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. 2024 Jan;18(1):44-61.
doi: 10.1002/1878-0261.13487. Epub 2023 Jul 17.

SETD2 loss in renal epithelial cells drives epithelial-to-mesenchymal transition in a TGF-β-independent manner

Tianchu Wang  1   2 , Ryan T Wagner  2 , Ryan A Hlady  2 , Xiaoyu Pan  2 , Xia Zhao  2 , Sungho Kim  2 , Liguo Wang  3 , Jeong-Heon Lee  4   5 , Huijun Luo  6 , Erik P Castle  7 , Douglas F Lake  8 , Thai H Ho  6 , Keith D Robertson  2
Affiliations

SETD2 loss in renal epithelial cells drives epithelial-to-mesenchymal transition in a TGF-β-independent manner

Tianchu Wang et al. Mol Oncol. 2024 Jan.

Abstract

Histone-lysine N-methyltransferase SETD2 (SETD2), the sole histone methyltransferase that catalyzes trimethylation of lysine 36 on histone H3 (H3K36me3), is often mutated in clear cell renal cell carcinoma (ccRCC). SETD2 mutation and/or loss of H3K36me3 is linked to metastasis and poor outcome in ccRCC patients. Epithelial-to-mesenchymal transition (EMT) is a major pathway that drives invasion and metastasis in various cancer types. Here, using novel kidney epithelial cell lines isogenic for SETD2, we discovered that SETD2 inactivation drives EMT and promotes migration, invasion, and stemness in a transforming growth factor-beta-independent manner. This newly identified EMT program is triggered in part through secreted factors, including cytokines and growth factors, and through transcriptional reprogramming. RNA-seq and assay for transposase-accessible chromatin sequencing uncovered key transcription factors upregulated upon SETD2 loss, including SOX2, POU2F2 (OCT2), and PRRX1, that could individually drive EMT and stemness phenotypes in SETD2 wild-type (WT) cells. Public expression data from SETD2 WT/mutant ccRCC support the EMT transcriptional signatures derived from cell line models. In summary, our studies reveal that SETD2 is a key regulator of EMT phenotypes through cell-intrinsic and cell-extrinsic mechanisms that help explain the association between SETD2 loss and ccRCC metastasis.

Keywords: SETD2 mutation; clear cell renal cell carcinoma; epithelial-to-mesenchymal transition; histone H3 lysine 36 trimethylation; paracrine signaling; transcription factors.

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Conflict of interest statement

THH: Advisory board participation: Surface Therapeutics, Exelixis, Genentech, Pfizer, Ipsen, Cardinal Health; research support‐Novartis. The remaining authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
SETD2 inactivation induces a TGF‐β‐independent EMT program. (A) Cell morphology of WT, TGF‐β‐treated WT, and SETD2 KO RPTEC. Magnification 10×. Scale bar, 400 μm. (B) RT‐qPCR results for expression of epithelial and mesenchymal genes in WT, TGF‐β‐treated WT, and SETD2 KO clones. Data from three replicates are represented as mean ± SEM. P‐value is calculated for epithelial and mesenchymal genes individually using one‐way ANOVA. ****P < 0.0001; ***P < 0.001; *P < 0.05; ns, P ≥ 0.05. (C) Western blot showing expression of EMT markers in the indicated cell lines. (D) Western blot for total SMAD2 and phospho (Ser465/467) SMAD2, and TGF‐β levels in WT, SETD2 KO, SETD2 KO rescue, and TGF‐β‐treated/untreated WT RPTEC cells. Images in C and D are representative of three independent experiments. (E) Top panel: volcano plot of differentially expressed genes between TGF‐β‐treated WT and untreated control. Bottom panel: differentially expressed genes between SETD2 KO and WT RPTEC (genes shown are common between the two independent KO1/2 clones). Average value of log2‐fold change for each gene is used as the expression value. Green: downregulated genes (log2‐fold change < −1 and P < 0.05). Red: upregulated genes (log2‐fold change > 1 and P < 0.05). EMT genes are labeled. RNA‐seq was run in duplicate. (F) Heatmaps of all differentially expressed genes between SETD2‐deficient or TGF‐β treatment and RPTEC WT (top), and a subset of key genes linked to EMT, IFNγ, and secreted factors shown in the lower panel. ‘a/b’ denote replicates. (G) Venn diagram of differentially expressed genes between parental RPTEC, SETD2 KO RPTEC, and TGF‐β‐treated WT RPTEC. (H) Heatmap comparing enrichment of select GSEA hallmark pathways for differentially expressed genes between RPTEC WT/SETD2 KO (yellow), RPTEC TGF‐β‐treated vs control (blue), and TCGA primary SETD2 mutant vs WT tumors (green). P‐values are derived from the GSEA algorithm (Broad Institute); *P < 0.05. H3, histone H3; MW, molecular weight.
Fig. 2
Fig. 2
SETD2 loss drives EMT and stemness phenotypes. (A) Wound healing assay assessing migratory phenotype for 72 h TGF‐β treatment of RPTEC WT (left panel) vs untreated control and SETD2 KO vs WT/rescue (right panel). P‐value for comparing wound closure among WT, SETD2 KO, and SETD2 KO rescue RPTEC, each performed in duplicate, is calculated using one‐way ANOVA. Data are represented as mean ± SEM. Magnification 4×. Scale bar, 1000 μm. (B) Transwell assay testing invasiveness of TGF‐β‐treated WT cells. Magnification 2.5×. Scale bar, 1200 μm. (C) Transwell assay testing invasiveness of WT, SETD2 KO, and SETD2 KO rescue RPTEC. For B and C images of crystal violet‐stained cells that invaded through the membrane are shown beside the graphs. Two‐way ANOVA is used for statistical testing; samples are run in duplicate. Data are represented as mean ± SEM. Magnification 2.5×. Scale bar, 1200 μm. (D) 3D spheroid formation assay evaluating stemness in SETD2 KO cells. Data are represented as mean ± SEM. Image of spheroids in ultra‐low attachment plates are shown at the right. P‐value is calculated using one‐way ANOVA. Magnification 4×. Scale bar, 1000 μm. ****P < 0.0001; ***P < 0.001; **P < 0.01; *P < 0.05; ns, P ≥ 0.05.
Fig. 3
Fig. 3
Global impact of SETD2 knockout and restoration on H3K36me3 distribution. (A) Western blot of H3K36me1, H3K36me2, and H3K36me3 levels in the indicated RPTEC lines. ‘Res’ is SETD2 KO rescue. Images are representative of three independent experiments. (B) MA plots of differential H3K36me3 peaks for the RPTEC SETD2 KO vs WT (top) and RPTEC SETD2 KO rescue vs KO (bottom) comparisons. ChIP‐seq was performed in duplicate for each cell line analysis. Significantly gained/lost peaks are highlighted in red (log2 fold change > 1, P < 0.05). (C) Principal component analysis of H3K36me3 ChIP‐seq data in RPTEC lines. Each sample was run twice as indicated by the colored circles. (D) Tag density plot of H3K36me3 enrichment for all protein‐coding genes at the gene body, 10 kb upstream of the TSS and 10 kb downstream of the TES. Data are derived from ChIP‐seq run in duplicate for each cell line. (E) Representative genome browser views of H3K36me3 levels at EMT‐related genes for the three isogenic RPTEC lines. TSS, transcription start site; TTS, transcription termination site.
Fig. 4
Fig. 4
SETD2 loss drives an EMT program through paracrine signaling. (A) GSEA enrichment plots for the NABA‐secreted factors pathway showing enrichment in SETD2 KO vs WT RPTEC, with significant reversal in the SETD2 rescue vs KO comparison. Normalized enrichment scores (NES) and adjusted P‐values (Padj) are shown. (B) A corresponding heatmap of a subset of NABA‐secreted factors from (A) in replicates of WT, KO, and rescue RPTEC. ‘a/b’ indicate replicates. The color scale is the same as in Fig. 1. (C) A bar chart and representative examples of a wound healing assay assessing migration capacity of WT RPTEC cells treated with CM from RPTEC WT, SETD2 KO, and SETD2 KO rescue cells. Magnification, 4×. Scale bar, 1000 μm. (D) Summary bar chart and representative examples from a transwell assay testing invasiveness of WT cells exposed to the same CMs as in C from RPTEC WT, KO, and rescue cells. Magnification, 2.5×. Scale bar, 1200 μm. One‐way ANOVA is used for statistical testing in B and C. ****P < 0.0001; ***P < 0.001; **P < 0.01; *P < 0.05; ns, P ≥ 0.05. All reactions were performed in duplicate.
Fig. 5
Fig. 5
SETD2 rescue partially restores global transcriptional patterns and reverses EMT and stemness transcriptional signatures. (A) Principal component analysis of all genes in untreated RPTEC WT, SETD2 KO, SETD2 KO rescue, and TGF‐β‐treated WT RPTEC derived from RNA‐seq (all lines are run in duplicate). (B) A scatterplot of RPTEC KO vs WT against SETD2 rescue vs KO showing reversal of genes differentially expressed with SETD2 loss with ectopic re‐expression of SETD2. A subset of key EMT genes is labeled in red. (C) A heatmap of hallmark pathways from GSEA altered in SETD2 KO and reversed with ectopic re‐expression. (D) A subset of reversed pathways from (C) demonstrating reversal of EMT and IFNγ pathways with reintroduction of SETD2. Two genes of interest are indicated (PRRX1 and OAS2), and the differential expression and gene rank in their respective comparisons are shown, along with the pathway normalized enrichment score (NES) and adjusted P‐value (Padj).
Fig. 6
Fig. 6
Global chromatin opening and novel transcriptional effectors of SETD2 loss revealed through ATAC‐seq. (A) Pairwise correlation of global ATAC‐seq data of WT, SETD2 KO, and SETD2 KO rescue RPTEC. Each cell line was run in triplicate for ATAC‐seq. (B) Differential analysis of ATAC‐seq peaks between RPTEC SETD2 KO vs WT, and SETD2 rescue vs KO as MA plots. Peaks with either log2‐fold change > 1 or < −1, P < 0.05 are highlighted in red. (C) Top panel: Venn diagram for overlapping ATAC‐seq peaks gained in SETD2 KO (67 346 peaks in B, left panel) and peaks lost in SETD2 KO rescue cells (34 053 peaks in B, right panel). Right panel: annotation of each of the three peak sets (gained, lost, and shared) to genomic features. Bottom panel: ATAC‐seq peaks lost in KO2 (25 108 peaks in B, left panel) and ATAC‐seq peaks gained in SETD2 KO rescue cells (19 114 peaks, right panel), and the distribution of these peaks in the genomic features indicated. P‐value for significance of enrichment of ATAC peaks within each feature relative to background is calculated using the Chi‐square test with Yates continuity correction. ****P < 0.0001. (D) Supervised clustering of genes whose expression is restored in SETD2 KO rescue cells and the correlation with corresponding reversed/rescued ATAC‐seq peaks. (E) TF motif analysis performed by AME in the MEME suite showing motifs significantly enriched in ATAC SETD2 KO rescued peaks in C (20 555 overlapped peaks in top panel and 6591 overlapped peaks in bottom panel). (F) Genome browser views of ATAC signals for representative EMT TFs in RPTEC WT, SETD2 KO, and rescue cell lines. Promoters and enhancers are indicated by red and gray lines, respectively. Regions of differential peaks are boxed.
Fig. 7
Fig. 7
SOX2, OCT2, and PRRX1 are downstream effectors of the SETD2‐regulated EMT program. (A) Expression of SOX2, OCT2, and PRRX1 in TGF‐β‐treated WT (72 h), SETD2 KO, and SETD2 rescue tested by RT‐qPCR (run in triplicate). (B) Migration capacity by wound healing assay, (C) invasiveness by transwell assay, and (D) stemness by 3D spheroid formation assay in RPTEC WT GFP (control vector), SETD2 KO1 and KO2, and SOX2/OCT2/PRRX1‐transduced WT RPTEC lines. Images are taken at 4× magnification, scale bar: 1000 μm for (B) and (D) and at 2.5× magnification, scale bar: 1200 μm for (C). Data are represented as mean ± SEM for triplicate reactions for B–D. P‐value is calculated by one‐way ANOVA in (A), (B), and (D). Two‐way ANOVA is used for statistical test for (C). ****P < 0.0001; ***P < 0.001; **P < 0.01; *P < 0.05; ns, P ≥ 0.05. (E) Model of the SETD2 loss‐driven EMT program through cell intrinsic (transcriptional) and cell extrinsic (paracrine) mechanisms.
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