Minimally-Invasive Radiation Biodosimetry
Functional Genomics Core
Core Leader: Sally Amundson, Columbia University
It is now well established that exposure of human cells to environmental stresses, including ionizing radiation, activates multiple signal transduction pathways, resulting in complex patterns of gene expression change. Expression of specific genes can be both dose- and stress-dependent, making gene-expression profiling a potentially informative approach for much-needed radiation biodosimetry. Several issues remain to be resolved, however, including variations in baseline and treated expression levels among the population, potential confounding effects, and identification of an optimally informative gene set.
Circulating lymphocytes represent a sensitive target for early radiation injury, highly responsive in terms of induced gene expression changes, and relatively easily biopsied. Peripheral blood cells will therefore be our primary model for development of a gene expression biodosimeter for radiation exposure. The Functional Genomics Core will:
Establish and refine gene expression signatures diagnostic of human radiation exposure and dose in support of Project 2.
Provide a resource for oversight of experimental design pertaining to functional genomics and training of staff in support of Project 3 and the Pilot Projects as needed.
By allowing measurement of gene expression changes across virtually the entire genome in a single experiment, the modern long oligonucleotide microarray approach is not only an efficient screen for potentially informative radiation biomarkers, but may also provide insight into the mechanistic basis of the human response to early radiation injury. Such information may suggest refinements of other biodosimetry techniques, and facilitate future collaborations between the Center for High-Throughput Minimally-Invasive Radiation Biodosimetry and the other Centers for Medical Countermeasures against Radiation, for instance by suggesting “druggable targets” and opportunities for development of chemoprotective intervention strategies.
KEY ASPECTS OF THE FUNCTIONAL GENOMICS CORE APPROACH
RNA Quality: Before any microarray hybridization can be undertaken, it is necessary to confirm that the RNA to be labeled is of acceptable quality. The core uses the Agilent Bioanalyzer (pictured right) to accomplish this step.
Amplification: In order to conserve clinical samples, and enable sharing of the same samples between all our projects, small amounts of RNA must often be amplified prior to labeling and hybridization. For amplification we use the Agilent system for direct incorporation.
Microarrays: While our facility can hybridize essentially any cDNA or spotted oligonucleotide microarray printed in the standard 1" x 3" glass slide format, the central core activities will use Agilent whole genome microarrays.
Hybridization: In the past we relied on the standard static waterbath hybridization with the targets beneath a coverslip. We now use active mixing and the Agilent DNA Microarray Hybridization Oven for increased sensitivity and reproducibility.
Scanning: Our core facility relies on the state-of-the-art Agilent DNA Microarray Scanner and its Feature Extraction Software, which is compatible with industry standard analysis applications.
Downstream Data Analysis: We will continue to use previously developed tools for data analysis (see links to tools below), while collaborating closely with our Bioinformatics Core in the development and application of more sophisticated and specific tools in the course of this project.
ACID - Array Clone Information Database, Lund University
Annotates lists of Gene IDs, Unigene Clusters, Accession numbers, or HUGO gene symbols. Information returned includes chromosome and cytoband, gene description, Locus Link ID, and gene ontology annotation. (Web Interface)
DAVID - Database for Annotation, Visualization and Integrated Discovery, NIAID
This set of tools provides integrated annotation and analysis of lists of GenBank Accession numbers and Affymetrix, Locus Link, or Unigene Identifiers. It also provides links to Gene ontology tools, and KEGG charts. (Web interface)
MatchMiner, NCI Genomics and Bioinformatics Group
This tool performs extensive annotations of gene lists including also Affymetrix identifiers. However, it appears to use an outdated Unigene build. (Web interface)
Gene Ontology Tools
EASE - Expression Analysis Systematic Explorer, NIAID
Gives GO annotations of gene lists with statistics adjusted for multiple comparisons to identify significantly over-represented biological functions. (Web interface and Windows)
GoMiner, NCI Genomics and Bioinformatics Group
GO annotations displayed in tree view or as a Directed Acyclic Graph. Statistical analysis of GO categories and integration to public databases including PubMed, Bio Carta, CGAP, GeneCards, etc. (Windows and Mac OSX)
High Throughput GoMiner, NCI Genomics and Bioinformatics Group
This is similar to GoMiner, but integrates GO information across multiple microarrays. Results can be displayed in “heatmap” or cluster format, and include statistical analyses. (Web interface or UNIX systems including OSX)
PANTHER, SRI International
Provides tools for protein classification and alignments, maps genes to biological processes and molecular functions. Can use gene expression levels in addition to gene lists, and overlay results on pathways. (Web interface)
Data Analysis Tools
BRB Array Tools, NCI Biometric Research Branch
This is a package for visualization and statistical analysis of DNA microarray gene expression data. Identifies genes differentially expressed in groups of experiments, builds and tests multivariate predictors for sample classification by gene expression profiles. Incorporates PCA visualization tools. (Windows – Excel Add-in)
Cytoscape, The Cytoscape Consortium
This is a standalone platform for visualizing molecular interaction networks and integrating these interactions with gene expression profiles. This can also be used as a module in geWorkBench. (Windows and Mac OSX)
SAM - Significance Analysis of Mircoarrays, Stanford University
This is a tool for statistical assessment of differential gene expression, giving false discovery rates. It often misses significant changes across experiments when there are large changes, and consequently large biological variability. (Windows – Excel Add-in)
PAM - Prediction Analysis of Microarrays, Stanford University
This tool performs sample classification from gene expression data using nearest shrunken centroids. It produces lists of genes that characterize defined classes, and provides cross-validation testing and false discovery rates. (Windows – Excel Add-in)
Sample Size and Power Calculations for Microarray Studies, Brigham and Women’s Hospital and Harvard Medical School
This tool may be useful for planning new array experiments based on the parameters of prior experiments with the same or similar systems. (Web interface)
LeFEminer, Genomics and Bioinformatics Group, NCI
A tool for interpreting gene microarray datasets using independently determined gene categories such as those defined by GO, KEGG and MSigDB. LeFEMiner applies a random forest machine learning algorithm in conjunction with a permutation based statistical method to determine which sets of functionally related genes (ie. gene categories or ensembles) are most biologically related to the observed biology or state of the samples. (Web interface)
Public Databases of Microarray Data
GEO - Gene Expression Omnibus, NCBI
ArrayExpress, EMBL-EBI European Bioinformatics Institute
Amundson S.A., Attinger D., Wong C.W., Systems and Methods for Biodosimetry with Biochip Using Gene Expression Signatures. Application Number: 11/844,906
PUBLICATIONS BASED ON THIS WORK
Alexander, G.A., Swartz, H.M., Amundson,S.A., Blakely, W.F., Buddemeier, B., Gallez, B., Dainiak, N., Goans, R.E., Hayes, R.B., Lowry, P.C., Noska, M.A., Okunieff, P., Salner, A.L., Schauer, D.A., Trompier, F., Turteltaub, K.W., Voisin, P., Wiley Jr. A.L. and Wilkins, R. (2007) BiodosEPR-2006 meeting: Acute dosimetry consensus committee recommendations on biodosimetry applications in events involving terrorist uses of radioactive materials and radiation accidents. Radiat Meas 42: 972-996. [doi:10.1016/j.radmeas.2007.05.035] [PDF]
Amundson S.A. (2008) Functional genomics in radiation biology: A gateway to cellular systems-level studies. Radiat Environ Biophys 47: 25-31. [abstract] [PDF]
Amundson, S.A., K.T. Do, L.C. Vinikoor, R.A. Lee, C.A. Koch-Paiz, J. Ahn, M. Reimers, Y. Chen, D.A. Scudiero, J.N. Weinstein, J.M. Trent, M.L. Bittner, P.S. Meltzer and A.J. Fornace, Jr. (2008) Integrating global gene expression and radiation survival parameters across the 60 cell lines of the national cancer institute anticancer drug screen. Cancer Res 68: 415-424. [abstract] [PDF] [supplemental data: SI figure 1, SI table 1, 2, 3 and 4] [microarray data]
Amundson, S.A .(2008) Functional Genomics and a New Era in Radiation Biology and Oncology. BioScience 58(6): 491-500. [abstract] [PDF]
Paul, S. and S.A. Amundson (2008) Development of gene expression signatures for practical radiation biodosimetry. Int J Radiat Onc Biol Phys 71(4): 1236-1244. [abstract] [PDF] [supplemental data: SI1_primers-probes, SI2 6h genes, SI3 24h genes, SI4 Dose genes, SI5 MDS by donor] [microarray data]
Straume, T., Amundson, S.A., Blakely, W.F., Burns, F.J., Chen, A., Dainiak, N., Franklin, S., Leary, J.A., Loftus, D.J., Morgan, W.F., Pellmar, T.C., Stolc, V., Turteltaub, K.W., Vaughan, A.T., Vijayakumar, S. and Wyrobek, A.J. (2008) Meeting Report – NASA Radiation Biomarker Workshop. Radiat Res 170: 393-405. [abstract] [PDF]
Brengues, M., Paap, B., Bittner, M., Amundson, S.A., Seligmann, B., Korn, R., Lenigk, R. and Zenhausern F. Biodosimetry on small blood volume using gene expression assay. Health Phys. 98:179-185 (2010). [abstract] [PDF]
OTHER MICROARRAY RELATED PUBLICATIONS
Amundson, S.A., M. Bittner, Y. Chen, J. Trent, P. Meltzer and A.J. Fornace, Jr. (1999) cDNA microarray hybridization reveals complexity and heterogeneity of cellular genotoxic stress responses. Oncogene, 18: 3666-3672. [abstract]
Amundson, S.A., K.T. Do, S. Shahab, M. Bittner, P. Meltzer, J. Trent and A.J. Fornace, Jr. (2000) Identification of potential mRNA biomarkers of human ionizing radiation exposure in peripheral blood lymphocytes. Radiation Research, 154(3): 342-346. [abstract]
Bittner, M, Y Chen, SA Amundson, J Khan A.J. Fornace, Jr., E Dougherty, PS Meltzer and JM Trent. (2000) Obtaining and evaluating gene expression profiles with cDNA microarrays. Pp. 5-25. In Genomics and Proteomics, S. Suhai (ed.), Kluwer Academic/ Plenum Publishers, New York.
Amundson, S.A., M Bittner, P Meltzer, J Trent and A.J. Fornace, Jr. (2001) Physiological function as regulation of large transcriptional programs: the cellular response to genotoxic stress. Comp. Biochem Physiol. (B), 129: 703-710. [abstract]
Amundson, S.A. and A.J. Fornace, Jr. (2001) Biological indicators for the identification of radiation exposure in humans. Expert Rev. Mol. Diagn, 1(2): 211-219. [abstract]
Amundson S.A., P Meltzer, J Trent, M Bittner and A.J. Fornace, Jr. (2001) Induction of gene expression as a monitor of ionizing radiation exposure. Radiation Research 156: 657-661. [abstract]
Amundson, S.A. and A.J. Fornace Jr. (2001) Gene expression profiles for monitoring radiation exposure. Radiation Protection Dosimetry, 97(1): 11-16. [abstract]
Amundson, S.A., A. Patterson, KT Do and A.J. Fornace, Jr. (2002) A nucleotide excision repair master-switch: p53 regulated coordinate induction of global genomic repair genes. Cancer Biology and Therapy: 1(2): 145-149. [abstract]
Amundson, S.A., R.A. Lee, C.A. Koch-Paiz, M.L. Bittner, P. Meltzer, J.M. Trent and A.J. Fornace, Jr. (2003) Differential responses of stress genes to low dose-rate gamma-irradiation. Molecular Cancer Research, 1(6): 445-452. [abstract]
Amundson, S.A. and A.J. Fornace, Jr. (2003) Microarray approaches for analysis of tumor suppressor gene function, pp. 141-154, in Tumor Suppressor Genes: Methods and Protocols (El-Deiry, WS, ed.) Humana Press, Totowa, NJ. [abstract]
Amundson, S.A. and A.J. Fornace, Jr. (2003) Monitoring human radiation exposure by gene expression profiling: Possibilities and pitfalls. Health Physics, 85(1): 36-42. [abstract]
Amundson, S.A. and A.J. Fornace, Jr (2003) Microarray approaches for analysis of cell cycle regulatory genes, pp. 125-141, in Cell Cycle Checkpoint Control Protocols (Lieberman HB, ed.) Humana Press, Totowa, NJ. [abstract]
Amundson, S.A., ML Bittner and A.J. Fornace, Jr. (2003) Functional genomics as a window on radiation stress signaling. Oncogene, 22(37): 5828-5833. [abstract]
Amundson, S.A. and A.J. Fornace, Jr. (2003) Complexity of stress signaling and responses, pp. 179-183, in the Handbook of Cell Signaling (Haley, M, ed.) Academic Press, San Diego, CA.
Koch-Paiz, C.A., S.A. Amundson, M.L. Bittner, P.S. Meltzer and A.J. Fornace, Jr. (2004) Functional genomics of UV radiation responses in human cells. Mutat. Res., 549(1-2): 65-78 [abstract]
Amundson, S.A., M.B. Grace, C.B. McLeland, M.W. Epperly, A. Yeager, Q. Zhan, J.S. Greenberger and A.J. Fornace, Jr. (2004) Human in vivo radiation-induced biomarkers: gene expression changes in radiotherapy patients. Cancer Research, 64: 6368-6371. [abstract] [dataset]
Amundson, S.A., K.T. Do, L. Vinikoor, C.A. Koch-Paiz, M.L. Bittner, J.M. Trent, P. Meltzer and A.J. Fornace, Jr. (2005) Stress-specific signatures: Expression profiling of p53 wild-type and null human cells. Oncogene, 24: 4572-4579. [abstract]
Center for Radiological Research, Columbia University
website updated 01/15/2010
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