Gene expression reversal toward pre-adult levels in the aging human brain and age-related loss of cellular identity

Abstract

It was previously reported that mRNA expression levels in the prefrontal cortex at old age start to resemble pre-adult levels. Such expression reversals could imply loss of cellular identity in the aging brain, and provide a link between aging-related molecular changes and functional decline. Here we analyzed 19 brain transcriptome age-series datasets, comprising 17 diverse brain regions, to investigate the ubiquity and functional properties of expression reversal in the human brain. Across all 19 datasets, 25 genes were consistently up-regulated during postnatal development and down-regulated in aging, displaying an “up-down” pattern that was significant as determined by random permutations. In addition, 113 biological processes, including neuronal and synaptic functions, were consistently associated with genes showing an up-down tendency among all datasets. Genes up-regulated during in vitro neuronal differentiation also displayed a tendency for up-down reversal, although at levels comparable to other genes. We argue that reversals may not represent aging-related neuronal loss. Instead, expression reversals may be associated with aging-related accumulation of stochastic effects that lead to loss of functional and structural identity in neurons.

Gene expression‐based drug repurposing to target aging

“…Aging is a time‐dependent, complex phenomenon, which induces subtler changes compared to development (Dönertaş et al, 2017), or to a disease state such as Alzheimer's (Avramopoulos, Szymanski, Wang, & Bassett, 2011). The “omics” profile reflects two potentially distinct contributions: the detrimental effects which occur with age (e.g., accumulation of mutations) and the potentially beneficial responses to those changes (e.g., the immune response).…”

“…However, in this study, we only considered samples above 20 years of age, which corresponds to the age at first reproduction in human societies (Walker et al, 2006). Previous human brain aging studies using transcriptome data have also suggested gene expression patterns before and after the age of 20 are discontinuous (Colantuoni et al, 2011; Dönertaş et al, 2017). As we are interested in finding consistent tendencies in terms of the direction of change, which can characterize aging, we only included samples above 20 years of age.…”

“…We used a permutation scheme that we developed earlier (Dönertaş et al, 2017), to simulate the null hypothesis that there is no association between age and the gene expression, while retaining the dependence between genes and the data sets. Particularly, the ages of individuals in each study are permuted (randomized) 1,000 times, and if that individual donated multiple samples for different brain regions, each sample is annotated with the same age.…”

Multi-faceted deregulation of gene expression and protein synthesis with age

“…In previous studies, such a pattern was not observed, probably because age-related gene expression changes were examined either only at two time points (14,32) or they did not include young animals (1 month old) (14,32,33). However, the U-shaped, "reversal" pattern of gene expression was previously reported for human (44,45) and rat (46) brain, with the turning points at ~3.5 and ~20 years for humans and between 6-12 months for rats.…”

Gene expression heterogeneity during brain development and aging: temporal changes and functional consequences

“…We also applied an additional correction 538 procedure for Somel2011 datasets, in which there was a batch effect influencing the expression levels, 539 as follows: for each probeset (1) calculate mean expression (M), (2) scale each batch separately (to 540 mean = 0, standard deviation = 1), (3) add M to each value. We excluded outliers given in 541 Supplementary Table S1, through a visual inspection of the first two principal components for the 543 Dönertaş et al, 2017). We mapped probeset IDs to Ensembl gene IDs 1) using the Ensembl database, 544 through the 'biomaRt' library 57 in R for the Somel2011 dataset, 2) using the GPL file deposited in GEO 545 for Kang2011, as probeset IDs for this dataset were not complete in Ensembl, and 3) using the Entrez 546 gene IDs in the GPL file deposited in GEO for the Colantuoni2011 dataset and converting them into 547 Ensembl gene IDs using the Ensemble database, through the "biomaRt" library in R. Lastly, we scaled 548 expression levels for genes (to mean = 0, standard deviation = 1) using the 'scale' function in R. Age 549 values of individuals in each dataset were converted to the fourth root of age (in days) to have a linear 550 relationship between age and expression both in development and aging.…”