Systematic discovery of structural elements governing stability of mammalian messenger RNAs


Decoding post-transcriptional regulatory programs in RNA is a critical step towards the larger goal of developing predictive dynamical models of cellular behaviour. Despite recent efforts1,2,3, the vast landscape of RNA regulatory elements remains largely uncharacterized. A long-standing obstacle is the contribution of local RNA secondary structure to the definition of interaction partners in a variety of regulatory contexts, including—but not limited to—transcript stability3, alternative splicing4 and localization3. There are many documented instances where the presence of a structural regulatory element dictates alternative splicing patterns (for example, human cardiac troponin T) or affects other aspects of RNA biology5. Thus, a full characterization of post-transcriptional regulatory programs requires capturing information provided by both local secondary structures and the underlying sequence3,6. Here we present a computational framework based on context-free grammars3,7 and mutual information2 that systematically explores the immense space of small structural elements and reveals motifs that are significantly informative of genome-wide measurements of RNA behaviour. By applying this framework to genome-wide human mRNA stability data, we reveal eight highly significant elements with substantial structural information, for the strongest of which we show a major role in global mRNA regulation. Through biochemistry, mass spectrometry and in vivo binding studies, we identified human HNRPA2B1 (heterogeneous nuclear ribonucleoprotein A2/B1, also known as HNRNPA2B1) as the key regulator that binds this element and stabilizes a large number of its target genes. We created a global post-transcriptional regulatory map based on the identity of the discovered linear and structural cis-regulatory elements, their regulatory interactions and their target pathways. This approach could also be used to reveal the structural elements that modulate other aspects of RNA behaviour.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Discovery of RNA structural motifs informative of genome-wide transcript stability.
Figure 2: The regulatory role of sRSM1.
Figure 3: HNRPA2B1 stabilizes transcripts through direct in vivo binding to sRSM1 structural motifs.
Figure 4: HNRPA2B1 regulates growth rate.

Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

The microarray and high-throughput sequencing data are deposited at GEO under the umbrella accession number GSE35800.


  1. 1

    Dölken, L. et al. High-resolution gene expression profiling for simultaneous kinetic parameter analysis of RNA synthesis and decay. RNA 14, 1959–1972 (2008)

    Article  Google Scholar 

  2. 2

    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)

    CAS  Article  Google Scholar 

  3. 3

    Rabani, M., Kertesz, M. & Segal, E. Computational prediction of RNA structural motifs involved in posttranscriptional regulatory processes. Proc. Natl Acad. Sci. USA 105, 14885–14890 (2008)

    ADS  CAS  Article  Google Scholar 

  4. 4

    Barash, Y. et al. Deciphering the splicing code. Nature 465, 53–59 (2010)

    ADS  CAS  Article  Google Scholar 

  5. 5

    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)

    CAS  Article  Google Scholar 

  6. 6

    Pavesi, G., Mauri, G., Stefani, M. & Pesole, G. RNAProfile: an algorithm for finding conserved secondary structure motifs in unaligned RNA sequences. Nucleic Acids Res. 32, 3258–3269 (2004)

    CAS  Article  Google Scholar 

  7. 7

    Searls, D. B. The language of genes. Nature 420, 211–217 (2002)

    ADS  CAS  Article  Google Scholar 

  8. 8

    Hofacker, I. L., Fekete, M. & Stadler, P. F. Secondary structure prediction for aligned RNA sequences. J. Mol. Biol. 319, 1059–1066 (2002)

    CAS  Article  Google Scholar 

  9. 9

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

    ADS  CAS  Article  Google Scholar 

  10. 10

    Goodarzi, H., Elemento, O. & Tavazoie, S. Revealing global regulatory perturbations across human cancers. Mol. Cell 36, 900–911 (2009)

    CAS  Article  Google Scholar 

  11. 11

    Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011)

    ADS  Article  Google Scholar 

  12. 12

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

    CAS  Article  Google Scholar 

  13. 13

    Windbichler, N. & Schroeder, R. Isolation of specific RNA-binding proteins using the streptomycin-binding RNA aptamer. Nature Protocols 1, 637–640 (2006)

    CAS  Article  Google Scholar 

  14. 14

    Biamonti, G., Ruggiu, M., Saccone, S., Della Valle, G. & Riva, S. Two homologous genes, originated by duplication, encode the human hnRNP proteins A2 and A1. Nucleic Acids Res. 22, 1996–2002 (1994)

    CAS  Article  Google Scholar 

  15. 15

    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)

    CAS  Article  Google Scholar 

  16. 16

    Michlewski, G. & Caceres, J. F. Antagonistic role of hnRNP A1 and KSRP in the regulation of let-7a biogenesis. Nature Struct. Mol. Biol. 17, 1011–1018 (2010)

    CAS  Article  Google Scholar 

  17. 17

    Ross, D. T. et al. Systematic variation in gene expression patterns in human cancer cell lines. Nature Genet. 24, 227–235 (2000)

    CAS  Article  Google Scholar 

  18. 18

    Jensen, K. B. & Darnell, R. B. CLIP: crosslinking and immunoprecipitation of in vivo RNA targets of RNA-binding proteins. Methods Mol. Biol. 488, 85–98 (2008)

    CAS  Article  Google Scholar 

  19. 19

    Keene, J. D., Komisarow, J. M. & Friedersdorf, M. B. RIP-Chip: the isolation and identification of mRNAs, microRNAs and protein components of ribonucleoprotein complexes from cell extracts. Nature Protocols 1, 302–307 (2006)

    CAS  Article  Google Scholar 

  20. 20

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

    ADS  CAS  Article  Google Scholar 

  21. 21

    Giannopoulou, E. G. & Elemento, O. An integrated ChIP-seq analysis platform with customizable workflows. BMC Bioinformatics 12, 277–294 (2011)

    Article  Google Scholar 

  22. 22

    Beer, M. A. & Tavazoie, S. Predicting gene expression from sequence. Cell 117, 185–198 (2004)

    CAS  Article  Google Scholar 

  23. 23

    Yang, Y. et al. RNA secondary structure in mutually exclusive splicing. Nature Struct. Mol. Biol. 18, 159–168 (2011)

    CAS  Article  Google Scholar 

  24. 24

    Greco, T. M., Yu, F., Guise, A. J. & Cristea, I. M. Nuclear import of histone deacetylase 5 by requisite nuclear localization signal phosphorylation. Mol. Cell Proteomics 10, M110.004317 (2011)

    Article  Google Scholar 

  25. 25

    Wiśniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nature Methods 6, 359–362 (2009)

    Article  Google Scholar 

  26. 26

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

    ADS  CAS  Article  Google Scholar 

Download references


We thank the members of the Tavazoie laboratory for comments on the project and manuscript. We are also grateful to N. Pencheva, B. Tsui, S. Tavazoie and L. Dölken for their intellectual and technical contributions. L.F. was supported by a Ruth L. Kirschstein National Research Service Award (T32-GM066699). S.T. was supported by grants from NHGRI (2R01HG003219) and the NIH Director's Pioneer Award.

Author information




H.G., H.S.N. and S.T. conceived and designed the study. H.G. and H.S.N. developed TEISER. R.S. contributed to the execution of the study. H.G., H.S.N., T.M.G., P.O., I.M.C. and S.T. designed the experiments. H.G., P.O., L.F. and T.M.G. performed the experiments. H.G., H.S.N. and T.M.G. analysed the results. H.G., H.S.N. and S.T. wrote the paper.

Corresponding author

Correspondence to Saeed Tavazoie.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-15, Supplementary Tables 1-2 and additional references. (PDF 2659 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing