- Open Access
Vex-seq: high-throughput identification of the impact of genetic variation on pre-mRNA splicing efficiency
© The Author(s). 2018
- Received: 19 September 2017
- Accepted: 27 April 2018
- Published: 1 June 2018
Understanding the functional impact of genomic variants is a major goal of modern genetics and personalized medicine. Although many synonymous and non-coding variants act through altering the efficiency of pre-mRNA splicing, it is difficult to predict how these variants impact pre-mRNA splicing. Here, we describe a massively parallel approach we use to test the impact on pre-mRNA splicing of 2059 human genetic variants spanning 110 alternative exons. This method, called variant exon sequencing (Vex-seq), yields data that reinforce known mechanisms of pre-mRNA splicing, identifies variants that impact pre-mRNA splicing, and will be useful for increasing our understanding of genome function.
One of the main goals of personalized medicine is to understand how genetic variations between individuals impact health. Genetic variants can impact health in a number of different ways, one of which is through altering pre-mRNA splicing efficiency. Alternative splicing is a process that is important for regulatory function and a primary source of proteome diversity in humans . Perturbations in splicing have also been shown to contribute to a number of different diseases [2, 3]. These splicing changes can manifest themselves through interrupting well-known interactions between the spliceosome and splicing elements, including the 3′ and 5′ splice sites, pyrimidine tract, or branchpoint sequences. However, splicing can also be perturbed by disrupting other sequences known to modulate splicing. Exonic splicing enhancers and silencers (ESEs and ESSs), as well as intronic splicing enhancers and silencers (ISEs and ISSs), are examples of splicing regulatory elements that can be perturbed and result in different splicing outcomes. Modulation of these splicing regulatory elements has been shown to be disease associated (for a review see ). Thus, understanding how both intronic and exonic variants impact splicing not only provides insights into the mechanisms of splicing, but also is important to understand the basis of certain genetic diseases.
Identifying variants that impact splicing regulatory elements and their splicing consequences are difficult to detect using conventional poly(A)+ RNA-seq alone because the variants are often spliced out of the mature mRNA. A number of different studies have aimed to address this issue. One approach has been the pursuit of deciphering the “splicing code” using computational techniques such as deep learning [5–7]. While these studies have yielded useful knowledge about splicing and do have predictive power, experimental confirmation of the behavior of these variants has been limited and the predictions are not perfect. Other groups have pursued the use of random sequences to understand the splicing code; however, it is hard to integrate datasets with contextual transcriptome information (i.e., CLIP) when studying the splicing behavior of random sequences . A more recent study tested a number of exonic disease-associated variants in parallel using a mini-gene system . The approach was to observe the allelic ratio of reference to variant in a plasmid pool, and compare with the ratios observed from splicing outcomes. This approach is useful for studying exonic variants but is unable to test intronic variants. Here we present a method that address some of these shortcomings using a barcoding approach called Variant exon sequencing (Vex-seq). Vex-seq is capable of testing many exonic and flanking intronic variants for the same exon simultaneously.
We first designed a pool of 2059 variants spanning 110 exons with reference, consensus splice site, and mutated splice site control sequences for each exon. To ensure reproducibility, each variant exon was associated with at least three unique eight-nucleotide barcodes. Common primer sequences and restriction enzyme sites were also added for proper library construction. We included a minimum of 50 bases of the upstream intron, which should be adequate to include the majority of branchpoints , as well as the exon itself and at least 20 bases of downstream intron. This allowed for construction of test exons up to 97 nucleotides in length. Alternative exons between the size of 68 and 97 nucleotides were randomly selected from Ensembl GRCh37.p13 annotations and variants from the ExAC database were intersected with the selected exons and their flanking intron sequences .
We amplified the oligonucleotide pool by PCR (Additional file 1: Table S1). This product was then subcloned into a modified version of the splicing reporter plasmid pcAT7-Glo1 in between the first intron and the 3′ UTR to generate a 1˚ library. Then restriction sites in between the barcode and the end of the test sequence were used to subclone in the second part of the second intron and third exon from the original plasmid (Fig. 1a). This results in a plasmid that encodes a transcript containing the first exon and part of the first intron of the globin gene, the test sequence, followed by the second intron and final exon of the reporter transcript, ending with the barcode and the 3′ UTR. We refer to this final library pool as the 2˚ library.
The 2˚ library was then transfected into K562 and HepG2 cell lines in biological triplicate. cDNA was then synthesized from the RNA isolated from the cells using a mini-gene specific primer, a ten-nucleotide random sequence which serves as a unique molecular identifier (UMI) and an Illumina Read 2 sequencing primer. PCR amplified the cDNA to attach the other necessary sequences for Illumina paired-end sequencing. The products were then sequenced on an Illumina MiSeq.
The data analysis pipeline uses custom python scripts to ensure that read 2 contains the third exon, the correct restriction site next to the barcode, and sorts the reads by barcode into bins. PCR replicate reads are collapsed into a single read using the UMI from the reverse transcription primer. The reads in each bin are then aligned using STAR to a reference specific to each variant . Percent spliced in (PSI or Ψ) and change in PSI (ΔΨ) from the reference sequence are then calculated (Fig. 1b). The amplicon based paired-end sequencing reads contain an unambiguous splicing outcome for each amplicon, making Ψ and ΔΨ calculations straightforward from the alignment outputs alone.
The impact of splice site strength has been well characterized and is known to impact splicing efficiency . The impacts of changes on maximum entropy of 3′ and 5′ splice sites calculated by MaxEntScan can be visualized in Fig. 7c, d . Intronic splicing regulatory elements have been typically studied downstream of the exon in question, which are outside or on the periphery of the context that Vex-seq currently has the capacity to study [16, 17]. However, we do still observe intronic variants changing splicing (particularly upstream of the 3′ splice site) that are outside of the conventionally measured effects of changes in 3′ and 5′ splice site strength. As many of the variants that do impact splicing are upsteam of the exon, yet outside the window studied for 3′ splice site strength, we examined whether these might disrupt branchpoint sequences. To do this, we used branchpointer, a machine learning program, to predict branchpoint probabilities of the reference and variant branchpoints . Surprisingly, the majority (53 out of 84) of variants impacting splicing in this region were not predicted to impact maximum branchpoint usage probability. The variants that do affect branchpoint probability did not show any significant correlation with ΔΨ. We also did not identify any association between changes in RNA secondary structure around the splice sites and changes in splicing, which have been previously reported .
We have developed a new assay to assess how variants can impact pre-mRNA splicing efficiency called Vex-seq. This method builds upon previous high-throughput splicing reporter assays. It utilizes a barcoding approach and designed sequences based on the transcriptome and genetic variants. Vex-seq’s approach of using designed sequences allows for the possibility of not deeply sequencing the plasmid pool, because barcode variant associations are already known. This assay is also able to test designed intronic variation which other recent methods have been unable to do, until very recently [9, 26]. Vex-seq is even able to account for the impacts that variants may have on transcription of reporter transcripts because of the barcoding approach. Vex-seq could be applied to a number of different applications, including fine mapping of GWAS variants that may be involved in splicing regulation, which has been shown to be linked to complex diseases . Additionally, this could be used to dissect the behavior of RNA binding proteins and their effect on splicing regulation, or even saturating mutagenesis of exons known to be important for diseases. Thus, Vex-seq has the potential to have an extremely high impact on our understanding of genome function and how non-coding sequence variants impact pre-mRNA splicing.
While Vex-seq offers certain advantages over current methods, there remain some obstacles with all of these splice reporter approaches [8, 9]. First, these massively parallel splicing assays lack the context of the entire gene and chromatin state of the native genes. Second, these assays have limitations in terms of barcode design and synthesis length constraints and also may have cryptic splice sites formed in the context of the mini-gene. As oligonucleotide synthesis technologies improve, more context can be added to exons tested in this way. With more context, we expect Vex-seq to be more accurate at identifying variants that impact splicing.
Despite only examining 110 alternative exons in this study, we are able to obtain some biological insights from these data. The first is the similarity between the splicing behavior of K562 and HepG2 cell lines. Although the precise Ψ of each exon variant is not necessarily identical between the two cell lines, the directionality of the ΔΨ induced by most variants is quite similar in each cell line (Fig. 5b). This may suggest that most variants tested in this context are acting upon splicing elements common across these cell lines. Of course there are exceptions to this behavior, which may mechanistically be related to the unique trans factors of each cell line or noise in the data. This observation may change when analyzing splicing changes in response to stimuli or in the context of a cell with more complex transcriptome regulation. Alternatively, this may suggest that regulatory factors important for cell type-specific splicing are generally outside of the window that we are testing in Vex-seq. The predictive power of conserved intronic splicing regulatory elements on Ψ generally being within 100 nucleotides upstream and downstream may suggest that this is the case . We have also been able to use this assay after UPF1 depletion to account for NMD as an experimental artifact, but also use it to predict the impact of NMD on variants which would cause NMD endogenously.
Data obtained from Vex-seq demonstrate the importance of variants on impacting pre-mRNA splicing efficiency. It shows that variant effect prediction, while useful for predicting protein changing variants, is insufficient to predict all splicing changes induced by variants. We also show that variants that tend to change splicing more are also generally more conserved than nucleotides that do not, particularly when the variants are otherwise not predicted to change protein products.
pcAT7-Glo1 was provided by Kristen Lynch. To eliminate a splice acceptor site in the middle of intron 1, a deletion of the pyrimidine tract and splice acceptor sequence was deleted. This was done through digestion of the vector with AflII and PstI and PCR amplifying an insert using two primers (FWD 5′-AAACTCTTAAGCTAATACGACTCACTATAGG-3′, REV 5′- GACTGAATGAGTCTGCAGAGGCAGAGAGAGTCAGTGG-3′). The insert digested with AflII and PstI was ligated in the vector digested with the same enzymes, resulting in the plasmid used for these studies.
Assembly of Vex-seq plasmid
The oligo pool (Additional file 1: Table S1) was amplified with a common primer set (FWD 5′- GTAGCGTCTGTCCGTCTGCA-3′; REV 5′-CTGTAGTAGTAGTTGTCTAG-3′) for 20 cycles, then digested with PstI and XbaI. These were subcloned into the modified pcAT7-Glo1 also using PstI and XbaI sites. The resulting plasmid pool, referred to as 1˚, was then digested with SpeI and MfeI. Exon 3 and intron 2 were PCR amplified from the original plasmid with primers (FWD 5′-GTGTGGAAGTCTCAGGATCG-3′, REV 5′-AACGGGCCCTCTAGAGC-3′) and digested with MfeI and XbaI. The resulting product was subcloned into the digested 1˚ vector resulting in the final plasmid pool (2˚).
Transfection and cell culture
HepG2 cells were grown to a density of 0.5 × 106 cells per well and transfected with 1 μg of plasmid DNA using Lipofectamine 2000. Transfected HepG2 cells were then selected with 1 mg/mL zeocin for 8 days. K562 cells were grown to a density of 1.0 × 106 cells per well and electroporated with 5 μg of plasmid DNA. Transfected K562 cells were then selected with 200 μg/mL of zeocin for 8 days. RNA from each cell line was isolated using Maxwell® 16 LEV simplyRNA Purification kits.
UPF1 knockdown experiments were performed by transducing K562 cells with shRNA TRCN0000022254 (TRC collection), hairpin sequence (5′-CCGG-GCATCTTATTCTGGGTAATAA-CTCGAG-TTATTACCCAGAATAAGATGC-TTTTT-3′). A scrambled shRNA (SHC002 Sigma-Aldrich; 5′-CCGG-CAACAAGATGAAGAGCACCAA-CTCGAG-TTGGTGCTCTTCATCTTGTTG-TTTTT-3′) was used as a non-specific control. Transfected cells were selected with puromycin for 5 days followed by transfection with the Vex-seq plasmid library. Cells were then harvested after 24 h and RNA was collected as above. Protein was isolated and western blotting performed using Wes.
Sequencing for the 1˚ library was constructed using a nested PCR reaction. The 1˚ library was amplified for 15 cycles using the following primers: FWD 5′- ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCACTGACTCTCTCTGCCTC-3′; REV 5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGCGGGTTTAAACGGGCCCT-3′. The 2˚ library was amplified for 15 cycles using the following primers: FWD 5′- ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGCAGCTAAATCCAGCTACCA-3′; REV 5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGCGGGTTTAAACGGGCCCT-3′. Each of these products was then amplified for ten cycles using the following primers: FWD 5′-AATGATACGGCGACCACCGAGATCTACAC-i5-INDEX-ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3′; REV 5′-CAAGCAGAAGACGGCATACGAGAT-i7-INDEX-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-3′. The cDNA was synthesized from the K562 and HepG2 RNA using SuperScript™ III reverse transcriptase and a gene specific primer (5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNNNNNNGCAACTAGAAGGCACAGTCGAGG-3′). The cDNA was then PCR amplified for ten cycles using the following primers: FWD 5′-AATGATACGGCGACCACCGAGATCTACAC-i5-INDEX-ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCAAGGTGAACGTGGATGAAG-3′; REV 5′-CAAGCAGAAGACGGCATACGAGAT-i7-INDEX-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-3′. Resulting samples were multiplexed and sequenced on a MiSeq using a v2 300-cycle kit. Read 1 and read 2 were 150 bases each.
Data analysis and interpretation
Plasmid quality control
Forward and reverse reads from plasmids were combined into a single read using FLASH . The 1˚ library reads were sorted into bins using the barcode and grouped by control exon backbone with separate bins for indels and control sequences. Reads were then aligned using Novoalign V3.02.13 (http://www.novocraft.com). Sam2tsv was then used to identify variants in each read and identify the barcode sequence . Barcodes with 15% or more of reads not containing the correct variant were filtered out during splicing analysis using custom python scripts. Barcodes identified 2˚ library reads using custom python scripts and barcodes without reads were filtered out of the analysis.
Splicing alignments and analysis
Reads were identified by barcode and sorted into bins for each variant. The duplicate reads in each bin were then collapsed into a single read by the UMI. Reads were then aligned to a variant-specific reference using STAR version 2.5.2b . The uniquely aligned annotated read junctions were identified and Ψ and ΔΨ were calculated. Reads which spanned unannotated splice junctions were discarded for calculating Ψ and ΔΨ. Ψ values for analysis, unless otherwise indicated, were the mean of the K562 and HepG2 Ψ values. Mutated and consensus splice site controls were removed for most analyses with the exceptions of Figs. 2 and 4. Annotations for each variant were done using the Ensembl Variant Effect Predictor tool using assembly GRCh37.p13 and the Ensembl transcript database . The variants used in the analysis were selected based on the first annotation output by VEP. We used 100-way vertebrate PhyloP conservation scores to examine conservation . Scripts to reproduce the post-processed data can be found at https://github.com/scottiadamson/Vex-seq.
We thank Kristen Lynch for the pcAT7-Glo1 plasmid and members of the Graveley lab for discussions. We wish to thank those who reviewed the manuscript for their constructive comments (Additional file 2).
This work was supported by a grant from the National Human Genome Research Institute (R21HG008799) and the John and Donna Krenicki Endowment Fund to BRG.
Availability of data and materials
The raw and processed datasets generated and/or analyzed during the current study are available using the GEO accession GSE113163 . Custom python scripts for data analysis are located on Github (https://github.com/scottiadamson/Vex-seq) and zenodo (https://doi.org/10.5281/zenodo.1217642) . The modified pcAT7-Glo1 plasmid is available from the lab upon request.
The review history for this manuscript is available as Additional file 2.
BG and SA conceived of the experiments, SA designed the oligo pool, built the Vex-seq library, and analyzed data. LZ performed cell culture and knockdown experiments. SA and BG wrote the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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