Nuclear Dishevelled targets gene regulatory regions and promotes tumor growth
2021; Springer Nature; Volume: 22; Issue: 6 Linguagem: Inglês
10.15252/embr.202050600
ISSN1469-3178
AutoresIsabel Castro‐Piedras, Monica Sharma, Jennifer Brelsfoard, David Vartak, Edgar G. Martinez, Cristian Rivera, Deborah Molehin, Robert K. Bright, Mohamed Fokar, Josée Guindon, Kevin Pruitt,
Tópico(s)Genomics and Chromatin Dynamics
ResumoArticle16 April 2021free access Source DataTransparent process Nuclear Dishevelled targets gene regulatory regions and promotes tumor growth Isabel Castro-Piedras Isabel Castro-Piedras Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Monica Sharma Monica Sharma Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Jennifer Brelsfoard Jennifer Brelsfoard Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author David Vartak David Vartak Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Edgar G Martinez Edgar G Martinez orcid.org/0000-0003-1542-8571 Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Cristian Rivera Cristian Rivera Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Deborah Molehin Deborah Molehin Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Robert K Bright Robert K Bright Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Mohamed Fokar Mohamed Fokar Center for Biotechnology and Genomics, Texas Tech University, Lubbock, TX, USA Search for more papers by this author Josee Guindon Josee Guindon Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX, USA Center of Excellence for Translational Neuroscience and Therapeutics, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Kevin Pruitt Corresponding Author Kevin Pruitt [email protected] orcid.org/0000-0002-6932-042X Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Isabel Castro-Piedras Isabel Castro-Piedras Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Monica Sharma Monica Sharma Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Jennifer Brelsfoard Jennifer Brelsfoard Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author David Vartak David Vartak Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Edgar G Martinez Edgar G Martinez orcid.org/0000-0003-1542-8571 Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Cristian Rivera Cristian Rivera Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Deborah Molehin Deborah Molehin Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Robert K Bright Robert K Bright Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Mohamed Fokar Mohamed Fokar Center for Biotechnology and Genomics, Texas Tech University, Lubbock, TX, USA Search for more papers by this author Josee Guindon Josee Guindon Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX, USA Center of Excellence for Translational Neuroscience and Therapeutics, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Kevin Pruitt Corresponding Author Kevin Pruitt [email protected] orcid.org/0000-0002-6932-042X Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA Search for more papers by this author Author Information Isabel Castro-Piedras1, Monica Sharma1, Jennifer Brelsfoard1,2, David Vartak1, Edgar G Martinez1, Cristian Rivera1, Deborah Molehin1, Robert K Bright1, Mohamed Fokar3, Josee Guindon2,4 and Kevin Pruitt *,1 1Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA 2Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX, USA 3Center for Biotechnology and Genomics, Texas Tech University, Lubbock, TX, USA 4Center of Excellence for Translational Neuroscience and Therapeutics, Texas Tech University Health Sciences Center, Lubbock, TX, USA *Corresponding author. Tel: +1 806 743 2523; Fax: +1 806 743 2334; E-mail: [email protected] EMBO Reports (2021)22:e50600https://doi.org/10.15252/embr.202050600 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Dishevelled (DVL) critically regulates Wnt signaling and contributes to a wide spectrum of diseases and is important in normal and pathophysiological settings. However, how it mediates diverse cellular functions remains poorly understood. Recent discoveries have revealed that constitutive Wnt pathway activation contributes to breast cancer malignancy, but the mechanisms by which this occurs are unknown and very few studies have examined the nuclear role of DVL. Here, we have performed DVL3 ChIP-seq analyses and identify novel target genes bound by DVL3. We show that DVL3 depletion alters KMT2D binding to novel targets and changes their epigenetic marks and mRNA levels. We further demonstrate that DVL3 inhibition leads to decreased tumor growth in two different breast cancer models in vivo. Our data uncover new DVL3 functions through its regulation of multiple genes involved in developmental biology, antigen presentation, metabolism, chromatin remodeling, and tumorigenesis. Overall, our study provides unique insight into the function of nuclear DVL, which helps to define its role in mediating aberrant Wnt signaling. SYNOPSIS DVL3 target genes act in diverse processes such as regulation of immune responses, signal transduction and metabolism. DVL3 binds to the histone methyltransferase KMT2D and co-localizes with it to regulatory regions. DVL3 depletion alters KMT2D binding to novel targets and the maintenance or deposition of H3K4me3 at these regions. DVL3 depletion reduces tumor growth in vivo. This study identifies novel gene targets of nuclear Dishevelled 3 and shows that DVL3 depletion decreases binding of the histone methyltransferase KMT2D, thereby altering epigenetic marks and mRNA levels of target genes and decreasing tumor growth. Introduction DVL plays a critical role in disease and developmental disorders, but its complex mechanisms of action remain poorly understood (Mlodzik, 2016; Gentzel & Schambony, 2017; Sharma et al, 2018). Wnt signaling is frequently altered in disparate pathologies due to a disruption in the normal pattern of Wnt ligand expression which results in dysregulated and constitutive signals transmitted across the plasma membrane. DVL integrates and transmits these signals arising from aberrant expression of Wnt ligands, antagonists, and membrane receptors (Gammons & Bienz, 2018; Sharma & Pruitt, 2020). Although DVL relays Wnt signals under normal and pathophysiological conditions, much remains unknown about the function of nuclear DVL. DVL has been studied primarily as a cytoplasmic scaffold which promotes processes such as β-catenin stabilization (Gao & Chen, 2010) or cell migration (Saxena et al, 2015). However, seminal studies show that nuclear localization of DVL is important even for canonical signaling (Itoh et al, 2005; Gan et al, 2008; Simmons et al, 2014; Wang et al, 2015). We were the first to report that novel post-translational lysine acetylation of DVL-1 serves as a novel regulatory switch to promote its nuclear localization (Sharma et al, 2019). Limited reports demonstrated that DVL binds to the promoters of four Wnt target genes such as cMyc, BMP4, cyclinD1, and/or FZD7 (Gan et al, 2008; Simmons et al, 2014), but beyond the identity of these classic Wnt target genes which have been known for many years, virtually nothing is known about the genes bound by DVL. Moreover, our previous study identified another novel DVL target gene, CYP19A1, which encodes aromatase and had not been previously linked with Wnt signaling (Castro-Piedras et al, 2018). In this study, we also observed an enrichment of H3K4me3, a mark associated with active transcription, relative to H3K27me3, a mark associated with transcriptional repression at CYP19A1 promoters where DVL3 bound (Castro-Piedras et al, 2018). The methylation of H3 at the lysine 4 position (H3K4) is a well-studied mark of transcriptional activity at regulatory regions like enhancers and promoters (Gardner et al, 2011). There are several enzymes responsible for the methylation of H3K4, which belong to the family of histone-lysine methyltransferases (KMTs) (Allis et al, 2007; Pruitt, 2016). It has been reported that these enzymes may have redundant functions in the methylation of H3K4. For example, only 5% of H3K4me3 on promoters is generated by KMT2A (Vallianatos & Iwase, 2015), while KMT2D is required to maintain broad H3K4me3 super-enhancer signals (Dhar et al, 2018). Similarly to DVL3, KMT2D functions in cell development, differentiation, cell migration, and tumor progression (Guo et al, 2013; Froimchuk et al, 2017). For a more comprehensive analysis of DVL nuclear function, we set out to identify novel DVL target genes. Since DVL shows nuclear localization in breast cancer cells (Dass et al, 2016; Castro-Piedras et al, 2018) and mediates oncogenic signaling, we conducted the first DVL3 ChIP-seq to identify novel gene targets in breast cancer cell lines. Our study shows that DVL3 localizes to both promoter and enhancer regulatory regions and differentially regulates novel gene targets involved in a number of processes including developmental biology, T-cell mediated immunity, and tumorigenesis. In loss-of-function studies, we show that DVL governs cell growth properties in vitro and in vivo. Additionally, bioinformatics analyses identify potential transcription factors which could be involved in DVL3-mediated transcriptional regulation. Furthermore, we show binding of DVL to a chromatin-modifying enzyme, KMT2D, and establish a role for DVL3 in H3K4 methylation. Hence, this study uncovers novel DVL targets and reveals a role in regulating epigenetic marks that are important for chromatin structure at transcription regulatory regions. Furthermore, this study addresses a critical question in the field regarding the role of nuclear DVL (Habas & Dawid, 2005; Weitzman, 2005) and reveals a surprisingly broader scope of DVL function (Torres & Nelson, 2000; Dass et al, 2016; Mlodzik, 2016) in cancer models. Results Nuclear DVL3 binding is enriched on specific motif sequences During mammary gland development, Wnt signaling is tightly regulated (Gavin & McMahon, 1992). While aberrant Wnt signaling has been observed in various cancer types (Liu et al, 2008; Xu et al, 2008; Akiri et al, 2009), much remains unknown about its contribution to breast cancer (Bafico et al, 2004; Benhaj et al, 2006; Schlange et al, 2007), where the constitutive Wnt signaling is frequently observed (Pruitt et al, 2006; Matsuda et al, 2009). DVL transmits signals from multiple Wnt ligands, receptors, and co-receptors to regulate critical cellular processes (Sharma et al, 2018). Recently, we and others demonstrated that DVL translocates to the nucleus (Itoh et al, 2005; Gan et al, 2008; Castro-Piedras et al, 2018); however, the role of nuclear DVL remains unclear. To investigate the novel nuclear roles of DVL, we selected two breast cancer cell lines: MCF7 and MDA-MB-468. These cell lines were chosen because (i) both lines possess constitutive autocrine signaling (Fig EV1A) and DVL3 contributes to oncogenic signaling in both MCF7 and MDA-MB-468 (Castro-Piedras et al, 2018), (ii) both lines show high levels of nuclear DVL3 (Castro-Piedras et al, 2018), (iii) MCF7 is a Tier 2 cell line for the ENCODE Project which allows us to compare our data with publicly available data, and iv) both lines represent two different BC subtypes, with MCF7 as an ER+ cell line and MDA-MB-468 as a triple-negative cell line. Click here to expand this figure. Figure EV1. Autocrine Wnt pathway activation in breast cancer cell lines RNA expression of selected Wnt ligands (WNT3A, WNT6, WNT4, and WNT7B) and Frizzled receptors (FZD7, FZD4, and FZD6) in human non-cancer mammary epithelial cell line (MCF10A and MCF12F), breast cancer cell lines (MCF-7 and MDA-MB-468), and colon carcinoma cell line (HCT116). A minus reverse transcriptase (−RT) control ensures no DNA contamination is present in the RNA preparation. Download figure Download PowerPoint To examine the role of nuclear DVL3, we performed three independent DVL3 ChIP-seq analyses in MDA-MB-468 and MCF7 cells. For the ChIP-seq experiments, we used an IgG-control ChIP-seq dataset as a reference for peak selection which showed a robust reproducibility between experiments in both cell lines. We obtained a total of 12,162 peaks for DVL3 in MDA-MB-468 (all of them ≥ 2-fold enrichment over IgG, intersecting 6,486 genes), and a total of 2,227 peaks for DVL3 in MCF7 (1848 filtered peaks, ≥ 2-fold enrichment over IgG; intersecting 901 genes). Interestingly, 37.7% of the filtered MCF7 peaks overlapped with MDA-MB-468 peaks (Fig 1A). To identify the DNA motifs predictive of the association of DVL3 with the genome, we analyzed the genomic DNA sequences from DVL3-binding peaks detected by ChIP-seq analyses. All the peaks underwent MEME-ChIP analysis to determine significant motifs that had a strong central enrichment among the DNA sequences for the ChIPs performed (Fig 1B for MDA-MB-468 cells, and 1C for MCF7 cells). To further investigate three specific sequence motifs enriched in DVL3 binding, we analyzed the novel DVL3 motifs identified in Fig 1C and D using the TOMTOM motif comparison tool. This in silico tool revealed similarity with some known transcription factor motifs like ZNF410, SOX1, SOX18, TEAD1, and TEAD3 for MDA-MB-468 (Fig EV2A) or STAT3, STAT1, Gfi, SOX1, SRY, SOX15, SOX3, SOX17, SOX9, and MEF2A transcription factors for MCF7 (Fig EV2A). Figure 1. ChIP-seq identified novel DVL3-binding sequences A. Venn diagram representing overlap of DVL3 ChIP-seq peaks between MDA-MB-468 and MCF7 cells. B. MEME-ChIP (Motif Analysis of Large Nucleotide Datasets) analysis of the DVL3 binding sites identified DVL3-specific motifs in MDA-MB-468 cells: TGGAATGGAATGGAATGGAAT in 622 fragments with a P value = 3.1e-4486, ATTCCATTCCATTC in 673 with a P value = 8.6e-1868, and TTCCATTCCATTCCATTCCA in 605 fragments with a P value = 2.9e-1614. The P value is the significance of the motif according to MEME, motif discovery program. C. MEME-ChIP analysis of the DVL3-binding sites identified DVL3-specific motifs in MCF7 cells: CAGAAKVATTCTCAGAAACTYCTTTGTGA in 1394 fragments with a P value = 1.5e-2815, GTGTGYRTTCAACTCACAGAGTTGAACSTT in 1143 fragments with a P value = 3.7e-2478 and KGAAACACTCTTTTTGTAKAWTYTGCAAG in 1359 fragments with a P value = 9.4e-2181. The P value is the significance of the motif according to MEME, motif discovery program. D, E. Pie graph showing enriched pathways of ChIP hits for MDA-MB-468 (D) and MCF7 (E) cell lines generated by Reactome pathway analysis. F, G. Bar graphs showing the −log10 P-values for the Reactome terms of the most significant pathways enriched using binomial test in DVL3 ChIP-seq for MDA-MB-468 (F) and MCF7 (G) cell lines. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Co-localization of ZBTB7B with respect to DVL3 genomic binding in breast cancer cells Table of transcription factor motifs matching DVL3-binding motif by TOMTOM analysis in MDA-MB-468 and MCF7 cell lines. The P-value, e-value, and q-value were obtained with TOTOM software. DVL3 co-immunoprecipitates ZBTB7B and vice versa in MCF7. IgG heavy chain (Hc) and light chain (Lc) were blotted for as a control for equal antibody capture for immunoprecipitation and whole cell extracts (WCE) as a positive control. An assembly of IgG (first row), DVL3 (second row), and ZBTB7B (third row) ChIP-seq data in MCF7 for the RFX5-GBAT2, GPD1-COX14, MAST3, and HLA-E genes, visualized by IGV. Venn diagram showing the overlap between MCF7 DVL3 ChIP-seq peaks and ZBTB7B ChIP-seq. ChIP experiments for Input, IgG, DVL3, and ZBTB7B were performed in MDA-MB-468 cells. Occupancy of DVL3 at three different genes (MAMDC2, DUX4, and SYNJ1) was analyzed by end-point PCR. Western blot for DVL3 expression in NTC and shDVL3 KD in MDA-MB-468 and MCF7 cell lines. Data information: (F) The membranes were probed with DVL-3-specific antibody (sc-8027; Santa Cruz Biotechnology, Inc), and GAPDH was included as a control. Download figure Download PowerPoint We set up a co-IP screen to find possible transcription factors that may bind DVL3 and confirmed by co-immunoprecipitation that the transcription factor ZBTB7B (Zinc Finger and BTB Domain Containing 7B) binds to DVL3 in the MCF7 cell line (Fig EV2B), indicating that DVL3 and ZBTB7B may be part of a transcription regulatory complex. Additionally, in MCF7 cells, we observed an overlap of 84% of the DVL3 peaks with published ZBTB7B ChIP-seq data (39) (Fig EV2C and D). In MDA-MB-468 cells, we also observed by ChIP-PCR, a co-localization of DVL3 and ZBTB7B at MAMDC2 (MAM Domain Containing 2), DUX4 (Double Homeobox 4), and SYNJ1 (Synaptojanin 1) genes (Fig EV2E). Pathway analysis based on the Reactome database indicates that the gene hits for both cell lines correspond mostly to the following pathways: immune system, signal transduction, metabolism, metabolism of proteins, and gene expression (Fig 1D for MDA-MB-468 cells and 1E for MCF7 cells). Interestingly, the pathways that were most significantly overrepresented in both cell lines are pathways associated with the regulation of or the interaction with the immune system. In general, in both cell lines, DVL localized to genes known to be involved in or known to regulate multiple aspects of antigen processing and presentation and peptide loading of class I MHC (Fig 1F and G). In MDA-MB-468 cells, we also observed significantly overrepresented other pathways related to interferon signaling, rRNA expression, TCF complex formation, DNA methylation, transcription, and polycomb repressive complex-mediated methylation (Fig 1F). In contrast, in MCF7 the other pathways significantly overrepresented not associated with the immune system involved the regulation of potassium channels, ion exchangers, TP53-linked transcription, and a host of other pathways dysregulated during tumorigenesis (Fig 1G). DVL3 localizes to gene promoters and regulates their expression We previously reported that DVL3 not only localizes to the nucleus of breast cancer cells, but also binds to aromatase promoters and regulates their transcription (Castro-Piedras et al, 2018). To investigate further the genomic localization of the DVL3 ChIP-seq hits, we used IGV track visualization of the bam files. We discovered that DVL3 localized in different promoter regions including, for example, ARAP1 (ArfGAP With RhoGAP Domain, Ankyrin Repeat And PH Domain 1), RFX5 (Regulatory Factor X5), GBAT2 (Glioblastoma Multiforme-Associated Transcript 2), WDR74 (WD Repeat Domain 74), POU2F3 (POU Class 2 Homeobox 3), SNORD3D (Small Nucleolar RNA, C/D Box 3D), and HLA-A (Major Histocompatibility Complex, Class I, A) in MDA-MB-468 cells and ARAP1, RFX5, GBAT2, ZNF672 (Zinc Finger Protein 672), SSC5D (Scavenger Receptor Cysteine Rich Family Member With 5 Domains), HLA-A, and HLA-E (Major Histocompatibility Complex, Class I, E) in MCF7 cells (Fig 2A for MDA-MB-468 cells and 2B for MCF7 cells). Figure 2. DVL3 genomic binding in MDA-MB-468 and MCF7 cells An assembly of IgG (first row) and DVL3 (second row) ChIP-seq data in MDA-MB-468 for the ARAP1, RFX5-GBAT2, WDR74, POU2F3, SNORD3D, and HLA-A genes, visualized by IGV. An assembly of IgG (first row) and DVL3 (second row) ChIP-seq data in MCF7 for the ARAP1, RFX5-GBAT2, ZNF672, SSC5D, HLA-A, and HLA-E genes, visualized by IGV. ChIP-qPCRs at ARAP1, RFX5-GBAT2, WDR74, and HAUS5 promoters for IgG and DVL3 in MDA-MB-468 (NTC, shA DVL3, and shB DVL3). ChIP-qPCRs at RFX5-GBAT2, COX14, HLA-E, and PSMB8 promoters for IgG and DVL3 in MCF7 (NTC, shA DVL3, and shB DVL3). RT–qPCR-based analysis of expression changes of RFX5, GBAT2, WDR74, HCG15, POU2F3, SNORD3B, APOC1P1, HLA-A, TAP1, PSMD8, PDG1, and JUNB genes in MDA-MB-468 (NTC, shA DVL3, and shB DVL3) cells. RT–qPCR-based analysis of expression changes of RFX5, GBAT2, PCAT6, ZNF672, SSC5D, APOC1P1, HLA-A, HLA-F, and TAP1 genes in MCF7 (NTC, shA DVL3, and shB DVL3) cells. Data information: For (A) and (B), each column is 4,000 bp wide. The third rows show the gene nearest to the ChIP-seq alignment including its location, and orientation. The medium thick dark lines are the UTRs of the gene, and the thicker dark regions are exons followed by thin lines with arrows which are the introns. C and D panels show a representative ChIP-qPCR of three independent ChIP-qPCR experiments. For C and D panels, an independent t-test was used for statistical comparison. *P < 0.05; n = 3, technical replicates; data show means ± SD. For E and F panels, transcript levels were normalized to actin transcript levels, an independent t-test was used for statistical comparison. *P < 0.05; data show means ± SD, n = 3, biological replicates. Download figure Download PowerPoint To validate further the binding of DVL3 to those regions, we developed DVL3 stable knockdown cell lines, using two short hairpin RNAs (shRNAs) that target different regions of DVL3 mRNA vs. a non-targeting control (NTC). These cell lines have reduced expression of DVL3 (Fig EV2F), and ChIP-qPCR confirmed reduced DVL3 binding in the DVL3-shRNA cell lines compared with the NTC-control cell lines (Fig 2C for MDA-MB-468 and 2D for MCF7). Remarkably, the expression of GBAT2, WDR4, HCG15, POU2F3, APOC1P1, HLA-A, TAP1, PSMD8, PDG1, and JUNB was reduced in DVL3-depleted MDA-MB-468 cells, suggesting a role of DVL3 in the regulation of these genes. In contrast, the lncRNA SNORD3B was upregulated, suggesting that DVL3 acts as a repressor of this lncRNA (Fig 2E). In MCF7 cells, DVL3 depletion led to an upregulation of RFX5, GBAT2, PCAT6, SSC5D, APOC1P1, HLA-A, HLA-F, and TAP1 and a downregulation of HAUS5 and ZNF672 (Fig 2F). Thus, the impact of DVL3 depletion with respect to some genes (such as GBAT2) show different trends in MCF7 vs. MDA-MB-468 cells, suggesting a complex role of DVL3 in regulating these genes. To investigate whether these findings could also be linked with breast cancer patients, we analyzed RNA-Seq data from 1904 breast cancer patients using public gene expression data of RNA-seq downloaded from UCSC Xena project (http://xena.ucsc.edu/) derived from the TCGA Breast Cancer (BRCA) dataset (Goldman et al, 2020). Assessing the transcriptomes of TCGA breast cancer patients with different levels of DVL3 expression, we found that DVL3 positively correlates with the top ChIP-seq genes analyzed (Fig EV3A). Among those genes, we found that DVL3 mRNA is strongly associated with gene transcripts that control cell cycle, chromatin organization, developmental biology, DNA repair, gene expression, immune system, metabolism, metabolism of proteins, metabolism of RNA, signal transduction, and vesicle-mediated transport (Table EV1). Click here to expand this figure. Figure EV3. Heatmap showing positive correlation between DVL3 and KMT2D expression and the expression of DVL3 ChIP-seq hit genes in breast cancer patients Expression of top DVL3 ChIP-seq target genes upregulated in breast cancer patients with high DVL3 expression. Expression of top DVL3 ChIP-seq target genes upregulated in breast cancer patients with high KMT2D expression. Download figure Download PowerPoint To further explore DVL3 function in gene transcription, we identified the DVL3-associated transcriptional profile by RNA-seq (Fig 3A). We found 223 transcripts that significantly changed their expression (fold change [FC] > 2 and P value < 0.01) in the DVL3-depleted MDA-MB-468 cells (shB DVL3) compared with the control (NTC). Among these, 41 (18%) were downregulated and 184 (82%) upregulated upon DVL3 depletion (Fig 3B, left panel). Furthermore, we found 4964 transcripts that significantly changed their expression in DVL3-overexpressed MDA-MB-468 cells (DVL3-WT) compared with the control (EV). Among these, 4842 (97.5%) were downregulated and 122 (2.5%) were upregulated upon DVL3 overexpression (Fig 3B, right panel). In MCF7 cells, we found 569 transcripts that significantly changed their expression in the in DVL3-depleted cells (shB DVL3) compared with the control (NTC). Among these, 41 (7.2%) were downregulated and 530 (92.8%) upregulated upon DVL3 depletion (Fig 3C, left panel). Furthermore, we found 410 transcripts that significantly changed their expression in DVL3-overexprresed MCF7 cells (DVL3-WT) compared with the control (EV). Among these, 205 (50%) were downregulated and 205 (50%) were upregulated upon DVL3 overexpression (Fig 3C, right panel). Figure 3. DVL3 regulates gene transcription Volcano plot represents DVL3 transcriptional targets identified by RNA-seq in MDA-MB-468 cells (NTC vs. shB DVL3 and EV vs. DVL3-WT, upper panel) and in MCF7 cells (NTC vs. shB DVL3 and EV vs. DVL3-WT, lower panel). In the upper panel, the green dots represent significant downregulated genes, the red dots represent significantly upregulated genes and the orange dots represent DVL3-ChIP hits in MDA-MB-468 cells. In the lower panel, the blue dots represent significant downregulated genes, the red dots represent significantly upregulated genes, and the orange dots represent DVL3-ChIP hits in MCF7 cells. Gene expression levels were quantified using Fisher's Exact Test Signal Search in the DNASTAR ArrayStar software package. Diagram showing the percentage of upregulated and downregulated genes in the RNA-seq experiment comparing NTC vs. shB DVL3 (left panel) and EV vs. DVL3-WT (right panel) in MDA-MB-468 cells. Diagram showing the percentage of upregulated and downregulated genes in the RNA-seq experiment comparing NTC vs. shB DVL3 (left panel) and EV vs. DVL3-WT (right panel) in MCF7 cells. Venn diagram showing overlapping between DVL3-bound genes and DVL3 transcriptional targets in MDA-MB-468 cells. A total of 32% of differentially expressed genes identified by RNA-seq comparing NTC and shB DVL3 were also DVL3 direct targets identified by ChIP-seq. From these, 69% were upregulated and 31% down-regulated. A total of 36% of differentially expressed genes identified by RNA-seq comparing EV and DVL3-WT were also DVL3 direct targets identified by ChIP-seq. From these, 1% were upregulated and 99% down-regulated. Venn diagram showing overlapping between DVL3-bound genes and DVL3 transcriptional targets in MCF7 cells. A total of 3% of differentially expressed genes identified by RNA-seq comparing NTC and shB DVL3 were also DVL3 direct targets identified by ChIP-seq. From these, 87.5% were upregulated and 12.5% down-regulated. A total of 5.6% of differentially expressed genes identified by RNA-seq comparing EV and DVL3-WT were also DVL3 direct targets identified by ChIP-seq. From these, 56.5% were upregulated and 43.5% down-regulated. Data information: For all panels n = 3, biological replicates. Download figure Download PowerPoint Next, we identified the DVL3 direct transcriptional targets by comparing the DVL3-associated transcriptional profile with the ChIP-seq data. In MDA-MB-468 cells, we observed that 32% and 36% of the genes that showed a DVL3 dependency for transcription were bound by DVL3 when comparing DVL3 depletion vs. DVL3 overexpression, respectively (Fig 3D). Interestingly, the proportion of direct target genes upregulated (69%) was higher than those downregulated (31%) in DVL3-depleted cells relative to the control. Similarly, the proportion of direct target genes upregulated (1%) was lower than those downregulated (99%) when we comparing DVL3-overexpressed cells to its control (Fig 3D). In MCF7 cells, the overall percent of total transcripts that change which overlapping with DVL3 ChIP-seq hits was much smaller. Of these gene subsets, the proportion of direct target genes upregulated (87.5%) was lower than those downregulated (12.5%) in DVL3-depleted cells relative to the control. Additionally, the proportion of direct target genes upregulated (56.5%) was somewhat higher than the direct downregulated genes (43.5%) in DVL3-overexpressed cells relative to its control (Fig 3E). These data suggest a potential role of DVL3 in the transcription with a greater impact in MDA-MB-468 cells. DVL3 localizes with H3K4me3 epigenetic mark The identific
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