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Mechanism and modeling of human disease-associated near-exon intronic variants that perturb RNA splicing

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Abstract

It is estimated that 10%–30% of disease-associated genetic variants affect splicing. Splicing variants may generate deleteriously altered gene product and are potential therapeutic targets. However, systematic diagnosis or prediction of splicing variants is yet to be established, especially for the near-exon intronic splice region. The major challenge lies in the redundant and ill-defined branch sites and other splicing motifs therein. Here, we carried out unbiased massively parallel splicing assays on 5,307 disease-associated variants that overlapped with branch sites and collected 5,884 variants across the 5′ splice region. We found that strong splice sites and exonic features preserve splicing from intronic sequence variation. Whereas the splice-altering mechanism of the 3′ intronic variants is complex, that of the 5′ is mainly splice-site destruction. Statistical learning combined with these molecular features allows precise prediction of altered splicing from an intronic variant. This statistical model provides the identity and ranking of biological features that determine splicing, which serves as transferable knowledge and out-performs the benchmarking predictive tool. Moreover, we demonstrated that intronic splicing variants may associate with disease risks in the human population. Our study elucidates the mechanism of splicing response of intronic variants, which classify disease-associated splicing variants for the promise of precision medicine.

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Fig. 1: MaPSy of near-exon intronic mutations.
Fig. 2: Significant splicing mutations demonstrate distinctive features.
Fig. 3: Distinctive features of 3′ splicing variants.
Fig. 4: Generalized linear model to summarize predictors of splice-altering intronic variants.
Fig. 5: Validation of predictive models.
Fig. 6: Generalized linear model to explain splicing efficiency.
Fig. 7: Functional branchpoint mutations in the TWB.

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Data availability

RNA Sequencing datasets generated during this study are available at the NCBI GEO: GSE179892. Other databases used in the study: UCSC PhyloP: https://genome.ucsc.edu/cgi-bin/hgTracks?hgsid=1351580935_14MOQtNDW7V78RaXEDp3Yy4m4PTb&c=chr2&hgTracksConfigPage=configure&hgtgroup_compGeno_close=0#compGenoGroup ATtRACT: https://attract.cnic.es/download; Ensembl: https://asia.ensembl.org/info/data/ftp/index.html.Source data are provided with this paper. Further information and requests for resources should be directed to and will be fulfilled by the corresponding author.

Code availability

Custom codes and the training features used in the study are available at https://github.com/chienlinglin/modeling-intron-variants/.

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Acknowledgements

We thank M.-C. Tsai, Senior Scientific Editor at Cell, for constructive advice and editing the manuscript. We thank the Genomics Core of Institute of Molecular Biology (IMB), Academia Sinica, for performing the amplicon sequencing. We thank all members of IMB, particularly H.-J. Cheng, J.-Y. Leu, S.-C. Cheng and S.-H. Chen, for tremendous help and support. This work was supported by Career Development Award and Multidisciplinary Health Cloud Research Program of Academia Sinica (AS-CDA-108-M03 and AS-PH-109-01-3), Career Development Award of National Health Research Institute, Taiwan (NHRI-EX109-10908BC) and Excellent Young Scholar Research Grants and Ta-You Wu Memorial Award of Ministry of Science and Technology, Taiwan (MOST 109-2628-B-001-014-MY1 and 108-2118-M-001-013-MY5).

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  1. These authors contributed equally: Hung-Lun Chiang, Yi-Ting Chen, Jia-Ying Su.

Authors and Affiliations

  1. Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan

    Hung-Lun Chiang, Yi-Ting Chen, Jia-Ying Su, Hsin-Nan Lin, Chen-Hsin Albert Yu, Yu-Jen Hung, Yun-Lin Wang & Chien-Ling Lin

  2. Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

    Jia-Ying Su & Yen-Tsung Huang

  3. Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan

    Jia-Ying Su

  4. Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Jia-Ying Su

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Contributions

H.-L.C., Y.-T.C., J.-Y.S., Y.-L.W. and C.-L.L. carried out experiments and analysis. H.-N.L., C.-H.A.Y., Y.-J.H., Y.-T.C. and C.-L.L. established the web server tool. Y.-T.H. supervised statistical analysis. H.-L.C., Y.-T.C., J.-Y.S. and C.-L.L. wrote the manuscript.

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Correspondence to Chien-Ling Lin.

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Nature Structural & Molecular Biology thanks Ana Fiszbein and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Sara Osman was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1

Analysis flow of the study.

Extended Data Fig. 2 Workflow of the library construction.

(a) Primers used in the overlap** PCR. (b) The procedure of overlap** PCR. In brief, oligo pools and the other parts of the splicing minigenes were amplified by 25 PCR cycles. The fragment containing the promoter and the first exon (PCR product 1) was stitched to the oligo pool (PCR product 2) by overlap** PCR using 20 amplification cycles. Then, the stitched product (PCR product 1+2) was further stitched with the fragment containing the 3rd exon and polyadenylation signal (PCR product 3) using 20 amplification cycles to obtain the final construct (PCR product 1+2+3).

Extended Data Fig. 3 Massively parallel splicing assay (MaPSy) showing high consistency between repeats.

(a) Spearman’s correlation between 4 RNA-seq total read counts after alignment. (b) Spearman’s correlation between 4 RNA-seq read count with spliced reads using GT-AG as splice sites. (c) Principle analysis for the spliced outcome of total RNA-seq read count. (d) Principle analysis for the spliced outcome of RNA-seq read count after GT-AG splice site filtration on spliced reads.

Extended Data Fig. 4 Individual WT/mt pair validation of the Massively parallel splicing assay (MaPSy).

Single minigene WT was transfected in HEK293T cells for splicing. The splicing outcome was examined by RT-PCR. Genomic coordinates, transcript ID, gene name and the corresponding introns were labeled accordingly. A representative experiment of three repeats is presented.

Source data

Extended Data Fig. 5 Characters of intronic 3’-end splicing variants.

(a) Significant splicing variants are significantly closer to the 3’ss. Identity of two distributions was examined by two-sided Kolmogorov-Smirnov test. (b) Mutation effect on splicing efficiency of variants of various pathogenic levels. n = 247 variants. (c) Genome-wide association between the number of intronic splicing regulatory elements near the 5’ss and the intron length, stratified by exon length, related to Fig. 2f. n = 704,953 introns. (d) Enrichment of add-AG variants in the significant splicing variants. (e) Differential exon-intron GC content for non-significant and significant add-AG variants, stratified by intron length, related to Fig. 3f. n = 281 add AG variants. **: P-value of two-sided Wilcoxon test between the non-significant and HC significant variants 9 × 10−4; * 0.01. The boxes in box plots represent medians (central line) and interquartile ranges (IQR; 25th to 75th percentile). The whiskers indicate ±1.5 × IQR from the box or the last data point within that and the dots show the outliers (b,c,e). (f) Preference of 3’ss with various 3’ss strengths was examined by 3’ss swap** assay with splicing minigenes. A representative experiment of three repeats is presented.

Source data

Extended Data Fig. 6 Sensitivity analysis of the generalized linear model that predicts splice-altering intronic mutations, related to Fig. 4.

(a) Segregation of intronic mutations into two models based on intronic location and AG addition, same as Fig. 4a. (b-d) The left-most presents the top 10 contributory factors predicting mutations in each category that affect splicing, same as Fig. 4b, d and f. Right next to the factors is the ‘variance inflation factor (VIF)’ that examines the collinearity of variables. VIF smaller than 5 is an indication of independence of variables without a collinear effect. Z-score of univariate GLM in the middle column shows the size of the marginal influence of each variable (without other variables in the model). The right-most figure shows the consistency of variable selection with 20 different random selections of training data.

Extended Data Fig. 7 Generalized linear model to synthesize predictors of splicing altering non-AG-creating intronic mutations and splicing efficiency without WT splicing efficiency.

(a) ROC curve of the non-AG model without WT splicing efficiency, similar to Fig. 4e. (b) Top 10 contributing factors to predict non-AG mutations affecting splicing without WT splicing efficiency, similar to Fig. 4d. (c) ROC curve of the 5’-end model without WT splicing efficiency, similar to Fig.4g. (d) Top 10 contributing factors to predict 5’-end mutations affecting splicing without WT splicing efficiency, similar to Fig. 4f. (e,g) Explanation power of each splicing efficiency model without WT splicing efficiency of (e) Novel AG mutations (g) non-AG mutations. The explanatory power of each model on the test dataset was estimated by Pearson’s correlation (two-tailed), R square, and RMSE (root-mean-square error). The gray area displays the 95% confidence interval for predictions from the linear model. (f,h) All contributing factors to predict (f) novel AG mutations and (h) non-AG mutations affecting splicing without WT splicing efficiency. An ‘alt’ factor refers to a canonical property in the context of sequence variation. A ‘△ (delta)’ factor refers to the difference of scores/motifs between the alt and WT sequence. A ‘novel’ factor refers to a new property associated with the novel 3’ss AG (f). More detailed descriptions of the factors can be found in Supplementary Table 2.

Extended Data Fig. 8 Intrinsic features of the BS and 3’ss regulate splice outcome.

(a) Genome-wide distribution of A-, C-, T- and G-branchpoints (bp) relative to the 3’ss. (b) Proportion of branchpoints supporting constitutive splicing, sorted according to relative distance to the 3’ss. (c) Minimum free energy of BS pairing with the U2 BS recognition region, represented by boxplots for each position. (d) PhyloP100 conservation level of bp relative to the 3’ss.

Supplementary information

Source data

Source Data Fig. 1

Unprocessed gels.

Source Data Fig. 7

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Source Data Extended Data Fig. 4

Unprocessed gels.

Source Data Extended Data Fig. 5

Unprocessed gels.

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Chiang, HL., Chen, YT., Su, JY. et al. Mechanism and modeling of human disease-associated near-exon intronic variants that perturb RNA splicing. Nat Struct Mol Biol 29, 1043–1055 (2022). https://doi.org/10.1038/s41594-022-00844-1

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