PDF(Pubmed)
Abstract：
BACKGROUND: 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.
RESULTS: 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.
CONCLUSIONS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：基因编码序列中的单核苷酸变体（SNV）可以显着影响前mRNA剪接，对致病机制和精准医学有着深远的影响。在这项研究中,我们的目标是利用完善的全长基因剪接测定（FLGSA）与SpleeAI结合，前瞻性地解释四外显子SPINK1基因内所有潜在编码SNV的剪接效应，与慢性胰腺炎相关的基因。
结果：我们的研究开始于对先前使用FLGSA评估的27个SPINK1编码SNV的回顾性分析，对35种新的FLGSA测试的SPINK1编码SNV进行了前瞻性分析，然后是数据外推，并以进一步的验证结束。总的来说,我们分析了67个SPINK1编码SNV，占720种可能的编码SNV的9.3%。在这67个FLGSA分析的SNV中，12个被发现影响拼接。通过对FLGSA结果和SpliceAI预测的详细比较，我们推断,SPINK1基因中剩余的653个未测编码SNV不太可能显著影响剪接.在12个改变剪接的事件中,九个产生正常剪接和异常剪接的转录本，而其余三个只产生异常剪接的转录本。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个改变剪接的事件中,11个是错义变体（506个潜在错义变体的2.17％），一个是同义的(164个潜在同义变异中的0.61%).值得注意的是,将SpliceAI截止值调整为0.30而不是常规的0.20将提高特异性而不降低灵敏度.
结论：通过将FLGSA与SpleeAI集成，我们已经确定,SPINK1中所有可能编码SNV的不到2%(1.67%)显著影响剪接结果.我们的发现强调了在研究基因的更广泛的基因组序列背景下进行剪接分析的关键重要性，并强调了与中间SpleeAI评分（0.20至0.80）相关的固有不确定性。这项研究通过成为第一个前瞻性地解释疾病相关基因中所有潜在编码SNV的高准确度，代表了在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。
结果：我们的研究开始于对先前使用FLGSA评估的27个SPINK1编码SNV的回顾性分析，对35种新的FLGSA测试的SPINK1编码SNV进行了前瞻性分析，然后是数据外推，并以进一步的验证结束。总的来说,我们分析了67个SPINK1编码SNV，占720种可能的编码SNV的9.3%。在这67个FLGSA分析的SNV中，12个被发现影响拼接。通过对FLGSA结果和SpliceAI预测的详细比较，我们推断,SPINK1基因中剩余的653个未测编码SNV不太可能显著影响剪接.在12个改变剪接的事件中,九个产生正常剪接和异常剪接的转录本，而其余三个只产生异常剪接的转录本。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个改变剪接的事件中,11个是错义变体（506个潜在错义变体的2.17％），一个是同义的(164个潜在同义变异中的0.61%).值得注意的是,将SpliceAI截止值调整为0.30而不是常规的0.20将提高特异性而不降低灵敏度.
结论：通过将FLGSA与SpleeAI集成，我们已经确定,SPINK1中所有可能编码SNV的不到2%(1.67%)显著影响剪接结果.我们的发现强调了在研究基因的更广泛的基因组序列背景下进行剪接分析的关键重要性，并强调了与中间SpleeAI评分（0.20至0.80）相关的固有不确定性。这项研究通过成为第一个前瞻性地解释疾病相关基因中所有潜在编码SNV的高准确度，代表了在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。
