The involvement of RIPK4 in TNF-α-stimulated IL-6 and IL-8 production by melanoma cells

Abstract

Purpose: The receptor-interacting protein kinase (RIPK4) has an oncogenic function in melanoma, regulates NF-κB and Wnt/β-catenin pathways, and is sensitive to the BRAF inhibitors: vemurafenib and dabrafenib which lead to its decreased level. As its role in melanoma remains not fully understood, we examined the effects of its downregulation on the transcriptomic profile of melanoma.

Methods: Applying RNA-seq, we revealed global alterations in the transcriptome of WM266.4 cells with RIPK4 silencing. Functional partners of RIPK4 were evaluated using STRING and GeneMANIA databases. Cells with transient knockdown (via siRNA) and stable knockout (via CRISPR/Cas9) of RIPK4 were stimulated with TNF-α. The expression levels of selected proteins were assessed using Western blot, ELISA, and qPCR.

Results: Global analysis of gene expression changes indicates a complex role for RIPK4 in regulating adhesion, migration, proliferation, and inflammatory processes in melanoma cells. Our study highlights potential functional partners of RIPK4 such as BIRC3, TNF-α receptors, and MAP2K6. Data from RIPK4 knockout cells suggest a putative role for RIPK4 in modulating TNF-α-induced production of IL-8 and IL-6 through two distinct signaling pathways-BIRC3/NF-κB and p38/MAPK. Furthermore, increased serum TNF-α levels and the correlation of RIPK4 with NF-κB were revealed in melanoma patients.

Conclusion: These data reveal a complex role for RIPK4 in regulating the immune signaling network in melanoma cells and suggest that this kinase may represent an alternative target for melanoma-targeted adjuvant therapy.

Keywords: BIRC3; Cytokines; Melanoma; RIPK4; RNA-seq; Transcriptome; p-38.

Fig. 1

RNA-seq analysis revealed significant changes in gene expression under downregulation of RIPK4 in WM266.4 cells. RIPK4 expression in cells transfected with neg.si and RIPK4.si was analyzed by qRT-PCR 48 h after transfection. The transcription level of RIPK4 was normalized to GAPDH (A). A scatter plot showing the correlation of transcript expression profile between RIPK4 silenced cells (RIPK4.si) and control cells (neg.si) using the DEBrowser platform (B). A heatmap showing the expression levels of significantly (FDR-adjusted p < 0.5) differentially expressed genes after transfection. The rows and columns were clustered based on similarity in predicted expression patterns. The red-blue gradient indicates the expression of the transcript in three independent experiments. Red = highest expression; blue = lowest expression (C). Classification of DEGs between RIPK4 silenced cells (RIPK4.si) and control cells (neg.si) based on their functions (molecular functions, cellular components, and biological processes) using BiNGO tool as a Cytoscape plugin. The percentages were calculated as the number of gene hits to the total number of hits in each category (D)

Fig. 2

Differentially expressed transcripts upon RIPK4 silencing in WM266.4 cells. Heatmap of DEGs in selected biological processes as immune system, cell adhesion/migration, cell differentiation, and cell proliferation associated with RIPK4. Red-blue gradient indicates the expression of transcript in three independent experiments: red = highest expression; blue = lowest expression. Selected processes were assigned to specific transcripts using Cytoscape platform (BiNGO apps). Genes selected for quantitative analysis with RT-PCR are marked with green squares (A). Validation of selected transcripts by qRT-PCR. RIPK4.si transfected cells were compared to neg.si samples. Relative mRNA levels of indicated transcripts were normalized to GAPDH. n = 3. **p < 0.001 (B). The expression levels of the P-p38, p38, BIRC3, and MAP2K6 proteins were analyzed using Western blotting along with densitometry. [n = 1–3] (C)

Fig. 3

A network of interactions between genes involved in the regulation of inflammation, in which transcript levels were altered by downregulation of RIPK4 expression in WM266.4 cells. The analysis was performed in the GeneMANIA (A) and STRING (B) databases. The network shows the following types marked according to the legend, interactions: activation, binding, catalysis, transcriptional regulation

Fig. 4

Relationship between NF-κB and RIPK4 expression in cutaneous melanomas (n = 17). p65 subunit of nuclear factor-κB immunofluorescence (upper row) in RIPK4 (lower row) showing low (left column) and high (right column) expressions in human cutaneous melanomas. p65 (AlexaFluor 488-green) and nuclei (PI-res) signals were captured using epifluorescence microscope with 475 nm and 542 nm excitation wavelengths, respectively, and merged. Scale bars = 50 μm (A). Percentage of p65 subunit of NFκB-positive melanoma cells nuclei (left) and NFκB staining intensity (right) in melanoma cells in melanoma cases showing RIPK4 low and RIPK4 high agranular and granular staining, respectively. Low and high expressions of RIPK4 were distinguished with the median value. Spearman’s correlation between NFκB and RIPK4 is indicated in the graph (B)

Fig. 5

Effect of downregulation of RIPK4 on selected protein expression after TNF-α stimulation. TNF-α concentration in the serum in melanoma patients (n = 32) and control group (n = 20). The p value represents statistical significance in Mann–Whitney test (A). Effect of TNF-α (10 ng/ml) in RIPK4.si and neg.si transfected cells on mRNA and secretion level of IL-8 (B) and mRNA level of MAP2K6 and BIRC3 transcripts normalized to GAPDH. n = 3, **p < 0.01. (C). Effect of TNF-α (10 ng/ml) on pre-treated WM266.4 cells with SB203580 (10 µM) on RIPK4 and P-p38 expression assessed by Western blot (D) and mRNA and secretion level of IL-8 (E). Selected protein levels in A375^RIPK4.KO and WM266.4^RIPK4.KO cells along with densitometry. GAPDH served as loading control. (F). Effect of TNF-α on IL-8 and IL-6 secretion in A375^RIPK4.KO and WM266.4^RIPK4.KO cells and their controls. n = 3, ND indicates not determined (G)