Gene gold turning to dust?
Governments are sinking further billions into genomics and related research but a new study finds no sign of revolution in healthcare.
ISIS Press Release 23/03/05
Biotech Wonder Tool in Disarray
DNA sequence information can’t predict the rich tapestry of
life, and researchers are turning to analysing downstream processes using the
biotech microarray wonder tool, only to end in disarray
Dr. Mae-Wan Ho
Sources for this article
are posted on ISIS members’ website.
Details here
Gene microarray studies (Box 1) have been growing exponentially since
the mid-1990s. By 2003, thousands of studies were carried out; but that was
when things started to unravel.
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Box 1
Microarray for comparing gene transcript
A microarray of short DNA sequences stuck on a glass plate allows
two populations of gene transcripts coding for proteins from different cells
(e.g., disease versus controls), or the same cells exposed to different
conditions, to be compared. One of them is labelled with a green fluorescent
dye, the other with a red fluorescent dye.
Spots that appear green are genes expressed preferentially in the
green-labelled population; those that appear red are preferentially expressed
in the red-labelled population. Those that appear yellow are expressed to the
same extent in both populations. The intensity of the colour is proportional to
the degree of gene expression. |
Margaret Cam, director of DNA Microarray Core at the National Institute
of Diabetes and Digestive and Kidney Diseases wanted to use microarrays to
study gene expression in pancreas cells. She and her research team used the
same RNA samples on DNA microarrays from 3 leading suppliers: Affymetrix,
Agilent, and Amersham, and got wildly discordant results. Out of 185 genes
common to all three arrays, the expression pattern of only 4 genes agreed with
one another. In other words, the noise level could be as high as 98%. The
results were in Nucleic Acids Research in 2003.
Marc Salit, a physical chemist at the National Institute of Standards
and Technology said Cam’s findings caused "one’s jaw to drop". Hers
was not the first paper to find such inconsistencies. A few ex-enthusiasts
think that the promise of gene arrays may have been oversold, especially for
diagnostics. Richard Klausner, former director of the National Cancer
Institute, now at the Bill and Melinda Gates Foundation in Seattle, Washington,
admitted to having been "naïve" to think that new hypothesis about disease
would emerge spontaneously from huge files of gene-expression data. The more
data he gathered on kidney tumour cells, the less significant they became.
Each company used different short DNA sequence probes spotted onto the
array; and they were not telling what exactly these sequences were, so each
sequence could be picking up different genes.
Supposedly different probes were responding to pieces of the same gene.
Targeting different parts of the same gene can be a problem because genes
contain many components that can be spliced into variant mRNAs. The probes have
not been designed to be specific to gene-splice variants, and no one has even
created a master list of variants for any gene.
Another confounding factor is promiscuous matches. Probes often respond
not only to gene products that exactly fit the sequence but also to those that
cross-hybridize with near matches. Moreover, many probes don’t correspond
to the annotated sequences in the public database.
The results from several high-profile papers have already proved
difficult to reproduce. Statistician Ulrich Mansmann and his team in the
University of Heidelberg pointed out that a series of papers published in high
prestige journals like Nature, NEJM, and The Lancet base
their impressive results on ad hoc methods, so it is nearly impossible
to assess the quality of the studies. They referred to microarray studies as "a
methodological wasteland".
"So, despite considerable hype, the published studies are far from the
level of evidence that would be accepted for virtually any other medical test."
Said the senior editors of PloS Medicine, one of whom, Virginia Barbour
is on the advisory board of the Microarray Gene Expression Data Society.
The problem doesn’t end there. Many aspects of modulation and
regulation of cellular activity cannot be investigated on the level of DNA or
RNA transcripts, but require analysis of the proteome (complete profile of
proteins). So microarrays of antibodies to proteins have already been
contemplated.
Several studies in yeast and higher organisms demonstrated a poor
correlation between mRNA and protein, due to a number of additional processes
such as posttranscriptional control of protein translation, post-translational
modification of proteins, and protein degradation. The current estimate is that
there are more than 200 types of protein modification; and that 5-10% of the
mammalian genes code for proteins that modify other proteins.
Consequently, the human proteome is expected to range from 100 000 to
several million different protein molecules, in striking contrast to the small
number of genes. Furthermore, no function is known for more than 75% of the
predicted proteins of multicellular organisms, and the dynamic range of protein
expression can be as large as 107.
"Knowledge of genomic sequences and transcriptional profiles do not
allow a reliable description of actual protein expression, let alone an
examination of protein-protein interaction or prediction of the protein’s
biochemical activities." Said Wlad Kusnezow and Jörg Hoheisel of
Functional Genome Analysis in Heidelberg, Germany.
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