Abstract
Human neurons function over an entire lifetime, yet the molecular mechanisms which perform their functions and protecting against neurodegenerative disease during aging are still elusive. Here, we conducted a systematic study on the human brain aging by using the weighted gene correlation network analysis (WGCNA) method to identify meaningful modules or representative biomarkers for human brain aging. Significantly, 19 distinct gene modules were detected based on the dataset GSE53890; among them, six modules related to the feature of brain aging were highly preserved in diverse independent datasets. Interestingly, network feature analysis confirmed that the blue modules demonstrated a remarkably correlation with human brain aging progress. Besides, the top hub genes including PPP3CB, CAMSAP1, ACTR3B, and GNG3 were identified and characterized by high connectivity, module membership, or gene significance in the blue module. Furthermore, these genes were validated in mice of different ages. Mechanically, the potential regulators of blue module were investigated. These findings highlight an important role of the blue module and its affiliated genes in the control of normal brain aging, which may lead to potential therapeutic interventions for brain aging by targeting the hub genes.
Highlights
Brain aging is characterized by a progressive loss of physiological integrity including loss of gray and white matter volume, a general loss of dendritic spines, loss of synaptic plasticity, increased axonal bouton turnover rates, and elevated inflammation, leading to impaired function and increased vulnerability to neurodegenerative disease (Salthouse, 2009; Dorszewska, 2013; Grillo et al, 2013; Lopez-Otin et al, 2013)
It is becoming clear that neuronal cell number is largely preserved and keeps their cognitive function relatively intact in the neocortex and hippocampus of the aging human brain, declining only in the setting of neurodegenerative disease (Gomez-Isla et al, 1996; Peters et al, 1998; Yankner et al, 2008; Lu et al, 2014)
Increasing studies focus on highthroughput sequencing approach to investigated the regulation of normal brain aging and weighted gene correlation network analysis (WGCNA) is characterized effectively and systematically to find modules and gene signatures highly related with the clinical trait, such as the trait of brain aging
Summary
Brain aging is characterized by a progressive loss of physiological integrity including loss of gray and white matter volume, a general loss of dendritic spines, loss of synaptic plasticity, increased axonal bouton turnover rates, and elevated inflammation, leading to impaired function and increased vulnerability to neurodegenerative disease (Salthouse, 2009; Dorszewska, 2013; Grillo et al, 2013; Lopez-Otin et al, 2013). The systematic cellular mechanisms behind the normal brain aging phenotypic changes in the absence of neurodegenerative disease of healthy older adults are only barely understood. Precision medicine has emerged as a new approach to Transcriptomic Signatures of Brain Aging health care base on the individual’s molecular drivers of disease (Montine and Montine, 2015). Applying this tailored and molecular mechanism-based approach to understand and reduce the negative impacts of brain aging are very promising. Even though recent reports have suggested the distinct changes in the expression of genes at the single neuron level (Kadakkuzha et al, 2013), the systematic cellular mechanisms behind the normal brain aging phenotypic changes in the healthy older adults are only barely understood. The biased process in large changes analysis of differential gene expression, as well as lacking the consideration of the relationship between changing genes as a whole are inevitable drawbacks for this method (Furlong, 2013; Lou et al, 2017)
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