Single-nucleotide variants (SNVs) within gene coding sequences can significantly impact pre-mRNA splicing, bearing profound implications for pathogenic mechanisms and precision medicine. In this study, we aim to harness the well-established full-length gene splicing assay (FLGSA) in conjunction with SpliceAI to prospectively interpret the splicing effects of all potential coding SNVs within the four-exon SPINK1 gene, a gene associated with chronic pancreatitis. Our study began with a retrospective analysis of 27 SPINK1 coding SNVs previously assessed using FLGSA, proceeded with a prospective analysis of 35 new FLGSA-tested SPINK1 coding SNVs, followed by data extrapolation, and ended with further validation. In total, we analyzed 67 SPINK1 coding SNVs, which account for 9.3% of the 720 possible coding SNVs. Among these 67 FLGSA-analyzed SNVs, 12 were found to impact splicing. Through detailed comparison of FLGSA results and SpliceAI predictions, we inferred that the remaining 653 untested coding SNVs in the SPINK1 gene are unlikely to significantly affect splicing. Of the 12 splice-altering events, nine produced both normally spliced and aberrantly spliced transcripts, while the remaining three only generated aberrantly spliced transcripts. These splice-impacting SNVs were found solely in exons 1 and 2, notably at the first and/or last coding nucleotides of these exons. Among the 12 splice-altering events, 11 were missense variants (2.17% of 506 potential missense variants), and one was synonymous (0.61% of 164 potential synonymous variants). Notably, adjusting the SpliceAI cut-off to 0.30 instead of the conventional 0.20 would improve specificity without reducing sensitivity. By integrating FLGSA with SpliceAI, we have determined that less than 2% (1.67%) of all possible coding SNVs in SPINK1 significantly influence splicing outcomes. Our findings emphasize the critical importance of conducting splicing analysis within the broader genomic sequence context of the study gene and highlight the inherent uncertainties associated with intermediate SpliceAI scores (0.20 to 0.80). This study contributes to the field by being the first to prospectively interpret all potential coding SNVs in a disease-associated gene with a high degree of accuracy, representing a meaningful attempt at shifting from retrospective to prospective variant analysis in the era of exome and genome sequencing.

中文翻译：

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结合全长基因检测和 SpliceAI 来解释所有可能的 SPINK1 编码变体的剪接影响

基因编码序列中的单核苷酸变异 (SNV) 可以显着影响前 mRNA 剪接，对致病机制和精准医学产生深远影响。在本研究中，我们的目标是利用成熟的全长基因剪接测定 (FLGSA) 与 SpliceAI 相结合，前瞻性地解释四外显子 SPINK1 基因（一种与慢性胰腺炎相关的基因）内所有潜在编码 SNV 的剪接效应。我们的研究首先对先前使用 FLGSA 评估的 27 个 SPINK1 编码 SNV 进行回顾性分析，然后对 35 个新的 FLGSA 测试的 SPINK1 编码 SNV 进行前瞻性分析，然后进行数据外推，并以进一步验证结束。我们总共分析了 67 个 SPINK1 编码 SNV，占 720 个可能的编码 SNV 的 9.3%。在这 67 个 FLGSA 分析的 SNV 中，发现 12 个会影响剪接。通过详细比较 FLGSA 结果和 SpliceAI 预测，我们推断 SPINK1 基因中剩余的 653 个未经测试的编码 SNV 不太可能显着影响剪接。在 12 个剪接改变事件中，有 9 个同时产生正常剪接和异常剪接转录本，而其余 3 个仅产生异常剪接转录本。这些影响剪接的 SNV 仅在外显子 1 和 2 中发现，特别是在这些外显子的第一个和/或最后一个编码核苷酸处。在 12 个剪接改变事件中，11 个是错义变异（506 个潜在错义变异的 2.17%），1 个是同义变异（164 个潜在同义变异的 0.61%）。值得注意的是，将 SpliceAI 截止值调整为 0.30 而不是传统的 0.20 将提高特异性而不降低灵敏度。通过将 FLGSA 与 SpliceAI 集成，我们确定 SPINK1 中所有可能的编码 SNV 中不到 2% (1.67%) 会显着影响剪接结果。我们的研究结果强调了在研究基因更广泛的基因组序列背景下进行剪接分析的至关重要性，并强调了与中间 SpliceAI 分数（0.20 至 0.80）相关的固有不确定性。这项研究首次以高精度前瞻性地解释了疾病相关基因中所有潜在的编码 SNV，为该领域做出了贡献，代表了外显子组和基因组测序时代从回顾性变异分析转向前瞻性变异分析的有意义的尝试。 。