in Prostate Cancer Gene Expression : Identification of ...

Characterization of Epithelial Senescence by Serial Analysis of Gene Expression : Identification of Genes Potentially Involved in Prostate Cancer Gerold Untergasser, Heike B. Koch, Antje Menssen, et al. Cancer Res 2002;62:6255-6262.

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[CANCER RESEARCH 62, 6255– 6262, November 1, 2002]

Characterization of Epithelial Senescence by Serial Analysis of Gene Expression: Identification of Genes Potentially Involved in Prostate Cancer1 Gerold Untergasser, Heike B. Koch, Antje Menssen, and Heiko Hermeking2 Molecular Oncology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18 A, D-82152 Martinsried/Munich, Germany

ABSTRACT Evasion of cellular senescence is required for the immortal phenotype of tumor cells. The tumor suppressor genes p16INK4A, pRb, and p53 have been implicated in the induction of cellular senescence. To identify additional genes and pathways involved in the regulation of senescence in prostate epithelial cells (PrECs), we performed serial analysis of gene expression (SAGE). The gene expression pattern of human PrECs arrested because of senescence was compared with the pattern of early passage cells arrested because of confluence. A total of 144,137 SAGE tags representing 25,645 unique mRNA species was collected and analyzed: 157 mRNAs (70 with known function) were up-regulated and 116 (65 with known function) were down-regulated significantly in senescent PrECs (P < 0.05; fold difference >2.5). The differential regulation of an exemplary set of genes during senescence was confirmed by quantitative realtime PCR in PrECs derived from three different donors. The results presented here provide the molecular basis of the characteristic changes in morphology and proliferation observed in senescent PrECs. Furthermore, the differentially expressed genes identified in this report will be instrumental in the further analysis of cellular senescence in PrECs and may lead to the identification of tumor suppressor genes and proto-oncogenes involved in the development of prostate cancer.

INTRODUCTION Mammalian somatic cells have a limited proliferative capacity when cultivated in vitro. For instance, human fibroblasts stop dividing after 50 –70 population doublings and enter a terminal arrest state termed replicative senescence (1). Senescent fibroblasts are strongly enlarged and refractory to mitogen stimulation. However, they are metabolically active and survive in culture for several month. A similar limitation of proliferative capacity has been observed for most other cell types (2, 3). Replicative senescence is induced by progressive telomere shortening, which occurs during each cell division (4). Telomere erosion presumably generates a DNA damage signal, which leads to activation of p53 and subsequent transcriptional induction of the cdk3 inhibitor p21CIP1. Therefore, prevention of telomere shortening by ectopic expression of the catalytic subunit of telomerase (hTERT) is sufficient to immortalize primary cells in vitro provided they are cultivated under the appropriate conditions. Induction of a senescence-like phenotype also occurs after aberrant mitogenic signaling and after environmental and genotoxic insults. This form of senescence has been termed cellular senescence as opposed to telomere-associated replicative senescence (5). Similar to apoptosis, cellular senescence is thought to be a mechanism of tumor suppression because it prevents the outgrowth of cells that have Received 4/26/02; accepted 9/5/02. 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. 1 Supplementary data for this article are available at Cancer Research Online (http:// cancerres.aacrjournals.org). 2 To whom requests for reprints should be addressed, at Max-Planck-Institute of Biochemistry, Molecular Oncology, Am Klopferspitz 18 A, D-82152 Martinsried/Munich, Germany. Phone: 49-(0)-89-8578-2875; Fax: 49-(0)-89-8578-2540; E-mail: herme@ biochem.mpg.de. 3 The abbreviations used are: Cdk, cyclin-dependent kinase; PrEC, prostate epithelial cell; SAGE, serial analysis of gene expression; qPCR, quantitative real-time PCR; ECM, extracellular matrix; TNF, tumor necrosis factor; TRAIL, TNF-related apoptosis-inducing ligand.

acquired mutations in genes rendering them cancerous (6). Consistent with this model, several tumor suppressor genes (e.g., p16) or their products (p53) are activated at the onset of cellular senescence. In addition, mice engineered to display elevated p53 activity show premature aging and a drastically decreased incidence of cancer, supporting a role of cellular senescence as a tumor suppressive mechanism relevant for the whole organism (7). Recently, it has been shown that mammary epithelial cells have the capacity to spontaneously escape replicative senescence and enter a phase of genomic instability, which may give rise to immortal cells (8). According to calculations by Morris (9), a similar evasion of replicative senescence has to occur for the development of any epithelial cancer. Complicating the issue, the presence of senescent fibroblasts promotes the proliferation of premalignant and malignant but not normal epithelial cells presumably by generating an altered microenvironment (10). Therefore, Krtolica et al. (10) suggested that senescence may promote carcinogenesis in aged organisms while it protects against cancer early in life. Prostatic cancer is the most frequent malignancy in the United States and the second leading cause of cancer deaths in men today (11–14). Among a variety of environmental and genetic factors favoring the development of prostatic cancer, aging is the most significant risk factor. It has been estimated that 15–30% of males over the age of 50 and as many as 80% of the males over the age of 80 harbor clinically undetected foci of prostate cancer (15). On the basis of the in vivo expression of pH 6.0 specific ␤-galactosidase, a marker of cellular senescence (16), it has been suggested that the accumulation of senescent prostate epithelial cells within prostatic glands might play a role in the development of prostatic diseases (17). The characterization of senescence in epithelial cells is still in its beginning. However, a detailed characterization of senescence in epithelial cells is necessary to understand how carcinoma circumvent this program. This approach may allow to identify genes involved in the development of prostate cancer, a disease for which relatively few causal genetic events are known. Furthermore, changes in gene expression during senescence of PrECs may provide insights into the aging mechanisms of the prostate. To characterize genome-wide expression during senescence of PrECs, we used SAGE, a quantitative method developed by Velculescu et al. (18). Here, we describe differentially expressed genes identified by SAGE, which presumably represent components of pathways and mechanisms involved in the induction and maintenance of senescence. Genetic inactivation or deregulation of these genes may lead to immortalization and neoplastic transformation of PrECs. MATERIALS AND METHODS

Cell Culture. PrECs used for SAGE were derived from a 17-year-old accident victim (Clonetics, San Diego, CA). PrECs were cultivated in PrEC growth medium (Clonetics) on collagen type I vented flasks (BioCoat; BD Falcon, Bedford, MA) according to the supplier’s instructions. PrECs were passaged at ⬃70% confluence by splitting 1:3 using collagenase 1S (Sigma, Deisenhofen, Germany). For qPCR analysis, additional PrEC samples were obtained from two prostate cancer patients (patient 1: 56 years old; patient 2: 63 years old). After 6255


EXPRESSION PROFILING OF SENESCENCE IN PROSTATE EPITHELIAL CELLS

radical prostatectomy, tissue wedges free of malignant cells were removed from the transition zone. These explants were minced into organoids of 1 mm3 and seeded on collagen I-coated plates in PrEC growth medium, allowing a homogeneous epithelial cell population to grow out. These cytokeratin-positive cells were passaged until senescence. Western Blots. Antibodies specific for p21 (clone: 6B6) were obtained from BD PharMingen (Bedford, MA). p16- (clone: C-20), p15- (clone: C-20), p53- (clone: Pab 1801), p27- (clone: C-19), or ␣-tubulin- (clone: TU-02) specific antibodies were obtained from Santa Cruz Biotechnology (Santa Cruz, CA). Enhanced chemiluminescence signals were detected on a Image Station 440CF (Kodak-Perkin-Elmer, Boston, MA). MicroSAGE. MicroSAGE was performed according to a protocol (version 1.0e) accessible online.4 We included additional purification steps of DNA intermediates after each PAGE step using Sephadex G-25 columns to ensure complete removal of contaminants. In brief, mRNA was isolated from human PrECs with the Dynabeads mRNA Direct Kit (Dynal, Smestad, Norway), and cDNA was synthesized on magnetic particles using the Superscript Choise System (Invitrogen, Groningen, the Netherlands). After cleaving the cDNA with NlaIII, linkers containing recognition sites for BsmFI were ligated to the cDNA. Linker tags were released by BsmFI digestion from the magnetic particles, ligated, and a 102-bp fragment was amplified with biotinylated primers. Ditags (⬃26 bp) were released by NlaIII cleavage. Biotinylated linkers were completely removed using streptavidin-linked magnetic beads and subsequent PAGE purification. The ditags were concatenated, and concatemers of 500 – 800 bp were subcloned into pZERO (Invitrogen). After colony PCR, products ⬎500 bp were sequenced using BigDye-terminator V2.0 reagents (Applied Biosystems, Lincoln, CA). Products were purified using Sephadex G-50 in filter plates (Millipore, Bedford, MA) and analyzed on an automated capillary DNA Sequencer (3700; Applied Biosystems). The results were analyzed using the SAGE2000 software provided by Dr. Ken Kinzler (Johns Hopkins University Medical School, Baltimore, MD). To exclude tags generated by sequencing errors, only tags that occurred at least twice were included in the analysis. After statistical analysis using Monte Carlo simulations, SAGE tags (P ⬍ 0.05; differential regulation ⬎2.5-fold) were assigned to cDNAs using the Unigene database (release 03/01). Tags with multiple matches were additionally analyzed after retrieving the 11th base of the tag. Matching and position of all tags listed in Tables 1 and 2 were confirmed using the tag-to-gene-mapper function provided online.5 qPCR. qPCR analysis was performed as described in detail by Menssen and Hermeking (19). The Light Cycler, software version 3.5.2, FastStart DNA Master SYBR Green I, and cDNA synthesis reagents were used according to the manufacturer’s instructions (Roche Applied Science, Mannheim, Germany). A total of 1 ␮g of RNA was reverse transcribed into cDNA. PCR efficiency of different primer pairs was determined using logarithmic dilutions of cDNA templates. After determining the slope of the reaction over a range of 20 –32 cycles, PCR primer efficiency was calculated according to the equation: E ⫽ 10⫺1/slope. Specificity of PCR products was confirmed by melting curve analysis, gel analysis, and direct sequencing (DNA Analyzer 310; Applied Biosystems). Primer sequences and examples of qPCR raw data are available as supplemental material. qPCR determinations were normalized with primers specific for eukaryotic elongation factor 1 ␣-1, which was equally represented in both SAGE libraries (316:369 tags). The difference in gene expression was calculated incorporating the efficiency (E) of each primer pair according to Pfaffl (20):

共EgeneX⵩⌬CP共confl. cDNA ⫺ senesc. cDNA兲geneX兲/ 共EELF1␣⵩⌬CP共confl. cDNA ⫺ senesc. cDNA兲ELF1␣兲 ⫽ fold induction RESULTS AND DISCUSSION Senescence of Human PrECs. Human PrECs derived from a 17-year-old accident victim were cultivated until they ceased to proliferate (10. passage, ⬃30 population doublings). During serial cultivation, the frequency of cells showing markers of senescence as 4 5

Internet address: www.sagenet.org. Internet address: www.ncbi.nlm.nih.gov/SAGE/SAGEtag.cgi.

cellular enlargement (Fig. 1a) and positive staining for ␤-galactosidase at pH 6.0 (data not shown) increased to ⬎90% of the population. The protein levels of the cdk inhibitor p16INK4A increased during senescence of PrECs (Fig. 1b). Furthermore, a minor increase in the protein levels of the cdk inhibitor p15INK4B could be detected (Fig. 1b). However, no significant changes in the protein levels of p53, its transcriptional target, the cdk inhibitor p21CIP1, and the cdk inhibitor p27KIP1 could be detected as PrECs became senescent (Fig. 1b). qPCR allowed to detect induction of p16INK4A mRNA in senescent PrECs (Fig. 1c). Expression of p15INK4B mRNA was induced in senescent PrECs, whereas p21CIP1 and p27KIP1 mRNA levels were not significantly altered (Fig. 1c). The qPCR results were in accordance with the Western blot analysis shown in Fig. 1b. Connective tissue growth factor, a gene previously found to be induced during senescence of PrECs (21), was induced as detected by qPCR (Fig. 1c). Induction of p16INK4A and unchanged p21CIP1 expression in senescent PrECs has been reported previously (22, 23). The lack of p53/p21CIP1 activation suggests that the cessation of proliferation observed in senescent PrECs was not because of shortening of telomeres and a subsequent DNA damage-mediated cell cycle arrest. Analysis of Senescence in PrECs Using MicroSAGE. To acquire a comprehensive, unbiased picture of changes in gene expression during senescence, SAGE was used. With SAGE, sequence tags of 10 –11 bp from the 3⬘-end of each transcript are isolated, concatenated, and sequenced to generate so-called SAGE libraries (18). The abundance of a specific tag in a SAGE library is proportional to the expression level of its corresponding mRNA. Because of the limited number of early passage PrECs, a protocol adapted to small amounts of mRNA was used (MicroSAGE, for details see “Materials and Methods”). A SAGE library of 72,068 tags was generated from subconfluent, terminally arrested, senescent PrECs at passage 10 and compared with a library of 72,069 tags derived from PrECs arrested because of confluence at passage 3. Both cell populations were cultivated in the presence of growth factors. A comparison of senescent to exponentially proliferating, early passage PrECs was avoided because many genes would have been differentially regulated because of the drastically different growth state and cell cycle distribution of arrested versus proliferating cells. The 144,137 SAGE tags collected in total correspond to 25,645 unique mRNA species. Using statistical analysis by Monte Carlo simulation, we determined that 273 tags showed significant differential expression (P ⬍ 0.05; fold difference ⬎2.5). The complete set of SAGE data can be accessed online.6 This web site allows the analysis and comparison of the differentially expressed tags/genes identified here with numerous other SAGE studies (24). Confirmation of SAGE Results by qPCR. To estimate the accuracy of the MicroSAGE analysis and to determine whether differential regulation of the identified transcripts occurs generally during senescence, qPCR was used to determine the abundance of exemplary transcripts in PrECs from three different donors (Fig. 2): differential regulation during senescence as detected by MicroSAGE was confirmed for all genes tested and is discussed in detail below. Therefore, the MicroSAGE results accurately reflect the levels of gene expression in the two cell populations analyzed. Furthermore, the changes in gene expression observed by SAGE in the senescent PrECs from one donor were also observed in senescent PrECs from two additional donors (Fig. 2). These results suggest that most of the changes in gene expression detected by SAGE in this study generally occur in senescent human PrECs. 6 Internet address: www.cgap.nci.nih.gov/SAGE. Library designations: “SAGE㛭prostate㛭primary㛭B㛭senescent” and “SAGE㛭prostate㛭primary㛭B㛭confluent.”

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diploid fibroblasts (28, 29), and muscle cells (30). Indeed, we identified several examples where similar senescence-specific gene expression could be found (Tables 1 and 2, right column): e.g., PAI-1 appears as a gene universally up-regulated during senescence in different cell types (Table 1). However, most of the changes detected here are specific for PrECs (Tables 1 and 2). The tag for ␤-galactosidase (TTACTTTTTT, Hs. 79222) was not significantly differentially regulated (8:13 tags), which is consistent with the hypothesis that an increase in lysosomal mass is responsible for the increase in ␤-galactosidase activity observed in senescent cells. Cell Cycle Regulation. Irreversible cell cycle arrest in the presence of otherwise mitogenic growth factors is a hallmark of senescence. The changes in gene expression we detected by MicroSAGE suggest the involvement of several key regulators in the establishment and maintenance of cell cycle arrest in senescent PrECs (Tables 1 and 2). It has been shown previously that ectopic expression of the

Fig. 1. Senescence of human PrECs. PrECs were cultivated in collagen type I-treated flasks. Cells were passaged by splitting in a 1:3 ratio until the population ceased to expand (passage 10, ⬃30 population doublings). a, morphology of PrECs during cultivation. Phase contrast images of early passage (left panel) and late passage cells (right panel, ⫻100 magnification). b, protein levels of cdk inhibitors and p53. Protein extracts were prepared from ⬃70% confluent cells of passage 5– 8 and subjected to Western blot analysis. See “Materials and Methods” for details. Total protein (50 ␮g) was loaded. ␣-Tubulin served as loading and transfer control. c, qPCR analysis of gene expression in senescent PrECs. The results shown correspond to the average of four measurements. For details, see “Materials and Methods.”

Classification of Senescence-specific Changes in Gene Expression. Differentially expressed SAGE tags were matched to the cDNAs of the Unigene database (release 03/01). Those transcripts, which corresponded to known genes and unambiguously contained the SAGE tag next to the most 3⬘ NlaIII-site, were sorted according to their function (Tables 1 and 2). Seventy genes induced in senescent PrECs are listed in Table 1, whereas 65 genes repressed during senescence are depicted in Table 2. Of the 157 tags significantly induced during senescence, 87 tags matched to functionally uncharacterized transcripts or had no matches in the database. Among the 116 repressed tags were 51 tags that had no functional assignment or matches in the database. Because we reasoned that transcripts up-regulated during senescence may be targets for down-regulation during tumor progression (and vice versa), the expression data were compared with previously published studies or public SAGE data analyzing differential gene expression in prostate cancer (Refs. 25, 26; Tables 1 and 2, right column). For a number of genes, e.g., DKK3, the proposed correlation could be confirmed. These interesting cases are discussed below. We also compared the SAGE results obtained here with other profiling studies on senescence performed with PrECs (27), human

Fig. 2. Confirmation of tag-to-gene assignments and reproducibility in PrECs samples from three different donors using quantitative PCR. Fold induction/repression indicates the differences in expression between confluent, early passage (2.–3.) and senescent, late passage (10.–12.) PrECs. The numbers of corresponding SAGE-tags in the respective libraries (confluent:senescent) are indicated below the gene symbol. qPCR analysis was performed on cDNA derived from PrECs, which were also used for SAGE (PrEC-SAGE), and from two additional donors [PrEC1 (56 years old) and PrEC2 (63 years old)]. Each bar corresponds to the average of 2– 4 measurements. For details, see “Materials and Methods.” a, transcripts induced during senescence of PrECs. b, transcripts repressed during senescence of PrECs. ACTB (31:26) served as an example of a transcript not altered in abundance.

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Table 1 Functional classification of genes up-regulated during cellular senescence Seventy significantly up-regulated tags are shown. The genes matching to these tags were assigned to functional classes. For some tags, the 11. base, which was retrieved using the SAGE2000 software, is indicated. The column “tag# con.” indicates the number of tags in the SAGE library derived from confluent, early passage PrECs, whereas the column labelled “tag# sen.” gives the tag abundance in the senescent PrECs derived library. P chance was obtained by Monte Carlo simulation. The right column indicates references of previously published expression data for the respective transcript, which shows a correlation to this study. A ⫽ SAGE analysis of normal prostate tissue and prostate tumor tissue obtained by manual microdissection of frozen tissue. These SAGE libraries are “SAGE_PR317_normal prostate” and “SAGE_PR317_prostate_tumor” accessible online.6 Functional class tag sequence

Tag# con.

Tag# sen.

Fold ind.

2 1

11 7

5.5 7

0.0124 0.0370

B-cell translocation gene 1, antiproliferative Cyclin D1

Hs.77054 Hs.82932

Cell cycle inhibition Cell cycle regulation

27 6 23 0 4 1 6 5

106 21 61 8 16 7 15 15

3.9 3.5 2.6 ⬎8 4 7 2.5 3

0.0000 0.0030 0.0000 0.0037 0.0056 0.0370 0.0400 0.0203

Plasminogen activator inhibitor type 1, PAI-1 Plasminogen activator inhibitor type 1, PAI-1 Cathepsin B Fibronectin 1 Microfibrillar-associated protein 2 Procollagen-lysine 2-oxoglutarate 5-dioxygenase 2 Matrix metalloproteinase 14, MMP-14 ␤4 integrin

Hs.82085 Hs.82085 Hs.297939 Hs.287820 Hs.83551 Hs.41270 Hs.2399 Hs.85266

ECM remodeling ECM remodeling Protease Cell adhesion/shape Extracellular matrix Collagen biosynthesis Protease Cell-matrix contact

Cellular shape and motility TCCAAATCGA TTAAAGATTT TGTAGAAAAA A GACTGTGCCA TCACCGGTCA CGAATGTCCT CAGCTGGCCC GCTAAGGAGA CCTTGCTTTT

5 4 1 6 1 3 0 1 0

21 15 9 18 8 11 6 8 6

4.2 3.7 9 3 8 3.6 ⬎6 8 ⬎6

0.0013 0.0101 0.0106 0.0122 0.0178 0.0301 0.0173 0.0178 0.0173

Vimentin Tropomyosin 1 ␣ ␤-tubulin Dynein, cytoplasmic Gelsolin, cytoplasmic, and secreted Keratin 6B Fibulin 1 RAC1, ras-related cdc42

Hs.297753 Hs.77899 Hs.336780 Hs.5120 Hs.290070 Hs.335952 Hs.79732 Hs.173737 Hs.146409

Intermediate filament Cytoskeleton Microtubuli Intracellular transport Actin fragmentation Intermediate filament Fibronectin-receptor bdg. Lamellipodia formation Filopodia formation

Transcription CCTACCACCA CTGCCCCACA CACAGGCAAA TTCCGGTTCC CCTTTCACAC

0 0 7 6 3

6 6 18 16 10

⬎6 ⬎6 2.5 2.6 3.3

0.0173 0.0173 0.0235 0.0236 0.0483

NFKB (p65)-associated inhibitor SKI (oncoprotein)-interacting protein, SKIP Basic leucine-zipper protein BZAP45 Nucleobindin 1 General transcription factor TF II-i

Hs.324051 Hs.79008 Hs.155291 Hs.172609 Hs.278589

Transcriptional regulation DNA binding protein Transcription factor DNA binding protein INR binding protein

Signaling CCCTCAGCAC ATGCTCCCTG A TGCAATAGGG CTTTCTTTGA G GGCCATCTCT CACGCAATGC T GTTTCCAAAA

2 0 0 3 5 3 6

16 8 5 11 14 10 20

8 ⬎8 ⬎5 3.6 2.8 3.3 3.3

0.0006 0.0037 0.0297 0.0301 0.0323 0.0483 0.0049

Annexin A8 90K serum protein (lectin 3 binding protein) Protein phosphatase 1, regulatory subunit 12C Dickkopf 3, Dkk3 14-3-3 tau Amino terminal enhancer of split, G-protein Gap junction protein, ␤ 2, connexin 26

Hs.87268 Hs.79339 Hs.235975 Hs.4909 Hs.74405 Hs.244 Hs.323733

Signal transduction Signal transduction Signal transduction wnt-signaling inhibition Signal transduction Signaling Cell-cell channels

Cytokine/growth factor GACGGCGCAG TAAAAATAAC CCACTACACT C

0 5 2

5 17 9

⬎5 3.4 4.5

0.0297 0.0082 0.0323

Endothelial cell growth factor 1, ECGF1 Parathyroid-hormone related protein, PTHRP TNF-related apoptosis-inducing ligand, TRAIL

Hs.73946 Hs.89626 Hs.83429

Endothelial cell specific Hormone Apoptosis induction

IFN related CGCCGACGAT ACCATTCTGC GTGTGCCTCC ACCTGTATCC

4 1 0 8

36 10 7 20

9 10 ⬎7 2.5

0.0000 0.0055 0.0075 0.0176

IFN IFN IFN IFN

␣-inducible protein (IFI-6-16) ind. transmembrane protein 2 (1–8D) regulatory factor 3, IRF3 ind. transmembrane protein 3 (1-8U)

Hs.265827 Hs.174195 Hs.75254 Hs.182241

Unknown Unknown Transcription factor Unknown

Intracellular transport GGGCCTGTGC C TCATTTTCCA A TTCATTTGTC AGTGCAAAAT GTGCAGGCTC TATTTATTGA A

9 2 0 0 0 2

29 12 5 7 6 10

3.2 6 ⬎5 ⬎7 ⬎6 5

0.0008 0.0066 0.0297 0.0075 0.0173 0.0210

Solute carrier family 16, member 3, MCT3 Solute carrier family 6, member 8, CT1 Solute carrier family 20, SLC20A1 Ion transport regulator 3 TAP1 Coat protein ␥-cop

Hs.85838 Hs.187958 Hs.78452 Hs.301350 Hs.352018 Hs.102950

Lactate ⫹ pyruvate transp. Creatin transporter Phosphate transporter Transport MHC-I peptide ER-import Transport

3 9 14 3 2 3 2 1 2

10 23 48 20 14 15 13 10 10

3.3 2.5 3.4 6.6 7 5 6.5 10 5

0.0483 0.0105 0.0000 0.0003 0.0022 0.0037 0.0038 0.0055 0.0210

Glyceraldehyde-3-phosphate dehydrogenase Lactate dehydrogenase A Phosphoglycerate kinase 1 Mevalonate (diphospho) decarboxylase Apolipoprotein E Fatty acid desaturase 3 Serum amyloid A1 ATP synthase, mit. F1 complex, ATP5E Similar to glucosamine-6-sulfatases


Glycolysis Glycolysis Glycolysis Cholesterol biosynthesis Lipid transport Lipid metabolism Associates w/HDL-prot. ATP synthesis

Protein synthesis GGGAAACCTT G TAAATATAAA TGGTGCAGCA TCTGCAAGAA AACTCTTGAA TAAATAATAC TACAAAACCA TGCACCACAG

2 0 0 0 4 1 1 4

9 5 8 6 13 7 7 13

4.5 ⬎5 ⬎8 ⬎6 3.2 7 7 3.2

0.0323 0.0297 0.0037 0.0173 0.0244 0.0370 0.0370 0.0244

Ribosomal protein S6 Mitochondrial, ribosomal protein L18 Mitochondrial, ribosomal protein S7 Mitochondrial, ribosomal protein S21 Translation initiation factor 3, s.u. 3 ␥ KIAA0111 gene product, initiation factor 4A-like Nucleolin Microsomal signal peptidase (18kD)


Protein synthesis Protein synthesis Protein synthesis Protein synthesis Protein synthesis Protein synthesis Ribosome synthesis Protease

Other functions TGCAATGACT AAGCAGAAGG GTGCTGGACC T TGGCTTAAAT G TTTTTGTATT AACATAGGAA TACATTTGGA C ATCATTCCCT GAGGCCATCC

34 0 1 2 2 1 1 3 6

170 5 15 ⬎15 8 8 10 5 10 5 7 7 7 7 10 3.3 15 2.5

0.0000 0.0000 0.0178 0.0210 0.0210 0.0370 0.0370 0.0483 0.0400

S100 calcium-binding protein A2 S100 calcium-binding protein A10, p11 Proteasome activator subunit 2 (PA28-␤) Hypoxia-inducible protein 2 Thioredoxin interacting protein CD59 antigen p18–20 CAAX box 1 dpy-30-like protein U6 snRNA-associated Sm-like protein LSm7


Stress response Stress response Protein degradation Unknown Unknown Cell surface protein Membrane protein Differentiation RNA processing

Cell cycle TCACAGCTGT AAAGTCTAGA Extracellular matrix TAAAAATGTT GGTTATTTTG TGGGTGAGCC ATCTTGTTAC GACCACCTTT TGTTAGAAAA GGGAGGGGTG G AAGGGGGCAA

Metabolism TAGACCCCTT TCTTGTGCAT GAAACAAGAT GGGAATAAAC CGACCCCACG TGTATTCAGC GTGCGGAGGA TTATGGCAGA GGAACTTTTA

P chance

Description (assigned mRNA)

Unigene accession no.

Function

6258


Correlation with ref.

A, 25 28 28 25 27 27 28, 29

A 25, 26 25 A

30

A

A A A

26

A

A, 30

A

25


Table 2 Functional classification of genes repressed during cellular senescence Sixty-five significantly repressed tags are depicted. The genes matching to these tags were assigned to functional classes. See legend of Table 1 for details. Functional class tag sequence

Tag# con.

Tag# sen.

Fold repr.

Cell cycle related TTAAAAGCCT TGCCATCTGT CCTAAGGCTA CGTTCCTGCG

37 11 9 24

9 2 2 7

4.1 5.5 4.5 3.4

0.0000 0.0119 0.0374 0.0017

CDC28 protein kinase 1, CKS-1 Cyclin B1 E2F4 Inhibitor of DNA binding 1, Id1

Hs.77550 Hs.23960 Hs.108371 Hs.75424

cdk regulation G2-M regulation Transcription factor Transcription factor

Transcription GGATATGTGG ACAGTGGGGA ATCCCTCAGT GCTGGTCTGA TGGGGATTAC CTCTGAGAGA GACACTACAC TTGAAGGGCC

18 15 11 5 13 9 17 10

3 5 3 0 3 2 3 1

6 3 3.6 ⬎5 4.3 4.5 5.6 10

0.0008 0.0200 0.0310 0.0319 0.0111 0.0374 0.0013 0.0058

Early growth response 1, EGR1 Unactive progesterone receptor (23 kD), ZNF6 ATF4, CREB2 HCNGP RNA polymerase I subunit, RPA12 TF IIIA, GTF3A p8 protein (candidate of metastasis 1), mitogenic TSC22-related leucin zipper protein


Transcription factor Transcription factor Transcriptional repressor Transcription factor RNA Pol I transcription 5S RNA Pol I transcr. HLH DNA-binding factor Transcriptional regulator

Signaling TGCATTAACT TTCTCTCTGT ATCTTTCTGG TACCTCTGAT

8 8 11 33

1 1 3 9

8 8 3.6 3.6

0.0184 0.0184 0.0310 0.0002

Cyclic AMP phosphoprotein, 19 kD ADP-ribosylation factor 5, ARF5 14-3-3␨ S100 calcium-binding protein P

Hs.7351 Hs.77541 Hs.75103 Hs.2962

Signal transduction GTP binding protein Signal transduction Signal transduction

Cytokine/growth factor GTATACCTAC GGGGCTGTAT

5 15

0 6

⬎5 2.5

0.0319 0.0401

Platelet-derived growth factor ␣, PDGF␣ Transforming growth factor ␤ 1, TGF-␤

Hs.37040 Hs.1103

Growth factor Growth factor

Cytoskeleton CATTAAATTC GCCGATCCTC

15 13

3 3

5 4.3

0.0037 0.0111

Cytoskeleton-associated protein 1 ␣-Tubulin-specific chaperone

Hs.31053 Hs.24930

Cytoskeleton Protein folding

Intracellular transport ATGATGATGA TTTCTAGTTT

35 22

14 8

2.5 2.7

0.0019 0.0076

Mitochondrial adenine translocator 2, ANT2 Transmembrane 4 ␣ protein, lysosomal

Hs.79172 Hs.111894

ADP/ATP translocase Transporter

DNA replication TGCAGCGCCT GGCGTGAACC

62 5

20 0

3.1 ⬎5

0.0000 0.0319

Uridine phosphorylase Proliferating cell nuclear antigen, PCNA

Hs.77573 Hs.78996

Nucleoside synthesis DNA-replication

Metabolism TAATGGTAAC GCCGCCATCT GGCCCAGGCC TCCTGAAAAA A TTGGGGAAAC ATGCAGCCAT CGGCTGAATT GCTTAACCTG TGTACTTCCT TGTGTTGTCA CCGTGCTCAT TTTGGAAAAA TAAAGACTTG

70 20 9 9 19 14 14 8 5 7 9 10 5

28 3 0 1 7 4 4 1 0 1 2 3 0

2.5 6.6 ⬎9 9 2.7 3.5 3.5 8 ⬎5 7 4.5 3.3 ⬎5

0.0000 0.0003 0.0020 0.0106 0.0149 0.0150 0.0150 0.0184 0.0319 0.0328 0.0374 0.0468 0.0319

Cytochrome c oxidase subunit Va Transketolase (Wernicke-Korsakoff syndrome) Aldehyde dehydrogenase 3 family, member A1 Spermidine/spermine N1-acetyl transferase Biliverdin reductase A Ornithine decarboxylase 1, ODC1 Phosphogluconate dehydrogenase Glutamate dehydrogenase 1 Ornithine aminotransferase Methylene tetrahydrofolate dehydrogenase Carbonyl reductase Glyceronephosphate O-acyltransferase Adenylate kinase 2

Hs.181028 Hs.89643 Hs.575 Hs.10846 Hs.81029 Hs.75212 Hs.75888 Hs.77508 Hs.75485 Hs.154672 Hs.9857 Hs.12482 Hs.171811

Respiratory chain Metabolic enzyme Alcohol metabolism Metabolic enzyme Metabolic enzyme Polyamine biosynth. Metabolic enzyme Nitrogen-metabolism Metabolic enzyme Metabolic enzyme Metabolic enzyme Phospholipid biosynthesis ATP-ADP cycle

RNA processing TTGATGTACA CGTGTTAATG TCCTAGCCTG

9 15 6

2 6 0

4.5 2.5 ⬎6

0.0374 0.0401 0.0171

Splicing factor 11, SFRS11 ZNF9 (myotonic dystophy 2) Splicing factor similar to DnaJ

Hs.11482 Hs.2110 Hs.74711

RNA processing RNA binding RNA splicing

Protein synthesis TTGGCGGGTC GAAGCCAGCC TCATCTTTGT GCCCAGCGGC C

15 14 5 10

6 4 0 2

2.5 3.5 ⬎5 5

0.0401 0.0150 0.0319 0.0184

Ribosomal protein S17 Translation initiation factor 4E bdg. prot. 1 Mitochondrial ribosomal protein L3 Mitochondrial ribosomal protein L4

Hs.5174 Hs.71819 Hs.79086 Hs.279652

Protein Protein Protein Protein

Protein degradation TAATTTGATT CAGCCAAATA GGCTCGGGAT

8 10 5

0 1 0

⬎8 10 ⬎5

0.0042 0.0058 0.0319

Ubiquitin-conjugating enzyme E2G 1 F-box protein FBX30 Calpain 1, (mu/l) large subunit

Hs.78563 Hs.321687 Hs.2575

Protein degradation Ubiquitination Protease

Other functions CTGCTAAAAG C GGAGCTGGCC TTTGGGGCTG GGGTGCTTGG GGAGCCATTC GATTACCTGT GAGGCGCTGG G GCGGGAGGGC CCCTATCACA GGGCCTGGGG A TAAGTTTAAT CTGGCCCGGA G TTTGGAATGT GCAGGGCCAG G CTTTTCAAGA A TGGGCTCTGA A CCCCCACCTA CAAATGAGGA

17 23 15 5 7 9 12 10 5 10 10 15 13 5 7 10 9 7

4 8 4 0 1 1 3 2 0 2 2 5 4 0 1 3 2 1

4.2 2.9 3.7 ⬎5 7 9 4 5 ⬎5 5 5 3 3.2 ⬎5 7 3.3 4.5 7

0.0035 0.0052 0.0104 0.0319 0.0328 0.0106 0.0179 0.0184 0.0319 0.0184 0.0184 0.0200 0.0249 0.0319 0.0328 0.0468 0.0374 0.0328

Cystatin A (stefin A) Artemin ATPase, H⫹ pump, lysosomal, 21kD ATPase, H⫹ pump, lysosomal, subunit 1 ATPase, H⫹ transp. lysosomal, member M Hexosaminidase A (alpha polypeptide) BAD ADP-ribosylation factor-like 2 CATX-8 protein, ras-related Epsin Sterol carrier protein 2, intracellular Vasodilator-stimulated phosphoprotein, VASP Matrin 3 XRCC1 Membrane cofactor protein (CD46) CD36L2, lysosomal Proteolipid protein 2 Gene upstream of NRAS, UNR

Hs.2621 Hs.194689 Hs.7476 Hs.6551 Hs.272630 Hs.119403 Hs.76366 Hs.154162 Hs.150826 Hs.279953 Hs.75760 Hs.93183 Hs.78825 Hs.98493 Hs.83532 Hs.323567 Hs.77422 Hs.69855

Proteinase inhibitor Neurotrophic factor Acidification Acidification of organelles Acidification of organelles Ganglioside catabolism Apoptosis regulation GTP binding protein GTP binding protein Endocytosis Cholesterol transporter Focal adhesion stability Nuclear matrix DNA-repair Measles virus receptor Receptor Unknown Unknown

P chance

Description (assigned mRNA)

Unigene accession no.

Function

synthesis synthesis inhibitor synthesis synthesis

6259


Correlation with ref. 27, 28, 29 A, 28, 29 25 A A A, 30 A A

27

27 28, 29 A 25

A A A, 25 A

A A

25

A, 25

A 25

A


helix-loop-helix factor Id1 is able to reactivate the cell cycle in senescent human fibroblasts (31), presumably by inhibitory association with ets transcription factors, which are required for elevated expression of p16INK4A (32). A similar mechanism may be operating in PrECs: in early passage PrECs, Id1 was expressed at high levels (Table 2). However, during senescence, Id1 decreased dramatically, which may explain the induction of p16INK4A mRNA and protein (Fig. 1, b and c). Elevated p16INK4A may then lead to a reduction of G1 phase-specific cdk activity and hypophosphorylation of pRb. Active pRb binds and inactivates members of the mitogenic E2F transcription factor family, which subsequently leads to inhibition of G1-S cell cycle progression (reviewed in Ref. 33). In addition to this mode of E2F inactivation, we observed a decreased expression of E2F4 (Table 2), which may additionally contribute to the inability of senescent PrECs to traverse the G1-S phase. E2F4 is expressed at elevated levels in immortal prostate cancer cells (25), suggesting that E2F4 may be involved in immortalization of PrECs. The reduced levels of CKS-1 expression in senescent PrECs may also contribute to the permanent cell cycle arrest observed in senescent PrECs (Fig. 2B). CKS-1 knockout mice have a profound defect in cell proliferation, suggesting that CKS-1 is necessary for full activity of Cdk2 (34). CKS proteins affect cdk activity by directly binding to cdk complexes and facilitating ubiquitin-mediated proteolysis of associated inhibitors like p27KIP1 (34). However, the levels of p27KIP1 protein do not increase significantly during senescence of PrECs (Fig. 1b). Therefore, it is likely that CKS-1 targets other proteins for degradation in early passage PrECs. Down-regulation of cyclin B1 expression was confirmed by qPCR (Fig. 2B) and may lead to a cell cycle arrest in the G2 phase. Consistent with this observation, cell cycle arrest of senescent cells is not restricted to arrest in the G1 phase but also occurs in the G2 phase (28). Paradoxically, expression of cyclin D1 was increased in terminally arrested prostate cells. Senescent human fibroblasts show a similar increase in cyclin D1 mRNA and protein levels (35).7 This may constitute a compensatory up-regulation, which results from inhibition of the cdk4/cyclin D1 pathway by p16INK4A. Extracellular Matrix. Elevated levels of enzymes involved in remodeling of the ECM has been observed previously during senescence of fibroblasts (28, 29). The deregulation of these genes during senescence may contribute to the altered ECM observed in aged tissues. In senescent PrECs, elevated levels of matrix metalloproteinase MMP-14 and cathepsin B expression were detected by SAGE (Table 1). On the other hand, PAI-1, an inhibitor of a matrix-degrading protease, was induced significantly (confirmed by qPCR, Fig. 2A). Up-regulation of PAI-1 has also been observed in other cell types undergoing senescence and presumably leads to disruption of ECM maintenance (36). Expression of the gene encoding the adhesion molecule fibronectin 1, which contains multiple binding sites for diverse ECM and cell surface molecules, was increased in senescent PrECs (confirmed by qPCR, Fig. 2A). Consistent with an antiproliferative role of fibronectin, its expression is generally reduced in transformed cells (37). Senescent PrECs showed increased expression of ␤4 integrin, a transmembrane receptor, which mediates cell-matrix interactions (confirmed by qPCR, Fig. 2A, Table 1). Down-regulation of ␤4 integrin is characteristic for prostate cancer and prostatic intraepithelial neoplasia (38). Cell Shape and Motility. Senescent PrECs undergo dramatic changes in size and shape (Fig. 1a). These changes could be because of the up-regulation of several key regulators and components of the cytoskeleton (Table 1): e.g., the gene encoding the intermediate filament forming protein vimentin was induced in senescent PrECs 7

H. Hermeking and A. Menssen, unpublished results.

(Table 1). Senescent human fibroblasts also show elevated expression of vimentin (39). In addition, the elevated expression of gelsolin (confirmed by qPCR, Fig. 2A), which fragments actin networks in a calcium-regulated manner, may be involved in the altered morphology of senescent cells. Interestingly, levels of gelsolin are diminished in breast cancer (40) and ectopic expression of gelsolin suppresses tumorigenicity (41). Expression of intermediate chain I of cytoplasmatic dynein (DNClI) was increased in senescent PrECs. Among several components of cytoplasmic dynein, up-regulation during senescence was shown to be unique for DNClI (42). The expression of tropomyosin 1-␣ (confirmed by qPCR, Fig. 2A) and fibulin-1 was increased in senescent PrECs. Interestingly, down-regulation of human epithelial tropomyosin has been observed in prostate carcinoma cells (43), and ectopic fibulin-1 expression inhibits motility and invasion of human ovarian and breast cancer cells (44). RAC1 and cdc42, which both encode GTP-binding, ras-like molecules, were induced in senescent PrECs and have been implicated in the reorganization of actin filaments during wound healing processes in fibroblasts: cdc42 expression is sufficient to induce filopodia, whereas RAC1 is required for lamellipodia formation (45). The induction of both genes is presumably involved in the characteristic spreading of senescent PrECs (Fig. 1a). Transcription. As discussed above, transcription factors like Id1 and E2F4 may have central roles in regulating or antagonizing cellular events, which are part of the senescence program. The transcription factor EGR1 is the product of an immediate early growth-response gene and directly induces TGF-␤ expression (46). Consistent with our finding that EGR1 is repressed in senescent PrECs, expression of TGF-␤ is diminished concomitantly (Table 2). The transcriptional repressor ATF4 (CREB2) was repressed, suggesting that genes regulated via cyclic AMP-response elements may be derepressed during senescence of PrECs. p8, which encodes a basic-helix-loop-helix transcription factor, was significantly repressed in senescent PrECs. Interestingly, mitogenic and metastatic potential has been assigned to p8 (47, 48). SKI-interacting protein (49), which was induced in senescent PrECs, functions as an antagonist of the oncogene product SKI, thereby presumably contributing to the terminal arrest of PrECs. Signaling Molecules and Growth Factors. Senescent PrECs are refractory to stimulation of proliferation by external growth factors, implying that repression of receptors or mediators of signaling events should be detectable. However, of the detected changes in mRNA levels only the repression of PDGF␣ fulfills this criterion. On the other hand, induction of negative regulators of signaling could lead to the unresponsiveness of senescent cells to mitogens: we observed induction of DKK3 (confirmed by qPCR, Fig. 2A), which presumably represents an antagonist of wnt-signaling. Up-regulation of DKK3 during senescence was also observed in human diploid fibroblasts.7 Ectopic expression of DKK3 inhibits tumor cell proliferation and expression of DKK3 is significantly down-regulated in non-small cell lung carcinomas (50). Interestingly, DKK3 is localized on 11p15, a locus often deleted in human cancer (50). IFNs are capable of generating a variety of cellular responses, e.g., cell cycle arrest, thereby having antitumor and antiviral effects. Senescent PrECs displayed elevated levels of IFN regulatory factor 3 mRNA. IFN regulatory factor 3 transactivates IFN-responsive genes through sequence specific binding of IFN response elements (51). IFN-␣-inducible protein IFI-6-16 was dramatically increased in senescent PrECs as determined by MicroSAGE and qPCR analysis (Fig. 2A). Furthermore, IFN-induced transmembrane protein 2 (1– 8D) was induced in senescent PrECs. Although the function of these genes is unknown, their induction could be involved in the dominant cell cycle arrest observed in senescent PrECs. Senescent PrECs showed increased connexin 26 expression. Con-

6260



nexin 26 protein forms intercellular channels present in gap junctions, which allow the transfer of ions and small signaling molecules between basal and luminal cells of the human prostate (52). Consistent with a role of connexin 26 in regulation of cell proliferation and differentiation, prostate cancer cell growth can be suppressed by ectopic expression of connexin 26 (53). Apoptosis. Senescent cells acquire an increased resistance towards apoptotic insults (54, 55). In senescent PrECs, this may, in part, be because of the down-regulation of the proapoptotic gene Bad detected in this study (Table 2). On the other hand, we observed induction of TRAIL (APO2L, confirmed by qPCR, Fig. 2), which binds to the TRAIL receptors TRAILR1/DR4 and TRAILR2/DR5 (56). TRAIL induces apoptosis in prostate cancer cells but also in normal PrECs (57). It will be interesting to determine whether increased TRAIL expression, which may occur in the prostate because of accumulation of senescent PrECs (17), contributes to suppression of tumor formation in the aging prostate. In the future, it will be important to analyze whether the differential regulation of genes identified in this study is required for the induction or maintenance of senescence in PrECs. During neoplastic transformation, genes required for the senescent phenotype may be inactivated through genetic (mutation, deletion) or epigenetic alterations (e.g., methylation). Furthermore, transcriptional repression of senescence-inducing genes may occur. Therefore, genome-wide analyses of changes in gene expression patterns and of genetic alterations, which occur during formation of prostate cancer, will be complementary to this study. Recent examples of gene expressing studies on prostate cancer cells (25, 58 – 60) include a report by Shou et al. (60), which shows that down-regulation of several IFN-regulated genes is characteristic for the transition from nontumorigenic benign prostatic hyperplasia to tumorigenic prostatic hyperplasia: one of these genes is IFI-6-16, which is strongly induced during senescence (Table 1, Fig. 2). These examples suggest that genes induced during senescence are good candidates for genes, which are inactivated/down-regulated during cancer initiation and/or progression. On the other hand, genes down-regulated during cellular senescence may represent potential therapeutic targets for inhibition of prostate cancer cell proliferation because specific inhibition of such gene products may lead to reactivation of the senescence program in immortal cancer cells. ACKNOWLEDGMENTS We thank Peter Palm for help with, and Dieter Oesterhelt for access to, automated sequencing, and Holger Rumpold and members of the lab for discussion and comments. Heiko Hermeking’s laboratory is supported by the Max-Planck-Society and the Deutsche Krebshilfe.

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50. Tsuji, T., Nozaki, I., Miyazaki, M., Sakaguchi, M., Pu, H., Hamazaki, Y., Iijima, O., and Namba, M. Antiproliferative activity of REIC/Dkk-3 and its significant downregulation in non-small cell lung carcinomas. Biochem. Biophys. Res. Commun., 289: 257–263, 2001. 51. Au, W. C., Moore, P. A., Lowther, W., Juang, Y. T., and Pitha, P. M. Identification of a member of the interferon regulatory factor family that binds to the interferonstimulated response element and activates expression of interferon-induced genes. Proc. Natl. Acad. Sci. USA, 92: 11657–11661, 1995. 52. El-Alfy, M., Pelletier, G., Hermo, L. S., and Labrie, F. Unique features of the basal cells of human prostate epithelium. Microsc. Res. Tech., 51: 436 – 446, 2000. 53. Mehta, P. P., Perez-Stable, C., Nadji, M., Mian, M., Asotra, K., and Roos, B. A. Suppression of human prostate cancer cell growth by forced expression of connexin genes. Dev. Genet. (Amsterdam), 24: 91–110, 1999. 54. Banerjee, S., Banerjee, P. P., and Brown, T. R. Castration-induced apoptotic cell death in the Brown Norway rat prostate decreases as a function of age. Endocrinology, 141: 821– 832, 2000. 55. Suh, Y., Lee, K. A., Kim, W. H., Han, B. G., Vijg, J., and Park, S. C. Aging alters the apoptotic response to genotoxic stress. Nat. Med., 8: 3– 4, 2002. 56. Pan, G., Ni, J., Wei, Y. F., Yu, G., Gentz, R., and Dixit, V. M. An antagonist decoy receptor and a death domain-containing receptor for TRAIL. Science (Wash. DC), 277: 815– 818, 1997. 57. Nesterov, A., Ivashchenko, Y., and Kraft, A. S. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) triggers apoptosis in normal prostate epithelial cells. Oncogene, 21: 1135–1140, 2002. 58. Luo, J., Duggan, D. J., Chen, Y., Sauvageot, J., Ewing, C. M., Bittner, M. L., Trent, J. M., and Isaacs, W. B. Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. Cancer Res., 61: 4683– 4688, 2001. 59. Mousses, S., Bubendorf, L., Wagner, U., Hostetter, G., Kononen, J., Cornelison, R., Goldberger, N., Elkahloun, A. G., Willi, N., Koivisto, P., Ferhle, W., Raffeld, M., Sauter, G., and Kallioniemi, O. P. Clinical validation of candidate genes associated with prostate cancer progression in the CWR22 model system using tissue microarrays. Cancer Res., 62: 1256 –1260, 2002. 60. Shou, J., Soriano, R., Hayward, S. W., Cunha, G. R., Williams, P. M., and Gao, W. Q. Expression profiling of a human cell line model of prostatic cancer reveals a direct involvement of interferon signaling in prostate tumor progression. Proc. Natl. Acad. Sci. USA, 99: 2830 –2835, 2002.

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