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The ribosome-engaged landscape of alternative splicing - PubMed

. 2016 Dec;23(12):1117-1123.

doi: 10.1038/nsmb.3317. Epub 2016 Nov 7.

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The ribosome-engaged landscape of alternative splicing

Robert J Weatheritt et al. Nat Struct Mol Biol. 2016 Dec.

Abstract

High-throughput RNA sequencing (RNA-seq) has revealed an enormous complexity of alternative splicing (AS) across diverse cell and tissue types. However, it is currently unknown to what extent repertoires of splice-variant transcripts are translated into protein products. Here, we surveyed AS events engaged by the ribosome. Notably, at least 75% of human exon-skipping events detected in transcripts with medium-to-high abundance in RNA-seq data were also detected in ribosome profiling data. Furthermore, relatively small subsets of functionally related splice variants are engaged by ribosomes at levels that do not reflect their absolute abundance, thus indicating a role for AS in modulating translational output. This mode of regulation is associated with control of the mammalian cell cycle. Our results thus suggest that a major fraction of splice variants is translated and that specific cellular functions including cell-cycle control are subject to AS-dependent modulation of translation output.

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Conflict of interest statement

The authors declare no competing financial interests

Figures

Fig 1
Fig 1. Most cassette alternative splicing events in medium to high abundance transcripts are engaged by the ribosome

(a) Box plots showing AS frequency (scored as the fraction of annotated exons in canonical transcripts that show skipping) for genes with different levels of RNA-Seq or ribosome-profiling read coverage in human cells. Simple, complex and microexon (i.e. 3-27 nt) cassette events were analyzed and only genes with detected AS events in RNA-Seq data were included. EEJ, exon-exon junction (b) Stacked bar plot comparing percentages of constitutive versus alternatively spliced exons for all genes with at least three exons detected. Between-sample normalization (BSN) of corrected reads per kilobase of transcript per million (cRPKM) was performed using DESeq. (c) Bar plot comparing fractions of total alternative spliced events identified in RNA-Seq data that are also identified as alternatively spliced in ribosome profiling (RP) data, at different expression levels. Events were only analyzed if both constitutive exons surrounding an alternative exon were detected in ribosome profiling data (n=5462). Error bar calculated form the standard error of the mean (d) Bar plots showing fractions of coding AS events detected in matched ribosome profiling (n=2,431) and RNA-Seq (n=8,797) datasets comprising exons divisible by 3 and not encoding an in-frame premature stop codons, or not divisible by 3 but partially overlapping UTR sequence. Error bar calculated form the standard error of the mean and p-value calculated using Fisher’s exact test (e) Bar plot showing fractions of coding AS events not divisible by 3 within CDS that display changes in PSI (percentage spliced in) between ribosomal profiling and matched RNA-Seq data (n=1226). ‘ORF-Preserving’ indicates that the PSI change promotes the inclusion of a frame-preserving exon; ‘ORF-Disrupting’ indicates that the inclusion of a frame-shifting exon with the potential to elicit NMD. Error bar calculated form the standard error of the mean and p-value calculated using Fisher’s exact test. Data for all panels obtained from. See Figure 2 and Online Methods for description of boxplots and statistical tests used. Source data are available online.

Fig 2
Fig 2. Detection of intron retention events in ribosomal profiling data

(a) Box plots showing intron retention frequency (fraction of annotated introns that show retention in canonical transcripts) for genes with different RNA-Seq/ribosome profiling read coverage. EIJ = Exon-intron Junction (b) Bar plot showing fraction of total intron retention events identified in RNA-Seq data that are also detected as retained in ribosome profiling (RP) data (n=847). Error bar calculated using the standard error of the mean and p-value calculated using Fisher’s exact test (c) Bar plot as in (b) showing intron retention events detected in 5´ UTR sequences and other regions (‘REST’) of transcripts with >100 cRPKM coverage (n=123). Error bar calculated form the standard error of the mean and p-value calculated using Fisher’s exact test (d) Bar plot showing the percentage change in detection of IR events in different transcript locations using ribosome profiling data (n=847) and RNA-Seq data from fractionated cells (Cytosol n=2980; Nuclear n = 3810), as compared to whole cell RNA-Seq data. Error bar calculated form the standard error of the mean and p-value calculated using Fisher’s exact test. Transcript locations are mapped based on Ensembl GTF annotations. (e) Box plots comparing average lengths of 5´-UTR retained introns identified in ribosome profiling data and for total retained introns. Error bar calculated form the standard error of the mean and p-value calculated using Wilcoxon-Test. (5´-UTR introns: n=123; All introns: n = 9,760) (f) Bar plot showing fractions of 5´ UTR retained introns identified in ribosome profiling data and total retained introns with evidence of intronization Error bar calculated form the standard error of the mean and p-value calculated using Fisher’s exact test (5´-UTR introns: n=123; All introns: n = 9,760) Sequencing data obtained from,–. See Supplementary Table 1 for details. For boxplots, the median value for each group of proteins is shown with a horizontal black line. Boxes enclose values between the first and third quartile. Interquartile range is calculated by subtracting the first quartile from the third quartile. All values outside this range are considered to be outliers and were removed from the graphs to improve visualization. The smallest and highest values that are not outliers are connected with the dashed line. The notches correspond to ~95% confidence interval for the median. Source data are available online.

Fig 3
Fig 3. Ribosome engaged intron retention events are enriched within the 5´ UTRs of essential genes

(a) Bar plots comparing the frequency of detection of 5´UTR retained introns identified in ribosome profiling data and total retained introns in genes scored as essential in cell viability assays and in housekeeping genes. Error bar calculated form the standard error of the mean and p-value calculated using Fisher’s exact test (5´-UTR introns: n=123; All introns: n = 9,760) (b) Enrichment map of functional categories identified for genes with ribosome-engaged intron retention events that overlap 5´-UTR sequences. Each node represents a Gene Ontology (GO) category with overlapping gene-set clusters linked together by edges and organized into clouds of similar function. (c) Heatmaps showing the percentage of transcripts with a percent intron retained (PIR) values for introns located within 5´-UTRs, coding sequence (CDS) and 3´-UTRs. Colour scales indicate PIR values and the colour shading reflects the gene function category as shown in panel (b). Sequencing data obtained from,–, see Supplementary Table 1 for details. Source data are available online.

Fig 4
Fig 4. Cell cycle-regulated AS events control ribosome engagement

(a) Cumulative frequency plot comparing changes in levels of ribosome engagement of transcripts at different cell cycle stages for genes with cell-cycle-dependent (periodic) splicing changes (n=411) compared to genes with general AS events (n=1068). General AS events are events found across ribosome-profiling datasets where no regulation has been assigned. Box plots below quantify these changes. (b) Box plots comparing peptide abundance changes between cell cycle stages for four different categories of genes: ‘All Genes’ are genes identified as expressed during the cell cycle (n=6195); ‘Periodic GE’ are genes that are differentially expressed between cell cycle stages (n=379); ‘Cell Type AS’ are genes with cell/tissue-specific AS events (n=315)(see Online Methods). (c) Box plots showing expression changes of transcripts between different cell cycle stages in transcripts per million (TPM) (d) Box plots displaying absolute difference in fraction of nucleotides detected as structured between an alternative exon and their surrounding constitutive exons. (e) Enrichment map of functional categories identified for genes with cell-cycle-dependent AS events and changes in ribosomal engagement between cell cycle stages (see also Supplementary Fig. 4). Each node represents a Gene Ontology (GO) category with overlapping gene-set clusters linked together by edges and organized into clouds of similar function. See Figure 2 and Online Methods for description of boxplots and statistical tests used. Sequencing data obtained from ,,,; see Supplementary Table 1 for details.

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