Genomic retargeting of p53 and CTCF is associated with transcriptional changes during oncogenic HRas-induced transformation

Schwartz, Michal; Portugez, Avital Sarusi; Attia, Bracha Zukerman; Tannenbaum, Miriam; Cohen, Leslie; Loza, Olga; Chase, Emily; Turman, Yousef; Kaplan, Tommy; Salah, Zaidoun; Hakim, Ofir

doi:10.1038/s42003-020-01398-y

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Published: 25 November 2020

Genomic retargeting of p53 and CTCF is associated with transcriptional changes during oncogenic HRas-induced transformation

Michal Schwartz ORCID: orcid.org/0000-0001-5442-0201¹^na1^nAff4,
Avital Sarusi Portugez¹^na1,
Bracha Zukerman Attia¹,
Miriam Tannenbaum¹,
Leslie Cohen¹,
Olga Loza¹,
Emily Chase¹,
Yousef Turman²,
Tommy Kaplan ORCID: orcid.org/0000-0002-1892-5461³,
Zaidoun Salah² &
…
Ofir Hakim ORCID: orcid.org/0000-0003-0063-2721¹

Communications Biology volume 3, Article number: 696 (2020) Cite this article

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Abstract

Gene transcription is regulated by distant regulatory elements via combinatorial binding of transcription factors. It is increasingly recognized that alterations in chromatin state and transcription factor binding in these distant regulatory elements may have key roles in cancer development. Here we focused on the first stages of oncogene-induced carcinogenic transformation, and characterized the regulatory network underlying transcriptional changes associated with this process. Using Hi-C data, we observe spatial coupling between differentially expressed genes and their differentially accessible regulatory elements and reveal two candidate transcription factors, p53 and CTCF, as determinants of transcriptional alterations at the early stages of oncogenic HRas-induced transformation in human mammary epithelial cells. Strikingly, the malignant transcriptional reprograming is promoted by redistribution of chromatin binding of these factors without major variation in their expression level. Our results demonstrate that alterations in the regulatory landscape have a major role in driving oncogene-induced transcriptional reprogramming.

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Introduction

Altered gene expression programs are a major factor in the development and expansion of cancer cells. These aberrant transcription patterns support and promote biological processes that are required along tumorigenesis, such as proliferation, resistance to cell death, and induction of angiogenesis¹. Transcriptional programs are driven by a complex network of transcription factors acting on regulatory DNA elements such as enhancers (reviewed in²). Although changes in transcriptional programs during tumorigenesis could be driven directly by DNA mutations, in many cases they are affected indirectly by various altered cellular pathways. Transcription factors may affect transcriptional programs due to changes in their DNA binding, without harboring mutations, or change in their expression levels. Much of the effort to characterize the pathways contributing to cancer development mostly focus on the identification of either upstream mutations³ or downstream RNA and protein expression patterns of cancer cells⁴. These are indeed very powerful approaches to reveal important drivers of cancer development and characterize cancer types. However, demarcating the intermediate molecular events triggering gene expression programs holds great promise for generating novel and improved diagnostic tools and identifying relevant targets for therapeutic interventions^5,6. Indeed, a recent pan-cancer analysis demonstrated global enhancer activation in most cancer types compared with matched normal tissues⁷. Thus studying the regulatory basis of transcription reprogramming associated with carcinogenesis can define a set of transcription factors that are involved in shaping the transcriptional landscape of the cancer cell.

Transcription programs are regulated by a complex network of transcription factors, cofactors, and chromatin regulators^2,8. This regulation occurs via binding of transcription factors to regulatory elements, i.e., enhancers, in a sequence-specific manner, and recruitment of transcriptional coactivators or corepressors^2,8. Enhancer sequences contain multiple binding sites for a variety of transcription factors, and mediate transcriptional control by their combinatorial binding^2,9. This regulation is further modulated by the epigenetic status of enhancers. Therefore, mapping active enhancers can be key to delineating regulatory networks driving transcriptional reprogramming during the process of carcinogenesis. Active enhancer regions are associated with different molecular characteristics that allow their identification, such as histone modifications, specifically, H3K27ac and H3K4me1^10,11,12,13, and occupancy of transcriptional co-activators, such as Ep300¹⁴. Importantly, for an enhancer to be bound by transcription factors and other regulatory DNA binding proteins, it must be accessible, therefore, a technique that is commonly used to map active regulatory elements is the detection of open chromatin by sensitivity to nucleases^15,16. ATAC-seq is a recently developed sensitive method to measure chromatin accessibility which enables mapping of active regulatory regions in a genome-wide manner¹⁷.

A major challenge, however, for inferring mechanistic principles of transcriptional regulation, is the difficulty to assign specific regulatory elements to their distant target genes. Enhancers and their regulated target genes can be located hundreds of kbs apart¹⁸. Importantly, the spatial organization of genomic information is non-random and is a major regulatory component of gene transcription^19,20. In recent years it was demonstrated that mammalian chromosomes are partitioned into units of internal high spatial connectivity²¹. These units, termed Topologically Associating Domains (TADs), are large (few tens of kb to 3 Mb) chromosomal units encompassing multiple genes and regulatory elements²². Although the borders between TADs are considered cell-type invariant, at the local scale, chromosomal contacts between genes and their regulatory elements (within TADs) show cell-type specificity¹⁹. Moreover, variation in gene expression within TADs is highly correlated during differentiation and response to external stimuli, suggesting that TADs insulate the activity of regulatory elements to specific genes^18,23,24. Therefore, TADs offer a proper and applicable framework to couple between regulatory elements and their target genes and study dynamic transcriptional regulation.

Here we combined, transcriptome analysis, genome-wide mapping of active regulatory sites with chromosome topology profiles to delineate the regulatory network underlying transcriptional reprogramming during carcinogenic transformation induced by overexpression of oncogenic HRas in human mammary epithelial cells. Our results reveal major gene expression changes accompanied by significant alterations in the regulatory landscape upon this transformation process. Linking, in 3D, differentially expressed genes to the catalog of dynamic regulatory elements revealed two candidate transcription factors, p53 and CTCF that contribute to these transcriptional changes. Strikingly, these factors act by redistribution of their chromatin binding without major variation in their expression level. Moreover, this work highlights the power of the approach by which the transcriptional regulatory landscape can be analyzed in view of the 3D organization of the genome to reveal dynamic regulatory networks underlying cancer phenotypes.

Results

Mutations in the HRas oncogene are among the most frequent in human tumors. To investigate transcriptional regulatory changes during early carcinogenesis, we introduced an oncogenic copy of HRas carrying a single point mutation at amino acid 12 (G12V) into MCF10A cells, a nontransformed, near-diploid, immortalized mammary epithelial cell line. These cells (from here on termed G12V) first undergo growth arrest but after a few weeks continue growing and exhibit higher proliferation rates and higher cell survival capacity compared to their MCF10A counterparts (Fig. 1a, b). The tumorigenicity of the G12V cells was tested by different means. Anchorage-independent survival and growth are hallmarks of carcinogenic cells. Indeed, G12V cells exhibit growth in soft agar (Fig. 1c) and adherence independent survival (Fig. 1d). Following HRas dependent transformation we noticed a morphological change towards a more mesenchymal morphology, we, therefore, tested invasive potential and found that the G12V cells harbor invasive properties as tested by Matrigel invasion assay (Fig. 1e). Finally, the examination of their tumorigenic potential in vivo shows that G12V cells are able, although with low penetrance, to form small tumors when injected into the mammary fat pad of Nod-SCID mice (Fig. 1f). Thus the introduction of G12V HRas oncogene into MCF10A rendered these cell tumorigenic.

**Fig. 1: Characterization of G12V cells.**

In order to define the transcriptional changes induced by the introduction of G12V HRas oncogene in MCF10A cells, we performed RNA-sequencing comparing G12V and MCF10A cells. We identified 1144 up-regulated and 1136 down-regulated genes (>1.3 fold, p < 0.05) accompanying this transformation process (Fig. 2a and Supplementary Data 1). Down-regulated genes are enriched in functions of cell death (p = 2.67E-18) and apoptosis (p = 2.53E-16) (Fig. 2b). Up-regulated genes are enriched in functions of invasion (p = 2.5E-50), cell survival (p = 1.42E-26), and cell movement (1.64E-25) and include tumor-related genes (Fig. 2c). The enriched molecular pathways (Fig. 2d) also include some signaling pathways tightly related to breast cancer, such as IL-8 signaling²⁵ and p53 signaling^3,4. The top enriched upstream factors that can explain these gene expression changes (Fig. 2e, upstream factors) also include p53. Thus the alteration of gene expression profiles accompanying the expression of the G12V HRas oncogene is indicative of tumorigenic process.

**Fig. 2: Transcriptional reprogramming induced by the G12V HRas oncogene.**

In order to examine the changes in regulatory chromatin that dictate the transcriptional changes, we applied ATAC-seq which allows the discovery of accessible chromatin loci genome-wide. We identified 42,546 accessible loci in MCF10A cells and 46,367 in G12V cells. Using stringent analysis of differential peaks in MACS we identified a few thousands of increased and decreased peaks representing Differentially Accessible Regions (DARs, Fig. 3a, b). 5,355 were induced in G12V compared to MCF10A cells (gained DARs) while 7,589 were reduced (lost DARs). Overall, the distribution of accessible regions in the genome (Fig. 3c) is similar to what was previously reported¹⁶ with ~30% of loci near promoters. Interestingly the proportion of DARs at promoters is much lower (5% in G12V and 8% in MCF10A) relative to the general distribution, indicating that the major changes in chromatin accessibility are at gene-distant regions (Fig. 3c). Examination of the connection between chromatin accessibility at promoters and gene expression reveals that up-regulated genes are more associated with gained accessibility while down-regulated genes associate more with loss of accessible regions (Fig. 3d). This reflects directional association between changes in gene expression and changes in their promoter accessibility.

**Fig. 3: G12V HRas induced changes in the transcription regulatory landscape.**

The data points to TSS-distant accessible loci as potential key elements in regulating the transcriptional changes during oncogene-induced tumorigenesis in this model. Thus, in order to define regulatory pathways that are important in this process, it is essential to connect between DARs and their distant target genes.

The human genome is segmented into domains of the high frequency of internal long-range chromosomal associations, or topologically associating domains (TADs). TADs facilitate associations between their resident enhancers and gene promoters, while constraining inter‐TAD contacts, thus constitute a spatial regulatory framework. Given that TADs are largely cell-type invariant, we tested the possibility to use available high-resolution Hi-C data from multiple cell types²⁶ for associating DARs with their gene targets. Using 4 C, we first tested for a few differentially expressing and a few stably expressing genes, whether their Hi-C defined topological domains from GM12878 cells are similar in G12V and MCF10A cells. As shown in Fig. 4a and Supplementary Fig. S1, indeed there is no change in the domains of the 5 genes we tested. We, therefore, took advantage of the comprehensive TAD database available²⁶ as a regulatory organizational framework, rather than genomic proximity, to link between TSS and regulatory elements (Fig. 4b)

**Fig. 4: TADs as the spatial framework of transcriptional regulation.**

The 3D organization of the genome has been shown to provide a functional framework for transcriptional regulation. Importantly, in line with this notion, we found that up-regulated and down-regulated genes are segregated between TADs and fall together in the same TAD much more rarely than expected by chance (Fig. 4c). Therefore, our data support that also in the case of oncogene-induced carcinogenic transformation, TADs are functionally relevant to transcriptional regulation, thus constitute an adequate framework to couple between transcriptional activity and regulatory loci as defined by chromatin accessibility. Importantly, cell-type-specific (MCF10A or G12V) DARs are enriched within TADs containing cell-type-specific expressed genes (Fig. 4d) suggesting that chromatin accessibility and genes are not only physically but also functionally linked within the 3D domains.

To discover candidate TFs involved in the transcriptional shift in this model of oncogene-induced transformation, we applied motif discovery analysis on different groups of regulatory sites defined by ATAC-seq, that are associated with differentially expressed genes in the same TAD.

Analysis of gained DARs, revealed enrichment for binding motifs of several TFs including ETV1 and Fli1 pro-oncogenes of the ETS family of transcription factors. Increased activity of ETS transcription factors was shown to be involved in all stages of tumorigenesis of several solid tumors, including prostate and breast cancer^27,28. RNA-seq analysis uncovered several ETS factors that are differentially expressed in G12V cells and may underlie some of the transcriptional alterations. One of the motifs that was found enriched in gained DARs is the CTCF motif. Interestingly, the CTCF motif is enriched in gained DARs located in TADs of both up-regulated and down-regulated genes in G12V cells (Fig. 5a) and in the global gained DARs dataset (8.97%). This suggests that the enrichment of the CTCF motif is associated with gained regulatory activity upon HRas oncogenic activation that is associated with both gene activation and gene repression. However, according to RNA-seq data, the levels of CTCF did not differ between G12V and parental MCF10A cells (Fig. 5B), nor CTCF protein levels, as tested by western blot analysis (Fig. 5b). Moreover, the integrity of CTCF coding region was not compromised in G12V cells (Supplementary Fig. S4). To validate that indeed there is a change in CTCF binding upon activation of the G12V HRas oncogene, we performed chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq) for CTCF in G12V cells and their MCF10A counterparts. This analysis revealed 30,642 ChIP-seq peaks in G12V cells and 22,376 peaks in MCF10A cells (Fig. 5c, Supplementary Fig. S5). As was predicted from the motif analysis, CTCF ChIP-seq data shows significantly higher CTCF binding in gained DARs compared to lost DARs located in TADs of both up-regulated and down-regulated genes (Fig. 5d, e). This strongly suggests the involvement of CTCF in regulating the differential gene expression upon HRas transformation in this system. Interestingly, this occurs via redistribution and probably increase of genomic CTCF binding upon HRas oncogene-induced transformation without a change in the level of CTCF expression.

**Fig. 5: CTCF motif and binding sites are enriched in gained DARs.**

In lost DARs, the most significantly enriched motif is the binding motif of p53 (Fig. 6a, GW 10.9%). This is in agreement with the fact that p53 is one of the top enriched upstream regulators with multiple of its targets being differentially expressed following HRas G12V overexpression in MCF10A cells (Fig. 2e). To confirm that variably expressed genes are regulated by p53 we asked whether genes that were down-regulated following G12V HRas transformation are responsive to p53 activation in MCF10A cells. To measure the likely direct transcriptional response to p53, RNA was extracted following a 4-hour Nutlin-3a treatment and sequenced. Gene expression changes were overall moderate. Importantly genes that were down-regulated following HRas induced transformation and were located within TADs harboring lost DARs that contain p53 motif showed the highest and significant response to p53 activation (Fig. 6d, and Supplementary Fig. S3). We, therefore, checked the level of p53 for changes that could explain the mis-regulation of its target genes. Surprisingly TP53 transcript and p53 protein levels, as well as its subcellular distribution, remain stable between G12V and MCF10A cells (Fig. 6b, c). Moreover, we confirmed that TP53 gene is not mutated in the G12V HRas expressing cells (Supplementary Fig. S5). Thus the reduction in chromatin accessibility at putative p53 binding sites and mis-regulation of p53 target genes in G12V cells is not due to its downregulation or change in cellular localization. To assess directly whether indeed the landscape of p53 chromatin binding was altered in MCF10A and G12V cells, ChIP-seq was performed. Overall, 3260 binding sites of p53 were found in both cell types, with 465 and 565 sites specific for MCF10A and G12V cells, respectively (Fig. 6g, Supplementary Fig. S5). As was predicted by the motif analysis, p53 occupancy in MCF10A is significantly higher in lost DARs relatively to gained DARs located in TADs of up- and down-regulated genes (Fig. 6e. f). Noteworthy, p53 binding loci in MCF10A undergo massive loss of chromatin accessibility (56% lost DARs, p < 2.2e-16, Supplementary Fig. S6), reinforcing that rewiring of p53 regulatory network contributes to transcriptional reprogramming in G12V cells. The variation in p53 binding was accompanied by a significant decrease in the proportion of canonical p53 binding motif in cell type-specific peaks (from 93 to 69%, p < 2.2e-16, proportional test, Supplementary Fig. S7), suggesting that p53 binding in G12V cells is mediated by another transcription factor. Interestingly, the binding motif of AP-1 family of transcription factors is highly enriched in G12V-specific p53 binding loci (24%) relatively to the entire ChIP-seq dataset in these cells (14%) and particularly relative to the MCF10A-specific binding loci (6%), which is close to the background level. This supports a major role for p53 in the transcriptional reprogramming during the early stages of HRas oncogene-induced transformation. Importantly, this reprogramming by p53 does not occur through changes in protein level or localization, but rather through changes in its chromatin binding.

**Fig. 6: Redistribution of p53 binding underlies genetic reprogramming and cancer phenotypes.**

In order to define how the redistribution of p53 binding is related to disease functions, we first assigned MCF10A-specific and G12V-specific binding sites to their 3D-associated differentially expressed genes. The 84 down-regulated genes associated with MCF10A-specific p53 binding sites were enriched in disease functions of the formation of cytoskeleton and actin filaments. GREAT analysis assigned to MCF10A-specific binding sites retrieved terms related to development and differentiation functions, in line with the loss of cellular identity and increased plasticity in cancer progression (Supplementary Data 1). The 95 up-regulated genes within TADs decorated with G12V-specific p53 binding sites were enriched in multiple cancer-related functions such as cell invasion, cell viability, and cell proliferation as well as cellular processes related to G1/S transition and response to DNA damage. Interestingly apoptosis-related functions were not enriched in these analyses (Supplementary Data 2). Thus, genes which may be directly reprogrammed by the redistribution of p53 binding are related to an important subset of p53 functions. Finally, we asked to what extent the cancerous phenotypes of the transformed cells are related to p53. While activation of p53 by Nutllin-3a inhibited cell proliferation of both, MCF10A and G12V cells, this response was initially attenuated in G12V cells, which also maintained their capacity to migrate (Fig. 6h, i). The disorganized growth of G12V cells in 3D cell culture setup is a hallmark of cell transformation (Fig. 6j). p53 activation by mild Nutlin-3a treatment reversed the effect of HRAS overexpression on mammosphere formation, while reduced the size of the mammospheres from MCF10A cells. These results indicate that the redistribution of p53 binding did not eliminate its protective capacity but may have diminished some of its arms.

Discussion

Cancer development is associated with altered gene expression programs which are key in the acquisition of biological capabilities that drive tumorigenesis. Defining the regulatory networks underlying carcinogenesis associated transcriptional reprogramming holds great promise for identifying transcription factors in this process that may not harbor mutations or change expression patterns. Transforming cells by overexpressing oncogenes is widely used and has been instrumental in understanding the molecular mechanisms involved in malignancy²⁹. These cell-based models allow exploring processes occurring in the early stages of oncogene-induced transformation. Using a cell-based mammary epithelial model we find that transformation induced by overexpression of oncogenic HRas is associated with dramatic changes in gene expression. Indeed, the changes we report are characteristic of a carcinogenic transformation process.

Regulatory sites are known to malfunction and thus cause major gene expression alterations related to cancer^30,31. Several chromatin characteristics are used as a proxy for activity of regulatory elements, including DNA methylation status, nucleosome occupancy, specific histone species and modifications in flanking regions. These were used in several studies to demonstrate major alterations in the DNA regulatory landscape in various cancer cell lines and tumor cells from different cancer types^{31,32,33,34,35}. By assaying chromatin accessibility, we demonstrate that even at early stages of transformation, induced by over expression of oncogenic G12V HRas variant, major alterations in the regulatory landscape are evident. Similar modulation of the regulatory landscape was described previously during Ras-dependent oncogenesis in drosophila³⁶ and using H3K27ac marking following disruption of the ERK signaling pathway in MEF cells³⁷. Strikingly, the examination of the distribution of regulatory chromatin between promoters and TSS-distant loci revealed that the latter are dramatically enriched in the varying fraction of accessible chromatin between transformed and non-transformed cells, illustrating that they have a substantial role in transcriptional reprogramming in oncogenic HRas induced transformation.

In order to interrogate more thoroughly specific transcriptional regulation of differentially expressed genes, it is necessary to assign altered regulatory elements to their target differentially expressed genes. Distance between regulatory elements and their target genes was shown to vary in the range of hundreds of kbs¹⁸, however, regulation of gene expression is constrained by the 3D organization of the genome³⁸. TADs are topological domains in the length of hundreds of kbs of high frequency physical association which are considered stable across different cell types and to some extent even different species^26,39,40. Therefore TADs are considered a framework within which enhancer-promoter dynamic interactions occur^19,38. Importantly, TADs were shown to act as co-regulated functional units in different processes from long-term differentiation processes⁴⁰ to short-term responses to external signals^23,24. Strikingly also in the process of oncogene-induced transformation we find strong segregation between up-regulated and down-regulated genes among TADs, which strongly supports that TADs comprise the regulatory context within which transcriptional alteration occurs during early stages of transformation. Moreover, we confirm that there are no major changes in TAD boundaries for a number of differentially expressed genes as a results of the transformation process.

Motif enrichment in gained DARs that are associated with differentially expressed genes can infer the related transcriptional regulation activity. The transcriptional regulator CTCF was found to be enriched in specific active regulatory sites in G12V cells despite lack of change in its expression. Mutations in CTCF are frequently found in breast tumors^3,41, some of which have been shown to affect its DNA binding. Interestingly, the effect of mutations on CTCF binding was found to be non-uniform, for instance different mutations in its zinc finger 3 domain led to selective inhibition of binding to different targets suggesting that there is a tumor-specific change of function rather than loss of function⁴¹. Furthermore, CTCF was shown to be elevated in breast cancer cell lines and breast tumors⁴², its overexpression was suggested to protect tumor cells from induction of apoptotic cell death⁴³ while its downregulation in breast cancer cell lines was shown to be associated with reduced cell proliferation^44,45, both effects via transcriptional regulation of target genes. In line with these results we find overall increased chromatin binding of CTCF in G12V cells, although without detectable elevation in its gene expression levels.

P53 is a known tumor suppressor and the TP53 gene is frequently mutated in breast cancer³. We found the p53 binding motif to be strongly enriched in domains of differentially expressed genes in response to HRas oncogenic activity. Moreover, these genes were indeed regulated by p53 as activation of p53 by Nutlin specifically increased their expression level. However, like CTCF, we found that p53 chromatin binding is redistributed while its expression levels are not changed as well as its localization in the cell. The involvement of p53 in cancers is associated with its DNA binding and transcriptional regulation activity⁴⁶. Our results suggest that distinct genomic p53 binding patterns reported in cancer cells^{47,48,49,50,51} may be important for the development of capabilities during the progression of cancer. Given that p53 binding profile is cancer-specific, it is unlikely that p53 alone reprograms the chromatin landscape⁵². Interestingly, redistribution of p53 binding is accompanied by a decrease in the proportion of its binding motif and an increase in AP-1 binding motif. This suggests a possible role for members of the AP-1 family of transcription factors in modulating p53 activity in the transformed cells by direct association with p53 binding sites⁵³. Functionally, this modulation may relate to transcriptional suppression, as p53 represses target genes of TFF2 via the AP-1 motif⁵⁴. In addition, ATF3 and FOSL1 subunits of the AP-1 transcription factor complex that were up-regulated in G12V cells, were shown to have regulatory interactions with p53^55,56.

The 3D conformation of the genome is the framework within which transcriptional regulation takes place. Here we take advantage of this framework together with DNA accessibility as proxy for active regulatory elements, to infer from changes in gene expression upon HRas oncogene overexpression in normal breast epithelial cells, on regulatory networks that are dis-functioning at the first stages of oncogene-induced carcinogenesis. Chromosome structure Hi-C data allowed combining regulatory loci that change their activity with their distant transcriptionally responsive gene-targets in their biologically relevant 3D context on a genome-wide scale. This focused approach coupled with motif discovery analysis revealed two regulatory factors, namely CTCF and p53, that regulate transcriptional variations associated with HRas oncogenic cellular transformation. Noteworthy, these factors carry out this modular activity, while their RNA, protein levels, and subcellular localization remain invariable, therefore could have not been identified by differential expression analysis. Our ATAC-seq and ChIP-seq data support that this effect is occurring through changes in DNA binding patterns. Thus the combination of differential expression analysis, and DNA accessibility using the framework of the 3D organization of the genome is a powerful tool to identify in an unbiased manner regulatory pathways that orchestrate transcriptional reprogramming during the early stages of cancer development.

Methods

Cells and treatments

MCF10A cells were grown as previously described (Debnath et al.) in DMEM media (Biological Industries) supplemented with 100 ng/ml cholera toxin (Sigma), 20 ng/ml epidermal growth factor (EGF, Peprotech), 0.01 mg/ml insulin (Sigma), 500 ng/ml hydrocortisone (Sigma), 1% penicillin-streptomycin (Biological Industries), 5% horse serum (Biological Industries). Introduction of the G12V HRas oncogene was done via lentiviral transduction along with a GFP expression vector in order to track transduction efficiency. After 72 h lentiviral transduction hygromycin was added to the media in order to select for cells carrying the G12V HRas expression vector. Nutlin treatment was done by adding 10 µM Nutlin-3A (Sigma) to the media for the periods of time indicated.

Lentiviruses

Lentiviral particles were prepared by cotransfecting either the G12V HRas overexpression vector pWZL hygro HRas V12 (addgene #18749) or a GFP expression vector with packaging vectors (CMVΔR8.91, CMV-VSV-G) into 293 T cells using Mirus TransLTi (Mirus Bio LLC, Madison, WI) according to the manufacturer’s instructions. Medium containing viral particles was collected 2 and 3 days post-transfection.

Proliferation assay

Cell proliferation was measured for 3days using an XTT based cell proliferation kit (Biological industries), according to the manufacturer’s instructions.

Colony formation assay

In total 100 cells per well were seeded in 6 well-plates in triplicates. After two weeks, cells were fixed and stained with Giemsa stain. Colonies larger than 5 mm were counted.

Soft agar assay

A total of 2 × 10⁴ cells were seeded in MCF10A standard media containing 0.3% 2-Hydroxyethyl Agarose on top of a solidified layer of MCF10A media containing 0.6% 2-Hydroxyethyl Agarose. Plates were incubated until colonies were visible by the naked eye at which point they were counted.

Anchorage-independent cell death assay

In total 5 × 10³ cells were plated onto poly-HEMA coated 12-well plates to prohibit attachment. After 4 days in suspension, cells were collected from the wells and live cells were counted manually using Trypan blue exclusion.

Matrigel invasion assay

Cells were placed in the upper chamber of a Boyden migration chamber. The upper and lower chambers were separated by matrigel coated PVP-free, 8-mm pore size polycarbonate filters (Costar Scientific). EGF containing conditioned medium of 3T3 fibroblasts was placed in the lower chamber. After overnight incubation, the filters were fixed and stained with Diff-Quick System (Dade Behring, Inc.) and cells on the lower surface were counted. Each assay was done in triplicates.

In vivo tumorigenesis

About 5 × 10⁶ cells were injected s.c. on both dorsal sides of 6 weeks old non-obese diabetic/severe combined immunodeficient (NOD/SCID) mice (4/group) and tumor formation and size were followed for 8 weeks, after which animals were killed. All studies with mice were approved by the Institutional Animal Care and Use Committee at the Hebrew University of Jerusalem.

RNA extraction, cDNA synthesis, and qPCR

Total RNA was extracted from the cultured cells using Quick-RNA MiniPrep kit (ZYMO RESEARCH) following manufacturer’s instructions, reverse transcribed using Quanta Bioscience qScript cDNA synthesis kit (95047-100) following manufacturer’s instructions. cDNA was measured by real-time PCR (Bio-Rad S1000) using sybr green mix (Bio-Rad) with primers spanning exon- intron junctions (Supplementary table S1) and normalized to GAPDH transcript. Results show average and SD of three replicates.

Wound healing assay

About 25 × 10⁴ cells/well were seeded in 12 well plate wells to form a 100% confluent layer. One day after the layer was wounded using the 10 μl pipet tip and monitored for wound healing at 0 h and 24 h in minimal growth factor medium.

3D culture assay

Matrigel was incubated on ice overnight, then 3000 cells were seeded on a solidified layer of growth factor reduced Matrigel measuring approximately 1–2 mm in thickness. The cells were grown in an assay medium containing 5 ng/ml EGF and 2% Matrigel then incubated in CO₂ incubator at 37 °C.

RNA-seq

Total RNA was extracted from cells using RNA purification kit (GeneAll) according to the manufacturer’s instructions. RNA quality was measured on a Bioanalyzer (Agilent) and the only RNA with RIN score >9 was used for library preparation. Messenger RNA (mRNA) was enriched from 1 μg of total RNA by Poly(A) mRNA Magnetic Isolation Module (New England Biolabs) according to the manufacturer’s instructions. cDNA libraries were constructed using the NEBNext Ultra RNA Library Prep Kit (New England Biolabs) following the manufacturer’s protocol. Library concentration was measured by DNA High Sensitivity Kit (Invitrogen) on a Qubit fluorometer (Invitrogen). Library quality and fragment sizes were assessed on a Bioanalyzer (Agilent) or on a Tape station (Agilent). RNA-Seq libraries from at least two biological replicas for each condition were sequenced on Illumina Hi-seq 2000 platform.

ATAC-seq

ATAC-seq was performed as previously described⁵⁷. Briefly, cells were lysed in NLB buffer (10 mM Tris-HCl pH 7.5, 10 mM NaCl, 3 mM MgCl2, 0.05% NP-40 and protease inhibitors (Sigma, P2714). Transposition reaction was performed on 10⁵ nuclei using 5 μl of Nextera TDEI enzyme (Illumina, FC-121-1030) for 30 min at 37 °C. DNA was then purified by Expin PCR SV (GeneAll, 103-102) and the library was amplified using NEBNext High-Fidelity 2× PCR Master Mix (New England Biolabs, M0541). The libraries were size-selected by a gel-free double-sided size-selection using Agencourt AMPure XP beads (Beckman, 63881), at 0.5X and 1.2X. Library concentration was measured by DNA High Sensitivity Kit (Invitrogen) on a Qubit fluorometer (Invitrogen). Library quality and fragment sizes were assessed on a Bioanalyzer (Agilent). ATAC-Seq libraries from two biological replicas for each condition were sequenced on Illumina Hi-seq 2000 platform.

ChIP-seq

Cells were cross-linked for 10 min at 37 °C in 1% formaldehyde followed by quenching with 125 mM glycine for 10 min. for CTCF, crosslinked cells were first lysed in cell lysis buffer (10 mM Hepes pH7.5, 85 mM KCl, 1 mM EDTA, 1% NP-40) supplemented with protease inhibitors, resuspended in RIPA buffer (10 mM Tris-HCl, pH 7.6, 1 mM EDTA, 0.1% SDS, 0.1% NaDeoxycholate, 1% Triton X100) supplemented with protease inhibitors, and sonicated for 40 cycles of 30 s ON and 30 s OFF (Bioruptor sonicator, Diagenode). Cleared chromatin was incubated overnight with 10 μg α-CTCF (Millipore 07-729) or 5 μg α-p53 (DO-1, Santa-Cruz) and additional 2 hours with 40ul protein A/G magnetic beads (ChIP grade, Pierce). Complexes were washed twice with RIPA buffer, twice with RIPA buffer supplemented with 300 mM NaCl, twice with LiCl buffer (10 mM Tris 7.5, 1 mM EDTA, 0.25 M LiCl, 0.5% NP40, 0.5% NaDeoxycholate), once with TE buffer (10 mM Tris, 1 mM EDTA, pH8) supplemented with 0.2% triton and once in TE buffer. For p53, cells were treated with 10uM Nutlin-3a (Sigma) for 4 h before fixation. Fixed cells were lysed with SDS Lysis Buffer (1% SDS, 50 mM Tris pH 8.1, 10 mM EDTA) supplemented with protease inhibitor and sonicated for 560 s (ME220 sonicator, Covaris). Cleared chromatin was diluted 1:10 with dilution Buffer (16.7 mM Tris-HCl 8.1; 1.2 mM EDTA; 167 mM NaCl; 1.1% Triton) and incubated overnight with 5 μg α-p53 (DO-1, Santa-Cruz) bound to magnetic beads (Dynabeads Protein A). Chromatin was washed with low salt buffer (20 mM Tris-HCl 8.1; 2 mM EDTA; 150 mM NaCl; 1% triton; 0.1% SDS), high salt buffer (20 mM Tris-HCl 8.1; 2 mM EDTA; 500 mM NaCl; 1% triton; 0.1% SDS), LiCl buffer (10 mM Tris-HCl 8.1; 1 mM EDTA; 1% NP-40; 250 mM LiCl) at 4 °C, and twice with TE (10 mM Tris-HCl 8.1; 1 mM EDTA) at room temp. Complexes were eluted with Elution buffer (10 mM Tris-HCl 8.1; 1 mM EDTA; 200 mM NaCl; 1% SDS). For both antibodies, crosslinks were reversed with 1 mg/mL Proteinase K overnight at 65 °C. Purified DNA was used to prepare sequencing libraries using NEBNext UltraII DNA Library Prep Kit (New England Biolabs). Library concentration was measured by DNA High Sensitivity Kit (Invitrogen) on a Qubit fluorometer (Invitrogen). Library quality and fragment sizes were assessed on a Bioanalyzer (Agilent). ChIP-seq libraries from two biological replicas for each condition were sequenced on Illumina Hi-seq 2000 platform.

4C-seq

4 C was performed as previously described^58,59. Cells were fixed with 2% formaldehyde for 10 min, cross-linked chromatin was digested overnight with an excess of HindIII enzyme (New England Biolabs) and then DNA ends were ligated under dilute conditions that favor junctions between cross-linked DNA fragments. The ligation junctions were then circularized by digestion with Csp6I (Thermo Scientific). Chromosomal contacts with the baits were amplified with inverse PCR primers (Supplementary Table S2) using Platinum Taq DNA Polymerase (LifeTechnologies). 4C-seq libraries were sequenced on Illumina Hi-seq 2000 platform.

Immunostaining

Cells were fixed with 3.7% formaldehyde, permeabilized with 0.1% triton, and blocked with 2% BSA. p53 was detected using a mouse monoclonal antibody (SC-126, Santa Cruz Biotechnology) followed by a Cy3 conjugated anti-mouse antibody (Jackson Immunoresearch Laboratories). Cells were stained with Dapi and viewed on an Axioimager fluorescent microscope (Zeiss).

Western blot analysis

Cells were lysed directly in sample buffer and protein extracts were separated on a 4–20% polyacrylamide gel (Bio-Rad). Blotted nitrocellulose membranes were incubated with primary antibodies (mouse α-p53, SC-126, Santa Cruz Biotechnology; rabbit α-CTCF, Millipore 07-729; rabbit α-GAPDH, cst-2118, Cell Signaling Technology) followed by detection with HRP conjugated secondary antibodies (Jackson Immunoresearch Laboratories). ECL was performed using EZ-ECL kit (Biological Industries) and imaged on an ImageQuant chemiluminescence camera (GE Healthcare).

Statistics and reproducibility

Two biological replicates were used for each genome-wide (GW) analysis. To assess the reproducibility of ATAC seq and ChIP-seq replicas correlation was calculated using sampling 10 M reads per replica, calling peaks, and calculating the correlation between the merged peaks. The correlation was between 0.79 and 0.9. The reproducibility of the RNA seq was evaluated using PCA and hierarchical clustering. Statistical tests were conducted using the R programing language.

RNA seq analysis

The alignment was done using TopHat⁶⁰. Reads count on transcripts was done using HTSeq⁶¹. Expression and differential analysis was done using “edgeR”⁶² that assigns to each gene a false discovery rate (FDR) value and calculates the log Fold Change (logFC) between the two conditions. Genes with FDR < 0.05 and logFC greater than |0.5| were considered differentially expressed. Functional enrichment was done using IPA (Qiagen,⁶³).

ATAC seq and ChIP-seq analysis

The alignment was done using Bowtie⁶⁴, allowing only unique reads to be considered. Since the replicas were well correlated (Supplementary Fig. S2), reads from both replicas of each condition were combined for further analysis. For detecting regions of local read enrichment (peaks), MACS algorithm⁶⁵ was applied with default parameters for ChIP-seq. For ATAC-seq, read start site was adjusted to represent the center of the transposon binding event as described⁶⁶ and the following parameters for MACS were applied – --tsize=51 --nomodel --shiftsize=75 --llocal=25000 -p 1e-04. Differentially accessible loci between MCF10A and G12V cells were detected by MACS algorithm, using one sample as the background of the other. Only peaks that were both detected by MACS analysis in either sample and in the comparative MACS analysis were considered as unique peaks and were used for further analysis.

For calculating the distribution of ATAC-seq peaks relatively to gene features, refseq annotations were used for TSS coordinates of each gene, and the longest transcript as gene body. Overlap of at least 1 bp between ATAC-seq peak with gene promoters (1000 bp upstream and 500 bp downstream to the TSS) or with distant regulatory regions (100 kb upstream and downstream to the TSS of each gene) was counted. Regulatory sites that did not overlap one of the two features were considered as far regulatory sites. Overlap analysis was performed by intersectBed from Bedtools⁶⁷. Each gene or peak was counted once.

4C-seq analysis

Reads were sorted, according to their barcodes, to different fastq files for each bait and condition and aligned to the human (hg19) genome using BOWTIE⁶⁴. Reads were then counted for each HindIII site. For domains detection, the number of reads on each HindIII site was counted in sliding windows of 50Kb with 25Kb steps. Hi-C heatmaps are from the 3D genome browser⁶⁸.

Association of genes and TADs

For genome-wide assignment of the gene to topologically associated domains (TADs), TADs from high-resolution Hi-C data of 7 human cell lines²⁶ were merged to one dataset and the smallest overlapping TAD was assigned for each gene. “Up” or “down” TADs were declared according to their containing up- or down-regulated genes after transformation by G12V HRas. Overlap between TADs, regulatory sites, and genes was calculated by intersectBed from Bedtools⁶⁷. For the GO analysis presented in Supplementary Data 1, cell-type-specific expressed genes within “Up” or “down” TADs that contain cell-type-specific ChIP peaks were used for IPA analysis. For GREAT analysis⁶⁹, differential factor binding loci were retrieved by EdgeR package from R (p.adj < 0.05) using merged peaks from all replicas as input.

Motif discovery analysis

To uncover DNA binding motifs of possible transcription factors (TFs) which are associated with regulatory loci, HOMER “findMotifsGenome.pl” program⁷⁰ with default parameters was applied for a 150 bp window with the highest read count within each peak (summit) that was defined using the custom made script in R. The enrichment of motifs in regulatory sites from MCF10A was measured against G12V cells and vice versa.

Reporting summary

Further information on experimental design is available in the link to this paper.

Data availability

RNA sequencing, ATAC sequencing, ChIP-sequencing, and 4 C sequencing data have been deposited at GEO, accession number GSE140254. Source data can be found in Supplementary Data 3. All other data can be obtained from the corresponding author on a reasonable request.

References

Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
Article CAS PubMed Google Scholar
Spitz, F. & Furlong, E. E. M. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).
Article CAS PubMed Google Scholar
Koboldt, D. C. et al. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).
Article CAS Google Scholar
Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).
Article CAS PubMed PubMed Central Google Scholar
Yeh, J. E., Toniolo, P. A. & Frank, D. A. Targeting transcription factors: promising new strategies for cancer therapy. Curr. Opin. Oncol. 25, 652–658 (2013).
Article CAS PubMed Google Scholar
Bradner, J. E., Hnisz, D. & Young, R. A. Transcriptional addiction in cancer. Cell 168, 629–643 (2017).
Article CAS PubMed PubMed Central Google Scholar
Chen, H. et al. A pan-cancer analysis of enhancer expression in nearly 9000 patient samples. Cell 173, 386–399.e12 (2018).
Article CAS PubMed PubMed Central Google Scholar
Lee, T. I. & Young, R. A. Transcriptional regulation and its misregulation in disease. Cell 152, 1237–1251 (2013).
Article CAS PubMed PubMed Central Google Scholar
Ong, C.-T. & Corces, V. G. Enhancer function: new insights into the regulation of tissue-specific gene expression. Nat. Rev. Genet. 12, 283–293 (2011).
Article CAS PubMed PubMed Central Google Scholar
Dunham, I. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Article CAS Google Scholar
Zentner, G. E., Tesar, P. J. & Scacheri, P. C. Epigenetic signatures distinguish multiple classes of enhancers with distinct cellular functions. Genome Res. 21, 1273–1283 (2011).
Article CAS PubMed PubMed Central Google Scholar
Rada-Iglesias, A. et al. A unique chromatin signature uncovers early developmental enhancers in humans. Nature 470, 279–283 (2011).
Article CAS PubMed Google Scholar
Creyghton, M. P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl Acad. Sci. USA 107, 21931–21936 (2010).
Article CAS PubMed PubMed Central Google Scholar
Visel, A. et al. ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature 457, 854–858 (2009).
Article CAS PubMed PubMed Central Google Scholar
Bowman, S. K. Discovering enhancers by mapping chromatin features in primary tissue. Genomics 106, 140–144 (2015).
Article CAS PubMed Google Scholar
Thurman, R. E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).
Article CAS PubMed PubMed Central Google Scholar
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Article CAS PubMed PubMed Central Google Scholar
Sanyal, A., Lajoie, B. R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).
Article CAS PubMed PubMed Central Google Scholar
Schwartz, M. & Hakim, O. 3D view of chromosomes, DNA damage, and translocations. Curr. Opin. Genet. Dev. 25, 118–125 (2014).
Article CAS PubMed Google Scholar
Denker, A. & De Laat, W. The second decade of 3C technologies: detailed insights into nuclear organization. Genes Dev. 30, 1357–1382 (2016).
Article CAS PubMed PubMed Central Google Scholar
Dekker, J. & Heard, E. Structural and functional diversity of topologically associating domains. FEBS Lett. 589, 2877–2884 (2015).
Article CAS PubMed PubMed Central Google Scholar
Sati, S. & Cavalli, G. Chromosome conformation capture technologies and their impact in understanding genome function. Chromosoma 126, 33–44 (2017).
Article PubMed Google Scholar
Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294 (2013).
Article CAS PubMed PubMed Central Google Scholar
Le Dily, F. L. et al. Distinct structural transitions of chromatin topological domains correlate with coordinated hormone-induced gene regulation. Genes Dev. 28, 2151–2162 (2014).
Article PubMed PubMed Central CAS Google Scholar
Singh, J. K., Simões, B. M., Howell, S. J., Farnie, G. & Clarke, R. B. Recent advances reveal IL-8 signaling as a potential key to targeting breast cancer stem cells. Breast Cancer Res. 15, 210 (2013).
Article PubMed PubMed Central CAS Google Scholar
Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
Article CAS PubMed PubMed Central Google Scholar
Sizemore, G. M., Pitarresi, J. R., Balakrishnan, S. & Ostrowski, M. C. The ETS family of oncogenic transcription factors in solid tumours. Nat. Rev. Cancer 17, 337–351 (2017).
Article CAS PubMed Google Scholar
Abeshouse, A. et al. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).
Article CAS Google Scholar
Hahn, W. C. & Weinberg, R. A. Modelling the molecular circuitry of cancer. Nat. Rev. Cancer 2, 331–341 (2002).
Article CAS PubMed Google Scholar
Sur, I. & Taipale, J. The role of enhancers in cancer. Nat. Rev. Cancer 16, 483–493 (2016).
Article CAS PubMed Google Scholar
Kron, K. J., Bailey, S. D. & Lupien, M. Enhancer alterations in cancer: a source for a cell identity crisis. Genome Med. 6, 77 (2014).
Article PubMed PubMed Central CAS Google Scholar
Stergachis, A. B. et al. Developmental fate and cellular maturity encoded in human regulatory DNA landscapes. Cell 154, 888–903 (2013).
Article CAS PubMed PubMed Central Google Scholar
Aran, D., Sabato, S. & Hellman, A. DNA methylation of distal regulatory sites characterizes dysregulation of cancer genes. Genome Biol. 14, R21 (2013).
Article PubMed PubMed Central CAS Google Scholar
Taberlay, P. C., Statham, A. L., Kelly, T. K., Clark, S. J. & Jones, P. A. Reconfiguration of nucleosome-depleted regions at distal regulatory elements accompanies DNA methylation of enhancers and insulators in cancer. Genome Res. 24, 1421–1432 (2014).
Article CAS PubMed PubMed Central Google Scholar
Lovén, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153, 320–334 (2013).
Article PubMed PubMed Central CAS Google Scholar
Davie, K. et al. Discovery of transcription factors and regulatory regions driving in vivo tumor development by ATAC-seq and FAIRE-seq open chromatin profiling. PLoS Genet. 11, 1–24 (2015).
Article CAS Google Scholar
Nabet, B. et al. Deregulation of the Ras-Erk signaling axis modulates the enhancer landscape. Cell Rep. 12, 1300–1313 (2015).
Article CAS PubMed PubMed Central Google Scholar
Smallwood, A. & Ren, B. Genome organization and long-range regulation of gene expression by enhancers. Curr. Opin. Cell Biol. 25, 387–394 (2013).
Article CAS PubMed PubMed Central Google Scholar
Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).
Article CAS PubMed PubMed Central Google Scholar
Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).
Article CAS PubMed PubMed Central Google Scholar
De La Rosa-Velázquez, I. A., Rincón-Arano, H., Benítez-Bribiesca, L. & Recillas-Targa, F. Epigenetic regulation of the human retinoblastoma tumor suppressor gene promoter by CTCF. Cancer Res. 67, 2577–2585 (2007).
Article CAS Google Scholar
Docquier, F. et al. Heightened expression of CTCF in breast cancer cells is associated with resistance to apoptosis. Cancer Res. 65, 5112–5122 (2005).
Article CAS PubMed Google Scholar
Méndez-Catalá, C. F. et al. A novel mechanism for CTCF in the epigenetic regulation of BAX IN BREAST CANCER Cells. Neoplasia 15, 898–IN14 (2013).
Article PubMed PubMed Central CAS Google Scholar
Lee, J. Y., Mustafa, M., Kim, C. Y. & Kim, M. H. Depletion of CTCF in breast cancer cells selectively induces cancer cell death via p53. J. Cancer 8, 2124–2131 (2017).
Article PubMed PubMed Central CAS Google Scholar
Mustafa, M., Lee, J. Y. & Kim, M. H. CTCF negatively regulates HOXA10 expression in breast cancer cells. Biochem. Biophys. Res. Commun. 467, 828–834 (2015).
Article CAS PubMed Google Scholar
Mello, S. S. & Attardi, L. D. Deciphering p53 signaling in tumor suppression. Curr. Opin. Cell Biol. 51, 65–72 (2018).
Article CAS PubMed Google Scholar
Bao, F., LoVerso, P. R., Fisk, J. N., Zhurkin, V. B. & Cui, F. p53 binding sites in normal and cancer cells are characterized by distinct chromatin context. Cell Cycle 16, 2073–2085 (2017).
Article CAS PubMed PubMed Central Google Scholar
Botcheva, K., McCorkle, S. R., McCombie, W. R., Dunn, J. J. & Anderson, C. W. Distinct p53 genomic binding patterns in normal and cancer-derived human cells. Cell Cycle 10, 4237–4249 (2011).
Article CAS PubMed PubMed Central Google Scholar
Fischer, M. Census and evaluation of p53 target genes. Oncogene 36, 3943–3956 (2017).
Article CAS PubMed PubMed Central Google Scholar
Nguyen, T.-A. T. et al. Revealing a human p53 universe. Nucleic Acids Res. 46, 8153–8167 (2018).
Article CAS PubMed PubMed Central Google Scholar
Andrysik, Z. et al. Identification of a core TP53 transcriptional program with highly distributed tumor suppressive activity. Genome Res. 27, 1645–1657 (2017).
Article CAS PubMed PubMed Central Google Scholar
Sammons, M. A., Zhu, J., Drake, A. M. & Berger, S. L. TP53 engagement with the genome occurs in distinct local chromatin environments via pioneer factor activity. Genome Res. 25, 179–188 (2015).
Article CAS PubMed PubMed Central Google Scholar
Madrigal, P. & Alasoo, K. AP-1 takes centre stage in enhancer chromatin dynamics. Trends Cell Biol. 28, 509–511 (2018).
Article CAS PubMed Google Scholar
Tu, S. P. et al. p53 inhibition of AP1-dependent TFF2 expression induces apoptosis and inhibits cell migration in gastric cancer cells. Am. J. Physiol. Liver Physiol. 297, G385–G396 (2009).
CAS Google Scholar
Talotta, F., Casalino, L. & Verde, P. The nuclear oncoprotein Fra-1: a transcription factor knocking on therapeutic applications’ door. Oncogene 39, 4491–4506 (2020).
Article CAS PubMed Google Scholar
Yan, C., Wang, H. & Boyd, D. D. ATF3 represses 72-kDa type IV collagenase (MMP-2) expression by antagonizing p53-dependent trans-activation of the collagenase promoter. J. Biol. Chem. 277, 10804–10812 (2002).
Article CAS PubMed Google Scholar
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Article CAS PubMed PubMed Central Google Scholar
Schwartz, M. et al. Comparative analysis of T4 DNA ligases and DNA polymerases used in chromosome conformation capture assays. Biotechniques 58, 195–199 (2015).
Article CAS PubMed Google Scholar
Hakim, O. et al. Diverse gene reprogramming events occur in the same spatial clusters of distal regulatory elements. Genome Res. 21, 697–706 (2011).
Article CAS PubMed PubMed Central Google Scholar
Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).
Article CAS PubMed PubMed Central Google Scholar
Anders, S., Pyl, P. T. & Huber, W. HTSeq-A Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Article CAS PubMed Google Scholar
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).
Article PubMed PubMed Central CAS Google Scholar
Krämer, A., Green, J., Pollard, J. & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523–530 (2014).
Article CAS PubMed Google Scholar
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
Article PubMed PubMed Central CAS Google Scholar
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Article PubMed PubMed Central CAS Google Scholar
Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. In Current Protocols in Molecular Biology 109, 21.29.1-21.29.9 (John Wiley & Sons, Inc., 2015).
Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Article CAS PubMed PubMed Central Google Scholar
Wang, Y. et al. The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. bioRxiv 112268 https://doi.org/10.1101/112268 (2017).
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
Article CAS PubMed PubMed Central Google Scholar
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Article CAS PubMed PubMed Central Google Scholar

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Acknowledgments

This work is supported by the Israel Science Foundation (grant 748/14), Marie Curie Integration grant (CIG)- FP7-PEOPLE-20013-CIG-618763 and I-CORE Program of the Planning and Budgeting Committee and The Israel Science Foundation grant no. 41/11. We thank the BIU imaging facility for helping with microscopy.

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Michal Schwartz
Present address: Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
These authors contributed equally: Michal Schwartz, Avital Sarusi Portugez.

Authors and Affiliations

The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
Michal Schwartz, Avital Sarusi Portugez, Bracha Zukerman Attia, Miriam Tannenbaum, Leslie Cohen, Olga Loza, Emily Chase & Ofir Hakim
Al-Quds-Bard College for Arts and Sciences, Al-Quds University, Abu Dis, Palestinian Terretories, Palestine
Yousef Turman & Zaidoun Salah
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
Tommy Kaplan

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Michal Schwartz
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M.S., B.Z.A., M.T., O.L., A.C., and Y.T. performed experiments, M.S., A.S.P., L.C., T.K., Z.S., and O.H. analyzed data. M.S., A.S.P., Z.S., and O.H. designed experiments. M.S., A.S.P., and O.H. wrote the manuscript.

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Schwartz, M., Portugez, A.S., Attia, B.Z. et al. Genomic retargeting of p53 and CTCF is associated with transcriptional changes during oncogenic HRas-induced transformation. Commun Biol 3, 696 (2020). https://doi.org/10.1038/s42003-020-01398-y

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Received: 28 September 2019
Accepted: 16 October 2020
Published: 25 November 2020
DOI: https://doi.org/10.1038/s42003-020-01398-y

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