Impact of Alternative Splicing on the Human Proteome

Abstract

SummaryAlternative 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.

Isoform‐resolved correlation analysis between mRNA abundance regulation and protein level degradation

“…We have developed an approach differentiating the major and unique AS isoforms for each AS group. Based on the abundance of the major transcript AS isoform, we confirmed that protein abundance is generally correlated to transcript AS levels in a spliceosome‐disrupted system (Larochelle, ; Liu et al , 2017b).…”

“…We then sought to identify AS switch events (i.e., the most abundant splicing isoform changed between conditions) for P7‐to‐P50 on the transcriptome level and to map these events to the protein level. Using a tool called SwitchSeq (preprint: Gonzàlez‐Porta & Brazma, ; Liu et al , 2017b), we identified 900 switch events between HeLa P7 and P50. As depicted in Fig C, transcripts that underwent switching from a RI in P7 to protein coding in P50 (i.e., RI to coding) showed a significant increase on protein level in P50 ( n = 77, Wilcoxon test, P = 0.0071; Figs C and EV5D).…”

“…As for protein identities (IDs), all IDs mapping to a peptide were preserved to keep full information about the splicing isoforms that map to a peptide. The data were log 2 transformed, triplicate measurements were averaged for all HeLa cell lines (median value; the peptide had to be quantified in at least two injections), and only peptide full profiles (i.e., peptides quantified in all twelve HeLa cell lines) were kept for the downstream analysis. Peptides mapped to several genes were excluded and were further classified based on the criteria whether they map uniquely to one splicing isoform of a gene ( unique peptides) or to multiple splicing isoforms of the same gene ( shared peptides). Both unique and shared peptides were then collapsed to create a matrix of quantified proteins and protein groups, respectively (by summing; at least two peptides were required for a protein/protein group). Similar approach was applied for the pSILAC data. After k loss calculation, only peptide k loss full profiles were used with peptides mapping to multiple genes removed. The k loss values were log 2 transformed and collapsed to create a protein and protein group matrix (average; at least two peptides were required for a protein/protein group). The two data sets were then merged in a protein‐centric way; i.e., the protein k loss values were mapped to the protein expression matrix using the protein/protein group IDs. To map the mRNA abundance data to the assembled protein matrix, we exploited the fact that in our protein FASTA DB, each entry was annotated by a unique Ensemble transcript ID (ENST) and thus could be easily mapped to its corresponding transcript abundance in the RNA‐Seq data (i.e., FPKM). We first mapped the unique proteins (UQ) with the abundance of the corresponding transcripts. For the protein groups quantified based on shared peptides quantities, we applied a sample‐specific protein inference similar to a strategy described previously (Liu et al , 2017b). For each splicing isoform included in a shared protein group, we retrieved its average abundance on mRNA level (the average was calculated from all HeLa variants). The major (i.e., the most abundant) splicing isoform on mRNA level was then selected as the best representative transcript ID for the whole protein group ( shared major, SM ) and was used for transcript abundance mapping.…”

Ribosome profiling at isoform level reveals an evolutionary conserved impact of differential splicing on the proteome

“…Computational and in-vitro studies have provided evidence that a change in relative isoform abundances can lead to the production of protein variants that impact the network of proteinprotein interactions in different contexts (Buljan et al 2012;Yang et al 2016;Climente-González et al 2017;Wojtowicz et al 2007). In contrast, quantitative proteomics of naturally occurring proteins has identified much fewer protein variants than those predicted with RNA sequencing (Ezkurdia et al 2015;Liu et al 2017). Using state-of-the-art proteomics, it was recently shown that splicing changes at RNA level lead to changes in the sequence and abundance of proteins produced, although this was detected only for a limited number of transcripts (Liu et al 2017).…”

Mass spectrometry-based proteomics in cancer research

“…With the establishment of the first large-scale proteogenomic study of breast cancer, more integrating genomic and proteomic data will be integrated to achieve a better picture of cancer biology. For instance, Liu et al could capture an unbiased quantitative snapshot of the constitutive and alternative splicing events on proteome by integrating RNA sequencing with the sequential window acquisition of all theoretical spectra-MS. Finding the RNA splicing links isoform expression in human transcriptome with proteomic diversity may help to study the perturbations associated with human diseases [11].…”