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COMPLETE TITLE: Age-Related Gene Expression Signature in Rats Demonstrate Early, Late, and Linear Transcriptional Changes from Multiple Tissues
SOURCE: Cell
Highlights
• A multi-tissue age-related gene expression signature (AGES) is constructed
• Linear, early, middle-, and late-life expression changes are discovered
• AGES points to potential mechanisms inducing age-related disorders
• Gene changes in multiple tissues include upregulation of interferon signaling
Summary
To understand the changes in gene expression that occur as a result of age, which might create a permissive or causal environment for age-related diseases, we produce a multi-time point age-related gene expression signature (AGES) from liver, kidney, skeletal muscle, and hippocampus of rats, comparing 6-, 9-, 12-, 18-, 21-, 24-, and 27-month-old animals. We focus on genes that changed in one direction throughout the lifespan of the animal, either early in life (early logistic changes), at mid-age (mid-logistic), late in life (late-logistic), or linearly, throughout the lifespan of the animal. The pathways perturbed because of chronological age demonstrate organ-specific and more-global effects of aging and point to mechanisms that could potentially be counter-regulated pharmacologically to treat age-associated diseases. A small number of genes are regulated by aging in the same manner in every tissue, suggesting they may be more-universal markers of aging.
Introduction
Aging is the strongest risk factor for many serious diseases and co-morbidities, including cancer, heart disease, kidney disease, dementia, Alzheimer’s disease, frailty, and sarcopenia (Armanios et al., 2015, Egerman and Glass, 2014, Johnson et al., 2013, Kirkland and Tchkonia, 2015, Niccoli and Partridge, 2012). Increasing evidence suggests that aging occurs in a regulated manner and that perturbation of discrete cell-signaling pathways can extend lifespan and delay age-related diseases and co-morbidities (Bitto et al., 2016, Chen et al., 2009, Flynn et al., 2013, Harrison et al., 2009, Miller et al., 2011, Miller et al., 2014, Zhang et al., 2014).
Multiple analyses of age-related changes in gene expression have been conducted in various tissues in mice (Barns et al., 2014, Braun et al., 2016, White et al., 2015, Benayoun et al., 2019) and rats (de Magalhães et al., 2009, Ibebunjo et al., 2013, Yu et al., 2014, Shavlakadze et al., 2018). There have been other strategies for the development of molecular signatures of aging; for example, researchers have determined DNA methylation patterns that correlate with biological age. An advantage of that method is that easily accessible tissue, such as blood, can be used to determine and cross-compare a human’s aging status (Gross et al., 2016, Sziráki et al., 2018, Thompson et al., 2018).
It is possible to obtain a more thorough molecular profile of aging by examining gene expression changes in multiple tissues throughout multiple time points in the lifespan of the animal and determining genes that are perturbed in a consistent direction—in other words—genes that consistently increase in expression throughout life or genes that consistently decrease in expression.
We were curious to know whether examination of multiple tissues at multiple times could lead to new insights into the possible global and tissue-specific mechanisms of aging that might be causal for age-related pathologies. Thus, we studied gene expression changes with age throughout the animal’s lifespan (at 6, 9, 12, 18, 21, 24, and 27 months) in liver, gastrocnemius muscle, kidney, and hippocampus. Rats were chosen because we had previously shown that rats are an excellent model for sarcopenia—the age-related loss of skeletal muscle (Ibebunjo et al., 2013). Here, we applied that experience to other tissues to develop a full multi-tissue aging signature. We discovered genes that change in common in every tissue; genes that are regulated in early logistic, mid-logistic, late-logistic, and linear fashion in particular tissues; and pathways that are regulated in multiple tissues, giving some indication of common mechanisms of aging. It is our hope that this dataset will serve as a valuable resource for further molecular insights into mechanisms of aging.
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