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Lineage-specific roles of the cytoplasmic polyadenylation factor CPEB4 in the regulation of melanoma drivers

View Article: PubMed Central - PubMed

ABSTRACT

Nuclear 3'-end-polyadenylation is essential for the transport, stability and translation of virtually all eukaryotic mRNAs. Poly(A) tail extension can also occur in the cytoplasm, but the transcripts involved are incompletely understood, particularly in cancer. Here we identify a lineage-specific requirement of the cytoplasmic polyadenylation binding protein 4 (CPEB4) in malignant melanoma. CPEB4 is upregulated early in melanoma progression, as defined by computational and histological analyses. Melanoma cells are distinct from other tumour cell types in their dependency on CPEB4, not only to prevent mitotic aberrations, but to progress through G1/S cell cycle checkpoints. RNA immunoprecipitation, sequencing of bound transcripts and poly(A) length tests link the melanoma-specific functions of CPEB4 to signalling hubs specifically enriched in this disease. Essential in these CPEB4-controlled networks are the melanoma drivers MITF and RAB7A, a feature validated in clinical biopsies. These results provide new mechanistic links between cytoplasmic polyadenylation and lineage specification in melanoma.

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RIP-Seq (RNA immunoprecipitation-sequencing) for the identification of CPEB4 targets in melanoma.(a) Correlated results of Cuffdiff or EdgeR-based analysis of CPEB4 RIP-seq analyses in SK-Mel-103, using shCPEB4-derivatives as a reference. This comparison was performed in two independent replicates. Graph depicts differential expression changes (Log2 fold change, Log2FC) obtained with each method for replicate (1). Replicate (2) is shown in Supplementary Fig. 5c. (b) Differential expression of CPEB4-bound mRNAs in SK-Mel-103 versus the RWP1 pancreatic cancer cell line. RIP-seq data from RWP1 was obtained from ref. 35 and analysed as for melanoma cells by Cuffdiff. Two replicates were processed for each cell line. Data in this panel correspond to Replicate (1) of melanoma and pancreatic cancers. Other replicates are depicted in Supplementary Fig. 5d. (c) Relative expression of CPEB4-bound mRNAs identified by RIP-seq in SK-Mel-103 cells and mined by GSEA across the CCLE data set. Graph represents the enrichment score in melanoma versus other tumours. Positive correlated genes in melanoma and negatively correlated in other tumours are highlighted in red and blue, respectively. (d) Heatmap from the GSEA analysis shown in c, represented for each of the indicated tumour cell types. Note the distinct clustering in melanoma. Pearson coefficient (P), Spearman rank correlation coefficient (r) and FDR values are indicated in the corresponding panels.
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f5: RIP-Seq (RNA immunoprecipitation-sequencing) for the identification of CPEB4 targets in melanoma.(a) Correlated results of Cuffdiff or EdgeR-based analysis of CPEB4 RIP-seq analyses in SK-Mel-103, using shCPEB4-derivatives as a reference. This comparison was performed in two independent replicates. Graph depicts differential expression changes (Log2 fold change, Log2FC) obtained with each method for replicate (1). Replicate (2) is shown in Supplementary Fig. 5c. (b) Differential expression of CPEB4-bound mRNAs in SK-Mel-103 versus the RWP1 pancreatic cancer cell line. RIP-seq data from RWP1 was obtained from ref. 35 and analysed as for melanoma cells by Cuffdiff. Two replicates were processed for each cell line. Data in this panel correspond to Replicate (1) of melanoma and pancreatic cancers. Other replicates are depicted in Supplementary Fig. 5d. (c) Relative expression of CPEB4-bound mRNAs identified by RIP-seq in SK-Mel-103 cells and mined by GSEA across the CCLE data set. Graph represents the enrichment score in melanoma versus other tumours. Positive correlated genes in melanoma and negatively correlated in other tumours are highlighted in red and blue, respectively. (d) Heatmap from the GSEA analysis shown in c, represented for each of the indicated tumour cell types. Note the distinct clustering in melanoma. Pearson coefficient (P), Spearman rank correlation coefficient (r) and FDR values are indicated in the corresponding panels.

Mentions: Next, RNA immunoprecipitation followed by RNA sequencing (RIP-seq) was performed for an unbiased identification of CPEB4-bound transcripts that are involved in the modulation of mitosis and the lineage-specific control of G1/S transition. In this context, we were interested in genes or pathways that may act beyond the control of pigmentation (that is, to identify true novel melanoma-associated traits). The melanoma SK-Mel-103 was selected for an initial screen, as a well-known example of MITF-negative amelanotic melanoma cells31. RNA collected from control or shCPEB4-transduced cells (two independent replicates each) was subjected to crosslinking and immunoprecipitation followed by elimination of ribosomal RNA (total reads are summarized in Supplementary Fig. 5a). Reads were aligned to the human genome (Ref Seq GRCh37/hg19) with TopHat-2.0.4 (ref. 42). For each read, we considered the best hit or allowed 20 multi-hits, obtaining an 80–85% overlap with the two approaches (Supplementary Fig. 5b). We thus proceeded with the best hit for subsequent analyses of differential expression. This was performed using Cufflinks or EdgeR, which rendered similar results as indicated by the high correlation found by Pearson and Spearman rank tests (Fig. 5a; see also Supplementary Fig. 5c). Filtering for significance (adjusted P value<0.05), this approach rendered 331 CPEB4-bound transcripts in melanoma cells (that is, downregulated in the CPEB4 shRNA counterparts; see additional detail in Supplementary Data 1). Similar analyses were performed for CPEB4 RIP-Seq in the pancreatic RWP1 cell line (which had also been performed in duplicates)35. Intriguingly, the overlap between RIP-Seq data of both systems was strikingly low (see Fig. 5b for one of the replicates of SK-Mel-103 and of RWP1, and Supplementary Fig. 5d for the rest of the comparisons). Thus, while known targets of CPEB4 such as the metallothionein proteins MT2A and MT1E were found in both cell types, 93% of the CPEB4-bound transcripts in melanoma (that is, 312/331) had not been reported in RWP1 (Supplementary Fig. 5e). Indeed, Gene Set Enrichment Analyses (GSEA) performed in the CCLE, indicated the CPEB4-bound targets in melanoma showed a distinct expression in this tumour type (see enrichment scores in Fig. 5c; the corresponding heatmaps in Fig. 5d; false discovery rate (FDR)=0.22). Therefore, these results support the hypothesis that CPEB4 has functions restricted to melanoma resulting (at least in part) from the regulation of a set of genes particularly enriched in this tumour type.


Lineage-specific roles of the cytoplasmic polyadenylation factor CPEB4 in the regulation of melanoma drivers
RIP-Seq (RNA immunoprecipitation-sequencing) for the identification of CPEB4 targets in melanoma.(a) Correlated results of Cuffdiff or EdgeR-based analysis of CPEB4 RIP-seq analyses in SK-Mel-103, using shCPEB4-derivatives as a reference. This comparison was performed in two independent replicates. Graph depicts differential expression changes (Log2 fold change, Log2FC) obtained with each method for replicate (1). Replicate (2) is shown in Supplementary Fig. 5c. (b) Differential expression of CPEB4-bound mRNAs in SK-Mel-103 versus the RWP1 pancreatic cancer cell line. RIP-seq data from RWP1 was obtained from ref. 35 and analysed as for melanoma cells by Cuffdiff. Two replicates were processed for each cell line. Data in this panel correspond to Replicate (1) of melanoma and pancreatic cancers. Other replicates are depicted in Supplementary Fig. 5d. (c) Relative expression of CPEB4-bound mRNAs identified by RIP-seq in SK-Mel-103 cells and mined by GSEA across the CCLE data set. Graph represents the enrichment score in melanoma versus other tumours. Positive correlated genes in melanoma and negatively correlated in other tumours are highlighted in red and blue, respectively. (d) Heatmap from the GSEA analysis shown in c, represented for each of the indicated tumour cell types. Note the distinct clustering in melanoma. Pearson coefficient (P), Spearman rank correlation coefficient (r) and FDR values are indicated in the corresponding panels.
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f5: RIP-Seq (RNA immunoprecipitation-sequencing) for the identification of CPEB4 targets in melanoma.(a) Correlated results of Cuffdiff or EdgeR-based analysis of CPEB4 RIP-seq analyses in SK-Mel-103, using shCPEB4-derivatives as a reference. This comparison was performed in two independent replicates. Graph depicts differential expression changes (Log2 fold change, Log2FC) obtained with each method for replicate (1). Replicate (2) is shown in Supplementary Fig. 5c. (b) Differential expression of CPEB4-bound mRNAs in SK-Mel-103 versus the RWP1 pancreatic cancer cell line. RIP-seq data from RWP1 was obtained from ref. 35 and analysed as for melanoma cells by Cuffdiff. Two replicates were processed for each cell line. Data in this panel correspond to Replicate (1) of melanoma and pancreatic cancers. Other replicates are depicted in Supplementary Fig. 5d. (c) Relative expression of CPEB4-bound mRNAs identified by RIP-seq in SK-Mel-103 cells and mined by GSEA across the CCLE data set. Graph represents the enrichment score in melanoma versus other tumours. Positive correlated genes in melanoma and negatively correlated in other tumours are highlighted in red and blue, respectively. (d) Heatmap from the GSEA analysis shown in c, represented for each of the indicated tumour cell types. Note the distinct clustering in melanoma. Pearson coefficient (P), Spearman rank correlation coefficient (r) and FDR values are indicated in the corresponding panels.
Mentions: Next, RNA immunoprecipitation followed by RNA sequencing (RIP-seq) was performed for an unbiased identification of CPEB4-bound transcripts that are involved in the modulation of mitosis and the lineage-specific control of G1/S transition. In this context, we were interested in genes or pathways that may act beyond the control of pigmentation (that is, to identify true novel melanoma-associated traits). The melanoma SK-Mel-103 was selected for an initial screen, as a well-known example of MITF-negative amelanotic melanoma cells31. RNA collected from control or shCPEB4-transduced cells (two independent replicates each) was subjected to crosslinking and immunoprecipitation followed by elimination of ribosomal RNA (total reads are summarized in Supplementary Fig. 5a). Reads were aligned to the human genome (Ref Seq GRCh37/hg19) with TopHat-2.0.4 (ref. 42). For each read, we considered the best hit or allowed 20 multi-hits, obtaining an 80–85% overlap with the two approaches (Supplementary Fig. 5b). We thus proceeded with the best hit for subsequent analyses of differential expression. This was performed using Cufflinks or EdgeR, which rendered similar results as indicated by the high correlation found by Pearson and Spearman rank tests (Fig. 5a; see also Supplementary Fig. 5c). Filtering for significance (adjusted P value<0.05), this approach rendered 331 CPEB4-bound transcripts in melanoma cells (that is, downregulated in the CPEB4 shRNA counterparts; see additional detail in Supplementary Data 1). Similar analyses were performed for CPEB4 RIP-Seq in the pancreatic RWP1 cell line (which had also been performed in duplicates)35. Intriguingly, the overlap between RIP-Seq data of both systems was strikingly low (see Fig. 5b for one of the replicates of SK-Mel-103 and of RWP1, and Supplementary Fig. 5d for the rest of the comparisons). Thus, while known targets of CPEB4 such as the metallothionein proteins MT2A and MT1E were found in both cell types, 93% of the CPEB4-bound transcripts in melanoma (that is, 312/331) had not been reported in RWP1 (Supplementary Fig. 5e). Indeed, Gene Set Enrichment Analyses (GSEA) performed in the CCLE, indicated the CPEB4-bound targets in melanoma showed a distinct expression in this tumour type (see enrichment scores in Fig. 5c; the corresponding heatmaps in Fig. 5d; false discovery rate (FDR)=0.22). Therefore, these results support the hypothesis that CPEB4 has functions restricted to melanoma resulting (at least in part) from the regulation of a set of genes particularly enriched in this tumour type.

View Article: PubMed Central - PubMed

ABSTRACT

Nuclear 3'-end-polyadenylation is essential for the transport, stability and translation of virtually all eukaryotic mRNAs. Poly(A) tail extension can also occur in the cytoplasm, but the transcripts involved are incompletely understood, particularly in cancer. Here we identify a lineage-specific requirement of the cytoplasmic polyadenylation binding protein 4 (CPEB4) in malignant melanoma. CPEB4 is upregulated early in melanoma progression, as defined by computational and histological analyses. Melanoma cells are distinct from other tumour cell types in their dependency on CPEB4, not only to prevent mitotic aberrations, but to progress through G1/S cell cycle checkpoints. RNA immunoprecipitation, sequencing of bound transcripts and poly(A) length tests link the melanoma-specific functions of CPEB4 to signalling hubs specifically enriched in this disease. Essential in these CPEB4-controlled networks are the melanoma drivers MITF and RAB7A, a feature validated in clinical biopsies. These results provide new mechanistic links between cytoplasmic polyadenylation and lineage specification in melanoma.

No MeSH data available.


Related in: MedlinePlus