Office
of Biological and Environmental Research
DOE
Lowdose Radiation Program Workshop IIIAbstract
_____________________________________________________________________
Title: Genome-scale Modeling of Low-Dose
Irradiation Responses Using Microarray Based Gene Networks.
Authors:
Matthew A. Coleman1, Leif Peterson2, Terence Critchlow1, and
Andrew J. Wyrobek1.
Institutions:
1. Biology & Biotechnology Research Program, Lawrence
Livermore National. Laboratory. 2. Department of Medicine,
Baylor College of Medicine.
Participating Consortium members: Tom Slezak
(LLNL), Dave Nelson (LLNL), Bertram Ludaescher (LBL), Amarnath
Gupta (LBL), Ilkay Altintas (LBL), Tom Potok (ORNL), Mladen
Vouk (NCSU), Calton Pu (Georgia Tech.), Ling Liu (Georgia
Tech.), David Buttler (Georgia Tech.), Dan Rocco (Georgia
Tech.), Henrique Paques (Georgia Tech.), Wei Han (Georgia
Tech.).
Cells
and tissues with similar radiation response phenotypes are
predicted to have common ionizing radiation (IR)-induced gene
expression profiles that are controlled by shared groups of
regulatory elements (also known as synergistic gene groups).
Our overall objective is to utilize genome-scale expression
microarray data in conjunction with DNA sequence/pattern databases
available on the Web, to build a computer-based gene-network
model for identifying, grouping and predicting regulatory
elements that control differential aspects of the early cellular
responses to IR. This research project is defined by: (1)
Grouping genes identified by microarray experiments into IR-responsive
clusters based on their relative-transcript abundance and
their differential IR radiation responses at low (10cGy) and
high doses, (2) Identifying regulatory elements (and their
locations relative to the open reading frame) that distinguish
among separate IR responsive gene clusters and (3) Comparing
IR responsive gene clusters identified “in silico”
to those IR responsive genes identified on microarrays in
many different laboratories. To accomplish this we have brought
together a diverse group of investigators to build a gene
pathway model of the cellular controls of radiation response.
This project takes advantage of stand alone software (CLUSFAVOR,
http://mbcr.bcm.tmc.edu/genepi/) to identify clusters and
groups of interesting IR responsive genes that are then used
to search relational database systems (http://www.llnl.gov/CASC/datafoundry/index.html,
SDM http://sdm.lbl.gov/sdmcenter/)
to parse information from genomic databases such as
GenBank, KEGG and UniGene. Obtained sequence information is
then used for promoter analysis (ModelInspector, http://genomatix.gsf.de/)
to identify synergistic gene groups that share conserved regulatory
elements. This database is being designed to allow data analysis
across multiple platforms (i.e., cDNA as well as oligo-array
data). In the future, experimental low-dose radiation array
data sets from microarray experiments will be compared to
the resulting database of synergistic groups and their regulatory
elements to assess the ability and accuracy of our ability
to predicted the same IR responsive genes and pathways. The
identification and characterization of regulatory element
profiles of IR-responsive genes will provide valuable understanding
of the genetic mechanisms of IR-response and should provide
powerful biological indicators of genetic susceptibilities
for tissue and genetic damage. The resulting model is being
developed as a foundation for a unique predictor of new genes
and for testing new hypotheses related to exposure of IR based
on coordinate gene/pathway interactions.
[This work was performed under the auspices of the U. S. Department
of Energy by the University of California, Lawrence Livermore
National Laboratory under Contract No. W-7405-Eng-48.]
