Mutations of Pre-mRNA Splicing Regulatory Elements: Are Predictions Moving Forward to Clinical Diagnostics?

For more than three decades, researchers have known that consensus splice sites alone are not sufficient regulatory elements to provide complex splicing regulation. Other regulators, so-called splicing regulatory elements (SREs) are needed. Most importantly, their sequence variants often underlie the development of various human disorders. However, due to their variable location and high degeneracy, these regulatory sequences are also very difficult to recognize and predict. Many different approaches aiming to identify SREs have been tried, often leading to the development of in silico prediction tools. While these tools were initially expected to be helpful to identify splicing-affecting mutations in genetic diagnostics, we are still quite far from meeting this goal. In fact, most of these tools are not able to accurately discern the SRE-affecting pathological variants from those not affecting splicing. Nonetheless, several recent evaluations have given appealing results (namely for EX-SKIP, ESRseq and Hexplorer predictors). In this review, we aim to summarize the history of the different approaches to SRE prediction, and provide additional validation of these tools based on patients’ clinical data. Finally, we evaluate their usefulness for diagnostic settings and discuss the challenges that have yet to be met

Splicing Enhancers at Intron–Exon Borders Participate in Acceptor Splice Sites Recognition

Acceptor splice site recognition (3′ splice site: 3′ss) is a fundamental step in precursor messenger RNA (pre-mRNA) splicing. Generally, the U2 small nuclear ribonucleoprotein (snRNP) auxiliary factor (U2AF) heterodimer recognizes the 3′ss, of which U2AF35 has a dual function: (i) It binds to the intron–exon border of some 3′ss and (ii) mediates enhancer-binding splicing activators’ interactions with the spliceosome. Alternative mechanisms for 3′ss recognition have been suggested, yet they are still not thoroughly understood. Here, we analyzed 3′ss recognition where the intron–exon border is bound by a ubiquitous splicing regulator SRSF1. Using the minigene analysis of two model exons and their mutants, BRCA2 exon 12 and VARS2 exon 17, we showed that the exon inclusion correlated much better with the predicted SRSF1 affinity than 3′ss quality, which were assessed using the Catalog of Inferred Sequence Binding Preferences of RNA binding proteins (CISBP-RNA) database and maximum entropy algorithm (MaxEnt) predictor and the U2AF35 consensus matrix, respectively. RNA affinity purification proved SRSF1 binding to the model 3′ss. On the other hand, knockdown experiments revealed that U2AF35 also plays a role in these exons’ inclusion. Most probably, both factors stochastically bind the 3′ss, supporting exon recognition, more apparently in VARS2 exon 17. Identifying splicing activators as 3′ss recognition factors is crucial for both a basic understanding of splicing regulation and human genetic diagnostics when assessing variants’ effects on splicing

Exon First Nucleotide Mutations in Splicing: Evaluation of <i>In Silico</i> Prediction Tools

<div><p>Mutations in the first nucleotide of exons (E⁺¹) mostly affect pre-mRNA splicing when found in AG-dependent 3′ splice sites, whereas AG-independent splice sites are more resistant. The AG-dependency, however, may be difficult to assess just from primary sequence data as it depends on the quality of the polypyrimidine tract. For this reason, <i>in silico</i> prediction tools are commonly used to score 3′ splice sites. In this study, we have assessed the ability of sequence features and <i>in silico</i> prediction tools to discriminate between the splicing-affecting and non-affecting E⁺¹ variants. For this purpose, we newly tested 16 substitutions <i>in vitro</i> and derived other variants from literature. Surprisingly, we found that in the presence of the substituting nucleotide, the quality of the polypyrimidine tract alone was not conclusive about its splicing fate. Rather, it was the identity of the substituting nucleotide that markedly influenced it. Among the computational tools tested, the best performance was achieved using the Maximum Entropy Model and Position-Specific Scoring Matrix. As a result of this study, we have now established preliminary discriminative cut-off values showing sensitivity up to 95% and specificity up to 90%. This is expected to improve our ability to detect splicing-affecting variants in a clinical genetic setting.</p></div

Analyzed sequences.

<p>The sequences are ordered according to the length of their longest PPS. Exons whose splicing was shown to depend on intact E⁺¹ position are underlined. The PPS are singly underlined and other polypyrimidine stretches are dashed underlined. Sites of mutations are showed in bold. Seq. = sequence. (A) Sequences of the test set. (B) Sequences of the borderline set.</p

Results of the splicing minigene analyses.

<p>RT-PCR analysis of the literature-derived E⁺¹ variations. The splicing affecting sequences are underlined. (A) The test set sequences. cDNA bands originating from <i>BTK</i> exon 10 mutated minigene are numbered as follows: 1) cryptic 3′ss utilization 31 nt upstream of the authentic splice site (the aberrant exon starts at c.840-31G), 2) normally spliced RNA. (B) The borderline set sequences.</p

Analysis of the two E⁺¹ mutations of the <i>BTK</i> gene, O13 (c.1482G>T) and HK08 (c.1883G>A).

<p>(A) Schematic sequences of mutated acceptor splice sites. Introns are shown in lower-case, and exons are shown in capital letters. Mutated nucleotides are bold and underlined. The PPS are singly underlined and other polypyrimidine stretches are dashed underlined. (B) RT-PCR of minigenes transfected into HeLa cells. cDNA bands originating from O13 mutated minigene are numbered as follows: 1) cryptic 3′ss utilization 81 nt upstream of the authentic splice site (the aberrant exon starts at c.1350-81G), 2) normally spliced RNA, 3) skipping of mutated exon. (C) RT-PCR from RNA extracted from patients’ blood. P = patient’s sample, HC = healthy control sample. The O13 cDNA bands are numbered as in (B).</p

Comparison of value ranges describing particular intronic parameters in the test set of G⁺¹ mutations.

<p>The BS and PPT parameters were predicted using prediction programs provided by Kol et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089570#pone.0089570-Kol1" target="_blank">[10]</a> or Schwartz et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089570#pone.0089570-Schwartz1" target="_blank">[11]</a> using the Sroogle engine. Computational predictions and other sequence parameters for an experimentally confirmed test set of splicing-affecting and non-affecting G⁺¹ mutations were subjected to statistical analysis using the Mann-Whitney test (see Table S1 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089570#pone.0089570.s006" target="_blank">File S1</a> for details). Significant differences (p<0.05) are marked in bold. dist. = distance, BS = branch point site, M-W test = Mann-Whitney test, perc. = percentile, PPS = the longest uninterrupted polypyrimidine stretch Py = number of pyrimidines (in 25 or 50 nt upstream from 3′ss).</p

Proposed cut-off values for the <i>in silico</i> tools that discriminate AG-dependent 3′ss from AG-independent 3′ss.

a<p>The cut-off values were proposed according to the predicted values obtained using the test set of naturally occurring G⁺¹ mutations (as used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089570#pone-0089570-t001" target="_blank">Tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089570#pone-0089570-t002" target="_blank">2</a>). ^bThe sensitivity and specificity calculated using Fu-mut set of G⁺¹ mutations, containing artificially mutated PPTs. ^cThe sensitivity and specificity calculated using the original test set of G⁺¹ mutations. The predicted scores and percentiles below the cut-off values and the differences between the predicted values for wild type and mutant sequences above the cut-off values are supposed to pertain to variants prone to affect splicing.</p