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CFIm25 links alternative polyadenylation to glioblastoma tumour suppression

Nature volume 510, pages 412–416 (2014)Cite this article

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Abstract

The global shortening of messenger RNAs through alternative polyadenylation (APA) that occurs during enhanced cellular proliferation represents an important, yet poorly understood mechanism of regulated gene expression1,2. The 3′ untranslated region (UTR) truncation of growth-promoting mRNA transcripts that relieves intrinsic microRNA- and AU-rich-element-mediated repression has been observed to correlate with cellular transformation3; however, the importance to tumorigenicity of RNA 3′-end-processing factors that potentially govern APA is unknown. Here we identify CFIm25 as a broad repressor of proximal poly(A) site usage that, when depleted, increases cell proliferation. Applying a regression model on standard RNA-sequencing data for novel APA events, we identified at least 1,450 genes with shortened 3′ UTRs after CFIm25 knockdown, representing 11% of significantly expressed mRNAs in human cells. Marked increases in the expression of several known oncogenes, including cyclin D1, are observed as a consequence of CFIm25 depletion. Importantly, we identified a subset of CFIm25-regulated APA genes with shortened 3′ UTRs in glioblastoma tumours that have reduced CFIm25 expression. Downregulation of CFIm25 expression in glioblastoma cells enhances their tumorigenic properties and increases tumour size, whereas CFIm25 overexpression reduces these properties and inhibits tumour growth. These findings identify a pivotal role of CFIm25 in governing APA and reveal a previously unknown connection between CFIm25 and glioblastoma tumorigenicity.

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Figure 1: CFIm25 depletion leads to consistent and robust 3′ UTR shortening of test genes.
Figure 2: The DaPars algorithm identifies broad targets of CFIm25 in standard RNA-seq data.
Figure 3: Increased pPAS usage after CFIm25 depletion results in increased protein translation and enhanced cell proliferation.
Figure 4: Altered expression of CFIm25 modulates glioblastoma tumour growth.

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Primary accessions

Gene Expression Omnibus

Data deposits

Raw sequence data has been deposited in the Gene Expression Omnibus under accession number GSE42420.

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References

  1. Elkon, R. et al. E2F mediates enhanced alternative polyadenylation in proliferation. Genome Biol. 13, R59 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Sandberg, R., Neilson, J. R., Sarma, A., Sharp, P. A. & Burge, C. B. Proliferating cells express mRNAs with shortened 3′ untranslated regions and fewer microRNA target sites. Science 320, 1643–1647 (2008)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. Mayr, C. & Bartel, D. P. Widespread shortening of 3′UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell 138, 673–684 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ji, Z., Lee, J. Y., Pan, Z., Jiang, B. & Tian, B. Progressive lengthening of 3′ untranslated regions of mRNAs by alternative polyadenylation during mouse embryonic development. Proc. Natl Acad. Sci. USA 106, 7028–7033 (2009)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Mangone, M. et al. The landscape of C. elegans 3′UTRs. Science 329, 432–435 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tian, B., Hu, J., Zhang, H. & Lutz, C. S. A large-scale analysis of mRNA polyadenylation of human and mouse genes. Nucleic Acids Res. 33, 201–212 (2005)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Di Giammartino, D. C., Nishida, K. & Manley, J. L. Mechanisms and consequences of alternative polyadenylation. Mol. Cell 43, 853–866 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chen, C.-Y. A. & Shyu, A.-B. AU-rich elements: characterization and importance in mRNA degradation. Trends Biochem. Sci. 20, 465–470 (1995)

    Article  CAS  PubMed  Google Scholar 

  9. Farh, K. K. et al. The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 310, 1817–1821 (2005)

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Wu, L. & Belasco, J. G. Let me count the ways: mechanisms of gene regulation by miRNAs and siRNAs. Mol. Cell 29, 1–7 (2008)

    Article  PubMed  Google Scholar 

  11. Singh, P. et al. Global changes in processing of mRNA 3′ untranslated regions characterize clinically distinct cancer subtypes. Cancer Res. 69, 9422–9430 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kubo, T., Wada, T., Yamaguchi, Y., Shimizu, A. & Handa, H. Knock-down of 25 kDa subunit of cleavage factor Im in Hela cells alters alternative polyadenylation within 3′-UTRs. Nucleic Acids Res. 34, 6264–6271 (2006)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yao, C. et al. Transcriptome-wide analyses of CstF64–RNA interactions in global regulation of mRNA alternative polyadenylation. Proc. Natl Acad. Sci. USA 109, 18773–18778 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Martin, G., Gruber, A. R., Keller, W. & Zavolan, M. Genome-wide analysis of pre-mRNA 3′ end processing reveals a decisive role of human cleavage factor I in the regulation of 3′UTR length. Cell Reports 1, 753–763 (2012)

    Article  CAS  PubMed  Google Scholar 

  15. Thomas, P. E. et al. Genome-wide control of polyadenylation site choice by CPSF30 in Arabidopsis. Plant Cell 24, 4376–4388 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Jan, C. H., Friedman, R. C., Ruby, J. G. & Bartel, D. P. Formation, regulation and evolution of Caenorhabditis elegans 3′UTRs. Nature 469, 97–101 (2011)

    Article  ADS  CAS  PubMed  Google Scholar 

  17. Shepard, P. J. et al. Complex and dynamic landscape of RNA polyadenylation revealed by PAS-Seq. RNA 17, 761–772 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bernard, D. et al. The methyl-CpG-binding protein MECP2 is required for prostate cancer cell growth. Oncogene 25, 1358–1366 (2006)

    Article  CAS  PubMed  Google Scholar 

  19. Sicinski, P. et al. Cyclin D1 provides a link between development and oncogenesis in the retina and breast. Cell 82, 621–630 (1995)

    Article  CAS  PubMed  Google Scholar 

  20. Weinstat-Saslow, D. et al. Overexpression of cyclin D1 mRNA distinguishes invasive and in situ breast carcinomas from non-malignant lesions. Nature Med. 1, 1257–1260 (1995)

    Article  CAS  PubMed  Google Scholar 

  21. Liu, W. et al. Reprogramming of proline and glutamine metabolism contributes to the proliferative and metabolic responses regulated by oncogenic transcription factor c-MYC. Proc. Natl Acad. Sci. USA 109, 8983–8988 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wang, J. B. et al. Targeting mitochondrial glutaminase activity inhibits oncogenic transformation. Cancer Cell 18, 207–219 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Klein, M. E. et al. Homeostatic regulation of MeCP2 expression by a CREB-induced microRNA. Nature Neurosci. 10, 1513–1514 (2007)

    Article  CAS  PubMed  Google Scholar 

  24. Deshpande, A. et al. 3′UTR mediated regulation of the cyclin D1 proto-oncogene. Cell Cycle 8, 3592–3600 (2009)

    Article  CAS  PubMed  Google Scholar 

  25. Gao, P. et al. c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature 458, 762–765 (2009)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Miura, P., Shenker, S., Andreu-Agullo, C., Westholm, J. O. & Lai, E. C. Widespread and extensive lengthening of 3′ UTRs in the mammalian brain. Genome Res. 23, 812–825 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ulitsky, I. et al. Extensive alternative polyadenylation during zebrafish development. Genome Res. 22, 2054–2066 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Nakayama, T. et al. Aberrant expression of Fra-2 promotes CCR4 expression and cell proliferation in adult T-cell leukemia. Oncogene 27, 3221–3232 (2008)

    Article  CAS  PubMed  Google Scholar 

  29. Wagner, E. J. & Garcia-Blanco, M. A. RNAi-mediated PTB depletion leads to enhanced exon definition. Mol. Cell 10, 943–949 (2002)

    Article  CAS  PubMed  Google Scholar 

  30. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ward, A. & Dutton, J. R. Regulation of the Wilms’ tumour suppressor (WT1) gene by an antisense RNA: a link with genomic imprinting? J. Pathol. 185, 342–344 (1998)

    Article  CAS  PubMed  Google Scholar 

  32. Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnol. 28, 511–515 (2010)

    Article  CAS  Google Scholar 

  33. Kaplan, P. J., Mohan, S., Cohen, P., Foster, B. A. & Greenberg, N. M. The insulin-like growth factor axis and prostate cancer: lessons from the transgenic adenocarcinoma of mouse prostate (TRAMP) model. Cancer Res. 59, 2203–2209 (1999)

    CAS  PubMed  Google Scholar 

  34. Fabian, M. R., Sonenberg, N. & Filipowicz, W. Regulation of mRNA translation and stability by microRNAs. Annu. Rev. Biochem. 79, 351–379 (2010)

    Article  CAS  PubMed  Google Scholar 

  35. Braulke, T., Dittmer, F., Gotz, W. & von Figura, K. Alteration in pancreatic immunoreactivity of insulin-like growth factor (IGF)-binding protein (IGFBP)-6 and in intracellular degradation of IGFBP-3 in fibroblasts of IGF-II receptor/IGF-II-deficient mice. Horm. Metab. Res. 31, 235–241 (1999)

    Article  CAS  PubMed  Google Scholar 

  36. Hu, J. F. et al. Lack of reciprocal genomic imprinting of sense and antisense RNA of mouse insulin-like growth factor II receptor in the central nervous system. Biochem. Biophys. Res. Commun. 257, 604–608 (1999)

    Article  CAS  PubMed  Google Scholar 

  37. Ellis, M. J. et al. Insulin-like growth factors in human breast cancer. Breast Cancer Res. Treat. 52, 175–184 (1998)

    Article  CAS  PubMed  Google Scholar 

  38. Friedman, R. C., Farh, K. K.-H., Burge, C. B. & Bartel, D. P. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19, 92–105 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Gómez-Angelats, M., Teeguarden, J. G., Dragan, Y. P. & Pitot, H. C. Mutational analysis of three tumor suppressor genes in two models of rat hepatocarcinogenesis. Mol. Carcinog. 25, 157–163 (1999)

    Article  PubMed  Google Scholar 

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Acknowledgements

We would like to thank members of the E.J.W., A.-B.S. and W.L. laboratories for helpful discussions, P. Carpenter for reviewing the manuscript, and Q. Zhu and T. Shan of LC Sciences for their efforts on the RNA-seq. This work was supported by a CPRIT grant to E.J.W. and A.-B.S. (RP100107), and in part by a Department of Defense grant to E.J.W. (W81XWH-11-1-0304), National Institutes of Health (NIH) grants to A.-B.S. (GM046454) and E.J.W. (CA167752 and CA166274), and an endowment from Houston Endowment (to A.-B.S.). Work in the W.L. laboratory is funded by grants from the Department of Defense (W81XWH-10-1-0501), CPRIT (RP110471-C3) and NIH (R01HG007538). Work in the M.L. laboratory is funded by grants from the Dr Marnie Rose Foundation and the William and Ella Owens Medical Research Foundation. C.P.M. acknowledges a Department of Defense Postdoctoral Visionary Award (W81XWH-12-1-0218).

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Author notes

  1. Chioniso P. Masamha and Zheng Xia: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, The University of Texas Medical School at Houston, Houston, 77030, Texas, USA

    Chioniso P. Masamha, Todd R. Albrecht, Ann-Bin Shyu & Eric J. Wagner

  2. Division of Biostatistics, Dan L Duncan Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, 77030, USA

    Zheng Xia & Wei Li

  3. The Vivian L. Smith Department of Neurosurgery, The University of Texas Medical School at Houston, Houston, 77030, Texas, USA

    Jingxuan Yang & Min Li

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  1. Chioniso P. Masamha
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Contributions

A.-B.S., W.L. and E.J.W. designed the study. C.P.M., T.R.A. and J.Y. performed the described experiments with conceptual advice from M.L. W.L. and Z.X. conducted bioinformatic analyses and developed the DaPars algorithm. C.P.M., Z.X., W.L., A.-B.S. and E.J.W. wrote the manuscript.

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Correspondence to Ann-Bin Shyu, Wei Li or Eric J. Wagner.

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Extended data figures and tables

Extended Data Figure 1 Design and optimization of the qRT–PCR assay to monitor APA of three test genes.

a, Schematic denotes the relative location of the common and distal primer annealing sites in each test gene and the approximate locations of the annotated proximal and distal poly(A) sites, depicted as pPAS and dPAS, respectively. The numbers demarcate where the 3′ UTR starts and ends according to ENSEMBL. b, Ethidium-stained agarose gel of RT–PCR products of equal cycle number from the different amplicons using HeLa cell mRNA. c, Both the common and distal cyclin D1 amplicons were cloned into the same pcDNA3 plasmid in tandem. Three dilutions of each plasmid were made and amplified individually with each amplicon in triplicate. The two lines on the graph depict the amplification curve for the common and distal amplicons. The expectation is that identical cycle threshold (CT) values should be attained for each, given that the PCR reactions were conducted using identical amounts of starting material. The average of three individual experiments is shown for each dilution and the average CT deviation of either amplicon at all of the dilutions was calculated as a correction factor. d, The experiment shown in c was repeated for DICER1 and TIMP2 to determine their respective correction factors, which was then applied to experiments shown in Fig. 1.

Extended Data Figure 2 Summary of RNA-seq alignment and reproducibility of PDUI and CFIm25-depletion-induced 3′ UTR shortening.

a, RNA-seq read statistics of the four biologically independent experiments where HeLa cells were treated with either control siRNA (Control) or CFIm25 siRNA (CFIm25kD). Pie chart on the right represents genomic distribution of reads that were mapped to human genome hg19. The percentage was calculated by averaging all samples. CDS, coding region. b, Histogram of gene expression of RefSeq genes with fragments per kilobase of transcript sequence per million mapped paired-end reads (FPKM) no less than 1. c, Scatterplot of the two biological replicates for each condition with high Pearson correlation (r ≥ 0.9) demonstrating a high level of reproducibility between sample PDUI scores. Each dot represents the PDUI of a transcript. d, Genome browser screen images from four independent RNA-seq experiments. Each represents an independent biological sample where HeLa cells were transfected with either the control siRNA (Con.) or an siRNA that knocked down CFIm25. Both VMA21 and SPCS3 were found to undergo 3′ UTR shortening after CFIm25 knockdown whereas FHL1 was found not to change.

Extended Data Figure 3 Shortened transcripts have more UGUA CFIm25-binding motifs than unaltered transcripts.

a, CFIm25 is known to bind to the UGUA motif. The number of UGUA motifs within the 3′ UTRs of genes with 3′ UTR shortening after CFIm25 knockdown relative to genes with unaltered 3′ UTRs was calculated and compared. Here we selected the genes without 3′ UTR change according to them having a ΔPDUI value ≤ 0.05. b, iCLIP tags from ref. 14 (Gene Expression Omnibus accession number GSE37398) were superimposed onto data derived from PDUI analysis of CFIm25 knockdown cells. The box plot demonstrates the enrichment of CFIm25 binding within 3′ UTRs that are altered after CFIm25 knockdown (P = 6.1 × 10−107, t-test).

Extended Data Figure 4 Gene expression changes of genes with shortened 3′ UTRs.

Pie chart was calculated from the list of 1,450 genes exhibiting shortened 3′ UTRs due to CFIm25 knockdown (dn, down). Differentially expressed gene analysis was performed using edgeR with FDR ≤ 0.05 (see Methods).

Extended Data Figure 5 The Pearson correlation between gene expression fold change and the number of lost negative regulatory elements.

Left, the number of lost AREs (AU-rich elements) due to 3′ UTR shortening was calculated using the ARE database and plotted against change in gene expression levels after CFIm25 knockdown (KD). Right, similar to the left except the number of lost patented miRNA target sites (Targetscan 6.2) was plotted.

Extended Data Figure 6 Overlap between shortening events in glioblastoma with low CFIm25 and shortening events in HeLa cells after CFIm25 knockdown.

Left, y-axis (red) represents the percentage of shortening events in low CFIm25 glioblastoma that are also shortened in HeLa cells after CFIm25 knockdown. Right, y-axis (blue) shows the number of shortening events in low CFIm25 glioblastoma (GBM) against different ΔPDUI cut-offs.

Extended Data Figure 7 Overexpression of CFIm25 reduces invasion and colony formation whereas CFIm25 depletion increases invasion and colony formation.

a, U251 cells were transfected with either GFP or CFIm25. Top left, Cells were replated in soft agar and the number of colonies/clusters formed were determined. Bottom left, Matrigel invasion assay for cells overexpressing CFIm25 or GFP. b, Top right, LN229 cells were transfected with either control or two different lentiviral plasmids targeting CFIm25 (KD1 and KD2). Stably transfected cells were plated on soft agar and the resulting colonies were counted for KD1 and KD2, respectively. Bottom right, LN229 cells were transfected with either control or two different siRNAs (KD1 and KD2) directed against CFIm25 and were replated for a Matrigel invasion assay. All the experiments were done in biological triplicates and shown is the mean ± s.d. All P values were from the two-tailed student t-test of the control versus sample. *P < 0.1, **P < 0.01, ***P < 0.001.

Extended Data Figure 8 Overexpression of CFIm25 in U251 tumours reduces their size and weight.

a, b, U251 subcutaneous (s.c.) xenograft tumours were isolated from nude mice on day 84 after implantation and measured for volume (a) and weight (b) (n = 10). U251-GFP indicates control U251 cells expressing GFP and U251-CFIm25 indicates cells transduced with a lentivirus that overexpresses CFIm25.

Extended Data Figure 9 Reduction in CFIm25 expression levels enhances LN229 tumour size and weight.

a, b, LN229 subcutaneous (s.c.) xenograft tumours were isolated from nude mice on day 40 after implantation and measured for volume (a) and weight (b) (n = 10). LN229-shCon. indicates control lentiviral transduced cells and LN229-shCFIm25 indicates cells transduced with a lentivirus that expresses shRNA targeting CFIm25.

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Masamha, C., Xia, Z., Yang, J. et al. CFIm25 links alternative polyadenylation to glioblastoma tumour suppression. Nature 510, 412–416 (2014). https://doi.org/10.1038/nature13261

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