Fight Aging!

The open access papers I'll point out today are but two among many similar publications, in which researchers catalog ever more of the age-related changes in omics data that take place in mice. The omics fields cover measurement and analysis of data related to the genome, epigenome, transcriptome, proteome, metabolome, and then specific subsets of these sizable volumes of data, such as the secretome of specific cell types. The genome is the DNA of a cell, the epigenome the regulatory chemical additions to the genome that govern gene expression. The transcriptome is the set of RNA transcripts produced by a cell at any given time, and the proteome the proteins presently circulating or found within specific cells. The metabolome is the broader set of molecules circulating in the body, not all of which are manufactured by cells.

Technological progress of the past few decades has resulted in a rapid increase in capability and reduction in cost in omics technologies. Enormous amounts of data are easily obtained, and are indeed constantly obtained by many research groups, but analysis and synthesis remain challenging. These latter efforts are now the bottlenecks to progress in this part of the field. It is one thing to better measure the details of a young organism and an old organism in order to flag the differences at the molecular level. It is quite another thing to make sense of that data, to arrange it into cause and consequence, to identify processes that produce the observed results, to move from observation to proposed therapy. Comparatively little has been accomplished on that latter front, as illustrated by the point that epigenetic (or transcriptomic, or proteomic) clocks that correlate with age and mortality risk are well established, but no-one can yet explain exactly why these epigenetic changes are associated so closely with the process of becoming old.

Tissue-specific Gene Expression Changes Are Associated with Aging in Mice

Aging is a complex process that can be characterized by functional and cognitive decline in an individual. Aging can be assessed based on the functional capacity of vital organs and their intricate interactions with one another. Thus, the nature of aging can be described by focusing on a specific organ and an individual itself. However, to fully understand the complexity of aging, one must investigate not only a single tissue or biological process but also its complex interplay and interdependencies with other biological processes.

Here, using RNA-seq, we monitored changes in the transcriptome during aging in four tissues (including brain, blood, skin and liver) in mice at 9 months, 15 months, and 24 months, with a final evaluation at the very old age of 30 months. We identified several genes and processes that were differentially regulated during aging in both tissue-dependent and tissue-independent manners. Most importantly, we found that the electron transport chain (ETC) of mitochondria was similarly affected at the transcriptome level in the four tissues during the aging process. We also identified the liver as the tissue showing the largest variety of differentially expressed genes (DEGs) over time. Lcn2 (Lipocalin-2) was found to be similarly regulated among all tissues, and its effect on longevity and survival was validated using its orthologue in Caenorhabditis elegans.

In conclusion, our study demonstrated that the molecular processes of aging are relatively subtle in their progress, and the aging process of every tissue depends on the tissue's specialized function and environment. Hence, individual gene or process alone cannot be described as the key of aging in the whole organism.

Mouse Age Matters: How Age Affects the Murine Plasma Metabolome

A large part of metabolomics research relies on experiments involving mouse models, which are usually 6 to 20 weeks of age. However, in this age range mice undergo dramatic developmental changes. Even small age differences may lead to different metabolomes, which in turn could increase inter-sample variability and impair the reproducibility and comparability of metabolomics results. In order to learn more about the variability of the murine plasma metabolome, we analyzed male and female C57BL/6J, C57BL/6NTac, 129S1/SvImJ, and C3HeB/FeJ mice at 6, 10, 14, and 20 weeks of age, using targeted metabolomics.

Our analysis revealed high variability of the murine plasma metabolome during adolescence and early adulthood. A general age range with minimal variability, and thus a stable metabolome, could not be identified. Age-related metabolomic changes as well as the metabolite profiles at specific ages differed markedly between mouse strains. This observation illustrates the fact that the developmental timing in mice is strain specific. We therefore stress the importance of deliberate strain choice, as well as consistency and precise documentation of animal age, in metabolomics studies.