Tissue-Specific Gene Expression Changes are Associated with Aging in Mice

Abstract

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. 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.

Integration of segmented regression analysis with weighted gene correlation network analysis identifies genes whose expression is remodeled throughout physiological aging in mouse tissues

“…We observed that, based on VST-normalized gene expression values, samples tend to cluster by tissue, which is in line with what Schaum et al . [ 6 ] found, as well as with previous observations [ 5 , 7 , 15 , 20 – 24 ]. For this reason, subsequent analyses were performed on each tissue separately ( Figure 1A ).…”

“…Another study across 17 mouse tissues, whose dataset we re-analyzed in this work, identified shift points of gene expression trajectories at around 6 months for extracellular matrix genes, 10 months for mitochondrial genes, 12 months for genes encoding heat shock proteins, and at around 15 months for immune response genes [ 6 ]. Other studies also suggest tissue-specific turning points in gene expression profiles [ 6 , 20 , 35 ]. For example, immune response gene expression was found to change in the mouse kidney between 13 and 20 months, in line with the previously described organismal trend, whereas in the spleen and lung this shift occurs later in life, at around 26 months [ 35 ].…”

“…The most commonly reported age-related dysregulations involve the immune system [ 9 , 11 – 13 ] where inflammatory response genes are upregulated even in the absence of pathogen infection [ 5 , 6 , 9 , 11 , 14 – 19 ]. Energy metabolism, redox homeostasis, and mitochondrial function alterations are also frequently observed in age-related studies [ 6 , 9 , 11 , 15 – 18 , 20 ], particularly the downregulation of genes encoding mitochondrial ribosomal proteins and components of the electron transport chain [ 5 , 11 , 14 – 16 , 18 ], protein synthesis machinery [ 5 , 11 , 17 ], developmental and cell differentiation genes [ 9 , 11 , 19 ], and extracellular matrix components [ 6 , 14 – 16 ]. Up-regulated genes are associated with the stress response and DNA repair [ 5 , 6 , 9 , 11 , 14 , 16 – 18 ], RNA processing [ 11 , 12 , 17 ] and cell cycle arrest [ 5 , 16 , 19 ].…”

“…Up-regulated genes are associated with the stress response and DNA repair [ 5 , 6 , 9 , 11 , 14 , 16 – 18 ], RNA processing [ 11 , 12 , 17 ] and cell cycle arrest [ 5 , 16 , 19 ]. Despite this, the existence of specific genetic signatures of aging continue to be a matter of debate as gene regulation is mostly tissue- [ 5 – 7 , 15 , 20 – 24 ] and cell-specific [ 25 , 26 ], but also because there is focus on comparisons between young and old individuals without much consideration of the dynamics of gene expression throughout the lifespan. Nonetheless, recent studies in humans and animal models shed light on these dynamics.…”

New Perspectives on Avian Models for Studies of Basic Aging Processes

“…Even though genomic data are a useful tool for examining the genetic architecture of a given species or individual, they often do not provide information about which genes are expressed and where (i.e., which tissue/organ); the magnitude of expression; or the development stage(s) at which they are expressed. Ultimately, the generation of organspecific transcriptomes will be possible at various points in a model organism's life history, as well as under specific experimental conditions, including anti-aging interventions As the field has grown, the human aging transcriptome has been shown to be a robust tool for estimating the biological age of an individual [152], as well as measuring age-related changes in tissue-specific transcriptomes of model organisms such as monkeys [153], mice [154], D. melanogaster [155,156], C. elegans [157,158], and yeast [159]. Not surprisingly, relatively little is known about the aging transcriptome in birds, with the exception of studies of brain [160] and thymus [161] transcriptomes in populations of domestic chicken.…”