Architecture of epigenetic reprogramming following Twist1-mediated epithelial-mesenchymal transition
- Gabriel G Malouf†1, 2,
- Joseph H Taube†3, 5,
- Yue Lu1,
- Tapasree Roysarkar3, 5,
- Shoghag Panjarian6,
- Marcos RH Estecio1,
- Jaroslav Jelinek1,
- Jumpei Yamazaki1,
- Noel J-M Raynal1,
- Hai Long1,
- Tomomitsu Tahara1,
- Agata Tinnirello3,
- Priyanka Ramachandran3,
- Xiu-Ying Zhang4,
- Shoudan Liang4,
- Sendurai A Mani3, 5Email author and
- Jean-Pierre J Issa1, 6Email author
© Malouf et al.; licensee BioMed Central Ltd. 2013
Received: 23 September 2013
Accepted: 24 December 2013
Published: 24 December 2013
Epithelial-mesenchymal transition (EMT) is known to impart metastasis and stemness characteristics in breast cancer. To characterize the epigenetic reprogramming following Twist1-induced EMT, we characterized the epigenetic and transcriptome landscapes using whole-genome transcriptome analysis by RNA-seq, DNA methylation by digital restriction enzyme analysis of methylation (DREAM) and histone modifications by CHIP-seq of H3K4me3 and H3K27me3 in immortalized human mammary epithelial cells relative to cells induced to undergo EMT by Twist1.
EMT is accompanied by focal hypermethylation and widespread global DNA hypomethylation, predominantly within transcriptionally repressed gene bodies. At the chromatin level, the number of gene promoters marked by H3K4me3 increases by more than one fifth; H3K27me3 undergoes dynamic genomic redistribution characterized by loss at half of gene promoters and overall reduction of peak size by almost half. This is paralleled by increased phosphorylation of EZH2 at serine 21. Among genes with highly altered mRNA expression, 23.1% switch between H3K4me3 and H3K27me3 marks, and those point to the master EMT targets and regulators CDH1, PDGFRα and ESRP1. Strikingly, Twist1 increases the number of bivalent genes by more than two fold. Inhibition of the H3K27 methyltransferases EZH2 and EZH1, which form part of the Polycomb repressive complex 2 (PRC2), blocks EMT and stemness properties.
Our findings demonstrate that the EMT program requires epigenetic remodeling by the Polycomb and Trithorax complexes leading to increased cellular plasticity. This suggests that inhibiting epigenetic remodeling and thus decrease plasticity will prevent EMT, and the associated breast cancer metastasis.
Epithelial-mesenchymal transition (EMT) is known to promote cellular plasticity during the formation of the mesoderm from epiblasts and the neural crest cells from the neural tube in the developing embryo as well as during adult wound healing . During EMT, epithelial cells lose their epithelial characteristics and acquire mesenchymal morphology, which facilitates cellular dissociation and migration. Similar to embryo development, neoplastic cells have been shown to reactivate EMT leading to cancer metastasis . Induction of EMT is also involved in the development of resistance to cytotoxic chemotherapy and targeted agents [3–5]. In addition, EMT imparts stem cell properties to differentiated cells . Since cancer cells seem to acquire stem cell properties dynamically in response to the tumor microenvironment and become differentiated at distant sites, it has been suggested that major epigenetic remodeling would occur during EMT to facilitate metastasis. Although DNA methylation changes at specific loci have been established during EMT [7, 8], changes in the global DNA methylation landscape are not well understood. Indeed, a recent report demonstrated that DNA methylation is largely unchanged during EMT mediated by transforming growth factor beta (TGF-β) , while another showed that EMT is associated with specific alterations of gene-related CpG-rich regions . Moreover, another report showed a striking difference in DNA methylation in non-small cell lung cancers between mesenchymal-like tumors and epithelial-like tumors, which display a better prognosis and exhibit greater sensitivity to inhibitors of epidermal growth factor receptor .
In addition to DNA methylation, EMT mediates epigenetic reprogramming through widespread changes in post-translational modifications of histones . However, it is unknown if switches in histone marks coordinate EMT and, in particular, whether genome regulation by Polycomb group (PcG) and Trithorax group (TrxG) proteins are critical regulators for this transition, as is the case for germ cell development and stem cell differentiation. Indeed, the TrxG complex activates gene transcription by inducing trimethylation of lysine 4 of histone H3 (H3K4me3) at specific sites, whereas the PcG complex represses gene transcription by trimethylation of lysine 27 on histone H3 (H3K27me3). Of note, a subset of promoters in embryonic stem cells are known to have methylation at both H3K4 and H3K27 (the bivalent state), which poise them for either activation or repression in different cell types upon differentiation . However, the transcriptional dynamics and the role of those bivalent genes in differentiated cells and during EMT are still poorly understood.
The development of genome-wide sequencing expanded our understanding of the plasticity of DNA methylation during differentiation of embryonic stem cells, tumorigenesis and metastasis [14, 15]. During the differentiation of embryonic stem cells into fibroblasts, the majority of DNA methylation changes occur outside of core promoters in partially methylated domains (PMDs), which represent large hypomethylated regions covering approximately 40% of our genome . Using genome-wide DNA methylation analyses, these PMDs have been shown to be hypomethylated in adipose tissue , placenta [17, 18], cultured breast cancer cells  and neuronal cells , as well as in several cancer types . PMDs overlap with domains of H3K27me3 and/or H3K9me3, transcriptional-repression associated histone marks, in IMR90 fibroblasts . In breast cancer, widespread DNA hypomethylation occurs primarily at PMDs in normal breast cells . However, whether DNA methylome changes during EMT recapitulate tumor formation remains unknown.
EMT is often a transient process, with changes in gene expression, increased invasiveness, and acquisition of stem cell properties such as increased tumor initiation, metastasis and chemotherapeutic-resistance. It’s transient nature suggests that significant features of an EMT could be regulated by epigenetic fluidity triggered by key transcription factors and signaling events in response to an alteration in the tumor microenvironment. We present genome-wide changes in DNA methylation and histone modifications in H3K4me3 and H3K27me3 following the induction of EMT by the ectopic expression of the transcription factor Twist1 using immortalized human mammary epithelial cells (HMLE) . Additionally, we compared the Twist1-expressing HMLE cells, hereafter HMLE Twist, cultured in a monolayer to the same cells cultured as mammospheres (MS), which enriches for cells with stem cell properties . We found that EMT is characterized by major epigenetic reprogramming required for phenotypic plasticity, with predominant alterations to polycomb targets. Moreover, we have shown that inhibition of the H3K27 methyltransferases EZH2 and EZH1 - part of the polycomb repressive complex 2 (PRC2) - either by short hairpin RNA (shRNA) or pharmacologically, blocks EMT and stemness properties.
Aberrant promoter DNA methylation induced by epithelial-mesenchymal transition is cell-type specific and regionally coordinated
Expression of Twist1 caused a dramatic change in DNA methylation both at CGIs and at non-CGIs (Figure 1B) whereas no changes were seen between cells with independent control vectors, suggesting that the methylation changes observed are related to Twist1 expression and not to random clonal drift (Figure 1C). To study the impact of these changes on gene expression, we focused on completely unmethylated genes (<1%) and identified 90 genes out of 3,008 (3%) that switched from <1% to >2% with an average gain of 5.4% DNA methylation. As expected, this was associated with about a four-fold decrease in the expression of these genes (P <0.0001; Figure 1D). The gain of methylation was higher in genes located within PMDs (12%; 37 out of 309) versus outside PMDs (2%; 53 out of 2,699; χ2 test, P <0.0001). Conversely, there were 39 genes that become unmethylated upon Twist1 expression, concomitant with around a two-fold increased expression of the respective genes (Figure 1D), such as FOXC2, a master regulator of EMT [26–28]. In contrast with promoter methylation, promoter hypomethylation was more frequent outside PMDs (4.6%; 31 out of 670) than within PMDs (1.8%; 8 out of 455; P <0.02). Gene ontology (GO) analysis for genes with methylation change associated with gene expression change showed enrichment for cell adhesion genes such as DSCAM, NID1 and NID2 (P = 0.002), consistent with the functional change of motility and migration of mesenchymal cells. Moreover, we found an enrichment of genes (P = 5e-05) involved in calcium binding protein coding genes (that is, FBN1, NPNT), suggesting a functional role for orchestrated calcium-binding proteins in EMT that may represent a novel therapeutic target for controlling cell plasticity. Collectively, these data suggest that induction of EMT by Twist1 results in a moderate change in the DNA methylation of core promoters.
Twist1 promotes global demethylation outside of core promoters
To understand the global methylation and demethylation changes that occur in response to induction of EMT by Twist1, we focused on 4,903 CpG sites with a threshold detection of a minimum of 100 tags that had a baseline methylation ≥70%, as is typical of most of the genome . Among these 4,903 CpG sites, one fifth (18.6%) lost DNA methylation following EMT (Table S2 in Additional file 1). We obtained comparable results using thresholds of 10 tags, and three tags per CpG site, covering 7,081 and 11,117 CpG sites respectively (data not shown). This widespread hypomethylation was mainly observed in PMDs (P <0.0001; Table S2 in Additional file 1) and was independent of the genomic CpG location in repeats and lamina-associated domains (Figure S2A-C in Additional file 3). Moreover, we found decreased methylation of repetitive elements at short interspersed nuclear elements, long interspersed nuclear elements and satellite repeats (Figure S2D in Additional file 3). Concomitant with global PMD demethylation, we also observed focal hypermethylation specific to those promoters (Figure S1C,D in Additional file 2), consistent with data recently reported in colon cancer . These data suggest that methylome change during EMT is reminiscent of methylome changes observed in cancer.
To understand the functional relevance of gene body methylation changes following the induction of EMT by Twist1, we performed Gene Set Enrichment Analysis (GSEA). GSEA is a computational method that assesses whether a defined set of genes (herein, gene bodies) shows statistically significant difference between two conditions (herein, between epithelial and mesenchymal states) . While there was no enrichment for any pathway associated with gain of gene body methylation, GSEA reveals enrichment for gene body hypomethylation for EMT targets in the CDH1-knockdown model (P <0.0001 ; Figure 1E), and for MIR34B and MIR34C targets  (Table S3 in Additional file 1). Concomitantly, average expression level of those hypomethylated genes was lower after knockdown of CDH1, as well as in basal-like compared to luminal-like breast cancer subtypes [32, 33] (P <0.004; Figure 1F). Collectively, these data suggest that following the induction of EMT by Twist expression, Twist reprograms the genome by demethylating gene bodies of epithelial cell-specific genes, leading to a decrease of their expression levels.
Twist1 increases the number of promoters with H3K4me3 by more than one fifth
Switches between H3K4me3 and H3K27me3 modulate transcriptional dynamics
Importantly, we sought to investigate if the changes we observed in Twist cells could be replicated in other EMT model systems such as Snail and TGF-β1-induced model systems. If we found similar findings across multiple EMT models, this would rule out adaptation and suggest that the effect we observed in Twist cells was due to EMT and not necessarily adaptation. In fact, we found that the majority of sites (14 out of 17) demonstrated the same directional change in H3K4me3 and/or H3K27me3 by ChIP-qPCR in HMLE Snail, TGF-β1 and Twist cells as we observed by ChIP-seq in HMLE Twist cells (Figure S4 in Additional file 5). The Pearson correlation coefficients for Snail versus Twist (r = 0.8982, P <0.0001), for Snail versus TGF-β1 (r = 0.4613, P = 0.006) and for TGF-β1 versus Twist (r = 0.1791, P = 0.3108) point to close similarities between Snail- and Twist-induced EMTs in their effects on H3K4me3 and H3K27me3 whereas expression of TGF-β1 has a less similar effect (Figure S5 in Additional file 6). We also observed similar results for the methylation of DNA elements assessed using bisulfite sequencing in the promoters of seven genes randomly chosen out of genes switching between H3K27me3 and H3K4me3 (Figure S6 in Additional file 7, Figure S7 in Additional file 8 and Figure S8 in Additional file 9). Collectively, our data suggest that our a majority of changes due to Twist expression are not due to adaptation but rather shared with cells undergoing EMT through other means.
Enrichment in bivalent genes upon Twist1 induction
Chromatin changes in spheroid cultures
Cells with stem cells properties are known to initiate sphere formation in non-attachment cultures including the cells induced to undergo EMT. Whereas previous work has shown that sphere culture actually decreases the number of bivalent genes , we observed an increase in the number of bivalent genes from 464 to 1,628 (Figure 5C). Furthermore, we compared the expression of genes, DNA methylation and histone modifications of HMLE Twist cells cultured in monolayers (two dimensional) or in MS (three dimensional) and found that the DNA methylation in the two states was highly similar (Spearman’s R >0.96, P <0.0001; Figure S9A in Additional file 10). By contrast, we found that 2.6% of the genes (849 out of 33,004) increased their expression more than four fold and 2.2% (737 out of 33,004) decreased their expression more than four fold when transitioned from monolayer to MS culture. GSEA analysis revealed positive enrichment for different pathways related to interferon responses (Table S7 in Additional file 1); by contrast, there was negative enrichment (exclusion) for pathways involved in proliferation , as well as for genes up-regulated in grade 3 versus grade 1 invasive breast cancer tumors  (Table S7 in Additional file 1). Consistent with earlier findings in an ovarian cancer model , there was a significant switch toward more genes marked by H3K27me3 in MS cells (3,607) compared to monolayer (2,411; Figure S9B in Additional file 10), but the majority of these genes were already transcriptionally silenced in monolayer culture in response to the overexpression of Twist1. This was also the case for the 186 genes switching from H3K4me3 in monolayer to H3K27me3 in MS. Remarkably, there was a loss of H3K4me3 mark in 2,894 genes when cultured in MS compared to monolayer (Figure S9B in Additional file 10). We then asked whether histone switches between HMLE vector and HMLE Twist cells cultured in spheres were consistent with gene expression changes despite culture condition, and found that this was the case (Figure S9C in Additional file 10). These data suggest that changes in H3K4me3 and H3K27me3 distribution accompany a Twist1-driven EMT, either cultured in monolayer or spheres.
Chromatin interplay between DNA methylation and histone modifications
To assess whether there is an opposing correlation between H3K4me3 and DNA methylation, we focused on the gene promoters that gain DNA methylation and decrease gene expression by two fold or more. Overall, 22 out of 30 genes lost their H3K4me3 mark, highly confirming the opposing relationship between DNA methylation and H3K4me3 (data not shown). Conversely, out of the 19 gene promoters losing DNA methylation and gaining expression, six genes gained a de novo H3K4me3 mark, while the 13 other genes that already had a H3K4me3 mark in HMLE vector cells kept it in HMLE Twist cells.
Epigenetic plasticity mediated by EZH2 is required for epithelial-mesenchymal transition
The findings presented here provide the first comprehensive genome-wide demonstration of the remodeling of the epigenome following Twist1-induced EMT, in terms of DNA methylation as well as trithorax and polycomb-related histone modifications, concurrent with quantitative gene expression analysis by RNA-seq. In particular, we show evidence that genome-wide DNA methylation changes involve focal hypermethylation and global hypomethylation of PMDs, reminiscent of methylome changes observed between normal mammary cells and breast cancers. This highlights, for the first time, that EMT recapitulates DNA methylation changes observed during breast cancer carcinogenesis . As with embryonic stem cell differentiation, the majority of changes occur outside core promoters, suggesting that cell plasticity, even in differentiated cells, involves a profound change in the DNA methylome and histone packaging. These data are in contrast with data reported recently showing unchanged DNA methylation during EMT mediated by TGF-β . One explanation could be that the 36-hour exposure to TGF-β was too short to observe changes in DNA methylation. In addition, our analysis of H3K4me3 and H3K27me3 ChIP data from HMLE Snail, Twist and TGF-β cells indicates that TGF-β cells are more divergent than EMT induced by Snail and Twist, an observation that was initially made using microarray data in Taube et al. . Nevertheless, our data provide powerful evidence to support the viewpoint recently brought forward by Pujadas and Feinberg that shifts in distinct regions of the epigenetic landscape, in our case PMDs, undergird cellular plasticity in both developmental and disease contexts .
Unexpectedly, our DREAM analysis found that a small gain of promoter DNA methylation is often coupled with a gain of H3K27me3, which strongly suggests that DNA methylation and PcG proteins act together. Indeed, DNA methylation is linked to polycomb protein-mediated repression; however, there is a debate as to whether DNA methylation and PcG proteins act together  or independently [44, 45] to silence gene expression. Deep sequencing allows more accurate measurement in the low methylation range and the etiology and functional significance of small changes in DNA methylation is unknown. Although DNA methylation could be contributing to gene silencing, it is also possible that silencing by PcG proteins weakens protection against DNA methylation and thus indirectly promotes small increases. It is interesting that remodeling at PMDs was associated with both DNA hypermethylation of promoters and global demethylation; we speculate that a redistribution of TET (ten-eleven-translocation) and DNMT (DNA (Cytosine-5-)-Methyltransferase) proteins may facilitate plasticity during EMT .
Most strikingly, cells that had undergone EMT displayed a large increase in the number of bivalent genes. Certainly, these poised genes might help mediate the stem cell properties previously reported in this model. How Twist1 leads to this gain of bivalency deserves further investigation; however, this is the first description of remodeling involving differentiated cells having similarities with the differentiation of embryonic stem cells .
Beyond the pure description of the landscape of DNA methylation and histone changes, these data provide a holistic framework for studying EMT-mediated changes in chromatin and gene expression. Indeed, our data show that key EMT markers switch between H3K4me3 and H3K27me3. As an example, we found an opposing histone switch for E-cadherin and N-cadherin, and that the expression change was associated with histones switches but not with DNA methylation changes. In fact, E-cadherin lost H3K4me3 and gained H3K27me3, whereas N-cadherin did the opposite. Moreover, other EMT regulators (different from EMT markers) were also found to be subject to H3K4me3 and H3K27me3 switches, such as ESRP1 and PDGF α, which are known to be involved in splicing and invadopodia formation respectively [34, 47]. Therefore, we speculate that other genes that exhibit histone modification switches are key EMT genes and deserve more focus in the study of this process. Indeed, with the development of deep sequencing and the decreased cost of sequencing, we speculate that alterations in histone landscape may be used in the future as a tool for drug discovery.
Lastly, we have shown that both the epigenetic modifiers EZH2 and EZH1 are essential for the stemness property of cells that have undergone EMT in response to Twist1 expression. EZH2 is a known marker of aggressive breast cancer , specifically through an influence on cancer stem cells , but a link to EMT has not yet been described. Because the expression of either EZH2 or EZH1 was not significantly altered by EMT, we hypothesize that the EMT-induced changes in the H3K27me3 landscape are mediated primarily by changes in EZH2 or EZH1 localization and function. Of note, the suppression of EZH2 by shRNA and pharmacologically by 3-deazaneplanocin A was sufficient to reduce both H3K27me3 and sphere formation, opening avenues for the use of EZH2 inhibitors to reverse EMT-induced tumor resistance to hypoxia or chemotherapeutics. Further work is called for to detail the mechanisms leading to these changes. Currently, there are active efforts to develop EZH2 inhibitors for cancer therapy, and our data suggest that they may also be useful to suppress epigenetic plasticity and its physiological consequences, such as metastasis and drug resistance.
We show that induction of EMT results in dramatic alterations in the epigenetic landscape involving significant changes in both DNA methylation (mainly outside core promoters) and histone modifications (that is, an increase in bivalent genes, gene switching between H3K4me3 and H3K27me3) and that these changes contribute to the stem cell properties and increase cellular plasticity. Thus, inhibiting epigenetic remodeling may block plasticity which facilitates EMT and associated breast cancer metastasis.
Characterization of human mammary epithelial cells
HMLE Twist cells were derived as shown in Yang et al. . Briefly, we overexpressed Twist1 using retroviral vectors and the transduced cells were selected using puromycin. This method yields a very high transduction rate (>99%). To further confirm the homogeneity of this population of cells, we performed immunofluorescence for VIM and FOXC2 markers, which are known to be induced following EMT (Figure S12 in Additional file 13). Similar results were obtained for HMLE Snail and HMLE TGF-β1 cells ( Figure S13 in Additional file 14).
Digital restriction enzyme analysis of methylation methods
DREAM was performed as reported previously . Briefly, genomic DNA was sequentially digested with a pair of enzymes recognizing the same restriction site (CCCGGG) containing a CpG dinucleotide, as previously reported. The first enzyme, SmaI, cut only at unmethylated CpG and left blunt ends. The second enzyme, XmaI, was not blocked by methylation and left a short 5′ overhang. The enzymes thus created methylation-specific signatures at the ends of digested DNA fragments. These were deciphered by next-generation sequencing using the Illumina Gene Analyzer II and Hiseq2000 platforms (Illumina, San Diego, CA, USA). Methylation levels for each sequenced restriction site were calculated based on the number of DNA molecules with the methylated or unmethylated signatures. Overall, we acquired around 36 million sequence tags per sample that were mapped to unique CpG sites in the human genome, using version hg18. Details of the DREAM method were previously reported by Challen et al. .
Genome annotation of DREAM data and statistical analysis
Genomic regions were defined according to National Center for Biotechnology Information coordinates downloaded from the University of California Santa Cruz website  in April 2010. Promoters were defined as regions between −1,000 bp and +1000 bp from TSSs for each RefSeq transcript. Gene bodies were defined as the transcribed regions, +1,000 bp from TSS to the end of the transcription sites for each RefSeq transcript. To calculate promoter methylation, we averaged the methylation level of all CpG sites located between −1000 bp and 1000 bp of the TSS. To estimate the FDR of promoter methylation using our method, we reasoned that a comparison of HMLE cells transduced with Twist1 according to culture conditions (monolayer versus MS) could be used because their methylation was remarkably identical (Spearman’s R >0.96, P <0.0001; Figure S3B in Additional file 4). The FDR was 0% for 5% gain of methylation for unmethylated genes (≤1%), and 0.001 (5 out of 4,655) for a difference of 2% of methylation for unmethylated genes (≤1%). Of note, a different transduction of HMLE with a different vector led to a minimal change of promoter methylation with 20 genes out of 3,933 genes gaining 2% methylation at unmethylated loci (≤1%). Using a minimum of 300 tags per SmaI site, only 7 out of 3,933 unmethylated genes gained more than 2% methylation, confirming the method’s high level of precision.
Because the majority of the genome is heavily methylated in contrast to CpG sites related to promoters located in CGI, we used different criteria to analysis DNA methylation changes at the genome level. Arbitrarily, we considered that CpG sites with methylation level ≤10% were unmethylated and a threshold gain of 20% was defined as hypermethylation; conversely, CpG sites with methylation level ≥70% were considered methylated and a threshold loss of 20% was defined as hypomethylation.
For the localization of CpG sites in PMDs or outside PMDs, we downloaded data published for fetal lung fibroblasts (IMR90) . The genes were considered to be located in PMDs if their promoters were located within PMDs. The graphs were prepared using GraphPad Prism 5.0 for Windows, GraphPad Software, San Diego California USA. For the graph of the distribution of CpG sites detected by DREAM, we average 25 to 50 neighbor points according to their distance to TSS, and the data were then smoothed using GraphPad Prism 5.0.
For gene body methylation, the average of CpG sites located +1000 bp from the start of transcription to the transcription end site was calculated. For GSEA, gene sets were downloaded from the Broad Institute’s MSigDB website . Gene set permutations were used to determine statistical enrichment of the gene sets using the difference of methylation between Twist1-transduced cells (monolayer) and vector cells.
Chromatin immunoprecipitation-sequence generation and mapping
ChIP-seq experiments performed for H3K4m3 and H3K27me3 produced more than 10 million uniquely mapped tags per chromatin modification. ChIP was performed according to the Abcam protocol  with few modifications. Library preparation and sequencing were performed on an Illumina/Solexa Genome Analyzer II or Hiseq 2000 in accordance with the manufacturer’s protocols. ChIP-seq reads were aligned to the human genome (hg18) using the Illumina Analyzer pipeline.
Unique reads mapped to a single genomic location were called peaks using the MACS software (version 220.127.116.11) for H3K4me3 marks (the window was 400 bp, and the P-value cutoff = 1e-5) .
For peak calling of H3K27me3, SICER (version 1.03) was used to detect peaks and enriched domains as the peaks were large and not as sharp as for H3K4me3 . The window size was set as 200 bp as default. The gap size was determined as recommended by Zang et al. , or at most 2 kb, since the performance worsens as the gap size increases beyond more than 10 times the window size. Following Wang et al. , the E-value was set at FDR ≤5%, which was estimated as E-value (the expected number of significant domains under the random background) divided by the number of identified candidate domains. The FDR cutoff to further filter out the candidate domains by comparing to control was set as 5%.
Sequencing reads for histone H3 DNA were used as control for MACS and SICER. Annotated RefSeq genes with a peak located at their promoters (−1 kb to +0.5 kb of TSS) were identified as being marked by H3K4me3 or H3K27me3 modifications. For the pathway analysis, GO analysis was done using DAVID [56, 57]. DAVID analyses were performed online using parameters of EASE value of <1 × 10–5, count of >10, fold enrichment of >2 and Bonfferroni of <1 × 10–2. For GSEA, gene sets were downloaded from the Broad Institute’s MSigDB website . Gene set permutations were used to determine statistical enrichment of the gene sets using the fold enrichment difference in histone modifications between H3K4me3 and H3K27me3 of mesenchymal cells (Twist1 cells) and vector cells.
To exclude the possibility of technical variations, we performed technical (independent IP) replicates for the ChIP of H3K4me3 and H3K27me3 in HMLE cells transduced with Twist1 and cultured in spheres followed by sequencing. Likewise, we performed a technical replicate for ChIP of H3K27me3 in HMLE vector cells. We obtained high correlations between the technical replicates (r >0.82; Table S10 in Additional file 1), suggesting that our findings were not due to chance. A list of primers used for ChIP-qPCR validation of selected genes is available in Table S11 in Additional file 1.
RNA-sequence library generation and mapping
RNA extraction from vector cells and Twist1-transduced cells (monolayer and sphere) were done with Trizol reagent (Invitrogen, 15596–026). Library preparation was done using a SOLiD™ Total RNA-seq Kit according to the manufacturer’s protocol (Life Technologies, Carlsbad, CA, USA). Reads sequenced produced by the SOLiD analysis pipeline were aligned with to the National Center for Biotechnology Information BUILD hg19 reference sequence. Short reads were mapped to the human reference genome (hg19) and exon junctions using the ABI Bioscope (version 1.21) pipeline with default parameters. Only the tags that mapped to the hg19 reference at full 35-nucleotide length were used. Reads that aligned to multiple positions were excluded. Tags mapped to RefSeq genes were counted to derive a measure of gene expression. To compare the gene expression values, we reasoned that cell type change associated with EMT could result in a change in the total amount of RNA. We therefore used the most conservative normalization by assuming most genes did not change their expression. This was done by constructing a histogram of expression ratio and by assuming that the maximum of the histogram corresponded to no change in gene expression. When compared to the normalization procedure where the total tags mapped to the genes were assumed to be constant, the differences were less than 10%.
All sequencing data and processed files are available on Gene Expression Omnibus accession number [GEO:GSE53026].
GGM and JHT are first co-authors.
SAM and JPI are senior co-authors.
chromatin immunoprecipitation sequencing
digital restriction enzyme analysis of methylation
false discovery rate
gene set enrichment analysis
human mammary epithelial cells
Polycomb group proteins
polymerase chain reaction
partially methylated domain
quantitative polymerase chain reaction
short hairpin RNA
transforming growth factor beta
Trithorax group proteins
transcription start site.
This work was supported in part by an MD Anderson Research Trust Fellow Award, funded by the George and Barbara Bush Endowment for Innovative Cancer Research (SAM) and DOD Breast Cancer Research Postdoctoral Fellowship (JHT). GGM was also funded by the following non-for-profit organizations: Fondation Nadine Midy, Foundation Nelia et Amadeo Barletta (FNAB) and the Association pour l’Aide à’la Recherche et l’Enseignement en Cancérologie (AAREC).
- Thiery JP, Acloque H, Huang RY, Nieto MA: Epithelial-mesenchymal transitions in development and disease. Cell. 2009, 139: 871-890. 10.1016/j.cell.2009.11.007.PubMedView ArticleGoogle Scholar
- Yang J, Mani SA, Donaher JL, Ramaswamy S, Itzykson RA, Come C, Savagner P, Gitelman I, Richardson A, Weinberg RA: Twist, a master regulator of morphogenesis, plays an essential role in tumor metastasis. Cell. 2004, 117: 927-939. 10.1016/j.cell.2004.06.006.PubMedView ArticleGoogle Scholar
- Ghoul A, Serova M, Astorgues-Xerri L, Bieche I, Bousquet G, Varna M, Vidaud M, Phillips E, Weill S, Benhadji KA, Lokiec F, Cvitkovic E, Faivre S, Raymond E: Epithelial-to-mesenchymal transition and resistance to ingenol 3-angelate, a novel protein kinase C modulator, in colon cancer cells. Cancer Res. 2009, 69: 4260-4269. 10.1158/0008-5472.CAN-08-2837.PubMedView ArticleGoogle Scholar
- Wang Z, Li Y, Kong D, Banerjee S, Ahmad A, Azmi AS, Ali S, Abbruzzese JL, Gallick GE, Sarkar FH: Acquisition of epithelial-mesenchymal transition phenotype of gemcitabine-resistant pancreatic cancer cells is linked with activation of the notch signaling pathway. Cancer Res. 2009, 69: 2400-2407. 10.1158/0008-5472.CAN-08-4312.PubMedPubMed CentralView ArticleGoogle Scholar
- Creighton CJ, Li X, Landis M, Dixon JM, Neumeister VM, Sjolund A, Rimm DL, Wong H, Rodriguez A, Herschkowitz JI, Fan C, Zhang X, He X, Pavlick A, Gutierrez MC, Renshaw L, Larionov AA, Faratian D, Hilsenbeck SG, Perou CM, Lewis MT, Rosen JM, Chang JC: Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features. Proc Natl Acad Sci USA. 2009, 106: 13820-13825. 10.1073/pnas.0905718106.PubMedPubMed CentralView ArticleGoogle Scholar
- Mani SA, Guo W, Liao MJ, Eaton EN, Ayyanan A, Zhou AY, Brooks M, Reinhard F, Zhang CC, Shipitsin M, Campbell LL, Polyak K, Brisken C, Yang J, Weinberg RA: The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell. 2008, 133: 704-715. 10.1016/j.cell.2008.03.027.PubMedPubMed CentralView ArticleGoogle Scholar
- Hiraguri S, Godfrey T, Nakamura H, Graff J, Collins C, Shayesteh L, Doggett N, Johnson K, Wheelock M, Herman J, Baylin S, Pinkel D, Gray J: Mechanisms of inactivation of E-cadherin in breast cancer cell lines. Cancer Res. 1998, 58: 1972-1977.PubMedGoogle Scholar
- Vrba L, Garbe JC, Stampfer MR, Futscher BW: Epigenetic regulation of normal human mammary cell type-specific miRNAs. Genome Res. 2011, 21: 2026-2037. 10.1101/gr.123935.111.PubMedPubMed CentralView ArticleGoogle Scholar
- McDonald OG, Wu H, Timp W, Doi A, Feinberg AP: Genome-scale epigenetic reprogramming during epithelial-to-mesenchymal transition. Nat Struct Mol Biol. 2011, 18: 867-874. 10.1038/nsmb.2084.PubMedPubMed CentralView ArticleGoogle Scholar
- Ruike Y, Imanaka Y, Sato F, Shimizu K, Tsujimoto G: Genome-wide analysis of aberrant methylation in human breast cancer cells using methyl-DNA immunoprecipitation combined with high-throughput sequencing. BMC Genomics. 2010, 11: 137-10.1186/1471-2164-11-137.PubMedPubMed CentralView ArticleGoogle Scholar
- Walter K, Holcomb T, Januario T, Du P, Evangelista M, Kartha N, Iniguez L, Soriano R, Huw LY, Stern HM, Modrusan Z, Seshagiri S, Hampton GM, Amler LC, Bourgon R, Yauch RL, Shames DS: DNA Methylation Profiling Defines Clinically Relevant Biological Subsets of Non-small Cell Lung Cancer. Clin Cancer Res. 2012, 18: 2360-2373. 10.1158/1078-0432.CCR-11-2635-T.PubMedView ArticleGoogle Scholar
- Wu CY, Tsai YP, Wu MZ, Teng SC, Wu KJ: Epigenetic reprogramming and post-transcriptional regulation during the epithelial-mesenchymal transition. Trends Genet. 2012, 28: 454-463. 10.1016/j.tig.2012.05.005.PubMedView ArticleGoogle Scholar
- Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, Fry B, Meissner A, Wernig M, Plath K, Jaenisch R, Wagschal A, Feil R, Schreiber SL, Lander ES: A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell. 2006, 125: 315-326. 10.1016/j.cell.2006.02.041.PubMedView ArticleGoogle Scholar
- Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo QM, Edsall L, Antosiewicz-Bourget J, Stewart R, Ruotti V, Millar AH, Thomson JA, Ren B, Ecker JR: Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009, 462: 315-322. 10.1038/nature08514.PubMedPubMed CentralView ArticleGoogle Scholar
- Hansen KD, Timp W, Bravo HC, Sabunciyan S, Langmead B, McDonald OG, Wen B, Wu H, Liu Y, Diep D, Briem E, Zhang K, Irizarry RA, Feinberg AP: Increased methylation variation in epigenetic domains across cancer types. Nat Genet. 2011, 43: 768-775. 10.1038/ng.865.PubMedPubMed CentralView ArticleGoogle Scholar
- Lister R, Pelizzola M, Kida YS, Hawkins RD, Nery JR, Hon G, Antosiewicz-Bourget J, O'Malley R, Castanon R, Klugman S, Downes M, Yu R, Stewart R, Ren B, Thomson JA, Evans RM, Ecker JR: Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature. 2011, 471: 68-73. 10.1038/nature09798.PubMedPubMed CentralView ArticleGoogle Scholar
- Aran D, Toperoff G, Rosenberg M, Hellman A: Replication timing-related and gene body-specific methylation of active human genes. Hum Mol Genet. 2011, 20: 670-680. 10.1093/hmg/ddq513.PubMedView ArticleGoogle Scholar
- Popp C, Dean W, Feng S, Cokus SJ, Andrews S, Pellegrini M, Jacobsen SE, Reik W: Genome-wide erasure of DNA methylation in mouse primordial germ cells is affected by AID deficiency. Nature. 2010, 463: 1101-1105. 10.1038/nature08829.PubMedPubMed CentralView ArticleGoogle Scholar
- Shann YJ, Cheng C, Chiao CH, Chen DT, Li PH, Hsu MT: Genome-wide mapping and characterization of hypomethylated sites in human tissues and breast cancer cell lines. Genome Res. 2008, 18: 791-801. 10.1101/gr.070961.107.PubMedPubMed CentralView ArticleGoogle Scholar
- Schroeder DI, Lott P, Korf I, LaSalle JM: Large-scale methylation domains mark a functional subset of neuronally expressed genes. Genome Res. 2011, 21: 1583-1591. 10.1101/gr.119131.110.PubMedPubMed CentralView ArticleGoogle Scholar
- Hon GC, Hawkins RD, Caballero OL, Lo C, Lister R, Pelizzola M, Valsesia A, Ye Z, Kuan S, Edsall LE, Camargo AA, Stevenson BJ, Ecker JR, Bafna V, Strausberg RL, Simpson AJ, Ren B: Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer. Genome Res. 2012, 22: 246-258. 10.1101/gr.125872.111.PubMedPubMed CentralView ArticleGoogle Scholar
- Dontu G, Abdallah WM, Foley JM, Jackson KW, Clarke MF, Kawamura MJ, Wicha MS: In vitro propagation and transcriptional profiling of human mammary stem/progenitor cells. Genes Dev. 2003, 17: 1253-1270. 10.1101/gad.1061803.PubMedPubMed CentralView ArticleGoogle Scholar
- Dumont N, Wilson MB, Crawford YG, Reynolds PA, Sigaroudinia M, Tlsty TD: Sustained induction of epithelial to mesenchymal transition activates DNA methylation of genes silenced in basal-like breast cancers. Proc Natl Acad Sci USA. 2008, 105: 14867-14872. 10.1073/pnas.0807146105.PubMedPubMed CentralView ArticleGoogle Scholar
- Jelinek J, Liang S, Lu Y, He R, Ramagli LS, Shpall EJ, Estecio MR, Issa JP: Conserved DNA methylation patterns in healthy blood cells and extensive changes in leukemia measured by a new quantitative technique. Epigenetics. 2012, 7: 1368-1378. 10.4161/epi.22552.PubMedPubMed CentralView ArticleGoogle Scholar
- Berman BP, Weisenberger DJ, Aman JF, Hinoue T, Ramjan Z, Liu Y, Noushmehr H, Lange CP, van Dijk CM, Tollenaar RA, Van Den Berg D, Laird PW: Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina-associated domains. Nat Genet. 2011, 44: 40-46. 10.1038/ng.969.PubMedPubMed CentralView ArticleGoogle Scholar
- Hader C, Marlier A, Cantley L: Mesenchymal-epithelial transition in epithelial response to injury: the role of Foxc2. Oncogene. 2010, 29: 1031-1040. 10.1038/onc.2009.397.PubMedPubMed CentralView ArticleGoogle Scholar
- Mani SA, Yang J, Brooks M, Schwaninger G, Zhou A, Miura N, Kutok JL, Hartwell K, Richardson AL, Weinberg RA: Mesenchyme Forkhead 1 (FOXC2) plays a key role in metastasis and is associated with aggressive basal-like breast cancers. Proc Natl Acad Sci USA. 2007, 104: 10069-10074. 10.1073/pnas.0703900104.PubMedPubMed CentralView ArticleGoogle Scholar
- Hollier BG, Tinnirello AA, Werden SJ, Evans KW, Taube JH, Sarkar TR, Sphyris N, Shariati M, Kumar SV, Battula VL, Herschkowitz JI, Guerra R, Chang JT, Miura N, Rosen JM, Mani SA: FOXC2 expression links epithelial-mesenchymal transition and stem cell properties in breast cancer. Cancer Res. 2013, 73: 1981-1992. 10.1158/0008-5472.CAN-12-2962.PubMedPubMed CentralView ArticleGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005, 102: 15545-15550. 10.1073/pnas.0506580102.PubMedPubMed CentralView ArticleGoogle Scholar
- Onder TT, Gupta PB, Mani SA, Yang J, Lander ES, Weinberg RA: Loss of E-cadherin promotes metastasis via multiple downstream transcriptional pathways. Cancer Res. 2008, 68: 3645-3654. 10.1158/0008-5472.CAN-07-2938.PubMedView ArticleGoogle Scholar
- Toyota M, Suzuki H, Sasaki Y, Maruyama R, Imai K, Shinomura Y, Tokino T: Epigenetic silencing of microRNA-34b/c and B-cell translocation gene 4 is associated with CpG island methylation in colorectal cancer. Cancer Res. 2008, 68: 4123-4132. 10.1158/0008-5472.CAN-08-0325.PubMedView ArticleGoogle Scholar
- Charafe-Jauffret E, Ginestier C, Monville F, Finetti P, Adelaide J, Cervera N, Fekairi S, Xerri L, Jacquemier J, Birnbaum D, Bertucci F: Gene expression profiling of breast cell lines identifies potential new basal markers. Oncogene. 2006, 25: 2273-2284. 10.1038/sj.onc.1209254.PubMedView ArticleGoogle Scholar
- Taube JH, Herschkowitz JI, Komurov K, Zhou AY, Gupta S, Yang J, Hartwell K, Onder TT, Gupta PB, Evans KW, Hollier BG, Ram PT, Lander ES, Rosen JM, Weinberg RA, Mani SA: Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes. Proc Natl Acad Sci USA. 2010, 107: 15449-15454. 10.1073/pnas.1004900107.PubMedPubMed CentralView ArticleGoogle Scholar
- Eckert MA, Lwin TM, Chang AT, Kim J, Danis E, Ohno-Machado L, Yang J: Twist1-induced invadopodia formation promotes tumor metastasis. Cancer Cell. 2011, 19: 372-386. 10.1016/j.ccr.2011.01.036.PubMedPubMed CentralView ArticleGoogle Scholar
- Reinke LM, Xu Y, Cheng C: Snail Represses the Splicing Regulator ESRP1 to Promote Epithelial-Mesenchymal Transition. J Biol Chem. 2012, 287: 36435-36442. 10.1074/jbc.M112.397125.PubMedPubMed CentralView ArticleGoogle Scholar
- Bapat SA, Jin V, Berry N, Balch C, Sharma N, Kurrey N, Zhang S, Fang F, Lan X, Li M, Kennedy B, Bigsby RM, Huang TH, Nephew KP: Multivalent epigenetic marks confer microenvironment-responsive epigenetic plasticity to ovarian cancer cells. Epigenetics. 2010, 5: 716-729. 10.4161/epi.5.8.13014.PubMedPubMed CentralView ArticleGoogle Scholar
- Rosty C, Sheffer M, Tsafrir D, Stransky N, Tsafrir I, Peter M, de Cremoux P, de La Rochefordiere A, Salmon R, Dorval T, Thiery JP, Couturier J, Radvanyi F, Domany E, Sastre-Garau X: Identification of a proliferation gene cluster associated with HPV E6/E7 expression level and viral DNA load in invasive cervical carcinoma. Oncogene. 2005, 24: 7094-7104. 10.1038/sj.onc.1208854.PubMedView ArticleGoogle Scholar
- Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, Nordgren H, Farmer P, Praz V, Haibe-Kains B, Desmedt C, Larsimont D, Cardoso F, Peterse H, Nuyten D, Buyse M, Van de Vijver MJ, Bergh J, Piccart M, Delorenzi M: Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006, 98: 262-272. 10.1093/jnci/djj052.PubMedView ArticleGoogle Scholar
- Cha TL, Zhou BP, Xia W, Wu Y, Yang CC, Chen CT, Ping B, Otte AP, Hung MC: Akt-mediated phosphorylation of EZH2 suppresses methylation of lysine 27 in histone H3. Science. 2005, 310: 306-310. 10.1126/science.1118947.PubMedView ArticleGoogle Scholar
- Lee ST, Li Z, Wu Z, Aau M, Guan P, Karuturi RK, Liou YC, Yu Q: Context-specific regulation of NF-kappaB target gene expression by EZH2 in breast cancers. Mol Cell. 2011, 43: 798-810. 10.1016/j.molcel.2011.08.011.PubMedView ArticleGoogle Scholar
- Tan J, Yang X, Zhuang L, Jiang X, Chen W, Lee PL, Karuturi RK, Tan PB, Liu ET, Yu Q: Pharmacologic disruption of Polycomb-repressive complex 2-mediated gene repression selectively induces apoptosis in cancer cells. Genes Dev. 2007, 21: 1050-1063. 10.1101/gad.1524107.PubMedPubMed CentralView ArticleGoogle Scholar
- Pujadas E, Feinberg AP: Regulated noise in the epigenetic landscape of development and disease. Cell. 2012, 148: 1123-1131. 10.1016/j.cell.2012.02.045.PubMedPubMed CentralView ArticleGoogle Scholar
- Vire E, Brenner C, Deplus R, Blanchon L, Fraga M, Didelot C, Morey L, Van Eynde A, Bernard D, Vanderwinden JM, Bollen M, Esteller M, Di Croce L, de Launoit Y, Fuks F: The Polycomb group protein EZH2 directly controls DNA methylation. Nature. 2006, 439: 871-874.PubMedView ArticleGoogle Scholar
- Kondo Y, Shen L, Cheng AS, Ahmed S, Boumber Y, Charo C, Yamochi T, Urano T, Furukawa K, Kwabi-Addo B, Gold DL, Sekido Y, Huang TH, Issa JP: Gene silencing in cancer by histone H3 lysine 27 trimethylation independent of promoter DNA methylation. Nat Genet. 2008, 40: 741-750. 10.1038/ng.159.PubMedView ArticleGoogle Scholar
- McGarvey KM, Greene E, Fahrner JA, Jenuwein T, Baylin SB: DNA methylation and complete transcriptional silencing of cancer genes persist after depletion of EZH2. Cancer Res. 2007, 67: 5097-5102. 10.1158/0008-5472.CAN-06-2029.PubMedView ArticleGoogle Scholar
- Song SJ, Poliseno L, Song MS, Ala U, Webster K, Ng C, Beringer G, Brikbak NJ, Yuan X, Cantley LC, Richardson AL, Pandolfi PP: MicroRNA-antagonism regulates breast cancer stemness and metastasis via TET-family-dependent chromatin remodeling. Cell. 2013, 154: 311-324. 10.1016/j.cell.2013.06.026.PubMedPubMed CentralView ArticleGoogle Scholar
- Brown RL, Reinke LM, Damerow MS, Perez D, Chodosh LA, Yang J, Cheng C: CD44 splice isoform switching in human and mouse epithelium is essential for epithelial-mesenchymal transition and breast cancer progression. J Clin Invest. 2011, 121: 1064-1074. 10.1172/JCI44540.PubMedPubMed CentralView ArticleGoogle Scholar
- Kleer CG, Cao Q, Varambally S, Shen R, Ota I, Tomlins SA, Ghosh D, Sewalt RG, Otte AP, Hayes DF, Sabel MS, Livant D, Weiss SJ, Rubin MA, Chinnaiyan AM: EZH2 is a marker of aggressive breast cancer and promotes neoplastic transformation of breast epithelial cells. Proc Natl Acad Sci USA. 2003, 100: 11606-11611. 10.1073/pnas.1933744100.PubMedPubMed CentralView ArticleGoogle Scholar
- Chang CJ, Yang JY, Xia W, Chen CT, Xie X, Chao CH, Woodward WA, Hsu JM, Hortobagyi GN, Hung MC: EZH2 promotes expansion of breast tumor initiating cells through activation of RAF1-beta-catenin signaling. Cancer Cell. 2011, 19: 86-100. 10.1016/j.ccr.2010.10.035.PubMedPubMed CentralView ArticleGoogle Scholar
- Challen G, Sun D, Jeong M, Luo M, Jelinek J, Berg J, Bock C, Vasanthakumar A, Gu H, Xi Y, Liang S, Lu Y, Darlington GJ, Meissner A, Issa JP, Godley LA, Li W, Goodell MA: Dnmt3a is essential for hematopoietic stem cell differentiation. Nat Genet. 2011, 4: 23-31.View ArticleGoogle Scholar
- UCSC genome browser. [http://genome.ucsc.edu/]
- Abcam protocol for chromatin immunoprecipitation. [http://www.abcam.com/ps/pdf/protocols/x_chip_protocol.pdf]
- Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS: Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008, 9: R137-10.1186/gb-2008-9-9-r137.PubMedPubMed CentralView ArticleGoogle Scholar
- Zang C, Schones DE, Zeng C, Cui K, Zhao K, Peng W: A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics. 2009, 25: 1952-1958. 10.1093/bioinformatics/btp340.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang Z, Zang C, Cui K, Schones DE, Barski A, Peng W, Zhao K: Genome-wide mapping of HATs and HDACs reveals distinct functions in active and inactive genes. Cell. 2009, 138: 1019-1031. 10.1016/j.cell.2009.06.049.PubMedPubMed CentralView ArticleGoogle Scholar
- da Huang W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4: 44-57.PubMedView ArticleGoogle Scholar
- da Huang W, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009, 37: 1-13. 10.1093/nar/gkn923.PubMedView ArticleGoogle Scholar
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