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.