MicroRNA, mRNA, and protein expression link
development and aging in human and macaque brain
Mehmet Somel,1,2,7,8Song Guo,1,7Ning Fu,3,7Zheng Yan,1Hai Yang Hu,1Ying Xu,1
Yuan Yuan,1,4Zhibin Ning,3Yuhui Hu,5Corinna Menzel,6Hao Hu,6Michael Lachmann,2
Rong Zeng,3Wei Chen,5,6,8and Philipp Khaitovich1,2,8
1Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai
200031, China;2Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany;3Key Laboratory of Systems Biology,
Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China;4Faculty of Science, Technology
and Engineering, La Trobe University, Melbourne, VIC 3086, Australia;5Max Delbru ¨ck Center for Molecular Medicine, Berlin Institute
for Medical Systems Biology, Berlin-Buch 13092, Germany;6Max Planck Institute for Molecular Genetics, Berlin 14195, Germany
Changes in gene expression levels determine differentiation of tissues involved in development and are associated with
functional decline in aging. Although development is tightly regulated, the transition between development and aging, as
well as regulation of post-developmental changes, are not well understood. Here, we measured messenger RNA (mRNA),
microRNA (miRNA), and protein expression in the prefrontal cortex of humans and rhesus macaques over the species’
life spans. We find that few gene expression changes are unique to aging. Instead, the vast majority of miRNA and gene
expression changes that occur in aging represent reversals or extensions of developmental patterns. Surprisingly, many
gene expression changes previously attributed to aging, such as down-regulation of neural genes, initiate in early
childhood. Our results indicate that miRNA and transcription factors regulate not only developmental but also post-
developmental expression changes, with a number of regulatory processes continuing throughout the entire life span.
Differential evolutionary conservation of the corresponding genomic regions implies that these regulatory processes,
although beneficial in development, might be detrimental in aging. These results suggest a direct link between de-
velopmental regulation and expression changes taking place in aging.
[Supplemental material is available online at http:/ /www.genome.org. All mRNA, miRNA, and protein expression data
from this study have been submitted to the NCBI Gene Expression Omnibus (http:/ /www.ncbi.nlm.nih.gov/geo) under
series accession no. GSE18069.]
The human brain changes dramatically during postnatal de-
velopment, both structurally and histologically (de Graaf-Peters
and Hadders-Algra 2006; Marsh et al. 2008). Some developmen-
tal processes, such as cortical axon myelinization, extend far
into adulthood, concluding at ;40 yr of age (Sowell et al. 2004).
Whereas between birth and adulthood, human cognitive abili-
ties undergo remarkable remodeling (Marsh et al. 2008), in later
life, the brain begins to change in a destructive manner. Such
changes include a decrease in brain volume, loss of synapses,
cognitive decline, and a rise in the frequency of neurological
disorders (Courchesne et al. 2000; Sowell et al. 2004; Peters et al.
2008; Salthouse 2009). Although developmental and aging-
related changes are clearly observed in histology and cognitive
function, their molecular bases are still not well understood.
During the last decade, messenger RNA (mRNA) expression
profiling has been widely used to investigate changes in gene ex-
pression levels in aging human and mammalian brains (Lee et al.
2000; Lu et al. 2004; Erraji-Benchekroun et al. 2005; Xue et al.
2007; Zahn et al. 2007; Loerch et al. 2008). These studies have
identified several mRNA expression patterns in the aging brain,
including an up-regulation of stress and immune response path-
ways, as well as a decrease in expression of genes involved in en-
ergy metabolism and neuronal functions. Many of the observed
changes in expression levels were attributed to oxidative stress and
associated accumulation of DNA damage, especially in gene pro-
moter regions (Lu et al. 2004). Interestingly, at 13 yr of age, some
of the gene expression changes observed in human brain aging
are already detectable (Erraji-Benchekroun et al. 2005). This either
reflects the early effects of DNA damage or indicates that some of
the expression changes seen in aging reflect regulatory patterns
established in brain development (Finch 1976; de Magalhaes and
Church 2005; de Magalha ˜es et al. 2009). Although the latter no-
tion is not well recognized, recent studies in Caenorhabditis elegans
have identified several developmental regulatory patterns that
persist into aging, effectively limiting the life span of the animals
studied (Boehm and Slack 2005; Budovskaya et al. 2008). Notably,
such regulation involved both microRNA (miRNA) and transcrip-
tion factors (TFs).
In mammals, development and aging are commonly studied
those involved in postnatal neural development, are known to be
controlled by regulatory programs (Polleux et al. 2007; Stefani and
Slack 2008; Schratt 2009), little is known about when and whether
these programs conclude or when aging-related changes com-
mence. To address these questions, here we survey mRNA, miRNA,
7These authors contributed equally to this work.
Article published online before print. Article and publication date are at
online through the Genome Research Open Access option.
20:1207–1218 ? 2010 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/10; www.genome.org
and rhesus macaques throughout the bulk of each species’ life
Using Affymetrix Human Gene 1.0 ST microarrays, we measured
and 24 healthy macaques, with ages ranging from 2 d to 98 yr and
from 16 d to 28 yr, respectively (Methods; Supplemental Tables S1,
S2). In humans, we reliably detect expression signal from a total of
12,396 genes. For these genes, technical replicates indicated little
experimental variation (Fig. 1A), and other factors, such as sex,
postmortem delay, or RNA quality, did not explain a significant
amount of expression variation among individuals (Supplemental
Text S1). In contrast, ;60% of the total variation in gene expres-
sion can be attributed to the age of individuals (permutation test
[PT], P < 0.01) (Supplemental Fig. S1). The effect of age on total
variation can also be observed from a Principal Components
genes (n = 4084), referred to as ‘‘age-related genes,’’ age explains
a significant proportion of the total variation (median 83%, F-test,
P < 1 3 10?4, false discovery rate [FDR] < 0.1%). Comparing age-
related changesidentifiedin this studywithtwo publishedhuman
brain data sets (0–83 yr, n = 44 [Somel et al. 2009], and 26–106 yr,
n = 30 [Lu et al. 2004]), we find that expression trajectories are
highly consistent across the studies (median [Pearson correlation]
r = 0.98 and r = 0.68, respectively) (Supplemental Fig. S1).
Similarly, using only probes that match the rhesus macaque
genome perfectly, we detect expression of 9628 genes in macaque
brains (Fig. 1D), 21% of which show significant change with age.
Notably, we find strong positive correlation between human and
macaque age-related expression changes (median r = 0.94; PT, P <
used in this study, as opposed to the humans, shared the same en-
vironment throughout their life, as well as the same postmortem
conditions, thus excluding a potential source of non-age-related
artifacts. The high reproducibility of the identified age-related gene
expression changes, across human and macaque species and across
several human data sets, indicates that technical errors or biological
factors unrelated to age do not have a large impact on our data.
Aging as the reversal or extension of development
age in the human prefrontal cortex using the 4084 age-related
genes. To analyze expression changes in the classical framework,
we separated human life span into ‘‘development’’ (0–20 yr) and
‘‘aging’’ (20–100 yr), following the commonly used age intervals
for pre- and post-adulthood (e.g., Lu et al. 2004; Rodwell et al.
Four important observations emerged. First, for 64% of age-
related genes, there was a reversal in the expression change trends
observed between development and aging (Supplemental Fig. S1C).
This can be seen visually by clustering gene expression profiles
into groups of genes with similar trajectories (Fig. 2; Supplemental
expression in human and rhesus macaque brains. The analysis was performed by singular value decomposition, using the ‘‘prcomp’’ function in the R
‘‘stats’’ package, with each gene scaled to unit variance before analysis. The numbers represent each individual’s age in years. The proportion of variance
explained by each principal component is shown in parentheses. For mRNA, arrows indicate pairs of technical replicates, and shades of blue represent two
experimental batches. Individuals group according to their age, indicating a substantial influence of age on total expression variation. (F) Distribution of
Pearson correlation coefficients between human and macaque expression time series, calculated for 3233 orthologous mRNAs (blue) or 98 orthologous
miRNAs (red), showing significant expression change with age in human (Supplemental Text S1). The y-axis shows the relative frequency (the Gaussian
kernel density estimate, calculated with the R ‘‘density’’ function) of genes showing a certain Pearson correlation coefficient.
mRNA, protein, and miRNA expression changes during life span. (A–E) The first two principal components of mRNA, miRNA, and protein
1208 Genome Research
Somel et al.
Fig. S2) (we use eight groups in the following analysis, but using
different numbers of groupings yields consistent results; see Sup-
plemental Figs. S3, S4). Figure 2 shows that for genes in five out of
eight coexpressed groups (groups 2, 5, 6, 7, and 8) there is reversal
in the trend of expression change between development and ag-
(group 3), show limited or no change during aging (group 1), or
show limited change during the first years of postnatal develop-
ment (group 4).
Second, there are no prominent expression change patterns
that can be exclusively related to aging. In fact, 0.5% of all genes
expressed in brain show substantial change during aging, but no
change during development (Supplemental Fig. S5). Importantly,
these genes cannot be functionally discerned from other age-re-
lated gene groups. In addition, expression changes in early de-
velopment have substantially larger amplitude than changes dur-
ing aging (Supplemental Fig. S6). This is consistent with the rate of
anatomical change in the brain during postnatal life (Courchesne
et al. 2000).
life expression trends. Here, for each age-related gene, we deter-
mined when one trend finishes and another begins (Methods). We
find that in humans, most transitions
occur at two particular time intervals:
;3–4 yr and/or 20–25 yr of age (Fig. 3A;
Supplemental Table S3). Interestingly,
many expression changes that had been
previously associated with aging, such as
down-regulation of genes involved in
neural functions (Lu et al. 2004; Loerch
et al. 2008), initiate in early childhood
(Fig. 3B; discussed below).
Finally, we find that the two tran-
sition intervals in macaques are shifted
to earlier ages compared to humans (Fig.
3A). This shift is consistent with life-
history differences such as time of re-
productive maturation and maximum
life span between the two species (de
Magalha ˜es and Costa 2009).
To test whether these observations
apply to protein expression changes with
age, we measured protein expression in
12 of the human samples with shotgun
mass spectrometry (Methods; Fig. 1C).
We used a label-free proteomic approach,
which allows quantification of protein
expression based on peptide counting
(Old et al. 2005; Fu et al. 2009). Although
both the sample size and the number of
reliably detected proteins (n = 2229, with
total peptide count $ 20, FDR = 3%) are
limited compared to the mRNA data set,
we find that age-related changes in pro-
tein expression are strongly correlated
with mRNA changes across all 901 age-
related genes (median r = 0.75) (Supple-
mental Fig. S1D). Accordingly, both the
reversal of expression change between
development and aging as well as the
timing of these transitions are reflected at
the protein level (Fig. 2; Supplemental
Fig. S3). Thus, at both mRNA and protein levels, gene expression
changes in the aging brain are connected to developmental pat-
miRNAs regulate expression changes in both development
To investigate whether some of the transitions between de-
velopmental and aging expression trends can be explained by the
regulatory effects of miRNA, we analyzed miRNA expression in 12
humans and 12 rhesus macaques, selected among the individuals
studied at the mRNA level, using Illumina high-throughput se-
quencing (Methods). In humans and macaques, we obtained a
total of 56,661,685 and 69,883,506 sequence reads, respectively,
that map perfectly to the corresponding genomes. Ninety-three
percent and 95% of the sequence reads match annotated human
miRNA and their macaque orthologs, respectively (Methods). Prin-
cipal component analyses indicate that, in both species, miRNA ex-
pression is highly influenced by age (Fig. 1B,E). Out of 373 miRNAs
represented by a total of $100 sequence reads in humans, 115
(31%) show significant expression changes with age (FDR < 0.1).
Comparing these results to published miRNA studies on postnatal
levels in eight coexpressed gene groups (see Methods). Dark blue points represent the mean expression
level of all genes in a group per individual. The y-axis shows standardized expression levels, where each
unit indicates one standard deviation difference from the mean. The x-axis shows age of individuals on
both developmental and adulthood periods). Bold vertical bars indicate the 25%–75% quantile range,
and thin bars indicate the 2.5%–97.5% quantile range. Mean expression change with age within each
group is summarized by spline curves. Green circles, green triangles, and purple crosses show the mean
expression levels for the same genes among individuals from two published mRNA data sets (Lu et al.
2004; Somel et al. 2009) (GEO data set accession nos. GSE1572 and GSE11512), and the protein data set
from the present study, respectively. (B) Major patterns of miRNA changes with age. Labels are as in A.
Major patterns of mRNA and miRNA changes with age. (A) Shows the average expression
1/4scale (i.e., thefourth root scale,which provided optimal resolutionofexpression changes during
A molecular link between development and aging
mouse cortex development (Smirnova et al. 2005; Dogini et al.
2008), we find consistent patterns of change for all reported cases
(Supplemental Table S4). Similarly, we find good positive correla-
tion between humans and macaques in miRNA expression
changes with age (median r = 0.89; PT, P < 0.02) (Fig. 1F).
We find that age-related miRNA expression changes closely
resemble those of mRNA. This includes the overall pattern of ex-
pression variation, the expression patterns of miRNA groups, and
the timing of transitions (Figs. 1–3). This correspondence suggests
that age-related mRNA changes might be directly shaped by
miRNA regulation. In this case, we expect that coexpressed and,
presumably, coregulated mRNA would be enriched for particular
miRNA binding sites. Furthermore, we expect to find negative
correlation between expression levels of these putative target
mRNAs and the corresponding miRNAs.
Using the TargetScan database (Lewis et al. 2005) to predict
miRNA-binding sites, we show that age-related miRNA expression
profiles are more negatively correlated with their targets’ expres-
sion profiles compared to randomly chosen miRNA–target con-
stellations (Fig. 4A). This holds for both mRNA and protein ex-
pression. We then separately tested the eight coexpressed gene
groups for target site enrichment. This yields 90 cases of miRNAs
with significant target enrichment in a gene group (at hyper-
geometric test [HT], P < 0.05; PT, P < 0.001) (Fig. 4B). Notably, this
enrichment is not uniform among the groups, but found mainly
in groups 1, 4, and 6 (Fig. 4B). We next examined the expression
profiles of the miRNAs targeting these three groups. As predicted,
these miRNAs show a significantly greater extent of negative cor-
relation with the targets’ expression profiles, compared to ran-
domly chosen miRNA–target constellations (49% vs. 20%; HT,
P = 5 3 10?67). Hence, both the overall miRNA–target expression
relationship and the correlations between miRNA–target pairs
identified,basedon targetsite enrichment (i.e.,targetsin groups1,
4, and 6), support the regulatory effect of miRNA on mRNA ex-
pression changes over the human life span.
Importantly, when we separately analyze expression changes
in ‘‘development’’ and ‘‘aging’’ (0–20 and 20–100 yr, respectively),
we find putative regulatory effects of miRNA in both periods (Fig.
5A). For genes in groups 1, 4, and 6, we identify 16 miRNAs with
a significant excess of binding sites and a significant negative ex-
pression correlation in development. Six miRNAs show a similar
excess of enrichment and negative correlation during aging (Sup-
plemental Table S5). Three of these—miR-34a, miR-222, and miR-
433—are correlated with their targets in both development and
aging and thus may regulate gene expression changes in both
used for dividing life span into two phases (Supplemental Fig. S7),
indicating that molecular changes in early and late phases of life
span are not distinct but form a continuum.
As our analyses depend on indirect assessment of regulatory
interactions, we conducted six additional analyses to support or
falsify the estimated regulatory relationships as follows:
1. For 15/16 and 5/6 of the miRNA–target gene pairs identified in
the mRNA expression data set, we find support for putative
regulatory effects at the protein expression level (Supplemental
Table S5) (odd’s ratio = 9.7; HT, P = 0.001).
miRNA–target correlations in macaque development (0–4 yr)
and aging(4–28 yr), respectively (Supplemental Table S5) (odd’s
ratio = 4.4; HT, P = 0.03).
3. We tested whether miRNA expression differences between
humans and macaques are reflected in expression of their pu-
tative target genes (Supplemental Fig. S8C,D).
determined on gene-by-gene basis. The y-axis shows the relative frequency (the Gaussian kernel density estimate, calculated with the R ‘‘density’’
function) of genes showing acertain transition point. The x-axis shows transition ageson the (age)
(purple) a published human mRNA data set (Somel et al. 2009); (gray) human protein; (red) human miRNA; (orange) macaque miRNA. Only age-related
genes following nonlinear trajectories and showing significant transition points are represented (Supplemental Table S3). (B) The transition point iden-
tification procedure illustrated using genes in groups 4 and 6 (as shown in Fig. 2A). The y-axes indicate mean normalized expression levels of genes in the
group. The x-axes show individuals’ ages in log2scale, allowing improved resolution of developmental changes (Methods; Supplemental Fig. S14). Blue
points. Dotted blue lines show linear regression of expression on age before and after the transition point. Purple and brown points/lines represent mean
expression levels/linear regression lines from two published data sets (Lu et al. 2004; Somel et al. 2009), respectively. Note that the results shown in A are
calculated per gene, and in B using the means of gene groups.
Transition points of expression change with age for mRNA, miRNA, and proteins. (A) The age distribution of expression transition points
1/4scale. (Blue) Human mRNA; (green) macaque mRNA;
Somel et al.
4. We investigated whether mutations in miRNA binding sites
present in the rhesus macaque genome, which are expected to
disrupt the regulatory relationship between miRNAs and their
target genes in macaques, indeed cause a loss of correlation
between miRNAs and target gene expression profiles in this
species (Supplemental Fig. S8E).
5. We tested whether negative correlations could be caused by
factors other than age, by using interpolated points instead of
the original expression values in the correlation tests (Supple-
mental Fig. S8A).
6. We compared our list of putative miRNA–target pairs to pub-
lished lists of experimentally identified regulators (Supple-
mental Table S6).
Taken together, the results of these six tests (Fig. 5; Supplemental
Fig. S8; Methods; Supplemental Text S1) indicate that the majority
of the identified miRNA–target gene pairs likely reflect genuine
regulatory relationships. Most importantly, this suggests that at
least part of expression changes occurring during aging are driven
by the same general mechanism and, in some cases, the same
regulatory factors as in postnatal development.
Besides miRNA, other gene expression regulators, such as TFs,
may influence mRNA expression during development and aging.
human age-related TFs with at least one target gene expressed in
the human brain. Remarkably, TFs show significant binding site
enrichment in the promoters of genes in coexpressed groups 1, 4,
and6, the very same gene groupspreferentially targetedby miRNA
(Fig. 4B). TF–target pairs show a significant excess of both positive
and negative correlations compared to randomly selected pairs
(Fig. 4A; Supplemental Fig. S9), indicating that TFs may play acti-
vator or repressor roles in the brain. Furthermore, we find a sig-
nificant excess of TF–target correlations for both the development
and aging periods (Fig. 5B; Supplemental Fig. S9). Notably, the
putative regulatory relationships found in development and in
aging in humans are also found in macaques (Fig. 5B; Supple-
mental Table S4). Hence, like miRNAs, TFs appear to regulate gene
expression changes in brain cortex throughout postnatal life.
Functional characterization of lifetime expression changes
development are optimized by purifying selection to yield a func-
tional and reproducing organism (de Magalhaes and Church
2005). In contrast, molecular changes during aging are typically
explained by accumulating somatic damage. Two models have
been proposed to account for aging-related gene expression level
changes: (1) down-regulations due to oxidative promoter damage
(Lu et al. 2004) and (2) responses to accumulating somatic damage
(Zahn et al. 2007). While the first type of change is probably in-
variably detrimental, the second type is presumably an adaptive
model, the extension of developmental patterns into old age, has
(de Magalha ˜es et al. 2009). Here we inspect the identified expres-
sion changes during development and aging with respect to the
described models, with a particular emphasis on gene groups
showing miRNA and TF regulation.
Strictly developmental regulation
throughout early development, but thereafter show no substantial
The y-axes show the relative frequency (the Gaussian kernel density esti-
mate, calculated with the R ‘‘density’’ function) of Pearson correlation co-
efficients.Shown are correlations between expression profiles of age-related
regulators (miRNA or TF) and their age-related target genes (Methods).
Coloredcurves representthedistributionof regulator–targetcorrelationsfor
miRNA–mRNA (red), miRNA–protein (orange), and TF–mRNA (blue). Gray
curves show the background distribution: correlations between regulators
regulators). (Right panels) The difference between the kernel density distri-
butions of regulator–target correlations and the background. The gray lines
represent100 simulation results, generated by randomlyselecting the same
correlation coefficient distributions is because we calculate correlations be-
tween age-related regulators and targets only; so, each pair shows some
degree of correlation, positive or negative. We therefore test the excess of
negative correlationsforpredicted miRNA–target pairs,relativeto randomly
paired age-relatedmiRNAand mRNA.Similarly, wetest the excessofstrong
positive and negative correlations for the predicted TF–target pairs (given
the dual role of TFs as activator and/or repressor of transcription), relative to
miRNA or TFs showing target enrichment among eight coexpressed gene
groups (at HT, P < 0.05).
miRNA and TF regulation of expression changes with age. (A)
A molecular link between development and aging
change (Figs. 1, 6A). With respect to their function, these genes
are mainly involved in regulation of transcription, nervous sys-
tem development, and cell division (Supplemental Fig. S10; Sup-
plemental Tables S7, S8). Among the candidate regulators of this
group is the miR-29 family, shown to have tumor suppressor roles
in multiple tissues (Fig. 6A; Fabbri et al. 2007; Wang et al. 2008).
Interestingly, miR-29 loss has been linked to sporadic Alzheimer’s
disease (Hebert et al. 2008). Thus, miR-29-driven down-regulation
of cell proliferation-associated genes may exemplify developmental
changes protecting against late-onset disease.
Reversal of development as a possible response to damage
Group 6 genes show development-aging reversal in their expres-
sion profiles: In old age, they are up-regulated to levels seen in
2 and 5. These are the mirror images of group 6 and contain energy
metabolism-related genes (Figs. 2, 6B; Supplemental Tables S7, S8).
in groups2 and 5 (odd’s ratio > 4; HT, P < 0.001). This indicates that
brain energy production peaks around 20 yr of age and then sub-
of aging-related expression changes in a wide range of tissues and
species (Zahn et al. 2007; de Magalha ˜es et al. 2009). The hypothe-
sized reason is that energy production results in accumulating ox-
so as to limit further harm (Zahn et al. 2007).
If attenuated energy metabolism at old age is triggered by
somaticdamageaccumulation from the energy production peak at
coexpressed gene groups in human or macaque cortex. The colored bars show the proportions of negative correlations among miRNA with significant
target enrichment within a gene group (at HT, P < 0.05) and their targets in that group at different correlation cutoffs. Hatched bars indicate the
proportions of negative correlations among miRNA without target enrichment in a gene group and their targets in that group (the background). The
asterisks indicate support for observed–background difference, calculated by bootstrapping the background set 1000 times; ***P < 0.001; **P < 0.01; *P <
0.05;oP < 0.10. Both observed and background correlations are calculated separately for developmental and aging periods. The names of identified
putative regulatory miRNA are shown above each gene group. Genes in group 1 show limited expression change during aging; therefore, we do not
estimate regulators forthis group atthis period. For macaque, regulators shown in the figureare predicted based onthe macaque data andindependently
of human analysis results, using the same significance levels. Additionally, ;80% of regulators predicted in humans show a tendency for negative
correlation with their targets in macaques (Supplemental Table S5). (B) Excess of negative and positive correlations among TF–target pairs in three gene
absolute Pearson correlation cutoff. The names of identified putative regulatory TFs are shown above each gene group. (C) A network of regulatory
interactions identified in groups 4 and 6. Only part of the full network, listed in Supplemental Table S5, is shown. The represented genes are those
containing the specific miRNA binding site and that show significant negative correlation with that miRNA’s expression profile, either in development
(green edges) or aging (blue edges). The figure was drawn using Cytoscape software (v 2.6.3).
miRNA and TF regulation in development and aging. (A) Excess of negative correlations among selected miRNA–target pairs in three
Somel et al.
1212 Genome Research
reproductive maturity, we expect this peak to also trigger damage
response pathways. Indeed, genes involved in DNA damage re-
transferase involved in DNA repair (Lin et al. 1999), is down-reg-
ulated by miR-222 during development and up-regulated during
aging (Fig. 6C). Thus, DNA damage response genes might be ac-
tively up-regulated in aging to curb further accumulation of oxi-
dative damage. The reason for down-regulation of damage repair
pathways in the developmental period, however, is less clear.
Reversal of development as a possible consequence of damage
Another common pattern seen in studies of brain aging is de-
creased neuronal gene expression (Lu et al. 2004; Loerch et al.
2008). This molecular trend might be associated with phenotypic
trends of age-related synapse loss and cognitive decline (Peters
et al. 2008; Salthouse 2009). In our data
set, genes involved in neuronal function
are enriched in two gene groups, 4 and 8
(52/170 age-related genes, odd’s ratio >
1.7; HT, P < 0.001). Supporting this,
groups 4 and 8 are significantly enriched
in genes showing neuron-specific ex-
pression (Fig. 6F). Both gene groups’ ex-
pression levels decrease during aging.
The timing, however, is different: Expres-
sion of neuronal genes in group 8 starts
decreasing around young adulthood,
whereas group 4 genes’ expression levels
start decreasing already in early child-
hood (Fig. 6D,E). Furthermore, for group
4, but not for group 8, we find indication
that this expression trend is actively reg-
ulated (discussed below). This implies
that an alternative mechanism is in-
volved in the down-regulation of group
8 genes. One possibility is that these
genes show high levels of oxidative dam-
age in their promoters, possibly due to
higher GC content of their promoter se-
quences (Lu et al. 2004). Indeed, gene
groups decreasing in expression with age
have higher promoter GC content than
other genes (Supplemental Fig. S11).
Group 8 gene promoters are also more
GC-rich than group 4 promoters (one-
sided Wilcoxon test, P = 0.015). Thus, the
expression levels of neuronal genes in
group 8 might decline in aging due to
accumulating promoter damage, rather
than trans regulation.
Aging as extended development
Group 4 genes are related to neural
development and function (e.g., axon
guidance and long-term depression), as
well as cell communication and cell–cell
adhesion (Supplemental Tables S7, S8).
Expression levels of these genes are con-
stant during the first few years of life, but
start decreasing in early childhood and
continue decreasing during aging (Fig. 6E). Thus, for these genes,
expression changes found in aging arguably represent the con-
tinuation of a developmental trend. Our results, from both
humans and macaques, suggest that a number of regulators, in-
cluding the TF early growth response protein 3 (EGR3) involved in
sympathetic neuron development (Eldredge et al. 2008), and miR-
34a, involved in the apoptosis (Yamakuchi et al. 2008) and tumor
suppression in neuroblastoma (Cole et al. 2008), might be re-
sponsible for at least some of these continuous expression changes
(Fig. 6E). What could the consequences of such extended regula-
tion be? Parallel to increased expression of miR-34a during normal
aging, a recent study showed that miR-34a is also up-regulated in
a mouse model of Alzheimer’s disease (Wang et al. 2009). Mean-
while, its knockdown attenuates the disease phenotype. Hence, we
speculate that the continuous up-regulation of miR-34a might be
a detrimental factor in human brain aging.
pression profiles of selected genes within coexpressed gene groups, and their putative miRNA regu-
lators. The empty triangles show mean standardized (z-transformed) human mRNA (blue) and miRNA
(red) expression levels, while empty circles show mean standardized macaque mRNA (green) and
scale. The lines correspond to cubic spline curves. The depicted genes are associated with specific Gene
Ontology functional terms significantly enriched within the given coexpressed group. For A, C, and E,
the genes are further targeted by specific miRNAs. (A) miR-29a and its four cancer-related targets in
group 1 (MMP2, TRAF4, COL4A2, COL4A1). (B) Seventeen genes involved in electron transport in group
5. (C) miR-222 and its target in group 6, REV1, involved in DNA damage repair. (D) Fifty-seven neuronal
genes in group 8. (E) miR-34a and its seven target neuronal genes in group 4 (GREM2, CAMSAP1,
TANC2, CALN1, RGMB, FKBP1B, RTN4RL1). (F) Cell-type specificity of gene groups. The y-axis shows the
percentage of cell-type-specific genes among the eight coexpressed age-related gene groups (based on
Cahoy et al. 2008; see Methods).
Functions, regulation, and specificity of coexpressed gene groups. Shown are mean ex-
A molecular link between development and aging
Here we analyzed gene expression changes in the human and
macaque brains across life span at the mRNA, protein, and miRNA
levels. Our results indicate that in both species, gene expression
regulation does not finalize at maturity but persists into old age.
This pattern is apparent at both mRNA and protein expression
levels and pertains to both miRNA and TF regulation.
Thissaid, our workhas a numberof limitations. First,we have
studied expression changes in one specific brain region, the su-
perior frontal gyrus. Patterns of expression changes with age are
known to diverge among tissues within the same organism (Zahn
et al. 2007). It therefore remains to be shown whether other brain
regions, or tissues, also show continuity between developmental
and aging expression profiles.
Second, given the limitations of the current miRNA and TF
target gene annotation (Chi et al. 2009), we expect that our anal-
ysis might miss many actual regulatory interactions. In addition,
our estimation of regulator effects is based on statistical evidence,
enrichment of binding sites, and correlation between regulator
and target gene expression, rather than direct experimental test-
ing. Thus, the identified interactions might contain false positives
(Methods). Nonetheless, a number of observations corroborate
the general validity of our predictions. These include the re-
producibility of the regulator–target pair estimations in the pro-
tein data and in rhesus macaque species and coordinated shifts in
expression timing of regulators and their targets between humans
and macaques, as well as overlap with experimentally verified
In fact, expression regulationby TFs and miRNAsin postnatal
central nervous system development is well recognized (Schratt
et al. 2006; Polleux et al. 2007; Schratt 2009). The novel finding
here is that gene regulation by miRNAs and TFs occurs to a similar
extent in both developmental and aging periods. Although the
functional significance of this regulation during aging remains
uncertain, some of our observations provide clues to its origin and
In humans, age-related expression changes first alter their di-
rection around 4 yr of age. This may be linked to key events in
neural development. Indeed, synaptic density peaks around early/
mid-childhood in the human prefrontal cortex (Huttenlocher
and Dabholkar 1997; Glantz et al. 2007; for review, see Toga et al.
2006), and in our data, genes associated with neural function
(groups 4 and 8) show transition at this point (Fig. 2A). The sec-
ond transition point takes place in humans at ;25 yr of age and
might correspond to conclusion of developmental processes such
as myelinization, known to continue past adolescence (Toga et al.
2006). It may also coincide with the onset of senescence. For in-
stance, genes associated with energy production, which decrease
their expression during aging across various tissues and species
(Zahn et al. 2006, 2007; de Magalha ˜es et al. 2009), start decreasing
at this transition point in our data (group 5; Fig. 2A). Hence, 25 yr
of age in humans may mark the beginning of systemic change
associated with certain senescence processes.
Conservation of expression changes with age
We observe that both developmental and aging expression pro-
files, as well as predicted regulatory interactions, are largely con-
served between humans and macaques (Figs. 1, 4). This result is
consistent with a previous study reporting similar expression
profiles in human and rhesus macaque aging, but not between
primates and mice (Loerch et al. 2008). Meanwhile, the most
prominent difference between the human and macaque species is
the delay in the timing of expression changes in humans com-
pared to macaques (Figs. 3A, 6; Supplemental Fig. S8). This delay is
in line with previous observations of developmental timing dif-
ferences in gene expression among primates (Somel et al. 2009)
and approximately corresponds to differences in life-history traits
between the two species (Supplemental Fig. S12). The coordinated
delay of molecular changes across the entire human life span,
coupled with our observation that some regulatory changes found
in aging are initiated in development, supports a proposed re-
lationship between the extension of human development and
human longevity (Kaplan et al. 2000). Human longevity, extreme
among primates, may have evolved as a consequence of regulatory
changes underlying human neotenic development (Somel et al.
In addition to timing differences, a small proportion of genes
in humans and macaques (Supplemental Fig. S13). Interestingly,
such differences are ;1.5 times more common in aging than in
purifying selection on the gene regulation at old age (discussed
below). These differences could also reflect extreme shifts in de-
velopmental timing between species, as well as technical artifacts.
Future studies, using additional species and alternative method-
ology, are needed to address this issue.
Aging as ‘runaway’ development
Our results suggest that gene expression changes in human brain
aging are best explained as a combination of multiple models: (1)
responses to damage accumulation, (2) direct products of damage
accumulation, and (3) uncontrolled or ‘‘runaway’’ continuation of
developmental regulatory traits. We cannotyet deduce the relative
influences of these mechanisms. Nevertheless, the paucity of ex-
pression patterns specific to aging, pervasive extension of de-
velopmental expression trends into aging among neuronal genes,
and persistence of some developmental regulatory effects into
aging, collectively indicate that ‘‘runaway’’ developmental regu-
lation might be a major factor shaping the aging brain.
In fact, a likely link between expression changes in de-
velopment and aging was previously suggested (Finch 1976; de
Magalhaes and Church 2005) and recently noted in the context of
lifelongup-regulation of blood coagulation potential(de Magalha ˜es
et al. 2009). Such a connection between development and aging
has two implications. First, if the two periods constitute a con-
tinuum, it should be preferable to study senescence-related mo-
lecular changes across life span, rather than restricting analyses to
adulthood. Second, this model may explain certain phenotypic
changes during aging. Specifically, following the antagonistic
pleiotropy hypothesis of aging (Williams 1957), such ‘‘runaway’’
regulatory processes might be beneficial in development but have
negative consequences with increased age. Observations of an-
tagonistic regulatory relationships between developmental and
aging changes have already been made in nematodes (Boehm and
Slack 2005; Budovskaya et al. 2008). Meanwhile, in the context of
brain development, this model could predict a connection be-
tween synaptic pruning in early life, required for efficient matu-
ration of cognitive processes (Toga et al. 2006; Gonzalez-Burgos
Somel et al.
1214 Genome Research
etal.2008),andsynapticlossin laterlife, associatedwithcognitive
decline (Peters et al. 2008).
Decrease in evolutionary conservation with age
Supporting the notion of a development-aging antagonism, we
observe that the extent of stabilizing/purifying selection, as
reflected by sequence conservation in protein-coding and regula-
tory regions, decreases over lifetime (Fig. 7A; Methods). Specifi-
cally, we find that genes highly expressed at old age are sub-
stantially less conserved than genes highly expressed in early
postnatal life. This observation is consistent with theories postu-
lating a decrease in stabilizing selection pressure past optimal re-
productive age (Rose 1991). This decrease in conservation remains
after controlling for other factors correlated with sequence con-
servation (Duret and Mouchiroud 2000), such as expression
breadth, cell type specificity, number of interaction partners, or
positive selection (Fig. 7B–D). Similarly, expression changes taking
place in aging are less conserved between humans and macaques,
compared to developmental changes (Supplemental Fig. S13A,B).
Evolutionarily, relaxation of stabilizing selection pressure on reg-
ulatory motifs of genes expressed at higher levels in old age would
facilitate fixation of regulatory traits detrimental in aging.
Taken together, our results indicate that the expression dy-
namics and regulatory interactions during development and aging
are not independent. Transcriptional regulation through miRNAs
complex nature of this biological phenomenon. Further studies
should determine the full set of regulatory interactions occurring
over lifetime in the brain and other tissues, and reveal the exact
role of developmental regulatory processes in determining the
onset and the progression of aging.
Analyses were conducted in the R environment (http://www.
www.mirbase.org/). We use HGNC gene symbols obtained from
Ensembl (v. 54; http://www.ensembl.org).
We collected superior frontal gyrus samples from postmortem
brains of healthy humans and rhesus macaques (Supplemental
Tables S1, S2; for details, see Supplemental Text S1).
mRNA isolation, hybridization to microarrays,
and data preprocessing
Somel et al. (2009) and are described in Supplemental Text S1.
Briefly, samples prepared from 2 mg of total RNA were processed
and hybridized to Affymetrix Human Gene 1.0 STarrays following
the standard Affymetrix protocol. We used the R Bioconductor
‘‘affy’’ library (Gautier et al. 2004) and in-house code to extract,
background-correct, quantile normalize, and summarize probe in-
tensities. For macaque data preprocessing, we only used probes that
perfectly and uniquely match the macaque genome (rhemac2).
miRNA isolation, sequencing, and quantification
The miRNA experiments and data preprocessing followed Hu et al.
(2009) and are described in Supplemental Text S1. Briefly, low-
molecular-weight RNA was isolated and sequenced following the
Small RNA Sample Preparation Protocol (Illumina). For data pre-
processing, trimmed sequences (18–26-nt long) were mapped to
the human genome(hg18), requiring a perfect match. To annotate
et al. 2006), only including sequences with copy number $ 2 and
mapping within the proximity of mature miRNAs. We also in-
cluded small RNA sequences mapping to the opposite arm of the
precursorhairpinas novelmiRNAs(Huetal. 2009).Foridentifying
rhesus macaque mature miRNA, we used reciprocal BLAST with
human sequences from miRBase. This yielded 306 orthologous
similarity between species (Supplemental Fig. S8F).
Protein sample preparation, sequencing,
and peptide identification
Protein expression measurements were performed using a label-
free proteomic approach and peptide (spectral) counting, using
a pH continuous online gradient (pCOG) 2D LC-MS/MS system
(Fu et al. 2009). This method provides comparably accurate mea-
surements for protein relative abundance (Old et al. 2005) and is
widely used in large-scale comparative proteomic studies (e.g.,
Domon and Aebersold 2006; Nesvizhskii et al. 2007; Lu et al.
2009). We followed the procedure described in Fu et al. (2009),
with a number of modifications (Supplemental Text S1). Briefly,
starting from frozen prefrontal cortex tissue from 12 humans (also
symbols and fitted spline curves show the stabilizing selection scores (SSS)
calculated for protein coding (blue and green), promoter (orange), and
39-untranslated (UTR) (red) regions (Methods). The SSS indicate correla-
tion between conservation values and standardized expression levels per
individual, across 4084 age-related genes. Conservation scores are cor-
rected for variation in mutation rates. The x-axis shows age of individuals
on the (age)
tween expression levels and sequence conservation among genes, at
a certain age. The dashed vertical line indicates 20 yr of age, when brain
maturation is largely complete (de Graaf-Peters and Hadders-Algra 2006).
(B) Same as A, but excluding genes possibly under positive selection
(Methods). (C) Same as A, but only using genes with enriched expression
levels in neurons. (D) Correlation between standardized expression levels
and potential confounding factors across age-related genes: number of
protein–protein interaction partners (blue), number of tissues (gray) or
cell types (brown) a gene is expressed in (i.e., expression breath).
Diminishing stabilizing selection pressure with age. (A) The
1/4scale. Positive SSS indicate above-average correlation be-
A molecular link between development and aging
used in the mRNA data set; Supplemental Table S1), we extracted
and trypsin-digested protein samples. These were loaded on ion
Fractions were automatically loaded on alternative trap columns
and analyzed on an LTQ mass spectrometer (ThermoFinnigan).
Peptides were identified by searching against the IPI human da-
tabase (IPI human v3.61). We estimated FDR of peptide identifi-
cation by reversed database searching. Peptide data were mapped
to Ensembl genes, summarized per gene, and quantile-normalized
(Supplemental Text S1).
Variance explained by age and other factors
We calculated the average expression variance explained by age
by fitting a cubic polynomial formula (Somel et al. 2009), while
transforming individual age to ranks (Supplemental Fig. S14; Sup-
plemental Text S1). We estimated the significance of the proportion
of variance explained by 300 random permutations of age. We cal-
culated variance explained by sex, RIN, and PMI in the human
cortex data set using the same procedure (Supplemental Text S1).
We tested the effect of age on expression level using polynomial
regression models, following Somel et al. (2009). For each gene, we
choose the best regression model with age as predictor and ex-
pression level as response, using families of cubic polynomial re-
gression models and the ‘‘adjusted r2’’ criterion (Faraway 2002).
The significance of the chosen regression model was estimated
using the F-test, and the FDR was calculated by 1000 random
permutations of age. The median of the permutation distribution
was used as the null expectation. For the mRNA and miRNA data
sets, genes with an age-test FDR < 0.1% were termed ‘‘age-related.’’
Clustering genes in groups
We used a modified version of the k-means algorithm to cluster
age-related genes into groups with similar expression profiles
(Supplemental Text S1).
Transition point analysis
We estimated the age when an early-life expression change trend
gives way to a late-life trend (e.g., in group 1 in Fig. 2A, when the
trend of a decrease in expression in childhood ceases at early
which compares the fit of two linear regression models (for early
and late life) to a single linear regression model (for the full life
span). We used the log2age scale (which allows resolution of de-
and the linear age scale for transition points in late life (Supple-
mental Table S3; Supplemental Text S1).
miRNA/TF binding site estimation
We used TargetScan5.0 (Lewis et al. 2005) for miRNA binding site
et al. 2003) for conserved TF binding site identification. For the
latter, we used 2000 bp +/? around the transcription start site
as proximal promoter, and 17-way vertebrate phastCons scores
(Siepel et al. 2005) for conservation (Supplemental Text S1).
Regulator miRNA/TF identification
The identification of miRNA regulators of brain development/ag-
ing is described in Supplemental Figure S15 and Supplemental
Text S1. For identifying an miRNA as a regulator, we required two
conditions: (1) enrichment of targets in a coexpressed gene group,
compared to other miRNAs and all other gene groups (at one-
sided HT, P < 0.05); and (2) excess of negative correlation (in ex-
pression profiles) with its targets in the enriched gene group,
compared to non-enriched miRNA (at one-sided binomial tests
[BT], P <0.05). We calculatedmiRNA–target correlations separately
for development and aging periods, using 20 yr and 5 yr as bor-
derlines for human and macaque, respectively, representing ap-
proximate timesofageat firstreproduction(Walkeretal.2006a,b).
The same test was applied to identify putative regulatory TFs. As
TFs (in contrast to miRNAs) showed both positive and negative
correlations with their targets, we tested excess of both positive
and negative correlations.
Testing predicted regulators
Additional tests to confirm the predicted target–regulator pairs are
described in Supplemental Text S1. Briefly:
1. For testing possible age-independent effects on miRNA–target
correlation, we used interpolated miRNA and mRNA profiles,
rather than individual expression levels (Supplemental Fig. S8A).
2. We identified regulators conserved between macaques and
humans by preselecting miRNAs with the same direction of
expressionchangewithage as in humansandtesting theexcess
of negative miRNA–target correlations (using miRNAs enriched
among human coexpressed gene groups; see above). We also
checked if putative regulator miRNAs identified in humans
show a tendency for excess negative correlations (mean r < 0)
with their targets in macaques (Supplemental Table S5). Note
that the macaque data, presumably due to their shorter age
range, showed less age-related change and weaker correlations
than human (both in miRNAs and mRNAs).
3. We tested whether miRNAs and their putative targets show
coordinated divergence between humans and macaques
(negative correlation in human–macaque differences between
genes (Supplemental Fig. S8C,D).
(but not human) has a mutation in the miRNA binding site, we
see miRNA–target negative correlation in humans, but not in
macaques (Supplemental Fig. S8E).
5. We compared our miRNA–target predictions with experimen-
tally verified miRNA target gene sets: Tarbase (http://diana.
et al. 2009), Mirwalk (http://www.ma.uni-heidelberg.de/apps/
zmf/mirwalk/contact.html), a published collection of results
from multiple experiments (Khan et al. 2009), and an miR-181
overexpression experiment (Baek et al. 2008).
6. We estimated the FDR of the binding site enrichment and the
regulator–target correlation tests, using permutations.
We used the Gene Ontology (GO) (Ashburner et al. 2000) and
KEGG (Kanehisa et al. 2008) databases for testing functional en-
richment among gene groups. For identifying cell-type-specific
expression, we used expression levels measured from purified
study (Cahoy et al. 2008). For details see Supplemental Text S1.
Evolutionary conservation analysis
For each human gene, we calculated conservation scores for
39-untranslated and proximal promoter regions using the
Somel et al.
1216 Genome Research
phastCons 18-way Placental Mammal Conservation Track (Siepel
et al. 2005). These were corrected for variance in mutation rates
among genes, using intronic conservation. We used dN/dSratios
from Ensemblas estimatesforamino acidconservation. Thescores
shown in Figure 7 were calculated as the Pearson correlation co-
efficient between sequence conservation levels and the standard-
ized expression levels of an individual, across age-related genes.
Because the score is calculated using expression levels standard-
ized per gene (z-transformed to mean = 0 and SD = 1) across the 23
humans, it is independent of the general positive correlation be-
tween mean expression levels and conservation. For details see
Supplemental Text S1.
We thank the NICHD Brain and Tissue Bank for Developmental
Disorders, the Chinese Brain Bank Center, and, in particular, H.
Lin Tang, Xiling Liu, Yi Huang, Jia Jia Xu, Kai Weng, and Ningyi
Shao for assistance; and R. Ed Green, Martin Vingron, Brian
Cusack, Dan Rujescu, Jing Dong Jackie Han, Jennifer E. Dent,
Joao Pedro de Magalha ˜es, and two anonymous reviewers, all
members of the Comparative Biology Group in Shanghai, for
helpful discussions and suggestions. We thank the Ministry of
Science and Technology of the People’s Republic of China (grant
no. 2007CB947004), the Chinese Academy of Sciences (grant
nos. KSCX2-YW-R-094 and KSCX2-YW-R-251), the Shanghai
Institutes for Biological Sciences (grant no. 2008KIT104), the
Forschung for financial support.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP,
Dolinski K, Dwight SS, Eppig JT, et al. 2000. Gene Ontology: Tool for the
unification of biology. Nat Genet 25: 25–29.
microRNAs on protein output. Nature 455: 64–71.
Boehm M, Slack F. 2005. A developmental timing microRNA and its target
regulate life span in C. elegans. Science 310: 1954–1957.
Budovskaya YV, Wu K, Southworth LK, Jiang M, Tedesco P, Johnson TE, Kim
elegans. Cell 134: 291–303.
Y, Lubischer JL, Krieg PA, Krupenko SA, et al. 2008. A transcriptome
understanding brain development and function. J Neurosci 28: 264–278.
Chi SW, Chi SW, Zang JB, Zang JB, Mele A, Mele A, Darnell RB, Darnell RB.
2009. Argonaute hits-clip decodes microRNA–mRNA interaction maps.
Cole KA, Attiyeh EF, Mosse YP, Laquaglia MJ, Diskin SJ, Brodeur GM, Maris
JM. 2008. A functional screen identifies miR-34a as a candidate
neuroblastoma tumor suppressor gene. Mol Cancer Res 6: 735–742.
Courchesne E, Chisum HJ, Townsend J, Cowles A, Covington J, Egaas B,
Harwood M, Hinds S, Press GA. 2000. Normal brain development and
aging: Quantitative analysis at in vivo MR imaging in healthy
volunteers. Radiology 216: 672–682.
de Graaf-Peters VB, Hadders-Algra M. 2006. Ontogeny of the human central
nervous system: What is happening when? Early Hum Dev 82: 257–266.
de Magalha ˜esJP,ChurchGM.2005. Genomes optimize reproduction: Aging
as a consequence of the developmental program. Physiology 20: 252–
de Magalha ˜esJ, Costa J.2009.A database of vertebrate longevity records and
their relation to other life-history traits. J Evol Biol 22: 1770–1774.
de Magalha ˜es JP, Curado J, Church GM. 2009. Meta-analysis of age-related
gene expression profiles identifies common signatures of aging.
Bioinformatics 25: 875–881.
Dogini DB, Ribeiro PA, Rocha C, Pereira TC, Lopes-Cendes I. 2008.
MicroRNA expression profile in murine central nervous system
development. J Mol Neurosci 35: 331–337.
Domon B, Aebersold R. 2006. Mass spectrometry and protein analysis.
Science 312: 212–217.
Duret L, Mouchiroud D. 2000. Determinants of substitution rates in
mutation rate. Mol Biol Evol 17: 68–70.
2008. Abnormal sympathetic nervous system development and
physiological dysautonomia in EGR3-deficient mice. Development 135:
Erraji-Benchekroun L, Underwood MD, Arango V, Galfalvy H, Pavlidis P,
Smyrniotopoulos P, Mann JJ, Sibille E. 2005. Molecular aging in human
prefrontal cortex is selective and continuous throughout adult life. Biol
Psychiatry 57: 549.
Fabbri M, Garzon R, Cimmino A, Liu Z, Zanesi N, Callegari E, Liu S, Alder H,
Costinean S, Fernandez-Cymering C, et al. 2007. MicroRNA-29 family
reverts aberrant methylation in lung cancer by targeting DNA
methyltransferases 3a and 3b. Proc Natl Acad Sci 104: 15805–15810.
Faraway J. 2002. Practical regression and ANOVA using R. http://cran.
aging. Q Rev Biol 51: 49–83.
Fu X, Fu N, Guo S, Yan Z, Xu Y, Hu H, Menzel C, Chen W, Li Y, Zeng R, et al.
BMC Genomics 10: 161. doi: 10.1186/1471-2164-10-161.
Gautier L, Cope L, Bolstad BM, Irizarry RA. 2004. affy––analysis of
Glantz LA, Gilmore JH, Hamer RM, Lieberman JA, Jarskog LF. 2007.
Synaptophysin and postsynaptic density protein 95 in the human
prefrontal cortex from mid-gestation into early adulthood. Neuroscience
Gonzalez-Burgos G, Kroener S, Zaitsev AV, Povysheva NV, Krimer LS,
Barrionuevo G, Lewis DA. 2008. Functional maturation of excitatory
synapses in layer 3 pyramidal neurons during postnatal development of
the primate prefrontal cortex. Cereb Cortex 18: 626–637.
Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. 2006.
miRBase: microRNA sequences, targets and gene nomenclature. Nucleic
Acids Res 34: D140–D144.
Hebert SS, Horre K, Nicolai L, Papadopoulou AS, Mandemakers W,
Silahtaroglu AN, Kauppinen S, Delacourte A, De Strooper B. 2008. Loss
of microRNA cluster miR-29a/b-1 in sporadic Alzheimer’s disease
correlates with increased BACE1/beta-secretase expression. Proc Natl
Acad Sci 105: 6415–6420.
Hu H, Yan Z, Xu Y, Hu H, Menzel C, Zhou YH, Chen W, Khaitovich P. 2009.
and flies. BMC Genomics 10: 413. doi: 10.1186/1471-2164-10-413.
Huttenlocher PR, Dabholkar AS. 1997. Regional differences in
synaptogenesis in human cerebral cortex. J Comp Neurol 387: 167–178.
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T,
Kawashima S, Okuda S, Tokimatsu T, et al. 2008. KEGG for linking
genomes to life and the environment. Nucleic Acids Res 36: D480–D484.
Kaplan H, Hill K, Lancaster J, Hurtado AM. 2000. A theory of human life
history evolution: Diet, intelligence, and longevity. Evol Anthropol 9:
Kel AE, Go ¨ssling E, Reuter I,Cheremushkin E, Kel-Margoulis OV,Wingender
E. 2003. MATCH: A tool for searching transcription factor binding sites
in DNA sequences. Nucleic Acids Res 31: 3576–3579.
Khaitovich P, Hellmann I, Enard W, Nowick K, Leinweber M, Franz H, Weiss
G, Lachmann M, Paabo S. 2005. Parallel patterns of evolution in the
genomes and transcriptomes of humans and chimpanzees. Science 309:
Khan AA, Betel D, Miller ML, Sander C, Leslie CS, Marks DS. 2009.
Transfection of small RNAs globally perturbs gene regulation by
endogenous microRNAs. Nat Biotechnol 27: 549–555.
Lee C, Weindruch R, Prolla TA. 2000. Gene-expression profile of the ageing
brain in mice. Nat Genet 25: 294–297.
Lewis BP, Burge CB, Bartel DP. 2005. Conserved seed pairing, often flanked
by adenosines, indicates that thousands of human genes are microRNA
targets. Cell 120: 15–20.
Lin W, Xin H, Zhang Y, Wu X, Yuan F, Wang Z. 1999. The human REV1 gene
codes for a DNA template-dependent DCMP transferase. Nucleic Acids
Res 27: 4468–4475.
Loerch PM, Lu T, Dakin KA, Vann JM, Isaacs A, Geula C, Wang J, Pan Y,
Gabuzda DH, Li C, et al. 2008. Evolution of the aging brain
transcriptome and synaptic regulation. PLoS ONE 3: e3329. doi:
Lu T, Pan Y, Kao S, Li C, Kohane I, Chan J, Yankner BA. 2004. Gene
regulation and DNA damage in the ageing human brain. Nature 429:
Lu A, Wis ´niewski JR, Mann M. 2009. Comparative proteomic profiling
of membrane proteins in rat cerebellum, spinal cord, and sciatic nerve.
J Proteome Res 8: 2418–2425.
Marsh R, Gerber AJ, Peterson BS. 2008. Neuroimaging studies of normal
brain development and their relevance for understanding childhood
A molecular link between development and aging
neuropsychiatric disorders. J Am Acad Child Adolesc Psychiatry 47: 1233– Download full-text
Nesvizhskii AI, Vitek O, Aebersold R. 2007. Analysis and validation of
proteomic data generated by tandem mass spectrometry. Nat Methods 4:
Old WM, Meyer-Arendt K, Aveline-Wolf L, Pierce KG, Mendoza A, Sevinsky
JR, Resing KA, Ahn NG. 2005. Comparison of label-free methods for
quantifying human proteins by shotgun proteomics. Mol Cell Proteomics
Papadopoulos GL, Reczko M, Simossis VA, Sethupathy P, Hatzigeorgiou AG.
2009. The database of experimentally supported targets: A functional
update of Tarbase. Nucleic Acids Res 37: D155–D158.
Peters A, Sethares C, Luebke JI. 2008. Synapses are lost during aging in the
primate prefrontal cortex. Neuroscience 152: 970–981.
axon guidance and synapse formation. Nat Rev Neurosci 8: 331–340.
Rodwell GEJ, Sonu R, Zahn JM, Lund J, Wilhelmy J, Wang L, Xiao W,
Mindrinos M, Crane E, Segal E, et al. 2004. A transcriptional profile of
aging in the human kidney. PLoS Biol 2: e427. doi: 10.1371/
Rose MR. 1991. Evolutionary biology of aging. Oxford University Press, New
Salthouse TA. 2009. When does age-related cognitive decline begin?
Neurobiol Aging 30: 507–514.
Schratt G. 2009. MicroRNAs at the synapse. Nat Rev Neurosci 10: 842–849.
Schratt GM, Tuebing F, Nigh EA, Kane CG, Sabatini ME, Kiebler M,
Greenberg ME. 2006. A brain-specific microRNA regulates dendritic
spine development. Nature 439: 283–289.
Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K,
Clawson H, Spieth J, Hillier LW, Richards S, et al. 2005. Evolutionarily
conserved elements in vertebrate, insect, worm, and yeast genomes.
Genome Res 15: 1034–1050.
Smirnova L, Gra ¨fe A, Seiler A, Schumacher S, Nitsch R, Wulczyn FG. 2005.
Regulation of miRNA expression during neural cell specification. Eur J
Neurosci 21: 1469–1477.
Somel M, Franz H, Yan Z, Lorenc A, Guo S, Giger T, Kelso J, Nickel B,
Dannemann M, Bahn S, et al. 2009. Transcriptional neoteny in the
human brain. Proc Natl Acad Sci 106:5743–5748.
Sowell ER, Thompson PM, Toga AW. 2004. Mapping changes in the human
cortex throughout the span of life. Neuroscientist 10: 372–392.
Stefani G, Slack FJ. 2008. Small non-coding RNAs in animal development.
Nat Rev Mol Cell Biol 9: 219–230.
Toga AW, Thompson PM, Sowell ER. 2006. Mapping brain maturation.
Trends Neurosci 29: 148–159.
Walker R, Burger O, Wagner J, Von Rueden CR. 2006a. Evolution of brain
size and juvenile periods in primates. J Hum Evol 51: 480–489.
Walker R, Gurven M, Hill K, Migliano A, Chagnon N, De Souza R, Djurovic
G, Hames R, Hurtado A, Kaplan H, et al. 2006b. Growth rates and life
Wang H, Garzon R, Sun H, Ladner KJ, Singh R, Dahlman J, Cheng A, Hall
BM, Qualman SJ, Chandler DS, et al. 2008. NF-kB-YY1-miR-29
regulatory circuitry in skeletal myogenesis and rhabdomyosarcoma.
Cancer Cell 14: 369–381.
Wang X, Liu P, Zhu H, Xu Y, Ma C, Dai X, Huang L, Liu Y, Zhang L, Qin C.
2009. miR-34a, a microRNA up-regulated in a double transgenic mouse
model of Alzheimer’s disease, inhibits BCL2 translation. Brain Res Bull
Williams GC. 1957. Pleiotropy, natural-selection, and the evolution of
senescence. Evolution 11: 398–411.
Xue H, Xian B, Dong D, Xia K, Zhu S, Zhang Z, Hou L, Zhang Q, Zhang Y,
Han JJ. 2007. A modular network model of aging. Mol Syst Biol 3: 147.
Yamakuchi M, Ferlito M, Lowenstein CJ. 2008. miR-34a repression of SIRT1
regulates apoptosis. Proc Natl Acad Sci 105: 13421–13426.
Zahn JM, Sonu R, Vogel H, Crane E, Mazan-Mamczarz K, Rabkin R, Davis
RW, Becker KG, Owen AB, Kim SK. 2006. Transcriptional profiling of
aging inhumanmusclerevealsacommon agingsignature.PLoSGenet2:
e115. doi: 10.1371/journal.pgen.0020115.
Taub DD, Gorospe M, Mazan-Mamczarz K, et al. 2007. AGEMAP: A gene
expression database for aging in mice. PLoS Genet 3: e201. doi: 10.1371/
Received February 22, 2010; accepted in revised form June 9, 2010.
Somel et al.
1218 Genome Research