Teraflop 73 - Novembre - cesca

mildly affected and terminally ill cells,
making it harder to find gene expression
changes. As we currently lack reliable
cellular markers for most neurological
and psychiatric conditions,
there is no obvious way to circumvent
this quandary.
Animal models of disease are an
alternative to the use of human tissue.
These models have had significant
success, particularly in conditions
such as Huntington’s and Alzheimer’s
diseases, for which we have a solid
mechanistic knowledge. But even in
these cases, the genomic analysis has
yet to deliver, as the insights from animal
models are still limited. As some
of the best models involve the insertion
of mutated human genes in the
genome of mice and flies, there might
be unforeseen problems related to
differences in cellular context —all of
the other proteins in the mouse and fly
cells are not human.
In the case of psychiatric disorders,
the situation is even more unfavourable,
as the available models of
these diseases reproduce but their
simplest phenotypes, owing to the fact
that our understanding of the biological
bases of these conditions is quite
elementary. I would therefore argue
that the study of psychiatric diseases
is not yet ripe for genomic analysis.
An embarrassment of riches
Despite the above problems, some
studies have risen to the challenge,
and reported significant and reproducible
gene expression differences
in the brains of several species in
physiological and pathological conditions.
But in addition to showing the
feasibility and value of the genomic
approach, these reports highlight another
of its limitations —the large
number of genes that can be differentially
expressed in a prototypical experiment.
It is common to find that several
dozens of genes are differentially expressed
between two samples. As the
genomic analysis is only the first step
in the characterization of these genes,
we need to confirm these differences
in expression using independent
means before moving forward with
any particular gene. It is clearly im-
practical for any researcher to analyse
in detail every gene, making it necessary
to face the tough decision of
which genes to study. An unfortunate
decision at this stage might lead the
experimenter to a blind alley.
But even assuming that we can
study all the genes that are differentially
expressed, the true problem is how
to make sense of the overwhelming
amount of data that is obtained. If we
find that many synaptic proteins, kinases,
transcription factors and receptors
change between experimental conditions,
will we be able to put all of these
changes into a coherent model of the
process that we are trying to understand?
Is it at all informative that
changes in all those kinds of protein
are found in, say, aging or schizophrenia?
Hardly, I would say. And until we
do not develop a basic understanding
of the core components of the process
that we hope to understand, it will be
very difficult to separate the wheat
from the chaff.
To conclude, I would like to emphasize
that I do not consider that
neurogenomics is a hopeless discipline
that will never yield fruit. But
the problems that I have listed (and
others that I could not discuss for
lack of space) are real, and we must
deal with them so that the brain can
take its rightful place in the postgenomic
era. ■
Typical stages in a microarray experiment. The false-colour image provides an indication of
what genes are differentially expressed in the two samples. If the dot is red or green, then the
gene is more highly expressed in A or B, respectively; if it is yellow, then the gene is equally expressed
in the two samples. Reproduced with permission from Geschwind, D. H. Nature Rev.
Neurosci. 2, 435–438 (2001).
TERAFLOP
Novembre 2003