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Metastasis-suppressor transcript destabilization through TARBP2 binding of mRNA hairpins

Abstract

Aberrant regulation of RNA stability has an important role in many disease states1,2. Deregulated post-transcriptional modulation, such as that governed by microRNAs targeting linear sequence elements in messenger RNAs, has been implicated in the progression of many cancer types3,4,5,6,7. A defining feature of RNA is its ability to fold into structures. However, the roles of structural mRNA elements in cancer progression remain unexplored. Here we performed an unbiased search for post-transcriptional modulators of mRNA stability in breast cancer by conducting whole-genome transcript stability measurements in poorly and highly metastatic isogenic human breast cancer lines. Using a computational framework that searches RNA sequence and structure space8, we discovered a family of GC-rich structural cis-regulatory RNA elements, termed sRSEs for structural RNA stability elements, which are significantly overrepresented in transcripts displaying reduced stability in highly metastatic cells. By integrating computational and biochemical approaches, we identified TARBP2, a double-stranded RNA-binding protein implicated in microRNA processing, as the trans factor that binds the sRSE family and similar structural elements—collectively termed TARBP2-binding structural elements (TBSEs)—in transcripts. TARBP2 is overexpressed in metastatic cells and metastatic human breast tumours and destabilizes transcripts containing TBSEs. Endogenous TARBP2 promotes metastatic cell invasion and colonization by destabilizing amyloid precursor protein (APP) and ZNF395 transcripts, two genes previously associated with Alzheimer’s and Huntington’s disease, respectively. We reveal these genes to be novel metastasis suppressor genes in breast cancer. The cleavage product of APP, extracellular amyloid-α peptide, directly suppresses invasion while ZNF395 transcriptionally represses a pro-metastatic gene expression program. The expression levels of TARBP2, APP and ZNF395 in human breast carcinomas support their experimentally uncovered roles in metastasis. Our findings establish a non-canonical and direct role for TARBP2 in mammalian gene expression regulation and reveal that regulated RNA destabilization through protein-mediated binding of mRNA structural elements can govern cancer progression.

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Figure 1: A family of GC-rich structural cis-regulatory RNA elements are enriched in transcripts destabilized in metastatic breast cancer cells.
Figure 2: TARBP2 binds and post-transcriptionally destabilizes sRSE-carrying transcripts.
Figure 3: Endogenous TARBP2 promotes metastatic colonization.
Figure 4: TARBP2 promotes metastatic cell invasion and colonization by destabilizing APP and ZNF395 transcripts.

Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

RNA-seq and microarray data have been deposited in the Gene Expression Omnibus under accession number GSE49649.

References

  1. Hollams, E. M., Giles, K. M., Thomson, A. M. & Leedman, P. J. mRNA stability and the control of gene expression: implications for human disease. Neurochem. Res. 27, 957–980 (2002)

    Article  CAS  PubMed  Google Scholar 

  2. Wilusz, C. J., Wormington, M. & Peltz, S. W. The cap-to-tail guide to mRNA turnover. Nature Rev. Mol. Cell Biol. 2, 237–246 (2001)

    Article  CAS  Google Scholar 

  3. Kumar, M. S., Lu, J., Mercer, K. L., Golub, T. R. & Jacks, T. Impaired microRNA processing enhances cellular transformation and tumorigenesis. Nature Genet. 39, 673–677 (2007)

    Article  CAS  PubMed  Google Scholar 

  4. Ma, L., Teruya-Feldstein, J. & Weinberg, R. A. Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 449, 682–688 (2007)

    Article  ADS  CAS  PubMed  Google Scholar 

  5. Song, S. J. et al. MicroRNA-antagonism regulates breast cancer stemness and metastasis via TET-family-dependent chromatin remodeling. Cell 154, 311–324 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tavazoie, S. F. et al. Endogenous human microRNAs that suppress breast cancer metastasis. Nature 451, 147–152 (2008)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Xiao, C. & Rajewsky, K. MicroRNA control in the immune system: basic principles. Cell 136, 26–36 (2009)

    Article  CAS  PubMed  Google Scholar 

  8. Goodarzi, H. et al. Systematic discovery of structural elements governing stability of mammalian messenger RNAs. Nature 485, 264–268 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Keene, J. D. RNA regulons: coordination of post-transcriptional events. Nature Rev. Genet. 8, 533–543 (2007)

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Minn, A. J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518–524 (2005)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cutroneo, K. R. & Ehrlich, H. Silencing or knocking out eukaryotic gene expression by oligodeoxynucleotide decoys. Crit. Rev. Eukaryot. Gene Expr. 16, 23–30 (2006)

    Article  CAS  PubMed  Google Scholar 

  12. Zuker, M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31, 3406–3415 (2003)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wan, Y., Kertesz, M., Spitale, R. C., Segal, E. & Chang, H. Y. Understanding the transcriptome through RNA structure. Nature Rev. Genet. 12, 641–655 (2011)

    Article  CAS  PubMed  Google Scholar 

  14. Wang, Y. et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671–679 (2005)

    Article  CAS  PubMed  Google Scholar 

  15. Gatignol, A., Buckler-White, A., Berkhout, B. & Jeang, K. T. Characterization of a human TAR RNA-binding protein that activates the HIV-1 LTR. Science 251, 1597–1600 (1991)

    Article  ADS  CAS  PubMed  Google Scholar 

  16. Gatignol, A., Buckler, C. & Jeang, K. T. Relatedness of an RNA-binding motif in human immunodeficiency virus type 1 TAR RNA-binding protein TRBP to human P1/dsI kinase and Drosophila staufen. Mol. Cell. Biol. 13, 2193–2202 (1993)

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Chendrimada, T. P. et al. TRBP recruits the Dicer complex to Ago2 for microRNA processing and gene silencing. Nature 436, 740–744 (2005)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Licatalosi, D. D. et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456, 464–469 (2008)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  19. Korpal, M. et al. Direct targeting of Sec23a by miR-200s influences cancer cell secretome and promotes metastatic colonization. Nature Med. 17, 1101–1108 (2011)

    Article  CAS  PubMed  Google Scholar 

  20. Kuhn, P. H. et al. ADAM10 is the physiologically relevant, constitutive α-secretase of the amyloid precursor protein in primary neurons. EMBO J. 29, 3020–3032 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tanaka, K., Shouguchi-Miyata, J., Miyamoto, N. & Ikeda, J. E. Novel nuclear shuttle proteins, HDBP1 and HDBP2, bind to neuronal cell-specific cis-regulatory element in the promoter for the human Huntington’s disease gene. J. Biol. Chem. 279, 7275–7286 (2004)

    Article  CAS  PubMed  Google Scholar 

  22. Fan, X. et al. miR-20a promotes proliferation and invasion by targeting APP in human ovarian cancer cells. Acta Biochim. Biophys. Sin. (Shanghai) 42, 318–324 (2010)

    Article  CAS  Google Scholar 

  23. Strieter, R. M. et al. Interleukin-8. A corneal factor that induces neovascularization. Am. J. Pathol. 141, 1279–1284 (1992)

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Soria, G. et al. Inflammatory mediators in breast cancer: coordinated expression of TNFα & IL-1β with CCL2 & CCL5 and effects on epithelial-to-mesenchymal transition. BMC Cancer 11, 130 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Singh, B. et al. COX-2 involvement in breast cancer metastasis to bone. Oncogene 26, 3789–3796 (2007)

    Article  CAS  PubMed  Google Scholar 

  26. MacRae, I. J., Ma, E., Zhou, M., Robinson, C. V. & Doudna, J. A. In vitro reconstitution of the human RISC-loading complex. Proc. Natl Acad. Sci. USA 105, 512–517 (2008)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  27. De Vito, C. et al. A TARBP2-dependent miRNA expression profile underlies cancer stem cell properties and provides candidate therapeutic reagents in Ewing sarcoma. Cancer Cell 21, 807–821 (2012)

    Article  CAS  PubMed  Google Scholar 

  28. 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 

  29. 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 

  30. Eddy, S. R. Computational genomics of noncoding RNA genes. Cell 109, 137–140 (2002)

    Article  CAS  PubMed  Google Scholar 

  31. Foat, B. C. & Stormo, G. D. Discovering structural cis-regulatory elements by modeling the behaviors of mRNAs. Mol. Syst. Biol. 5, 268 (2009)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Lunde, B. M., Moore, C. & Varani, G. RNA-binding proteins: modular design for efficient function. Nature Rev. Mol. Cell Biol. 8, 479–490 (2007)

    Article  CAS  Google Scholar 

  33. Ray, D. et al. Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins. Nature Biotechnol. 27, 667–670 (2009)

    Article  CAS  Google Scholar 

  34. Ray, D. et al. A compendium of RNA-binding motifs for decoding gene regulation. Nature 499, 172–177 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Elemento, O., Slonim, N. & Tavazoie, S. A universal framework for regulatory element discovery across all genomes and data types. Mol. Cell 28, 337–350 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zuker, M. & Stiegler, P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 9, 133–148 (1981)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kertesz, M. et al. Genome-wide measurement of RNA secondary structure in yeast. Nature 467, 103–107 (2010)

    Article  ADS  CAS  PubMed  Google Scholar 

  38. Oikonomou, P., Goodarzi, H. & Tavazoie, S. Systematic identification of regulatory elements in conserved 3′ UTRs of human transcripts. Cell Rep. 7, 281–292 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Chi, S. W., Zang, J. B., Mele, A. & Darnell, R. B. Argonaute HITS-CLIP decodes microRNA–mRNA interaction maps. Nature 460, 479–486 (2009)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. Giannopoulou, E. G. & Elemento, O. An integrated ChIP-seq analysis platform with customizable workflows. BMC Bioinformatics 12, 277 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

  41. Zhang, C. & Darnell, R. B. Mapping in vivo protein–RNA interactions at single-nucleotide resolution from HITS-CLIP data. Nature Biotechnol. 29, 607–614 (2011)

    Article  CAS  Google Scholar 

  42. Pencheva, N. et al. Convergent multi-miRNA targeting of ApoE drives LRP1/LRP8-dependent melanoma metastasis and angiogenesis. Cell 151, 1068–1082 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hatzis, C. et al. A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer. J. Am. Med. Assoc. 305, 1873–1881 (2011)

    Article  CAS  Google Scholar 

  44. Lu, X. et al. Predicting features of breast cancer with gene expression patterns. Breast Cancer Res. Treat. 108, 191–201 (2008)

    Article  CAS  PubMed  Google Scholar 

  45. Hofacker, I. L. et al. Fast folding and comparison of RNA secondary structures. Monatsh. Chem. 125, 167–188 (1994)

    Article  CAS  Google Scholar 

  46. Manley, J. L. SELEX to identify protein-binding sites on RNA. Cold Spring Harb. Protoc. 2013, 156–163 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

  47. Harfe, B. D., McManus, M. T., Mansfield, J. H., Hornstein, E. & Tabin, C. J. The RNaseIII enzyme Dicer is required for morphogenesis but not patterning of the vertebrate limb. Proc. Natl Acad. Sci. USA 102, 10898–10903 (2005)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank C. Alarcon, N. Halberg, N. Pencheva, P. Furlow and A. Nguyen for technical assistance and comments on previous versions of this manuscript. We thank H. Lee for her help with orthotopic injections. We thank P. Oikonomou for providing the reporter constructs. We thank the Darnell laboratory for their input on the HITS-CLIP methodology. We are also grateful to M. McManus and Z. Mourelatos for providing Dicer-null cells. We thank C. Zhao, W. Zhang and S. Dewell of the Rockefeller Genomics Resource Center for assistance with next-generation RNA-sequencing and microarray profiling. H.G. is an Anderson Cancer Center Fellow at Rockefeller University. S.F.T. is a Department of Defense Era of Hope Scholar and a Department of Defense Breast Cancer Collaborative Scholars and Innovators Award recipient. S.T. was supported by the National Human Genome Research Institute (R01 HG003219). We also acknowledge the efforts of Expression Project for Oncology (ExpO) and International Genomics Consortium (IGC) in providing public access to breast cancer gene expression profiles and annotations.

Author information

Authors and Affiliations

Authors

Contributions

H.G., S.T. and S.F.T. conceived the project and designed the experiments. S.F.T. and S.T. supervised all research. H.G., S.Z., S.T. and S.F.T. wrote the manuscript. H.G., S.Z., C.G.B. and L.F. performed the experiments. S.Z. performed cancer cell invasion and reporter assays. L.F. and H.G. performed the HITS-CLIP experiment. C.G.B. and H.G. performed lung colonization assays. H.G. performed the analyses.

Corresponding authors

Correspondence to Saeed Tavazoie or Sohail F. Tavazoie.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 The sRSE family of structural cis-regulatory RNA elements is enriched in transcripts exhibiting reduced stability in metastatic MDA-LM2 cells.

a, The sRSE family is composed of 318 GC-rich structural elements with sRSE1 serving as the most informative representative. These elements were enriched in the transcripts destabilized in MDA-LM2 cells. Here, we have included a subset of structural elements that reside in this family. These motifs display a lower statistical significance relative to sRSE1 and have a conditional information ratio of less than 2.0 with respect to sRSE1; in other words, these motifs form an overlapping family of structural elements for which sRSE1 is the best representative. For the analyses presented in this study, we have provided enrichment/depletion patterns for the entire sRSE family as well as sRSE1 as the representative. b, sRSE1 was significantly enriched in transcripts downregulated in MDA-LM2 cells relative to the parental population.

Extended Data Figure 2 The sRSE-carrying transcripts show correlated expression in human gene expression data sets and are modulated in in-culture cellular titration experiments.

a, The enrichment pattern of the sRSE regulon in two human whole-genome co-expression data sets. The significant enrichment/depletion pattern in different co-expression clusters further supports the role of sRSEs as potentially functional regulatory elements. b, The distribution of Spearman correlation coefficients between the expression of active sRSE-carrying transcripts across a compendium of breast cancer profiles19. Active targets were defined as transcripts that carry sRSEs and showed a differential decay rate score of less than −0.8. For reference, we also generated a background distribution by selecting a random set of transcripts (of equal size) and calculated pair-wise correlations. The sRSE-carrying transcripts showed a significantly higher correlated expression than expected by chance (the calculated P value is based on a one-tailed Mann–Whitney test). c, The transcripts with sequence but no structural matches to sRSEs (the sequence component of sRSEs) were stratified based on whether or not the sequence match also satisfies the structural restrictions (labelled as structured or linear). As shown here, the sequence restrictions alone showed no enrichment among the destabilized transcripts. d, Decoy (blue) and scrambled (red) sequences were chosen based on an identical randomly generated backbone sequence with the bold sequences matching the chosen sRSE1 sites (along with 30-nucleotide flanking sequences). Only the underlined regions, which correspond to exact matches to the sRSE1 motif description, were shuffled to generate the scrambled sequences. e, sRSE1 was significantly enriched among transcripts upregulated in MDA-LM2 cells upon the transfection of decoy RNA in comparison to scrambled oligonucleotides.

Extended Data Figure 3 In vitro secondary structure probing of the sRSE1 decoy and measuring the functionality of exemplary sRSE variants in reporter systems.

a, We used a medium-throughput approach to probe the secondary structure of a given sRSE1 decoy sequence (see Methods). Shown here are in silico and in vitro secondary structure predictions for each functional sRSE. The histogram for each nuclease digestion shows the percentage of clones (from a total of 15 tested clones) with truncation at each specific site. The in silico folding was constrained based on the digestion sites. b, The reporter construct used for testing the functionality of the engineered elements. A Gateway site in the 3′ UTR of mCherry was used to insert elements downstream of the mCherry coding sequence; acGFP served as the internal control. qRT–PCR of mCherry and GFP transcripts was then used to assay the effects of the cloned sRSE variants. c, Decoy, scrambled, structured and unstructured sequences (along with their predicted secondary structure) that were used in the mCherry/GFP reporter system. In the scrambled sequence, the nucleotides were shuffled in order to compromise both the sequence and the structure of the element. In the structured mimetic sequence, the nucleotides in the stem are swapped while maintaining the sequence of the loop, ensuring that the structure of the stem is maintained while its sequence is changed. In the unstructured mimetic sequence, only the nucleotides in the stem are mutated to disrupt the structure while maintaining the sequence identity of the loop. The in silico folding was guided based on the information provided by batch V1/S1 digestion of RNA variants followed by cloning and sequencing.

Extended Data Figure 4 Identification of TARBP2 as the potential modulator of the sRSE regulon and the regulatory consequences of TARBP2 modulations.

a, Three RBPs, TARBP2, HEXIM1 and PPRC1, were selected as potential regulators of the sRSE regulon on the basis of their positive/negative correlations with this regulon at the transcript level (Methods). Here, the Spearman correlations (and their associated t-statistics) between the sRSE target signature and each of the candidate RBPs have been shown. We have also included the distribution of Spearman correlations for each candidate RBP along with the associated P values (see Methods). b, Unlike TARBP2, RNAi-mediated knockdown of PPRC1 and HEXIM1 did not result in a significant deregulation of the sRSE-carrying transcripts. c, In addition to MDA-LM2 cells, a significant increase in the expression sRSE-carrying transcripts was also observed in RNAi-mediated TARBP2 knockdown cells in CN-LM1a and 293T backgrounds. d, The sRSE1 motif showed a similar enrichment pattern to that of the entire sRSE family with significant overrepresentations among the transcripts upregulated upon TARBP2 silencing in both CN-LM1a and 293T backgrounds. e, f, The enrichment/depletion patterns of sRSE1-carrying transcripts, as representative of the sRSE family, in transcript stability and gene expression profiles of cells in which TARBP2 levels were modulated. g, Linear regression analysis of gene-expression profiles from TARBP2 siRNA knockdown and sRSE1 in-culture cellular titration experiments. The smoothed density at each point in the scatterplot has been shown as a colour map. The P value represents the significance of the reported Spearman correlation (Rho).

Extended Data Figure 5 TARBP2 directly interacts with and modulates the sRSE regulon in vivo.

a, TARBP2 HITS-CLIP results were significantly correlated between the two replicates. b, TARBP2-bound sequences (determined by HITS-CLIP) were annotated and presented as a pie chart. The majority of interactions were intronic; however, direct interactions with exons, small nucleolar/microRNAs and long non-coding (lnc)RNAs were also observed. c, Crosslinking-induced mutation sites (CIMS) analysis of TARBP2 HITS-CLIP data (Methods), similar to the results obtained from ChIPseeqer-identified binding sites, also showed an enrichment of the sRSE family in the TARBP2-binding site relative to their shuffled counterparts (1st order Markov model; see Methods). d, Similar to the case for the entire sRSE family, sRSE1 was also significantly enriched among the TARBP2-binding sites identified by ChIPseeqer and CIMS respectively. e, We observed a highly significant overlap between transcripts that we identified as direct interacting partners of TARBP2 (using HITS-CLIP; see Methods) and the sRSE-carrying transcripts (53% overlap, hypergeometric P-value <10−22). f, The TARBP2 HITS-CLIP clusters, identified as the CIMS common between the two biological replicates, were folded in silico and the resulting terminal stem-loops were extracted. Consistent with the GC-rich nature of sRSE elements, these stem-loops displayed a significant shift towards higher GC contents. The associated P value was calculated using a Wilcoxon rank-sum test with μ = 0.45 (GC content of annotated genes in human genome marked by the light blue line) as the null hypothesis. g, In silico folding of the identified TARBP2 CIMS binding sites (Vienna RNA Package45) displayed more stable secondary structures formed by real sequences relative to their shuffled counterparts (1st order Markov model). The associated P value was calculated using a paired Mann–Whitney test between free-energy estimates for real and shuffled sequences. h, The relative stability of sRSE1/scrambled-fused mCherry reporters assayed in control and TARBP2 knockdown cells. Samples in quadruplicate were treated with α-amanitin and total RNA was extracted at the 0 and 8 h time points. Following DNase digestion and cDNA synthesis, transcript levels were quantified using qPCR in order to measure relative stability for mCherry (with GFP as the endogenous control). Error bars indicate s.e.m. *P < 0.05 using a Mann–Whitney test. i, j, In this study, we initially used the sRSE family as a proxy to track the behaviour of a broader regulon. Upon identification of TARBP2 as the trans factor, however, we could directly assess the targets that are bound by TARBP2 in vivo. As shown here, consistent with our previous observations, these transcripts showed an enrichment pattern identical to that of sRSE-carrying transcripts: they are enriched among the transcripts that are downregulated/destabilized in MDA-LM2 cells, upregulated and stabilized in TARBP2-knockdown cells, and downregulated when TARBP2 is overexpressed. We observed similar upregulations upon TARBP2 knockdown in CN-LM1a and 293T cells. These observations further support the coherence of our findings and the use of sRSE as a surrogate for probing this regulon.

Extended Data Figure 6 TARBP2 interacts with GC-rich stem-loops in vitro.

a, b, A qRT–PCR-based assay for quantification of RNA variants immunoprecipitated in the presence and absence of tagged TARBP2. This assay, which has been widely adopted for microRNA qRT–PCR, relies on poly-A tailing and oligo-dT annealing for cDNA synthesis followed by a universal reverse primer and variant specific forward primers for quantitative PCR. We combined our four stem-loop variants presented in Extended Data Fig. 5c (that is, sRSE1, scrambled, structured and unstructured mimetics) in equal amounts and subjected them to co-purification and precipitation in the presence and absence of His-tagged TARBP2 (20 nmol of RNA plus 20 μg of TARBP2 per sample in triplicate) as described before46. The relative quantity of each RNA variant was then measured using the assay described earlier. For the sRSE1 and structured variants, we observed an 8-fold enrichment after TARBP2 co-purification (n = 3 in each group). c, RNA electrophoretic mobility shift assays for the four variants also support the binding of the sRSE1 and structured variants to TARBP2 in vitro. Two-hundred picomoles of 3′ biotinylated RNAs were incubated in the presence of 200 ng of TARBP2 and three different non-specific competitors (that is, 10 ng μl−1 DNA, 10 ng μl−1 DNA plus 1 μg μl−1 fragmented yeast RNA, and 10 ng μl−1 DNA plus 0.1 μg μl−1 yeast transfer RNA) as described before16. d, We also used a degenerate library of 5′ phosphorylated 24-nucleotide-long RNA molecules to select for sequences that preferentially bind TARBP2 in vitro in one round of selection (a strategy termed RNAcompete34). Upon co-precipitating 20 nmol of randomized RNA with 20 μg of TARBP2 (in duplicate), we prepared the input as well as the selected libraries for small-RNA high-throughput sequencing. Upon linker removal, the 24-nucleotide reads from the two replicates were combined and collapsed into unique sequences (35 million unique reads in the input). Calculating the GC content of the selected versus input library showed a significant enrichment of sequences with higher GC content. e, In silico folding (using RNAfold) of the input and selected sequences also showed a significant shift towards sequences with higher propensity to form secondary structures. f, The breakdown of nucleotide frequencies in the selected sequences showing a significantly higher preference of TARBP2 for GC-rich sequences. g, The number of reads for each sequence was generally consistent between the two populations. In silico folding of sequences with high read counts (>80% sub-optimality) form GC-rich stem-loops similar to those described for the HITS-CLIP tags. For example, we have included a number of these stem-loops along with their fold enrichment over the input library. h, sRSEs were highly enriched among the in vitro selected RNA population relative to the input. Together, these in vitro binding experiments show that TARBP2 binds GC-rich stem-loops. i, GC-rich structural elements validated in in vitro TARBP2-binding experiments. The first two stem-loops (sRSE1 and structured mimetic) were also tested using reporter assays. j, Scrambled and unstructured mimetic showed minimal or no binding to TARBP2 in vitro and were not functional in reporter assays. An AT-rich stem-loop and a GC-rich stem-loop with a proximal bulge showed significantly diminished binding to TARBP2 in our in vitro binding assays (17% and 12% higher than scrambled control relative to >2-fold for functional stem-loops, respectively). k, A set of exemplary sRSE instances probably bound by TARBP2 discovered among the TARBP2-bound sequences (HITS-CLIP). Error bars in all panels indicate s.e.m. unless otherwise specified.

Extended Data Figure 7 TARBP2 silencing suppresses metastatic colonization and results in fewer metastatic nodules formed in the lungs of xenografted mice.

a, Western blots were performed for TARBP2 in MDA, LM2, CN34 and LM1a cells using tubulin as an internal control. TARBP2 relative protein abundance was then quantified with each sample measured in triplicate. Quantification was performed using ImageJ. Statistical significance was assessed using a Mann–Whitney test (*P = 0.05). b, Relative expression levels of TARBP2 mRNA in metastatic (stage IV) tumours compared to early stage (stages I and II) tumours (n = 59; see Methods). Error bars indicate s.e.m. *P < 0.05 by a one-tailed Student’s t-test. c, Bioluminescence imaging plot of lung metastasis by MDA-LM2 cells expressing shRNAs targeting TARBP2 (shTARBP2) or a control hairpin (shControl); n = 5 and 6, respectively. The area under the curve was also calculated for each mouse. *P < 0.05, **P < 0.01, ***P < 0.001 by a Student’s t-test unless specified otherwise. d, Haematoxylin and eosin (H&E)-stained images of representative lung sections from a tail-vein-injected mouse cohort extracted at day 33 post-injection (Fig. 3b). e, In vitro proliferation rates (day−1) for cells expressing shControl or TARBP2 shRNAs (n = 4 for each sample). f, Cell invasion capacity of MDA-LM2 cells quantified upon transwell matrigel invasion assays; n = 8 for each sample comparing shTARBP2 to shControl cells (two independent sets of biological quadruplicates). Also shown are representative images of transwell inserts along with the median count (m) for each experiment. **P < 0.01 by a one-sample Mann–Whitney test. g, Lung bioluminescence (7 days after tumour extraction) measured upon extraction ex vivo (n = 3 for shControl and 4 for shTARBP2 cohorts). Error bars in all panels indicate s.e.m. unless otherwise specified. *P < 0.05, **P < 0.01, ***P < 0.001 by a one-tailed Mann–Whitney test unless specified otherwise.

Extended Data Figure 8 AP2A2, ZNF395, APP and LAMB1 are post-transcriptionally regulated by TARBP2.

a, Shown are the HITS-CLIP cluster-tags common in both replicates for the identified TARBP2-binding site. These sites mark the interaction sites between TARBP2 and its targets, APP and ZNF395. b, c, Relative expression levels of direct TARBP2 targets APP, ZNF395, AP2A2 and LAMB1 in MDA, MDA-LM2, CN34 and CN-LM1a cells; n = 4 in each sample. d, The observed downregulation of these targets resulted from reduced stability as measured by α-amanitin-mediated inhibition of transcription. For this, the relative abundance of each transcript was measured at the 0 and 18 h time points (using 18S rRNA as an endogenous control; n = 4 in each sample). e, These targets displayed significantly higher stability in the context of TARBP2 knockdown in LM2 cells (shC, shControl; sh1 and sh2, two independent shRNAs; n = 4 in each sample). f, Epistatic interactions were not observed between the TARBP2-regulated genes as the knockdown of each gene did not significantly modulate the levels of the other transcripts. Error bars in all panels indicate s.e.m. unless otherwise specified. *P < 0.05, **P < 0.01, ***P < 0.001 by a one-tailed Student’s t-test unless otherwise specified.

Extended Data Figure 9 APP and ZNF395 suppress metastatic cell invasion and colonization.

a, Bioluminescence imaging plot of lung metastases by cells expressing shRNAs targeting one of four TARBP2 targets or a control shRNA (shControl) in a TARBP2 knockdown background in CN-LM1a lines; n = 5 in each cohort except for shZNF395 where n = 4. Area-under-the-curve summaries are also shown for each cohort along with bioluminescence images from representative mice from significant cohorts (APP and ZNF395). *P < 0.05, **P < 0.01, by a one-tailed Mann–Whitney test. b, The number of nodules quantified per lung section for each cohort (n = 3) in both MDA-LM2 and CN-LM1a backgrounds. *P < 0.05, **P < 0.01, by a one-tailed Student’s t-test. c, H&E-stained images of representative lung sections show an increase in the number of metastatic nodules in shAPP and shZNF395 cells. d, APP and ZNF395 were individually knocked down in LM2 and LM1a cells expressing shRNAs targeting TARBP2. The resulting populations were subjected to transwell invasion assays using a control shRNA in parallel (n = 8 for each sample). Representative (median) fields are shown here. e, Cancer cell invasion capacity relative to shControl cells measured for APP and ZNF395 knockdown cells in the TARBP2 knockdown background; n = 8 for each sample. *P < 0.05, **P < 0.01, by a one-tailed Mann–Whitney test. f, Tumours with lower than median expression of both APP and ZNF395 displayed a significantly lower survival rate in various cohorts of patients14,19,43. Error bars in all panels indicate s.e.m. unless otherwise specified.

Extended Data Figure 10 TARBP2-mediated downregulation of APP and ZNF395 is DICER-independent.

a, Cancer cell invasion capacity measured for MDA-LM2 cells with exogenously added amyloid-α, amyloid-α* (that is, amyloid-α(304-612)), amyloid-β, and BSA as a control; n = 6 or 8 for each sample. **P < 0.01 by a one-tailed Mann–Whitney test. b, A volcano plot, based on the microarray analysis of ZNF395 knockdown cells versus control samples, depicting a large number of probes that were significantly deregulated in ZNF395 knockdown cells (n = 8 per sample). c, qRT–PCR validation for three pro-metastatic/cell invasion genes upregulated in ZNF395 knockdown samples (n = 3 per samples). **P < 0.01 by one-tailed Student’s t-test. d, We achieved 8-fold DICER knockdown in the MDA-LM2 cells expressing an shRNA targeting DICER (TRCN0000290486, Sigma). This level of DICER knockdown, unlike TARBP2 knockdown, did not result in an upregulation of APP and ZNF395 (n = 3 per samples). e, To ensure functional knockdown of DICER in these cells, we performed qRT–PCR for four microRNAs (TaqMan MicroRNA Assays as per manufacturer’s instructions in triplicate) ranging from low (miR-614) to medium (miR-29b) to high expression (let7). In all cases, we observed a significant and substantial reduction in mature microRNA levels in this DICER knockdown background (n = 3 per samples). *P < 0.05, **P < 0.01 by a one-tailed Student’s t-test. f, We achieved 10-fold TARBP2 transcript knockdown using siRNA transfections in the DICER knockdown background (determined using qPCR in triplicate). We also performed qRT–PCR for ZNF395 and APP, which were significantly upregulated (expression of APP and ZNF395 was not upregulated upon DICER silencing; n = 3 for each sample). *P < 0.05, **P < 0.01 by one-tailed Student’s t-test. g, The observed upregulation of TARBP2 targets is not limited to ZNF395 and APP. As shown here, the sRSE-regulon and TARBP2-bound transcripts showed a similar upregulation pattern as the TARBP2 knockdown in LM2 cells (upregulated transcripts in shDICER background were defined as logFC >0.23). h, We achieved 7-fold Tarbp2 knockdown in Dicer-null mouse embryonic fibroblasts47, which in turn resulted in 2.5-fold and 2-fold upregulation in App and Zfp395 (an orthologue of ZNF395), respectively (n = 3 for each sample). *P < 0.05, ***P < 0.001 by one-tailed Student’s t-test. i, Gene expression profiling of Tarbp2 knockdown cells showed an enrichment of sRSE-carrying transcripts and the transcripts whose human homologues were bound by TARBP2 among the upregulated genes. We used two independent siRNAs (biological replicates) and three siControl-transfected samples as control. Upregulated transcripts were defined as the set with logFC >0.1 (Illumina MouseRef-8 arrays were used for profiling, and the Lumi package in R was used for normalization). The sRSE regulon was regenerated for the mouse transcriptome and was found to be significantly enriched among the upregulated transcripts. The homologous TARBP2-bound transcripts in mice were used to ensure their enrichment among the upregulated set as well. Error bars in all panels indicate s.e.m. unless otherwise specified.

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Goodarzi, H., Zhang, S., Buss, C. et al. Metastasis-suppressor transcript destabilization through TARBP2 binding of mRNA hairpins. Nature 513, 256–260 (2014). https://doi.org/10.1038/nature13466

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