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死亡，目前是全球死亡的主要原因。衰老过程显然与心血管疾病有关，然而，衰老与心脏功能之间的确切关系尚不完全清楚。此外，尚未综合考虑心脏衰老的整体观点，将早期生命发育的特征与老年时观察到的变化联系起来。在这里，我们重新利用了我们小组之前收集的 RNA 测序数据，研究了不同年龄组野生型小鼠之间的基因表达差异，这些差异代表了小鼠寿命中的关键发育里程碑。DESeq2 的广义线性模型应用两种假设检验方法来识别不同年龄组之间以及所有年龄段小鼠之间的差异表达 (DE) 基因。成对比较确定了与特定年龄转变相关的基因，而对所有年龄组的比较则确定了一大组与更广泛的衰老过程相关的基因。然后应用无监督的机器学习方法从这组与年龄相关的基因中提取常见的表达模式。一组具有线性和非线性表达轨迹的基因被鉴定出来，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，而且还涉及较早年龄的基因表达程序的重新激活。总的来说，我们对小鼠整个生命周期的心脏基因表达模式进行了全面的转录组分析。