five

Data_Sheet_5_Cross-Sectional Transcriptional Analysis of the Aging Murine Heart.CSV

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frontiersin.figshare.com2023-06-04 更新2025-01-21 收录
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https://frontiersin.figshare.com/articles/dataset/Data_Sheet_5_Cross-Sectional_Transcriptional_Analysis_of_the_Aging_Murine_Heart_CSV/13005482/1
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Cardiovascular disease accounts for millions of deaths each year and is currently the leading cause of mortality worldwide. The aging process is clearly linked to cardiovascular disease, however, the exact relationship between aging and heart function is not fully understood. Furthermore, a holistic view of cardiac aging, linking features of early life development to changes observed in old age, has not been synthesized. Here, we re-purpose RNA-sequencing data previously-collected by our group, investigating gene expression differences between wild-type mice of different age groups that represent key developmental milestones in the murine lifespan. DESeq2's generalized linear model was applied with two hypothesis testing approaches to identify differentially-expressed (DE) genes, both between pairs of age groups and across mice of all ages. Pairwise comparisons identified genes associated with specific age transitions, while comparisons across all age groups identified a large set of genes associated with the aging process more broadly. An unsupervised machine learning approach was then applied to extract common expression patterns from this set of age-associated genes. Sets of genes with both linear and non-linear expression trajectories were identified, suggesting that aging not only involves the activation of gene expression programs unique to different age groups, but also the re-activation of gene expression programs from earlier ages. Overall, we present a comprehensive transcriptomic analysis of cardiac gene expression patterns across the entirety of the murine lifespan.

心血管疾病每年导致数百万人的死亡,目前已成为全球死亡率最高的原因。衰老过程与心血管疾病之间存在着明显的关联,然而,衰老与心脏功能之间的确切关系尚未完全明了。此外,对心脏衰老的全面视角,即联系早期发育特征与老年期观察到的变化,尚未得到综合。在本研究中,我们重新利用我们团队先前收集的RNA测序数据,探究了代表小鼠生命周期中关键发育里程碑的不同年龄组野生型小鼠之间的基因表达差异。通过DESeq2的广义线性模型,采用两种假设检验方法来识别差异表达(DE)基因,既包括年龄组对之间的比较,也包括所有年龄段小鼠之间的比较。成对比较识别了与特定年龄转变相关的基因,而跨所有年龄组的比较则识别出了一大组与衰老过程更广泛相关的基因。随后,采用无监督机器学习方法从这组与年龄相关的基因中提取共同的表达模式。识别出具有线性和非线性表达轨迹的基因集,这表明衰老不仅涉及不同年龄组特有的基因表达程序的激活,还涉及早期年龄基因表达程序的重新激活。总体而言,我们展示了小鼠生命周期全过程中心脏基因表达模式的全转录组分析。
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