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 [35]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.

   Keywords: normal brain aging, prefrontal cortical regions,
   transcriptomic, weighted gene correlation network analysis (WGCNA), hub
   gene

Introduction

   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
   ([36]Salthouse, 2009; [37]Dorszewska, 2013; [38]Grillo et al., 2013;
   [39]Lopez-Otin et al., 2013). However, 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. Inspiringly, precision medicine has emerged as a new
   approach to health care base on the individual’s molecular drivers of
   disease ([40]Montine and Montine, 2015). Therefore, 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 ([41]Kadakkuzha et al.,
   2013), the systematic cellular mechanisms behind the normal brain aging
   phenotypic changes in the healthy older adults are only barely
   understood. One method to study molecular mechanisms of aging is the
   high-throughput technology. However, 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 ([42]Furlong, 2013; [43]Lou et
   al., 2017).

   In order to explore the dynamic changes for understanding the
   system-level properties of normal brain aging progress in an unbiased
   manner, one network approach, named weighted gene correlation network
   analysis (WGCNA) is proposed. It can group functionally correlated
   genes into modules ([44]Langfelder and Horvath, 2008). These modules
   are constructed by calculating a correlation network analysis of large,
   high-dimensional datasets, which are based on pairwise correlations
   between genes due to their similar expression profile, and can
   correlate with different stages of clinical traits ([45]Langfelder and
   Horvath, 2008). The R package for WGCNA has been successfully applied
   in various biological contexts, e.g., cancer ([46]Heiland D. et al.,
   2017; [47]Sun et al., 2017), mouse genetics ([48]Savas et al., 2017),
   and analysis of brain imaging data ([49]Heiland D. H. et al., 2017),
   which can also be used to describe the correlation structure between
   gene expression profiles, image data, genetic marker data, proteomics
   data, and other high-dimensional data ([50]Langfelder and Horvath,
   2008). The R package along with its source code and additional material
   are freely available at
   [51]https://cran.r-project.org/web/packages/WGCNA/WGCNA.pdf. Even
   though, WGCNA approach has provided a comprehensive characterization of
   the transcriptomic changes for disease’s functional interpretation and
   led to new insights into the molecular aspects of clinical-pathological
   factors, there are very few reports applying WGCNA to identify gene
   co-expression networks associated with normal brain aging. To fulfill
   this gap, we conduct a WGCNA method by calculating module-trait
   correlations based on [52]GSE53890 public microarray dataset, which
   include 41 samples and 24,455 genes. This approach identifies six
   meaningful co-expression modules significantly related to normal brain
   aging and highly preserved in other brain aging datasets. Besides, hub
   genes contributing to normal brain aging are also verified. Herein,
   this paper is devoted to discovering novel gene signatures that greatly
   impact the progression of normal brain aging by WGCNA approach.

Materials and Methods

mRNA Expression Data

   First, the microarray-based expression dataset [53]GSE53890 provided by
   [54]Lu et al. (2014) was downloaded from the NCBI Gene Expression
   Omnibus (GEO^[55]1). This dataset contained quantile normalized
   genome-wide expression profiles of adult human brain samples from
   prefrontal cortical regions, including samples from 12 young (<40
   years), 9 middle aged (40–70 years), 16 normal aged (70–94 years), and
   4 extremely aged (95–106 years). And these postmortem brain tissue
   samples used in this study were neuropathologically normal for age, and
   were derived from non-demented individuals (Supplementary File [56]1,
   Table S16). The dataset was produced using Affymetrix Human Genome U133
   plus 2.0 arrays, which allowed the expression analysis of over 47,000
   transcripts. The other microarray datasets referenced during the study
   ([57]GSE1572, [58]GSE71620, [59]GSE30272, [60]GSE21779, and
   [61]GSE11882) were also available in the public repository from NCBI
   GEO datasets. All the other datasets supporting the findings of this
   study were available within the article and provided it as
   Supplementary File [62]2, Table S1. For the public datasets, its
   detailed experimental methods and descriptions could be found in the
   original references. Notably, only the human normal brain aging samples