Miller JA, Oldham MC, Geschwind DH.
A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging.
J Neurosci. 2008 Feb 6;28(6):1410-20.
PubMed.
One of the driving questions in Alzheimer’s research has been its nature—is
AD simply an extension of normal aging, or is it a disease unto itself?
If it is a disease unto itself, what changes over time in the brain
to make the aged tissue so much more vulnerable to attack? Using high-level
bioinformatic approaches, Miller, Oldham, and Geschwind take a closer look
at the interplay between aging and AD using transcriptional profiles from
previously published data sets.
On a technical scale, this work is extremely thorough and careful. It is a
terrific example of a well-reasoned meta-analysis in its truest form, paying
attention to statistical probabilities at each stage of the analyses (e.g.,
probability of overlap between the two studies based on the discovery power
in either). Rather than simply comparing lists of genes from two studies,
the authors stripped the information from each study down to its most
raw form (at least as raw as they could get it, i.e., CEL files from Affymetrix
array scans), and used a consistent probe level algorithm across both
studies to create new data sets that are more comparable.
Using the innovative WGCNA approach to clustering, the authors established
modules of genes based on similar behavior across disease states or aging.
Within these modules, the authors established connectivity among genes and
identified “hub” genes that appeared to be most often linked to other genes
within the module. Among these, two genes stood out: VDAC1 in the
mitochondrial module of AD, and YWHAZ, a fairly uncharacterized, but
extremely abundant gene product in brain in a module that stubbornly refused
to reveal a functional association. Further, well-known PSEN1 was found to
be related to myelinating processes in a “guilt-by-association” module of
myelin-related gene products. The authors found that two of the modules from the AD data set (synaptic and mitochondrial) were related to a single module
from the aging study that contained both functional categories.
The implication here is that aging and AD do share some common processes.
However, the question remains as to whether AD represents a frankly
different process than aging. That’s because there are both intriguing agreements (discussed in the paper), and disagreements (for instance, that the
mitochondrial and synaptic genes split into two distinct modules in AD, but
resided in one aging module) between the two studies compared here.
It is remarkable that these relationships were seen even despite the confounds
on a technical level of different labs, and times, as well as the biological
confounds of different tissue type (hippocampal CA1 versus forebrain cortex). It
would be interesting to know, in future studies using this approach, what the
level of agreement would be between two studies attempting to examine the
same disease state—such as AD—in different brain regions. Our own
observations are that changes are more consistent at the functional group
level as opposed to the per gene level.
Other interesting further work might include determining the consequences of
various cutoff decisions in the course of implementing the algorithm, e.g.,
number of presence calls, variability for inclusion, lack of connectivity
for exclusion, should number of connections for determining hub genes be
adjusted for number of genes in module, etc. Also interesting would be to determine “anti-modules,” functional groups that remain static across groups.
Comments
University of Kentucky
One of the driving questions in Alzheimer’s research has been its nature—is
AD simply an extension of normal aging, or is it a disease unto itself?
If it is a disease unto itself, what changes over time in the brain
to make the aged tissue so much more vulnerable to attack? Using high-level
bioinformatic approaches, Miller, Oldham, and Geschwind take a closer look
at the interplay between aging and AD using transcriptional profiles from
previously published data sets.
On a technical scale, this work is extremely thorough and careful. It is a
terrific example of a well-reasoned meta-analysis in its truest form, paying
attention to statistical probabilities at each stage of the analyses (e.g.,
probability of overlap between the two studies based on the discovery power
in either). Rather than simply comparing lists of genes from two studies,
the authors stripped the information from each study down to its most
raw form (at least as raw as they could get it, i.e., CEL files from Affymetrix
array scans), and used a consistent probe level algorithm across both
studies to create new data sets that are more comparable.
Using the innovative WGCNA approach to clustering, the authors established
modules of genes based on similar behavior across disease states or aging.
Within these modules, the authors established connectivity among genes and
identified “hub” genes that appeared to be most often linked to other genes
within the module. Among these, two genes stood out: VDAC1 in the
mitochondrial module of AD, and YWHAZ, a fairly uncharacterized, but
extremely abundant gene product in brain in a module that stubbornly refused
to reveal a functional association. Further, well-known PSEN1 was found to
be related to myelinating processes in a “guilt-by-association” module of
myelin-related gene products. The authors found that two of the modules from the AD data set (synaptic and mitochondrial) were related to a single module
from the aging study that contained both functional categories.
The implication here is that aging and AD do share some common processes.
However, the question remains as to whether AD represents a frankly
different process than aging. That’s because there are both intriguing agreements (discussed in the paper), and disagreements (for instance, that the
mitochondrial and synaptic genes split into two distinct modules in AD, but
resided in one aging module) between the two studies compared here.
It is remarkable that these relationships were seen even despite the confounds
on a technical level of different labs, and times, as well as the biological
confounds of different tissue type (hippocampal CA1 versus forebrain cortex). It
would be interesting to know, in future studies using this approach, what the
level of agreement would be between two studies attempting to examine the
same disease state—such as AD—in different brain regions. Our own
observations are that changes are more consistent at the functional group
level as opposed to the per gene level.
Other interesting further work might include determining the consequences of
various cutoff decisions in the course of implementing the algorithm, e.g.,
number of presence calls, variability for inclusion, lack of connectivity
for exclusion, should number of connections for determining hub genes be
adjusted for number of genes in module, etc. Also interesting would be to determine “anti-modules,” functional groups that remain static across groups.
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