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Science Forum Index » Bio Evolution Forum » News: New Tool To Understand Evolution Of Multi-domain...
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| Robert Karl Stonjek... |
 Posted: Tue May 20, 2008 7:49 am |
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New Tool To Understand Evolution Of Multi-domain Genes Developed
ScienceDaily (May 18, 2008) - Carnegie Mellon scientists have discovered
critical flaws in the standard method used to analyze gene evolution.
Standard methods fail when applied to genes that encode multi-domain
proteins, an important class of proteins crucial to human health.
Computational biologist Dannie Durand and colleagues have for the first time
tackled the dilemma of how to study the ancestry of multi-domain genes.
Correctly identifying gene ancestry is a linchpin of computational genomics.
Genes passed down from a common ancestor tend to perform similar functions
in the cell. Scientists exploit this similarity to perform tasks such as
predicting gene function, mapping human chromosomal regions to corresponding
regions in model organisms, and reconstructing the regulatory circuitry that
turns genes on and off.
Although computational biologists have developed methods to identify genes
that share a common ancestor, current methods often lead to spurious
conclusions when applied genes encode multi-domain proteins. Domains are
sequence fragments that encode the basic building blocks of protein
structure. Evolution makes new genes by mixing and matching domains in novel
combinations, much like a child who builds a house, a car and a helicopter
from the same LEGO kit by combining LEGO blocks in different ways. This
process, called domain shuffling, creates complex proteins that perform
specific, critical tasks such as cell communication and binding to other
cells. When one of these proteins fails, cancer is often the result. Domain
shuffling allows rapid evolution of new proteins, but it also makes it close
to impossible for scientists to determine their ancestry.
In a paper published online in Public Library of Science Computational
Biology May 15, Durand's team presents a novel method to determine whether a
pair of similar genes evolved from a common ancestor, or whether they just
look similar because the same domain was inserted into both genes. Their
method, called "Neighborhood Correlation," is the first to tackle this
problem.
"We needed a completely new approach to determine which multi-domain
proteins share a common ancestor, and we are the first group to propose such
a method," Durand said. "Ours is the first approach to define and analyze
common ancestry in a traditional vertical way, even when domain shuffling
occurs."
Neighborhood Correlation exploits the structure of a statistically weighted
sequence similarity network to differentiate multi-domain genes with shared
ancestries from multi-domain genes that result from domain shuffling. Gene
duplication creates a specific signature in the network, while domain
insertion creates a different characteristic signature. Neighborhood
Correlation captures these signatures, giving pairs that arose through
duplication, and hence share common ancestry, a higher score than genes that
share an inserted domain, but not a common ancestor.
The Carnegie Mellon scientists tested Neighborhood Correlation against 20
protein families -- including Kinases, the largest multi-domain family found
in humans -- whose ancestral relationships are well established through
lab-based research. The tool worked remarkably well in verifying the
ancestral patterns of multi-domain gene evolution for these families, much
better than the tools we use today, Durand said.
Today's computational tools use sequence similarity, assuming that genes
with similar sequences indicate common ancestry. Those methods also use the
length of the similar region to rule out similarity that arose due to
inserted domains. They reason that the longer the sequence shared by two
multi-domain genes, the more likely that those two genes share a common
ancestor.
But Durand's tests showed that this assumption often does not hold. Her team
found disturbing results when they compared sequence similarity to their
Neighborhood Correlation method in evaluating the 20 gene families with
established histories. The sequence similarity method actually yielded false
ancestral associations and missed true ancestral relationships.
Neighborhood Correlation is successful because it takes both gene
duplication and domain insertion into account.
"Not only do we show that Neighborhood Correlation works empirically, we
also provide a sound evolutionary argument as to why it should work," Durand
observed. "Our results show that the organization of sequence similarity
network contains evidence of ancient evolutionary processes. This has
exciting implications for future studies. We hope that comparing the
sequence similarity networks of different species will reveal how
evolutionary processes differ in plants, animals and fungi," Durand said.
"Multicellularity evolved independently in each of those groups. To go from
a single cell to many cells acting together, each time nature had to solve
the same problems of cellular communication and control. But are the
solutions the same in each lineage" How those problems were solved is a
fascinating question."
Although designed for multi-domain families, Durand notes that Neighborhood
Correlation also accurately predicts ancestry in single domain sequences.
The researchers hope that scientists will begin to apply the analysis to
genomic studies to better understand the role multi-domain proteins play in
important evolutionary events, such as the emergence of multicellular
animals and the vertebrate immune system.
Other study authors include Carnegie Mellon's Nan Song, Jacob Joseph and
George Davis. Team members are affiliated with the Ray and Stephanie Lane
Center for Computational Biology, the Department of Biological Sciences and
the School of Computer Science.
The study was funded by the National Science Foundation, National Institutes
of Health, and the David and Lucille Packard Foundation.
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Adapted from materials provided by Carnegie Mellon University, via
EurekAlert!, a service of AAAS.
Carnegie Mellon University (2008, May 1 . New Tool To Understand Evolution
Of Multi-domain Genes Developed. ScienceDaily. Retrieved May 19, 2008, from
http://www.sciencedaily.com/releases/2008/05/080515205640.htm
Posted by
Robert Karl Stonjek |
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