Abstract
Background
Aging-related cognitive decline is associated with brain structural
changes and synaptic loss. However, the molecular mechanisms of
cognitive decline during normal aging remain elusive.
Results
Using the GTEx transcriptomic data from 13 brain regions, we identified
aging-associated molecular alterations and cell-type compositions in
males and females. We further constructed gene co-expression networks
and identified aging-associated modules and key regulators shared by
both sexes or specific to males or females. A few brain regions such as
the hippocampus and the hypothalamus show specific vulnerability in
males, while the cerebellar hemisphere and the anterior cingulate
cortex regions manifest greater vulnerability in females than in males.
Immune response genes are positively correlated with age, whereas those
involved in neurogenesis are negatively correlated with age.
Aging-associated genes identified in the hippocampus and the frontal
cortex are significantly enriched for gene signatures implicated in
Alzheimer’s disease (AD) pathogenesis. In the hippocampus, a
male-specific co-expression module is driven by key synaptic signaling
regulators including VSNL1, INA, CHN1 and KCNH1; while in the cortex, a
female-specific module is associated with neuron projection
morphogenesis, which is driven by key regulators including SRPK2, REPS2
and FXYD1. In the cerebellar hemisphere, a myelination-associated
module shared by males and females is driven by key regulators such as
MOG, ENPP2, MYRF, ANLN, MAG and PLP1, which have been implicated in the
development of AD and other neurodegenerative diseases.
Conclusions
This integrative network biology study systematically identifies
molecular signatures and networks underlying brain regional
vulnerability to aging in males and females. The findings pave the way
for understanding the molecular mechanisms of gender differences in
developing neurodegenerative diseases such as AD.
Keywords: brain aging, gender differences, gene co-expression network,
key regulators, Alzheimer’s disease
Introduction
Aging-associated changes in the human brain contribute to the decline
of cognitive functions and the development of various neurodegenerative
disorders ([45]Hsu et al., 2008; [46]Lopez-Otin et al., 2013).
Gender-specific changes in aging processes may contribute to the
differences in the prevalence of several neurological disorders between
males and females. For instance, the incidence rate of Parkinson’s
disease in males is about 2-fold as in females (19.0/100K in males vs.
9.9/100K in females) ([47]Picillo et al., 2017), while there are more
females (3.3 million) living with Alzheimer’s disease than males (2.0
million) ([48]Alzheimer’s Association, 2015; [49]Nebel et al., 2018).
Neuro-imaging studies have found many gender differences in structural
changes during brain aging ([50]Murphy et al., 1996; [51]Raz et al.,
1997; [52]Coffey et al., 1998; [53]Hsu et al., 2008; [54]Kiraly et al.,
2016). For example, age-associated brain volume loss in the frontal and
temporal lobes is much more significant in men than women, while in the
hippocampus and the parietal lobes, the loss is greater in women
([55]Murphy et al., 1996). Another study found that the age-related
decrease in brain grey matter volume at the caudate nucleus, putamen
and thalamic regions is greater in men with a faster rate of decline
than in women ([56]Kiraly et al., 2016). In contrast, females have a
lower white matter volume in the right deep temporal regions than males
([57]Hsu et al., 2008). White matter alterations during aging in the
precentral, cingulate, and anterior temporal regions also showed
significant differences between males and females ([58]Hsu et al.,
2008). These findings suggest sex dimorphism in aging-associated
microstructural changes across brain regions. However, molecular
mechanisms underlying gender-specific age-related structural changes
remain elusive ([59]Flood et al., 1987; [60]Pakkenberg et al., 2003;
[61]Bertoni-Freddari et al., 2007). The sex dimorphism at the brain
morphological levels is probably induced by various molecular changes
such as different hormone levels and epigenetic modifications and gene
expression alterations.
There were 9 established cellular and molecular hallmarks of aging
([62]Lopez-Otin et al., 2013), including genomic instability,
epigenetic alterations, telomere attrition, mitochondrial dysfunction,
proteostasis dysfunction, cellular senescence, deregulated nutrient
sensing, stem cell exhaustion, and altered intercellular communication.
Many studies have shown that these hallmarks are sexually dimorphic
factors associated with aging ([63]Barrett and Richardson, 2011;
[64]Dulken and Brunet, 2015; [65]Gaignard et al., 2015; [66]Gentilini
et al., 2015; [67]Fischer and Riddle, 2018). For example, genomic
instability, such as mutation and genetic mosaicism rate, is higher in
males than in females ([68]Machiela et al., 2015; [69]Podolskiy et al.,
2016). On the other hand, aging hallmarks showed differences in gene
expression patterns between males and females. Two studies of the
Zebrafish brain showed that aging alters gene expression and molecular
dynamics of synapses in a sexually dimorphic pattern ([70]Arslan-Ergul
and Adams, 2014; [71]Karoglu et al., 2017). Sexually dimorphism is also
found in aging-related neuroinflammation in the mouse hippocampus
([72]Mangold et al., 2017). Compared to animal studies, very few
studies have examined sex differences in human brain aging at a global
molecular level. A study of the microarray data of 40 samples from 4
brain regions including entorhinal cortex (EC), hippocampus (HIPP_MA),
postcentral gyrus (PCG), prefrontal cortex (PFC) and superior frontal
gyrus (SFG) by Berchtold et al., showed that more genes are changed in
males than in females across 4 brain regions during aging
([73]Berchtold et al., 2008). Expression of the genes involved in
energy metabolism and protein synthesis was found to decrease with
aging, while immune-related genes were activated in both sexes during
aging, with greater alteration in the female brains ([74]Berchtold et
al., 2008).
In this study, we aim to systematically investigate sex differences of
normal aging by examining RNA-Seq data collected from 13 regions in the
Genotype-Tissue Expression (GTEx) project ([75]GTEx Consortium, 2015).
We identified aging-associated gene expression patterns, gene
co-expression network modules and key network drivers in male and
female brains. We further studied the aging associated molecular
signatures and modules shared by both genders or specific to a gender
group as well as their roles in neurodegenerative disorders such as
Alzheimer’s disease.
Materials and methods
GTEx data processing
GTEx RNA-Seq read counts and metadata were downloaded from GTEx Portal
phs000424.v7.p2.^[76]1 Raw read counts for brain regions were extracted
and a total of 1,671 samples from 13 brain regions were retained
([77]Table 1). The demographic and neuropathological information of
those subjects was summarized in [78]Supplementary Table 1. Lowly
expressed genes with expression levels of at least 1 count per million
in less than 20% of samples were removed. A total of 16,494 genes were
retained for further analysis. Next, normalization factors were
computed on the filtered data matrix using the weighted trimmed mean of
M-values (TMM) method, followed by log2 transformation and voom
([79]Law et al., 2014) mean-variance analysis in preparation for Limma
linear modeling. The normalized expression data were then split into
matrices for males and females in each of the 13 brain regions.
TABLE 1.
Summary of sample sizes for gender and age in the 13 brain regions.
Region Region (full name) Male Female
Young Middle Aged Young Middle Aged
AMY Amygdala 8 29 29 5 11 18
ACC Anterior cingulate cortex 7 36 41 6 10 21
CD Caudate 9 52 54 8 16 21
CBH Cerebellar hemisphere 10 45 44 5 14 18
CB Cerebellum 13 57 50 6 17 30
CT Cortex 10 50 48 7 18 25
FC Frontal cortex 8 43 41 3 14 20
HIPP Hippocampus 10 35 38 5 13 22
HTH Hypothalamus 9 38 40 4 12 18
NAC Nucleus accumbens 9 48 46 6 16 22
PT Putamen 7 45 38 4 14 16
SC Spinal cord (cervical c-1) 5 25 26 4 16 15
SN Substantia nigra 4 26 29 5 9 15
[80]Open in a new tab
Identification of age-correlated genes (ACGs)
Pearson correlation coefficients were calculated between expression
levels of each gene and age for males and females, respectively, in
each of the 13 brain regions. P-values were corrected by the
Benjamini-Hochberg (BH) procedure ([81]Benjamini and Hochberg, 1995) to
adjust for multiple testing. To identify the differentially expressed
genes (DEGs) between aged and young brains, we split samples in each
brain region into 3 groups: young adult (age < 45), middle-aged (45 ≤
age < 60) and aged adult (age ≥ 60). DEGs between each pair of the 3
groups were identified using the eBayes method to fit gene expression
to a linear model implemented by the limma R package ([82]Ritchie et
al., 2015). Sample labels were permuted 1,000 times to obtain a null
distribution of t-statistics across all genes. The original t-statistic
of each gene was compared to the null distribution to calculate the FDR
of that gene. Genes identified as age-positively correlated genes
(APCGs) or age-negatively correlated genes (ANCGs) in at least one
region for both males and females were classified as consistent APCGs
(or ANCGs). Genes identified as APCGs (or ANCGs) in at least one region
for either only males or only females were classified as male-specific
or female-specific APCGs (or ANCGs).
To test the influence of sample size in the identification of ACGs and
DEGs, we randomly chose a subset of samples from each region to
identify ACGs and DEGs with the same procedure as above. From the male
samples in each age group, we randomly selected the same number of
samples as a corresponding female group. The samples of the 3 groups
were then used to identify ACGs and DEGs with the above-described
procedures. This process was repeated 1,000 times to estimate
distributions for the numbers of ACG and DEG in each region. P-value
was calculated as the number of tests with ACGs/DEGs no less than the
number of ACGs/DEGs identified in the original sample set in that brain
region, dividing the number of repeats (i.e., 1,000).
We also performed an interaction analysis on age and gender with all
genes in addition to the analysis of each gender. We built our model as
follows:
[MATH:
Express
ion
=β0+β1BMI+β2TRISCHD+β3SEX+β4AGE :MATH]
[MATH: +β5SEX:AGE+<
/mo>μ1RACE<
mo>+μ2DTHHRDY+ε :MATH]
where BMI is Body Mass Index, DTHHRDY is a factor of death
classification, disease or injury, leading to the cause of death listed
in immediate cause of death. TRISCHD is the ischemic time interval
between actual death, presumed death, or cross-clamp application and
the start of the GTEx procedure. β indicates fixed linear effects and μ
is a random effect.
dbGaP data sets preprocessing
To validate the ACGs identified from the GTEx dataset, we downloaded an
RNA-seq data set from the study of mRNA Sequencing of Human Cerebral
Frontal Cortex (dbGaP Study Accession: [83]phs001353.v1.p1) in North
American Brain Expression Consortium (NABEC) ([84]Dillman et al.,
2017). Raw sequencing data were aligned to human genome HG38 using the
STAR aligner ([85]Dobin et al., 2013) and assigned to genes using the
featureCounts ([86]Liao et al., 2014). Count matrix was preprocessed
and normalized using the same procedure of GTEx above. To match the age
range with the GTEx cohort, individuals younger than 20 or older than
70 were excluded from the following analysis. After correcting for
post-mortem interval, ACGs of the dbGaP FC were identified using the
Pearson correlation for male and female, respectively. FC region
male-specific APCGs and ANCGs were identified by removing APCGs and
ANCGs in females, respectively, and vice versa.
Human samples for validation
Formalin-fixation paraffin-embedded human brain tissues from the
hippocampal formation were obtained from the Neuropathology Brain Bank
& Research CoRE, in accordance with the relevant guidelines and
policies of the Icahn School of Medicine at Mount Sinai (ISMMS). The
experimental procedures involving human sample handling were approved
by the appropriate committee at James J. Peters VA Medical Center (JJP
VAMC) and ISMMS. Demographic information with 5–6 individuals/group is
provided in [87]Table 4. A comprehensive neuropathological assessment
was performed on each brain to rule out the presence of a major
neurodegenerative disease in excess of normal aging. Selection criteria
was based on biological sex and age of death and the absence of any
clinical or neuropathological neurodegenerative features. Middle-age
subjects included anyone between the ages of 42–59 years old and “aged”
subjects included anyone over the age of 60. Clinical exclusion
criteria included dementia or movement disorder diagnosis following a
comprehensive chart review. Neuropathological exclusion criteria
included any macro or microscopic neurodegenerative changes (i.e., Lewy
bodies, neuritic plaques, neuronal atrophy, neocortical neurofibrillary
tangles, etc.) with the exception of vascular pathology and/or mild
age-related changes (i.e., primary age-related tauopathy or other
common age-related changes) ([88]Crary et al., 2014).
TABLE 4.
Demographic information of the subjects for validation.
Group ID Age Clinical diagnosis Neuropathological diagnosis PMI (h)
Middle-aged female 1 56 Squamous cell carcinoma No diagnostic
abnormality 24
2 57 Cardiomyopathy Ischemic infarcts 24
3 53 Severe Anemia No diagnostic abnormality 25
4 57 Squamous cell carcinoma Cerebrovascular disease 24
5 59 Acute myeloid leukemia Cerebrovascular disease 31
Middle-aged male 6 56 Diabetes Ischemic encephalopathy 24
7 50 Coronary artery disease Cerebrovascular disease 26
8 59 Myeloproliferative neoplasms No diagnostic abnormality 19
9 50 Acute necrotizing pancreatitis Cerebrovascular disease 23
10 52 Diabetic ketoacidosis Cerebrovascular disease 20
11 42 End-stage renal disease Cerebrovascular disease 58
Aged female 12 74 Adenocarcinoma Arteriosclerosis 24
13 86 Cardiac arrest Primary age-related tauopathy 29
14 82 Myocardial infarction Primary age-related tauopathy 22
15 77 Diabetes Primary age-related tauopathy 14
16 88 Metastatic adenocarcinoma No diagnostic abnormality 7
Aged male 17 83 Adenocarcinoma Arteriosclerosis 89
18 80 Fibromuscular stroma Cerebrovascular disease 16
19 82 Cholecystitis No diagnostic abnormality 11
20 97 Multiple organ failure Cerebrovascular disease 53
21 89 Pancreatic cancer Cerebrovascular disease 22
[89]Open in a new tab
Real-time quantitative polymerase chain reaction (RT-qPCR)
The total RNA was extracted using the RNeasy FFPE kit following the
instructions provided by the manufacture (Qiagen). The mRNA levels of
genes of interest (CD99) were determined by RT-qPCR analysis. The CD99
mRNAs were normalized to actin and then transformed to log2 fold
changes when testing the significance of the differences between the
groups using the Student’s t-test.
Cell type and neuron loss rate estimation
For each sample, proportions of 6 brain cell types were inferred from
the normalized gene expression data using the digital sorting algorithm
([90]Zhong et al., 2013) with the cell markers of the 6 brain cell
types extracted from the BRETIGEA package ([91]McKenzie et al., 2018).
Spearman correlation coefficient and p-value were then calculated
between the proportion of each cell type and age for each brain region.
The p-values were corrected using the BH procedure ([92]Benjamini and
Hochberg, 1995). The R package DGCA ([93]McKenzie et al., 2016) was
used to calculate the correlation difference of cell-type proportions
and age between males and females. The adjusted p-values of the DGCA
analysis were estimated using 1,000 permutations. To measure the median
cell type change rate across age, we also fitted the cell type
proportions with age using the non-parametric Theil–Sen estimator
([94]Sen, 1968), which is insensitive to outliers by calculating the
median of the slopes of all fitting lines through pairs of points. In
addition, a down-sampling analysis was performed on the male samples
for calculated the correlation coefficient 1,000 times to test whether
neuron proportion was significantly correlated with age in the males
with the same number of samples as the females.
Co-expression network construction and downstream analysis
Gene co-expression networks were constructed based on normalized gene
expression levels for the males or females in each of the 13 brain
regions using the Multiscale Embedded Gene Co-expression Network
Analysis (MEGENA) ([95]Song and Zhang, 2015). For each gender group in
each brain region, a planar-filtered network was first constructed, and
then a multiscale clustering analysis was performed to identify gene
co-expression modules at multiple compactness scales. Modules were then
compared to random PFN modules generated by shuffling the link weights
of the parent cluster to calculate statistical significance. Lastly, a
multiscale hub analysis was conducted to identify highly connected hubs
of each significant module. Modules with too many (>5,000) or too few
(<50) genes were excluded from further analysis.
For each brain region in each gender group, genes in the identified
modules were mapped with their respective rankings of correlation
coefficients with age and fold changes (log2 transformed) between aged
and young adults to quantify modules’ relevance with aging. The
significance of a module’s association with aging was calculated using
a logistic regression approach LRpath ([96]Sartor et al., 2009), and
p-values were then adjusted with Bonferroni correction. A module of a
brain region in the males or females was defined as an age-associated
module if it was significantly enriched with ACGs and/or aged-young
DEGs in the same gender of that region.
Gene ontology (GO) biological process and pathway enrichment analysis
GO biological process and KEGG pathway enrichment analysis was
performed using the R package GO-function ([97]Wang et al., 2012). GO
biological processes with at least 1 human gene annotated in the
org.Hs.eg.db were extracted from GO.db data package in Bioconductor
version v3.5 ([98]Huber et al., 2015). Similarly, KEGG pathways with at
least one human gene annotated were extracted from the KEGG.db data
package ([99]Huber et al., 2015). The input for the analysis was either
a set of co-expression modules or a gene signature such as age
positively-correlated genes (APCGs), age negatively-correlated genes
(ANCGs), up-regulated genes or down-regulated genes in aged brains
versus young brains in male/female group. The p-value for an
intersection was calculated by the hypergeometric distribution test and
corrected using the BH procedure ([100]Benjamini and Hochberg, 1995).
Module preservation and visualization
Module preservation was calculated among modules between the male and
female networks for each brain region. To identify common and unique
modules, we applied the following procedures: firstly, the significance
of the overlap between any two modules was calculated using FET.
P-value was corrected by the BH procedure ([101]Benjamini and Hochberg,
1995). We then defined set overlap as follows:
[MATH: setov<
/mi>erlap(AB)=2|A∩B<
/mi>||A|+|B| :MATH]
where A and B were sets of genes in two modules under consideration.
Lastly, two modules were considered preserved if the adjusted FET
p-value was less than 0.05 and the set overlap size was at least 30%. A
male module and a female module were defined as preserved
aging-associated modules if they were preserved and significantly
enriched with ACGs and/or aged-young DEGs. Otherwise, an
aging-associated module was defined as a gender-specific
aging-associated module if it was not enriched with ACGs/DEGs or not
preserved in the other gender network. Circos plots were employed to
visualize various features of the modules in a co-expression network
using the R package NetWeaver ([102]Wang et al., 2016). Global
co-expression networks and modules were visualized using Cytoscape
(v3.3.0) ([103]Shannon et al., 2003).
Results
Males are more vulnerable to brain aging with greater neuron loss and
microglia gain
The sample demographics of the 13 brain regions are summarized in
[104]Table 1. The age range of the subjects is between 20 and 70. There
is no significant difference in the age distribution between the two
gender groups in each of the 13 brain regions (Kolmogorov–Smirnov test,
p ≥ 0.18; [105]Supplementary Figure 1). Notably, the number of male
samples is approximately twice of female ones in each region. The
neuropathological characters and confounding factors, including
neurodegenerative diseases, post-mortem delay and immediate cause of
death, are summarized for each brain region in [106]Supplementary Table
1.
We first inferred the proportions of 6 brain cell types for each
sample. Generally, we found that the proportion of neurons was
negatively correlated with age, while the proportion of microglia was
positively correlated with age ([107]Table 2). Specifically, under an
FDR cutoff of 0.05, the proportion of neurons was negatively correlated
with age in the CD, CBH, CT, FC, HIPP, HTH, NAC and SC regions in the
males, while in the other 5 regions, the negative correlation was
non-significant with adjusted p-values < 0.2 (0.064–0.184) ([108]Table
2). In contrast, the negative correlation with age was significant in 3
brain regions in the females, i.e., ACC, CT and CB. To estimate the
cell type rate of changes during aging in these brain regions, we
fitted the cell type proportions with age using Theil–Sen estimator. We
found that neuronal loss was faster in the males than in the females in
9 brain regions, including AMY, CD, CBH, CB, HIPP, HTH, NAC, PT and SC
([109]Supplementary Table 2), while the females showed a more neuronal
loss in the CT and SN regions than the males. However, the difference
was not significant in most of the regions except SC, in which males (ρ
= −0.379) and females (ρ = 0.044) showed a significant difference in
correlations between neuron proportions and age (adjusted p-value =
0.03). In contrast, the males and the females had a similar rate of
increase in microglia proportion with aging across all the brain
regions except CD, in which the males gained more microglia than the
females. In addition, the astrocyte proportion was altered in a
regional- and sex-specific manner during aging. Specifically, the
astrocyte proportion increased with age in the CT region of both males
and females and the male FC and CB regions. However, in the SC region,
the astrocyte proportion was negatively correlated with age in the
females (ρ = −0.437, adjusted p = 0.05) but positively correlated with
age in the males (ρ = 0.253, adjusted p = 0.16) ([110]Supplementary
Table 2). In summary, these results indicate that brain aging is
accompanied by the decreased neuron proportion and the decreased
microglia proportion.
TABLE 2.
Correlations of cell proportions and age in the males and
females[111]^*.
Region Neuron Microglia
Male ρ (adj. p) Female ρ (adj. p) Male ρ (adj. p) Female ρ (adj. p)
AMY −0.324 (0.079) −0.260 (0.091) 0.573 (0.002) 0.265 (0.051)
ACC −0.332 (0.064) −0.275 (0.037) 0.535 (0.003) 0.283 (0.022)
CD −0.282 (0.012) −0.032 (0.833) 0.296 (0.004) 0.200 (0.222)
CBH −0.297 (0.012) −0.089 (0.710) −0.070 (0.530) −0.418 (0.022)
CB −0.185 (0.184) −0.314 (0.003) −0.009 (0.947) −0.034 (0.770)
CT −0.296 (0.012) −0.408 (0.014) 0.087 (0.483) 0.378 (0.022)
FC −0.236 (0.038) −0.211 (0.342) 0.083 (0.511) 0.355 (0.051)
HIPP −0.365 (0.038) −0.421 (0.001) 0.562 (0.002) 0.370 (0.005)
HTH −0.269 (0.031) −0.222 (0.342) 0.292 (0.016) 0.548 (0.005)
NAC −0.234 (0.038) −0.168 (0.390) 0.197 (0.085) 0.415 (0.022)
PT −0.158 (0.148) −0.183 (0.390) 0.238 (0.052) 0.327 (0.085)
SC −0.379 (0.013) 0.044 (0.833) 0.233 (0.122) 0.301 (0.102)
SN −0.285 (0.148) −0.218 (0.212) 0.328 (0.122) 0.018 (0.888)
[112]Open in a new tab
*See [113]Supplementary Table 2 in additional file 3 for the
correlation of all 6 cell types.
ACGs can be reproducibly identified in males and females
We performed a standard transcriptome-wide association study to
identify genes whose expression levels were associated with age. With a
false discovery rate (FDR) cutoff of 5%, correlations between gene
expression and age in the 13 brain regions were calculated separately
for the male and female groups ([114]Table 3). Nine regions in the
males have over 100 genes correlated with age, but only 4 regions in
the females have over 100 age-correlated genes (ACGs). In the males,
there were 546 to 3,709 genes whose expression levels were positively
or negatively correlated with age in the AMY, CD, CBH, CB, CT, FC, HIPP
and HTH regions while the numbers of ACGs in the ACC, NAC, PT and SC
regions were much smaller. Notably, more than 95% of the ACGs in the SC
were negatively correlated with age.
TABLE 3.
Number of age-correlated genes identified in the males and females.
Region Male Female
# APCGs # ANCGs # APCGs # ANCGs
AMY 286 387 0 0
ACC 3 2 41 61
CD 842 1442 430 530
CBH 276 270 672 683
CB 663 513 2 4
CT 534 829 468 555
FC 311 410 0 0
HIPP 1563 2146 0 1
HTH 1639 1728 0 0
NAC 1 2 0 0
PT 31 9 0 0
SC 7 187 0 0
SN 0 0 0 0
[115]Open in a new tab
In general, there are fewer ACGs in females than in males.
Specifically, we identified only 6 ACGs in the female CB region and 1
for the HIPP region. No ACG was identified in the female AMY, FC, HTH,
NAC, PT, SC and SN regions. For CD and CT regions, we identified
approximately 1,000 ACGs in the females, which were also less than
those in males of these regions. However, we identified 102 and 1,355
ACGs in the female ACC and CBH regions, respectively, which were more
than the ACG numbers in the same regions of the males though the female
sample sizes were smaller. We identified a similar number of ACGs using
Spearman correlation ([116]Supplementary Table 3). These results
suggested that the ACC and CBH region were probably more vulnerable in
females than in males. In addition, we also identified more DEGs
between the aged and young individuals in males than in females
([117]Supplementary Table 4). To summarize, we identified more ACGs in
the males than in the females across 11 brain regions except for the
ACC and CBH regions.
To investigate the overlap between ACGs in the males and females across
the 13 brain regions, we performed Fisher’s Exact Test (FET) for ACG
sets with at least one gene. [118]Figure 1A shows the overlaps between
these ACG signatures. Note that the reported significance was corrected
for multiple testing. There were significant overlaps between the APCG
signatures in different brain regions within each gender group and
across two groups. A similar pattern was also observed for the ANCG
signatures. In contrast, there was no significant overlap between the
APCG and ANCG signatures across the 13 regions and two gender groups.
To further compare the ACGs from the brain with ACGs signatures from
other organs, we performed an enrichment analysis using Fisher’s exact
test. After correcting the p-values using the BH procedure, we found
that the APCGs and ANCGs of the brain regions were significantly
enriched for their counterparts in the heart and artery ([119]Yang et
al., 2015; [120]Supplementary Table 5). To validate the ACG signatures,
we used an independent RNA-seq dataset in the FC from dbGaP (Study
Accession [121]phs001353.v2.p1). To keep the same range as the GTEx
dataset, we removed individuals younger than 20 or older than 70,
resulting in 102 males and 39 females for the validation study. At an
FDR of 5%, 81 APCGs and 91 ANCGs were identified in the male FC, while
3 APCGs and 4 ANCGs were identified in the female FC. Among those ACGs,
23 of the 91 ANCGs and 8 of the 81 APCGs are also identified as ANGCs
and APGCs in the male FC of the GTEx, which are significantly more than
expected by chance (Fisher’s exact test, p = 3.02E-17 for the ANCGs, p
= 1.42E-04 for the APCGs). As no ACG was identified in the female FC in
GTEx, we cannot evaluate the conservation of the ACGs in the female FC.
On the other hand, we also reproducibly identified 22 male-specific
APCGs and 27 male-specific ANCGs in the dbGaP FC dataset, which are
also significantly more than expected by chance (Fisher’s exact test, p
= 2.54E-03 for the male-specific APCGs and p = 7.78E-04 for the
male-specific ANCGs).
FIGURE 1.
[122]FIGURE 1
[123]Open in a new tab
Age-correlated gene (ACG) identified in the GTEx, validation in the
human hippocampus and Gene Ontology enrichment analysis of the ACGs.
(A) The numbers of ACGs are shown in the diagonal of the heatmap. The
“+” sign after the abbreviation of each brain region indicates the
signatures positively correlated with age while “−” stands for those
negatively correlated with age. Color intensity indicates adjusted
p-values of the enrichment test between a pair of ACG signatures. AMY,
amygdala; ACC, anterior cingulate cortex (BA24); CD, caudate (basal
ganglia); CBH, cerebellar hemisphere; CB, cerebellum; CT, cortex; FC,
frontal cortex; HIPP, hippocampus; HTH, hypothalamus; NAC, nucleus
accumbens (basal ganglia); PT, putamen (basal ganglia); SC, spinal cord
(cervical c-1); SN, substantia nigra. (B) The mRNA expression levels of
CD99 that were measured by RT-qPCR and normalized to the actin
expression levels in each sample and log2 transformed. Pearson
correlation was used to calculate the correlation coefficients between
the normalized CD99 expression levels and age in the males (blue) and
females (red). (C) The normalized CD99 expression levels in the
middle-aged and aged healthy male and female hippocampal region.
log[2]FC: 1.198 ± 0.147 in middle-aged male subjects versus 0.206 ±
0.127 in middle-aged female subjects; versus 1.584 ± 0.146 in aged male
subjects; versus 0.606 ± 0.098 in aged female subjects; *p < 0.05,
****p < 0.0001 with Student’s t-test. (D) Top biological processes and
pathways enriched with the age-positively correlated genes (APCGs) or
age-negatively correlated genes (ANCGs) consistant in both males and
females as well as specific to each of the 2 gender groups.
To further validate the ACGs, we collected 38 human hippocampal samples
(from 11 male and 10 female subjects, subject demographic information
provided in [124]Table 4) and measured the mRNA expression levels of
CD99 using qPCR. CD99’s mRNA level was significantly correlated with
age in both males (the CD, CT and HIPP regions) and females (the CT
region) in the GTEx dataset ([125]Supplementary Table 6A). In our
validation hippocampal samples, CD99 expression level wassignificantly
correlated with age in both males (ρ = 0.47, p = 0.038) and females (ρ
= 0.50, p = 0.034) ([126]Figure 1B). Furthermore, the CD99 expression
levels were significantly higher in the aged males than the middle-aged
males (t-test, p = 0.032), as well as higher in the aged females than
middle-aged females (t-test, p = 0.012) ([127]Figure 1C). In addition,
CD99 expression levels were significantly higher in males than in
females, in both the middle-aged (t-test, p = 3.86E-05) and the aged
group (t-test, p = 2.40E-05). In summary, the validation experiment
shows that CD99 expression level is significantly positively correlated
with age in both males and females but shows a significant difference
between the two sex groups, consistent with the transcriptomics based
prediction. These results demonstrated the reproducibility of our ACG
signatures.
We then classified the ACGs across the 13 brain regions into
sex-consistent ACGs, male-specific ACGs and female-specific ACGs
([128]Supplementary Table 6A). In total, we identified 774
sex-consistent APCGs and 998 sex-consistent ANCGs. On the other hand,
we identified 519 female-specific APCGs and 494 female-specific ANCGs
that are correlated with age in one or more brain regions in females
but not in males. In males, we identified 2,524 male-specific APCGs and
2,742 male-specific ANCGs that are correlated with age in only one or
more brain regions in males but not in females. To study the functions
and pathways of the ACGs, we performed a Gene Ontology enrichment
analysis for each of the ACG lists. Generally, the top enriched
pathways and biological processes for both sex-consistent ANCGs and
male-specific ANCGs are “synaptic signaling”, “cognition”, “learning or
memory”, “mitochondrion organization” and “neurogenesis” ([129]Figure
1D and [130]Supplementary Table 6B). Besides, the sex-consistent ANCGs
are also enriched in the “axonal transport” and “vesicle transport
along microtubule” biological processes, while the male-specific ANCGs
are enriched in the “myelination”, “regulation of synapse organization”
and “axon ensheathment” pathways ([131]Figure 1D). On the other hand,
both the male-specific APCGs and sex-consistent APCGs are enriched in
the “immune response” and “immune system process” pathways. In
addition, the male-specific APCGs are also enriched in the “regulation
of RNA biosynthetic process” and “response to stress” pathways
([132]Figure 1D). In contrast, females-specific ANCGs and APCGs were
significantly enriched in the “cellular metabolic process” and
“regulation of cell communication” pathways, respectively.
We then asked which genes were differentially correlated with age
between males and females. To test whether the correlation coefficients
of age and gene expression were significantly different between males
and females, we performed a differential correlation analysis for each
region using an R package DGCA ([133]McKenzie et al., 2016). With a 5%
FDR cutoff under 1,000 times of permutation, we identified 65 to 805
genes differentially correlated with age between males and females. We
summarized the results in [134]Supplementary Table 7.
Identification of key gene subnetworks and regulators underlying aging in
males and/or females
To gain insights into the global structures as well as the detailed
local organizations of co-expression and co-regulation of the
above-identified gene signatures underlying aging, we performed gene
co-expression network analysis of the gene expression data from each
brain region in each gender group using the multiscale embedded gene
co-expression network analysis (MEGENA) ([135]Song and Zhang, 2015).
The modules, comprised of highly co-expressed genes, were first
identified in each region in each gender group and were then evaluated
for relevance to aging by the enrichment for respective ACG signatures.
Many top-ranked, aging-associated, region-wide gene modules in the
males are conserved in their respective female networks, and vice versa
([136]Supplementary Tables 8–[137]20). For instance, the module M11 of
the male CBH network (encoded as CBH-Male-M11) is significantly
enriched for the ANCGs in the male CBH (fold enrichment (FE) = 14.36,
corrected p = 8.07E-53) and the down-regulated genes in aged versus
young male CBH (FE = 6.48, corrected p = 1.69E-06) ([138]Figure 2A).
Among the 268 genes in the module CBH-Male-M11, 156 genes (66%) fall
into an aging-associated female module (CBH-Female-M194) comprised of
205 genes (FE = 46.83, corrected p = 2.78E-252; see [139]Supplementary
Table 12). The module CBH-Female-M194 is also significantly enriched
for the ANCGs in the female CBH (FE = 3.30, corrected p = 2.37E-31) and
the down-regulated genes in aged versus young female CBH (FE = 2.63,
corrected p = 3.86E-29) ([140]Figure 2B). Furthermore, 6 hub genes in
the module CBH-Male-M11 ([141]Figure 2C) are also hubs of the module
CBH-Female-M194 ([142]Figure 2D), including MOG, ENPP2, MYRF, ANLN, MAG
and PLP1. Both modules were significantly enriched for
myelination-related biological processes ([143]Supplementary Table 21),
such as “ensheathment of neurons” and “myelination”. To further
validate the shared hub genes identified in the 2 modules conserved
between the males and females, we examined gene signatures in Plp1^–/–
mice or Myrf^–/– mice from our previous study ([144]McKenzie et al.,
2017) and by overlaying them onto the 2 modules (CBH-Male-M11 and
CBH-Female-M194). We found up-regulated genes identified from Plp1^–/–
cerebellum were significantly enriched in CBH-Male-M11 (FE = 2.85,
adjusted p = 8.77E-06, [145]Figure 2C) and CBH-Female-M194 (FE = 3.07,
adjusted p = 9.69E-06, [146]Figure 2D). Similarly, down-regulated genes
identified from cultured mouse Myrf^–/– oligodendrocytes were
significantly enriched in CBH-Male-M11 (FE = 2.22, adjusted p =
1.84E-07) and CBH-Female-M194 (FE = 2.71, adjusted p = 8.42E-11). As
myelination is an important function in the central nervous system, we
further examined the myelination-associated module in the other 12
regions. We found that the myelination-associated module was
significantly enriched with ANCGs and conserved across all the brain
regions ([147]Supplementary Table 22). Moreover, the hub genes in those
modules are very consistent across the 13 brain regions. The most
frequent hub genes in the myelination modules are MOG, MYRF, PLP1, CNP
and MAG ([148]Supplementary Table 22). In summary, the above results
indicate that certain aging-associated processes, such as
down-regulation of the myelination/nerve ensheathment modules, are well
conserved between males and females across all the brain regions.
FIGURE 2.
[149]FIGURE 2
[150]Open in a new tab
Aging-associated co-expression module shared between male and female
cerebellar hemisphere (CBH). (A) Circos plot for the modules in the
male CBH network ranked by enrichment of the ACG and DEG signatures
between aged versus young males. (B) Circos plot for the top modules in
the female CBH network ranked by enrichment of the ACG and DEG
signatures in the CBH between aged versus young females. (C) Subnetwork
of the module CBH-Male-M11 in the male CBH network, which is conserved
in the female CBH network. Blue nodes are the genes whose expression
levels are negatively correlated with age in the male CBH, while blue
labels are the genes down-regulated in the CBH of the aged males versus
that of the young males. Large nodes are the hub genes of the module.
Nodes with red borders are genes up-regulated in the CBH region of the
Plp1^–/– mice versus the wild-type mice. (D) Subnetwork of the module
CBH-Female-M194 in the female CBH network. Blue nodes are the genes
whose expression levels are negatively correlated with age in the
female CBH, while blue labels are the genes down-regulated in the CBH
of the aged females versus that of the young females. Large nodes are
the hub genes of the module. Nodes with red borders are genes
up-regulated in the CBH region of the Plp1^–/– mice versus the
wild-type mice.
In the AMY, HIPP, HTH and FC brain regions, we observed many male
aging-associated modules that don’t overlap with female
aging-associated modules ([151]Supplementary Tables 8–[152]20). For
instance, in the male hippocampal network ([153]Figure 3A),
Hipp-Male-M3 ([154]Figures 3B, C) was significantly enriched for the
ANCGs (FE = 3.22, corrected p = 5.71E-174) and the down-regulated DEGs
between aged versus young hippocampus (FE = 3.63, corrected p =
3.50E-142). These genes in the module Hippocampus-Male-M3 were
implicated in the pathways such as “nervous system development”,
“synaptic signaling” and “neuron development” ([155]Supplementary Table
21). Moreover, top hub genes in this module ([156]Figure 3D) were
associated with the development of Alzheimer’s disease, including VSNL1
([157]Kirkwood et al., 2016), INA ([158]Dickson et al., 2005), CHN1
([159]Kato et al., 2015), NMNAT2 ([160]Ljungberg et al., 2012), and
MAP7D2 ([161]Khundakar et al., 2016). In particular, INA, VSNL1, MYT1L
and MAP7D2 were identified as male-specific aging-associated hub genes
across multiple brain regions (see [162]Supplementary Tables 6A,
[163]8–[164]20). In addition to the Hipp-Male-M3, we also identified
many modules specific to the male gene coexpression networks
([165]Supplementary Tables 8–[166]20), such as the modules
HIPP-Male-M9, CB-Male-M48, SC-Male-M100, and HIPP-Male-M27. These
modules are significantly enriched for the glia and neuron transmission
functions such as “regulation of catabolic process”, “glial cell
differentiation”, “vesicle-mediated transport in synapse”, and
“chemical synaptic transmission” ([167]Supplementary Table 21).
FIGURE 3.
[168]FIGURE 3
[169]Open in a new tab
Male-specific aging-associated co-expression network in the
hippocampus. (A) The global MEGENA network from the male hippocampus.
Each color indicates a highly connected module. The top age-associated
modules enriching for the ACGs and DEGs of the male hippocampus are
highlighted with red and cyan frames. (B) Sunburst plot of the
multi-scale modules in the male hippocampus network. Red blocks show
the modules enriched for APCGs of the male hippocampus; blue blocks
show the modules enriched for ANCGs of the male hippocampus; yellow
blocks show the modules enriched for both APCGs and ANCGs. (C) Heatmap
of the top 25 modules in the male hippocampus network enriched with
ACGs. The left panel shows the adjusted p-values of ACG and DEG
enrichments of the 25 modules. The right panel shows the adjusted
p-values of enrichment for the top 2 Gene Ontology biological processes
in each of the 25 modules. (D) Subnetwork of the top age-associated
module Hippocampus-Male-M3. Nodes with blue labels are ANCG hub genes
in the male hippocampus. Red and blue nodes are up- and down-regulated
genes in aged males versus young males, respectively.
Similarly, female-specific aging-associated modules have been
identified in other brain regions such as CD and CT. For example,
several modules in the female CT network ([170]Figure 4A) were
significantly enriched for the ACG and DEG signatures in the female CT
without significant overlap with the aging-associated module in the
male CT network ([171]Supplementary Table 13). For example,
CT-Female-M38, the 2nd module most associated with aging ([172]Figures
4B, C), was significantly enriched for the ANCGs in the female CT
(corrected p = 1.75E-63) and the down-regulated DEGs in CT between aged
versus young females (corrected p = 3.89E-72) ([173]Figure 4D). The
genes in the module CT-Female-M38 were associated with “neuron
projection morphogenesis” (FE = 2.25, corrected p = 3.67E-06) and “axon
development” (FE = 2.31, corrected p = 6.91E-05) ([174]Supplementary
Table 21), and regulated by female-specific key drivers include SRPK2,
REPS2 and FXYD1. Moreover, there are also many female-specific modules
such as CT-Female-M10, NAc-Female-M6, AMY-Female-M25 and CT-Female-M148
([175]Supplementary Tables 8–[176]20) and they are enriched for
pathways like calcium ion regulated exocytosis, protein targeting to
ER, inflammatory response, and cellular response to cytokine stimulus
([177]Supplementary Table 21).
FIGURE 4.
[178]FIGURE 4
[179]Open in a new tab
Female-specific aging-associated co-expression network and modules in
the cortex. (A) The global MEGENA network from the female cortex. Each
color indicates a highly connected module. The top age-associated
modules enriching for the ACGs and DEGs of the female cortex are
highlighted with red and cyan frames. (B) Sunburst plot of the
multi-scale modules in the female cortex network. Red blocks show the
modules enriched for APCGs of the female cortex; blue blocks show the
modules enriched for ANCGs of the female cortex; yellow blocks show the
modules enriched for both APCGs and ANCGs. (C) Heatmap of the top 25
modules in the female cortex network enriched with ACGs. The left panel
shows the adjusted p-values of ACG and DEG enrichments of the 25
modules. The right panel shows the adjusted p-values of enrichment for
the top 2 Gene Ontology biological processes in each of the 25 modules.
(D) Subnetwork of the module CT-Female-M38. Red and blue nodes
represent APCG and ANCG hub genes in the female cortex, respectively.
Aging-related modules and key genes are associated with Alzheimer’s diseases
To investigate the association between normal brain aging and
Alzheimer’s disease (AD), we performed an enrichment analysis between
the ACG signatures and previous AD gene signatures ([180]Colangelo et
al., 2002; [181]Liang et al., 2008; [182]Webster et al., 2009;
[183]Avramopoulos et al., 2011; [184]Blalock et al., 2011;
[185]Szymanski et al., 2011; [186]Miller et al., 2013; [187]Zhang et
al., 2013; [188]Satoh et al., 2014; [189]Mostafavi et al., 2018;
[190]Klein et al., 2019; [191]Mathys et al., 2019). As shown in
[192]Figure 5, the ACG signatures identified from various brain regions
were significantly enriched for the AD signatures identified from
previous studies. More importantly, the APCG signatures were
significantly enriched for the genes positively correlated with AD
phenotypes (Braak staging and atrophy) or genes up-regulated in AD
versus control, while the ANCG signatures were significantly enriched
for the genes negatively correlated with AD phenotypes or
down-regulated in AD. Specifically, the APCGs in the male FC, CB, CBH,
CD, CT, HIPP, HTH and AMY regions were significantly enriched for the
genes positively correlated with Braak stages and brain atrophy in PFC
and CB identified by [193]Zhang et al. (2013) and the genes
up-regulated with AD versus control in the HIPP CA1 and CA3
sub-regions. On the other hand, the ANCGs in the male AMY, CB, CD, CT,
FC, HIPP and HTH regions significantly overlapped the genes negatively
correlated with Braak stages and brain atrophy in the PFC ([194]Zhang
et al., 2013) and genes down-regulated in thalamocortical radiations,
superior temporal gyrus and hippocampal CA1 and CA3 regions of AD
brains ([195]Webster et al., 2009; [196]Szymanski et al., 2011;
[197]Miller et al., 2013). In summary, AD and aging showed many
consistent transcriptomic alterations as aging is the key vulnerability
for AD development.
FIGURE 5.
[198]FIGURE 5
[199]Open in a new tab
Overlapping between ACGs and Alzheimer’s disease gene signatures of
previous studies. The numbers in the heat map show the shared gene
numbers. Color intensity indicates the adjusted p-value of Fisher’s
Exact Test.
Interestingly, 134, 95 and 42 down-regulated genes identified from AD
PFC excitatory neurons in the Mathys et al. single-cell study
([200]Mathys et al., 2019) were significantly enriched for the ANCGs in
the male HIPP (corrected p = 9.44E-11), male HTH (corrected p =
1.57E-4) and female CBH (corrected p = 0.015) regions, respectively. On
the other hand, 38 and 14 up-regulated genes in AD excitatory neurons
were enriched for the APCGs in the male FC (corrected p = 2.18E-14) and
the female CBH (corrected p = 1.31E-03) regions, respectively. By
contrast, dysregulated genes in AD inhibitory neurons showed no
enrichment for the ACGs. Furthermore, the up-regulated genes in AD
astrocytes were significantly enriched for the APCGs in six brain
regions, including CB, CD, CT, FC, HIPP and HTH. The results suggested
that aging may have stronger effects on excitatory neurons and
astrocytes than other cell types during AD development. Thus, aging
effects on excitatory neurons and astrocytes may increase vulnerability
to AD development and progression.
Discussion
Previous studies showed that the proportion of neuronal loss varies not
only in different brain regions (ranging from no more than 10%
([201]Pannese, 2011) to 50% ([202]Devaney and Johnson, 1980), but also
in different neuron subtypes ([203]Hua et al., 2008). Our data shows
that ACGs are enriched for the genes differentially expressed in AD
excitatory neurons in comparison with control, but not those
differentially expressed in inhibitory neurons, confirming that the
loss of neurons in certain neuron subtypes is more severe than others
during aging. Morphological studies showed that synaptic function was
also significantly altered during aging ([204]Pannese, 2011), with a
decrease in dendrites and axons as well as the loss of dendritic spines
and myelin sheaths. Moreover, transcriptomic analyses indicate that
synapse-related genes and co-expression modules are extensively
down-regulated across different brain regions ([205]Berchtold et al.,
2013; [206]Dillman et al., 2017). In this study, we showed that the
males have faster neuronal loss rates during aging than the females in
9 brain regions (including AMY, CD, CBH, CB, HIPP, HTH, NAC, PT and
SC), while the females had a faster rate of neuronal loss in the CT and
SN regions during aging. This supports the previous findings that males
are generally aging faster than the females measured by many aging
hallmarks ([207]Barrett and Richardson, 2011; [208]Dulken and Brunet,
2015; [209]Gaignard et al., 2015; [210]Gentilini et al., 2015;
[211]Fischer and Riddle, 2018). Further studies should be designed to
investigate the gender differences in proteostasis dysfunction,
cellular senescence, deregulated nutrient sensing and altered
intercellular communication during aging since no study focuses on the
gender differences of these 4 hallmarks. Opposite to the decreased
neuron proportion during aging, the microglia proportion increases with
age in most of the brain regions in both gender groups. The role of
increased microglia proportion in the brain during aging and its
contribution to gender differences in brain aging await further
investigation.
In this study, we identified more aging-associated genes in the male
HIPP, HTH, FC, CD, AMY and CB brain regions than in the respective
female regions. This is consistent with a prior study, which showed
that subcortical regions in males were aging faster than in females
([212]Kiraly et al., 2016). In contrast, we identified more ACGs in the
female ACC and CBH brain regions than the respective male ones,
suggesting that sex differences in aging-associated gene expression
changes are region-specific. In the CT, CD and CBH regions, hundreds of
ACGs were identified in both males and females, while both gender
groups have only a few or no ACGs in the NAC, PT, SC and SN regions. As
there are twice as many males than females in most of the brain regions
in the current GTEx data, the statistical power is larger in the male
group, which may contribute to more ACGs identified in the male brain
regions than the corresponding female ones. Nevertheless, the CBH
region in the females has more ACGs than the males, suggesting that the
CBH may age faster in the females than the males, which is further
supported by the finding of the increased proportion of microglia in
the females than the males. Using an independent human dataset from the
dbGap and our hippocampal RT-qPCR cohort, we reproducibly identified
many ACGs in the GTEx and validated CD99 as a ACG in both male and
female. Due to the relatively low statistical power in the female group
in the GTEx cohort, many ACGs in the females are yet to be identified.
Age-related gene expression changes across the central nervous system
may contribute to the development of neurodegenerative disorders and
functional deficits. Understanding the normal brain aging process helps
elucidate the contribution of aging to neurodegenerative disorders and
impairment of the brain and offers the potential to prevent, mitigate,
and even reverse the impairment with potential therapeutics targeting
the dysregulated pathways ([213]Ali et al., 2017). Previous studies
suggested that age-related cognitive decline was associated with mTOR
signaling, chromatin modification, oxidative stress and dysregulation
of mitochondrial function ([214]Bishop et al., 2010; [215]Wyss-Coray,
2016; [216]Mostafavi et al., 2018). These changes during aging could
account for the vulnerability of neurons to neurodegenerative stressors
because of their high energetic demands ([217]Bishop et al., 2010).
Indeed, many key drivers in the aging-related modules identified in
both sexes have been demonstrated to contribute to the development of
AD and other neurodegenerative disorders. For instance, our study shows
that a gene encoding nicotinamide nucleotide adenylyltransferase 2
(NMNAT2), a critical enzyme in the NAD biosynthetic process, is
significantly down-regulated during aging in both the males and
females. This finding is consistent with the decreased expression of
NMNAT2 in Alzheimer’s, Huntington’s, and Parkinson’s diseases ([218]Ali
et al., 2016, [219]2017). The decreased expression of NMNAT2 would
reduce the biosynthesis of NAD and then decrease bioenergy generation,
which may contribute to the vulnerability of neurons. However, we still
do not know the roles of many key drivers identified in this study in
the development of neurodegenerative diseases, such as REPS2 and FXYD1.
Estrogen acts as anti-aging-hallmark roles in the brain, such as
promoting mitochondrial function, elevating DNA repair enzymes and
increasing synaptic plasticity ([220]Zarate et al., 2017). In the GTEx
cohort, the genes coding estrogen receptors (ESR1 and ESR2) and
aromatase (CYP19A1, HSD17B1 and HSD17B2) were lowly expressed in the 13
brain regions studied here and they were not correlated with age. For
their low expression levels, ESR2, HSD17B2 and CYP19A1 were excluded
from the further analyses. This suggested that brain neurons are
probably affected by the estrogen level decreasing in blood, especially
in females after menopause. This should be confirmed by providing more
pieces of evidence in future brain aging studies.
Nevertheless, this study has some limitations. Firstly, estimation of
the cell proportions from the bulk tissue RNA-seq data was based on the
expression levels of the known marker genes of six brain cell types.
The age-associated changes of cell type proportions in males and
females need be further investigated with single-cell RNA-Sequencing
data with sufficient number of young, middle-aged and aged brains.
Secondly, the differences in epigenetic alterations ([221]Nativio et
al., 2018; [222]Klein et al., 2019) between males and females are
important sources of the gender differences in gene expression changes
during aging. What are the gender-specific epigenetic alterations
during brain aging? What are the roles of those gender-specific aging
epigenetic alterations in neurodegenerative disease? These questions
need more data to answer in future studies of gender differences in
brain aging. Next, although we did not find a significant interaction
between age and gender due to the relatively sample size there may be
interactions between age and gender if enough samples are obtained.
Lastly, as there were twice as many males as females in most of the
brain regions in the current GTEx data, the statistical power is larger
in the male group, which may contribute to higher numbers of ACGs
identified in the male brain regions. Nevertheless, the CBH region in
the females showed more ACGs than the males, suggesting that the CBH
may age faster in the females than the males, which is further
supported by the finding of the increased proportion of microglia in
the females than the males.
Conclusions
Dramatic differences in brain cell type proportion and gene expression
changes during aging between males and females were observed in several
brain regions. Key molecular networks and targets underlying regional
vulnerability to aging in males and females were further identified.
These findings pave the way for understanding the molecular mechanisms
of gender differences in aging and developing neurodegenerative
diseases such as AD.
Data availability statement
The original contributions presented in this study are included in the
article/[223]Supplementary material, further inquiries can be directed
to the corresponding author.
Ethics statement
The studies involving human participants were reviewed and approved by
Ethics Committee at James J. Peters VA Medical Center (JJP VAMC) and
Icahn School of Medicine at Mount Sinai. The patients/participants
provided their written informed consent to participate in this study.
Author contributions
BZ conceived and designed the project. XZ, MW, LG, AM, and JY collected
the data. XZ analyzed the data. JC, LZ, KF, DC, and JFC performed
RT-qPCR validation. XZ, JC, LZ, MW, LG, ZT, DC, and BZ wrote and edited
the manuscript. All authors read and approved the final manuscript.
Funding Statement
This work was supported in parts by grants from the National Institutes
of Health (NIH)/National Institute on Aging (R01AG046170, RF1AG057440,
R01AG057907, U01AG052411, R01AG062355, and U01AG058635), NIH/National
Institute of Allergy and Infectious Diseases (U01AI111598),
NIH/National Institute of Dental and Craniofacial Research
(R03DE026814), NIH/National Institute of Diabetes and Digestive and
Kidney Diseases (R01DK118243) to BZ, NIH R01AG048923 to DC,
(RF1AG054014, RO1AG068030, and R56AG058655) to DC and BZ, Department of
Veteran Affairs BLRD (I01BX003380) and RR&D (I01RX002290) to DC, grant
from NIA/NIH (F32AG056098) to KF, grants from NIH (R01AG054008 and
R01NS095252) to JFC, and grant from NIH (R01AG055501) to ZT. This work
was also supported in part by the computational resources and staff
expertise provided by Scientific Computing at the Icahn School of
Medicine at Mount Sinai.
Footnotes
^1
[224]https://www.gtexportal.org/home/datasets
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s note
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that may be made by its manufacturer, is not guaranteed or endorsed by
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Supplementary material
The Supplementary Material for this article can be found online at:
[225]https://www.frontiersin.org/articles/10.3389/fnagi.2023.1153251/fu
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References