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Functional genomics in radiation biology: a gateway to cellular systems-level studies

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Abstract

Cells respond to ionizing radiation through an intricate network of interacting signaling cascades that are engaged in the regulation of diverse cellular functions, such as cell cycle arrest, DNA repair, and apoptosis. While changes in protein modification, activity, and sub-cellular localization may directly mediate these responses, alterations in gene expression also represent a central component of the pathways involved. Studies of altered gene expression have historically played an important role in elucidating the molecular mechanisms underlying cellular radiation response. In recent years, functional genomics approaches, such as microarray profiling, have been developed that can simultaneously monitor changes in gene expression across essentially the entire genome. However, analogous methods for global measurements of protein expression or modification have lagged behind. As global transcription profiling has become increasingly accessible, the quantity of information on gene expression responses to irradiation has increased dramatically. While many such experiments have provided improved insight into various aspects of radiation response, the diversity of experimental models and details of radiation dose, timing, and data analysis that have been employed means that no single consistent picture has emerged yet. More sophisticated methods for data analysis, data mining, and reverse engineering to reconstruct the underlying response pathways are continually being developed, and can extract additional value from profiling studies. As methods for the global study of other biomolecules become more routine, it will be important to integrate the results of radiation response profiling across multiple biological levels, and to build from simpler experimental systems toward more complex multi-cellular and in vivo systems. The future development of “integromic” models of radiation response should add substantially to the understanding gained from gene expression studies alone.

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References

  1. Fornace AJ, Alamo IJ, Hollander M (1988) DNA damage-inducible transcripts in mammalian cells. Proc Natl Acad Sci USA 85:8800–8804

    Article  ADS  Google Scholar 

  2. Fornace AJ, Nebert D, Hollander M, Luethy J, Papathanasiou M, Fargnoli J, Holbrook N (1989) Mammalian genes coordinately regulated by growth arrest signals and DNA-damaging agents. Mol Cell Biol 9:4196–4203

    Google Scholar 

  3. Kastan M, Zhan Q, El-Deiry WS, Carrier F, Jacks T, Walsh W, Plunkett B, Vogelstein B, Fornace AJ (1992) A mammalian cell cycle checkpoint pathway utilizing p53 and GADD45 is defective in ataxia-telangiectasia. Cell 71:587–597

    Article  Google Scholar 

  4. El-Deiry WS, Tokino T, Velculescu V, Levy D, Parsons R, Trent J, Lin D, Mercer WE, Kinzler K, Vogelstein B (1993) WAF1, a potential mediator of p53 tumor suppression. Cell 75:817–825

    Article  Google Scholar 

  5. Sheikh MS, Carrier F, Papathanasiou M, Hollander M, Zhan Q, Yu K, Fornace AJ (1997) Identification of several human homologs of hamster DNA damage-inducible transcripts. Cloning and characterization of a novel UV-inducible cDNA that codes for a putative RNA-binding protein. J Biol Chem 272:26720–26726

    Article  Google Scholar 

  6. Monks A, Scudiero D, Skehan P, Shoemaker R, Paull K, Vistica D, Hose C, Langley J, Cronise P, Vaigro-Wolff A, Gray-Goodrich M, Campbell H, Mayo J, Boyd M (1991) Feasibility of a high-flux anticancer drug screen using a diverse panel of cultured human tumor cell lines. J Natl Cancer Inst 83:757–766

    Article  Google Scholar 

  7. Paull K, Shoemaker R, Hodes L, Monks A, Scudiero D, Rubinstein L, Plowman J, Boyd M (1989) Display and analysis of patterns of differential activity of drugs against human tumor cell lines: development of mean graph and COMPARE algorithm. J Natl Cancer Inst 81:1088–1092

    Article  Google Scholar 

  8. Shoemaker R (2006) The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6:813–823

    Article  Google Scholar 

  9. Weinstein J (2006) Spotlight on molecular profiling: “Integromic” analysis of the NCI-60 cancer cell lines. Mol Cancer Ther 5:2601–2605

    Article  Google Scholar 

  10. O’connor P, Jackman J, Bae I, Myers T, Fan S, Mutoh M, Scudiero D, Monks A, Sausville E, Weinstein J, Friend S, Fornace AJ Jr, Kohn K (1997) Characterization of the p53 tumor suppressor pathway in cell lines of the National Cancer Institute anticancer drug screen and correlations with the growth-inhibitory potency of 123 anticancer agents. Cancer Res 57:4285–4300

    Google Scholar 

  11. Amundson S, Myers T, Scudiero D, Kitada S, Reed J, Fornace AJ Jr (2000) An informatics approach identifying markers of chemosensitivity in human cancer cell lines. Cancer Res 60:6101–6110

    Google Scholar 

  12. Bae I, Smith M, Sheikh MS, Zhan Q, Scudiero D, Friend SH, O’connor P, Fornace AJ Jr (1996) An abnormality in the p53 pathway following g-irradiation in many wild-type p53 human melanoma lines. Cancer Res 56:840–847

    Google Scholar 

  13. Carrier F, Georgel PT, Pourquier P, Blake M, Kontny HU, Antinore M, Gariboldi M, Myers T, Weinstein J, Pommier Y, Fornace AJ (1999) Gadd45, a p53-responsive stress protein, modifies DNA accessibility on damaged chromatin. Mol Cell Biol 19:1673–1685

    Google Scholar 

  14. Liang P, Pardee A (1992) Differential display of eukaryotic messenger RNA by means of the polymerase chain reaction. Science 257:967–971

    Article  ADS  Google Scholar 

  15. Velculescu V, Zhang L, Vogelstein B, Kinzler K (1995) Serial analysis of gene expression. Science 270:484–487

    Article  ADS  Google Scholar 

  16. Schena M, Shalon D, Davis R, Brown P (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470

    Article  ADS  Google Scholar 

  17. Amundson S, Do K, Fornace AJ Jr (1999) Induction of stress genes by low doses of gamma rays. Radiat Res 152:225–231

    Article  Google Scholar 

  18. Schena M, Shalon D, Heller R, Chai A, Brown P, Davis R (1996) Parallel human genome analysis: microarray-based expression monitoring of 1,000 genes. Proc Natl Acad Sci USA 93:10614–10619

    Article  ADS  Google Scholar 

  19. Pease A, Solas D, Sullivan E, Cronin M, Holmes C, Fodor S (1994) Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc Natl Acad Sci USA 91:5022–5026

    Article  ADS  Google Scholar 

  20. Amundson S, Bittner M, Chen Y, Trent J, Meltzer P, Fornace AJ Jr (1999) cDNA microarray hybridization reveals complexity and heterogeneity of cellular genotoxic stress responses. Oncogene 18:3666–3672

    Article  Google Scholar 

  21. Chen Y, Dougherty E, Bittner M (1997) Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomed Opt 2:364–374

    Article  Google Scholar 

  22. Kim S, Dougherty E, Chen Y, Sivakumar K, Meltzer P, Trent J, Bittner M (2000) Multivariate measurement of gene expression relationships. Genomics 67:201–209

    Article  Google Scholar 

  23. Amundson S, Shahab S, Bittner M, Meltzer P, Trent J, Fornace AJ Jr (2000) Identification of potential mRNA markers in peripheral blood lymphocytes for human exposure to ionizing radiation. Radiat Res 154:342–346

    Article  Google Scholar 

  24. Amundson S, Patterson A, Do K, Fornace AJ (2002) A nucleotide excision repair master-switch: p53 regulated coordinate induction of global genomic repair genes. Cancer Biol Ther 1:145–149

    Google Scholar 

  25. Amundson S, Lee R, Koch-Paiz CA, Bittner ML, Meltzer P, Trent J, Fornace AJ (2003) Differential responses of stress genes to low dose-rate gamma irradiation. Mol Cancer Res 1:445–452

    Google Scholar 

  26. Wang H, Long X, Sun Z, Rigaud O, Xu Q, Huang Y, Sui J, Bai B, Zhou P (2006) Identification of differentially transcribed genes in human lymphoblastoid cells irradiated with 0.5 Gy of gamma-ray and the involvement of low dose radiation inducible CHD6 gene in cell proliferation and radiosensitivity. Int J Radiat Biol 82:181–190

    Article  Google Scholar 

  27. Little JB (2006) Cellular radiation effects and the bystander response. Mutat Res 597:113–118

    Google Scholar 

  28. Azzam E, De Toledo SM, Little JB (2003) Expression of CONNEXIN43 is highly sensitive to ionizing radiation and other environmental stresses. Cancer Res 63:7128–7135

    Google Scholar 

  29. Azzam E, De Toledo SM, Little J (2001) Direct evidence for the participation of gap junction-mediated intercellular communication in the transmission of damage signals from alpha-particle irradiated to nonirradiated cells. Proc Natl Acad Sci USA 98:473–478

    Article  ADS  Google Scholar 

  30. Zhou H, Ivanov V, Gillespie J, Geard C, Amundson S, Brenner D, Yu Z, Lieberman H, Hei T (2005) Mechanism of radiation-induced bystander effect: role of the cyclooxygenase-2 signaling pathway. Proc Natl Acad Sci USA 102:14641–14646

    Article  ADS  Google Scholar 

  31. Chaudhry M (2006) Bystander effect: biological endpoints and microarray analysis. Mutat Res 597:98–112

    Google Scholar 

  32. Yin E, Nelson D, Coleman M, Peterson L, Wyrobek A (2003) Gene expression changes in mouse brain after exposure to low-dose ionizing radiation. Int J Radiat Biol 79:759–775

    Article  Google Scholar 

  33. Ding L, Shingyoji M, Chen F, Hwang J, Burma S, Lee C, Cheng J, Chen D (2005) Gene expression profiles of normal human fibroblasts after exposure to ionizing radiation: a comparative study of low and high doses. Radiat Res 164:17–26

    Article  Google Scholar 

  34. Franco N, Lamartine J, Frouin V, Le Minter P, Petat C, Leplat JJ, Libert F, Gidrol X, Martin MT (2005) Low-dose exposure to gamma rays induces specific gene regulations in normal human keratinocytes. Radiat Res 163:623–635

    Article  Google Scholar 

  35. Goldberg Z, Rocke D, Schwietert C, Berglund S, Santana A, Jones A, Lehmann J, Stern R, Lu R, Hartmann Siantar C (2006) Human in vivo dose-response to controlled, low-dose low linear energy transfer ionizing radiation exposure. Clin Cancer Res 12:3723–3729

    Article  Google Scholar 

  36. Rocke D, Goldberg Z, Schweitert C, Santana A (2005) A method for detection of differential gene expression in the presence of inter-individual variability in response. Bioinformatics 21:3990–3992

    Article  Google Scholar 

  37. Svensson J, Stalpers L, Esveldt-Van LRE, Franken N, Haveman J, Klein B, Turesson I, Vrieling H, Giphart-Gassler M (2006) Analysis of gene expression using gene sets discriminates cancer patients with and without late radiation toxicity. PLoS Med 3:1904–1914

    Article  Google Scholar 

  38. Margolin A, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7(Suppl 1):S7

    Article  Google Scholar 

  39. Basso K, Margolin A, Stolovitzky G, Klein U, Dalla-Favera R, Califano A (2005) Reverse engineering of regulatory networks in human B cells. Nat Genet 37:382–390

    Article  Google Scholar 

  40. Shannon P, Markiel A, Ozier O, Baliga NS, Wang J, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504

    Article  Google Scholar 

  41. Nikitin A, Egorov S, Daraselia N, Mazo I (2003) Pathway studio––the analysis and navigation of molecular networks. Bioinformatics 19:2155–2157

    Article  Google Scholar 

  42. Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng J, Bumgarner R, Goodlett D, Aebersold R, Hood L (2001) Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292:929–934

    Article  ADS  Google Scholar 

  43. Workman CT, Mak H, Mccuine S, Tagne J, Agarwal M, Ozier O, Begley TJ, Samson LD, Ideker T (2006) A systems approach to mapping DNA damage response pathways. Science 312:1054–1059

    Article  ADS  Google Scholar 

  44. Birrell GW, Giaever G, Chu A, Davis R, Brown J (2001) A genome-wide screen in Saccharomyces cerevisiae for genes affecting UV radiation sensitivity. Proc Natl Acad Sci USA 98:12608–12613

    Article  ADS  Google Scholar 

  45. Lee W, St Onge RP, Proctor M, Flaherty P, Jordan MI, Arkin A, Davis R, Nislow C, Giaever G (2005) Genome-wide requirements for resistance to functionally distinct DNA-damaging agents. PLoS Genet 1:e24

    Article  Google Scholar 

  46. Game J, Birrell GW, Brown J, Shibata T, Baccari C, Chu A, Williamson M, Brown J (2003) Use of a genome-wide approach to identify new genes that control resistance of Saccharomyces cerevisiae to ionizing radiation. Radiat Res 160:14–24

    Article  Google Scholar 

  47. Birrell GW, Brown J, Wu H, Giaever G, Chu A, Davis R, Brown J (2002) Transcriptional response of Saccharomyces cerevisiae to DNA-damaging agents does not identify the genes that protect against these agents. Proc Natl Acad Sci USA 99:8778–8783

    Article  ADS  Google Scholar 

  48. Said M, Begley T, Oppenheim A, Lauffenburger D, Samson L (2004) Global network analysis of phenotypic effects: protein networks and toxicity modulation in Saccharomyces cerevisiae. Proc Natl Acad Sci USA 101:18006–18011

    Article  ADS  Google Scholar 

  49. Mousses S, Caplen NJ, Cornelison R, Weaver D, Basik M, Hautaniemi S, Elkahloun A, Lotufo RA, Choudary A, Dougherty E, Suh E, Kallioniemi O (2003) RNAi microarray analysis in cultured mammalian cells. Genome Res 13:2341–2347

    Article  Google Scholar 

  50. Barcellos-Hoff MH, Costes SV (2006) A systems biology approach to multicellular and multi-generational radiation responses. Mutat Res 597:32–38

    Google Scholar 

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Acknowledgments

This work was supported by the Office of Science (BER), US Department of Energy, Grant no. DE-FG02-07ER46336, and by National Institutes of Health Grant CA 49062.

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Authors and Affiliations

  1. Center for Radiological Research, Columbia University Medical Center, 630 W. 168th St., New York, NY, 10032, USA

    Sally A. Amundson

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  1. Sally A. Amundson
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Correspondence to Sally A. Amundson.

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Presented at the First International Workshop on Systems Radiation Biology, February 14–16, 2007, GSF-Research Centre, Neuherberg, Germany.

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Amundson, S.A. Functional genomics in radiation biology: a gateway to cellular systems-level studies. Radiat Environ Biophys 47, 25–31 (2008). https://doi.org/10.1007/s00411-007-0140-1

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