and 1096, have been well characterized, and are known to interact with a number of adaptor molecules to stimulate signaling through phosphatidylinositol kinase (PI3K)/AKT, PLC-γ, RAS/extracellular signal-regulated kinase (ERK), p38MAPK, c-Jun NH2-terminal kinase (JNK), and ERK5 pathways (15, 16, 17, 18, 19). In response to these signals, cell type-specific responses are initiated that implement the varied functional roles of RET. An additional level of complexity of RET signaling is added by alternative splicing of the RET gene (20), which leads to functionally distinct RET isoforms, termed RET9 and RET51. These isoforms differ in their COOH-terminal amino acids, having either 9 or 51 unique residues, and have distinct transforming and differentiative potentials in vitro(21, 22). In transgenic mice, animals expressing only the RET9 isoform are viable and appear normal, whereas monoisoformic RET51 animals have kidney dysplasia and lack enteric ganglia (23). These differences likely reflect differences in signaling potential of these RET isoforms. An additional phosphotyrosine, Y1096, present only in RET51, has been shown to bind GRB2, activating PI3K and RAS/MAPK pathways (24, 25, 26, 27). In addition, RET9 and RET51 differ in protein interactions with phosphotyrosine 1062, which is the last amino acid common to both isoforms and lies in different amino acid contexts in each protein (20). Tyrosine 1062 has been shown to act as a binding site for multiple adaptor proteins including SHC, docking protein (DOK)1, DOK4, DOK5, fibroblast growth factor receptor substrate 2 (FRS2), and insulin receptor substrate 1 (IRS1) (15, 26, 28, 29, 30); however, the relative binding of these molecules to RET9 and to RET51 varies both quantitatively and qualitatively. For example, SHC binds both RET9 and RET51 but, whereas RET51 binds only to the SHC-PTB domain, RET9 can also bind through the SHC-SH2 domain potentiating differences in the specific downstream interactions and/or the magnitude of the signals transduced (26).

Although many of the pathways through which RET transduces extracellular signals have been identified or predicted, little is known of the gene targets that are specifically modulated in response to receptor activation. In this study, we have used gene expression microarray analyses to evaluate targets of RET activation in an embryonic kidney-derived cell line. In addition to predictable targets involved in cell–cell interaction, cell proliferation, and neuroendocrine differentiation, our data suggest that activation of the RET receptor may specifically target genes for proteins integral to inducible cellular stress response. This study may provide a direct link between the up-regulation of heat shock proteins (HSPs), seen in many primary tumor types and as a neuroprotective event, and the stimulation of receptor tyrosine kinase activity.

Expression Constructs.

Full-length human cDNAs encoding either the 1072 amino acid (RET9) or the 1114 amino acid (RET51) isoforms of RET (31) were cloned into CH269, an episomal expression vector derived from vector pCEP4 (Invitrogen, Burlington, ON, Canada) under the control of the cytomegalovirus promoter. The sequence of each construct was verified by restriction digestion, and direct sequencing (Mobix, Hamilton, ON, Canada). The GFRα1 expression construct has been described previously (32, 33).

Cell Culture and Transfection Experiments.

E293, a transformed embryonic kidney cell line, was maintained in DMEM, supplemented with 10% fetal bovine serum, penicillin, streptomycin, and G418. For transient transfections, E293 cells were seeded into six-well plates and grown to approximately 70–80% confluence. Cells were transfected with 1 μg of expression construct or empty vector, with the FUGENE6 reagent (Roche Applied Science, Laval, QC, Canada) according to the manufacturer’s instructions, and were incubated for 48 h. In RET activation experiments, cells stably or transiently transfected with RET constructs were cotransfected with 1 μg of the GFRα1 expression construct, serum-starved for 1 h, and treated with 100 ng/ml of GDNF (Promega, Madison, WI) for 10 min, before harvesting.

Cell lines stably expressing RET9 and RET51 were generated by transfection of E293 cells with the RET constructs, as described above, and by selection of RET-expressing clones with 400 μg/ml hygromycin. Stable cell lines were maintained in 100 μg/ml hygromycin and were regularly screened for RET expression.

Immunoprecipitations and Western Blotting.

RET expression was confirmed by Western analysis. Cells were washed three times with ice-cold PBS and lyzed in 20 mm Tris-HCl (pH 7.8), 150 mm NaCl, 1 mm sodium orthovanadate, 1% Igepal, 1 mm phenylmethylsulfonyl fluoride, 10 μg/ml aprotinin, 10 μg/ml leupeptin, and 2 mm EDTA, as described previously (21). Lysates were cleared at 12,000 × g, 4°C for 15 min. Protein concentration was determined by BCA Protein Assay (Pierce, Rockford, IL.). For direct analysis, lysates were combined at 1:1 with reducing Laemmli buffer. For RET immunoprecipitation, lysates were combined with a 1:50 dilution of antibodies specific to the COOH terminus of either the RET9 isoform (C-19; Santa Cruz Biotechnology, Santa Cruz, CA) or the RET51 isoform (C-20; Santa Cruz Biotechnology), for 2 h at 4°C, with shaking. Antibody complexes were collected with Protein A-Sepharose CL-4B (Amersham Biosciences, Baie d’Urfé, QC, Canada) or Protein G PLUS Agarose (Santa Cruz Biotechnology), depending on the primary antibody host species, and were washed with cold lysis buffer, taken up in Laemmli buffer, and frozen. Immunoprecipitates or total protein lysates were boiled 1 min and separated on either 6 or 10% SDS/PAGE gels. Protein was transferred to nitrocellulose membrane (Bio-Rad, Mississauga, ON, Canada) and were blocked overnight in 5% nonfat milk in Tris-buffered saline–Tween 20. RET expression was detected using the C-19 or C-20 antibody and RET tyrosine phosphorylation was detected using the 4G10 antibody (Upstate Technologies, Lake Placid, NY). Coimmunoprecipitation of RET-associated proteins SHC, and GRB2 was detected using appropriate antibodies (Upstate Technologies). Heat shock transcription factor 1 (HSF1) was detected on Western blots of whole cell lysates using a specific antibody (Stressgen, Victoria, BC, Canada). Proteins of interest were detected by incubation with the appropriate primary antibody (1–2 h at 37°C with shaking), washing with Tris-buffered saline–Tween 20, and incubating with horseradish peroxidase-conjugated secondary antibodies. Antibody binding was visualized using an Enhanced Chemiluminescence Detection system (Amersham Biosciences, Baie D’Urfé, QC, Canada).

Immunokinase Assay.

For in vitro kinase assays, equivalent amounts of cell lysates were immunoprecipitated with appropriate anti-RET serum in a kinase lysing buffer [30 mm Tris-HCl (pH 8.0), 1% Triton X-100, 150 mm NaCl, 1 mm EDTA, 0.3 mm Na3VO4], as described above. Precipitated antibody complexes were washed with kinase lysing buffer, then were incubated for 20 min (30°C) in 10 mm Tris-HCl (pH 7.4), 5 mm MgCl2, with 2 μg myelin basic protein (MBP) as substrate and 10 μC of [γ-32P]ATP (34). Reactions were stopped by adding Laemmli buffer, and denatured samples were assayed on 10% PAGE gels. Gels were fixed and dried and 32P-labeled bands were detected, and signal intensity quantified, by phosphorimager.

Microarray Analyses.

Total RNA was isolated from cultured cells using TRIZOL reagent, according to the manufacturer’s instructions (Invitrogen). cDNA microarrays containing 19,000 (19K) or 1700 (1.7K) expressed sequence tags (ESTs) were obtained from the University Health Network Microarray Centre. The EST content of the 1.7K array partially overlapped that of the 19K array.1 Arrays were probed with differentially labeled cDNAs using optimized protocols.2 Briefly, in a 40-μl reaction, 10 μg of total RNA was reverse transcribed using Superscript II according to the manufacturer’s instructions (Invitrogen) with 3.75 μm Anchored-T primer (5′-T20VN-3′); dATP, dGTP, and dTTP (500 μm each); and 50 μm dCTP, 10 mm DTT, 1 ng of control RNA (artificial Arabadopsis transcripts), and either 25 μm Cy3-dCTP or Cy5-dCTP (Mandel-NEN, Guelph, ON, Canada) at 42°C for 2–3 h. RNA was hydrolized and Cy5 and Cy3-labeled cDNA were combined, isopropanol precipitated, and resuspended in water. Labeled cDNAs were hybridized to cDNA microarrays in a medium consisting of DIG Easy Hyb solution (Roche Applied Science, Laval, QC, Canada) containing 50 μg of yeast tRNA (Invitrogen) and 50 μg of sheared calf thymus DNA (Sigma, Oakville, ON, Canada) per 100 μl in a 37°C dark humid chamber over night. Slides were washed with 1× SSC, 0.1% SDS at 50°C, rinsed in 1× SSC, and spun dry. Arrays were scanned with a ScanArray 4000 scanner using ScanArray software, and intensities were quantified using QuantArray software (Perkin-Elmer Life Sciences, Boston, MA). The expression ratios of cDNAs, for each comparison, were calculated using background-corrected hybridization intensities normalized to corresponding intensity averages for the whole array. Artifacts were removed from the data sets after visually inspecting spots on the array images. Low-intensity hybridization signals (≤500) were excluded from our analyses. After exclusion of artifacts and low-intensity hybridization, we were able to analyze more than 80% of array spots. MA plots of these normalized data suggested that differences in gene expression detected were not due to intensity-dependent dye-label effects between the compared samples (Fig. 2). Paired t tests were performed on log-transformed data (35, 36) and the significance of gene expression differences (P < 0.1) is indicated in Tables 1,2,3. Relative expression values are averages of ratios of normalized values.

Validation by Northern and Quantitative Real-Time PCR Analyses.

Northern blots were prepared and hybridized using standard methods. The full-length RET coding sequence, described above, was used as a probe to detect RET expression. All other hybridization probes were generated by PCR using specific primers selected from the EST sequences found on our microarrays and used to generate PCR products. For all other genes, primers were selected from longer published cDNA sequences. The relative differences in expression levels of some transcripts were also confirmed by quantitative real-time reverse transcription (RT)-PCR (qRT-PCR) using the LightCycler System with the QuantiTect SYBR Green RT-PCR kit (Qiagen Inc, Mississauga, ON, Canada) with 200 ng total RNA as template according to manufacturer’s instructions at an annealing temperature of 55°C. All of the PCR products were designed to detect only cDNA if possible. Each assay was repeated at least three times. To confirm the specificity of product, melt curves were generated over a 60°C–95°C range and a negative control, without cDNA, was run with each assay. Relative copy number was calculated using the crossing threshold method and assuming an efficiency of 2 [relative copy number = 2dCT(37)].

Primers used to make hybridization probes for genes shown here included: HSPA1L (5′-GAGCTCGATTTGAAGAGTTG-3′/5′-ATTGTGGGGCCTGTGGCAGG-3′), HSPA1A (5′-TGTGCTCCGACCTGTTCCGA-3′/5′-AATGGCCTGAGTTAAGTGTA-3′), HSPA1B (5′-TGTGCTCCGACCTGTTCCGA-3′/5′-TACATTCCCAGCCTTTGTAG-3′), STIP1 (5′-AGCGG ACGGA TTCGATTCAA-3′/5′-AGGAGTTGCCAATTCGAGCA-3′), STUB1 (5′-GAGATGGAGAGCTATGATGA-3′/5′-AAAGCGATGCTGAGAGGGGA-3′), RNF19 (5′-TCATCTGTGAGCTTGCCTTC-3′/5′-ACATCCTTGCCTTCATAGCG-3′). Primers used for qRT-PCR of genes shown here included: RET (5′-AATTTGGAAAAGTGGTCAAGGC-3′)/(5′-CTGCAGGCCCCATACAAT-3′), {186 bp}; DNAJC3 (5′-AACAGAGCCAAGCATTGCTG-3′)/(5′-GGTTCCATCTGTAAAACTTC-3′), {125 bp}; RNF19 (5′-AACTAACACAGCTGTAGACA-3′)/(5′-TCACTCAGGTTGTCTCGGAT-3′) {152 bp}; and HSPA1B (5′-GGTCCCAAGGCTTTCCAGAG-3′)/(5′-ATGCCGGTGCCCTGCTCTGTGGGCTCCGCT-3′), {156 bp}. Primer sequences used to amplify other EST sequences are available on request.

Isoform-Specific RET Cell Lines.

We generated constructs containing the full-length RET9 or RET51 sequence under control of a minimal cytomegalovirus promoter. Constructs were transfected into the E293 embryonic kidney cell line, which does not express detectable RET protein, and cell lines stably expressing either RET9 or RET51 were generated (Fig. 1). Both the E293+RET9 (RET9) and E293+RET51 (RET51) lines express a Mr 155,000 and a Mr 175,000 protein detectable with RET isoform-specific antisera (Fig. 1,B). These correspond to the previously reported partially and fully glycosylated RET protein forms, respectively. As predicted, the Mr 175,000 protein isoform showed significant autophosphorylation, which was increased further in the presence of RET coreceptor GFRα1 on addition of the RET ligand, GDNF (Fig. 1 B). Immunoprecipitated RET9 and RET51 proteins from our stable cell lines also efficiently phosphorylated an exogenous substrate, myelin basic protein, in in vitro immunokinase assays (data not shown), indicating that our cell lines expressed functional RET kinases.

To confirm that they were fully functional, we investigated the ability of our RET protein isoforms to bind normal RET substrates. In both transient transfections and stable lines, we demonstrated that RET9 and RET51 bound SHC, whereas GRB2 could be coimmunoprecipitated only with the RET51 isoform (Fig. 1 C). These data are consistent with previous observations that SHC binds RET at phosphotyrosine 1062, a position common to both protein isoforms, whereas GRB2 interacts directly with phosphotyrosine 1096, which is found only in the RET51 isoform (reviewed in Refs. 13 and 38).

RET-Induced Gene Expression.

RET expression can stimulate cell division, transformation, migration, or differentiation, yet little is known about the differences in gene expression through which these processes are mediated. We used gene expression microarrays to investigate downstream target genes with altered expression when RET-mediated signaling occurs. Because previous studies of transgenic animals suggest that RET9 expression may have the most profound effects on cells (23), we focused initially on gene expression related to this isoform. We performed multiple replicates of comparisons using E293 [RET negative (RET-ve)] and RET9 RNAs with both 1.7K (n = 4 arrays) and 19K (n = 3 arrays) cDNA microarrays1 (Fig. 2). RET9- and RET51-expressing lines were also directly compared with four repetitions using each of these arrays. Differences in gene expression between the E293 (RET-ve) and RET51 cells were confirmed with the 1.7K array. Individual cDNAs were represented on these arrays from two to six times per array. The relative expression ratios between pairs of RNA sources for each cDNA spot were calculated, as described above, and data for all of the arrays for a given comparison were pooled. Threshold-intensity ratios of ≥2 for up-regulation and ≤0.5 for down-regulation were used to identify significant expression differences. ESTs were chosen for further evaluation if relative expression differences exceeded the threshold values for a minimum of three array spots, based on normalized ratios, and if differences in log-transformed data between RNA sources were significant in paired t tests (P < 0.1) (35, 36). We chose these stringent selection criteria to minimize the number of false-positive expression differences in our study.

We identified 99 ESTs reproducibly differentially expressed in RET9-expressing cells as compared with the parental E293 cell line (RET-ve). Eighty ESTs, including 43 defined genes, were up-regulated in the presence of RET9. An additional 19 ESTs (nine genes) had lower expression in the presence of RET. Here, we have focused exclusively on ESTs representing defined genes or predicted genes that encode proteins for which a function is known or inferred (Table 1). Validation of our microarray data were performed by more sensitive Northern and/or qRT-PCR analyses. As we anticipated, the gene differing most significantly in expression on our microarrays was RET, which was more than 7.8-fold overexpressed in our RET9-expressing stable cell line (P < 0.001). These differences were consistent with our Western, Northern, and qRT-PCR analyses of RET expression (Figs. 1, 3, and 4), which identified no RET expression in our untransfected E293 cells. Interestingly, our Northern and qRT-PCR analyses indicated that the relative expression of RET9 was much greater than predicted by microarray analysis (>130-fold and >680-fold increase, respectively), suggesting the relatively lower sensitivity of the microarray analysis, and confirming the necessity for validation of these results.

Our analyses suggest that genes with a broad range of functions may be modulated specifically in response to RET expression. In particular, we noticed increased expression of transcripts that are normally expressed in neural or neuroendocrine cell types but are not normally highly expressed in E293 cells, including PER3, SDK1, SUPT5H, INSM1, CLU, DUSP8, and AKAP9 (Table 1). An overlapping group of genes with expression primarily associated with tumors of neuroendocrine or other RET-expressing cell types such as INSM1, CLU, NOV, and GRP, were also up-regulated. Increased expression of several genes associated with cytoskeletal interactions and formation of tight junctions was detected including SYMPK, CLDN5, and CD151 (Table 1).

RET-Mediated Expression of Stress Response Genes.

The most strikingly altered pattern of gene expression identified in response to RET was up-regulated expression of genes encoding inducible members of the stress response protein families. In preliminary analyses, we noted increased expression of genes encoding several members of the heat shock 70 (HSP70) family. Specifically, we found that RET9 expression reproducibly up-regulated expression of three stress inducible members of the HSP70 gene family, HSPA1A, HSPA1B, and HSPA1L, by more than 2-fold on microarray analysis (Tables 1 and 2). Using Northern analyses, we confirmed that each gene was up-regulated in both RET9 and RET51 cell lines, but not in parental E293 cells nor in an E293 cell line expressing only an empty hygromycin resistance vector (RET-ve) but not RET (Figs. 3 and 4). This suggested that this expression pattern was not a nonspecific response to transfection or selection of our stable lines but was directly related to RET expression. Using gene-specific probes from the 5′ and 3′ untranslated sequences of each gene, we showed that HSPA1A and HSPA1B were expressed at similar levels and that both were more highly expressed than HSPA1L, although all three were up-regulated on RET expression (Fig. 3). Although all three genes form a single gene cluster on chromosome 6p21.3 (39), other genes, such as BAT3, which lie in the same region (40) showed no difference in expression in response to RET (Table 2), confirming the specificity of the observed expression pattern.

The HSP70 proteins are one of several HSP families involved in formation of protein complexes that target cellular proteins either for refolding and maturation or for ubiquitinization and degradation (reviewed in Refs. 41 and 42). A variety of HSPs, cochaperones, and a number of interacting molecules form part of these complexes, which are variable in size and composition, depending on the processes and interactions they promote. To investigate whether RET induced these HSP70-related processes, we reviewed the genes in Table 1 to determine whether other genes with known or predicted roles in cellular stress responses, were also modulated on RET expression. We identified four additional genes that were modulated by RET and are known to be expressed in response to stress and/or to interact with HSPs. Three of these, MAPKAPK2, FKBP4, and SCARA3, were up-regulated by more than 2-fold when RET was expressed. Ring finger protein 19 (RNF19), an E3 ubiquitin ligase that is localized in Lewy bodies in Parkinson’s disease (43, 44) was significantly down-regulated on RET9 expression (Tables 1 and 2).

Because our conditions for identifying genes modulated through RET were stringent, and because many genes associated with cellular stress responses are not highly expressed, we reviewed our total microarray data set for alterations in expression of other HSP genes, cochaperones, and additional proteins with an established relationship with HSP70. In particular, we reviewed these data for expression of any additional members of HSP27, HSP40, HSP60, HSP70, HSP90, and HSP100 gene families. We were able to identify 27 genes with known or predicted roles in stress response for which microarray data were available (Table 2). In addition, we investigated several genes not found on our microarrays but with known important roles in stress response by Northern or qRT-PCR analyses (e.g., Fig. 5).

Our data suggest that expression of a number of additional stress-related genes, but not all such genes, are significantly increased in our RET-expressing cells, although these did not meet our initial stringent selection criteria (e.g., expression levels below our thresholds, fewer than three spots with ratios ≥2). In addition to the seven genes described above, our microarray analyses identified three additional genes, including ST13 (also known as HIP), and two members of the HSP40 gene family, DNAJB2 and DNAJC3, with more than 2-fold differences in expression (P < 0.1) on RET expression. If a less stringent threshold ratio was used (e.g., ≥ 1.3 fold difference and P < 0.1) an additional 6 genes with significant expression differences on microarrays and one additional gene on Northern analysis (Fig. 5) were recognized. This included members of the HSP40, HSP60, HSP70, HSP90, and HSP100 families (HSPCA, HSPH1, DNAJA1, HSPA8, and HSPD1) as well as the genes for HSP70 interacting proteins UBQLN2 (CHAP1) (Table 2) and STIP1 (Fig. 5). The only additional gene identified as down-regulated in our full microarray data set, the HSP40 gene DNAJC3, was reduced by 2.3 fold in the presence of RET9 but was not reduced on RET51 expression (Table 2, Fig. 4). Further, the STUB1 gene, which encodes the ubiquitin ligase CHIP, and was not represented on our microarrays, was down-regulated moderately in the presence of RET, particularly RET51, on Northern analyses (Fig. 5). Additional members of the HSP70 and HSP40 gene families and other stress response related genes, showed no consistent pattern of expression in response to RET or were clearly not regulated through RET activation (Table 2). These data further suggest that the stimulation of stress response genes we have observed is specific and not a generalized phenomenon affecting all heat shock family genes or even all of the members of a single heat shock family.

HSF1 and RET Expression.

Interestingly, the heat shock transcription factor, HSF1, known to be a primary inducer of stress related expression of HSP proteins, and particularly of HSP70 genes, did not differ in expression, irrespective of RET expression (Table 2). However, in the presence of RET, we detected an increase in phosphorylated, activated forms of HSF1, which correlated with increased transcription of HSP70 genes (Fig. 6). Our data suggest that RET-mediated HSP70 gene expression is induced by activation of HSF1. If increased HSF1 phosphorylation is a direct result of RET activation, we would predict that other genes with heat shock elements (HSEs), the recognition sequence for HSF family transcription factors, in their promoters, might also be regulated through this mechanism, in response to RET. To investigate this, we examined the proximal promoter region of each of our candidate genes using TFSearch (45) to predict the presence of HSEs, the recognition sequence for HSF1 binding. As we would predict, HSEs were associated with the genes for 10/14 of the stress response proteins up-regulated in response to RET (Table 2). Only two additional genes up-regulated by RET, SYMPK, and S100A10, contained HSEs. We found no HSEs associated with the genes down-regulated by RET expression or differentially expressed between RET isoforms (Table 1).

Evaluation of Predicted RET Targets.

Previous studies of gene expression patterns associated with RET activation have been limited. Many of these have relied on predicted candidates known to lie downstream of other receptor tyrosine kinases or to be up-regulated in response to neuro-differentiative agents (46, 47, 48, 49, 50, 51, 52, 53, 54). In one study, Watanabe et al.(48) used a relatively insensitive method, differential display analysis, to identify candidate genes expressed in NIH 3T3 cells on RET activation, although the RET isoform was not specified and few of these were confirmed on rigorous validation (55). Here, we used sensitive microarray analyses to evaluate whether these candidates were also modulated by RET in our E293 cell model system. Using our total microarray data, we were able to evaluate expression of 21 of the candidate genes previously predicted to show altered expression in response to RET (Table 3). None of these showed ≥2-fold difference in expression. Nine genes had >1.3-fold increased expression in our RET9 cell line as compared with RET-ve cells, but this difference was significant (P < 0.1) for only four of these, the immediate early genes FOS and JUN, the translation initiation factor E1F4G3, and CFL1 (Table 3).

RET Isoform-Related Gene Expression Differences.

The RET9 and RET51 protein isoforms have been shown to differ in normal expression and function, as well as in their ability to transform (21, 23, 56). Here, we used gene expression microarray analysis to investigate the differences in gene expression stimulated by these two RET isoforms. We identified eight ESTs (four genes) more highly expressed in the presence of the RET9 isoform (Table 1). Four ESTs (three genes) were more highly expressed in the presence of the RET51 isoform (Table 1; Figs. 4 and 5). Although we found that the differences in the relative expression of RET in these lines was not significant (P = 0.76), we cannot exclude the possibility that differences observed were related to subtle variation in the levels of RET expressed.

Although the RET receptor has important roles in cell growth, differentiation, and survival, and has been shown to be a critical determinant of the development of kidney and neural crest lineages, little is known about the specific genes through which these effects are mediated. Limited previous studies have focused on known or predicted candidate genes linked to other receptor tyrosine kinases (Table 3) but have been less successful in predicting novel target genes related specifically to RET. Microarray analysis represents a powerful tool for screening large numbers of ESTs simultaneously to identify such target genes and to elucidate novel functions or interactions of RET. Here, we have generated a series of RET isoform-specific stable cell lines and have used these to address the nature of genes that lie downstream of RET activation. We identified 43 known genes up-regulated, and 9 genes down-regulated, in response to RET activation. As might be predicted as a result of proto-oncogene activation, the up-regulated genes included a number of genes involved in cell–cell or cell–cytoskeletal interactions. Disruption of normal cell interactions is a common feature of oncogene activation. In fact, RET has previously been associated with changes in phosphorylation of cytoskeletal molecules such as paxillin, p130cas, and focal adhesion kinase (57, 58, 59). Our data indicate that this change in phosphorylation is not accompanied by increased expression of either paxillin or focal adhesion kinase (data not shown). However, we showed that the genes for SYMPK, CLDN5, CD151, and MPP3, proteins associated with tight junctions or known to interact with integrins, were up-regulated, as were several proteins linked to lamina interaction and synapse formation (e.g., SDK1, AKAP9). Previous studies have suggested that disruption of adhesion-dependent signaling may contribute to medullary thyroid carcinoma (59) and that RET stimulation by its ligands has a role in regulating development of synapses (60). Our data suggest that cells expressing RET may have altered interactions due to increased expression of a number of cell surface molecules that modulate cell interactions with its neighbors or with the substrate.

Changes in gene expression and in cellular interactions may, in part, be explained by an increase in differentiation signals provided by RET activation. In fact, we saw increased expression of a number of neuroendocrine cell type-specific transcripts including PER3, KCNJ10, SDK1, SUPT5H, DUSP8, and AKAP9, as well as transcripts specific to other RET-sensitive tissue types such as developing spermatogonia (SYMPK, CLU, DDX9) or thyroid (JTB), suggesting that these genes may be induced as part of the normal developmental roles of RET. RET has been shown to be required for neural crest cell migration, maturation of neural cell types of the peripheral and central nervous systems, and for cell fate decisions in undifferentiated cells of the spermatogonia (61, 62, 63), and these genes may lie downstream of RET in these processes. However, RET also has cell type-specific proliferative roles. RET activation can promote proliferation of some populations of neuroblasts, thyroid C-cells and adrenal chromaffin cells and is implicated in both medullary and papillary thyroid carcinoma and pheochromocytoma (reviewed in Refs. 13 and 38). Consistent with this role, we saw increased expression of several transcripts specifically associated with other endocrine or renal tumor types including INSM1(64), NOV(65), and GRP(66). Overexpression of other tumorigenesis-related genes, such as CLU, which confers apoptosis resistance in a variety of cell types (67), was also detected in response to RET.

The most intriguing gene expression pattern noted in this study on RET expression, was the up-regulation of proteins associated with stress response, and in particular, inducible members of the HSP70 family. Initiation of a complex pattern of protein expression, termed the stress response, occurs as a result of a variety of environmental cues including heat shock, heavy metals, oxidative stress, inflammation, and others (41, 68). These proteins form complexes that recognize misfolded or immature peptide chains and either assist in their appropriate folding and maturation, or target these peptides for ubiquitinization and proteosomal degradation (41, 68; Fig. 7). HSP40, 70 and 90 family members have roles in both of these processes and the ultimate fate of the immature peptide is dependent on the combination of chaperones, cochaperones, proteases, ubiquitin ligases and/or other proteins that comprise the specific stress response complex.

Members of five of the seven HSP gene families were represented on our microarrays (HSP100, HSP90, HSP70, HSP60, and HSP40). We saw altered expression of multiple family members in response to RET (Table 2) but particularly notable was significant up-regulation in expression of the stress-inducible members of these gene families. For example, we saw increased expression of the inducible members of the HSP70 family, HSPA1A, HSPA1B, and HSPA1L (Fig. 3), whereas constitutively expressed family members had no consistent pattern of expression in the presence of RET (↑HSPA8, ↓HSPA9B, no change HSPA2, STCH). The HSP40 gene, DNAJB2 was also up-regulated, whereas DNAJC3, an HSP40 family member that inhibits stress response (69), was down-regulated (Table 2). In addition, our data suggest that a number of proteins associated with HSP70 protein complexes in peptide refolding, including HSP70/90-interacting proteins ST13, STIP1, and FKBP4, as well as HSP40 and 90 family members, seem generally to be increased in expression (Table 2; Figs. 5 and 7). Conversely, genes more prominently associated with HSP-mediated peptide degradation, such as HSP70-interacting cochaperones BAG1 and BAT3, and ubiquitin ligases STUB1 and RNF19, either showed no change in expression or were relatively decreased in the presence of RET (Table 2; Fig. 7). The induction of stress proteins seen in our study was not a nonspecific response to overexpression of any exogenous protein, because our RET-ve cells, expressing the hygromycin resistance gene, did not have increased expression of these proteins, nor are these up-regulated generally in other expression array studies.

Together, our data suggest that RET expression favors the production of HSP complexes that target immature proteins for refolding or maturation rather than for ubiquitinization and degradation in response to cellular stresses. This preference is consistent with the neuroprotective role found for RET and its ligand GDNF (70, 71, 72). In a number of neuronal lineages, the RET ligand GDNF has been strongly linked to survival and blockage of apoptotic responses to ischemia, cell damage, nitric oxide, and metal ions (4, 5, 6, 7, 8, 9, 10, 11). The inducible members of the HSP families are also up-regulated by similar stresses (68). For example, GDNF, RET, and HSP70s are all up-regulated in brain in response to ischemia (70, 71, 72) and in adult nerve cells in response to injury (73, 74, 75). In each case, these molecules are thought to be critical to survival, and possibly regeneration, of the damaged neurons (73, 74, 75). Whereas RET- and HSP-mediated survival signals may be independent events, our data suggest that GDNF effects are mediated through RET, at least in part, by enhancing or stimulating stress response that in turn triggers prosurvival or antiapoptotic pathways. High levels of inducible HSP70s prevent stress-induced apoptosis and block caspase activity, mitochondrial damage, and nuclear fragmentation (76, 77). RET can also have proapoptotic activity in the absence of its ligand (no signaling) that is blocked by the presence of GDNF and the induction of downstream signals (78). It is interesting to speculate that RET expression may contribute to up-regulation of HSP proteins and, thus, acts as part of the normal stress response in some tissues and may even help to modulate it.

Increased expression of HSPs, particularly members of the HSP70 and HSP90 families, has also been frequently recognized in a variety of tumor types including gastric, endometrial, and breast cancers, in which it is associated with poor prognosis and resistance to therapy (79, 80). Stress response is also thought to serve an antiapoptotic function in these cell types, leading to clonal outgrowth. RET activation is increased in several neuroendocrine tumors including papillary and medullary thyroid carcinoma, pheochromocytoma, seminomas, and lung and renal tumors (reviewed in Refs. 13, 81, and 82). We, thus, might also predict that RET expression may enhance or regulate HSP expression in these and other tumor types, as well as in neural tissues.

Our data suggest that increased expression of RET triggers phosphorylation but not increased expression of HSF1 (Fig. 6), the major vertebrate transcription factor associated with cellular and organismal stress responses. HSF1 expression is not usually up-regulated on induction of environmental stresses. Instead, existing monomers of HSF1 become activated by phosphorylation, oligomerization, and translocation to the nucleus, in which they bind specifically to HSEs and induce gene transcription (reviewed in Ref. 83). HSEs are found in the promoters of most stress-inducible genes but also have important roles in normal developmental gene expression (84). We identified HSEs close to the transcription start site of a number of stress response genes up-regulated by RET (Table 2). However, very few of the genes identified in our initial expression microarray screen were predicted to be regulated through HSF1, because only two additional genes, SYMPK and S100A10, contained HSEs. As we would expect, if RET does activate HSF1, none of the genes down-regulated by RET expression contain HSEs. The data available on the promoters of many of the genes modulated by RET are incomplete and future analyses will be needed to evaluate the contribution of HSF1/HSE to RET-modulated gene expression. However, our present data indicate that stimulation of the stress response through HSF1 activation represents only one of the mechanisms by which RET modulates gene expression, because 36 of the 42 genes up-regulated by RET do not contain HSEs.

As we would predict, we found more gene expression differences between RET-positive and RET-ve cell lines than between lines expressing different RET isoforms. We detected only four genes up-regulated and three genes down-regulated in RET9 cells as compared with RET51. This surprisingly small number of genes may, in part, be due to differences in the relative expression of RET9 and RET51 in our cell lines, which would have masked some variation. Previous studies have shown that RET9 and RET51 are functionally different, having distinct capacities for binding downstream adaptors and activating signaling pathways (reviewed in Refs. 13 and 38). Using monoisoformic RET transgenic mouse models, de Graaff et al.(23) have shown that RET9, but not RET51, is required for development of the kidney and enteric nervous system. Conversely, RET51, but not RET9, is required for metabolism and growth of mature sympathetic neurons (85). Thus, it is likely that RET9 and RET51 may be associated with different gene expression patterns in kidney and neural cell types. In this study, we have used kidney-derived cell lines to investigate gene expression associated with either RET9 or RET51, but it will be interesting to expand these studies to compare gene expression in other cell types, such as neural lineages.

Our data show that RET leads to expression of a broad range of genes including genes not previously linked to receptor tyrosine kinases and others with known or predicted roles in cell proliferation and differentiation. Interestingly, our data show a strong and specific up-regulation of genes associated with stress response and, in particular, the inducible members of the HSP70 family. These findings may suggest a direct role for RET in stress responses and may, in part, explain the neuroprotective function of RET and of GDNF in response to brain or nerve injury.

Grant support: This work was supported by grants from the National Cancer Institute of Canada and the Canadian Institutes of Health Research.

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.

Requests for reprints: Lois M. Mulligan, Queen’s Cancer Research Institute, Botterell Hall, Room 329, Queen’s University, Kingston, Ontario, Canada, K7L 3N6. Phone: (613) 533-6310; Fax: (613) 548-1348; E-mail: [email protected]

1

See website: http://www.microarrays.ca/.

2

See website: http://www.microarrays.ca/support/proto.html.

Fig. 1.

RET isoform expression. A, schematic representation of the expressed RET isoforms showing the alternatively spliced COOH-terminal sequences: SP, signal peptide; TM, transmembrane domain. , COOH-terminal amino acids encoding the RET9-specific sequences;, COOH-terminal amino acids encoding the RET51-specific sequences. B, Western blot analysis showing RET expression and phosphorylation in E293 cells stably expressing the RET9 or RET51 isoform and in the parental E293 cell line. Cells were either untreated (-) or transiently transfected with a GFRα1 coreceptor expression construct and treated with 100 ng/ml GDNF for 48 h and 10 min before harvesting, respectively (+). RET proteins were immunoprecipitated (IP) with isoform-specific RET antisera and were immunoblotted (IB) either with the same antibodies (α RET) or with an antiphosphotyrosine antibody (α pY). C, interaction of RET9 and RET51 with adaptor proteins SHC and GRB2. Expression of SHC isoforms (p46SHC, p52SHC) and GRB2 was detected in lysates from E293 transiently transfected with RET9 or RET51 expression constructs or empty vector (top 2 panels). Equal amounts of cell lysates from these transfected cells were immunoprecipitated with RET isoform-specific antibodies and interaction with SHC and GRB2 was confirmed by immunoblotting with appropriate antibodies (bottom 2 panels). The RET9-specific antibody was used for immunoprecipitation of the empty vector control. An IgG band detected in RET9 immunoprecipitates results from the detection of the anti-RET9 antibody used in immunoprecipitations by the rabbit secondary antibody used to detect SHC. In B and C, kDa, Mr in thousands.

Fig. 1.

RET isoform expression. A, schematic representation of the expressed RET isoforms showing the alternatively spliced COOH-terminal sequences: SP, signal peptide; TM, transmembrane domain. , COOH-terminal amino acids encoding the RET9-specific sequences;, COOH-terminal amino acids encoding the RET51-specific sequences. B, Western blot analysis showing RET expression and phosphorylation in E293 cells stably expressing the RET9 or RET51 isoform and in the parental E293 cell line. Cells were either untreated (-) or transiently transfected with a GFRα1 coreceptor expression construct and treated with 100 ng/ml GDNF for 48 h and 10 min before harvesting, respectively (+). RET proteins were immunoprecipitated (IP) with isoform-specific RET antisera and were immunoblotted (IB) either with the same antibodies (α RET) or with an antiphosphotyrosine antibody (α pY). C, interaction of RET9 and RET51 with adaptor proteins SHC and GRB2. Expression of SHC isoforms (p46SHC, p52SHC) and GRB2 was detected in lysates from E293 transiently transfected with RET9 or RET51 expression constructs or empty vector (top 2 panels). Equal amounts of cell lysates from these transfected cells were immunoprecipitated with RET isoform-specific antibodies and interaction with SHC and GRB2 was confirmed by immunoblotting with appropriate antibodies (bottom 2 panels). The RET9-specific antibody was used for immunoprecipitation of the empty vector control. An IgG band detected in RET9 immunoprecipitates results from the detection of the anti-RET9 antibody used in immunoprecipitations by the rabbit secondary antibody used to detect SHC. In B and C, kDa, Mr in thousands.

Close modal
Fig. 2.

Representative MA plot. MA plot showing the intensity-dependant ratio of our normalized microarray data. The log expression ratio for each spot was plotted against the average of the log intensities of hybridization for each spot. Our normalized data clustered around zero, suggesting minimal systemic differences (e.g., dye-label incorporation, detection bias) affecting our gene expression predictions. RET-ve, RET negative.

Fig. 2.

Representative MA plot. MA plot showing the intensity-dependant ratio of our normalized microarray data. The log expression ratio for each spot was plotted against the average of the log intensities of hybridization for each spot. Our normalized data clustered around zero, suggesting minimal systemic differences (e.g., dye-label incorporation, detection bias) affecting our gene expression predictions. RET-ve, RET negative.

Close modal
Fig. 3.

Expression of HSP70 gene family in response to RET. Northern analysis of RET and HSP70 family genes HSPA1A, HSPA1B, and HSPA1L in cell lines stably expressing RET9, RET51 (two independent clones) or a hygromycin resistance vector RET−ve and in untransfected E293 cells. Expression for each transcript was normalized to GAPDH and is indicated as fold expression relative to E293. Variation in RET transcript sizes reflect differences in the full-length cDNAs of RET9 (4.08 kb) and RET51 (3.53 kb) because of different 3′UTR sequences.

Fig. 3.

Expression of HSP70 gene family in response to RET. Northern analysis of RET and HSP70 family genes HSPA1A, HSPA1B, and HSPA1L in cell lines stably expressing RET9, RET51 (two independent clones) or a hygromycin resistance vector RET−ve and in untransfected E293 cells. Expression for each transcript was normalized to GAPDH and is indicated as fold expression relative to E293. Variation in RET transcript sizes reflect differences in the full-length cDNAs of RET9 (4.08 kb) and RET51 (3.53 kb) because of different 3′UTR sequences.

Close modal
Fig. 4.

Real-time PCR quantitation of gene expression in response to RET. Quantitations were performed by the crossing threshold (CT) method (37). A, amounts of RET expression in RET-ve, RET9, and RET51 cells were compared after PCR amplification and fluorescence monitoring of SYBR green for each cycle. B, crossing thresholds and relative fold differences in expression for genes modulated by RET were averaged over four to five experiments. RET itself and HSPA1B are used for comparison. Relative differences in expression calculated from microarray analyses are also shown.

Fig. 4.

Real-time PCR quantitation of gene expression in response to RET. Quantitations were performed by the crossing threshold (CT) method (37). A, amounts of RET expression in RET-ve, RET9, and RET51 cells were compared after PCR amplification and fluorescence monitoring of SYBR green for each cycle. B, crossing thresholds and relative fold differences in expression for genes modulated by RET were averaged over four to five experiments. RET itself and HSPA1B are used for comparison. Relative differences in expression calculated from microarray analyses are also shown.

Close modal
Fig. 5.

Expression of stress-related proteins in cell lines stably expressing RET. A, Northern analysis of STIP1, STUB1, and RNF19 expression in RET-ve, RET9, and RET51 cells. Expression in each sample was normalized to GAPDH and relative expression, compared with the vector control, is indicated.

Fig. 5.

Expression of stress-related proteins in cell lines stably expressing RET. A, Northern analysis of STIP1, STUB1, and RNF19 expression in RET-ve, RET9, and RET51 cells. Expression in each sample was normalized to GAPDH and relative expression, compared with the vector control, is indicated.

Close modal
Fig. 6.

Induction of HSP70 transcripts by RET through HSF1 activation. Western analyses showing that, in the presence of RET51, inactive HSF1 (←) becomes phosphorylated (bracket). Northern blot showing HSPA1B expression increases only in the presence of RET51 and increased HSF1 phosphorylation. RET-ve, RET negative.

Fig. 6.

Induction of HSP70 transcripts by RET through HSF1 activation. Western analyses showing that, in the presence of RET51, inactive HSF1 (←) becomes phosphorylated (bracket). Northern blot showing HSPA1B expression increases only in the presence of RET51 and increased HSF1 phosphorylation. RET-ve, RET negative.

Close modal
Fig. 7.

Schematic diagram of HSP70-related proteins. The relationships of stress response proteins analyzed in this study are shown. Relative change in expression in response to RET of each transcript is indicated (↑, up-regulated; ↓, down-regulated; −, not significantly altered). Phosphorylated forms of HSF1 (P) are indicated. HSE, heat shock element.

Fig. 7.

Schematic diagram of HSP70-related proteins. The relationships of stress response proteins analyzed in this study are shown. Relative change in expression in response to RET of each transcript is indicated (↑, up-regulated; ↓, down-regulated; −, not significantly altered). Phosphorylated forms of HSF1 (P) are indicated. HSE, heat shock element.

Close modal
Table 1

Genes differentially expressed in response to RET isoforms and for which a protein of known or predicted function has been defined

Genes with known or predicted roles in stress response are indicated in bold. Gene names represent the most current designations according to the Human Genome Organization (HUGO) Gene Nomenclature Committee (www.gene.ucl.ac.uk/nomenclature/). Identification of one or more potential HSEs within the proximal promoter of each gene is indicated (+). Data are sorted on the ratio of gene expression in the presence of RET9 over RET51 or RET-ve. Average ratios represent the total available data and may not demonstrate a two-fold difference in expression overall. However, within this data set for each gene, a minimum of three spots did show a two-fold expression difference. All of the genes shown had a significant difference in expression (P < 0.1) based on paired t tests of log-transformed data.

Gene SymbolGene Name/DescriptionRelative Expression
RatioPHSE
RET9:RET-ve
RET9>RET-ve     
RET RET proto-oncogene 7.82 <0.001  
PER3 Period homolog 3 (Drosophila3.51 <0.001  
MAPKAPK2 Mitogen-activated protein kinase-activated protein kinase 2 3.39 0.003  
SYMPK Symplekin; Huntingtin interacting protein I 3.35 0.002 
KCNJ10 Potassium inwardly-rectifying channel, subfamily J, member 10 3.14 <0.001  
HSPA1A Heat shock 70 kD protein 1 3.05 <0.001 
DRLM Down-regulated in liver malignancy 2.98 0.001  
EEA1 Early endosome antigen 1, 162 kD 2.93 0.005  
CLDN5 Claudin 5 (transmembrane protein deleted velocardiofacial syndrome) 2.88 0.001  
MRPL23 Ribosomal protein L23-like 2.88 <0.001  
SDK1 Sidekick homolog 1 (chicken) 2.85 <0.001  
HSPA1B Heat shock 70 kDa protein 1B 2.83 <0.001 
POLR2I Polymerase (RNA) II (DNA directed) polypeptide I, 14.5 kDa 2.60 0.001  
CROC4 Transcriptional activator of the c-fos promoter 2.55 <0.001  
SUPT5H Suppressor of Ty (Saccharomyces cerevisiae) 5 homolog 2.48 <0.001  
ATP2C1 ATPase, Ca++ transporting, type 2C, member 1 2.47 0.001  
FADS3 Fatty acid desaturase 3 2.42 <0.001  
INSM1 Insulinoma-associated 1 2.38 <0.001  
LOC149420 Casein kinase 2.35 <0.001  
DKFZp727A071 Similar to tRNA synthetase class II 2.27 <0.001  
CLU Clusterin 2.21 0.015  
ABO26190 Kelch motif containing protein 2.20 <0.001  
CYB5R1 Cytochrome b5 reductase 1 (B5R.1) 2.17 <0.001  
FKBP4 FK506-binding protein 4 (59 kD) 2.16 0.007 
SLC16A3 Solute carrier family 16 (monocarboxylic acid transporters)-3 2.14 <0.001  
SCARA3 Scavenger receptor class A, member 3 2.11 <0.001  
JTB Jumping translocation breakpoint 2.07 0.037  
HSPA1L Heat shock 70 kD protein-like 1 2.06 <0.001 
DUSP8 Dual specificity phosphatase 8 2.04 0.052  
ENDOG Endonuclease G 2.03 0.022  
NOV Nephroblastoma overexpressed gene 2.02 0.003  
AKAP9 A kinase (PRKA) anchor protein 9 2.01 <0.001  
MOCS3 Molybdopterin synthase sulfurylase 2.01 <0.001  
DDX9 DEAD/H box polypeptide 9 (RNA helicase A, leukophysin) 1.81 0.026  
DELGEF Deafness locus associated putative guanine nucleotide exchange factor 1.80 0.001  
GRP Gastrin-releasing peptide 1.80 <0.001  
CTSC Cathepsin C 1.76 0.038  
SCHIP1 Schwannomin interacting protein 1 1.76 0.013  
S100A10 S100 calcium-binding protein A10 [annexin II ligand, calpactin I, light polypeptide (p11)] 1.73 0.006 
HEY1 Hairy/enhancer-of-split related with YRPW motif 1 1.60 0.094  
PFKL Phosphofructokinase, liver 1.56 0.071  
CD151 CD151 antigen 1.51 0.043  
MPP3 Membrane protein, palmitoylated 3 (MAGUK p55 subfamily 3) 1.45 0.010  
RET-ve>RET9     
RNF19 Ring finger protein 19, E3 ubiquitin ligase 0.43 0.001  
CIT Citron (rho-interacting, serine/theorine kinase 21) 0.45 <0.001  
PCBP2 Poly(rC)-binding protein 2 0.47 <0.001  
THH Trichohyalin 0.50 <0.001  
C6orf32 Chromosome 6 open reading frame 32 0.52 <0.001  
HIC2 Hypermethylated in cancer 2 0.53 <.001  
CGI-67 CGI-67 protein 0.54 <0.001  
PSG11 Pregnancy specific β1-glycoprotein 11 0.71 0.026  
RPL4 Ribosomal protein L4 0.72 0.001  
  RET9:RET51   
RET9>RET51     
SYN1 Synapsin 1 1.73 0.015  
BPAG1 Bullous pemphigoid antigen 1 1.63 0.074  
ELK4 ETS-domain protein 1.48 0.059  
TSC Tescalcin 1.28 0.034  
RET51>RET9     
DNAJC3 DnaJ (Hsp40) homolog 0.52 0.008  
AQP1 Aquaporin 1 0.77 0.012  
RNF19 Ring finger protein 19, E3 ubiquitin ligase 0.79 0.020  
Gene SymbolGene Name/DescriptionRelative Expression
RatioPHSE
RET9:RET-ve
RET9>RET-ve     
RET RET proto-oncogene 7.82 <0.001  
PER3 Period homolog 3 (Drosophila3.51 <0.001  
MAPKAPK2 Mitogen-activated protein kinase-activated protein kinase 2 3.39 0.003  
SYMPK Symplekin; Huntingtin interacting protein I 3.35 0.002 
KCNJ10 Potassium inwardly-rectifying channel, subfamily J, member 10 3.14 <0.001  
HSPA1A Heat shock 70 kD protein 1 3.05 <0.001 
DRLM Down-regulated in liver malignancy 2.98 0.001  
EEA1 Early endosome antigen 1, 162 kD 2.93 0.005  
CLDN5 Claudin 5 (transmembrane protein deleted velocardiofacial syndrome) 2.88 0.001  
MRPL23 Ribosomal protein L23-like 2.88 <0.001  
SDK1 Sidekick homolog 1 (chicken) 2.85 <0.001  
HSPA1B Heat shock 70 kDa protein 1B 2.83 <0.001 
POLR2I Polymerase (RNA) II (DNA directed) polypeptide I, 14.5 kDa 2.60 0.001  
CROC4 Transcriptional activator of the c-fos promoter 2.55 <0.001  
SUPT5H Suppressor of Ty (Saccharomyces cerevisiae) 5 homolog 2.48 <0.001  
ATP2C1 ATPase, Ca++ transporting, type 2C, member 1 2.47 0.001  
FADS3 Fatty acid desaturase 3 2.42 <0.001  
INSM1 Insulinoma-associated 1 2.38 <0.001  
LOC149420 Casein kinase 2.35 <0.001  
DKFZp727A071 Similar to tRNA synthetase class II 2.27 <0.001  
CLU Clusterin 2.21 0.015  
ABO26190 Kelch motif containing protein 2.20 <0.001  
CYB5R1 Cytochrome b5 reductase 1 (B5R.1) 2.17 <0.001  
FKBP4 FK506-binding protein 4 (59 kD) 2.16 0.007 
SLC16A3 Solute carrier family 16 (monocarboxylic acid transporters)-3 2.14 <0.001  
SCARA3 Scavenger receptor class A, member 3 2.11 <0.001  
JTB Jumping translocation breakpoint 2.07 0.037  
HSPA1L Heat shock 70 kD protein-like 1 2.06 <0.001 
DUSP8 Dual specificity phosphatase 8 2.04 0.052  
ENDOG Endonuclease G 2.03 0.022  
NOV Nephroblastoma overexpressed gene 2.02 0.003  
AKAP9 A kinase (PRKA) anchor protein 9 2.01 <0.001  
MOCS3 Molybdopterin synthase sulfurylase 2.01 <0.001  
DDX9 DEAD/H box polypeptide 9 (RNA helicase A, leukophysin) 1.81 0.026  
DELGEF Deafness locus associated putative guanine nucleotide exchange factor 1.80 0.001  
GRP Gastrin-releasing peptide 1.80 <0.001  
CTSC Cathepsin C 1.76 0.038  
SCHIP1 Schwannomin interacting protein 1 1.76 0.013  
S100A10 S100 calcium-binding protein A10 [annexin II ligand, calpactin I, light polypeptide (p11)] 1.73 0.006 
HEY1 Hairy/enhancer-of-split related with YRPW motif 1 1.60 0.094  
PFKL Phosphofructokinase, liver 1.56 0.071  
CD151 CD151 antigen 1.51 0.043  
MPP3 Membrane protein, palmitoylated 3 (MAGUK p55 subfamily 3) 1.45 0.010  
RET-ve>RET9     
RNF19 Ring finger protein 19, E3 ubiquitin ligase 0.43 0.001  
CIT Citron (rho-interacting, serine/theorine kinase 21) 0.45 <0.001  
PCBP2 Poly(rC)-binding protein 2 0.47 <0.001  
THH Trichohyalin 0.50 <0.001  
C6orf32 Chromosome 6 open reading frame 32 0.52 <0.001  
HIC2 Hypermethylated in cancer 2 0.53 <.001  
CGI-67 CGI-67 protein 0.54 <0.001  
PSG11 Pregnancy specific β1-glycoprotein 11 0.71 0.026  
RPL4 Ribosomal protein L4 0.72 0.001  
  RET9:RET51   
RET9>RET51     
SYN1 Synapsin 1 1.73 0.015  
BPAG1 Bullous pemphigoid antigen 1 1.63 0.074  
ELK4 ETS-domain protein 1.48 0.059  
TSC Tescalcin 1.28 0.034  
RET51>RET9     
DNAJC3 DnaJ (Hsp40) homolog 0.52 0.008  
AQP1 Aquaporin 1 0.77 0.012  
RNF19 Ring finger protein 19, E3 ubiquitin ligase 0.79 0.020  
Table 2

Expression of stress response-related genes in response to RET, analyzed by microarray

Genes were selected based on established relationships with stress response. Data are sorted on the ratio of gene expression in the presence versus the absence of RET expression (RET9:RET-ve).

Gene SymbolGene Name/DescriptionRelative expressionHSEa
RatioP
RET9: RET-ve
MAPKAPK2 Mitogen-activated protein kinase-activated protein kinase 2 3.39 0.003b  
ST13 Suppression of tumorigenicity 13, Hsp70 interacting protein (HIP) 3.21 0.007b 
HSPA1A Heat shock 70 kDa protein 1 3.05 <0.001b 
HSPA1B Heat shock 70 kDa protein 1B 2.83 <0.001b 
DNAJB2 DnaJ (Hsp40) homolog, subfamily B, member 2 2.55 0.046b  
FKBP4 FK506-binding protein 4 (immunophilin family) 2.16 0.007b 
SCARA3 Scavenger receptor class A, member 3 2.11 <0.001b  
HSPA1L Heat shock 70 kDa protein-like 1 2.06 <0.001b 
HSPCA Heat shock 90 kDa protein 1α 1.79 0.022b 
HSPH1 Heat shock 105/110 kDa protein 1 1.65 0.011b 
DNAJA1 DnaJ (Hsp40) homolog, subfamily A, member 1 1.47 <0.001b 
HSPA8 Heat shock 70 kDa protein 8, constitutive 1.45 <0.001b 
UBQLN2 Ubiquilin 2 (CHAP1) 1.45 0.015b  
AUH AU RNA-binding protein/enoyl-Coenzme A hydratase 1.44 0.254  
HSPD1 Heat shock 60 kDa protein 1 (chaperonin) 1.34 0.021b 
BAT3 HLA-B associated transcript 3, (Scythe) 1.17 0.131  
HSF1 Heat shock transcription factor 1 1.17 0.510  
HSPA2 Heat shock 70 kDa protein 2 1.15 0.432  
CDC37 CDC37 cell division cycle 37 homolog (S. cerevisiae1.14 0.030b 
BAG1 BCL2-associated athanogene 1.07 0.201  
DNAJB1 DnaJ (Hsp40) homolog, subfamily B, member 1 1.04 0.630 
RNF19 Ring finger protein 19, E3 ubiquitin ligase 0.43 0.001b  
DNAJC3 DnaJ (Hsp40) homolog, subfamily C, member 3 (P58IPK) 0.45 0.002b  
HSPA9B Heat shock 70 kDa protein 9B (mortalin-2), constitutive 0.79 0.002b  
STCH Stress 70 protein chaperone, microsome-associated, constitutive 0.83 0.151  
HSPE1 Heat shock 10 kDa protein 1 (chaperonin 10) 0.97 0.395 
APG-1 Heat shock protein (hsp110 family) 0.98 0.674  
Gene SymbolGene Name/DescriptionRelative expressionHSEa
RatioP
RET9: RET-ve
MAPKAPK2 Mitogen-activated protein kinase-activated protein kinase 2 3.39 0.003b  
ST13 Suppression of tumorigenicity 13, Hsp70 interacting protein (HIP) 3.21 0.007b 
HSPA1A Heat shock 70 kDa protein 1 3.05 <0.001b 
HSPA1B Heat shock 70 kDa protein 1B 2.83 <0.001b 
DNAJB2 DnaJ (Hsp40) homolog, subfamily B, member 2 2.55 0.046b  
FKBP4 FK506-binding protein 4 (immunophilin family) 2.16 0.007b 
SCARA3 Scavenger receptor class A, member 3 2.11 <0.001b  
HSPA1L Heat shock 70 kDa protein-like 1 2.06 <0.001b 
HSPCA Heat shock 90 kDa protein 1α 1.79 0.022b 
HSPH1 Heat shock 105/110 kDa protein 1 1.65 0.011b 
DNAJA1 DnaJ (Hsp40) homolog, subfamily A, member 1 1.47 <0.001b 
HSPA8 Heat shock 70 kDa protein 8, constitutive 1.45 <0.001b 
UBQLN2 Ubiquilin 2 (CHAP1) 1.45 0.015b  
AUH AU RNA-binding protein/enoyl-Coenzme A hydratase 1.44 0.254  
HSPD1 Heat shock 60 kDa protein 1 (chaperonin) 1.34 0.021b 
BAT3 HLA-B associated transcript 3, (Scythe) 1.17 0.131  
HSF1 Heat shock transcription factor 1 1.17 0.510  
HSPA2 Heat shock 70 kDa protein 2 1.15 0.432  
CDC37 CDC37 cell division cycle 37 homolog (S. cerevisiae1.14 0.030b 
BAG1 BCL2-associated athanogene 1.07 0.201  
DNAJB1 DnaJ (Hsp40) homolog, subfamily B, member 1 1.04 0.630 
RNF19 Ring finger protein 19, E3 ubiquitin ligase 0.43 0.001b  
DNAJC3 DnaJ (Hsp40) homolog, subfamily C, member 3 (P58IPK) 0.45 0.002b  
HSPA9B Heat shock 70 kDa protein 9B (mortalin-2), constitutive 0.79 0.002b  
STCH Stress 70 protein chaperone, microsome-associated, constitutive 0.83 0.151  
HSPE1 Heat shock 10 kDa protein 1 (chaperonin 10) 0.97 0.395 
APG-1 Heat shock protein (hsp110 family) 0.98 0.674  
a

HSE, heat shock element; +, presence of predicted HSEs within the proximal promoter region of the gene.

b

Genes for which the ratio of relative expression was significant in paired t tests (P < 0.1);

Table 3

Assessing relative expression of previously predicted RET target genes by microarray analysis

For each gene, expression in the presence of RET9 relative to expression in RET-negative cells was evaluated, and genes were sorted on relative expression ratio.

Gene symbolGene name/descriptionRelative expression
RatioP
RET9:RET-veReferencea
     
FOS v-fos FBJ murine osteosarcoma viral oncogene homolog 1.96 0.001b (44, 45) 
EIF4G3 Eukaryotic translation initiation factor 4γ, 3 1.84 0.005b  (46)  
JUN v-jun avian sarcoma virus 17 oncogene homolog 1.53 0.016b  (45)  
LOX Lysyl oxidase 1.46 0.135  (46)  
ITGA6 Integrin, α6 1.38 0.142  (46)  
CCND3 Cyclin D3 1.38 0.102  (47)  
CFL1 Cofilin, non-muscle isoform 1.37 0.021b  (46)  
CDKN1B Cyclin-dependent kinase inhibitor 1B (p27, Kip1) 1.35 0.235  (47)  
CCND1 Cyclin D1 1.34 0.510  (46)  
CTSL Cathepsin L 1.27 0.125  (46)  
VEGF Vascular enothelial growth factor precursor 1.24 0.359 (48495051) 
ENO2 Enolase 2(γ, neuronal) 1.17 0.030b  (48)  
EGR1 Early growth response protein 1 1.16 0.673 (44, 48, 49, 50) 
CXCL12 Chemokine ligand 12, stromal cell-derived factor 1 precursor 1.09 0.067  (46)  
GJA1 Gap junction protein, α1, 43 kDa (connexin 43) 1.08 0.835  (52)  
PTN Pleiotrophin precursor 1.06 0.900  (46)  
PLOD2 Procollagen-lysine, 2-oxoglutarate 5-dioxygenase (lysine hydroxylase) 2 1.05 0.701  (46)  
STC1 Stanniocalcin 1.01 0.903  (46)  
DCN Decorin 0.99 0.525  (46)  
TIMP3 Tissue inhibitor of metalloproteinase 3 0.90 0.073b  (46)  
ANXA4 Annexin IV 0.86 0.848  (46)  
Gene symbolGene name/descriptionRelative expression
RatioP
RET9:RET-veReferencea
     
FOS v-fos FBJ murine osteosarcoma viral oncogene homolog 1.96 0.001b (44, 45) 
EIF4G3 Eukaryotic translation initiation factor 4γ, 3 1.84 0.005b  (46)  
JUN v-jun avian sarcoma virus 17 oncogene homolog 1.53 0.016b  (45)  
LOX Lysyl oxidase 1.46 0.135  (46)  
ITGA6 Integrin, α6 1.38 0.142  (46)  
CCND3 Cyclin D3 1.38 0.102  (47)  
CFL1 Cofilin, non-muscle isoform 1.37 0.021b  (46)  
CDKN1B Cyclin-dependent kinase inhibitor 1B (p27, Kip1) 1.35 0.235  (47)  
CCND1 Cyclin D1 1.34 0.510  (46)  
CTSL Cathepsin L 1.27 0.125  (46)  
VEGF Vascular enothelial growth factor precursor 1.24 0.359 (48495051) 
ENO2 Enolase 2(γ, neuronal) 1.17 0.030b  (48)  
EGR1 Early growth response protein 1 1.16 0.673 (44, 48, 49, 50) 
CXCL12 Chemokine ligand 12, stromal cell-derived factor 1 precursor 1.09 0.067  (46)  
GJA1 Gap junction protein, α1, 43 kDa (connexin 43) 1.08 0.835  (52)  
PTN Pleiotrophin precursor 1.06 0.900  (46)  
PLOD2 Procollagen-lysine, 2-oxoglutarate 5-dioxygenase (lysine hydroxylase) 2 1.05 0.701  (46)  
STC1 Stanniocalcin 1.01 0.903  (46)  
DCN Decorin 0.99 0.525  (46)  
TIMP3 Tissue inhibitor of metalloproteinase 3 0.90 0.073b  (46)  
ANXA4 Annexin IV 0.86 0.848  (46)  
a

Previous studies showing altered gene expression in response to RET.

b

Genes for which the ratio of relative expression was significant in paired t tests (P < 0.1).

We thank Drs. Harriet Feilotter and Scott Andrew, and Andrew Day and Julie Shaw for helpful discussion.

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