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采用转录组捕获和超高深度长读 RNA 测序 (CAPLRseq) 诊断 HNPCC/Lynch 综合征。

Transcript capture and ultradeep long-read RNA sequencing (CAPLRseq) to diagnose HNPCC/Lynch syndrome.

机构信息

Medizinisch Genetisches Zentrum (MGZ), Munich, Germany.

Klinikum der Universität München, Munich, Germany.

出版信息

J Med Genet. 2023 Aug;60(8):747-759. doi: 10.1136/jmg-2022-108931. Epub 2023 Jan 2.

Abstract

PURPOSE

Whereas most human genes encode multiple mRNA isoforms with distinct function, clinical workflows for assessing this heterogeneity are not readily available. This is a substantial shortcoming, considering that up to 25% of disease-causing gene variants are suspected of disrupting mRNA splicing or mRNA abundance. Long-read sequencing can readily portray mRNA isoform diversity, but its sensitivity is relatively low due to insufficient transcriptome penetration.

METHODS

We developed and applied capture-based target enrichment from patient RNA samples combined with Oxford Nanopore long-read sequencing for the analysis of 123 hereditary cancer transcripts (capture and ultradeep long-read RNA sequencing (CAPLRseq)).

RESULTS

Validating CAPLRseq, we confirmed 17 cases of hereditary non-polyposis colorectal cancer/Lynch syndrome based on the demonstration of splicing defects and loss of allele expression of mismatch repair genes , , and . Using CAPLRseq, we reclassified two variants of uncertain significance in and as either likely pathogenic or benign.

CONCLUSION

Our data show that CAPLRseq is an automatable and adaptable workflow for effective transcriptome-based identification of disease variants in a clinical diagnostic setting.

摘要

目的

尽管大多数人类基因编码具有不同功能的多个 mRNA 异构体,但评估这种异质性的临床工作流程尚不可用。考虑到多达 25%的致病基因突变被怀疑会破坏 mRNA 剪接或 mRNA 丰度,这是一个很大的缺陷。长读测序可以轻松描绘 mRNA 异构体的多样性,但由于转录组的穿透不足,其灵敏度相对较低。

方法

我们开发并应用了基于患者 RNA 样本的捕获靶向富集,结合牛津纳米孔长读测序,用于分析 123 种遗传性癌症转录本(捕获和超深度长读 RNA 测序(CAPLRseq))。

结果

通过验证 CAPLRseq,我们证实了 17 例遗传性非息肉病结直肠癌/林奇综合征,方法是证明错配修复基因 、 和 的剪接缺陷和等位基因表达缺失。使用 CAPLRseq,我们将 和 中的两个意义不明的变体重新分类为可能致病性或良性。

结论

我们的数据表明,CAPLRseq 是一种自动化且可适应的工作流程,可在临床诊断环境中有效基于转录组鉴定疾病变体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd16/10423559/1c6e4584aa50/jmg-2022-108931f01.jpg

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