We investigated mTOR regulation of gene expression by studying rapamycin effect in two hepatic cell lines, the non-tumorigenic WB-F344 cells and the tumorigenic WB311 cells. The latter are resistant to the growth inhibitory effects of rapamycin, thus providing us with an opportunity to study the gene expression effects of rapamycin without confounding effects on cell proliferation.
The hepatic cells were exposed to rapamycin for 24 hr. Microarray analysis on total RNA preparations identified genes that were affected by rapamycin in both cell lines and, therefore, modulated independent of growth arrest. Further studies showed that the promoter regions of these genes included E-box-containing transcription factor binding sites at higher than expected rates. Based on this, we tested the hypothesis that c-Myc is involved in regulation of gene expression by mTOR by comparing genes altered by rapamycin in the hepatic cells and by c-Myc induction in fibroblasts engineered to express c-myc in an inducible manner. Results showed enrichment for c-Myc targets among rapamycin sensitive genes in both hepatic cell lines. However, microarray analyses on wild type and c-myc null fibroblasts showed similar rapamycin effect, with the set of rapamycin-sensitive genes being enriched for c-Myc targets in both cases.
There is considerable overlap in the regulation of gene expression by mTOR and c-Myc. However, regulation of gene expression through mTOR is c-Myc-independent and cannot be attributed to the involvement of specific transcription factors regulated by the rapamycin-sensitive mTOR Complex 1.
Citation: Jimenez RH, Lee J-S, Francesconi M, Castellani G, Neretti N, Sanders JA, et al. (2010) Regulation of Gene Expression in Hepatic Cells by the Mammalian Target of Rapamycin (mTOR). PLoS ONE 5(2): e9084. https://doi.org/10.1371/journal.pone.0009084
Editor: Matt Kaeberlein, University of Washington, United States of America
Received: December 17, 2009; Accepted: January 12, 2010; Published: February 5, 2010
Copyright: © 2010 Jimenez et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: R.H.J. is supported by National Research Service Award (NRSA) grant F31HD041893. Additional support was provided by National Institutes of Health grants R01HD024455 and R01HD035831 (to P.G.), 1R01AG035328 (to J.S.), K25AG028753 (to N.N.) and P20RR017695 to J.A.S. J.-S. Lee is supported by FG-4-2 of the 21C Frontier Functional Human Genome Project from the Ministry of Science and Technology in Korea. G. Castellani is supported by grants from the National Institute of Nuclear Physics (INFN) in support of the SHEILA and EXCALIBUR projects. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The mammalian Target of Rapamycin (mTOR) is a central regulator of many biological processes that are essential for cell growth, including cell cycle progression, protein translation, ribosomal biogenesis, autophagy and metabolism , . In addition, alterations in mTOR signaling have been associated with the pathophysiology of cancer ,  and aging . Among the effects of mTOR signaling, regulation of gene expression and transcription is well characterized for several specific genes, including DNA polymerase I (Pol I), Insulin Growth Factor II (IGF II), ribosomal DNA – and regulators of mitochondrial oxidative function . However, general effects on gene expression have not been well characterized, and the mechanisms by which mTOR signaling affects the expression of a broad array of genes are less well defined than is the case for its other biological effects.
mTOR interacts with a number of protein partners, including raptor (regulatory associated protein of TOR) and rictor (rapamycin-insensitive companion of TOR), which form complexes with mTOR termed mTORC1 and mTORC2, respectively . mTORC1-mediated signaling events are sensitive to the macrolytic lactone, rapamycin, while events downstream from mTORC2 are, in general, rapamycin insensitive .
The role of TOR in the regulation of transcription has been best characterized in yeast  where the nuclear localization and the activity of several nutrient and stress-responsive transcription factors are regulated by TORC1-dependent phosphorylation . Yeast TOR signals to a number of specific effectors including Tap42, Mks1p, Ure2p, Gln3p, Npr1, Tip41, and Gat1p, all of which can elicit changes in the expression levels of enzymes involved in metabolic pathways , . As a nutrient-sensing kinase, TOR signaling regulates the subcellular localization of the so-called unconventional prefoldin RPB5 interactor (URI), which is involved in the regulation of nutrient-sensitive, TORC1-controlled transcription pathways in yeast and mammals .
Examples of rapamycin-sensitive mTORC1 signaling to transcriptional regulators have been identified in eukaryotes. mTOR phosphorylates the signal transducers and activators of transcription 1 and 3 (STAT1 and STAT3) and, as a result, induces the transcription of the nuclear receptor peroxisome proliferator-activated receptors-gamma (PPARγ) . In addition, mTOR signaling occupies a central role in the global regulation of ribosomal RNA synthesis through the TIF-IA transcription factor . Finally, mTOR-mediated regulation of mitochondrial oxidative function occurs through regulation of a transcription factor termed YY1 .
We have characterized the effects of rapamycin on gene expression in two related rat liver epithelial cell lines . The slow growing, non-tumorigenic WB-F344  cell line is sensitive to the anti-proliferative effects of rapamycin while the faster growing, tumorigenic WB311 cell line, spontaneously derived from the WB-F344 cell line , is not. We used microarray analysis to characterize the effects of rapamycin on gene expression in these two cell lines. The results identified 106 genes whose expression was altered in response to rapamycin in both cell lines and, therefore, affected independent of rapamycin-induced growth arrest. We hypothesized that this set of genes would be regulated through effects on specific transcription factors and, therefore, be suitable for further study aimed at identifying transcriptional control mechanisms downstream from mTORC1.
Culture conditions for the WB-F344  and WB311  cell lines have been previously described. Conditions for maintaining the TGR-1 (c-myc+/+) and HO15.19 (c-myc−/−) cells have been published as well . All cell lines were grown at 37°C with 5.0% CO2 and plated at densities providing for approximately 60 to 80 percent confluence at the time of each experiment. After plating, cells were allowed to recover for at least 18 hr before exposure to rapamycin.
Identification of Predicted Transcription Factor Binding Sites
The TOUCAN 2 web based program , , which integrates several data and algorithmic resources including TRANSFAC, JASPAR, Ensembl, MotifScanner and MotifSearcher, was used to identify overrepresented transcription factor binding sites in the proximal promoters or the distal non-coding sequence in defined sets of co-regulated or co-expressed genes. For the retrieval of promoter sequences from the Ensemble database, the following settings were used: Get_Seq from Ensembl/Biomart, comma separated, rat species, AFFY_Rat230_2 chip, 5′Upstreatm Exon 1, 1,000 base pairs (bp) upstream, 200 bp downstream, add to current list, Rev Compl automatically. To search for transcription factor binding sites, MotifScanner was used with the following settings: motif Position Weight Matrix (PWM) scoring, Motif Scanner, P2: TRANSFAC 7.0, P3: Prior 0.1, P4: background model (mouse DBTSS promoters ), stand integer 1. Note that the background model for the rat was not available in the TOUCAN2 program. Therefore, we used mouse as the background model.
The TOUCAN2 analysis was followed by statistical analysis to identify overrepresented transcription factor binding sites. The following parameters were used: Expected Frequencies file: epd_mus_musculus_prior 0.1.freq. Note that the expected frequencies file was not available for the rat, so the file for mouse was used. The prior that was used was the same as for the MotifScanner run. The expected frequencies file, located on the TOUCAN FTP-site, is based on the Eukaryotic Promoter Database (http://www.epd.isb.ch/seq_download.html).
Analysis of Myc Family Gene Expression
WB-F344 cells were plated in 100 mm plates and allowed to attach overnight. Vehicle (DMSO) or 50 nM rapamycin was added for 6 or 24 hr. Total RNA was prepared from triplicate plates using TRIzol reagent. RNA integrity was determined using an Agilent Bioanalyzer. RNase protection assays were performed using the RPA III kit (Ambion; Austin, TX) and 32P-labeled probes generated from the RiboQuant mMyc multiprobe template (BD Biosciences; San Jose, CA) using the T7 MAXIscript kit (Ambion).
RNA Isolation and Microarray Hybridization for the HOMyc-ER12 Fibroblasts
The experimental conditions for expression profiling of c-Myc target genes induced by 4-hydroxytamoxifen (OHT) in HOMycER12 fibroblasts have been described previously . Genes were clustered according to their expression pattern over time. Unsupervised hierarchical clustering analysis with correlation-based metrics resulted in 9 clusters. Cluster 1 (1,712 probesets) consists of rapidly downregulated c-Myc target genes that remain downregulated for 24 hr. Cluster 2 (1,643 probesets) represents rapidly upregulated genes that remain upregulated. Cluster 3 (616 probesets) contains genes that are quickly downregulated after exposure to OHT then return towards the baseline but remain downregulated at 24 hr. Cluster 4 (178 probesets) represents genes that are quickly upregulated from 1 to 6 hr then fall to baseline. Cluster 5 genes (30 probesets) are rapidly downregulated by OHT from 1 to 6 hr then return towards the baseline by 24 hr. Clusters 6 through 9 had fewer than 5 genes each, so they were not included in the present analyses.
Identification of c-Myc Targets among Rapamycin Responsive Genes
The dataset of genes that were differentially expressed in response to rapamycin in the WB-F344 and WB311 hepatic cells and the c-Myc target gene dataset in HOMycER12 fibroblasts were compared to search for c-Myc targets in hepatic cells and the fibroblast cell lines. To accomplish this, the ID Converter Database (http://idconverter.bioinfo.cnio.es/) was used to obtain the Ensembl Gene ID and Entrez ID for the list of rapamycin-sensitive genes in the WB-F344 and WB311 hepatic cells and the c-Myc target genes in HOMycER12 fibroblasts. Genes that contained more than one probe ID in the list of differentially expressed genes affected by rapamycin in the WB-F344 and WB311 cells were counted only one time when compared to the existing list of c-Myc targets to avoid overestimating the intersection of the two groups. Therefore, while the total number of Affymetrix probe ID for genes affected by rapamycin in the WB-F344 cells was 2,034, only 1,927 were used for this analysis. While the total number of Affymetrix probe IDs affected by rapamycin in the WB311 cells was 1,236, only 1,130 were used for this analysis.
In addition to the above analysis, the dataset of rapamycin-sensitive genes in the WB-F344 and WB311 cells was aligned with 341 confirmed NFκB target genes previously identified in multiple human cell lines (http://people.bu.edu/gilmore/nf-kb/target/index.html). The purpose of this approach was to perform a second analysis using a gene expression regulating system other than c-Myc to assess the specificity of our findings.
E-Box Content and Distribution for Rapamycin Responsive Genes in Hepatic Cells
The Affymetrix gene features affected by rapamycin in the WB-F344 and WB311 cells were subjected to further analysis. Promoter regions of annotated genes extending from 3,000 bp upstream to 500 bp downstream of the transcription start sites were extracted from our datasets. The search for E-boxes was restricted to a perfect match with the canonical E-box CACGTG in the promoter sequences . Statistical significance of the results was assessed using a Fisher exact test.
Profiling of Rapamycin Effect on Gene Expression in Rat Fibroblasts
TGR-1 cells (c-myc+/+) and HO15.19 cells (c-myc−/−) were plated in 100 mm plates and allowed to attach overnight. Vehicle (DMSO) or 50 nM rapamycin was added for 24 hr. Total RNA was prepared from three independent biological replicates using TRIzol reagent. RNA processing, hybridization and data analysis were carried out at the W. M. KECK Foundation Biotechnology Resources Laboratory at Yale University (New Haven, CT). Gene expression was analyzed using the Illumina RatRef-12 Expression BeadChips and the fluorescence signals were normalized using quantile normalization implemented in BeadStudio (Illumina Inc., San Diego, CA). Illumina data analysis was performed using Partek® software, version 6.3 (Partek Inc., St. Louis, MO). For gene expression comparisons, genes whose change had a level of significance of P>0.01 or fold change less than 1.2-fold were excluded from further analyses. A two-sample t-test using a P-value cutoff of 0.05 with multiple test correction (Benjamini and Hochberg false discovery rate) was applied for each gene to determine if the gene was differentially expressed in the pairwise comparisons of triplicate analyses .
Significantly altered genes from each comparison were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) biopathway database (http://www.genome.ad.jp/kegg/pathway.html). For the Fisher exact test, P-values were employed to determine the enriched gene ontology categories and the overrepresented pathways. The list of differentially expressed genes in each cell line was used for the network reconstruction of pathway analysis, which was performed as described elsewhere .
Data Access and Analysis
The microarray studies described in this paper have been deposited in the NCI's Gene Expression Omnibus (GEO; ) and comply with the Minimal Information About a Microarray Experiment (MIAME) standard developed by the MGED Society (http://www.mged.org/Workgroups/MIAME/miame.html). Data derived from the hepatic cell lines may be accessed through the GEO SuperSeries accession number GSE17677 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17677). Data derived from the rat fibroblast cell lines may be accessed using accession number GSE18845 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18845).
Chi square analysis was used to determine whether observed frequencies differed from the expected frequencies in the various analyses.
We previously characterized the gene expression response to rapamycin for the WB-F344 and WB311 hepatic cell lines . Cells were exposed to DMSO vehicle or rapamycin (50 nM) for 24 hr prior to preparation of RNA for microarray analysis. A total of 2,346 gene features showed significant differences across the four experimental groups (2 cell lines, each without or with rapamycin). Hierarchical clustering (Figure 1A) showed that the vehicle control and rapamycin groups segregated for the rapamycin-sensitive WB-F344 cells but not for the rapamycin-resistant WB311 cells. We proceeded to perform a cross-comparison analysis of expression patterns in these two cell lines.
WB-F344 and WB311 cells were exposed to DMSO vehicle or rapamycin (50 nM) for 24 hr. Total RNA was prepared and processed for microarray analysis. Panel A shows a clustering analysis and heat map for the expression of all gene features that showed a significant change in response to rapamycin across the four experimental conditions (two cell lines, each plus and minus rapamycin). The accompanying Venn diagram shows the gene features affected in one cell line, the other or in both. Panel B shows a magnification of the gene expression pattern of the 106 gene features in the middle box of Panel A, representing those that were affected by rapamycin in both cell lines. The blue boxes at the top and in the middle of the heat map represent gene features that were regulated in opposite directions in the WB-F344 and WB311 cells after treatment with rapamycin. The gene features that are not included in the blue boxes represent genes that were co-regulated in the two cell lines.
Gene expression ratios were median-centered across the 6 analyses of each cell line. A cross comparison of expression patterns (Figure 1A) showed that 791 gene features were exclusively altered in WB-F344 cells after rapamycin treatment while 484 gene features were exclusively altered in the WB311 cells. Of particular interest were 106 gene features that were affected by rapamycin in both cell lines. Examination of the 106 gene features (Figure 1B) showed that the expression of 26 of the genes was affected in opposite directions in the WB-F344 and WB311 cells. The 80 gene features that were co-regulated in response to rapamycin in the two cell lines (Table S1) were selected for further study. Thirty-three of these genes were upregulated in both cell lines while 47 were downregulated.
Identification of Overrepresented Transcription Factor Binding Sites in Rapamycin-Regulated Genes in Hepatic Cells
Genes co-regulated by rapamycin in the two cell lines are necessarily regulated independent of growth arrest since the WB311 cells are rapamycin resistant. Our initial hypothesis was that these genes share a common transcriptional control mechanism and, therefore, specific transcription factor binding sites. Of the 80 co-regulated gene features, only 61 had Ensemble IDs and could therefore be recognized by TOUCAN 2. Analysis of the promoter regions of these genes revealed five high-scoring transcription factor matrices that were overrepresented in the designated set of promoters. All five represented binding sites with E-boxes (CACGTG) that corresponded to the consensus sites for the transcription factors Max, MycMax_01, MycMax_02, MycMax_03, and Arnt (Table 1). Nineteen of the 61 genes contained binding sites for these transcription factors (Table 2). Some of these genes contained more than one E-box-containing transcription factor binding in its promoter region. In addition, the binding site that correlated with a specific transcription factor differed in some cases. For example, the transcription factor M00119-V$Max_01 was predicted to bind the motifs TAATCACGTGATTG and CAATCACGTGATTA in the promoter of the Eif4b gene while it was predicted to bind the motifs GCAACACGTGACTC and GAGTCACGTGTTGC in the promoter of the Ecm1 gene.
Enrichment of c-Myc Targets in Rapamycin-Modulated Genes in the WB-F344 and WB311 Hepatic Cell Lines
Based on the overrepresentation of E-box-containing transcription factor binding sites among rapamycin-regulated genes, we hypothesized that c-Myc plays a role in the regulation of gene expression by mTOR. Using the WB-F344 cells, we examined the effect of mTOR on members of the Myc signaling network using a multiplex RNase protection assay . Results (Figure 2) showed that rapamycin exposure for 6 hr induced a decrease in the steady state expression of Max and Mad 4. However, the effect was not as marked as the effect on the two genes intended for use as internal standards, ribosomal protein L32 and GAPDH. Of note, members of the Myc family were not affected. At 24 hr, there were no persistent effects on any of the components of the Myc signaling network, L32 or GAPDH. The upregulation of c-myc at the longer time point did not reach statistical significance.
WB-F344 cells were treated with rapamycin for 6 hr (Panel A) or 24 hr (Panel B). At the end of the incubation period, total RNA was prepared from triplicate wells for each condition. RNA (21 µg) was analyzed by multiplex RNase protection assay. Results are expressed as the mean + 1SD. The asterisks indicate a significant difference from the control group as determined by unpaired t-test. The densitometry units are arbitrary and should not be used to compare the relative expression of one gene versus another, only the effect of rapamycin. GAPDH, glyceraldehyde 3-phosphate dehydrogenase.
We next tested the hypothesis that c-Myc targets would be enriched among the set of mTORC1 targets in both the WB-F344 and WB311 cells. We compared the set of rapamycin-responsive genes in the two hepatic cell lines with a set of c-Myc targets identified in HOMycER12 fibroblasts during c-myc induction for up to 24 hr. Results (Figure 3, upper panel) showed that 699 of the 1,927 rapamycin-responsive genes identified in the WB-F344 cells were c-Myc targets. This was significantly greater than the expected number of 377 (P<0.000001). Examination of the distribution of genes among the clusters representing various temporal responses to c-myc induction revealed a higher than expected proportion of genes contained in clusters 1 (P<0.000001), 2 (P<0.000001) and 3 (P<0.01).
Microarray results identifying genes affected by rapamycin in WB-F344 cells (Upper Panel) and WB311 cells (Lower Panel) were compared to c-Myc targets. The latter were identified as genes whose expression was altered in response to c-myc induction by OHT in HOMycER12 fibroblasts. The expected (unfilled), observed (black), upregulated (red) and downregulated (green) c-Myc targets regulated in response to rapamycin are shown for the temporal response clusters 1, 2 and 3 in the HOMycER12 fibroblasts and for the total population of genes affected in these cells. *, P<0.05 versus corresponding control were determined by chi square analysis.
We performed an analysis to determine if mTORC1 signaling and c-Myc might induce parallel changes in gene expression and that rapamycin effect would reflect this (Figure 3, upper panel). For cluster 1, c-Myc targets that were rapidly and persistently downregulated following c-myc induction in the HOMycER12 fibroblasts, there was no difference between the number of c-Myc targets that were upregulated and downregulated in response to rapamycin in the WB-F344 cells. For cluster 2, which represents genes that were upregulated following c-myc induction, the majority of c-Myc targets in this cluster (268 of 293 probesets) were downregulated in response to rapamycin. For cluster 3 (genes that were rapidly downregulated then moved towards baseline but remained downregulated at 24 hr following c-myc induction), the majority of c-Myc targets (108 of 118 probesets) were downregulated in response to rapamycin. These results indicated limited consistency in the effects of mTORC1 inhibition and c-Myc on the expression of c-Myc target genes.
In the WB311 cell line (Figure 3, lower panel), 342 of 1130 rapamycin-modulated genes were present in the c-Myc target dataset. This number surpassed the expected number of 221, a difference that was significant (P<0.000001). A significant association between rapamycin effect and the effect of c-myc induction was confined to clusters 1 and 3. For cluster 1, of the 178 probesets that responded to rapamycin (versus the expected number of 111 probesets), the expression of 111 c-Myc targets was increased while 67 were repressed (P<0.000001). For cluster 3, the observed number of c-Myc targets (44 probesets) exceeded the expected number (37 probesets) (P<0.05). Approximately equal numbers were upregulated and downregulated.
To assess the specificity of the relationship between c-Myc and mTORC1 signaling, we performed a similar analysis using the set of 341 genes that are targets of NFκB. We chose NFκB because, like c-Myc, it is involved in the regulation of a large, diverse set of genes involved in a spectrum of cellular processes . The results of this comparison disclosed that only 27 of the 1,927 rapamycin-responsive genes in the WB-F344 cells were included among the NFκB targets. This number was not significantly greater than the expected number. A similar result was obtained for the WB311 cell line in which only 26 of the 1,130 rapamycin-responsive genes were included in the NFκB dataset.
E-Box Distribution in the Promoter Regions of Rapamycin-Modulated Genes
We hypothesized that the distribution of E-boxes would differ in the promoter regions of genes that were up-regulated versus down-regulated in response to rapamycin. For the population of rapamycin-responsive genes in the two hepatic cell lines, E-boxes showed a positional bias in that most were located within a region 100 nucleotides upstream of the transcriptional start site (Figures 4A and 4B). There was no apparent difference in the distribution of E-boxes in the two cell lines. In addition, the number of E-box-containing sites among promoters for rapamycin-responsive genes showed similar results for the two cell lines (Figures 4C and 4D). However, the number of promoters with two E-boxes was higher for genes that were downregulated by rapamycin in the WB-F344 cells compared to the genes that were upregulated. This was not the case for the WB311 cells.
Identification of the E-boxes and their distribution was performed as described in the methods section. The promoter sequences spanning −3 kb upstream to +500 bases downstream of the transcriptional start site (TSS) were used in this analysis. Distribution (frequency as a function of position relative to +1, the TSS) of each E-box sequence is shown for WB-F344 cells (Panel A) and WB311 cells (Panel B). The distribution of the number of E-boxes along the promoters of genes affected by rapamycin is shown for the WB-F344 cells (Panel C) and the WB311 cells (Panel D). Density (Y-axis) is a probability function representing the normalized frequency of E-boxes. Black lines denote the total number of E-boxes detected in the database, red lines represent E-boxes in the promoters of genes upregulated by rapamycin and blue lines represent the genes downregulated by rapamycin.
Dependence of Rapamycin Effect on c-Myc in Fibroblasts
To assess the dependence of rapamycin-induced changes in gene expression on c-Myc, we performed studies on two rat fibroblast cell lines, TGR-1 (c-myc +/+) and HO15.19 (c-myc −/−). Preliminary studies (data not shown) confirmed that the phosphorylation of ribosomal protein S6 (Ser235/236) was highly sensitive to inhibition by rapamycin in both cell lines. Studies using 3H-thymidine showed that both cell lines were resistant to the growth inhibitory effects of rapamycin (data not shown) based on the criteria that we had established using hepatic cells .
We hypothesized that the effect of rapamycin on gene expression would be c-Myc-dependent and that this would be reflected in there being fewer rapamycin-sensitive genes in the HO15.19 fibroblasts (c-myc−/−) than in TGR-1 cells (c-myc+/+). Both cell lines were exposed to DMSO vehicle or rapamycin (50 nM) for 24 hr. Total RNA was prepared and processed for microarray analysis. Results for the TGR-1 cells showed that 989 gene features (4.5% of the total number on the array) were affected by rapamycin with 521 sequence tags upregulated and 463 downregulated. In the HO15.19 fibroblasts 1,140 probes (5.2% of the total number of gene features on the array) were affected by rapamycin (553 upregulated and 590 downregulated). The number of modulated genes in the HO15.19 fibroblasts was greater than the number in the TGR-1 cells, thus refuting our hypothesis. This conclusion was further supported by the observation that the number of genes showing a 1.5-fold or greater change in expression was higher in the HO15.19 cells than in the TGR-1 cells (data not shown).
Based on the results in hepatic cell lines, we hypothesized that the genes affected by rapamycin in the TGR-1 and HO15.19 cells would show enrichment of c-Myc targets. Results (Figure 5) confirmed this hypothesis in both cells lines. In the TGR-1 cells, 430 of 989 rapamycin modulated genes were in the set of c-Myc targets, far exceeding the expected 192 genes (P<0.000001). As in the hepatic cell lines, the number of rapamycin-sensitive c-Myc targets identified in the two fibroblast lines in clusters 1, 2 and 3 exceeded the number expected.
TGR-1 cells (c-myc+/+; Upper Panel) and c-HO15.19 cells (c-myc −/−; Lower Panel) were treated with rapamycin (50 nM) for 24 hr. At the end of that time, cells were processed for the preparation of RNA, which was used for microarray analysis. The genes affected by rapamycin were compared to c-Myc targets that were identified as genes whose expression was altered in response to c-Myc induction by OHT in HOMycER12 fibroblasts. The expected (unfilled), observed (black), upregulated (red) and downregulated (green) c-Myc targets regulated in response to rapamycin are shown for the temporal response clusters 1, 2 and 3 in the HOMycER12 fibroblasts and for the total population of genes affected in these cells. *, P<0.05 versus corresponding control were determined by chi square analysis.
The direction of regulation in response to rapamycin was examined. For TGR-1 cells (Figure 5, upper panel) cluster 1 genes (downregulated in response to c-myc induction), rapamycin induced upregulation of the expression of 167 c-Myc targets while only 29 were downregulated. Among cluster 2 genes (upregulated in response to c-myc induction), rapamycin upregulated the expression of 23 c-Myc targets while 159 were downregulated. Similar results were obtained for the HO15.19 cell line (Figure 5, lower panel).
Pathway analysis revealed that rapamycin modulated the expression of genes involved in a number of biochemical and metabolic pathways in both the TGR-1 and HO15.19 fibroblasts (Table 3). These pathways included those involved in steroid, carbohydrate and amino acid metabolism. As was the case for the WB-F344 and WB311 hepatic cells , rapamycin affected the glycolysis/gluconeogenesis pathway with phosphoglycerol kinase 1 (Pgk1) being downregulated in both cell lines. This was the only case of a gene being regulated in a manner that determined activity of a pathway in all four cell lines.
Pathway network reconstruction analysis was also performed in order to identify critical genes affected by rapamycin among the interphase of connected pathways. This analysis has already been reported for the WB-F344 and WB311 cells . It showed that two critical genes, c-myc and ccnd1 (cyclin D1) were connected with critical pathways in both cell lines. In the TGR-1 cells treated with rapamycin, a number of overrepresented pathways were similarly interconnected to these two genes (Figure 6A). Both genes were upregulated by rapamycin in the TGR-1 cells. Neither was identified in the pathway network reconstruction analysis for the HO15.19 cell line (Figure 6B), perhaps consistent with the fact that these cells lack c-Myc.
The microarray analyses described for Figure 5 were analyzed for networks of pathways that were significantly affected by rapamycin. Pathways are represented by circles and genes by squares. Gray circles indicate non significant pathways. Red and blue circles indicate significant pathways that are overrepresented and underrepresented, respectively. The green tone on the squares indicates the degree of pathways membership (from light green representing genes connected to few pathways to dark green for hubs). Panel A shows the pathways and related critical genes affected by rapamycin in TGR-1 cells. Panel B shows the pathways affected by rapamycin in HO15.19 cells. The Mapk1 gene appeared as critical but was not connected to any significant pathways. No significant gene was associated with the overrepresented pathways.
The present studies were undertaken for the purpose of identifying new mechanisms by which signaling via the mTOR pathway regulates gene expression at the transcriptional level. Our starting point was a series of studies on two hepatic cell lines, one that is sensitive to the anti-proliferative effects of rapamycin and one that is not. We reasoned that the set of genes whose expression was modulated in response to rapamycin in both cell lines would represent genes whose control was independent of growth arrest. We further expected that examination of the promoter regions of these genes would allow for the identification of transcription factor binding domains for mTORC1-responsive transcription factors.
The results we obtained were unexpected. The only transcription factor binding sites that were identified contained E-boxes, the consensus sequence of which is CANNTG, with a palindromic canonical sequence of CACGTG , . We interpreted this result as indicating that rapamycin-induced inhibition of signaling by mTORC1 might affect transcriptional control by the c-Myc network . Such a conclusion was supported by the commonality in the biological functions of mTOR and c-Myc. Both regulate cell growth, metabolism, gene expression, ribosome biogenesis and protein translation , , , .
In order to test the hypothesis that there was a functional intersection between mTORC1 signaling and c-Myc regulation of gene expression, we interrogated the list of rapamycin-sensitive genes for their presence in a population of genes whose expression was regulated in response to the induction of c-myc in rat fibroblasts. This approach to identifying c-Myc targets involved not just the detection of changes in expression but also a kinetic expression profiling analysis . By using this approach, we were able to assess not just the intersection between the populations of genes regulated by c-Myc and mTORC1, but also the upregulation versus downregulation of these genes. The results of this analysis demonstrated that c-Myc targets were indeed over-represented among the population of rapamycin-sensitive genes. With regard to direction of regulation, there was a modest concordance that was consistent with co-regulation by c-Myc and mTORC1, two positive regulators of cell growth and proliferation. Furthermore, these observations pertained to both the rapamycin-sensitive and rapamycin-resistant cell lines. The significance of these observations was supported by a similar analysis using a population of genes identified as targets for NFκB. In this case, no relationship with mTORC1 effects was identified.
Based on the intersection between the populations of genes regulated by mTORC1 and c-Myc, we hypothesized that c-Myc might be a direct or indirect participant in the pathway by which mTORC1 regulates gene expression. To test this hypothesis, we characterized rapamycin-induced changes in gene expression in the HO15.19 cell line. These cells were originally derived from TGR-1 cells, which were in turn derived from the Rat-1 fibroblast cell line . HO15.19, made null for c-myc by homologous recombination , are an appropriate model system for studying the role of c-myc in a variety of cell processes. In addition, the TGR-1 and HO15.19 cells represented a second model system in which we were able to confirm the intersection in the regulation of gene expression by mTORC1 and c-Myc.
To further assess the relationship between c-Myc and mTORC1 in the regulation of transcription, we examined the distribution of E-boxes among rapamycin responsive genes in the WB-F344 and WB311 hepatic cells. The significance of E-boxes lies in their role in c-Myc function in that c-Myc regulation of target genes appears to involve direct binding to E-boxes. Mutations in the E-boxes of two c-Myc target genes, nucleolin and BN51, result in the abrogation of transcriptional activation by c-Myc . Mutations in E-boxes are also associated with the removal of the repression of gene activation induced by the c-Myc binding partner Mad . Further, recent studies have shown that upregulated c-Myc targets in the HOMycER12 fibroblasts induced by OHT were enriched for E-boxes (manuscript in preparation, Sedivy et al.). We found that the E-box distribution along the promoter region of rapamycin-regulated genes in the WB-F344 and WB311 cells was similar and that they resembled the distribution observed in drosophila  and humans . The E-box distribution in all of these cases tends to show location near the transcription start site. We found that the majority of promoters in rapamycin-regulated genes in the WB-F344 and WB311 cells contained one E-box. We did not find a difference in the number or distribution of E-boxes among genes that were upregulated versus downregulated in response to rapamycin. In summary, our data indicate that c-Myc and mTORC1 may overlap in their regulation of transcription via E-boxes, but that this overlap is independent of c-Myc itself.
Although c-Myc and mTOR are known to be central regulators of comparable biological processes, this is the first study, to our knowledge, that has identified a set of genes that are targets of both c-Myc and mTORC1. mTOR ,  and c-Myc  are known to regulate cellular metabolism and, more specifically, glucose metabolism , . We found that rapamycin affected various metabolic and biosynthetic pathways in the TGR-1 and HO15.19 cells, similar to our observations in the WB-F344 and WB311 cells. The glycolysis/gluconeogenesis pathway was affected by rapamycin in all four cell lines with the gene for phosphoglycerate kinase-1 (Pgk1) being downregulated in every case. Further study would be warranted to determine the significance of this observation.
With regard to the intersection between c-Myc and mTOR signaling, our pathway network reconstruction analyses indicated a role for c-myc in mTORC1 action in the WB-F344, WB311 and TGR-1 cell lines. Prior studies have shown that rapamycin can downregulate both the transcription  and translation – of c-myc in several cell types. It may be that the effect of rapamycin on the expression of c-myc and cyclin D is not uniform and is cell type specific. Other studies examining the intersection between c-Myc and mTOR have demonstrated an ability of c-Myc to modulate signaling through the mTOR pathway via transcriptional downregulation of the tuberous sclerosis complex genes , , which are negative regulators of mTOR-mediated translation activation. Finally, c-Myc has been shown to abrogate the regulation of translation by mTOR by controlling the expression and activity of the key translation regulator, 4EBP-1 .
In summary, we have demonstrated an intersection between mTORC1 and c-Myc regulation of gene expression. This was observed in both hepatic cells and fibroblasts. However, control of gene expression by mTORC1 was independent of c-Myc, indicating that the intersection between these two systems is likely a reflection of their biological function, not their mechanism of gene expression control. Furthermore, our inability to detect the involvement of specific transcription factor binding domains in the regulation of gene expression by mTORC1 may indicate the involvement of epigenetic mechanisms, microRNAs or both in mTORC1 signaling.
Identification of genes that are regulated in both the WB-F344 and WB311 cells in response to rapamycin. The X to the left of a row indicates that the gene is one that was co-regulated in the two cell lines and one for which an Ensembl ID allowed further analysis in Toucan2. Arrowheads indicate the direction of the effect of rapamycin (increase or decrease in expression following exposure to rapamycin). The magnitude of rapamycin effect is given as log-base 2 of the fold-change.
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We thank Ms. Joan Boylan for her contribution to several experiments, her expert advice and her insight into these studies.
Conceived and designed the experiments: RHJ JMS PAG. Performed the experiments: RHJ JAS. Analyzed the data: RHJ JSL MF GC NN JAS JMS. Wrote the paper: RHJ PAG. Mentored the first author, a graduate student: PAG.
- 1. Hall MN (2008) mTOR-what does it do? Transplant Proc 40: S5–S8.MN Hall2008mTOR-what does it do?Transplant Proc40S5S8
- 2. Polak P, Hall MN (2009) mTOR and the control of whole body metabolism. Curr Opin Cell Biol 21: 209–218.P. PolakMN Hall2009mTOR and the control of whole body metabolism.Curr Opin Cell Biol21209218
- 3. Sabatini DM (2006) mTOR and cancer: insights into a complex relationship. Nat Rev Cancer 6: 729–734.DM Sabatini2006mTOR and cancer: insights into a complex relationship.Nat Rev Cancer6729734
- 4. Guertin DA, Sabatini DM (2007) Defining the role of mTOR in cancer. Cancer Cell 12: 9–22.DA GuertinDM Sabatini2007Defining the role of mTOR in cancer.Cancer Cell12922
- 5. Harrison DE, Strong R, Sharp ZD, Nelson JF, Astle CM, et al. (2009) Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature 460: 392–395.DE HarrisonR. StrongZD SharpJF NelsonCM Astle2009Rapamycin fed late in life extends lifespan in genetically heterogeneous mice.Nature460392395
- 6. James MJ, Zomerdijk JC (2004) Phosphatidylinositol 3-kinase and mTOR signaling pathways regulate RNA polymerase I transcription in response to IGF-1 and nutrients. J Biol Chem 279: 8911–8918.MJ JamesJC Zomerdijk2004Phosphatidylinositol 3-kinase and mTOR signaling pathways regulate RNA polymerase I transcription in response to IGF-1 and nutrients.J Biol Chem27989118918
- 7. Erbay E, Park IH, Nuzzi PD, Schoenherr CJ, Chen J (2003) IGF-II transcription in skeletal myogenesis is controlled by mTOR and nutrients. J Cell Biol 163: 931–936.E. ErbayIH ParkPD NuzziCJ SchoenherrJ. Chen2003IGF-II transcription in skeletal myogenesis is controlled by mTOR and nutrients.J Cell Biol163931936
- 8. Mayer C, Zhao J, Yuan X, Grummt I (2004) mTOR-dependent activation of the transcription factor TIF-IA links rRNA synthesis to nutrient availability. Genes Dev 18: 423–434.C. MayerJ. ZhaoX. YuanI. Grummt2004mTOR-dependent activation of the transcription factor TIF-IA links rRNA synthesis to nutrient availability.Genes Dev18423434
- 9. Cunningham JT, Rodgers JT, Arlow DH, Vazquez F, Mootha VK, et al. (2007) mTOR controls mitochondrial oxidative function through a YY1-PGC-1alpha transcriptional complex. Nature 450: 736–740.JT CunninghamJT RodgersDH ArlowF. VazquezVK Mootha2007mTOR controls mitochondrial oxidative function through a YY1-PGC-1alpha transcriptional complex.Nature450736740
- 10. Abraham RT (2002) Identification of TOR signaling complexes: More TORC for the cell growth engine. Cell 111: 9–12.RT Abraham2002Identification of TOR signaling complexes: More TORC for the cell growth engine.Cell111912
- 11. Loewith R, Jacinto E, Wullschleger S, Lorberg A, Crespo JL, et al. (2002) Two TOR complexes, only one of which is rapamycin sensitive, have distinct roles in cell growth control. Mol Cell 10: 457–468.R. LoewithE. JacintoS. WullschlegerA. LorbergJL Crespo2002Two TOR complexes, only one of which is rapamycin sensitive, have distinct roles in cell growth control.Mol Cell10457468
- 12. Dever TE (2002) Gene-specific regulation by general translation factors. Cell 108: 545–556.TE Dever2002Gene-specific regulation by general translation factors.Cell108545556
- 13. Wullschleger S, Loewith R, Hall MN (2006) TOR signaling in growth and metabolism. Cell 124: 471–484.S. WullschlegerR. LoewithMN Hall2006TOR signaling in growth and metabolism.Cell124471484
- 14. Raught B, Gingras AC, Sonenberg N (2001) The target of rapamycin (TOR) proteins. Proc Natl Acad Sci U S A 98: 7037–7044.B. RaughtAC GingrasN. Sonenberg2001The target of rapamycin (TOR) proteins.Proc Natl Acad Sci U S A9870377044
- 15. Xie MW, Jin F, Hwang H, Hwang S, Anand V, et al. (2005) Insights into TOR function and rapamycin response: chemical genomic profiling by using a high-density cell array method. Proc Natl Acad Sci U S A 102: 7215–7220.MW XieF. JinH. HwangS. HwangV. Anand2005Insights into TOR function and rapamycin response: chemical genomic profiling by using a high-density cell array method.Proc Natl Acad Sci U S A10272157220
- 16. Gstaiger M, Luke B, Hess D, Oakeley EJ, Wirbelauer C, et al. (2003) Control of nutrient-sensitive transcription programs by the unconventional prefoldin URI. Science 302: 1208–1212.M. GstaigerB. LukeD. HessEJ OakeleyC. Wirbelauer2003Control of nutrient-sensitive transcription programs by the unconventional prefoldin URI.Science30212081212
- 17. Grewal SS, Evans JR, Edgar BA (2007) Drosophila TIF-IA is required for ribosome synthesis and cell growth and is regulated by the TOR pathway. J Cell Biol 179: 1105–1113.SS GrewalJR EvansBA Edgar2007Drosophila TIF-IA is required for ribosome synthesis and cell growth and is regulated by the TOR pathway.J Cell Biol17911051113
- 18. Jimenez RH, Boylan JM, Lee JS, Francesconi M, Castellani G, et al. (2009) Rapamycin response in tumorigenic and non-tumorigenic hepatic cell lines. PLoS ONE 4: e7373.RH JimenezJM BoylanJS LeeM. FrancesconiG. Castellani2009Rapamycin response in tumorigenic and non-tumorigenic hepatic cell lines.PLoS ONE4e7373
- 19. Tsao MS, Smith JD, Nelson KG, Grisham JW (1984) A diploid epithelial cell line from normal adult rat liver with phenotypic properties of ‘oval’ cells. Exp Cell Res 154: 38–52.MS TsaoJD SmithKG NelsonJW Grisham1984A diploid epithelial cell line from normal adult rat liver with phenotypic properties of ‘oval’ cells.Exp Cell Res1543852
- 20. Lee LW, Tsao MS, Grisham JW, Smith GJ (1989) Emergence of neoplastic transformants spontaneously or after exposure to N-methyl-N'-nitro-N-nitrosoguanidine in populations of rat liver epithelial cells cultured under selective and nonselective conditions. Am J Pathol 135: 63–71.LW LeeMS TsaoJW GrishamGJ Smith1989Emergence of neoplastic transformants spontaneously or after exposure to N-methyl-N'-nitro-N-nitrosoguanidine in populations of rat liver epithelial cells cultured under selective and nonselective conditions.Am J Pathol1356371
- 21. Mateyak MK, Obaya AJ, Adachi S, Sedivy JM (1997) Phenotypes of c-Myc-deficient rat fibroblasts isolated by targeted homologous recombination. Cell Growth Differ 8: 1039–1048.MK MateyakAJ ObayaS. AdachiJM Sedivy1997Phenotypes of c-Myc-deficient rat fibroblasts isolated by targeted homologous recombination.Cell Growth Differ810391048
- 22. Aerts S, Van Loo P, Thijs G, Mayer H, de Martin R, et al. (2005) TOUCAN 2: the all-inclusive open source workbench for regulatory sequence analysis. Nucleic Acids Res 33: W393–W396.S. AertsP. Van LooG. ThijsH. MayerR. de Martin2005TOUCAN 2: the all-inclusive open source workbench for regulatory sequence analysis.Nucleic Acids Res33W393W396
- 23. Veerla S, Hoglund M (2006) Analysis of promoter regions of co-expressed genes identified by microarray analysis. BMC Bioinformatics 7: 384.S. VeerlaM. Hoglund2006Analysis of promoter regions of co-expressed genes identified by microarray analysis.BMC Bioinformatics7384
- 24. Morrish F, Neretti N, Sedivy JM, Hockenbery DM (2008) The oncogene c-Myc coordinates regulation of metabolic networks to enable rapid cell cycle entry. Cell Cycle 7: 1054–1066.F. MorrishN. NerettiJM SedivyDM Hockenbery2008The oncogene c-Myc coordinates regulation of metabolic networks to enable rapid cell cycle entry.Cell Cycle710541066
- 25. Kim J, Lee JH, Iyer VR (2008) Global identification of Myc target genes reveals its direct role in mitochondrial biogenesis and its E-box usage in vivo. PLoS ONE 3: e1798.J. KimJH LeeVR Iyer2008Global identification of Myc target genes reveals its direct role in mitochondrial biogenesis and its E-box usage in vivo.PLoS ONE3e1798
- 26. Hochberg Y, Benjamini Y (1990) More powerful procedures for multiple significance testing. Stat Med 9: 811–818.Y. HochbergY. Benjamini1990More powerful procedures for multiple significance testing.Stat Med9811818
- 27. Francesconi M, Remondini D, Neretti N, Sedivy JM, Cooper LN, et al. (2008) Reconstructing networks of pathways via significance analysis of their intersections. BMC Bioinformatics 9: Suppl 4S9.M. FrancesconiD. RemondiniN. NerettiJM SedivyLN Cooper2008Reconstructing networks of pathways via significance analysis of their intersections.BMC Bioinformatics9Suppl 4S9
- 28. Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30: 207–210.R. EdgarM. DomrachevAE Lash2002Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.Nucleic Acids Res30207210
- 29. Sanders JA, Gruppuso PA (2005) Coordinated Regulation of c-Myc and Max in Rat Liver Development. Am J Physiol Gastrointest Liver Physiol 290: G145–G155.JA SandersPA Gruppuso2005Coordinated Regulation of c-Myc and Max in Rat Liver Development.Am J Physiol Gastrointest Liver Physiol290G145G155
- 30. Cortes SM, Rodriguez FV, Sanchez PI, Perona R (2008) The role of the NFkappaB signalling pathway in cancer. Clin Transl Oncol 10: 143–147.SM CortesFV RodriguezPI SanchezR. Perona2008The role of the NFkappaB signalling pathway in cancer.Clin Transl Oncol10143147
- 31. Ephrussi A, Church GM, Tonegawa S, Gilbert W (1985) B lineage–specific interactions of an immunoglobulin enhancer with cellular factors in vivo. Science 227: 134–140.A. EphrussiGM ChurchS. TonegawaW. Gilbert1985B lineage–specific interactions of an immunoglobulin enhancer with cellular factors in vivo.Science227134140
- 32. Sen R, Baltimore D (1986) Multiple nuclear factors interact with the immunoglobulin enhancer sequences. Cell 46: 705–716.R. SenD. Baltimore1986Multiple nuclear factors interact with the immunoglobulin enhancer sequences.Cell46705716
- 33. Hay N, Sonenberg N (2004) Upstream and downstream of mTOR. Genes Dev 18: 1926–1945.N. HayN. Sonenberg2004Upstream and downstream of mTOR.Genes Dev1819261945
- 34. Eilers M, Eisenman RN (2008) Myc's broad reach. Genes Dev 22: 2755–2766.M. EilersRN Eisenman2008Myc's broad reach.Genes Dev2227552766
- 35. Prouty SM, Hanson KD, Boyle AL, Brown JR, Shichiri M, et al. (1993) A cell culture model system for genetic analyses of the cell cycle by targeted homologous recombination. Oncogene 8: 899–907.SM ProutyKD HansonAL BoyleJR BrownM. Shichiri1993A cell culture model system for genetic analyses of the cell cycle by targeted homologous recombination.Oncogene8899907
- 36. Greasley PJ, Bonnard C, Amati B (2000) Myc induces the nucleolin and BN51 genes: possible implications in ribosome biogenesis. Nucleic Acids Res 28: 446–453.PJ GreasleyC. BonnardB. Amati2000Myc induces the nucleolin and BN51 genes: possible implications in ribosome biogenesis.Nucleic Acids Res28446453
- 37. Hulf T, Bellosta P, Furrer M, Steiger D, Svensson D, et al. (2005) Whole-genome analysis reveals a strong positional bias of conserved dMyc-dependent E-boxes. Mol Cell Biol 25: 3401–3410.T. HulfP. BellostaM. FurrerD. SteigerD. Svensson2005Whole-genome analysis reveals a strong positional bias of conserved dMyc-dependent E-boxes.Mol Cell Biol2534013410
- 38. Shaw RJ, Cantley LC (2006) Ras, PI(3)K and mTOR signalling controls tumour cell growth. Nature 441: 424–430.RJ ShawLC Cantley2006Ras, PI(3)K and mTOR signalling controls tumour cell growth.Nature441424430
- 39. Buller CL, Loberg RD, Fan MH, Zhu Q, Park JL, et al. (2008) A GSK-3/TSC2/mTOR pathway regulates glucose uptake and GLUT1 glucose transporter expression. Am J Physiol Cell Physiol 295: C836–C843.CL BullerRD LobergMH FanQ. ZhuJL Park2008A GSK-3/TSC2/mTOR pathway regulates glucose uptake and GLUT1 glucose transporter expression.Am J Physiol Cell Physiol295C836C843
- 40. Fingar DC, Blenis J (2004) Target of rapamycin (TOR): an integrator of nutrient and growth factor signals and coordinator of cell growth and cell cycle progression. Oncogene 23: 3151–3171.DC FingarJ. Blenis2004Target of rapamycin (TOR): an integrator of nutrient and growth factor signals and coordinator of cell growth and cell cycle progression.Oncogene2331513171
- 41. Ikezoe T, Nishioka C, Bandobashi K, Yang Y, Kuwayama Y, et al. (2007) Longitudinal inhibition of PI3K/Akt/mTOR signaling by LY294002 and rapamycin induces growth arrest of adult T-cell leukemia cells. Leuk Res 31: 673–682.T. IkezoeC. NishiokaK. BandobashiY. YangY. Kuwayama2007Longitudinal inhibition of PI3K/Akt/mTOR signaling by LY294002 and rapamycin induces growth arrest of adult T-cell leukemia cells.Leuk Res31673682
- 42. Murooka TT, Rahbar R, Fish EN (2009) CCL5 promotes proliferation of MCF-7 cells through mTOR-dependent mRNA translation. Biochem Biophys Res Commun 387: 381–386.TT MurookaR. RahbarEN Fish2009CCL5 promotes proliferation of MCF-7 cells through mTOR-dependent mRNA translation.Biochem Biophys Res Commun387381386
- 43. Ravitz MJ, Chen L, Lynch M, Schmidt EV (2007) c-myc Repression of TSC2 contributes to control of translation initiation and Myc-induced transformation. Cancer Res 67: 11209–11217.MJ RavitzL. ChenM. LynchEV Schmidt2007c-myc Repression of TSC2 contributes to control of translation initiation and Myc-induced transformation.Cancer Res671120911217
- 44. Schmidt EV, Ravitz MJ, Chen L, Lynch M (2009) Growth controls connect: interactions between c-myc and the tuberous sclerosis complex-mTOR pathway. Cell Cycle 8: 1344–1351.EV SchmidtMJ RavitzL. ChenM. Lynch2009Growth controls connect: interactions between c-myc and the tuberous sclerosis complex-mTOR pathway.Cell Cycle813441351
- 45. Balakumaran BS, Porrello A, Hsu DS, Glover W, Foye A, et al. (2009) MYC activity mitigates response to rapamycin in prostate cancer through eukaryotic initiation factor 4E-binding protein 1-mediated inhibition of autophagy. Cancer Res 69: 7803–7810.BS BalakumaranA. PorrelloDS HsuW. GloverA. Foye2009MYC activity mitigates response to rapamycin in prostate cancer through eukaryotic initiation factor 4E-binding protein 1-mediated inhibition of autophagy.Cancer Res6978037810