The alternative splicing landscape of infarcted mouse heart identifies isoform level therapeutic targets.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-10-18 DOI:10.1038/s41597-024-03998-3

Binbin Xia, Jianghua Shen, Hao Zhang, Siqi Chen, Xuan Zhang, Moshi Song, Jun Wang

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Abstract

Alternative splicing is an important process that contributes to highly diverse transcripts and protein products, which can affect the development of disease in various organisms. Cardiovascular disease (CVD) represents one of the greatest global threats to humans, particularly acute myocardial infarction (MI) and subsequent ischemic reperfusion (IR) injury, which involve complex transcriptomic changes in heart tissues associated with metabolic reshaping and immunological response. In this study, we used a newly developed ONT full-length transcriptomic approach and performed transcript-resolved differential expression profiling in murine models of MI and IR. We built an analytical pipeline to reliably identify and quantify alternative splicing products (isoforms), expanding on the currently available catalog of isoforms described in mice. The updated alternative splicing landscape included transcripts, genes, and pathways that were differentially regulated during IR and MI. Our study establishes a pipeline to profile highly diverse isoforms using state-of-the-art long-read sequencing, builds a landscape of alternative splicing in the mouse heart during MI and IR.

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梗死小鼠心脏的替代剪接图谱确定了同工酶水平的治疗目标。

替代剪接是一个重要的过程，可产生高度多样化的转录本和蛋白质产物，从而影响各种生物体的疾病发展。心血管疾病（CVD）是人类面临的最大全球性威胁之一，尤其是急性心肌梗塞（MI）和随后的缺血再灌注（IR）损伤，涉及心脏组织中与代谢重塑和免疫反应相关的复杂转录组变化。在这项研究中，我们使用了一种新开发的 ONT 全长转录组方法，并在 MI 和 IR 的小鼠模型中进行了转录本分辨差异表达谱分析。我们建立了一个分析管道来可靠地识别和量化替代剪接产物（同工酶），扩展了目前可用的小鼠同工酶目录。更新后的替代剪接图谱包括在IR和MI期间受到不同调控的转录本、基因和通路。我们的研究利用最先进的长线程测序技术建立了一个分析高度多样化异构体的管道，构建了小鼠心脏在心肌梗死和心肌梗死期间的替代剪接图谱。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.