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Differential histone acetylation and super-enhancer regulation underlie melanoma cell dedifferentiation
Karen Mendelson, Tiphaine C. Martin, Christie B. Nguyen, Min Hsu, Jia Xu, Claudia Lang, Reinhard Dummer, Yvonne Saenger, Jane L. Messina, Vernon K. Sondak, Garrett Desman, Dan Hasson, Emily Bernstein, Ramon E. Parsons, Julide Tok Celebi
Karen Mendelson, Tiphaine C. Martin, Christie B. Nguyen, Min Hsu, Jia Xu, Claudia Lang, Reinhard Dummer, Yvonne Saenger, Jane L. Messina, Vernon K. Sondak, Garrett Desman, Dan Hasson, Emily Bernstein, Ramon E. Parsons, Julide Tok Celebi
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Research Article Dermatology

Differential histone acetylation and super-enhancer regulation underlie melanoma cell dedifferentiation

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Abstract

Dedifferentiation or phenotype switching refers to the transition from a proliferative to an invasive cellular state. We previously identified a 122-gene epigenetic gene signature that classifies primary melanomas as low versus high risk (denoted as Epgn1 or Epgn3). We found that the transcriptomes of the Epgn1 low-risk and Epgn3 high-risk cells are similar to the proliferative and invasive cellular states, respectively. These signatures were further validated in melanoma tumor samples. Examination of the chromatin landscape revealed differential H3K27 acetylation in the Epgn1 low-risk versus Epgn3 high-risk cell lines that corroborated with a differential super-enhancer and enhancer landscape. Melanocytic lineage genes (MITF, its targets and regulators) were associated with super-enhancers in the Epgn1 low-risk state, whereas invasiveness genes were linked with Epgn3 high-risk status. We identified the ITGA3 gene as marked by a super-enhancer element in the Epgn3 invasive cells. Silencing of ITGA3 enhanced invasiveness in both in vitro and in vivo systems, suggesting it as a negative regulator of invasion. In conclusion, we define chromatin landscape changes associated with Epgn1/Epgn3 and phenotype switching during early steps of melanoma progression that regulate transcriptional reprogramming. This super-enhancer and enhancer-driven epigenetic regulatory mechanism resulting in major changes in the transcriptome could be important in future therapeutic targeting efforts.

Authors

Karen Mendelson, Tiphaine C. Martin, Christie B. Nguyen, Min Hsu, Jia Xu, Claudia Lang, Reinhard Dummer, Yvonne Saenger, Jane L. Messina, Vernon K. Sondak, Garrett Desman, Dan Hasson, Emily Bernstein, Ramon E. Parsons, Julide Tok Celebi

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Figure 3

Reorganization of super-enhancer and enhancer landscape in Epgn1/Epgn3 melanoma cells.

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Reorganization of super-enhancer and enhancer landscape in Epgn1/Epgn3 m...
(A) Immunoblotting of the chromatin fraction of Epgn1 and Epgn3 cells for H3K27ac, H3K18ac, H3K9me3, H3K9ac, and H3K4me3. Total H3 indicates loading. (B) IHC of H3K27ac in YUPEET, a representative Epgn1 cell line and YUCHIME, a representative Epgn3 cell line. Scale bars: 50 μm (left), 25 μm (right). (C) Quantification of data generated in 2 Epgn1 cell lines WM983A and YUPEET (red) as compared with 2 Epgn3 cell lines WM902B and YUCHIME (light blue). P = 0.0004. One-way ANOVA test was used. (D and E) ChIP-Seq for H3K27ac using representative Epgn1 cells, WM35 and YUPEET, and representative Epgn3 cells, WM1552C and YUCHIME. PCA plot indicating the separation of groups. Volcano plot showing a total of 85,858 peaks from differential peak analysis: 7,924 upregulated peaks in the Epgn3 group, and 31,511 downregulated peaks in the Epgn3 group (upregulated in the Epgn1 group). (F) Heatmap of differential peak analysis. Data are presented on ± 5 kb around the peak center. DiffBind package was used. (G) Heatmap of differential enhancer analysis. Data are centered on ± 5 kb window. A total of 2,220 significant enhancers were identified in the Epgn3 cell lines and 9,074 significant enhancers were identified in the Epgn1 cell lines. (H) Heatmap of differential super-enhancer analysis. Data are shown ± 1 kb upstream and downstream of the super-enhancer. A total of 83 and 533 significant super-enhancers were identified in the Epgn3 and Epgn1 cell lines, respectively. ROSE algorithm was used. (I) GSEA (KEGG pathway) analysis identifies super-enhancers and enhancers associated with functional pathways. K, KEGG; E, Elsevier.

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ISSN 2379-3708

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