The widespread increase in inter-individual variability of gene expression in the human brain with age.

Veronika R. Kedlian,Janet M. Thornton,Handan Melike Donertas

doi:10.18632/aging.101912

Veronika R. Kedlian, Janet M. Thornton + Show 1 more

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https://doi.org/10.18632/aging.101912

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Abstract

Aging is broadly defined as a time-dependent progressive decline in the functional and physiological integrity of organisms. Previous studies and evolutionary theories of aging suggest that aging is not a programmed process but reflects dynamic stochastic events. In this study, we test whether transcriptional noise shows an increase with age, which would be expected from stochastic theories. Using human brain transcriptome dataset, we analyzed the heterogeneity in the transcriptome for individual genes and functional pathways, employing different analysis methods and pre-processing steps. We show that unlike expression level changes, changes in heterogeneity are highly dependent on the methodology and the underlying assumptions. Although the particular set of genes that can be characterized as differentially variable is highly dependent on the methods, we observe a consistent increase in heterogeneity at every level, independent of the method. In particular, we demonstrate a weak but reproducible transcriptome‐wide shift towards an increase in heterogeneity, with twice as many genes significantly increasing as opposed to decreasing their heterogeneity. Furthermore, this pattern of increasing heterogeneity is not specific but is associated with a wide range of pathways.

Highlights

Aging is commonly defined as a time-dependent decrease in the functional and structural integrity of an organism
In order to study the change in gene expression variability during aging, we used one of the biggest published human brain transcriptome datasets, generated using microarray technology [23]
We applied four batch correction strategies to account for technical and biological confounders (Supplemental Figure 1): i) only www.aging-us.com quantile normalization (QN), ii) QN followed by linear regression, iii) QN followed by ComBat [24], and iv) QN followed by Surrogate Variable Analysis (SVA) [25]

Summary

Introduction

Aging is commonly defined as a time-dependent decrease in the functional and structural integrity of an organism. Age-related expression variability has been detected in many different cell and tissue types including mice stem cells, cardiomyocytes and immune cells [7,8,9], rat neural retina [10], fruit-fly, mice and human brain [6,11,12,13,14] as well as human pancreas, lung, blood, skin, fat and human fibroblasts in vitro [13,15,16,17] Despite these reports, there is no agreement on the underlying mechanisms, extent and functional consequences. Suggested mechanisms include somatic [7,15] and germline mutations [11,17], changes in the DNA methylation [9,17,18] and chromatin modifications [5] and resulting chromatin compaction [12] as well as global dysregulation, caused by the change in transcription factor or miRNA expression [19]

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