PPARγ disease gene network and identification of therapeutic targets for prostate cancer

1

Introduction

Peroxisome proliferator-activated receptors (PPARs) belong to the nuclear hormone receptor superfamily of ligand-activated transcription factors (Mangelsdorf et al., 1995). Upon ligand activation, PPAR translocates from cytoplasm to nucleus and heterodimerizes with retinoic X receptor (RXR) to regulate gene expression. Three iso-forms (α, β, and γ) for PPAR have been identified so far in Xenopus, mouse, human, rats, and hamsters. The PPAR subtypes share a highly conserved DNA-binding domain

that matches with specific DNA sequences known as peroxisome proliferator response elements (PPREs) (Houseknecht et al., 2002). These three PPARs are encoded by different genes, perform different functions, and exhibit different tissue localizations (Hara et al., 2002). Emerging evidence indicates that PPARs modulate the expression of numerous target genes that play a central role in regu-lating glucose, lipid, and cholesterol metabolism where imbalances can lead to diabetes, obesity, cardiovascular diseases, and cancer. This has made PPARs attractive

RESEARCH ARTICLE

PPARγ disease gene network and identification of therapeutic targets for prostate cancer

Gireedhar Venkatachalam1,3,*, Alan Prem Kumar4,5,*, Kishore R. Sakharkar6,*, Sindhu Thangavel2, Marie-Véronique Clement1,3, and Meena Kishore Sakharkar2

1Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 2School of Life and Environmental Science, University of Tsukuba, Tsukuba, Ibaraki, Japan, 3NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, 4Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 5Cancer Science Institute of Singapore, National University of Singapore, Singapore, and 6Infectious Disease Cluster, University Sains Malaysia, Penang, Malaysia.

AbstractPeroxisome proliferator-activated receptor (PPAR) belongs to the nuclear hormone receptor superfamily. Recently published reports demonstrate the importance of a direct repeat 2 (DR2) as a PPARγ-responsive element in addition to the canonical direct repeat 1 (DR1) Peroxisome proliferator response elements (PPREs). However, a comprehensive and systematic approach to constructing de novo disease-specific gene networks for PPARγ is lacking, especially one that includes PPARγ target genes containing either DR1 or DR2 site within their promoter region. Here, we computationally identified 1154 PPARγ direct target genes and constructed the PPARγ disease gene network, which revealed 138 PPARγ target genes that are associated with 65 unique diseases. The network shows that PPARγ target genes are highly associated with cancer and neurological diseases. Thirty-eight PPARγ direct target genes were found to be involved in prostate cancer and two key (hub) PPARγ direct target genes, PRKCZ and PGK1, were experimentally validated to be repressed upon PPARγ activation by its natural ligand, 15d-PGJ

2 in three prostrate cancer cell lines. We

proposed that PRKCZ and PGK1 could be novel therapeutic targets for prostate cancer. These investigations would not only aid in understanding the molecular mechanisms by which PPARγ regulates disease targets but would also lead to the identification of novel PPARγ gene targets.

Keywords: PPARγ disease gene network, direct repeat 2, PPARγ target genes in prostate cancer, genome-wide DR1 and DR2 PPARγ genes, PPARγ protein–protein network

*Authors contributed equally to this work.Address for Correspondence: Kishore R. Sakharkar, Infectious Disease Cluster, University Sains Malaysia, Penang, Malaysia. E-mail: [email protected]; Marie Véronique Clement, Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597. E-mail: [email protected]; Meena Kishore Sakharkar, School of Life and Environmental Science, University of Tsukuba, Tsukuba, Ibaraki, Japan. E-mail: [email protected]

(Received 04 February 2011; accepted 24 February 2011)

Journal of Drug Targeting, 2011, 1–16, Early Online© 2011 Informa UK, Ltd.ISSN 1061-186X print/ISSN 1029-2330 onlineDOI: 10.3109/1061186X.2011.568062

Journal of Drug Targeting

2011

00

00

000

000

04 February 2011

00 00 0000

24 February 2011

1061-186X

1029-2330

© 2011 Informa UK, Ltd.

10.3109/1061186X.2011.568062

GDRT

568062

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.

mailto:[email protected]; Marie V�ronique Clement, Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597. E-mail: [email protected]; Meena Kishore Sakharkar, School of Life and Environmental Science, University of Tsukuba, Tsukuba, Ibaraki, Japan. E-mail: [email protected]



http://informahealthcare.com/doi/abs/10.3109/1061186X.2011.568062

2 G. Venkatachalam et al.


therapeutic targets and the pharmaceutical industry has invested enormous amount of its drug discovery efforts into developing PPAR modulating agents for the treat-ment of diseases. Extensive data has established the role of PPARγ in adipocyte differentiation, lipid storage, glu-cose homeostasis and its transcriptional regulation of a number of genes crucial for these processes (Evans et al., 2004). As the master regulator of fat-cell formation, PPARγ is required for the accumulation of adipose tissue and hence contributes to obesity (Lehrke and Lazar, 2005). PPARγ has also been reported to be involved in limiting inflammation (Hsueh and Bruemmer, 2004). Moreover, clinically relevant ligands for PPARγ comprise thiazoli-dinediones (TZDs), the insulin-sensitizing drugs licensed for use in selected patients with Type 2 diabetes mellitus (Krentz and Friedmann, 2006; Sakharkar and Sakharkar, 2008). Besides its anti-inflammatory and metabolic activities, PPARγ is overexpressed in many malignancies. With >1.6 million patients taking antidiabetic drugs that function as PPARγ ligands, investigations on their long-term effects on tumor prevention or progression is of high significance (Ondrey et al., 2009).

Of note, though the exact physiological function of PPARγ in normal epithelial cells is largely unknown, PPARγ activation is reported to inhibit the prolifera-tion of malignant cells from different lineages such as liposarcoma (Tontonoz et al., 1997), breast adenocarci-noma (Elstner et al., 1998; Mueller et al., 1998), prostate carcinoma (Kubota et al., 1998), colorectal carcinoma (DuBois et al., 1998; Sarraf et al., 1998), non-small cell lung carcinoma (Chang and Szabo, 2000), pancreatic carcinoma (Motomura et al., 2000), bladder cancer cells (Guan et al., 1999), and gastric carcinoma (Sato et al., 2000) both in vitro and in vivo. PPARγ-selective targets include genes linked to growth regulatory pathways, and immune modulation (Gupta et al., 2001). Drg-1, a putative suppressor gene in human colorectal cancer (Guan et al., 1999), and PTEN, a tumor suppressor gene that modulates several cellular functions are found to be controlled by PPARγ agonists in colon cancer cell lines (Patel et al., 2001). In hepatocellular carcinoma cell lines, PPARγ agonists elicited growth inhibition through up-regulation of cyclin-dependent kinase inhibitors, p21 and p27 proteins (Barak et al., 1999). Recently, we showed that activation of PPARγ inhibits the expression of Na+/H+ exchanger 1 (NHE1) that was associated with the sensitization of breast cancer cells to chemothera-peutic treatment (Venkatachalam et al., 2009).

Prostate cancer is the most common cancer among men and the second leading cause of male cancer death (Jemal et al., 2003). Although surgical resection or radio-therapy is potentially curative for localized disease, advanced prostate cancer is associated with poor progno-sis. Conventional chemotherapy and radiotherapy are of limited effectiveness. The shortage of curative therapies for advanced disease has provided impetus for the devel-opment of novel therapies. PPARγ is expressed in human prostate cancer cell lines and human prostate cancer

specimens (Mueller et al., 2000). In fact, members of the prostaglandin J series, which are PPARγ ligands have been shown to cause G1 arrest at lower doses and at higher concentrations cause G2–M arrest leading to apoptosis (Kim et al., 1993; Fukushima et al., 1994). Butler et al. also showed that 15d-PGJ

2, an endogenous PPARγ ligand,

induces non-apoptotic cell death in human prostate can-cer cells (Butler et al., 2000). Although these studies and others show the effects of PPARγ in prostate cancer cell death, none of them clearly demonstrate the molecular mechanisms, pathways, or genes that are modulated by PPARγ in prostate cancer cell death. Hence, modulation of PPARγ receptor action in these diseases may be of therapeutic value.

It is of interest to mention that DNA-binding activity of PPARγ is modulated by the isotype of the RXR heterodi-meric partner. PPREs described previously consisted of juxtaposed degenerate hexamer AGGTCA separated by one nucleotide, direct repeat 1 (DR1). However, several recent reports clearly demonstrate that a DR2 (direct repeats separated by two nucleotides) site is also a PPAR-responsive element (Gervois et al., 1999; Fontaine et al., 2003; Kumar et al., 2004, 2009; Venkatachalam et al., 2009), suggesting that both DR1 and DR2 sites could be bound by PPAR. Moreover, 5′ flanking sequence of PPRE is also a critical determinant for binding of PPARγ/RXR heterodi-mer (Gervois et al., 1999) However, a comprehensive and systematic approach to constructing de novo disease-spe-cific gene networks for PPARγ is lacking. The main objec-tive of this study is to identify both DR1 and DR2 target genes specifically regulated by PPARγ followed by the con-struction of a disease-specific gene network. Constructing disease gene network for PPARγ regulatory genes would aid in understanding the molecular mechanisms by which PPARγ regulates disease targets and would also lead to the identification of novel PPARγ gene targets.

In this work, first, we identified PPARγ regulated genes that contain either DR1 or DR2 PPRE elements in the human genome. We used these genes and mapped with DOLite database (Disease Ontology Lite) to identify the gene–disease associations. The identified disease gene associations were then mapped with International Classification of Diseases (ICD)-10 database and grouped in disease groups. It was observed that cancer disease group is one of the major disease groups, in which prostate cancer was found to have majority of PPARγ target genes. Concurrently, studies have shown that PPARγ upon activation by its ligands induces an anti-angiogenic effect in tumor cells (Keshamouni et al., 2005; Panigrahy et al., 2005). Therefore, regulating these identified hub genes could provide a mechanism for PPARγ in preventing angiogenesis in prostate cancer.

Materials and methods

Promoter sequences collectionThe sequences 5-kb upstream of annotated transcrip-tion starts of Refseq genes of human were downloaded

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.

PPARγ disease gene network 3


from UCSC genome browser (http://genome.ucsc.edu/) (Rhead et al., 2010). The promoter sequences down-loaded were from February 2009 assembly of the human genome (hg19, GRCh37 Genome Reference Consortium Human Reference 37(GCA_000001405.1).

PPRE predictionThe PPRE motifs were searched in the 5-kb promoter region of the genes using the PPRESearch tool. PPRESearch is a database searching tool that not only contains a compre-hensive collection of experimentally validated PPRE motifs, which are reported so far in the literature, but also allows the user to search for PPRE motifs in the DNA sequences of interest. PPRESearch is available at http://www.classicrus.com/PPRE/ (Venkatachalam et al., 2009). Only PPREs specific to PPARγ, which have reported binding efficiency greater than 80%, were used to search for PPRE sites in the promoter region. Gene promoters, whose PPREs matches exactly with experimental PPREs, were selected. Both DR1 and DR2 PPREs were searched. Since the gene sequences were represented in transcript ID, their corresponding gene symbols and gene names were retrieved using the gene ID conversion tool from DAVID http://david.abcc.ncifcrf.gov/ (Dennis et al., 2003). Genes that have more than one transcript ID (due to gene polymorphism) have same gene symbol and same PPRE sites and hence were grouped together under one gene name.

Gene ontology—biological and molecular functionsThe direct PPARγ/RXR targets are assumed to be respon-sible for a large part of the observed biological effects of PPARγ. To classify the PPARγ target genes based on the biological and molecular functions, we character-ized them using Protein ANalysis THrough Evolutionary Relationships (PANTHER). The PANTHER classifica-tion system is a unique resource that classifies genes by their functions, using published scientific experimental evidence and evolutionary relationships to predict func-tion even in the absence of direct experimental evidence (Thomas et al., 2003). This is available at http://www.pantherdb.org/.

Disease gene ontology and disease classificationPredicted PPARγ target genes and their association to diseases were mapped using Disease Ontology Lite (DOLite) (Osborne et al., 2009). DOLite is a simplified vocabulary list from the Disease Ontology (DO). It has been used in the Functional Disease Ontology (FunDO) Web application (Du et al., 2009). Diseases that mapped to PPARγ target genes were further classified into disease groups using ICD-10th revision version 2007 by World Health Organization (WHO) which is available at http://apps.who.int/classifications/apps/icd/icd10online/.

Protein–protein interaction database and prostrate cancer gene networkApart from the effects of PPARγ on cell death, none of the studies on the effect of PPARγ ligands on prostate cancer

demonstrate the molecular mechanisms, pathways, or genes that are modulated by PPARγ to cause cell death. Singh and Agarwal (2006) summarized potential tar-gets for chemoprevention that are pertinent in prostate cancer growth and progression. Using PANTHER data-base, we mapped 17 out of 138 PPARγ target genes on these target pathways for chemoprevention. We further identified other pathways that have been reported to be involved in prostrate cancer from literature and using the 718 gene list identified another 21 PPARγ target genes, which could possibly be involved in prostrate cancer. This possibly indicates that DOLite does not represent a complete dataset on human disease gene set. Table 1 lists all 38 (17 + 21) PPARγ direct target genes that are involved in prostate cancer pathways. The PPRE sites present in 5′ promoter region of the genes and DR1/DR2 column indi-cates the type of directed repeat present in each gene. Supplementary table 1 provides the complete names for these gene symbols. The associated signaling pathways in prostate cancer for these 38 genes are shown in Figure 4. The 38 (17 + 21) PPARγ-targeted disease genes involved in prostrate cancer were mapped to the Human Protein Reference Database (HPRD) protein–protein interaction (PPI) database (Keshava Prasad et al., 2009) to identify the interacting partners for the target genes. Here, it must be mentioned that protein annotation information catalogued in HPRD is derived through manual cura-tion using published literature and hence information retrieved from this database is accurate and more reli-able. HPRD can be accessed at http://www.hprd.org/.

Network constructionDisease gene network (for PPARγ target genes) and pro-tein interaction network (for PPARγ target genes involved in prostrate cancer) were drawn using Cytoscape. Cytoscape is an open source bioinformatics software platform for visualizing molecular interaction networks and also integrates these interactions with gene expres-sion profiles and other state data (Cline et al., 2007). It is available at http://www.cytoscape.org/.

Though scale free networks do not have a well-defined threshold for a node to be a hub, several researchers have defined a degree threshold. Gaci recently proposed that any node with a degree higher that 3/2 times of z (aver-age degree) can be considered as a hub (Gaci, 2010). We have used the above procedure for identification of hubs in prostrate cancer network. Network param-eters such as the node degree, betweenness centrality, and stress centrality were calculated using Cytoscape plug-in CentiScaPe (Scardoni et al., 2009). All the cen-tralities are calculated simultaneously in this special plug-in. Two genes PRKCZ and PGK1 were selected for experimental validation on their regulation by PPARγ. The details are presented in the next section.

Reagents and antibodiesRoswell Park Memorial Institute (RPMI) 1640 medium, phosphate-buffered saline (PBS), fetal bovine serum

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.

http://genome.ucsc.edu/

http://www.classicrus.com/PPRE/

http://www.classicrus.com/PPRE/

http://david.abcc.ncifcrf.gov/


http://www.pantherdb.org/.

http://www.pantherdb.org/.

http://apps.who.int/classifications/apps/icd/icd10online/.

http://apps.who.int/classifications/apps/icd/icd10online/.

http://www.hprd.org/.

http://www.cytoscape.org/.



(FBS), l-glutamine, HEPES, sodium pyruvate and trypsin were purchased from Hyclone (Logan, UT). Pepstatin A, phenylmethanesulfonyl fluoride (PMSF), leupeptin, bovine serum albumin (BSA), mouse anti-β-actin monoclonal antibody were supplied by Sigma-Aldrich (St. Louis, MO). Aprotinin was pur-chased from Applichem, Darmstadt, Germany. Rabbit anti-HuPRKCZ antibody was purchased from Cell signaling Technology Inc. (Danvers, MA). Mouse anti-HuPGK1 monoclonal antibodies were purchased from Santa Cruz Biotechnology (Santa Cruz, CA). Stabilized goat anti-mouse horseradish peroxidase (HRP) was

obtained from Pierce (Rockford, IL). The PPARγ ago-nist, 15d-PGJ

2, was purchased from Alexis Biochemical

(San Diego, CA).

Cell lines, culture, and drug treatmentHuman PC3, DU145, and LNCaP prostate carcinoma cells were obtained from the American Type Culture Collection (ATCC, Rockville, MD). PC3, DU145, and LNCaP cells were propagated and maintained in RPMI medium containing 10% FBS, 2 mM l-glutamine, HEPES, and sodium pyruvate at 37°C and 5% CO

2. Cultures were

replenished with fresh medium every 2 to 3 days and

Table 1. List of PPARγ-regulated genes involved in prostate cancer pathophysiology.Gene symbol Gene name PPRE DR1 or DR2 site DR1/DR2AGXT2L2 HYPOTHETICAL PROTEIN LOC85007, ISOFORM 2 AGGTCAAGAGTTCA 2ARHGAP8 RHO GTPASE ACTIVATING PROTEIN 8 AGGTCAAGAGTTCA 2ASAH1 N-ACYLSPHINGOSINE AMIDOHYDROLASE (ACID CERAMIDASE) 1 AAGTCAAACTTCA 1BAG3 BCL2-ASSOCIATED ATHANOGENE 3 AGGTCAAGAGTTCA 2CDK10 CYCLIN-DEPENDENT KINASE (CDC2-LIKE) 10 AGGCTAAAGGTCA 1CDK9 CYCLIN-DEPENDENT KINASE 9 (CDC2-RELATED KINASE) AGGGTAGAGTTCA 1CNR1 CANNABINOID RECEPTOR 1 (BRAIN) AGGTCATAAGTCA 1EGLN3 HYPOTHETICAL PROTEIN FLJ21620 AGGTCACAGGTTA 1ENO1 ENOLASE 1 AGGTCAGGAGATCA 2FGF1 FIBROBLAST GROWTH FACTOR 1 (ACIDIC) AGGTCATGAGTTCA 2FGFR2 FIBROBLAST GROWTH FACTOR RECEPTOR 2 (BACTERIA-EXPRESSED KINASE,

KERATINOCYTE GROWTH FACTOR RECEPTOR, CRANIOFACIAL DYSOSTOSIS 1, CROUZON SYNDROME, PFEIFFER SYNDROME, JACKSON-WEISS SYNDROME)

AGGTCAGCAGTTCA 2

GAB1 GRB2-ASSOCIATED BINDING PROTEIN 1 AGGTCAGAAGTTCA 2GFI1 GROWTH FACTOR INDEPENDENT 1 AGGTCAAGAGTTCA 2IRF1 INTERFERON REGULATORY FACTOR 1 AGGTCAAAGATGT 1IRF7 INTERFERON REGULATORY FACTOR 7 AGGACAGAGGCCA 1MAP3K2 MITOGEN-ACTIVATED PROTEIN KINASE KINASE KINASE 2 AGGTCAAAGGTTA 1MAPK15 MITOGEN-ACTIVATED PROTEIN KINASE 15 AGGAGAGAGGGGA 1MARK2 MAP/MICROTUBULE AFFINITY-REGULATING KINASE 2 AGGTCAAAGGGCA 1MIF MACROPHAGE MIGRATION INHIBITORY FACTOR (GLYCOSYLATION-

INHIBITING FACTOR)AGGGCACAGGTCA 1

NR1H3 NUCLEAR RECEPTOR SUBFAMILY 1, GROUP H, MEMBER 3 AGTACAAAGTTCA 1PCNT PERICENTRIN (KENDRIN) AGGTCAGGAGGTCG 2PDLIM7 PDZ AND LIM DOMAIN 7 (ENIGMA) AGGCCACAGGGGA 1PGK1 PHOSPHOGLYCERATE KINASE1 AGGTCAGGAGTTCG 2PIK3CG PHOSPHOINOSITIDE-3-KINASE, CATALYTIC, GAMMA POLYPEPTIDE AGGTCAGAAGTTCA 2PLA2G4B PHOSPHOLIPASE A2, GROUP IVB (CYTOSOLIC) AGGAGATAGGGGA 1PLCG1 PHOSPHOLIPASE C, GAMMA 1 AGGGGAAAGGTCA 1PRKCZ PROTEIN KINASE C, ZETA AGGTCAGGAGTTCG 2RING1 RING FINGER PROTEIN 1 AGGCCAGAGGGAC 1RPS27A RIBOSOMAL PROTEIN S27A AGGGCATAGGTCA 1RPS6KA1 RIBOSOMAL PROTEIN S6 KINASE, 90KDA, POLYPEPTIDE 1 AGGTCAGAAGTTCA 2SENP1 SUMO1/SENTRIN SPECIFIC PEPTIDASE 1 AGGTCACAGGTGA 1SET SET TRANSLOCATION (MYELOID LEUKEMIA-ASSOCIATED) AGGTCAGAAGTTCA 2STAT2 SIGNAL TRANSDUCER AND ACTIVATOR OF TRANSCRIPTION 2, 113 KDA AGGGGAAAGGTCA 1SQSTM1 SEQUESTOSOME 1 AGGTAAGAGGTCA 1SERPINA5 SERPIN PEPTIDASE INHIBITOR, CLADE A (ALPHA-1 ANTIPROTEINASE,

ANTITRYPSIN), MEMBER 5AGGGAAGAGGTCA 1

SERPINA3 SERPIN PEPTIDASE INHIBITOR, CLADE A (ALPHA-1 ANTIPROTEINASE, ANTITRYPSIN), MEMBER 3

AGGACAGAGGCCA 1

SLC39A1 SOLUTE CARRIER FAMILY 39 (ZINC TRANSPORTER), MEMBER 1 AGGTCAGAAGTTCA 1TSGA10 TESTIS SPECIFIC, 10 AGGTCATGAGGTCA 2Genes from disease genes network are highlighted. Respective gene names, PPRE sites, and type of directed repeats found in its 5-kb promoter region are also shown.

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.



passaged 1:3 when they reached 80% confluence. The prostate cancer cell lines were treated with 5, 10, 15 µM 15 d-PGJ

2 for 48 h and then cells were harvested for west-

ern blot analysis.

Crystal violet growth assayCell density after drug treatment was assessed by deter-mining the number of intact cells using crystal violet uptake assay. Cells were seeded at 1.5 × 105 cells per well in a 12-well plate (Greiner bio-one - Frickenhausen, Germany). After respective treatment, the medium was removed from the wells and cells were washed with 1× PBS. Five hundred microliters of crystal violet solution (0.75% crystal violet, 50% ethanol, 1.75% formaldehyde, and 0.25% NaCl) was added to stain the cells. After incubation for 10 min, the wells were washed with deionized water a few times to remove any traces of the crystal violet solu-tion and left to air-dry. The cells were lysed with 300 mL of 1% SDS in PBS and transferred to 96-well microtiter plate. Cell density was assessed by measuring the color intensity of crystal violet at 595 nm using Spectrofluoro Plus plate reader (TECAN, GmbH, Grodig, Austria).

Western blot analysisWhole-cell lysates were prepared with RIPA lysis buffer containing 10 mM Tris–HCl pH 7.4, 30 mM NaCl, 1 mM EDTA, 1% Nonidet P-40, supplemented with 1 mM Na

3VO

4, 1 μg/mL leupeptin, 1 μg/mL pepstatin A, 1 μg/

mL aprotinin, and 1 mM PMSF before use. Protein con-centration was determined for each sample and equal amounts of protein were boiled for 5 min, with 1× SDS sample buffer and resolved by 8% SDS-PAGE. Thereafter, proteins were transferred onto nitrocellulose mem-brane, blocked for 1 h at room temperature (RT) with 5% non-fat milk, and incubated overnight at 4°C with the primary antibody. After probing with secondary antibody for 1 h at 25°C, protein bands were detected by using the Supersignal West Pico Chemiluminescence (Pierce). β-Actin protein was used as a loading control.

Results

The steps involved in the identification of potential DR1 and DR2 PPARγ target genes and understanding of their association with diseases are outlined in Figure 1.

Prostrate cancer genes reported inliterature and pathway identification

Prostrate cancer gene list and mapping ofchemoprevention pathways

Whole human genome(5 kb upstream promoter region of genes)

PPRESearch analysis

DR1 and DR2 genes

Map to databases

Disease gene network

Mapping to HPRD-protein protein interaction data and network construction

Identification of important/hub PPARγ target genes involved in prostrate cancer

Experimental validation of two potential targets of PPARγ-PRKCZ and PGK1

Disease groups

PANTHER dbDOLite

ICD

Figure 1. Basic schema of the methodology used in the current study for identification of two target genes regulated by PPARγ.

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.



Figure 2. PPARγ PPRE sequence logo and biological significances. (A) PPARγ PPRE sequence logo shows the frequency distribution of nucleotides in each location of DR1 and DR2 PPREs used by PPRESearch and the distribution of predicted DR1 and DR2 PPREs present in the 5-kb promoter region of the genes. (B) The 1154 PPARγ genes with DR1 or DR2 or DR1 and DR2 PPREs are predicted of which 748 genes have DR1 PPREs and 406 genes have DR2 PPREs and 118 genes have DR1 and DR2 PPREs. (C) Percentage of predicted PPARγ target genes with PPRE sites in each chromosomes of human. (D) Shows the percentage of DR1 PPARγ target genes involved various biological processes as classified by PANTHER db. (E) Shows the percentage of DR2 PPARγ target genes involved various biological processes as classified by PANTHER db.

PPARγ(DR1 PPREs used)

PPARγ(DR2 PPREs used)

PPARγ target genes(DR1 PPREs)

PPARγ target genes(DR2 PPREs)

0

chr 1

chr 2

chr 3

chr 4

chr 5

chr 6

chr 7

chr 8

chr 9

chr 1

0

chr 1

1

chr 1

2

chr 1

3

chr 1

4

chr 1

5

chr 1

6

chr 1

7

chr 1

8

chr 1

9

chr 2

0

chr 2

1

chr 2

2

chr X

chr Y

Chromosome number

% G

enes

on

each

chr

omos

ome

1

2

3

4

5

6

7

8

9

10Genes with PPRE sites

A

C

B

Genes randomly selected with no PPRE sitesGenes randomly selected (with with/without PPRE sites)

DR1 & DR2(118 gene)

DR1(748 genes)

DR2(406 genes)

Metabollicprocess19.96% Metabollic

process25.00%

Cell cycle4.27%

D EImmune system

process7.06%

Cell adhesion2.70% Cell

communication11.52%

Cellular process16.22%

Cellularcomponentorganization

3.94%

Apoptosis2.18%

Systemprocess7.06%

Response tostimulus4.88%

Regulation ofbiologicalprocess0.09%

Developmentalprocess9.06%

Generation ofprecursor

metabolites andenergy0.95%

Reproduction1.38%

Localization0.19%

Transport8.53%

Cellularcomponentorganization

2.92%Apoptosis

2.44%

Systemprocess5.70%Response to

stimulus3.50%

Regulation ofbiological process

0.67%

Developmentalprocess6.03%

Generation ofprecursor

metabolites andenergy1.48%

Reproduction3.36%

Transport10.30%

Cellular process14.89%

Localization0.14%

Cell cycle6.03%

Immune systemprocess5.41% Cell adhesion

1.96%Cell

communication10.15%

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.



PPARγ target genes, logos, chromosome distribution, and biological functionSequence logo for PPRE motifs are drawn using Weblogo (Crooks et al., 2004), which can be accessed at http://weblogo.berkeley.edu/. PPARγ PPRE sequence logo shows the nucleotide frequency distribution for each location of PPREs used by PPRESearch (Venkatachalam et al., 2009). DR1 genes sequence logo and DR2 genes sequence logo show the nucleotide frequency distri-bution in each location of PPREs found in predicted PPARγ genes having DR1 and direct repeat 2 (DR2) PPRE motifs, respectively (Figure 2A). A total of 1154 genes of which 748 DR1 genes and 406 DR2 genes and 118 DR1 and DR2 genes with PPRE sites in the 5-kb pro-moter region were obtained using the PPRESearch pre-diction (Figure 2B) (Supplementary table 2a). Since the gene sequences from UCSC browser are represented in transcript id, their corresponding gene symbols and gene names were retrieved using the gene ID conver-sion tool from DAVID http://david.abcc.ncifcrf.gov/ (Dennis et al., 2003). Many transcripts have same gene symbol and same PPRE sites, hence they are grouped together under one gene name. Seven hundred and eighteen genes (with gene names) were finalized and used for further analysis. A list of PPARγ DR1 and DR2 target genes from these 718 genes are shown in the Supplementary table 2b.

The chromosomal distribution (% age) of the 718 predicted PPARγ target genes (genes containing PPRE sites) along with 718 genes randomly selected from

human genome (with or without PPRE sites) and 718 genes randomly selected from human genome (with no PPRE sites) is shown Figure 2C. The chromosomal distribution of these genes shows that there is a greater probability (P < 0.05) of finding these genes on chro-mosome 21 (when compared with random gene sets). Interestingly, chromosome 21 is the smallest human autosome. Though, it has been reported to be involved in several disorders ranging including Down’s syndrome, the exact reason for this bias needs further investigation (Figure 2C).

Gene ontology (GO) terms (biological process) as derived from PANTHER, for the predicted 748 PPARγ tar-get genes containing DR1 sites (Figure 2D) and predicted 406 PPARγ target genes containing DR2 sites (Figure 2E) are shown.

Predicted PPARγ disease gene networkA total of 718 predicted PPARγ target genes (DR1 and DR2) and their association with various diseases were mapped using DOLite database, of which 138 PPARγ-targeted genes were found to be associated with diseases (480 interactions). PPRE genes that are associated with more than one disease are used to construct the disease gene network. This led to the removal of 19 genes from the network. A total of 119 genes were used for con-struction of the diseases gene network (Figure 3). IDC classification of diseases reveals predominant role of PPARγ-regulated genes in tumors of colon, breast, pros-tate, stomach, brain, skin, ovary, bladder, and lung. In

Figure 3. PPARγ disease gene network. The genes are mapped with diseases using the information provided by DOLite. The diseases are then grouped using ICD-10 disease information provided by WHO. Node size is based on the degree of connectivity. PPARγ genes nodes are colored in green. PPARγ DR1 target genes node shape is shown in rounded rectangles and DR2 target genes node shape is shown in triangle. Diseases groups are colored as shown in the legend. PPARγ genes are mainly associated with neurological diseases and cancer.

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.

http://weblogo.berkeley.edu/.

http://weblogo.berkeley.edu/.




Table 2. Disease groups and diseases associated with PPARγ.

Disease groups Diseases (total PPARγ disease genes)

Cancer (22 diseases) Cancer (25), prostate cancer(17), colon cancer(10), breast cancer (9), embryoma (9), liver cancer (5), lung cancer (5), melanoma (5), stomach cancer (5), brain tumor (4), lymphoma (4), neuroblastoma (4), ovarian cancer (4), adenocarcinoma (3), bladder cancer (3), liver tumor (3), squamous cell cancer (3), adenoma (2), carcinoma (2), gastrointestinal stromal tumor (2), metastasis to lymph nodes (2)

Nervous system diseases and disorder (19 diseases)

Alzheimer’s disease (9), Parkinson’s disease(6), schizophrenia (6), lupus erythematosus (5), psychotic disorder (5), hypertension (4), mental retardation (4), bipolar disorder (3), encephalopathies (3), epilepsy (3), neurodegenerative disorder (3), neurofibromatosis (3), stroke (3), autistic disorder (2), behavior disease (2), depression (2), polyneuropathy (2), pre-eclampsia (2), spinocerebellar ataxias (2)

Circulatory system diseases (6 diseases)

Atherosclerosis (7), myopathy (4), aortic aneurysm (3), cardiovascular disease (2), celiac disease (2), heart failure (2)

Metabolic disease (5 diseases)

Diabetes mellitus (9), obesity (9), hyperlipidemia (3), fatty liver (2), hypercholesterolemia (2)

Musculoskeletal system and connective tissue diseases (4 diseases)

Rheumatoid arthritis (9), arthritis (2),bone disease (2), brain disease (2)

Blood-related disorder (3 diseases)

Hemolytic–uremic syndrome (3), Fanconi’s anemia (2), Sickle cell disease (2)

Skin and subcutaneous diseases (2 diseases)

Dermatitis (3), systemic scleroderma (2)

Neoplasm (2 diseases) Leukemia (12), neoplasm metastasis(4)

Respiratory system diseases Asthma (3)

Digestive system diseases Cholelithiasis (6)

Genitourinary system diseases Endometriosis (7)

Column 2 (Diseases (total PPARγ disease genes)) shows diseases that are associated with PPARγ target genes, and numbers in the brackets indicate total PPARγ genes involved in each disease. Column 1 (Disease groups) shows the disease groups into which PPARγ target genes-associated diseases are classified.

PROSTATECANCER

IGFR signalingpathway

(RPS6KA1)

EGF signaling pathway(PLCG1,MAPK15,GAB1,STAT2,PIK3CG,PRKCZ,

MAP3K2,RPS27A)

Cell cycle regulators(CDK9,CDK10)

HIF activated pathway(PIK3CG,EGLN3)

Toll like receptorpathway(IRF7)

Glycolysis(EN01,PGK1)

Others(CNR1,GFI1,MARK2,MIF,

NR1H3,PCNT,PDLIM7,SENP,SERPINA3,

SERPINA5,SET,SLC39A1,SSQSTM1,TSGA10)

STAT pathway(STAT2)

VEGF signaling(PLCG1,ARHGAP8,

PIK3CG,PRKCZ,PLA2G4B)

Angiogenesis(PLCG1,ARHGAP8,

FGFR2,PRKCZ,PRKCZ,PLA2G4B,PDLIM7,FGF1,AGXT2L2)

Apoptosis signalingpathway

(PIK3CG,PRKCZ,BAG3)

Figure 4. PPARγ target genes in prostate cancer. Thirty-eight PPARγ genes involved in prostate cancer pathophysiology mapped onto the chemopreventive pathways based on literature and Singh et al. Pathway names are represented in bold and genes are shown within the brackets.

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.



order to confirm the statistical significance of our claim, we generated two random datasets. One containing 718 genes selected randomly from the human genome (irrespective of whether they contain PPRE or not—set 1) and another random set from human genome with 718 genes that do not contain the PPRE site (set 2). We further mapped them on DoLite and observed that 121 genes in set 1 are involved in diseases, whereas 118 genes in set 2 are involved in diseases. We further mapped these diseases onto the IDC and observed that ~91.3% of the 138 genes with PPRE are involved in cancer and ~47.8% are involved in neurological disorders. This number is ~50.4% and ~7.4% for genes in set 1 and ~43.2% and ~11.8% in set 2. This suggests that genes with PPRE sites in their promoter region are more likely (P < 0.05) to be involved in cancer and neu-rological disorders (Supplementary figure 1). This result is in agreement with previous studies (Sarraf et al., 1998; Sato et al., 2000; Han et al., 2001, 2003; Mössner et al., 2002; Theocharis et al., 2002; Nakashiro et al., 2003;

Feilchenfeldt et al., 2004; Nijsten et al., 2005; Kumar et al., 2009). Interestingly, the cancer disease group is related with 22 different types of cancer, of which 25 PPARγ-targeted genes are associated with cancer in general, 17 genes are specifically related with prostate cancer, 10 genes with colon cancer, nine genes with breast cancer and embryoma each (Table 2).

Notably, neurological diseases and disorder group is associated with 19 diseases. Nine PPARγ target genes are associated with Alzheimer disease and six genes are asso-ciated Parkinson’s disease and schizophrenia each. This is interesting because recent studies have shown a role for PPARγ in the pathobiology of various neurological disor-ders, such as experimental autoimmune encephalomy-elitis (Niino et al., 2001; Feinstein et al., 2002), Alzheimer’s disease (Heneka et al., 2001; Watson and Craft, 2003), ischemic stroke (Aronowski et al., 2002; Shimazu et al., 2005; Sundararajan et al., 2005), and Parkinson’s disease (Breidert et al., 2002; Dehmer et al., 2004). In addition to the above disease groups, PPARγ-targeted genes were

Figure 5. Protein–protein interaction network for PPARγ target genes involved in prostrate cancer. Blue is interacting proteins. Pink triangles are DR1 and yellow diamonds are DR2 genes. Node size is proportional to the degree of connectivity.

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.



also found to be linked to other disease groups such as circulatory-related diseases, metabolic diseases, mus-culoskeletal system and connective tissue diseases, blood-related disorder, skin and subcutaneous diseases, neoplasm, respiratory system diseases, digestive system diseases, and genitourinary system diseases.

PPARγ target genes involved in prostate cancerThirty-eight direct target genes involved in prostrate cancer pathways are reported (Figure 4). To further understand the importance of these PPARγ-regulated genes in prostate cancer, a PPI network was constructed (Figure 5). The degree and the centrality parameters (for these 38 genes) were used to identify the hubs in the network. All nodes above the degree value of 3.4 were

considered as hubs in this analysis (Gaci, 2010). The highest value for node degree, betweenness, and stress was 98, 109167.8683, and 312968, respectively. The low-est calculated values for degree, betweenness, and stress was 1.0 and 0, respectively. The average value of the node degree, betweenness, and stress was 2.3, 1897.98, and 7271.68, respectively. PLCG1 scores the highest for all the three centrality parameters followed by the PRKCZ with values of 71, 90,051.42, and 356,432 for degree, between-ness, and stress, respectively. The node degree of PGK1 is four, betweenness and stress is 8526 and 40188, respec-tively (Table 3).

We further identified two genes for experimen-tal validation based on their degree value (must be a hub) and biological importance in the network and

Table 3. Network parameters for the list of PPARγ-regulated genes involved in prostate cancer pathophysiology.Gene name Betweenness Degree StressPLCG1 109167.8683 98 392168PRKCZ 90051.42614 71 356432SQSTM1 31631.55277 38 134136CDK9 49642.07744 32 89868RPS6KA1 24783.0827 31 111072GAB1 14673.05412 29 53360SET 19343.60823 23 66364PDLIM7 15849.5566 21 27754FGFR2 18390.2782 20 60262SERPINA3 26665.84908 19 102476RPS27A 10848.26088 19 46170SERPINA5 14878.32906 18 64972PIK3CG 12782.08486 18 49904NR1H3 14190 16 45990FGF1 24998.1743 16 85828STAT2 11132.13362 13 77886MAP3K2 17297.40189 13 61776RING1 9510 12 15130IRF7 14042.78179 11 101660MIF 10106.23434 10 20114BAG3 25490.65169 9 39854SLC39A1 3446.255475 8 7286PCNT 56 8 56MARK2 8223.083703 7 43214MAPK15 3881.423665 7 13640IRF1 5785.085257 7 29018ENO1 14080 6 66850CNR1 4780 6 19090PGK1 8526 4 40188EGLN3 2874 4 13428ARHGAP8 2874 4 6432GFI1 6 3 6SENP1 960 2 6530STAT1 1073.6459 2 4844TSGA10 960 2 4478AGXT2L2 0 1 0CDK10 0 1 0PLA2G4B 0 1 0 Average—1897.98; min—0;

max—109167.8683Average—2.33; min—1;

max—98Average—7271.68; min—0;

max—392168

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.



chose PRKCZ and PGK1. PRKCZ was chosen because it has higher degree and centrality values and hence has significance in the network. Phosphoglycerate kinase 1 (PGK1) is a glycolytic gene and have been ear-lier reported to be overexpressed in prostrate cancer (Altenberg and Greulich, 2004). Otto Warburg in 1924 postulated that “Cancer cells take up higher amounts of glucose than normal cells and exhibit increased rates of glycolysis and lactate production even in the presence of oxygen”(Warburg et al., 1924). Altenberg and Greulich (2004), using the results from NIH public database, found that glycolytic genes are overexpressed in 24 cancer cell lines.

Activation of PPARγ and its anti-proliferation effects on prostate cancer cellsTo assess if inhibition of cells’ growth by PPARγ ligand, 15d-PGJ

2 is a function of PPARγ expression, we first

screened three representative prostate cancer cell lines, PC3, DU145, and LNCaP for expression of PPARγ pro-tein. We observed PC3 cells expressed the highest level of PPARγ protein followed by DU145, whereas LNCaP cells have very little PPARγ protein (Figure 6A). Interestingly,

the antiproliferative effect of the PPARγ ligand 15d-PGJ2

on the three cell lines corroborated with the level of PPARγ protein (Figure 6B).

Activation of PPARγ decreases expression of protein kinase C zeta in prostate cancer cells with high levels of PPARγ proteinThe prostate cancer PPI network shows that protein kinase C zeta (PRKCZ) is one of the highly connected hubs. Interestingly, it has been reported that PRKCZ is one of the known therapeutic targets in prostate can-cer with a role in prostate cancer progression (Su et al., 2001). Interestingly, PRKCZ has the same DR2 PPRE site AGGTCAGGAGTTCG that was found in our previ-ously described new PPARγ target gene, NHE1 (Kumar et al., 2009) (Figure 7A). Multiple sequence alignment of PRKCZ promoter region flanking both sides of the DR2 site with that of the NHE1 gene shows a well-conserved PPRE DR2 site (Figure 7B). We then validated our PP prediction to show that activation of PPARγ by 15d-PGJ

2

down-regulated the expression of PRKCZ only in the PC3 and DU145 cells that express high levels of PPARγ, com-pared with LNCaP cells (Figures 6A and 7C).

5 10

Pro

lifer

atio

n re

lativ

e to

unt

reat

ed c

ontro

l

PC3

0

15dPGJ2(µM)

20

40

60

80

100

120

β-Actin

PPAR γ 58

A

B

PC

3

DU

145

LNC

aP

50

4237

15 5 10

DU145

15 5 10

LNCaP

15

Figure 6. Activation of PPARγ and its anti-proliferation effects on prostate cancer cells. (A) Shows the basal PPARγ protein expression in PC3, DU145, and LNCaP prostate cancer cell lines. (B) PC3, DU145, and LNCaP cells were exposed to 5, 10, and 15 μM 15d-PGJ

2 for 48 h and

cell growth were assessed as described in “Materials and methods.”

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.



Activation of PPARγ decreases the expression of phosphoglycerate kinase 1 in prostate cancer cells with high levels of PPARγ proteinIt has been reported that PGK1 is a critical downstream target of the chemokine axis and an important regulator of an “angiogenic switch” that is essential for tumor and metastatic growth of prostate cancer (Wang et al., 2007). From our predicted PPARγ target genes, we found that PGK1 is one of the glycolytic genes with a PPRE sites in their 5-kb promoter (Figures 4 and 8A). Interestingly, PGK1 gene is overexpressed in prostate cancer (Altenberg and Greulich, 2004). Multiple sequence alignment of PGK1 promoter region flanking both sides of the DR2 site with that of the NHE1 gene shows a well-conserved PPRE DR2 site (Figure 8B). This observation opens door to explore if suppressing PGK1 by PPARγ activation could result in glycolytic inhibition, ATP depletion eventually leading to cancer cell death. Indeed, we showed activa-tion of PPARγ by 15d-PGJ

2 down-regulated expression of

PGK1 only in cells expressing high levels of PPARγ, PC3,

and DU145 cells but not in cells with very low PPARγ pro-tein such as LNCaP cells (Figure 8C).

Discussion

PPARs are nuclear hormone receptors. PPARγ, the best characterized of the PPARs, has been reported to play a crucial role in adipogenesis and insulin sensitization. Furthermore, PPARγ has also been reported to affect cell proliferation/differentiation pathways in various malig-nancies. Hence, identification of PPARγ target genes may provide novel approaches to the treatment of several diseases and will also help to understand the molecular basis of several diseases.

We identified 1154 genes from human genome that contain the PPRE site and could possibly be regulated by PPARγ. Interestingly, majority of DR1 and DR2 PPARγ target genes are involved in cell communication, cellular processes, transportation, developmental and metabolic processes. This signifies that both DR1 and DR2 PPARγ

DR2 PPRE in ALU

15dPGJ2(µM)

0 5 10 0 5 10

4237

75PC3

DU145

LNCaP

75

75

4237

4237

A

C

B

NHE1PRKCζ

PRKCζ β-Actin

Figure 7. Activation of PPARγ decreases expression of protein kinase C zeta. (A) Putative PPREs in promoter of PRKCZ. Human PRKCZ promoter sequence from UCSC browser. Transcriptional start site (T) and ATG translational start codon shown in figure. Predicted PPREs in the promoter region are highlighted. (B) Shows the promoter alignment between NHE1 and PRKCZ. Best alignment is observed between the PPREs of NHE1 and PRKCZ. (C) Activation of PPARγ represses PRKCZ gene repression. PC3, DU145, and LNCaP cells were exposed to 5, 10, and 15 μM 15d-PGJ

2 for 48 h before protein levels were assessed as described in “Materials and methods.” PRKCZ protein expression

in PC3, DU145, and LNCaP was analyzed by western blot following 15d-PGJ2 at doses indicated for 48 h. Equal loading ascertained using

detection of β-actin.

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.



genes in general regulate the same biological processes (Figure 2D and 2E). Other important biological func-tions that PPARγ target genes are associated with include apoptosis, cell cycle, neuronal activities, muscle contrac-tion, lipid and fatty acid metabolism, cell proliferation and differentiation. These data suggest the important role of these genes in the listed disorders and could pos-sibly contribute toward understanding their molecular mechanism.

Recent progress in genomics has led to an appreciation of the effects of gene mutations in multiple disorders and provides the opportunity to study human diseases all at once rather than one at a time. The disease gene network for PPARγ target genes shows that PPARγ-targeted genes in general are highly associated with two major disease groups—cancer and neurological diseases. MIF is the highest connected gene involved in nervous, cancer, adenoma, metabolic, mucoskeletal, and skin diseases with a degree of connectivity of 16. Due to its broad regu-latory properties, MIF is a critical mediator of a number

of immune and inflammatory diseases, including septic shock (Calandra et al., 1998; Bozza et al., 1999; Calandra et al., 2000), rheumatoid arthritis (Mikulowska et al., 1997; Leech et al., 1998, 1999), delayed-type hypersensitivity (Bernhagen et al., 1996), inflammatory lung diseases (Makita et al., 1998; Rossi et al., 1998), and cancer (del Vecchio et al., 2000; Kamimura et al., 2000). Interestingly, 38 genes with PPRE sites were found to be involved in prostrate cancer pathways. Construction of the molecu-lar interaction network based on PPI data from HPRD for these 38 genes helped us identify several hub genes. These genes were mapped onto target pathways that could pos-sibly be involved in prostrate cancer. These data could be useful toward chemopreventive agent options and also in combination treatment options in the management of prostrate cancer. Here, this is promising because tumors have many molecular targets, which function aberrantly and therefore for the best results in chemoprevention, as well as therapy of prostrate cancer, a multi-molecular targeting approach is essential.

DR2 PPRE in ALU

15dPGJ2(µM)

0 5 10 0 5 10

42

3742PC3

DU145

LNCaP

42

42

4237

4237

A

C

B

NHE1PGK1

PGK1 β-Actin

Figure 8. Activation of PPARγ decreases expression of phosphoglycerate kinase 1. (A) Putative PPREs in promoter of PGK1. Human PGK1 promoter sequence from UCSC browser. Transcriptional start site (T) and ATG translational start codon shown in figure. Predicted PPREs in the promoter region are highlighted. (B) Shows the promoter alignment between NHE1 and PGK1. Best alignment is observed between the PPREs of NHE1 and PGK1. (C) Activation of PPARγ represses PGK1 gene repression. PC3, DU145, and LNCaP cells were exposed to 5, 10, and 15 μM 15d-PGJ

2 for 48 h before protein levels were assessed as described in “Materials and methods.” PGK1 protein expression

in PC3, DU145, and LNCaP was analyzed by western blot following 15d-PGJ2 at doses indicated for 48 h. Equal loading ascertained using

detection of β-actin.

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.



Experimental validation on regulation of two hub genes (PRKCZ and PGK1) (possible targets) shows that they are regulated by PPARγ and show that activation of PPARγ indeed decreases expression of PGK1 and PRKCZ in prostate cancer cells expressing PPARγ protein. PGK1 is a glycolytic gene. Inhibition of glycolytic genes results in depletion of ATP which in turn leads to apoptosis or necrosis (Lieberthal et al., 1998). Inhibiting glycolysis has been reported to result in an increase of in vivo thera-peutic activity in osteosarcoma (Maschek et al., 2004; Scatena et al., 2008). PGK1 catalyzes the conversion of 1,3-bisphosphoglycerate to 3-phosphoglycerate coupled with the generation of ATP from ADP. The profiling studies by Nielsen et al. (2008) also demonstrate that PGK1 has PPAR-binding motifs in its 5′ promoter region (Nielsen et al., 2008). Since this step is involved in conversion of ADP to ATP, repressing this gene would have an effect on glycolysis that eventually could decrease energy produc-tion in cancer cells.

PRKCZ is overexpressed in prostate cancer and is also one of the known therapeutic targets in prostate cancer. PRKCZ is a member of the PKC family of serine/threonine kinases that are involved in a variety of cellu-lar processes such as proliferation, differentiation, and secretion. A very recent report provided evidence that PRKCZ is a promising target for the development of drugs that interfere with the development or progres-sion of the metastatic phenotype (Della Peruta et al., 2010). Having shown from our network that PRKCZ is potentially a direct target of PPARγ, we validated this experimentally to show that activation of PPARγ leads to a decrease in PRKCZ protein expression in cells with high levels of PPARγ protein but not in cells that express very low levels of PPARγ, suggesting for the use of PPARγ ligands for effective management of pros-tate cancer. However, more detailed investigations are needed for clinical implications.

Conclusion

PPARs, in particular PPARγ, is emerging as a hot thera-peutic target to combat numerous diseases. Herein, we constructed a disease PPARγ gene network that sheds new light on understanding the molecular mechanisms and new gene targets by which PPARγ may regulate in targeting a particular disease. Given the effort on discov-ering the underlying molecular mechanisms and gene targets modulated by PPARγ in diseases, this study will aid ongoing pharmaceutical research to pursue the use of PPARγ ligands with enhanced therapeutic efficacy and superior margins of safety.

Acknowledgements

M.V.C. and A.P.K. were supported by National Medical Research Council, Singapore [Grant R-183-000-204–213 and Grant R-713-000-124–213]. A.P.K. was also sup-ported by the Cancer Science Institute of Singapore,

Experimental Therapeutics I Program [Grant R-713-001-011–271].

Declaration of interest

We have no conflict of interest.

ReferencesAltenberg B, Greulich KO. (2004). Genes of glycolysis are ubiquitously

overexpressed in 24 cancer classes. Genomics, 84, 1014–1020.Aronowski J. (2002). Peroxisome proliferator-activated receptor-

reduces damage produced by focal ischemia. Abstr-Soc Neurosci, Program No. 389.6.

Barak Y, Nelson MC, Ong ES, Jones YZ, Ruiz-Lozano P, Chien KR, Koder A, Evans RM. (1999). PPAR gamma is required for placental, cardiac, and adipose tissue development. Mol Cell, 4, 585–595.

Bernhagen J, Bacher M, Calandra T, Metz CN, Doty SB, Donnelly T, Bucala R. (1996). An essential role for macrophage migration inhibitory factor in the tuberculin delayed-type hypersensitivity reaction. J Exp Med, 183, 277–282.

Bozza M, Satoskar AR, Lin G, Lu B, Humbles AA, Gerard C, David JR. (1999). Targeted disruption of migration inhibitory factor gene reveals its critical role in sepsis. J Exp Med, 189, 341–346.

Breidert T, Callebert J, Heneka MT, Landreth G, Launay JM, Hirsch EC. (2002). Protective action of the peroxisome proliferator-activated receptor-gamma agonist pioglitazone in a mouse model of Parkinson’s disease. J Neurochem, 82, 615–624.

Butler R, Mitchell SH, Tindall DJ, Young CY. (2000). Nonapoptotic cell death associated with S-phase arrest of prostate cancer cells via the peroxisome proliferator-activated receptor gamma ligand, 15-deoxy-delta12,14-prostaglandin J2. Cell Growth Differ, 11, 49–61.

Calandra T, Echtenacher B, Roy DL, Pugin J, Metz CN, Hültner L, Heumann D, Männel D, Bucala R, Glauser MP. (2000). Protection from septic shock by neutralization of macrophage migration inhibitory factor. Nat Med, 6, 164–170.

Calandra T, Spiegel LA, Metz CN, Bucala R. (1998). Macrophage migration inhibitory factor is a critical mediator of the activation of immune cells by exotoxins of Gram-positive bacteria. Proc Natl Acad Sci USA, 95, 11383–11388.

Chang TH, Szabo E. (2000). Induction of differentiation and apoptosis by ligands of peroxisome proliferator-activated receptor gamma in non-small cell lung cancer. Cancer Res, 60, 1129–1138.

Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B, Hanspers K, Isserlin R, Kelley R, Killcoyne S, Lotia S, Maere S, Morris J, Ono K, Pavlovic V, Pico AR, Vailaya A, Wang PL, Adler A, Conklin BR, Hood L, Kuiper M, Sander C, Schmulevich I, Schwikowski B, Warner GJ, Ideker T, Bader GD. (2007). Integration of biological networks and gene expression data using Cytoscape. Nat Protoc, 2, 2366–2382.

Crooks GE, Hon G, Chandonia JM, Brenner SE. (2004). WebLogo: a sequence logo generator. Genome Res, 14, 1188–1190.

Dehmer T, Heneka MT, Sastre M, Dichgans J, Schulz JB. (2004). Protection by pioglitazone in the MPTP model of Parkinson’s disease correlates with I kappa B alpha induction and block of NF kappa B and iNOS activation. J Neurochem, 88, 494–501.

del Vecchio MT, Tripodi SA, Arcuri F, Pergola L, Hako L, Vatti R, Cintorino M. (2000). Macrophage migration inhibitory factor in prostatic adenocarcinoma: correlation with tumor grading and combination endocrine treatment-related changes. Prostate, 45, 51–57.

Della Peruta M, Giagulli C, Laudanna C, Scarpa A, Sorio C. (2010). RHOA and PRKCZ control different aspects of cell motility in pancreatic cancer metastatic clones. Mol Cancer, 9, 61.

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.



Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. (2003). DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol, 4, P3.

Du P, Feng G, Flatow J, Song J, Holko M, Kibbe WA, Lin SM. (2009). From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations. Bioinformatics, 25, i63–i68.

DuBois RN, Gupta R, Brockman J, Reddy BS, Krakow SL, Lazar MA. (1998). The nuclear eicosanoid receptor, PPARgamma, is aberrantly expressed in colonic cancers. Carcinogenesis, 19, 49–53.

Elstner E, Müller C, Koshizuka K, Williamson EA, Park D, Asou H, Shintaku P, Said JW, Heber D, Koeffler HP. (1998). Ligands for peroxisome proliferator-activated receptorgamma and retinoic acid receptor inhibit growth and induce apoptosis of human breast cancer cells in vitro and in BNX mice. Proc Natl Acad Sci USA, 95, 8806–8811.

Evans RM, Barish GD, Wang YX. (2004). PPARs and the complex journey to obesity. Nat Med, 10, 355–361.

Feilchenfeldt J, Bründler MA, Soravia C, Tötsch M, Meier CA. (2004). Peroxisome proliferator-activated receptors (PPARs) and associated transcription factors in colon cancer: reduced expression of PPARgamma-coactivator 1 (PGC-1). Cancer Lett, 203, 25–33.

Feinstein DL, Galea E, Gavrilyuk V, Brosnan CF, Whitacre CC, Dumitrescu-Ozimek L, Landreth GE, Pershadsingh HA, Weinberg G, Heneka MT. (2002). Peroxisome proliferator-activated receptor-gamma agonists prevent experimental autoimmune encephalomyelitis. Ann Neurol, 51, 694–702.

Fontaine C, Dubois G, Duguay Y, Helledie T, Vu-Dac N, Gervois P, Soncin F, Mandrup S, Fruchart JC, Fruchart-Najib J, Staels B. (2003). The orphan nuclear receptor Rev-Erbalpha is a peroxisome proliferator-activated receptor (PPAR) gamma target gene and promotes PPARgamma-induced adipocyte differentiation. J Biol Chem, 278, 37672–37680.

Fukushima M, Sasaki H, Fukushima S. (1994). Prostaglandin J2 and related compounds. Mode of action in G1 arrest and preclinical results. Ann N Y Acad Sci, 744, 161–165.

Gaci O. A topological description of hubs in amino acid interaction networks (2010). Adv Bioinform., 257512. (No other details in PubMed)

Gervois P, Chopin-Delannoy S, Fadel A, Dubois G, Kosykh V, Fruchart JC, Najib J, Laudet V, Staels B. (1999). Fibrates increase human REV-ERBalpha expression in liver via a novel peroxisome proliferator-activated receptor response element. Mol Endocrinol, 13, 400–409.

Guan YF, Zhang YH, Breyer RM, Davis L, Breyer MD. (1999). Expression of peroxisome proliferator-activated receptor gamma (PPARgamma) in human transitional bladder cancer and its role in inducing cell death. Neoplasia, 1, 330–339.

Gupta RA, Brockman JA, Sarraf P, Willson TM, DuBois RN. (2001). Target genes of peroxisome proliferator-activated receptor gamma in colorectal cancer cells. J Biol Chem, 276, 29681–29687.

Han S, Inoue H, Flowers LC, Sidell N. (2003). Control of COX-2 gene expression through peroxisome proliferator-activated receptor gamma in human cervical cancer cells. Clin Cancer Res, 9, 4627–4635.

Han SW, Greene ME, Pitts J, Wada RK, Sidell N. (2001). Novel expression and function of peroxisome proliferator-activated receptor gamma (PPARgamma) in human neuroblastoma cells. Clin Cancer Res, 7, 98–104.

Hara M, Alcoser SY, Qaadir A, Beiswenger KK, Cox NJ, Ehrmann DA. (2002). Insulin resistance is attenuated in women with polycystic ovary syndrome with the Pro(12)Ala polymorphism in the PPARgamma gene. J Clin Endocrinol Metab, 87, 772–775.

Heneka MT, Landreth GE, Feinstein DL. (2001). Role for peroxisome proliferator-activated receptor-gamma in Alzheimer’s disease. Ann Neurol, 49, 276.

Houseknecht KL, Cole BM, Steele PJ. (2002). Peroxisome proliferator-activated receptor gamma (PPARgamma) and its ligands: a review. Domest Anim Endocrinol, 22, 1–23.

Hsueh WA, Bruemmer D. (2004). Peroxisome proliferator-activated receptor gamma: implications for cardiovascular disease. Hypertension, 43, 297–305.

Jemal A, Murray T, Samuels A, Ghafoor A, Ward E, Thun MJ. (2003). Cancer statistics. CA Cancer J Clin., 53,1, 5–26.

Kamimura A, Kamachi M, Nishihira J, Ogura S, Isobe H, Dosaka-Akita H, Ogata A, Shindoh M, Ohbuchi T, Kawakami Y. (2000). Intracellular distribution of macrophage migration inhibitory factor predicts the prognosis of patients with adenocarcinoma of the lung. Cancer, 89, 334–341.

Keshamouni VG, Arenberg DA, Reddy RC, Newstead MJ, Anthwal S, Standiford TJ. (2005). PPAR-gamma activation inhibits angiogenesis by blocking ELR+CXC chemokine production in non-small cell lung cancer. Neoplasia, 7, 294–301.

Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, Somanathan DS, Sebastian A, Rani S, Ray S, Harrys Kishore CJ, Kanth S, Ahmed M, Kashyap MK, Mohmood R, Ramachandra YL, Krishna V, Rahiman BA, Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A. (2009). Human Protein Reference Database—2009 update. Nucleic Acids Res, 37, D767–D772.

Kim IK, Lee JH, Sohn HW, Kim HS, Kim SH. (1993). Prostaglandin A2 and delta 12-prostaglandin J2 induce apoptosis in L1210 cells. FEBS Lett, 321, 209–214.

Krentz AJ, Friedmann PS. (2006). Type 2 diabetes, psoriasis and thiazolidinediones. Int J Clin Pract, 60, 362–363.

Kubota T, Koshizuka K, Williamson EA, Asou H, Said JW, Holden S, Miyoshi I, Koeffler HP. (1998). Ligand for peroxisome proliferator-activated receptor gamma (troglitazone) has potent antitumor effect against human prostate cancer both in vitro and in vivo. Cancer Res, 58, 3344–3352.

Kumar AP, Quake AL, Chang MKX, Lim KSY, Eng CB, Singh R, Hewitt RE, Salto-Tellez M, Pervaiz S, Clement MV. (2009). Repression of the Na+/H+ exchanger 1 expression by PPARg activation is a potential new approach for specific inhibition of tumor cells’ growth in vitro and in vivo. Cancer Res., 69, 22, 8636–8644.

Kumar AP, Piedrafita FJ, Reynolds WF. (2004). Peroxisome proliferator-activated receptor gamma ligands regulate myeloperoxidase expression in macrophages by an estrogen-dependent mechanism involving the −463GA promoter polymorphism. J Biol Chem, 279, 8300–8315.

Leech M, Metz C, Hall P, Hutchinson P, Gianis K, Smith M, Weedon H, Holdsworth SR, Bucala R, Morand EF. (1999). Macrophage migration inhibitory factor in rheumatoid arthritis: evidence of proinflammatory function and regulation by glucocorticoids. Arthritis Rheum, 42, 1601–1608.

Leech M, Metz C, Santos L, Peng T, Holdsworth SR, Bucala R, Morand EF. (1998). Involvement of macrophage migration inhibitory factor in the evolution of rat adjuvant arthritis. Arthritis Rheum, 41, 910–917.

Lehrke M, Lazar MA. (2005). The many faces of PPARgamma. Cell, 123, 993–999.

Lieberthal W, Menza SA, Levine JS. (1998). Graded ATP depletion can cause necrosis or apoptosis of cultured mouse proximal tubular cells. Am J Physiol, 274, F315–F327.

Makita H, Nishimura M, Miyamoto K, Nakano T, Tanino Y, Hirokawa J, Nishihira J, Kawakami Y. (1998). Effect of anti-macrophage migration inhibitory factor antibody on lipopolysaccharide-induced pulmonary neutrophil accumulation. Am J Respir Crit Care Med, 158, 573–579.

Mangelsdorf DJ, Thummel C, Beato M, Herrlich P, Schütz G, Umesono K, Blumberg B, Kastner P, Mark M, Chambon P, Evans RM. (1995). The nuclear receptor superfamily: the second decade. Cell, 83, 835–839.

Maschek G, Savaraj N, Priebe W, Braunschweiger P, Hamilton K, Tidmarsh GF, De Young LR, Lampidis TJ. (2004). 2-Deoxy-d-glucose increases the efficacy of adriamycin and paclitaxel in human osteosarcoma and non-small cell lung cancers in vivo. Cancer Res, 64, 31–34.

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.



Mikulowska A, Metz CN, Bucala R, Holmdahl R. (1997). Macrophage migration inhibitory factor is involved in the pathogenesis of collagen type II-induced arthritis in mice. J Immunol, 158, 5514–5517.

Mössner R, Schulz U, Krüger U, Middel P, Schinner S, Füzesi L, Neumann C, Reich K. (2002). Agonists of peroxisome proliferator-activated receptor gamma inhibit cell growth in malignant melanoma. J Invest Dermatol, 119, 576–582.

Motomura W, Okumura T, Takahashi N, Obara T, Kohgo Y. (2000). Activation of peroxisome proliferator-activated receptor gamma by troglitazone inhibits cell growth through the increase of p27KiP1 in human. Pancreatic carcinoma cells. Cancer Res, 60, 5558–5564.

Mueller E, Sarraf P, Tontonoz P, Evans RM, Martin KJ, Zhang M, Fletcher C, Singer S, Spiegelman BM. (1998). Terminal differentiation of human breast cancer through PPAR gamma. Mol Cell, 1, 465–470.

Mueller E, Sarraf P, Tontonoz P, Evans RM, Martin KJ, Zhang M, Fletcher C, Singer S, Spiegelman BM. (1998). Terminal differentiation of human breast cancer through PPAR gamma. Mol Cell, 1, 465–470.

Mueller E, Smith M, Sarraf P, Kroll T, Aiyer A, Kaufman DS, Oh W, Demetri G, Figg WD, Zhou XP, Eng C, Spiegelman BM, Kantoff PW. (2000). Effects of ligand activation of peroxisome proliferator-activated receptor gamma in human prostate cancer. Proc Natl Acad Sci USA, 97, 10990–10995.

Nakashiro K, Begum NM, Uchida D, Kawamata H, Shintani S, Sato M, Hamakawa H. (2003). Thiazolidinediones inhibit cell growth of human oral squamous cell carcinoma in vitro independent of peroxisome proliferator-activated receptor gamma. Oral Oncol, 39, 855–861.

Nielsen R, Pedersen TA, Hagenbeek D, Moulos P, Siersbaek R, Megens E, Denissov S, Børgesen M, Francoijs KJ, Mandrup S, Stunnenberg HG. (2008). Genome-wide profiling of PPARgamma:RXR and RNA polymerase II occupancy reveals temporal activation of distinct metabolic pathways and changes in RXR dimer composition during adipogenesis. Genes Dev, 22, 2953–2967.

Niino M, Iwabuchi K, Kikuchi S, Ato M, Morohashi T, Ogata A, Tashiro K, Onoé K. (2001). Amelioration of experimental autoimmune encephalomyelitis in C57BL/6 mice by an agonist of peroxisome proliferator-activated receptor-gamma. J Neuroimmunol, 116, 40–48.

Nijsten T, Geluyckens E, Colpaert C, Lambert J. (2005). Peroxisome proliferator-activated receptors in squamous cell carcinoma and its precursors. J Cutan Pathol, 32, 340–347.

Ondrey F. (2009). Peroxisome proliferator-activated receptor gamma pathway targeting in carcinogenesis: implications for chemoprevention. Clin Cancer Res, 15, 2–8.

Osborne JD, Flatow J, Holko M, Lin SM, Kibbe WA, Zhu LJ, Danila MI, Feng G, Chisholm RL. (2009). Annotating the human genome with disease ontology. BMC Genom, 10 (Suppl 1), S6.

Panigrahy D, Huang S, Kieran MW, Kaipainen A. (2005). PPARgamma as a therapeutic target for tumor angiogenesis and metastasis. Cancer Biol Ther, 4, 687–693.

Patel L, Pass I, Coxon P, Downes CP, Smith SA, Macphee CH. (2001). Tumor suppressor and anti-inflammatory actions of PPARgamma agonists are mediated via upregulation of PTEN. Curr Biol, 11, 764–768.

Rhead B, Karolchik D, Kuhn RM, Hinrichs AS, Zweig AS, Fujita PA, Diekhans M, Smith KE, Rosenbloom KR, Raney BJ, Pohl A, Pheasant M, Meyer LR, Learned K, Hsu F, Hillman-Jackson J, Harte RA, Giardine B, Dreszer TR, Clawson H, Barber GP, Haussler D, Kent WJ. (2010). The UCSC Genome Browser database: update 2010. Nucleic Acids Res, 38, D613–D619.

Rossi AG, Haslett C, Hirani N, Greening AP, Rahman I, Metz CN, Bucala R, Donnelly SC. (1998). Human circulating eosinophils

secrete macrophage migration inhibitory factor (MIF). Potential role in asthma. J Clin Invest, 101, 2869–2874.

Sakharkar MK, Sakharkar KR. (2008). Genetic and pharmacological interaction network of targets and drugs. Int J Integ Biol, 4, 1–8.

Sarraf P, Mueller E, Jones D, King FJ, DeAngelo DJ, Partridge JB, Holden SA, Chen LB, Singer S, Fletcher C, Spiegelman BM. (1998). Differentiation and reversal of malignant changes in colon cancer through PPARgamma. Nat Med, 4, 1046–1052.

Sato H, Ishihara S, Kawashima K, Moriyama N, Suetsugu H, Kazumori H, Okuyama T, Rumi MA, Fukuda R, Nagasue N, Kinoshita Y. (2000). Expression of peroxisome proliferator-activated receptor (PPAR)gamma in gastric cancer and inhibitory effects of PPARgamma agonists. Br J Cancer, 83, 1394–1400.

Scardoni G, Petterlini M, Laudanna C. (2009). Analyzing biological network parameters with CentiScaPe. Bioinformatics, 25, 2857–2859.

Scatena R, Bottoni P, Pontoglio A, Mastrototaro L, Giardina B. (2008). Glycolytic enzyme inhibitors in cancer treatment. Expert Opin Investig Drugs, 17, 1533–1545.

Shimazu T, Inoue I, Araki N, Asano Y, Sawada M, Furuya D, Nagoya H, Greenberg JH. (2005). A peroxisome proliferator-activated receptor-gamma agonist reduces infarct size in transient but not in permanent ischemia. Stroke, 36, 353–359.

Singh RP, Agarwal R. (2006). Mechanisms of action of novel agents for prostate cancer chemoprevention. Endocr Relat Cancer, 13, 751–778.

Su AI, Welsh JB, Sapinoso LM, Kern SG, Dimitrov P, Lapp H, Schultz PG, Powell SM, Moskaluk CA, Frierson HF Jr, Hampton GM. (2001). Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res, 61, 7388–7393.

Sundararajan S, Gamboa JL, Victor NA, Wanderi EW, Lust WD, Landreth GE. (2005). Peroxisome proliferator-activated receptor-gamma ligands reduce inflammation and infarction size in transient focal ischemia. Neuroscience, 130, 685–696.

Theocharis S, Kanelli H, Politi E, Margeli A, Karkandaris C, Philippides T, Koutselinis A. (2002). Expression of peroxisome proliferator activated receptor-gamma in non-small cell lung carcinoma: correlation with histological type and grade. Lung Cancer,36,3, 249–255.

Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A. (2003). PANTHER: a library of protein families and subfamilies indexed by function. Genome Res, 13, 2129–2141.

Tontonoz P, Singer S, Forman BM, Sarraf P, Fletcher JA, Fletcher CD, Brun RP, Mueller E, Altiok S, Oppenheim H, Evans RM, Spiegelman BM. (1997). Terminal differentiation of human liposarcoma cells induced by ligands for peroxisome proliferator-activated receptor gamma and the retinoid X receptor. Proc Natl Acad Sci USA, 94, 237–241.

Venkatachalam G, Kumar AP, Yue LS, Pervaiz S, Clement MV, Sakharkar MK. (2009). Computational identification and experimental validation of PPRE motifs in NHE1 and MnSOD genes of human. BMC Genomics, 10 (Suppl 3), S5.

Venkatachalam G, Sakharkar MK, Kumar AP, Clement MV. (2009). PPRESearch: Peroxisome Proliferator Activator Element Search Database. Int J Integ Biol, 8, 37–42.

Wang J, Wang J, Dai J, Jung Y, Wei CL, Wang Y, Havens AM, Hogg PJ, Keller ET, Pienta KJ, Nor JE, Wang CY, Taichman RS. (2007). A glycolytic mechanism regulating an angiogenic switch in prostate cancer. Cancer Res, 67, 149–159.

Warburg O, Posener K, Negelein E. (1924). Ueber den Stoffwechsel der Tumoren. Biochemische Zeitschrift, 152, 319–344.

Watson GS, Craft S. (2003). The role of insulin resistance in the pathogenesis of Alzheimer’s disease: implications for treatment. CNS Drugs, 17, 27–45.

Jour

nal o

f D

rug

Tar

getin

g D

ownl

oade

d fr

om in

form

ahea

lthca

re.c

om b

y 11

4.48

.172

.196

on

08/2

2/11

For

pers

onal

use

onl

y.

PPARγ disease gene network and identification of therapeutic targets for prostate cancer

Documents

Transcript of PPARγ disease gene network and identification of therapeutic targets for prostate cancer