. A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J Neurosci. 2008 Feb 6;28(6):1410-20. PubMed.


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  1. 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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