Man is only man at the surface. Remove the skin, dissect,
and immediately you come to machinery.
Paul Valéry (1871-1945)
eTag, You're It!
Researchers use microarrays to cull from the 30,000 genes identified by the
Human Genome Project those that interact with environmental toxicants. Generally,
anywhere from 50 to 200 genes stand out and are further scrutinized by time-consuming
bioinformatics algorithms. Now a new tool called the eTag Assay System rapidly
identifies not only genes but also related proteins without the need for complex
sample preparation and follow-up bioinformatics. eTags further allow scientists
to look at multiple concentrations of an environmental toxicant to determine
at what dose it becomes toxic, says Sharat Singh, inventor of the eTag chemistry
and senior vice president of technology and assay development at Aclara Biosciences
in Mountain View, California.
Short for "electrophoretic tags," eTags are small fluorescent molecules linked
to nucleic acids or antibodies and are designed to bind one specific messenger
RNA or protein, respectively. After the eTag binds its target, a special proprietary
enzyme cleaves the bound eTag from the target. The signal generated from the
released eTag, called a "reporter," is proportional to the amount of target
messenger RNA or protein in the sample.
The eTag reporters are identified by capillary electrophoresis using a capillary-based
DNA sequencer. The unique charge-to-mass ratio of each eTag reporter--that is,
its electrical charge divided by its molecular weight--makes it show up as a
specific peak on the capillary electrophoresis readout.
Unlike microarrays, samples used with the eTag system do not require expensive
preparation steps, such as amplification by polymerase chain reaction. Nanogram
quantities of cells, tissue, blood, or bodily fluids are mixed directly with
eTag reagents in standard 96-well laboratory microplates.
The ability to look at genes and proteins at the same time yields multiple
advantages for researchers. Running only one assay means less sample is needed,
and time and equipment costs are lower. This ability also increases the specificity
of the assay by confirming that certain genes express certain proteins. Finally,
because some genes are regulated at the transcriptional level and others at
the translational level, measuring genes and proteins gives a better understanding
of the regulation of gene expression.
Because of the costs related to running microarrays, researchers often obtain
data for just one dose of a given compound. Yet, "it would be enlightening to
look at one hundred compounds at ten concentrations and ten time points to fully
understand toxic reactions," says Singh. Such a comprehensive experiment would
require hundreds of microarrays (at about $300 apiece) and a few months to complete.
In contrast, eTags could accomplish the task in days, at a cost of approximately
$200.
Fifty different eTags corresponding to 50 reactions can be combined in one
well. Aclara offers eTag assay kits to detect certain pathways, such as cytokines
and cytochrome P450s associated with toxicity, or they can customize sets of
eTags to suit a researcher's needs.
At Stanford University School of Medicine, rheumatologist Paul J. Utz received
funding from the National Heart, Lung, and Blood Institute to use eTags to identify
cytokines and chemokines as biomarkers for systemic lupus erythematosus and
rheumatoid arthritis. In mouse models of rheumatoid arthritis, environmental
toxicants such as mercury trigger the production of characteristic autoantibodies,
which may also be pathogenic. In past experiments, Utz has relied on homemade
protein arrays to detect biomarkers such as autoantibodies and cytokines. However,
these protein arrays "are a pain to print, they do not allow us to study all
proteins, and the sensitivity is not as good as what we get with eTags," he
says. Moreover, Utz adds, eTags "are easier to use and have great flexibility."
Because eTags are small molecules that do not interfere with biological processes,
the opportunities for their use are broad, says Singh, ranging from detecting
biowarfare agents such as anthrax and smallpox, to discovering biomarkers of
cancer. Aclara scientists have created 600 eTags for genes and proteins relevant
to toxicology and human illnesses, providing significant coverage of key targets.
The ultimate goal is to create 1,000 eTags by the end of 2004, says Singh.
Carol Potera
Silencing of Mutant Genes with RNAi
RNA works hard at the business of expressing genetic information. It carries
instructions from DNA in the cell nucleus into the cytoplasm, where basic housekeeping
functions are carried out and proteins manufactured. When messenger RNA arrives
in the cytoplasm, it binds to the ribosomes and guides the assembly of amino
acids into proteins. Now advances in genomics have led to the discovery that,
in addition to its transport and manufacturing roles, RNA can silence gene expression
by a process called RNA interference (RNAi). RNAi provides a new tool for investigating
gene function that also has potential for developing novel clinical treatments
for certain previously untreatable diseases.
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Cross-species application.
Because RNA interference is evolutionarily conserved, it may be useful for
silencing genes in various animal models of disease.
image credits: Photodisc, Art Explosion, Chris Reuther/EHP |
RNAi is an evolutionarily conserved cellular mechanism in worms, plants, and
animals. In the RNAi pathway, long pieces of double-stranded RNA are cut into
smaller pieces by the "dicer" enzyme to form small interfering RNAs (siRNAs)
that are about 21 nucleotides long. These siRNAs bind with other molecules to
form the RNA-induced silencing complex, which allows the siRNAs to target specific
messenger RNAs to block production of protein.
In the 10 June 2003 issue of Proceedings of the National Academy of Sciences,
graduate research assistant Victor M. Miller, associate neurology professor
Henry L. Paulson, and colleagues at the University of Iowa report results that
will faciliate development of siRNA therapies for heritable diseases such as
Machado-Joseph disease (MJD) and other dementias in which defective proteins
clump together and impair brain and nervous system function. In MJD, for example,
a mutation of the MJD1 gene produces multiple copies of the amino acid
glutamine, which makes a protein that is toxic to cells. And in frontotemporal
dementia with parkinsonism, a mutation of the tau gene produces defective
tau protein consisting of the tangled filaments that lead to cell death in dementia
disorders such as Alzheimer disease.
Miller and colleagues conducted experiments using siRNAs to silence genes
of these two diseases. In the experiments, siRNAs were produced in a test tube,
then added to cells to see if they inhibited or suppressed expression of the
targeted gene. Sequences for siRNAs that worked were inserted into a plasmid
for production of short hairpin RNA, which the cell converts into siRNA, using
the specific sequence for each different siRNA the team wanted to clone.
Targeting a single-nucleotide difference between the mutant and healthy MJD1
gene enabled the scientists to almost completely eliminate production of the
defective protein in a human cell model system. The experiments using siRNAs
to knock down expression of the tau gene also succeeded in reducing production
of the protein that causes disease.
Paulson says, "Because the human genome is full of polymorphisms, including
countless single-nucleotide polymorphisms, it is conceivable that some of these
might be associated with diseases, or traits, that allow them to be the 'hook'
by which selective targeting [of a gene] can occur." The ability of siRNAs to
knock down disease-causing proteins coded by dominant genes offers hope for
new and effective treatment of diseases that other genetic engineering strategies--such
as gene replacement therapy--cannot address.
According to Hui Zhang, an associate professor of genetics at the Yale University
School of Medicine, the paper is highly significant in that it provides a conceptual
as well as potential way of treating diseases that contain point mutations through
interfering with normal cellular function. "The authors provided detailed analysis
for allele-specific silencing of the disease genes using the siRNA approach,
which may provide a therapeutic answer to many mutation-based diseases," says
Zhang. "Their conclusion . . . is consistent with our understanding of the siRNA
targeting mechanism reported by many others."
The next big hurdle will be to test siRNAs in an animal model. In a collaboration
between Paulson and Beverly Davidson, the Roy J. Carver Associate Professor
of Internal Medicine at the University of Iowa, the team is now employing a
viral vector to introduce siRNAs into mouse models of human neurodegenerative
disease.
Although human therapy is the ultimate goal, there are a number of challenges
ahead before this new technology will be available. These include possible rapid
degradation of siRNA in the cell, nonspecific effects on gene expression, and
the need for high specificity to prevent unwanted side effects of treatment,
such as possible interference with other proteins or biological pathways, or
elicitation of immune system responses.
Mary W. Eubanks
ADDLs: A New Explanation for Alzheimer Disease?
Clumps of large, sticky proteins forming senile plaques have been observed
in the brains of people with Alzheimer disease ever since neurologist Alois
Alzheimer first described the disorder nearly a century ago. However, only in
the last few years have researchers begun to understand how the primary component
of these proteins, a compound known as ß-amyloid (Aß), disables
and kills brain cells. Recent work shows that other forms of Aß known
as Aß-derived diffusible ligands, or ADDLs, may, along with senile plaques,
play a key role in the pathogenicity of Alzheimer disease. True to the promise
of environmental medicine, these findings could contribute to better methods
for diagnosing Alzheimer disease, as well as new therapies to halt its progress.
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Addled synapses? In a newly
discovered Alzheimer disease process, ligands known as ADDLs bind to synapses
and disrupt the formation of memories.
image credits: Photodisc |
Recent research by postdoctoral fellow Yuesong Gong, neurobiology and physiology
professor William Klein, and other researchers at Northwestern University, published
in the 2 September 2003 Proceedings of the National Academy of Sciences,
shows that ADDLs bind to synapses, connection points that allow the exchange
of signals between neurons. There they disrupt the signaling needed to form
memories, says Klein, a member of the Northwestern Cognitive Neurology and Alzheimer's
Disease Center.
Aß is formed by the breakdown of the amyloid precursor protein, a molecule
of unknown function that is embedded in the membrane of some cells. It is not
unusual for cells to make Aß, but in persons with Alzheimer disease, for
reasons that are not yet known, production of Aß either dramatically increases
or its breakdown decreases. The Northwestern team found up to 70 times as many
ADDLs in brain tissue from persons with Alzheimer disease compared to brain
tissue from persons without the disease.
Once formed, Aß molecules can fold on themselves and bind to each other.
Early in this process, they link in globules of 1224 molecules to form
ADDLs. Eventually, in a process described in the 18 April 2003 issue of Science
by a team including Carl Cotman, director of the University of California, Irvine,
Institute for Brain Aging and Dementia, ADDLs can bind to cells until they begin
to appear as diffuse plaques.
In the early 1990s, researchers thought that plaques were the most toxic form
of Aß. But recent research indicates that ADDLs may be more damaging.
Studies in mice have shown that memory loss correlates more strongly with the
presence of ADDLs than with the presence of senile plaques, and that treatments
to reduce ADDL levels can actually reverse memory loss. "It's very likely that
[senile plaques] can be bioactive; they've been seen attached to the sides of
nerve cells, but the damage they cause is probably limited," says Klein, who
further speculates, "The ADDLs are much more insidious because they diffuse
between cells until they find just the right target."
Cotman's team and others have also found that ADDLs are similar in size and
shape to prions, molecules that have been linked to the transmission of bovine
spongiform encephalopathy ("mad cow disease") and other neurodegenerative diseases.
They may therefore share a common mechanism of toxicity.
ADDLs have been measured in cerebrospinal fluid. Measurement of ADDL levels
could eventually serve as a diagnosis of Alzheimer disease, especially in its
early stages, and could be more accurate than the cognitive evaluations currently
used, says Klein. His team is working to see if ADDL levels that correlate to
the disease can be detected in less-invasive blood tests.
Recent ADDL discoveries could also contribute to research into potential anti-amyloid
therapies, which currently fall into three groups: immunotherapies that prompt
the body's immune system to destroy Aß, antiaggregants that keep the molecules
from clumping, and enzyme (secretase) modulators that prevent the creation of
Aß or hasten its destruction. "All three approaches show some efficacy
in transgenic animal models," says Samuel Gandy, vice chair of the medical and
scientific advisory council of the Alzheimer's Association and director of the
Farber Institute for Neurosciences at Thomas Jefferson University.
An immunotherapy approach using vaccine has shown mixed results so far. Although
some patients in phase II trials showed cognitive benefits, some 5% also developed
acute allergic encephalitis for reasons yet unknown. Klein speculates that a
vaccine that focused on Aß in ADDL form, as opposed to plaques, could
be more effective, cause less inflammation, and require less vaccine. Further,
he says, there are no reasons a priori that should prevent development
of ADDL-specific therapeutic antibodies, which would constitute a "passive"
vaccine. "It's a challenging and exciting approach, but there's a lot of work
to go yet," says Cotman.
Several drugs to inhibit Aß clumping are in clinical trials, according
to Gandy. Enzyme-based therapies could use existing compounds--recent studies
indicate that some antioxidants, including vitamin E and curcumin (the main
ingredient in curry), may inhibit Aß accumulation. Other research has
shown that statins (cholesterol-lowering drugs) and estrogen activate a form
of secretase that destroys Aß, says Gandy. There are indications that
other lifestyle and environmental factors may also decrease Aß levels.
For example, some epidemiology studies link exercise with delayed onset of Alzheimer
disease, says Cotman. "Ultimately," he says, "we may be looking at a combination
of behavior, new drugs, and nutrients to treat Alzheimer's disease."
Kris Freeman
Crunching the Bio-numbers
The quest is on to extract useful information from the growing mountain of
data from today's high-throughput, high-tech biology, and there is a constant
demand for new data-mining techniques that are faster and smarter. The work
presented at a recent symposium session on microarray and gene expression analysis
shows how bio-number crunchers are contributing many ingenious new approaches
to finding the scientific needles in the raw data haystacks.
The session was part of the Atlantic Symposium on Computational Biology and
Genome Informatics, which was one of 11 conferences, symposia, and workshops
convened under the umbrella of the 7th Joint Conference on Information Sciences,
held 26-30 September 2003 in Research Triangle Park, North Carolina. The conference
was sponsored by the NIEHS, the Association for Intelligent Machinery, Duke
University, the journal Information Sciences, and the Harbin Institute
of Technology in China.
According to session chair Björn Olsson, a lecturer in computer sciences
at the University of Skövde, Sweden, the presentations showed that the
field of bioinformatics is coming to terms with the capabilities it has to offer.
"Going back a few years, when this type of data was completely new and everyone
was excited about it, I think people were fumbling in the dark a bit about what
to do with all of this data," he says. "It's becoming more clear now what directions
we can go in."
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More than a mouthful. A recent
bioinformatics meeting highlighted the many data-crunching technologies
being brought to bear on the “-omics.”
image credit: Brand X Pictures |
Simon Lin, manager of the Duke Bioinformatics Shared Resource, led off the
session by presenting the results of a study he and colleagues recently completed
proposing an improved method of data classification in proteomics-based research
using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF)
mass spectrometry, an enhanced version of the original technology used for protein
identification. Peaks from MALDI-TOF raw data (which exhibit the proteins extant
in a biosample) must be brought into registration to correct for random fluctuations
before they can be used to classify samples (for example, as diseased or nondiseased).
Lin and his team used a new algorithmic approach to the registration problem,
employing a statistical model based on the chemical analysis of normal mixtures
to achieve registration.
Applying this method to an existing data set of 11 tissue samples from cancerous
lungs and 11 samples from healthy lungs achieved a classification rate of 90.9%,
with a false-positive rate of 0%. Further, the researchers correctly identified
two previously known lung cancer protein markers in the cancerous samples, and
discovered seven novel markers worthy of further investigation for biologic
relevance.
Improving the accurate classification of diseased versus nondiseased samples
is one of the ongoing challenges in the world of bioinformatics. "Decision trees"
help researchers classify samples by offering sequential tests of individual
attributes. The result of each test determines which test, or branch, should
be applied next, until a final classification is reached. Olsson presented work
in which he and his colleagues applied the decision tree algorithm C4.5 to microarray-based
gene expression data in order to induce decision trees for identification of
breast cancer patients.
Using the expression values of the 108 genes identified in the literature
as breast cancerrelated as input to the decision tree algorithm, the team
analyzed gene expression data from 75 women, 53 of whom had been diagnosed with
breast cancer. The decision tree method achieved 89% accuracy in classifying
samples, based on their gene expression data. Olsson also described the potential
utility of decision tree algorithms to study signaling pathways based on gene
expression data, as well as to discover additional cancer-related genes.
From decision trees, graduate student Tao Shi of the Department of Human Genetics
at the University of California, Los Angeles, took the audience into the woods
as he described the use of "random forest" predictors to derive information
from microarray data. The random forest approach, which uses a suite of decision
trees, can be used to detect clusters in the data, a vital and informative but
often difficult step in accurate classification. Shi showed that the random
forest approach could help meet the challenge of using gene expression data
to classify tumor types, which is increasingly important in molecular biology
efforts to characterize cancer subtypes.
Rounding out the session, mathematician Takeharu Yamanaka and research fellow
Fred Parham of the NIEHS Laboratory of Computational Biology and Risk Analysis
presented a new method of analyzing gene expression to infer genetic interactions,
which can help identify signal transduction pathways crucial to the sequence
of biochemical events that control cellular function. The method uses a Bayesian
network, a type of mathematical framework useful for representing known or hypothesized
causal relationships. The network measures levels of messenger RNA from different
genes and uses conditional assumptions to represent the influence one gene has
on another. "By using the Bayesian network," says Yamanaka, "we can incorporate
statistical thought into the analysis, unlike present methods." Parham adds,
"Also, with the Bayesian networks, we can hopefully . . . say not just that
this group of genes is related, but we can also see the causality."
As today's automated, high-throughput instruments routinely churn out masses
of data that would have been unimaginable just a decade ago, innovations in
bioinformatics such as those presented at the session will be required to bring
method to the madness, and ultimately help deliver the improvements in human
and environmental health promised by molecular biology.
Ernie Hood
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Harvard Institute of Proteomics
The discovery, study, and characterization of the vast number of proteins
in the human body is an enormous challenge. One research center that has
been formed to take on this challenge is the Harvard Institute of Proteomics
(HIP). The HIP's website, located at http://www.hip.harvard.edu/,
outlines the institute's research program. Founded within the Harvard
Medical School in 1999, the HIP is at present laying the groundwork for
determining the function of every protein encoded by the human genome
in order to help understand how protein malformations contribute to disease.
Toward this goal, HIP scientists are developing a novel robotics-based
resource known as the FLEXGene repository, which will contain and distribute
cloned copies of 20,000 human genes. Once this repository is complete,
its developers say it will allow researchers to look at protein expression
in all experimental formats and at any chosen scale. Whereas traditional
DNA subcloning is time-consuming and labor-intensive, using clones from
FLEXGene is quick, inexpensive, and efficient.
image credit: HIP
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The research section of the site contains contact information for and
descriptions of the eight areas of ongoing HIP research. One of these,
the Breast Cancer 1,000 Project, is focused on discovering and understanding
the biological functions of proteins related to breast development and
breast cancer. Eventually, this research group will develop a repository
of clones for 1,000 full-length complementary DNAs for genes that may
contribute to the onset of breast cancer. The group is also working to
convert their findings into technologies to support a broad range of functional
experiments. The products generated through these efforts should also
prove useful to research on other cancers that have been linked to this
group of genes.
HIP projects devoted to the sequencing of the genomes of human pathogen
organisms and the building of an expression-ready gene repository for
these gene sets are described on the Pseudomonas page of the Research
section of the HIP site. Currently HIP investigators are focusing on developing
gene sets for Pseudomonas aeruginosa PAO1 (the leading cause of
death in cystic fibrosis patients), the malaria parasite Plasmodium
falciparum 3D7, and strains of arboviruses including West Nile virus
and dengue virus strains 2 and 3.
Institute researchers are also pushing forward to clone the entire range
of kinases encoded within the human genome and to transfer them to a database
that supports cell-based assays relevant to processes including growth
factor/cytokine signaling, apoptosis, and immunosuppression. Information
on this project is available in the Research section, as are snapshots
of projects to develop high-throughput methods for protein expression
and purification, and to devise a method for producing protein microarrays
that is more streamlined than current processes and that reduces the need
for direct manipulation of the proteins.
The Informatics section of the HIP site contains links to databases
and web-based programs used for proteomics applications. For example,
the MedGene database mines text citations from PubMed to create a "co-occurrence
network" that normalizes and ranks reported human genedisease relationships.
MedGene can generate input for disease-specific microarrays, sort gene
profiling data, and compile lists of genes for use in screening experiments.
The programs available on this page include PCR Oligo Calculator, which
lets researchers design batch polymerase chain reaction primers for amplifying
the open reading frames for a given set of genes, and Batch Gene Retriever,
which allows downloads of full-length sequence information from National
Center for Biotechnology Information databases in batch mode.
Erin E. Dooley |