Biotech Wonder Tool in Disarray

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23 Mar 2005 14:09:53 -0000

 

Biotech Wonder Tool in Disarray

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ISIS Press Release 23/03/05

 

Biotech Wonder Tool in Disarray

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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

 

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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.

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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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