INTRODUCTION:
Aging is one of the poorly understood processes despite its
importance. It is a complex process characterised by gradual
and progressive decline in physiological factors and
homeostasis which eventually leads to death. However ,the
research shows that aging processes are attributed to
biochemical and genetic dissection in both human and model
organisms.
Both environmental and genetic alterations in model organisms show profound
effects on aging and lifespan. For example dietary restriction has been found to
dramatically increase lifespan of yeast, flies ,nematodes , and mammals
(mechanism still under investigation). Additionally ,inactivating myriad single
genes increases average lifespan of model organisms. For example, genes
regulating lifespan in nematode Caenorhabditis elegans belong to the insulin/
IGF (Insulin like Growth Factor) signaling pathway .
Free Radical Theory.
This was introduced by Harman ,almost half a century ago. A class of genes
involved in aging is related to production and scavenging of molecules known as
Reactive Oxygen Species (ROS). Together with rate of living theory , it states that
aging is partly due to deleterious side effects of aerobic respiration.
Mitochondrial electron transport chain produces ROS such as hydroxyl
radical(OH*) and hydrogen peroxide (H2O2) that cause damage to DNA ,lipids
and proteins.
Recent study of aging in human brain demonstrated that oxidative damage to
DNA can be caused by mitochondrial dysfunction which accumulate in areas of
genome including promoters, resulting in low level of transcription.
Studies from model organisms show that decreasing ROS levels leads to increase
in lifespan.
In this same study, aging associated gene expression change in the frontal pole of
human brain cortex were measured using DNA microarrays , and genes that had
decreased transcription with age were shown to be the ones that are most susceptible to
oxidative damage.
Different regions of human brain accumulate DNA damage at different rates, hence their
ability to show different gene expression changes with age.
Aging process cannot be understood solely by laboratory evolution experiments or
theoretical work.
Approach to answer the question lies at the level of gene expression:Do orthologous genes undergo the same patterns of expression changes with age in
diverse species , or can common factors such as ROS lead to different gene expression
patterns in different organisms? Using DNA microarrays, this question can now be
addressed in a systematic genome wide manner.
One study found that a small but significant portion of aging related gene expression
changes are shared by the very distantly related nematode and fruit fly ,another study
comparing aging patterns in muscle cells of two more closely related species , mouse
and human ,also found a great deal of divergence in aging patterns.
Aging in human brain is also important because ROS induced damage and age are both
major risk factors in many neurodegenerative diseases such as Alzheimer’s , Parkinson’s
and Amyotrophic Lateral Sclerosis.
Two Main Questions Addressed in The Study :
(1) While using published data, is the pattern of gene expression change previously
observed in the frontal pole representative of other regions of the human brain?
(2) Using data generated from this project, what are the similarities between aging
associated changes in gene expression in human brain and closest living relative , the
chimpanzee?
.In order to test whether different regions of the human brain
show similar patterns of change with age, three independently
published microarray expression data sets were used.
(1) Here ,Lu et al [7], frontal pole region of 30 individuals aged
26 – 106 years were used to identify hundreds of genes with clear
up or down regulation associated with age.
(2) The other one in which gene expression patterns of six brain
regions i.e. prefrontal cortex , primary visual cortex ,anterior
cingulate cortex ,Broca’s area ,caudate nucleus ,and cerebellum
were studied in three individuals aged 45, 45 and 70 years;
Khaitovich et al [19].
(3) Finally ,Evans et al. [20], in which three brain regions , cortex
,anterior cingulate cortex , and cerebellum from seven individuals
aged 18- 70 years were studied . The latter two studies were
conducted to examine gene expression differences between
regions of human brain; the data was not previously analysed with
respect to aging. All three studies used the same microarray
platform (Affymetrix HG U95Vv2), facilitating comparison
between them.
To achieve the most comprehensive picture, the pattern of aging
in all six brain regions from Khaitovich et al.[19] were first
studied , where it was found that in up regulation ,the old sample
is highly expressed than the young sample., in down regulation,
the old sample is more weakly expressed or neither the old
sample is between the young samples
Since thousands of genes are expected to show each of these
three patterns even in the absence of the genuine aging related
changes in gene expression. With available data it could be
determined whether the genes whose expression changes with
age in frontal pole showed the same direction of change in each
of six other brain regions .In order to do this , data from Lu et
al [7] were reanalysed and 841 genes showed a significant
(p<0.01) spearman rank correlation between age and expression
level in frontal pole, irrespective of their fold change in
expression were identified . Most of these were expected to be
true positives , because only approximately 126 genes are
expected to pass this significant threshold by change
(corresponding to an estimated false discovery rate [21] of
126/841=>15.0%).
The 841 genes were classified as having either increased or
decreased expression with age in frontal pole , and then as
either increasing , decreasing or constant in each of the six
regions. After discarding genes with no direction of change within each of the
six brain regions , because they lack information about aging changes , the
testing is done to determine how well the frontal pole data agree with each
data from each of the six regions.
If datasets showed a similar aging patterns , an excess of genes in the latter 2
categories is expected , whereas no excess would be expected in the absence
of a shared pattern .
To quantify the pattern statistically , nonparametric spearman rank
correlation coefficient was used (Abbr=r) . Values of r close to one indicate
good agreement between aging patterns , whereas those close to zero
indicate lack of agreement .To assess the significance of these correlations ,
the ages of the sample were randomly permuted and probability of observing
a random correlation as strong as that found in real data calculated .
All four regions of cerebral cortex which had expression data (prefrontal
cortex , broca’s area , primary visual cortex and anterior cingulate cortex)
showed excellent agreement with the aging pattern in frontal pole (fig2) r>0.8
and p>0.4 for each
In order to further test the similarity of aging patterns within the brain , a third
independent dataset was compared to the data from Lu et al [7] and Khaitovich et al
[19].
As already described, Evans et al [20] sampled 3 brain regions from each of seven
individuals . It was first tested whether the aging patterns in the two cortex regions
from Evans et al [20] correlated more highly with the frontal pole aging changes, than
did the cerebellum samples , fig 2A. Classifying the same 841 genes showing significant
with age in frontal pole as either up regulated or down regulated with age in each region
of this new dataset, the same general pattern of correlations as with the data from
Khaitovich et al [19] was found . Cerebellum showed a weaker correlation with frontal
pole than did either cortex sample ;-prefrontal cortex, r=0.70; anterior cingulate cortex ,
r=0.61; cerebellum , r=0.38 .
Even thought the cerebellum correlation is stronger here (than in fig 2A) , it is still not
significantly different from zero (p=0.17) , even though the two cortex samples are both
significant (p<0.01 each) . This finding supports the conclusion that cerebellum ages
differently from cortex .
For a third test of aging patterns throughout the human brain , the correlation of aging
patterns between a single cortex region from Evans et al [20] with all six of the brain
regions from Khaitovick et al [19] were determined to facilitate comparison with
cerebellum , analysis was extended to all 12,558 probe sets present on the microarray ;
however in order to increase the signal /noise ration, genes with no aging changes were
excluded ( age vs. expression , r<0.5) in either dataset . This comparison showed the
expected reproducibility of aging patterns across all four regions of the cortex; r>0.76
In contrast , neither cerebellum , nor caudate nucleus showed a significant correlation
(fig 2B) ; r<0.04 and p>0.04 for each ) , as expected from their lack of correlation with
frontal pole data shown in fig 2A.
Human Cerebellum Ages less than Cortex .
There are two possible explanations for this
1. Cerebellum and caudate nucleus have their own aging patterns distinct from that in
cortex
2. They have fewer gene changing expressions with age than cortex , hence they lack
reproducible pattern of aging associated gene expression changes altogether
Because this analysis was carried out on all informative genes (age vs. expression ; r>0.5
fig 2B , instead of just the 841 with expression changes in the frontal pole , any
reproducible changes in cerebellum should be found .
Comparison of cerebellum aging from Evans et al [20] with all six regions from
Khaitovich et al [19] gave an unambiguous result;Not a single region had a significant
correlation (fig 2C ; r<0.2 and p>0.4 for each) including the cerebellum comparison.
From these results, it was concluded that cerebellum has a different aging pattern from
cortex because significantly fewer genes appear to change expression with age in
cerebellum.
In order to further characterize the differences in aging pattern between cortex and
cerebellum, the average expression levels in one representative region of prefrontal
cortex were calculated for 841 genes changing strongly with age in frontal pole , in both
young (45yrs) and old (70yrs) samples from Khaitovich et al [19]. When separated into
two groups by their direction of change with age , clear differences were seen between
cerebellum expression levels, an intresting trend emerged: Although the genes that are
up regulated in the cortex are also slightly up regulated in cerebellum, those that are
down regulated in cortex show almost no change at all in cerebellum (fig3) . Thus , the
difference in aging patterns between these two brain regions arises mainly from genes
down regulated in the cortex. The reason for this may be related to metabolic
differences between cerebral cortex and cerebellum.
Chimpanzee Cortex Ages Differently from Human Cortex.
In order to study the relationship between brain aging patterns in humans and
chimpanzees , gene expression data from chimpanzees brain was used .
The data was generated by measuring gene expression levels in three regions of
chimpanzee brain : prefrontal cortex , anterior cingulate cortex and cerebellum.
Samples were taken from five individuals aged 7 to 45 yrs , and one additional
cerebellum sample and two additional prefrontal cortex samples. Because of high
sequence similarity between human and chimpanzees, microarrays designed for
human was used.
Aging patterns of different regions within chimpanzee brain were compared using the
same method as in human brain . As in fig 2B and 2 C (age vs. expression r >0.5) present
on microarray. Although there was no significant correlation when comparing
cerebellum to either cortex region (fig 4A; r<0.07 and p>0.3 for both comparisons) ,
there was a strong agreement when comparing aging patterns at prefrontal cortex to
anterior cingulate cortex (fig 4A;r=0.894 , p<0.005) . This result is precisely analogous to
findings in human, where the entire cerebral cortex shares a single patterns of gene
Similarity between the brain aging in chimps and human was later tested .Using the
841 genes that change expression with age in human frontal pole, the agreement
between the aging related changes in frontal pole and in each four regions of
chimpanzees brain were tested .
None of the three regions showed any significant correlations with human frontal pole
(fig4B r<0.13 , p>0.4 for all three) .
This led to the conclusion that chimpanzee cortex has a reproducible pattern of aging
associated gene expression changes, but this pattern is completely different from that
of human cortex.
Discussion
Three main observations were made from this study:
1. Aging related gene expression changes are similar throughout all five tested
regions of the human cerebral cortex.
2. This pattern of human cortex aging is not found in cerebellum or caudate nucleus
and at least in cerebellum this appears to be due to far fewer genes changing
expression with age.
3. Although chimpanzee cortex, has reproducible pattern of expression changes with
age , it shares no detectable similarity with the aging pattern in human cortex .
These conclusions raise a number of questions:
1. Why does human cerebellum age differently from cortex? The difference is because
genes down – regulated with age in cortex are not down-regulated in cerebellum
(fig3); because fewer genes change with age in cerebellum than in cortex (fig2) , this
is mostly due to fewer genes being down regulated with age.
2. What causes fewer genes to have reduced transcription over time in cerebellum
than in cerebral cortex?
Cerebellum has a lower metabolic rate than cortex in human regardless of age. These
observations of lower metabolic activity in cerebellum imply that if a consequence of
aerobic respiration is ROS induced DNA damage, then such damage should be greater
in cortex than in cerebellum. It has been shown that cerebellum has far fewer
mitochondrial DNA deletions than cortex especially in old humans, and it
accumulates less oxidative damage to both mitochondrial DNA and nuclear DNA than
does cortex . Therefore , if the accumulation of DNA damage causes gene expression
down regulation , then we expected to see fewer aging related gene expression level
reductions in cerebellum than in cortex.
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