The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Structural disruption of exonic stem–loops immediately upstream of the intron regulates mammalian splicing
Open accessPublished version of a preprint
Abstract
Recognition of highly degenerate mammalian splice sites by the core spliceosomal machinery is regulated by several protein factors that predominantly bind exonic splicing motifs. These are postulated to be single-stranded in order to be functional, yet knowledge of secondary structural features that regulate the exposure of exonic splicing motifs across the transcriptome is not currently available. Using transcriptome-wide RNA structural information we show that retained introns in mouse are commonly flanked by a short (≲70 nucleotide), highly base-paired segment upstream and a predominantly single-stranded exonic segment downstream. Splicing assays with select pre-mRNA substrates demonstrate that loops immediately upstream of the introns contain pre-mRNA-specific splicing enhancers, the substitution or hybridization of which impedes splicing. Additionally, the exonic segments flanking the retained introns appeared to be more enriched in a previously identified set of hexameric exonic splicing enhancer (ESE) sequences compared to their spliced counterparts, suggesting that base-pairing in the exonic segments upstream of retained introns could be a means for occlusion of ESEs. The upstream exonic loops of the test substrate promoted recruitment of splicing factors and consequent pre-mRNA structural remodeling, leading up to assembly of the early spliceosome. These results suggest that disruption of exonic stem–loop structures immediately upstream (but not downstream) of the introns regulate alternative splicing events, likely through modulating accessibility of splicing factors.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · cited 1×
“1) since it represents a strong promoter (transcription rate ∼6–7 mRNAs per hour) near the median strength of endogenous mammalian promoters (35,36).”
BackgroundSpliceosome Structure and Function
Will, C. L., & Luhrmann, R. (2011) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a003707 · Cited by 1,829
Coupling mRNA processing with transcription in time and space
Bentley, D. L. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3662 · Cited by 828
In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features
Ding, Y., Tang, Y., Kwok, C. K., Zhang, Y., Bevilacqua, P. C., & Assmann, S. M. (2014) · Nature · doi:10.1038/nature12756 · Cited by 775
Pre-mRNA splicing is facilitated by an optimal RNA polymerase II elongation rate
Fong, N., Kim, H., Zhou, Y., Ji, X., Qiu, J., Saldi, T., Diener, K., Jones, K., Fu, X.-D., & Bentley, D. L. (2014) · Genes & Development · doi:10.1101/gad.252106.114 · Cited by 287
Flagged first, then by citations
Research works printed but not linked: 55
- Wong, 2018, Quantitative activity profile and context dependence of all human 5′ splice sites, Mol. Cell, 71, 1012, 10.1016/j.molcel.2018.07.033 · DOI
- Goren, 2006, Comparative analysis identifies exonic splicing regulatory sequences–The complex definition of enhancers and silencers, Mol. Cell, 22, 769, 10.1016/j.molcel.2006.05.008 · DOI
- Ke, 2011, Quantitative evaluation of all hexamers as exonic splicing elements, Genome Res., 21, 1360, 10.1101/gr.119628.110 · DOI
- Hollander, 2016, How are short exons flanked by long introns defined and committed to splicing, Trends Genet., 32, 596, 10.1016/j.tig.2016.07.003 · DOI
- Zhou, 2013, Regulation of splicing by SR proteins and SR protein-specific kinases, Chromosoma, 122, 191, 10.1007/s00412-013-0407-z · DOI
- Änkö, 2012, The RNA-binding landscapes of two SR proteins reveal unique functions and binding to diverse RNA classes, Genome Biol., 13, R17, 10.1186/gb-2012-13-3-r17 · DOI
- Pandit, 2013, Genome-wide analysis reveals SR protein cooperation and competition in regulated splicing, Mol. Cell, 50, 223, 10.1016/j.molcel.2013.03.001 · DOI
- Van Nostrand, 2016, Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP), Nat. Methods, 13, 508, 10.1038/nmeth.3810 · DOI · Paper page
- Sanford, 2009, Splicing factor SFRS1 recognizes a functionally diverse landscape of RNA transcripts, Genome Res., 19, 381, 10.1101/gr.082503.108 · DOI
- Screaton, 1995, Identification and characterization of three members of the human SR family of pre-mRNA splicing factors, EMBO J., 14, 4336, 10.1002/j.1460-2075.1995.tb00108.x · DOI
- Chasin, 2007, Alternative Splicing in the Postgenomic Era, 85, 10.1007/978-0-387-77374-2_6 · DOI
- Lu, 2016, RNA duplex map in living cells reveals higher-order transcriptome structure, Cell, 165, 1267, 10.1016/j.cell.2016.04.028 · DOI
- Spitale, 2015, Structural imprints in vivo decode RNA regulatory mechanisms, Nature, 519, 486, 10.1038/nature14263 · DOI
- Corley, 2017, An RNA structure-mediated, posttranscriptional model of human α-1-antitrypsin expression, Proc. Natl Acad. Sci. U.S.A., 114, E10244, 10.1073/pnas.1706539114 · DOI
- Mahen, 2010, mRNA secondary structures fold sequentially but exchange rapidly in vivo, PLoS Biol., 8, e1000307, 10.1371/journal.pbio.1000307 · DOI
- Taliaferro, 2016, RNA sequence context effects measured in vitro predict in vivo protein binding and regulation, Mol. Cell, 64, 294, 10.1016/j.molcel.2016.08.035 · DOI
- Lambert, 2014, RNA bind-n-Seq: quantitative assessment of the sequence and structural binding specificity of RNA binding proteins, Mol. Cell, 54, 887, 10.1016/j.molcel.2014.04.016 · DOI
- Mustoe, 2018, Pervasive regulatory functions of mRNA structure revealed by high-resolution SHAPE probing, Cell, 173, 181, 10.1016/j.cell.2018.02.034 · DOI
- Pan, 2006, RNA folding during transcription, Annu. Rev. Biophys. Biomol. Struct., 35, 161, 10.1146/annurev.biophys.35.040405.102053 · DOI
- Jimeno-González, 2015, Defective histone supply causes changes in RNA polymerase II elongation rate and cotranscriptional pre-mRNA splicing, Proc. Natl. Acad. Sci. U.S.A., 112, 14840, 10.1073/pnas.1506760112 · DOI · Paper page
- Voong, 2016, Insights into nucleosome organization in mouse embryonic stem cells through chemical mapping, Cell, 167, 1555, 10.1016/j.cell.2016.10.049 · DOI
- Wong, 2017, Intron retention is regulated by altered MeCP2-mediated splicing factor recruitment, Nat. Commun., 8, 15134, 10.1038/ncomms15134 · DOI
- Hiller, 2007, Pre-mRNA secondary structures influence exon recognition, PLoS Genet., 3, e204, 10.1371/journal.pgen.0030204 · DOI
- Cho, 2011, Interaction between the RNA binding domains of Ser-Arg splicing factor 1 and U1-70K snRNP protein determines early spliceosome assembly, Proc. Natl. Acad. Sci. U.S.A., 108, 8233, 10.1073/pnas.1017700108 · DOI
- Chandler, 1997, RNA splicing specificity determined by the coordinated action of RNA recognition motifs in SR proteins, Proc. Natl. Acad. Sci. U.S.A., 94, 3596, 10.1073/pnas.94.8.3596 · DOI
- Kohtz, 1994, Protein-protein interactions and 5′-splice-site recognition in mammalian mRNA precursors, Nature, 368, 119, 10.1038/368119a0 · DOI
- McGinnis, 2009, High-throughput SHAPE and hydroxyl radical analysis of RNA structure and ribonucleoprotein assembly, Methods Enzymol., 468, 67, 10.1016/S0076-6879(09)68004-6 · DOI
- Schindelin, 2012, Fiji: an open-source platform for biological-image analysis, Nat. Methods, 9, 676, 10.1038/nmeth.2019 · DOI · Paper page
- Mathews, 2014, RNA secondary structure analysis using RNAstructure, Curr. Protoc. Bioinformatics, 10.1002/0471250953.bi1206s46 · DOI
- Darty, 2009, VARNA: interactive drawing and editing of the RNA secondary structure, Bioinformatics, 25, 1974, 10.1093/bioinformatics/btp250 · DOI
- Darzacq, 2007, In vivo dynamics of RNA polymerase II transcription, Nat. Struct. Mol. Biol., 14, 796, 10.1038/nsmb1280 · DOI · Paper page
- Mayeda, 1999, Methods in Molecular Biology (RNA-protein interaction protocols), 309, 10.1385/1-59259-676-2:309 · DOI
- Das, 1999, Resolution of the mammalian E complex and the ATP-dependent spliceosomal complexes on native agarose mini-gels, RNA, 5, 1504, 10.1017/S1355838299991501 · DOI
- Marcais, 2011, A fast, lock-free approach for efficient parallel counting of occurrences of k-mers, Bioinformatics, 27, 764, 10.1093/bioinformatics/btr011 · DOI · Paper page
- Zubradt, 2017, DMS-MaPseq for genome-wide or targeted RNA structure probing in vivo, Nat. Methods, 14, 75, 10.1038/nmeth.4057 · DOI
- Nicolaides, 1990, A simple, efficient method for the separate isolation of RNA and DNA from the same cells, BioTechniques, 8, 154
- Bernhart, 2006, Local RNA base pairing probabilities in large sequences, Bioinformatics, 22, 614, 10.1093/bioinformatics/btk014 · DOI
- Lange, 2012, Global or local? Predicting secondary structure and accessibility in mRNAs, Nucleic Acids Res., 40, 5215, 10.1093/nar/gks181 · DOI
- Lorenz, 2011, ViennaRNA Package 2.0, Algorith. Mol. Biol.: AMB, 6, 26, 10.1186/1748-7188-6-26 · DOI · Paper page
- Marquez, 2015, Unmasking alternative splicing inside protein-coding exons defines exitrons and their role in proteome plasticity, Genome Res., 25, 995, 10.1101/gr.186585.114 · DOI
- Boutz, 2015, Detained introns are a novel, widespread class of post-transcriptionally spliced introns, Genes Dev., 29, 63, 10.1101/gad.247361.114 · DOI
- Krainer, 1984, Normal and mutant human β-globin pre-mRNAs are faithfully and efficiently spliced in vitro, Cell, 36, 993, 10.1016/0092-8674(84)90049-7 · DOI
- Bennett, 1992, Protein components specifically associated with prespliceosome and spliceosome complexes, Genes Dev., 6, 1986, 10.1101/gad.6.10.1986 · DOI
- Bailey, 1994, Fitting a mixture model by expectation maximization to discover motifs in biopolymers, Proc. Int. Conf. Intell. Syst. Mol. Biol., 2, 28
- Bailey, 1998, Combining evidence using P-values: application to sequence homology searches, Bioinformatics, 14, 48, 10.1093/bioinformatics/14.1.48 · DOI
- Buratti, 2004, Influence of RNA secondary structure on the pre-mRNA splicing process, Mol. Cell. Biol., 24, 10505, 10.1128/MCB.24.24.10505-10514.2004 · DOI
- Rouskin, 2014, Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo, Nature, 505, 701, 10.1038/nature12894 · DOI
- Zhu, 2000, Pre-mRNA splicing in the absence of an SR protein RS domain, Genes Dev., 14, 3166, 10.1101/gad.189500 · DOI
- Hertel, 1999, Serine–arginine (SR)-rich splicing factors have an exon-independent function in pre-mRNA splicing, Proc. Natl. Acad. Sci. U.S.A., 96, 2651, 10.1073/pnas.96.6.2651 · DOI
- Fu, 1993, Specific commitment of different pre-mRNAs to splicing by single SR proteins, Nature, 365, 82, 10.1038/365082a0 · DOI
- Eperon, 2000, Selection of alternative 5′ splice sites: role of U1 snRNP and models for the antagonistic effects of SF2/ASF and hnRNP A1, Mol. Cell. Biol., 20, 8303, 10.1128/MCB.20.22.8303-8318.2000 · DOI
- Kralovicova, 2018, PUF60-activated exons uncover altered 3′ splice-site selection by germline missense mutations in a single RRM, Nucleic Acids Res., 46, 6166, 10.1093/nar/gky389 · DOI
- Ramanouskaya, 2017, The determinants of alternative RNA splicing in human cells, Mol. Genet. Genomics: MGG, 292, 1175, 10.1007/s00438-017-1350-0 · DOI
- Kim, 2017, Widespread intra-dependencies in the removal of introns from human transcripts, Nucleic Acids Res., 45, 9503, 10.1093/nar/gkx661 · DOI
- Soemedi, 2017, The effects of structure on pre-mRNA processing and stability, Methods, 125, 36, 10.1016/j.ymeth.2017.06.001 · DOI
Metadata
- Authors
- Saha, K., England, W., Fernandez, M. M., Biswas, T., Spitale, R. C., & Ghosh, G.
- Affiliations
- University of California San Diego (US)
- University of California, Irvine (US)
- Corresponding author
- Spitale, Robert C
- Ghosh, Gourisankar
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-05-13
- Volume
- 48
- Issue
- 11
- Pages
- 6294-6309
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC7293017
- OpenAlex
- W3024286496
- Cited by
- 25
- References
- 5
- Citations by year
- 2026: 1 · 2025: 4 · 2023: 4 · 2022: 4 · 2021: 10 · 2020: 2
- FWCI
- 1.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (GM085490, GM084277, 5UM1HG009443)
- Helmsley Charitable Trust
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Keywords
- intron retention, alternative splicing, exonic splicing enhancers, pre-mRNA secondary structure, splice site recognition, splicing factors, mouse transcriptome, RNA structure
- MeSH terms
- Mouse Embryonic Stem Cells, Adenoviridae, Animals, Base Sequence, Enhancer Elements, Genetic, Exons, Introns, Mutation, RNA Precursors, RNA Splicing, Spliceosomes, Base Pairing, Gene Silencing, Mice, beta-Globins, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review