Systematic evaluation of the effect of common SNPs on pre‐mRNA splicing

The evolutionary and biomedical importance of differential mRNA splicing is well established. Numerous studies have assessed patterns of differential splicing in different genes and correlated these patterns to the genotypes for adjacent single‐nucleotide polymorphisms (SNPs). Here, we have chosen a reverse approach and screened dbSNP for common SNPs at either canonical splice sites or exonic splice enhancers (ESEs) that would be classified as putatively splicing‐relevant by bioinformatic tools. The 223 candidate SNPs retrieved from dbSNP were experimentally tested using a previously established panel of 92 matching DNAs and cDNAs. For each SNP, 16 cDNAs providing a balanced representation of the genotypes at the respective SNP were investigated by nested RT‐PCR and subsequent sequencing. Putative allele‐dependent splicing was verified by the cloning of PCR products. The positive predictive value of the bioinformatics tools turned out to be low, ranging from 0% for ESEfinder to 9% (in the case of acceptor‐site SNPs) for a recently reported neural network. The results highlight the need for a better understanding of the sequence characteristics of functional splice‐sites to improve our ability to predict in silico the splicing relevance of empirically observed DNA sequence variants. Hum Mutat 0, 1–9, 2009. © 2009 Wiley‐Liss, Inc.     

Assessing predictions of the impact of variants on splicing in CAGI5

Precision medicine and sequence‐based clinical diagnostics seek to predict disease risk or to identify causative variants from sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype‐phenotype prediction challenges; participants build models, undergo assessment, and share key findings. In the past, few CAGI challenges have addressed the impact of sequence variants on splicing. In CAGI5, two challenges (Vex‐seq and MaPSY) involved prediction of the effect of variants, primarily single‐nucleotide changes, on splicing. Although there are significant differences between these two challenges, both involved prediction of results from high‐throughput exon inclusion assays. Here, we discuss the methods used to predict the impact of these variants on splicing, their performance, strengths, and weaknesses, and prospects for predicting the impact of sequence variation on splicing and disease phenotypes.     

Predicting the change of exon splicing caused by genetic variant using support vector regression

Alternative splicing can be disrupted by genetic variants that are related to diseases like cancers. Discovering the influence of genetic variations on the alternative splicing will improve the understanding of the pathogenesis of variants. Here, we developed a new approach, PredPSI‐SVR to predict the impact of variants on exon skipping events by using the support vector regression. From the sequence of a particular exon and its flanking regions, 42 comprehensive features related to splicing events were extracted. By using a greedy feature selection algorithm, we found eight features contributing most to the prediction. The trained model achieved a Pearson correlation coefficient (PCC) of 0.570 in the 10‐fold cross‐validation based on the training data set provided by the “vex‐seq” challenge of the 5th Critical Assessment of Genome Interpretation. In the blind test also held by the challenge, our prediction ranked the 2nd with a PCC of 0.566 that demonstrates the robustness of our method. A further test indicated that the PredPSI‐SVR is helpful in prioritizing deleterious synonymous mutations. The method is available on https://github.com/chenkenbio/PredPSI‐SVR .     

Assessing predictions on fitness effects of missense variants in calmodulin

This paper reports the evaluation of predictions for the “CALM1” challenge in the fifth round of the Critical Assessment of Genome Interpretation held in 2018. In the challenge, the participants were asked to predict effects on yeast growth caused by missense variants of human calmodulin, a highly conserved protein in eukaryotic cells sensing calcium concentration. The performance of predictors implementing different algorithms and methods is similar. Most predictors are able to identify the deleterious or tolerated variants with modest accuracy, with a baseline predictor based purely on sequence conservation slightly outperforming the submitted predictions. Nevertheless, we think that the accuracy of predictions remains far from satisfactory, and the field awaits substantial improvements. The most poorly predicted variants in this round surround functional CALM1 sites that bind calcium or peptide, which suggests that better incorporation of structural analysis may help improve predictions.     

Intronic variants in BRCA1 and BRCA2 that affect RNA splicing can be reliably selected by splice-site prediction programs

A large number of sequence variants identified in BRCA1 and BRCA2 cannot be distinguished as either disease-causing mutations or neutral variants. These so-called unclassified variants (UVs) include variants that are located in the intronic sequences of BRCA1 and BRCA2. The purpose of this study was to assess the use of splice-site prediction programs (SSPPs) to select intronic variants in BRCA1 and BRCA2 that are likely to affect RNA splicing. We performed in vitro molecular characterization of RNA of six intronic variants in BRCA1 and BRCA2. In four cases (BRCA1, c.81-6T>A and c.4986+5G>T; BRCA2, c.7617+2T>G and c.8754+5G>A) a deleterious effect on RNA splicing was seen, whereas the c.135-15_-12del variant in BRCA1 showed no effect on RNA splicing. In the case of the BRCA2 c.68-7T>A variant, RNA analysis was not sufficient to establish the clinical significance. Six SSPPs were used to predict whether an effect on RNA splicing was expected for these six variants as well as for 23 intronic variants in BRCA1 for which the effect on RNA splicing has been published. Out of a total of 174 predictions, 161 (93%) were informative (i.e., the wild-type splice-site was recognized). No false-negative predictions were observed; an effect on RNA splicing was always predicted by these programs. In four cases (2.5%) a false-positive prediction was observed. For DNA diagnostic laboratories, these programs are therefore very useful to select intronic variants that are likely to affect RNA splicing for further analysis.     

Dysfunctional gene splicing as a potential contributor to neuropsychiatric disorders

Alternative pre‐mRNA splicing is a major mechanism by which the proteomic diversity of eukaryotic genomes is amplified. Much akin to neuropsychiatric disorders themselves, alternative splicing events can be influenced by genetic, developmental, and environmental factors. Here, we review the evidence that abnormalities of splicing may contribute to the liability toward these disorders. First, we introduce the phenomenon of alternative splicing and describe the processes involved in its regulation. We then review the evidence for specific splicing abnormalities in a wide range of neuropsychiatric disorders, including psychotic disorders (schizophrenia), affective disorders (bipolar disorder and major depressive disorder), suicide, substance abuse disorders (cocaine abuse and alcoholism), and neurodevelopmental disorders (autism). Next, we provide a theoretical reworking of the concept of “gene‐focused” epidemiologic and neurobiologic investigations. Lastly, we suggest potentially fruitful lines for future research that should illuminate the nature, extent, causes, and consequences of alternative splicing abnormalities in neuropsychiatric disorders. © 2011 Wiley‐Liss, Inc.     

Characterization of cryptic splicing in germline PTEN intronic variants in Cowden syndrome

Germline mutations in the tumor‐suppressor gene PTEN predispose to subsets of Cowden syndrome (CS), Bannayan–Riley–Ruvalcaba syndrome, and autism. Evidence‐based classification of PTEN variants as either deleterious or benign is urgently needed for accurate molecular diagnosis and gene‐informed genetic counseling. We studied 34 different germline PTEN intronic variants from 61 CS patients, characterized their PTEN mRNA processing, and analyzed PTEN expression and downstream readouts of P‐AKT and P‐ERK1/2. While we found that many mutations near splice junctions result in exon skipping, we also identified the presence of cryptic splicing that resulted in premature termination or a shift in isoform usage. PTEN protein expression is significantly lower in the group with splicing changes while P‐AKT, but not P‐ERK1/2, is significantly increased. Our observations of these PTEN intronic variants should contribute to the determination of pathogenicity of PTEN intronic variants and aid in genetic counseling.     

Predicting the impact of single nucleotide variants on splicing via sequence‐based deep neural networks and genomic features

Single nucleotide mutations in exonic regions can significantly affect gene function through a disruption of splicing, and various computational methods have been developed to predict the splicing‐related effects of a single nucleotide mutation. We implemented a new method using ensemble learning that combines two types of predictive models: (a) base sequence‐based deep neural networks (DNNs) and (b) machine learning models based on genomic attributes. This method was applied to the Massively Parallel Splicing Assay challenge of the Fifth Critical Assessment of Genome Interpretation, in which challenge participants predicted various experimentally‐defined exonic splicing mutations, and achieved a promising result. We successfully revealed that combining different predictive models based upon the stacked generalization method led to significant improvement in prediction performance. In addition, whereas most of the genomic features adopted in constructing machine learning models were previously reported, feature values generated with DSSP, a DNN‐based splice site prediction tool, were novel and helpful for the prediction. Learning the sequence patterns associated with normal splicing and the change in splicing site probabilities caused by a mutation was presumed to be helpful in predicting splicing disruption.     

ESRP1剪接变异体的克隆及其对乳腺癌MDA-MB-231细胞增殖的影响

目的克隆ESRP1剪接变异体并构建其慢病毒表达载体,研究其对乳腺癌细胞MDA-MB-231细胞增殖的影响。方法以OVCAR3细胞的c DNA为模板,PCR扩增ESRP1剪接变异体,将其连接到带有Myc标签的pCMV-Myc真核表达载体;再以该真核表达载体为模板,将含有Myc标签的ESRP1剪接变异体克隆到慢病毒表达载体p LVt TR/KRAB-Red上,从而构建带有Myc标签的ESRP1剪接变异体慢病毒表达载体;用该慢病毒表达载体转染293T细胞,包装慢病毒;用该慢病毒感染MDA-MB-231细胞,用MTT法检测该细胞增殖,用Western blot检测该细胞中PARP蛋白的表达。结果成功构建带有Myc标签的ESRP1剪接变异体慢病毒表达载体,并包装出慢病毒;用该慢病毒感染MDA-MB-231细胞后,与空载体对照组比较,ESRP1过表达能明显抑制MDA-MB-231细胞增殖(P0.05);ESRP1过表达能诱导PARP蛋白的剪切。结论成功构建带有Myc标签的ESRP1剪接变异体的慢病毒表达载体;ESRP1过表达能抑制MDA-MB-231细胞增殖,这可能与细胞死亡有关。     

肿瘤细胞中基因选择性剪接的研究进展

选择性剪接是高等真核细胞在转录后水平调控基因表达以及产生蛋白质组多样性的重要机制。选择性剪接过程受多种顺式作用元件和反式作用因子相互作用调节。肿瘤癌基因、抑癌基因、肿瘤转移抑制基因可发生选择性剪接，与肿瘤发生发展关系密切，其蛋白异构体参与基因转录、细胞周期和凋亡等生命过程，对肿瘤生长有一定作用。以选择性剪接蛋白异构体为靶点或干预选择性剪接过程，可望进行肿瘤的分子治疗。     

Contribution of bioinformatics predictions and functional splicing assays to the interpretation of unclassified variants of the BRCA genes

A large fraction of sequence variants of unknown significance (VUS) of the breast and ovarian cancer susceptibility genes BRCA1 and BRCA2 may induce splicing defects. We analyzed 53 VUSs of BRCA1 or BRCA2, detected in consecutive molecular screenings, by using five splicing prediction programs, and we classified them into two groups according to the strength of the predictions. In parallel, we tested them by using functional splicing assays. A total of 10 VUSs were predicted by two or more programs to induce a significant reduction of splice site strength or activation of cryptic splice sites or generation of new splice sites. Minigene-based splicing assays confirmed four of these predictions. Five additional VUSs, all at internal exon positions, were not predicted to induce alterations of splice sites, but revealed variable levels of exon skipping, most likely induced by the modification of exonic splicing regulatory elements. We provide new data in favor of the pathogenic nature of the variants BRCA1 c.212+3A>G and BRCA1 c.5194−12G>A, which induced aberrant out-of-frame mRNA forms. Moreover, the novel variant BRCA2 c.7977−7C>G induced in frame inclusion of 6 nt from the 3′ end of intron 17. The novel variants BRCA2 c.520C>T and BRCA2 c.7992T>A induced incomplete skipping of exons 7 and 18, respectively. This work highlights the contribution of splicing minigene assays to the assessment of pathogenicity, not only when patient RNA is not available, but also as a tool to improve the accuracy of bioinformatics predictions.     

Reduced fidelity of branch point recognition and alternative splicing induced by the anti-tumor drug spliceostatin A

Spliceostatin A (SSA) is a stabilized derivative of a Pseudomonas bacterial fermentation product that displays potent anti-proliferative and anti-tumor activities in cancer cells and animal models. The drug inhibits pre-mRNA splicing in vitro and in vivo and binds SF3b, a protein subcomplex of U2 small nuclear ribonucleoprotein (snRNP), which is essential for recognition of the pre-mRNA branch point. We report that SSA prevents interaction of an SF3b 155-kDa subunit with the pre-mRNA, concomitant with nonproductive recruitment of U2 snRNP to sequences 5' of the branch point. Differences in base-pairing potential with U2 snRNA in this region lead to different sensitivity of 3' splice sites to SSA, and to SSA-induced changes in alternative splicing. Indeed, rather than general splicing inhibition, splicing-sensitive microarray analyses reveal specific alternative splicing changes induced by the drug that significantly overlap with those induced by knockdown of SF3b 155. These changes lead to down-regulation of genes important for cell division, including cyclin A2 and Aurora A kinase, thus providing an explanation for the anti-proliferative effects of SSA. Our results reveal a mechanism that prevents nonproductive base-pairing interactions in the spliceosome, and highlight the regulatory and cancer therapeutic potential of perturbing the fidelity of splice site recognition.