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Cross-Sectional Transcriptional Analysis of the Aging Murine Heart

Authors
  • Greenig, Matthew1
  • Melville, Andrew2
  • Huntley, Derek1
  • Isalan, Mark1, 3
  • Mielcarek, Michal1, 3
  • 1 Department of Life Sciences, Imperial College London, London , (United Kingdom)
  • 2 Department of Mathematics, Imperial College London, London , (United Kingdom)
  • 3 Imperial College Center for Synthetic Biology, Imperial College London, London , (United Kingdom)
Type
Published Article
Journal
Frontiers in Molecular Biosciences
Publisher
Frontiers Media SA
Publication Date
Sep 25, 2020
Volume
7
Identifiers
DOI: 10.3389/fmolb.2020.565530
PMID: 33102519
PMCID: PMC7545256
Source
PubMed Central
Keywords
License
Unknown

Abstract

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.

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