 |
November 04, 2005
Technique Offers New View of Dynamic Biological Landscape
A new technique for analyzing the network of genetic interactions
promises to change how researchers study the dynamic biological
landscape of the cell. The technology, which is called epistatic mini
array profiles (E-MAP), has already been used to assign new functions
to known genes, to uncover the roles of previously uncharacterized
proteins, and to define how biochemical pathways and proteins interact
with one another.
E-MAP will enable new understanding of how genes and proteins
function in the cell, said Jonathan S. Weissman, a Howard Hughes
Medical Institute (HHMI) investigator at the University of California,
San Francisco (UCSF) and leader of the team that developed the
technique. For example, E-MAPs of human gene interactions could enable
researchers to optimize drug treatments to patients' genetic
backgrounds. It might also be possible to use E-MAP to develop
effective combinations of antiviral drugs that target proteins produced
by interacting genes. Such a strategy would help to prevent these genes
from acting together to compensate for an attack on just one protein,
said Weissman.
“It gives you a less hypothesis-biased, more objective way of looking at the structure of biological systems.”
Jonathan S. Weissman
The researchers, led by Weissman, Maya Schuldiner, a post-doctoral
fellow working in his lab, and Nevan Krogan at the University of
Toronto, described initial studies of E-MAP in yeast in the November 4,
2005, issue of the journal Cell. Weissman and his colleagues at
UCSF collaborated on the studies with researchers at the University of
Toronto.
 | | | | |  | | | | | |  |  | Early Secretory Pathway
A comprehensive and objective view of functional architecture in the Early Secretory Pathway... more
Illustration: Reprinted from Cell, Vol.123, 507-519, Schuldiner, M., Collins, S.R., Thompson, N.J., Denic, V., Bhamidipati, A., Punna T., Ihmels, J., Andrews, B., Boone, C., Greenblatt, J.F., Weissman, J.S., and Krogan, N.J., © 2005, with permission from Elsevier.
| |
|
| | |
| | | |
Previous techniques for analyzing epistatic interactions — how the
activity of one gene affects that of another — involved altering
single genes and analyzing their impact on growth in combination with
all other genes in the yeast genome. "The one-to-one method has been an
extremely powerful way of studying biological systems,” said
Weissman. “But we wanted to approach such analyses in a
systematic way and to use the new generation of high-throughput
technology to quantitatively explore large numbers of epistatic genetic
interactions at once."
The E-MAP technique consists of selectively "dialing down" the
activity of a multitude of gene pairs and comparing the effects of
those changes to those that result when each gene is dialed down
individually. Many genes' activity could be reduced by eliminating them
entirely, but for the subset of genes that are essential for yeast
growth — whose complete deletion would kill the cell — the
researchers invented a high throughput technique to manipulate the
half-life of their messenger RNA (mRNA). Since mRNA is a genetic
intermediate during the conversion of a gene to protein, reducing its
lifespan by mutating the mRNA message lowers the amount of protein the
cell can produce. The group called this approach “decreased
abundance by mRNA perturbation” (DAmP).
"The DAmP technique gave us a way of lowering the abundance of a
target gene's messenger RNA while maintaining its natural regulation,"
said Weissman. "Most of the mRNAs in yeast have half-lives of ten
minutes or so, but our alterations destabilized them to have only a
half-life of a couple of minutes. Consequently, they produce five- to
ten-fold less protein," he said.
In developing E-MAP, the researchers faced a significant hurdle:
Even yeast's relatively modest 6,000 genes would generate nearly 20
million possible gene pairs that would need to be tested. To narrow the
number of possible interactions, they adopted a strategy called
neighborhood clustering, which restricts analysis to genes that have
related functions and that also cluster in one place in the cell. In
the Cell paper, they applied the E-MAP technique to a "mini
array" of 442 yeast genes that define a biological pathway called the
early secretory pathway. This compartmentalized, interconnected pathway
synthesizes and regulates lipids and secreted proteins in yeast.
Weissman and his colleagues also needed a way to quantify the
epistatic effects of interacting mutant genes on the cells' viability.
Since yeast form round colonies when grown in culture dishes, they
could measure the mutant cells' colony size in an automated fashion and
use that to calculate their growth rates. To determine epistatic
effects, they compared the growth rate for each cell containing
mutations in two genes with the growth rate of mutant cells carrying
mutations in only one of those genes.
"The analysis of these epistatic interactions gave us a unique and
coherent perspective on the function and structure of this network in
yeast," Weissman explained. "And it also proved a great way to find new
gene functions or to figure out how known genes were functioning and
the processes they were likely to be involved in. But on top of that,
we could identify groups of genes that were acting in a coherent way,
to produce protein complexes. And then on a more global level, we could
see how the different processes were interacting with each other.
"By contrast, in classical genetics, you begin with a process you're
interested in — for example secretion — and look for all the genes
that affect secretion. It's a productive approach, but it's very
process oriented,” he said. “You might find a given gene
that's involved in secretion, but it doesn't tell you about the many
other processes it could be involved in.
With the E-MAP approach, however, the researchers start with the
gene and ask about all the processes that it affects. “It gives
you a less hypothesis-biased, more objective way of looking at the
structure of biological systems," Weissman said.
In future studies, Weissman and his colleagues plan to develop
better quantitative measures of the effects of epistatic interactions
and to extend their technique to other organisms, ultimately to
humans.
In addition to offering important basic insights into the roles of
proteins and genes, E-MAPs will also contribute to understanding
evolutionary processes. “In evolutionary theory, the structure of
epistatic gene interactions is critical,” he said. “To
understand how different variations, or alleles, of a gene affect an
organism's evolution, you have to understand for each gene how it's
affected by the genetic background in which it operates.”
Moving beyond the theoretical, E-MAPs might also have a role in
clinical applications. "In the field of pharmacogenomics, clinicians
seek to tailor drug therapies to an individual's genetic makeup,”
said Weissman. “They are essentially asking the very questions
about epistatic interactions that E-MAPs can answer. They want to know
whether if they inhibit a protein — in this case with a drug instead
of knocking down the mRNA — how other genes interact with that
inhibition.”
Knowing the interactions a target gene participates in could also
enable clinicians to predict the variability of effects of a drug among
different people. Understanding such interactions could also give
pharmaceutical researchers clues to the magnitude of possible side
effects of drugs under development.
The development of combination drug therapies, such as for cancer or
viruses, could also benefit from the E-MAP approach. “In such
cases, clinicians want to know — if drugs that inhibit each of two
proteins slow down a cancer or virus — whether the two proteins
interact epistatically, such that inhibiting both produces a much
greater effect than the sum of the two,” he said.
|
|
Versión en español
|
|
Jim Keeley
