Impact of Alternative Splicing on the Human Proteome

Abstract

Alternative splicing is a critical determinant of genome complexity and, by implication, is assumed to engender proteomic diversity. This notion has not been experimentally tested in a targeted, quantitative manner. Here, we have developed an integrative approach to ask whether perturbations in mRNA splicing patterns alter the composition of the proteome. We integrate RNA sequencing (RNA-seq) (to comprehensively report intron retention, differential transcript usage, and gene expression) with a data-independent acquisition (DIA) method, SWATH-MS (sequential window acquisition of all theoretical spectra-mass spectrometry), to capture an unbiased, quantitative snapshot of the impact of constitutive and alternative splicing events on the proteome. Whereas intron retention is accompanied by decreased protein abundance, alterations in differential transcript usage and gene expression alter protein abundance proportionate to transcript levels. Our findings illustrate how RNA splicing links isoform expression in the human transcriptome with proteomic diversity and provides a foundation for studying perturbations associated with human diseases.

Keywords: RNA; alternative splicing; proteomics.

Graphical abstract

Figure 1

Framework to Study Contribution of Alternative Splicing to Proteomic Composition and Diversity Experimental and analysis workflow. Top: RNA splicing can result in generation of multiple transcripts as indicated in this hypothetical example, including different protein coding transcripts (transcripts A–C), as well as transcripts with retained introns (transcript D). Protein coding exons are represented by solid color bars, 5′ and 3′ untranslated regions are represented by white boxes, introns are represented by black lines, and a retained intron is represented by a dark red bar. We selectively perturbed RNA splicing by depleting the core spliceosome factor PRPF8 and used RNA-seq to assess the transcriptomic changes (left) and mass spectrometry to assess the effects at the protein level (right). PRPF8 depletion can alter the relative transcript abundance of the 4 transcripts, resulting in differential transcript usage (DTU), intron retention, or unchanged transcript usage. We have defined DTU to include cases where there is a change in transcript relative abundance between conditions. Differential gene expression (DGE) may also result, where the relative transcript abundances are unchanged between conditions, but changes in expression at the gene level are observed. We used SWATH-MS (sequential window acquisition of all theoretical spectra) to assess the effects at the protein level, which were validated by targeted SRM (selective reaction monitoring). We integrated the complete proteomic dataset based on knowledge from our RNA-seq experiments in order to guide the peptide assignments (bottom panel). Because peptide 2 maps uniquely to transcript B, it is assigned to transcript B. Peptide 1 maps to multiple transcripts in the same gene (A and C), but after PRPF8 depletion, the expression of only one of these transcripts is changed. The change affects the dominant expressed isoform for this gene (known as a major transcript), hence, peptide 1 is assigned to transcript A. In contrast, peptide 3 maps simultaneously to multiple differentially used transcripts and is therefore considered ambiguous, precluding assignment to any transcript.

Figure 2

Changes in Isoform Usage Manifest Themselves at the Proteome Level (A) Uniquely mapping peptides and SWATH-MS. Schematic indicating peptide to transcript mapping for uniquely mapping peptides is shown on left. Scatterplot comparing changes in expression of differently used transcripts (DTU) (log₂ fold change RNA-seq) to changes in expression of the peptides that uniquely map to them (log₂ fold change SWATH-MS) after PRPF8 depletion is shown on right. Spearman and Pearson correlation coefficients and associated p values are shown in top left corner. (B) Major transcripts and SWATH-MS. Schematic indicating peptide to transcript mapping for major transcripts is shown on left. Similar scatterplot is shown on right for transcripts whose most highly expressed isoform (major transcript) changes in expression after PRPF8 depletion with corresponding peptide evidence. (C) Use of an alternative integration strategy for peptide assignment where information about transcript expression levels was not considered increases dataset size but reduces correlation coefficient. Specifically, if a peptide maps to multiple transcripts in the same gene, but the expression of only one of these transcripts was changed after PRPF8 depletion, then this peptide was assigned to that particular transcript regardless of its expression level. In contrast, peptides that map simultaneously to multiple differentially used transcripts were considered ambiguous and were not used for further analysis. A scatterplot comparing changes in expression of differently used transcripts (DTU) (log₂ fold change RNA-seq) to changes in expression of their corresponding peptides (log₂ fold change SWATH-MS) after PRPF8 depletion is shown. Spearman and Pearson correlations coefficient and associated p values are shown in top left corner. See also Figure S2 and Table 1.

Figure 3

Peptides Encoded by Major Transcripts Are More Frequently Detected by SWATH-MS (A) Expression levels (log₁₀ FPKMs [fragments per kilobase of transcript per million mapped reads]) of major and minor transcripts with or without peptide evidence are indicated for Control and PRPF8-depleted samples. Only uniquely mapping peptides were used for this analysis and one biological replicate for each condition is shown. (B) Lowly expressed major transcripts displaying DTU are not detectable as expressed protein product within dynamic range of SWATH mass spectrometry. Expression levels (log₁₀ FPKMs) for major transcripts displaying DTU with or without peptide evidence are indicated for Control and PRPF8-depleted samples. One biological replicate for each condition is shown.

Figure 4

Validation Using Selective Reaction Monitoring Mass Spectrometry (SRM) (A) Uniquely mapping peptides and SRM. Scatterplot comparing changes in expression of differently used transcripts (DTU) (log₂ fold change RNA-seq) to changes in expression of the peptides that uniquely map to them (log₂ fold change SRM-MS) after PRPF8 depletion. Peptide expression information was obtained using SRM. (B) Major transcripts and SRM. Similar scatterplot is shown for transcripts whose most highly expressed isoform (major transcript) changes in expression after PRPF8 depletion with corresponding peptide evidence. See also Figure S3C and Table S2.

Figure 5

Biological Impact of Functional mRNA Isoforms (A) Transcript representation of LAP2 isoforms and starplots of transcript relative abundance in control siRNA-treated and PRPF8-depleted cells. One biological replicate is shown. (B) LAP2 switch event with corresponding peptide evidence. Column plots show fold change in expression of transcripts (left two columns) and peptides (right columns) after PRPF8 depletion. A negative fold change is represented in yellow (LAP2β), and a positive fold change in turquoise (LAP2α). Each peptide detected by SWATH or SRM is shown individually, and the region of the transcript to which it maps is represented in (A) by different colored ovals. (C and D) Changes in LAP2 isoform expression are confirmed at the RNA (C) and protein (D) levels using probes and antibodies that recognize each specific isoform. (E) LAP2 isoform localization is altered after PRPF8 depletion. Immunofluorescence of LAP2β and LAP2α isoforms is shown in control siRNA-treated and PRPF8-depleted Cal51 cells using antibodies that recognize each specific isoform and both isoforms respectively (scale bar, 5 μm). Scanning analysis of LAP2 isoform intensity is also shown with the scanning axes indicated by white lines. Pairs of nuclei of same scan width as determined by DAPI were used for scanning using ImageJ (NIH). Experiments in (C)–(E) were replicated independently 3 times and one representative experiment is shown. See also Figures S4 and S5.

Figure 6

Intron Retention and Differential Gene Expression Functionally Tune the Human Proteome (A) Intron retention reduces protein levels. Boxplot representing the ratio of protein expression (PRPF8 depletion/Control) is shown for retained introns (n = 270) and non-retained introns (n = 473) with peptide evidence. p value is indicated (Wilcoxon test). (B) Alterations in gene expression alter protein abundance proportionally to transcript levels. Scatterplot comparing changes in expression of differentially expressed genes (DGE) (log₂ fold change RNA-seq) to changes in expression of the peptides that map to them (log₂ fold change SWATH-MS) after PRPF8 depletion. Spearman’s correlation coefficient is shown in top left corner. Differently expressed genes whose corresponding peptides change significantly in expression (adjusted p value < 0.1, t test, Holm method) are indicated in red and associated correlation coefficient is also shown in red. See also Figure S6 and Table S3.