DNA methylation data for identification of epigenetic targets of resveratrol in triple negative breast cancer cells

Previous studies revealed that some bioactive food components have anti-cancer effects. However epigenetic effects of dietary compound resveratrol are largely unknown in breast cancer cells (M.A. Dawson, T. Kouzarides, 2012) [1]. Here we provide novel data and comparisons of DNA methylation status of promoter gene regions in response to resveratrol treatment at 24 h and 48 h versus untreated MDA-MB-231 breast cancer cells. DNA methylation changes were measured using Array-PRIMES method (aPRIMES) followed by whole-genome hybridization using human DNA methylation promoter microarray NimbleGen HG18 Refseq Promoter 3×720 K array. Our data were associated to corresponding changes in mRNA expression in a set of cancer-related genes. Using gene ontology analysis we also identify cancer-related cellular processes and pathways that can be epigenetically reprogramed by resveratrol. Data in this article are associated to the research articles “Methylation Landscape of Human Breast Cancer Cells in Response to Dietary Compound Resveratrol”. Medina Aguilar et al., PLoS ONE 11(6): e0157866. doi:10.1371/journal.pone.0157866 2016 (A.R. Medina, P.C. Pérez, L.A. Marchat, P. Gariglio, M.J. García, C.S. Rodríguez, G.E. Ruíz, et al., 2016) [2]; and “Resveratrol inhibits cell cycle progression by targeting Aurora kinase A and Polo-like kinase 1 in breast cancer cells” in Oncology Reports. Medina Aguilar et al., 2016 Jun; 35(6):3696-704. doi: 10.3892/or.2016.4728 (A.R. Medina, P. Gariglio, M.J. García, O.E. Arechaga, S.N. Villegas, C.M. Martínez et al., 2016) [3].


a b s t r a c t
Previous studies revealed that some bioactive food components have anti-cancer effects. However epigenetic effects of dietary compound resveratrol are largely unknown in breast cancer cells (M.A. Dawson, T. Kouzarides, 2012) [1]. Here we provide novel data and comparisons of DNA methylation status of promoter gene regions in response to resveratrol treatment at 24 h and 48 h versus untreated MDA-MB-231 breast cancer cells. DNA methylation changes were measured using Array-PRIMES method (aPRIMES) followed by whole-genome hybridization using human DNA methylation promoter microarray NimbleGen HG18 Refseq Promoter 3 Â 720 K array. Our data were associated to corresponding changes in mRNA expression in a set of cancer-related genes. Using gene ontology analysis we also identify cancer-related cellular processes and pathways that can be epigenetically repro-  Provides genomic data indicating that resveratrol impacts the epigenetic landscape by changing DNA methylation status of specific oncogenes and tumor suppressor genes in breast cancer cells.
Analyses indicate that resveratrol epigenetically alters regulation of particular genes involved in cancer in triple negative breast cancer cells.
Data provided here serves as a novel and free resource for researchers working in the field of epigenetic regulation of cancer related genes in response to naturally occurring dietary compounds.

Genome-wide analysis of DNA methylation
High molecular weight DNA from MDA-MB-231 triple negative breast cancer cell line untreated and treated with resveratrol (100 mM) at 24 h and 48 h was extracted using the DNeasyKit (Qiagen, Germany) according to the manufacturer's instructions. For detection of the methylation status of CpG islands (CGIs), we used array-based profiling of reference-independent methylation status (aPRIMES) technology in MDA-MB-231 cells untreated and treated with resveratrol at 24 h and 48 h. This method is based on the differential restriction and competitive hybridization of methylated and unmethylated DNA by methylation-specific and methylation-sensitive restriction enzymes, and NimbleGen HG18 Refseq Promoter 3 Â 720 K array to measure the differential DNA methylation as described in Fig. 1. Briefly, genomic DNA (500 ng) was restricted with MseI enzyme (New England Biolabs) and ligated to adapter primers according to the recommendations of the supplier. Then, onehalf of the ligated MseI fragments were digested with the methylation-sensitive restriction enzymes HpaII and BstUI to cut unmethylated CGIs, and the remaining half is digested with the methylationspecific enzyme McrBC to cut CGIs methylated. Restricted samples were then subjected to 20 cycles of linker-mediated PCR, differentially labeled with fluorescent dyes Cy3 and Cy5, and competitively hybridized to a NimbleGen HG18 Refseq Promoter 3 Â 720 K array following the conditions recommended by the supplier. DNA methylation analysis raw data was normalized and differential intensity Table 4 (continued )   PXDN, RADIL, RASGEF1A, RBMX, RFTN1, RGS12, RHOB, RHOH, RNF213, RNF43,  RPL5, RPTOR, RUNDC2A (SNX29), RXRA, RYR1, SALL3, SCN11A, SCN5A,  SDHAF2, SEMA5B, SEPT7P2, SETBP1, SH3GL1, SIGLEC1, SMC3, SORCS2, SOX9,  SPTBN4, SRCAP, SS18L1, ST6GAL2, STC1, STK11, STK19, SYNE2, TAL2, TBX18,  TCF3, TCHH, TERT, TFDP1, TFPT, TLL2, TLR2, TMEM132D, TMPRSS2, TMSL3  (TMSB4XP8), TNFRSF14, TOP1, TP53, TRAF5, TRAF7, TRRAP, TSC2, TTC18  (CFAP70), TUBA3C, WAS, WDR24, WHSC1, WIF1, WIPF2, WT1, XPA, XPC, ZFR2,  ZMYND10, ZNF331, ZNF469, ZNF497, ZNF536, ZZEF1. of each probe compared with experimental IP sample (IP) and control input sample (input) was calculated using the NimbleGen software DEVA. Average fold change (IP versus input) each 50 bp bin for a range of 2.44 kb upstream and 610 bp downstream window from RefSeq transcription start sites (TSS). The methylation peak values were mapped to features using DEVA software. Regions showing enrichment at 4 or more consecutive loci were integrated together to form a single "peak". Clusters of enriched regions separated by more than 500 base pairs were integrated as separate peaks, which reflected the probability of methylation for the designated peak and/or gene at a p-value of less than 0.01. The functional annotation of target genes based on Gene Ontology was performed using DAVID (Database for Annotation, Visualization and Integrated Discovery).

Raw data processing and statistical analysis
A two way ANOVA was performed to identify differentially methylated genes. Only genes with statistically significant differences in DNA methylation levels (p-value o0.05) were included.