Utargetome: A targetome prediction tool for modified U1-snRNAs to identify distal-target positions with improved selectivity.

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-09-23 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1013534
Paolo Pigini, Federico Manuel Giorgi, Keng Boon Wee
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引用次数: 0

Abstract

The endogenous U1 small nuclear RNA (U1-snRNA) plays a crucial role in splicing initiation through base-pairing to donor splice sites (5'-SSs). Likewise, modified U1s that carry a mutation-adapted 5'-terminal sequence have been demonstrated to rescue exon splicing when this is disrupted by genetic mutations within the 5'-SS. Given the base-pairing flexibility of the endogenous U1, the selectivity of modified U1s requires investigation. We developed a computational pipeline (Utargetome) that considers combinations of mismatches and alternative annealing registers to predict the transcriptome-wide binding sites (or targetome) of a U1. The pipeline accuracy was tested by recapitulating well-established alternative annealing registers and specificity for 5'-SSs in the predicted targetome of the human endogenous U1. It was then applied to analyse the targetome of 54 modified U1s that have been demonstrated to restore exon inclusion when affected by 5'-SS pathogenic mutations. While the targetome size was found to be wide-ranging, the off-target load appeared to be reduced for U1s targeting distal sites from the canonical U1-binding position. This feature was predicted also for a large set of 30,204 newly designed U1s targeting 839 5'-SS pathogenic mutations that were expected to affect exon inclusion. Targetome analysis indeed revealed an optimal distal-targeting position at 3 nucleotides downstream from the canonical 5'-SS, for which a modified U1 is likely to have minimal off-targets at 5'-SSs and acceptor splice sites (3'-SSs). Based on these insights, we propose to implement targetome prediction in the design and optimization of therapeutic U1s with improved selectivity.

utargeome:修饰的u1 - snrna的目标组预测工具,以提高选择性识别远端靶标位置。
内源性U1小核RNA (U1- snrna)通过碱基配对到供体剪接位点(5’- ss),在剪接起始过程中起着至关重要的作用。同样,当外显子剪接被5‘-SS内的基因突变破坏时,携带突变适应5’端序列的修饰U1s已被证明可以挽救外显子剪接。考虑到内源性U1的碱基配对灵活性,修饰后的U1s的选择性需要研究。我们开发了一个计算管道(Utargetome),它考虑了错配和替代退火寄存器的组合来预测U1的转录组范围结合位点(或目标组)。通过总结已建立的替代退火寄存器和人类内源性U1预测靶组中5'- ss的特异性,验证了管道的准确性。然后将其应用于分析54个修饰U1s的目标组,这些修饰U1s已被证明在受到5'-SS致病突变影响时可以恢复外显子包含。虽然靶组的大小范围很广,但U1s靶向典型的u1结合位置的远端位点时,脱靶负荷似乎减少了。对于30204个新设计的靶向839个5'-SS致病突变的U1s,也预测了这一特征,这些突变预计会影响外显子的包含。靶组分析确实揭示了一个最佳的远端靶向位置,在规范的5'-SS下游3个核苷酸处,因此修饰的U1可能在5'-SS和受体剪接位点(3'-SS)上有最小的脱靶位置。基于这些见解,我们建议在设计和优化具有更高选择性的治疗性u1时实现靶组预测。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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