Identification of new branch points and unconventional introns in <i>Saccharomyces cerevisiae</i>

Gould, Genevieve M.; Paggi, Joseph M.; Guo, Yuchun; Phizicky, David V; Zinshteyn, Boris; Wang, Eric T.; Gilbert, Wendy V.; Gifford, David K.; Burge, Christopher B.

doi:10.1261/rna.057216.116

Cited by 33 publications

(48 citation statements)

References 61 publications

(133 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4d) 20 . Indeed, we observed a similar pattern in G, C content in a dataset of 718 budding yeast branchpoints 21 (Supplementary Fig. 3d).…”

Section: Cc-by-nd 40 International License Peer-reviewed) Is the Autsupporting

confidence: 70%

A sequence-based, deep learning model accurately predicts RNA splicing branchpoints

Paggi

Bejerano

2017

Preprint

Self Cite

View full text Add to dashboard Cite

Experimental detection of RNA splicing branchpoints, the nucleotide serving as the nucleophile in the first catalytic step of splicing, is difficult. To date, annotations exist for only 16-21% of 3' splice sites in the human genome and even these limited annotations have been shown to be plagued by noise. We develop a sequence-only, deep learning based branchpoint predictor, LaBranchoR, which we conclude predicts a correct branchpoint for over 90% of 3' splice sites genome-wide. Our predicted branchpoints show large agreement with trends observed in the raw data, but analysis of conservation signatures and overlap with pathogenic variants reveal that our predicted branchpoints are generally more reliable than the raw data itself. We use our predicted branchpoints to identify a sequence element upstream of branchpoints consistent with extended U2 snRNA base pairing, show an association between weak branchpoints and alternative splicing, and explore the effects of variants on branchpoints.

show abstract

“…4d) 20 . Indeed, we observed a similar pattern in G, C content in a dataset of 718 budding yeast branchpoints 21 (Supplementary Fig. 3d).…”

Section: Cc-by-nd 40 International License Peer-reviewed) Is the Autsupporting

confidence: 70%

A sequence-based, deep learning model accurately predicts RNA splicing branchpoints

Paggi

Bejerano

2017

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Merging the list of novel splicing events found here with those reported in recent transcriptome-wide studies creates a database of alternative and cryptic splicing events for S. cerevisiae (Supplemental Table S1). This includes 21 alternative splicing events that have been reported repeatedly (Kawashima et al 2014;Schreiber et al 2015;Gould et al 2016) and hundreds of new splicing events that were not in those data sets ( Fig. 2A).…”

Section: Altering Rnapii Elongation Rate Affects Splicing Fidelitymentioning

confidence: 99%

“…These events likely make use of more 5 ′ suboptimal BPs for splicing. For example, two novel 3 ′ SS events in RPL13B are located 330 and 322 nt upstream of the annotated 3 ′ SS, and lariat sequencing (Gould et al 2016) has detected a cryptic BP (AACTAAT) located 21 and 13 nt upstream of these cryptic 3 ′ SSs, respectively (Supplemental Fig. S4).…”

Section: Altering Rnapii Elongation Rate Affects Splicing Fidelitymentioning

confidence: 99%

Transcription rate strongly affects splicing fidelity and cotranscriptionality in budding yeast

et al. 2017

View full text Add to dashboard Cite

The functional consequences of alternative splicing on altering the transcription rate have been the subject of intensive study in mammalian cells but less is known about effects of splicing on changing the transcription rate in yeast. We present several lines of evidence showing that slow RNA polymerase II elongation increases both cotranscriptional splicing and splicing efficiency and that faster elongation reduces cotranscriptional splicing and splicing efficiency in budding yeast, suggesting that splicing is more efficient when cotranscriptional. Moreover, we demonstrate that altering the RNA polymerase II elongation rate in either direction compromises splicing fidelity, and we reveal that splicing fidelity depends largely on intron length together with secondary structure and splice site score. These effects are notably stronger for the highly expressed ribosomal protein coding transcripts. We propose that transcription by RNA polymerase II is tuned to optimize the efficiency and accuracy of ribosomal protein gene expression, while allowing flexibility in splice site choice with the nonribosomal protein transcripts.[Supplemental material is available for this article.]Splicing is the process of removing introns from precursor messenger RNAs (pre-mRNAs) and joining adjacent exons to produce spliced mRNA. The excised intron, in the form of a branched lariat, is rapidly debranched and discarded. If genes contain multiple introns, alternative splicing pathways can give rise to distinct mRNA and protein isoforms by using alternative splice sites or by including or excluding particular exons. In human cells, ∼95% of transcripts are alternatively spliced, thereby greatly expanding the coding capacity of the genome (Kornblihtt et al. 2013). Moreover, alternative splicing events that introduce translational stop codons in mRNAs are generally coupled with nonsensemediated decay (NMD) to down-regulate that spliced isoform, offering an additional layer of gene expression regulation (Lewis et al. 2003;Sayani et al. 2008;Kawashima et al. 2014). In Saccharomyces cerevisiae (budding yeast) only ∼5% of genes contain an intron, although they produce ∼27% of total mRNA, because many intron-containing genes are highly expressed (Ares et al. 1999). There is extensive evidence that in both metazoans and budding yeast, the process of splicing occurs as soon as the intron is transcribed and before transcription termination, that is, cotranscriptionally (Alexander et al. 2010b;Ameur et al. 2011;Carrillo Oesterreich et al. 2016). As a result of splicing being cotranscriptional, RNA polymerase II (RNAPII) elongation rate can influence splicing. According to one model, referred to as the "kinetic coupling" model, variations in RNAPII elongation rate can alter the time available, or the "window of opportunity" for upstream splice sites to be recognized before competing downstream splice sites are produced (Roberts et al. 1998;de la Mata et al. 2003;Kornblihtt 2007;Naftelberg et al. 2015;Saldi et al. 2016). Consistent with this model, ...

show abstract

“…In animals, the main outcome of alternative splicing is exon skipping, whereas in plants, intron retention predominates (Kornblihtt et al 2013). Only 5% of genes in budding yeast contain introns and alternative splicing is rare (Naftelberg et al 2015;Gould et al 2016). In contrast, most animal and plant genes comprise multiple introns and the majority undergoes alternative splicing (Nilsen and Graveley 2010;Marquez et al 2012).…”

Section: Introductionmentioning

confidence: 99%

A genetic screen implicates a CWC16/Yju2/CCDC130 protein and SMU1 in alternative splicing in Arabidopsis thaliana

Kanno

Lin

et al. 2017

RNA

View full text Add to dashboard Cite

To identify regulators of pre-mRNA splicing in plants, we developed a forward genetic screen based on an alternatively spliced GFP reporter gene in Arabidopsis thaliana. In wild-type plants, three major splice variants issue from the GFP gene but only one represents a translatable GFP mRNA. Compared to wild-type seedlings, which exhibit an intermediate level of GFP expression, mutants identified in the screen feature either a "GFP-weak" or "Hyper-GFP" phenotype depending on the ratio of the three splice variants. GFP-weak mutants, including previously identified prp8 and rtf2, contain a higher proportion of unspliced transcript or canonically spliced transcript, neither of which is translatable into GFP protein. In contrast, the coilindeficient hyper-gfp1 (hgf1) mutant displays a higher proportion of translatable GFP mRNA, which arises from enhanced splicing of a U2-type intron with noncanonical AT-AC splice sites. Here we report three new hgf mutants that are defective, respectively, in spliceosome-associated proteins SMU1, SmF, and CWC16, an Yju2/CCDC130-related protein that has not yet been described in plants. The smu1 and cwc16 mutants have substantially increased levels of translatable GFP transcript owing to preferential splicing of the U2-type AT-AC intron, suggesting that SMU1 and CWC16 influence splice site selection in GFP pre-mRNA. Genome-wide analyses of splicing in smu1 and cwc16 mutants revealed a number of introns that were variably spliced from endogenous pre-mRNAs. These results indicate that SMU1 and CWC16, which are predicted to act directly prior to and during the first catalytic step of splicing, respectively, function more generally to modulate splicing patterns in plants.

show abstract

Identification of new branch points and unconventional introns in Saccharomyces cerevisiae

Cited by 33 publications

References 61 publications

A sequence-based, deep learning model accurately predicts RNA splicing branchpoints

A sequence-based, deep learning model accurately predicts RNA splicing branchpoints

Transcription rate strongly affects splicing fidelity and cotranscriptionality in budding yeast

A genetic screen implicates a CWC16/Yju2/CCDC130 protein and SMU1 in alternative splicing in Arabidopsis thaliana

Contact Info

Product

Resources

About