Notch1-MAPK Signaling Axis Regulates CD133+ Cancer Stem Cell-Mediated Melanoma Growth and Angiogenesis  Dhiraj Kumar, Santosh Kumar, Mahadeo Gorain, Deepti Tomar, Harshal S. Patil, Nalukurthi N.V. Radharani, Totakura V.S. Kumar, Tushar V. Patil, Hirekodathakallu V. Thulasiram, Gopal C. Kundu  Journal of Investigative Dermatology  Volume 136, Issue 12, Pages 2462-2474 (December 2016) DOI: 10.1016/j.jid.2016.07.024 Copyright © 2016 The Authors Terms and Conditions

Figure 1 Melanoma cells constitute a heterogeneous CSC subpopulation. (a) Bar graph represents the flow cytometry analyses of a heterogeneous subpopulation of CSCs using specific markers (CD133, CD20, CD271, ABCG2, ABCG5, VEGFR1, CD166, and CD44) in mouse melanoma B16F10 and B16F1 as well as human melanoma A375, SK-MEL-2, and SK-MEL-28 cells. Data are presented as the mean ± SEM of three independent experiments. (b) FACS analysis of CD133+ and CD133− subpopulations and their purity in B16F10 cells. (c) Immunoblot analyses of stemness-specific markers (Oct3/4, Nanog, and Sox10) in unsorted, CD133+, and CD133– B16F10 cells. ABC, ATP-binding cassette; CD, cluster of differentiation; CSC, cancer stem cell; PE, phycoerythrin; SEM, standard error of the mean; VEGF, vascular endothelial growth factor. Journal of Investigative Dermatology 2016 136, 2462-2474DOI: (10.1016/j.jid.2016.07.024) Copyright © 2016 The Authors Terms and Conditions

Figure 2 Molecular profile, tumorigenicity, and growth kinetics of CD133+ cells. (a–c) Scatter plots, cluster analysis, and volcano plot of differentially expressed genes in CD133+ versus CD133– cells. (d–f) CD133+, CD133–, or unsorted B16F10-Luc cells were injected subcutaneously (s.c.) into NOD/SCID mice and tumor images were captured using IVIS, quantified and represented in mean flux (n = 6 mice). *P < 0.007, **P < 0.0007. Tumor volumes were measured twice weekly. *P < 0.007. (g) CD133 expression was analyzed by immunoblot in tumor lysates. (h) In vivo limiting dilution analyses of indicated cells in C57BL/6J mice (n = 10 mice). *P < 0.00004, **P < 0.000006, ***P < 0.0000007. (i, j) CD133+ and CD133– cells were injected s.c. into NOD/SCID mice to generate primary isografts. Further, primary isograft-derived cultures were reimplanted for secondary isografts. *P < 0.05, **P < 0.02 (n = 6 mice). (k) Schematic representation of experimental outline for (i) and (j). Data are mean ± SEM. IVIS, in vivo imaging system; NOD, nonobese diabetic; SCID, severe combined immunodeficiency; SEM, standard error of the mean. Journal of Investigative Dermatology 2016 136, 2462-2474DOI: (10.1016/j.jid.2016.07.024) Copyright © 2016 The Authors Terms and Conditions

Figure 3 Chemoresistance and angiogenic properties of CD133+ cells. (a) SP analysis of CD133+ and CD133– cells. (b) Effect of dacarbazine, doxorubicin, dabrafenib, and trametinib on CD133+ and CD133– cells’ viability. *P < 0.004 (n = 3). (c) Vasculogenic mimicry in CD133+ and CD133– cells. Scale bars = 30 μm. Bar graph represents the number of tube junctions/hpf; *P < 0.0001 (n = 3). (d) Migration of HUVECs toward CD133+ or CD133– cells and their quantitation in a comigration assay. Scale bars = 30 μm. *P < 0.00005 (n = 3). (e) Tube formation using HUVECs (1 × 104) in the presence of conditioned medium (CM) of CD133+ or CD133– cells and their quantitation. Scale bars = 30 μm. *P < 0.00004 (n = 3). (f, left) qRT-PCR analysis of VEGF in indicated cells (n = 3). (f, right) Immunoblot of VEGF in CD133+ and CD133– cells. (g) Flow cytometry analysis of CD31, VEGFR2, and VEGFR1 in unsorted, CD133+, and CD133– cells. (h) CD31 and VEGF expression in tumor sections derived from CD133+ and CD133– cells based on immunofluorescence and data quantitation. Scale bars = 20 μm. *P < 0.0005 (n = 3). Data are mean ± SEM. HUVECs, human umbilical vein endothelial cells; SEM, standard error of the mean; SP, side population; VEGF, vascular endothelial growth factor. Journal of Investigative Dermatology 2016 136, 2462-2474DOI: (10.1016/j.jid.2016.07.024) Copyright © 2016 The Authors Terms and Conditions

Figure 4 Role of CD133+ cells in metastasis and transcriptional regulation of CD133 by Notch1. (a) Wound migration at 0 and 12 hours in indicated cells. *P < 0.0006 (n = 3). (b) Wound migration in CD133 silenced CD133+ cells. *P < 0.00008 (n = 3). (c) qRT-PCR of MMP-2/-9 (n = 3). (d) Zymography in indicated cells. (e) CD133+ and CD133– B16F10-Luc cells were injected through the tail vein. Metastatic sites were captured by IVIS. (f) Immunostaining of antimelanoma antigens. Scale bars = 100 μm. (g, h) Immunoblot and immunofluorescence of Notch1 pathway-associated proteins. Scale bars = 20 μm. (i, j) Effect of GSI-IX (γ-secretase inhibitor) on CD133 expression as shown by flow cytometry and immunoblot. Blue indicates CD133+ and red represents CD133– subpopulations. (k) Flow cytometry analysis of CD133 in NICD1-overexpressing B16F10 cells. (l, m) Schematic representation of NICD1-binding sites on a CD133 promoter. Chromatin immunoprecipitation was performed using CD133+ cells with anti-NICD1 antibody and PCR amplified with CD133 promoter-specific primers. (n) CD133 promoter activity by luciferase reporter assay in cells and conditions as indicated. *P < 0.009; #P < 0.003 (n = 3). Data are mean ± SEM. IVIS, in vivo imaging system; MMP, matrix metalloproteinase; NICD1, Notch1 intracellular domain; SEM, standard error of the mean; siRNA, small interfering RNA. Journal of Investigative Dermatology 2016 136, 2462-2474DOI: (10.1016/j.jid.2016.07.024) Copyright © 2016 The Authors Terms and Conditions

Figure 5 Role of Notch1 signaling on CD133-dependent MAPK activation. (a) Immunoblots of p-MEK3/6, p-p38, c-Fos, and c-Jun in unsorted, CD133+, or CD133– cells. (b) AP-1-DNA binding in cells as indicated in (a) by EMSA. (c) Effect of SB203580 (p38 MAPK inhibitor) on p-p38, c-Jun, c-Fos, and CD133 expression in CD133+ cells as shown by immunoblots. (d) Effect of GSI-IX on NICD1, p-p38, c-Jun, and c-Fos levels in CD133+ cells. (e, f) Immunoblot of p-p38, c-Jun, Notch1, and CD133 in Notch1- or CD133-silenced CD133+ cells. (g) Effect of GSI-IX and SB203580 on migration of HUVECs toward CD133+ cells. The data are mean ± SEM (n = 3). *P < 0.005; **P < 0.0005. (h) Wound migration assay at indicated conditions. The data are mean ± SEM (n = 3). *P < 0.002; **P < 0.0006. (i) Notch1, CD133, or c-Jun was silenced by their specific siRNA in CD133+ cells, and the expression of metastasis- and angiogenesis-specific genes was analyzed by qRT-PCR. Bar graph represents mean ± SEM (n = 2). AP-1, activator protein-1; EMSA, electrophoretic mobility shift assay; GSI, γ-secretase inhibitor; HUVECs, human umbilical vein endothelial cells; MAPK, mitogen-activated protein kinase; MEK, MAPK/ERK kinase; MMP, matrix metalloproteinase; NICD1, Notch1 intracellular domain; SEM, standard error of the mean; siRNA, small interfering RNA; VEGF, vascular endothelial growth factor. Journal of Investigative Dermatology 2016 136, 2462-2474DOI: (10.1016/j.jid.2016.07.024) Copyright © 2016 The Authors Terms and Conditions

Figure 6 Andrographolide (Andro) abrogates tumorigenic and metastatic potential of CD133+ cells by targeting Notch1-dependent MAPK pathway. (a) A cell viability assay was performed in CD133+, CD133–, and unsorted B16F10 cells treated with Andro at indicated doses. Bar graph denotes mean ± SEM (n = 3). *P < 0.008 and **P < 0.0008 compared with untreated cells. (b) Immunoblots of Notch1 regulated signaling molecules in Andro-treated CD133+ cells. (c) Effect of Andro on CD133+ cell-derived tumors in C57BL/6J mice (n = 6). Data are mean ± SEM. *P < 0.005; **P < 0.0007; ***P < 0.0006. (d) Immunoblots of NICD1, p-p38, c-Jun, and c-Fos from mice tumors lysate treated with Andro. (e) CD133+ B16F10-Luc (1 × 103) cells were injected through the tail vein of NOD/SCID mice, and mice were treated with Andro intraperitoneally. Metastatic sites were captured by IVIS. (f) H&E staining of lung metastases. Scale bars = 100 μm. (g) Immunofluorescence of VEGF in tumor sections as indicated. Scale bars = 20 μm. (h) Schematic representation of Notch1 signaling that regulates CD133-mediated MAPK activation. MAPK activation leads to AP-1-dependent VEGF and MMP-2/-9 expression, which contributes to melanoma growth, angiogenesis, and metastasis. AP-1, activator protein-1; H&E, hematoxylin and eosin; IVIS, in vivo imaging system; MAPK, mitogen-activated protein kinase; MMP, matrix metalloproteinase; NICD1, Notch1 intracellular domain; NOD, nonobese diabetic; SCID, severe combined immunodeficiency; SEM, standard error of the mean; VEGF, vascular endothelial growth factor. Journal of Investigative Dermatology 2016 136, 2462-2474DOI: (10.1016/j.jid.2016.07.024) Copyright © 2016 The Authors Terms and Conditions