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Identification of Genomic Targets of Transcription Factor Aebp1 and Its Role in Survival of Glioma Cells

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Identification of Genomic Targets of Transcription Factor Aebp1 and Its Role in Survival of Glioma Cells Published OnlineFirst June 20, 2012; DOI: 10.1158/1541-7786.MCR-11-0488 Molecular Cancer Cancer Genes and Genomics Research Identification of Genomic Targets of Transcription Factor Aebp1 and its role in Survival of Glioma Cells Jayashree Ladha, Swati Sinha, Vasudeva Bhat, Sainitin Donakonda, and Satyanarayana M.R. Rao Abstract A recent transcriptome analysis of graded patient glioma samples led to identification of AEBP1 as one of the genes upregulated in majority of the primary GBM as against secondary GBM. Aebp1 is a transcriptional repressor that is involved in adipogenesis. It binds to AE-1 element present in the proximal promoter of aP2 gene that codes for fatty acid binding protein (FABP4). A comprehensive study was undertaken to elucidate the role of AEBP1 overexpression in glioblastoma. We employed complementary gene silencing approach to identify the genes that are perturbed in a glioma cell line (U87MG). A total of 734 genes were differentially regulated under these conditions (1.5-fold, P 0.05) belonging to different GO categories such as transcription regulation, cell growth, proliferation, differentiation, and apoptosis of which perturbation of 114 genes of these pathways were validated by quantitative real time PCR (qRT-PCR). This approach was subsequently combined with ChIP-chip technique using an Agilent human promoter tiling array to identify genomic binding loci of Aebp1 protein. A subset of these genes identified for Aebp1 occupancy was also validated by ChIP-PCR. Bioinformatics analysis of the promoters identified by ChIP-chip technique revealed a consensus motif GAAAT present in 66% of the identified genes. This consensus motif was experimentally validated by functional promoter assay using luciferase as the reporter gene. Both cellular proliferation and survival were affected in AEBP1-silenced U87MG and U138MG cell lines and a significant percentage of these cells were directed towards apoptosis. Mol Cancer Res; 10(8); 1039–51. Ó2012 AACR. Introduction (5). However, secondary GBM often exhibits P53 muta- tions, PDGF/PDGFR overexpression, RB loss, and CDK4 Glioblastoma multiforme (GBM) is the most common fi and malignant form of primary tumor of CNS in adults, ampli cations (6). Recent studies have shown, however, that which is characterized by a median survival of less than a year. there is an overlapping spectrum of mutations in these 2 types of GBM (7, 8). In one of our earlier studies we had found The prognostic behavior of GBMs is rather poor and hence AEBP1 there have been efforts to identify molecular signatures and expression to be upregulated in primary GBMs as opposed to progressive secondary GBMs (9). Aebp1 was also to discover new biomarkers for characterizing different fi types and stages of GBMs (1–4). GBM is broadly classified originally identi ed as a transcriptional repressor that binds to into primary and secondary GBM (WHO), each one arising adipocyte enhancer 1 (AE-1 element) located in the proximal de promoter region of the adipose P2 gene, which codes for through distinct genetic pathways. Primary GBM arises fi novo and is frequently associated with amplification and/or adipocyte speci c fatty acid binding protein 4 (FABP4; EGFR PTEN ref. 10). Aebp1 is also overexpressed in transgenic mouse overexpression of and deletion combined with ERBB2 INK4A/ARF and CDKN2A losses and MDM2 amplification probasin-Neu ( ) induced advanced prostate cancer (11). However, the exact role of AEBP1 in tumorigenesis is not clear and hence we set out to identify the genomic targets Authors' Affiliation: Chromatin Biology Laboratory, Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific of this transcription factor to understand its biology in the Research, Bangalore, India cellular context. Toward this direction we have undertaken a Note: Supplementary data for this article are available at Molecular Cancer detailed study to analyze the Aebp1 genomic targets by Research Online (http://mcr.aacrjournals.org/). transcriptome profiling of AEBP1 downregulated U87MG cells and its role in cell proliferation, growth, and survival. J. Ladha and S. Sinha have made equal contributions to this article. V. Bhat and S. Donakonda have made equal contributions to this article. Materials and Methods Corresponding Author: Satyanarayana M.R. Rao, Chromatin Biology Cell culture and AEBP1 silencing Laboratory, Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India. Phone: +91 U87MG and U138MG cells (ATCC) were grown in 9886233032; Fax: +91-80-22082766/23602468; E-mail: Eagle's Minimal Essential Medium supplemented with [email protected] 10% FBS (Sigma-Aldrich). Cells were transfected with doi: 10.1158/1541-7786.MCR-11-0488 100 nmol/L siRNA pool targeted against AEBP1 (Dharma- Ó2012 American Association for Cancer Research. con Inc.). Quantitative real-time PCR (qRT-PCR) was done www.aacrjournals.org 1039 Downloaded from mcr.aacrjournals.org on September 27, 2021. © 2012 American Association for Cancer Research. Published OnlineFirst June 20, 2012; DOI: 10.1158/1541-7786.MCR-11-0488 Ladha et al. using Eva Green (Biorad) on a Biorad iQ5 cycler. Down- intraarray median normalization to remove dye bias and regulation of AEBP1 was assessed by qRT-PCR and Western interarray median normalization to bring the distribution blot analysis. All the primers used in this study are listed in uniform across replicates. The significantly enriched genes Supplementary Table S1. were detected based on the statistical p-value using White- head Per Array Neighborhood Model. False discovery rate Global gene expression analysis and ChIP-chip promoter analysis (13) was then applied to 11,659 enriched genes for tiling array Aebp1 promoter occupancy using Bioconductor R software Total RNA was isolated from 4 independent scrambled (14). Peaks were considered significant at a P-value 0.05. siRNA and 5 test siRNA–AEBP1 transfected U87MG cells Two biological replicate experiments were carried out for and hybridized to Affymetrix U133Plus 2.0 gene chip that Aebp1 occupancy analysis. queried 47,000 genes. The results were analyzed initially using Gene-Chip operating software and the data were subsequently Promoter sequences retrieval and motif prediction processed using ArrayAssist (Agilent) to statistically analyze Promoter sequences (À1 kb) of perturbed genes were changesingeneexpression.qRT-PCRvalidationfor114genes retrieved using 3 major databases viz., transcription reg- was done using Eva Green (Biorad) on a Biorad iQ5 cycler. ulatory element database (TRED), eukaryotic promoter ChIP assays were done according to the previously described database (EPD), and UCSC genome browser (20–22). method (12). Briefly, log-phase U87MG cells were fixed with De novo motif discovery was done using CisFinder algo- formaldehyde and chromatin was sonicated to generate an rithm (23) to identify motifs in most enriched sequences average length of 200 to 800 base pairs. After preclearing, the by ChIP experiments. Position frequency matrices were chromatin solution was incubated with affinity-purified rabbit estimated from counts of n-mer words with and without polyclonal Aebp1 antibody (SantaCruz) or purified rabbit IgG gaps and clustered to generate nonredundant sets of antibody. The abundance of genomic DNA containing a motifs. Web logo was used to construct sequence logos promoter was determined by PCR amplification using (24). To test the validity of motifs predicted from ChIP- sequence-specificprimerpairsflanking Aebp1 binding site chip data, we built control data set of 5810 random identified through position weighted matrix analysis within sequences each of approximately 50mer length from À1 kb promoters. For ChIP-chip analysis, amplified immu- human, using RSAT tools (25) and motif analysis were noprecipitated DNA and input genomic DNA was labeled conducted for these random sequences. with Cy5 and Cy3 fluorophores respectively, using random primer labeling kit (Invitrogen Corp.). Five micrograms each Correlation analysis of immunoprecipitated and genomic DNA was combined Pearson's correlation coefficients were calculated between along with human Cot-1 DNA and hybridized to each of the all replicates in gene expression and promoter tiling arrays Agilent human promoter tiling array (2 Â 224) containing using R statistical computing (14). 474,393 probes excluding control features. Transcription factor Network Analysis Microarray data analysis The DNA binding sites of 25 transcription factors, which Gene expression data was normalized using PLIER algo- were perturbed upon AEBP1 gene silencing and also vali- rithm in ArrayAssist (Agilent) and expression changes were dated by qRT-PCR, were mined from the literature. These filtered at >1.5-fold between experiments. Genes were binding sites were searched in À1 kb promoters of each of considered significantly perturbed at a p-value of 0.05. the transcription factor genes and transcription factor gene The method of Benjamini and Hochberg (13) for false network among these 25 transcription factors was generated. discovery rate was set to 0.05 using R software (14). These The heat maps were constructed using Java Tree view genes were then subjected to an unsupervised 2-way average software (16). linkage hierarchical cluster analysis with uncentered corre- lation as similarity metric using Cluster 3.0 software (15). Identification of Aebp1 binding site by functional Java Tree view version 1.1.4 was used to visualize structure of promoter assay the data (16). Functional
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