This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Zhao, W. Articles by Gius, D. Search for Related Content PubMed PubMed Citation Articles by Zhao, W. Articles by Gius, D.

Distinct Effects of Ionizing Radiation on In vivo Murine Kidney and Brain Normal Tissue Gene Expression

Authors' Affiliations: ¹ Department of Radiation Oncology, Brain Tumor Center of Wake Forest University, Wake Forest University School of Medicine, Winston-Salem, North Carolina; ² Biomedical Engineering Group, Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan; and ³ Radiation Oncology Sciences Program, Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland

Requests for reprints: David Gius, Radiation Oncology Sciences Program, Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Building 10, B3B69, Bethesda, MD 20892. Phone: 301-496-5457; Fax: 301-480-5439; E-mail. giusd{at}mail.nih.gov.

	Abstract

Top Abstract Materials and Methods Results Discussion References

 
Purpose: There is a growing awareness that radiation-induced normal tissue injury in late-responding organs, such as the brain, kidney, and lung, involves complex and dynamic responses between multiple cell types that not only lead to targeted cell death but also acute and chronic alterations in cell function. The specific genes involved in the acute and chronic responses of these late-responding normal tissues remain ill defined; understanding these changes is critical to understanding the mechanism of organ damage. As such, the aim of the present study was to identify candidate genes involved in the development of radiation injury in the murine kidney and brain using microarray analysis.

Experimental Design: A multimodality experimental approach combined with a comprehensive expression analysis was done to determine changes in normal murine tissue gene expression at 8 and 24 hours after irradiation.

Results: A comparison of the gene expression patterns in normal mouse kidney and brain was strikingly different. This observation was surprising because it has been long assumed that the changes in irradiation-induced gene expression in normal tissues are preprogrammed genetic changes that are not affected by tissue-specific origin.

Conclusions: This study shows the potential of microarray analysis to identify gene expression changes in irradiated normal tissue cells and suggests how normal cells respond to the damaging effects of ionizing radiation is complex and markedly different in cells of differing origin.

Radiation-induced late effects, once viewed solely as a late consequence of clonogenic cell loss, now seems to be due to a complex interrelationship between cell loss and changes in gene expression (4, 5). It has long been assumed that the damage to normal tissues from therapeutic irradiation is inevitable, progressive, and untreatable; however, it is now viewed in terms of dynamic interactions between multiple cell types within a particular organ (6–9) that can be modulated (2, 10, 11). Some of the important lesions include fibrosis, necrosis, atrophy, and vascular damage. Irradiated normal cells are not passive bystanders, merely dying as they attempt to divide, but are active participants in an orchestrated, yet limited, response to injury.

In general, it has been hypothesized that irradiating normal tissues leads to an acute activation of stress kinases and transcription factors (12) and increased production of inflammatory cytokines that may play a central role in the resulting damaging late effects (13). This is followed by an aberrant chronic inflammatory/wound-healing response, in which vascular and parenchymal cell dysfunction and loss, associated with chronic overproduction of particular cytokines and growth factors, result in fibrosis and/or necrosis, depending on the particular organ involved (9). More recently, chronic oxidative stress has been suggested to contribute to the progression of radiation-induced late effects (14).

Despite this paradigm shift, details of the specific genes and/or mechanisms involved in the pathogenesis of radiation-induced late effects remain unclear. The development of technological advances to determine changes in gene expression using cDNA and oligonucleotide microarray analysis presents the potential to increase our understanding of the complex nature of normal tissue responses to radiation (15). It has also enabled investigators to obtain a global view of gene expression patterns in both normal and tumor cells to profile how these cells may respond to cellular stress, including that induced by anticancer agents.

A genomic approach has been used to identify candidate genes of interest in the irradiated mouse kidney and rectum at 10 and 20 weeks after irradiation with a single dose of 16-Gy, 250-kV X rays (16, 17), as well as in the mouse brain after exposure to low dose (0.1 or 2.0 Gy ¹³⁷Cs -rays) ionizing radiation (18). Here, we report microarray results obtained 8 and 24 hours after irradiating the mouse brain and kidney with a single dose of 10 Gy ¹³⁷Cs -rays. These data show not only the potential of microarray analysis to identify gene expression changes in irradiated normal tissues but also that the radiation-induced changes in irradiated normal cells vary markedly between cells from different tissue types.

	Materials and Methods

Top Abstract Materials and Methods Results Discussion References

 
Irradiation of murine kidney and brain. A total of 12 adult male C57Bl6/J mice, 12 to 14 weeks old at the time of irradiation, were entered into this study. The animals were housed up to five per cage with free access to drinking water and standard mouse chow (Harlan Teklad, Madison, WI). The experimental protocol was reviewed and approved by the Institutional Animal Care and Use Committee at Wake Forest University School of Medicine. Unanesthetized mice were placed in a Perspex box and irradiated with a single dose of 10-Gy ¹³⁷Cs -rays whole-body irradiation using a 12,000 Ci self-shielded ¹³⁷Cs irradiator and a dose rate of 4 Gy/min. Sham-irradiated mice were placed similarly in the Perspex box and positioned in the ¹³⁷Cs irradiator for 2.5 minutes without -ray exposure. Groups of mice (n = 3) were euthanized using an overdose of pentobarbital (Nembutal, 90 mg/kg body weight, given i.p.) at 8 and 24 hours after irradiation. Kidneys and brains were removed, frozen in liquid nitrogen, and stored at –70°C.

RNA extraction. Total RNA of the kidneys and brains was isolated using TRIzol according to the protocol supplied by the manufacturer (Invitrogen, Carlsbad, CA); the concentration of RNA was determined spectrophotometrically at 260 nm. RNA was further purified using Rneasy Mini kit according to the manufacturer's recommendations (Qiagen, Valencia, CA) with the addition of DNase digestion with RNase-free DNase set (Qiagen).

Microarray fabrication. The microarrays used for this study contained 7,680 human cDNA clones and were prepared from the Research Genetics Named Genes set (Huntsville, AL). These cDNA clones are enriched for known genes. All 7,680 cDNAs were spotted onto poly-L-lysine-coated slides (National Cancer Institute ROSP 8k Human Array) using an OmniGrid arrayer (GeneMachines, San Carlos, CA) as described in Eisen and Brown (19).

Probe labeling, microarray hybridization, image, and data analysis. Methodologies for the probe labeling reaction and microarray hybridization were the same as described previously (20). Microarrays were scanned at 10-µm resolution on a GenePix 4000A scanner (Axon Instruments, Inc., Foster City, CA). The Cy5- and the Cy3-labeled cDNA samples were scanned at 635 and 532 nm, respectively. The resulting TIFF images were analyzed by GenePix Pro 3.0 software (Axon Instruments).

Each sample was tested in duplicate with alternating of the dyes. Thus, a total of four microarrays for each sample of every brain and kidney sample was done. This was necessary to have enough data points for statistical significance. The ratios of the sample intensity to the reference intensity (green [Cy3]/red [Cy5]) for all targets were determined. Because a normal distribution could not be applied to all components of the data set, a Mann-Whitney test was used to ascertain statistical significance among microarray replicates (21). Well fluorescence was corrected for background fluorescence, and ratios of intensity were established relative to appropriate controls. We selected a 1.5-fold threshold in differences because the multiple repeats in our experimental scheme increase the likelihood of statistical reliability. In some cases, additional genes were included so that the hierarchical cluster map would show novel clusters of important genes. In addition, we do not refer to individual genes as only increased by 1.5-fold but rather to gene clusters based on the multiple experimental conditions. Pearson's moment correlation coefficients (r) were calculated from fluorescent ratio data and relate the overall similarity in gene expression patterns between the two data sets under comparison. Values of r range from –1 (weak to no correlation) to 1 (strong or positive correlation).

Real-time quantitative reverse transcription-PCR. Eight genes were selected for reverse transcription-PCR validation with four increased and four decreased at both 8 and 24 hours after irradiation. For each gene, PCR reactions were done thrice on one sample. These reactions were done as previously described (22). Gene-specific primers used are shown at our web site.⁵ Standard curves for tested genes and ß-actin were done for each PCR, and the relative amounts of transcript of the tested genes were normalized by ß-actin. The average ratios of relative amounts of transcript in irradiated and nonirradiated cells from three replicate runs were calculated.

	Results

Top Abstract Materials and Methods Results Discussion References

 
Differentially expressed genes after irradiation. A total of 12 adult male C57Bl6/J mice, 12 to 14 weeks old, were irradiated with a single dose of 10-Gy ¹³⁷Cs -rays whole-body irradiation using a 12,000-Ci self-shielded ¹³⁷Cs irradiator at a dose rate of 4 Gy/min. Sham-irradiated mice were treated in an identical manner. Groups of mice (n = 3) were euthanized at 8 and 24 hours after irradiation following the removal of kidneys and brains that were subsequently frozen in liquid nitrogen and stored at –70°C. Using this number of mice allowed for a more robust, complete, and statistically significant analysis.

Methodologies for the probe labeling reaction and microarray hybridization were the same as described previously (20). When analyzing the microarray data from control and irradiated mouse brain and kidney, a change of >1.5-fold change of the gene expression between the two tissues was considered significant. These changes in gene expression levels were predetermined before the initiation of the study and are consistent with previous microarray analysis from our group (22). The resulting differentially expressed genes, coupled with a query of the GeneCards database,⁶ enabled us to reconstruct the biological interpretation of a selection of differentially expressed genes.

Analysis of kidneys exposed to radiation showed a total of 42 genes that met the criteria for differential expression (change of >1.5-fold), of which 19 genes were up-regulated, and 23 genes were down-regulated (Table 1 ). (The entire list of genes for the tissues exposed to ionizing radiation can be found at the NIH genomic dedicated web site⁷). The genes that were differentially expressed at 8 and 24 hours after exposure include several known acute-response genes, such as cell cycle checkpoint regulators that might be expected. However, the majority of genes identified were regulators of metabolism, transcription, translation, and signal transduction. In addition, the number of total genes increased (13) and decreased (9) 2-fold was also determined (Table 1).

Analysis of the functional gene groups showed that the largest number of changes in brain RNA patterns involved genes regulating metabolism (20), with cell cycle genes (8) next. If analyzed as a function of changes of >2-fold, a total of 25 genes were identified. Redox genes represented the group containing the highest number of gene expression changes in the brain when comparing both brain and kidney gene patterns. This is an intriguing result and suggests some fundamental differences in how these differing tissues are responding to the damaging and potentially cytotoxic effects of therapeutic irradiation.

Validation of microarray data. To further confirm the results of microarray analysis, real-time, quantitative reverse transcription-PCR analysis was conducted for eight randomly selected genes from both kidney and brain samples at 8 and 24 hours after exposure to -irradiation. Four up-regulated (Fig. 1A ) and four down-regulated (Fig. 1B) genes are shown. The sequences of primers used can be found at our National Cancer Institute web site.⁵ Comparison of the reverse transcription-PCR results showed a complete (100%) concurrence in terms of increases or decreases in gene expression with those measured using the microarray data, with no more than a 25% difference in relative values. Thus, results of the experiments in Fig. 1 indicate an excellent agreement between the microarray and real-time, reverse transcription-PCR analyses.

View larger version (20K): [in this window] [in a new window]   Fig. 1. Real-time RT-PCR verification of gene expression was conducted for eight randomly selected genes from both kidney and brain samples at both 8 and 24 hours after exposure to irradiation. Validation of cDNA microarray data using the real-time RT-PCR assay to verify gene expression results. A, up-regulated genes. (B) down-regulated genes. Total RNA of the kidneys and brains was isolated, and RT-PCR was done as previously described (22) using primers described in the supplementary section (http://www.org/cgi/-content/full/6/4/361/2006).

View larger version (21K): [in this window] [in a new window]   Fig. 2. Hierarchical clustering of gene expression in kidney and brain samples at both 8 and 24 hours after exposure. Ratios of gene expression values for (A) brain and (B) kidney were generated and clustered hierarchically. The mean gene expression values were determined from experimental replicates, calculated as a ratio of HCT116 expression, and clustered hierarchically using Cluster (v. 2.20) and TreeView (v. 1.60) software (19). Up-regulation (i.e., red) and down-regulation (i.e., green) identified gene expression changes, and yellow boxes denote boundaries of the clusters.

These experiments present the changes in genomic gene expression patterns in two normal tissues, kidney, and brain, following exposure to ionizing radiation. For this study, we chose to collect samples at 8 and 24 hours after exposure to 10 Gy of irradiation, and whole tissues were used to isolate RNA for microarray analysis. The most surprising result was the almost total lack of common genes whose RNA levels were changed when comparing the exposed kidney to brain tissues. In addition, there were a surprising number of genes up-regulated that have been previously shown to play a role in buffering changes in the intracellular oxidation/reduction status or redox.

	Discussion

Top Abstract Materials and Methods Results Discussion References

 
This study represents a comprehensive integrated experimental approach to investigate the genomic changes in gene expression in normal tissues exposed to ionizing radiation. In this regard, we did extensive replicate experiments with multiple exposed mice and several RNA samples from each normal mouse kidney and brain tissues exposed to clinically relevant doses of ionizing radiation. The outcome of this analysis yielded insights into how normal tissues respond to the damaging and often cytotoxic effects of irradiation.

These experiments present the changes in genomic gene expression patterns in two normal tissues (kidney and brain) following exposure of ionizing radiation. For this study, we chose to collect samples at 8 and 24 hours after exposure to 10 Gy of irradiation, and whole tissues were used to isolate RNA for microarray analysis. The most surprising result from this microarray analysis is the near complete lack of common genes whose expression is altered in kidney when compared with brain following exposure to ionizing radiation.

A greater examination of the specific genes altered showed that 42 genes were altered in kidney compared with 47 genes in brain > 1.5-fold. Although the number up-regulated and down-regulated in kidney versus brain was also very similar, there was very little in common other than the total number changes (Tables 3 and 4). This can also be seen when comparing the hierarchical clustering pattern of genes changed in the kidney and brain (Fig. 2) as well as when examining the specific genes up-regulated or down-regulated. In fact, only 2 common genes of 100 genes are altered at least 1.5-fold following exposure. This result suggests markedly differing mechanism(s) of genetic preprogrammed tissue cellular response in these two normal tissues.

The results of these experiments raise several surprising questions. The first of which is that the long held hypothesis that cellular genetic response to ionizing radiation in normal cells is likely to be similar is clearly incorrect. There are several possible explanations for this result. For example, the genetic response to irradiation may be more a function of microenvironment than any inherent preprogrammed cellular response to stress. Or, more likely, inherent tissue-specific intracellular signaling pathways, which are different in each different tissue type, are used by differing tissues to respond to the stress of irradiation. This is suggested by the differing number of metabolism genes altered in the brain tissue in contrast to kidney cells. This would seem logical because the metabolic demand to brain tissues/cells is greater than that for kidney cells and, as such, brain cells already have increased redox buffering pathways. Thus, it seems reasonable that brain cells would use these inherently active intracellular pathways to respond to the damaging effects of irradiation. This may explain the greater number of redox-sensitive genes increased in brain tissue in contrast to kidney.

It is also possible that the local cellular microenvironment as well as the connective and stromal cells may play a critical role in the genomic differences between kidney and brain tissues. For example, in the central nervous system, neural cells cannot proliferate, although astrocytes can, whereas most kidney cells can cycle if appropriately stressed. This might explain the observations that there were more changes in cell cycle genes in irradiated kidney than brain tissue cells.

It is tempting to compare these results with several other studies that have used genomics to investigate the changes in gene expression following exposure to ionizing radiation. However, the methods used in these studies, as well as the model systems, were quite different, making any significant conclusions difficult. For example, in our study, RNA was isolated from whole tissue biopsies to generate RNA for microarray analysis, whereas other studies have used microdissected tissues. In addition, other studies have used similar methods but chose time points much farther out than used in this study or focused their primary analysis on genes that play a role in radioresistance in contrast to normal tissues. Furthermore, other genomic studies have used tissue culture model systems and as such makes comparison with our study difficult. Therefore, no significant attempt was made to compare our results with that of others.

In this study, we chose to use whole organs to isolate RNA, and this method, like many model systems, has inherent shortcomings that may obscure important genetic response (profiling) data. For example, it seems likely that the complex three-dimensional structure of the brain and kidney contains specific subregions that may and should respond very differently to the damaging and cytotoxic effects of ionizing radiation. As such, a 2- or 3-fold in expression levels in one of these subregions could easily be washed out (or averaged out) by the other nonresponding part of the organ. Thus, the size of the dominator may prevent the identification of a up-regulated or down-regulated gene, and as such, this approach may be biased towards a sort of "averaged" tissue response across all tissues and not a "true" reflection of important alterations. This may be one of many explanations for the differing results from microarray studies done on normal tissues from several other laboratories (17, 23–25).

Another surprising finding was the almost complete absence of genes or gene families representative of activation of the inflammatory response. It is well documented that exposure to ionizing radiation can induce changes in normal tissues that clinically seem similar to a local inflammation. In addition, it has been previously been published that many genes and signaling factors involved in inflammation are activated following exposure. One possible explanation for this result may be the timing chosen to harvest the tissues. It is possible that at 8 and 24 hours, there is a minimal inflammatory effect in normal tissues, and such a genomic effect would be observed at longer time points after exposure to ionizing radiation.

These experiments continue to expand the knowledge of the genome and changes in gene expression following exposure to anticancer agents generally and ionizing radiation specifically. The differing and diverse changes in expression patterns clearly show the complex manner in which normal cells respond to this type of cellular stress and suggest that cells of differing origin respond to the damaging effects of irradiation in very differing ways.

	Footnotes

 
Grant support: Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research, Radiation Oncology Branch, and grants DK51612 and CA82722 (M.E.C. Robbins).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Note: W. Zhao and E.Y. Chuang contributed equally to this work.

⁴ http://plan.cancer.gov/public/survivor.htm.

⁵ http://www.org/cgi/-content/full/6/4/361/2006.

⁶ http://nciarray.nci.nih.gov/cards/.

⁷ http://www.X.org/cgi/content/full/6/4/361/yy.

Received 11/ 7/05; revised 2/ 4/06; accepted 4/13/06.

	References

Top Abstract Materials and Methods Results Discussion References

 

1. Hill RP, Rodemann HP, Hendry JH, Roberts SA, Anscher MS. Normal tissue radiobiology: from the laboratory to the clinic. Int J Radiat Oncol Biol Phys 2001;49:353–65.[CrossRef][Medline]
2. Stone HB, Coleman CN, Anscher MS, McBride WH. Effects of radiation on normal tissue: consequences and mechanisms. Lancet Oncol 2003;4:529–36.[CrossRef][Medline]
3. Coleman CN, Blakely WF, Fike JR, et al. Molecular and cellular biology of moderate-dose (1-10 Gy) radiation and potential mechanisms of radiation protection: report of a workshop at Bethesda, Maryland, December 17-18, 2001. Radiat Res 2003;159:812–34.[CrossRef][Medline]
4. Withers HR, Peters LJ, Kogelnik HD. The pathobiology of late effects in irradiation. In: Meyn RE, Withers HR, editors. Radiation biology in cancer research. New York: Raven Press; 1980. p. 439–48.
5. Rubin P, Casarett GW. Clinical radiation pathology as applied to curative radiotherapy. Cancer 1968;22:767–78.[Medline]
6. Jaenke RS, Robbins ME, Bywaters T, Whitehouse E, Rezvani M, Hopewell JW. Capillary endothelium. Target site of renal radiation injury. Lab Invest 1993;68:396–405.[Medline]
7. Schultheiss TE, Stephens LC. Invited review: permanent radiation myelopathy. Br J Radiol 1992;65:737–53.[Medline]
8. Rubin P, Finkelstein J, Shapiro D. Molecular biology mechanisms in the radiation induction of pulmonary injury syndromes: interrelationship between the alveolar macrophage and the septal fibroblast. Int J Radiat Oncol Biol Phys 1992;24:93–101.[Medline]
9. Denham JW, Hauer-Jensen M. The radiotherapeutic injury: a complex ‘wound’. Radiother Oncol 2002;63:129–45.[CrossRef][Medline]
10. Stone HB, McBride WH, Coleman CN. Modifying normal tissue damage postirradiation. Report of a workshop sponsored by the Radiation Research Program, National Cancer Institute, Bethesda, Maryland, September 6-8, 2000. Radiat Res 2002;157:204–23.[CrossRef][Medline]
11. Coleman CN, Stone HB, Moulder JE, Pellmar TC. Modulation of radiation injury. Science 2004;304:693–4.[Abstract/Free Full Text]
12. Dent P, Yacoub A, Contessa J, et al. Stress and radiation-induced activation of multiple intracellular signaling pathways. Radiat Res 2003;159:283–300.[Medline]
13. Hallahan DE. Radiation-mediated gene expression in the pathogenesis of the clinical radiation response. Semin Radiat Oncol 1996;6:250–67.[Medline]
14. Robbins ME, Zhao W. Chronic oxidative stress and radiation-induced late normal tissue injury: a review. Int J Radiat Biol 2004;80:251–9.[CrossRef][Medline]
15. Snyder AR, Morgan WF. Gene expression profiling after irradiation: clues to understanding acute and persistent responses? Cancer Metastasis Rev 2004;23:259–68.[CrossRef][Medline]
16. Kruse JJ, te Poele JA, Velds A, et al. Identification of differentially expressed genes in mouse kidney after irradiation using microarray analysis. Radiat Res 2004;161:28–38.[CrossRef][Medline]
17. Kruse JJ, te Poele JA, Russell NS, Boersma LJ, Stewart FA. Microarray analysis to identify molecular mechanisms of radiation-induced microvascular damage in normal tissues. Int J Radiat Oncol Biol Phys 2004;58:420–6.[CrossRef][Medline]
18. Yin E, Nelson DO, Coleman MA, Peterson LE, Wyrobek AJ. Gene expression changes in mouse brain after exposure to low-dose ionizing radiation. Int J Radiat Biol 2003;79:759–75.[Medline]
19. Eisen MB, Brown PO. DNA arrays for analysis of gene expression. Methods Enzymol 1999;303:179–205.[Medline]
20. Chuang YY, Chen Y, Gadisetti, et al. Gene expression after treatment with hydrogen peroxide, menadione, or t-butyl hydroperoxide in breast cancer cells. Cancer Res 2002;62:6246–54.[Abstract/Free Full Text]
21. Troyanskaya OG, Garber ME, Brown PO, Botstein D, Altman RB. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 2002;18:1454–61.[Abstract/Free Full Text]
22. Gius D, Cui H, Bradbury CM, et al. Distinct effects on gene expression of chemical and genetic manipulation of the cancer epigenome revealed by a multimodality approach. Cancer Cell 2004;6:361–71.[CrossRef][Medline]
23. McBride WJ, Kerns RT, Rodd ZA, et al. Alcohol effects on central nervous system gene expression in genetic animal models. Alcohol Clin Exp Res 2005;29:167–75.[Medline]
24. Mahmoud-Ahmed AS, Atkinson S, Wong CS. Early gene expression profile in mouse brain after exposure to ionizing radiation. Radiat Res 2006;165:142–54.[Medline]
25. Lin SW, Cook M, Finnberg N, Bernhard E, Baldwin D, El-Deiry WS. p53-dependent induction of serine proteases in irradiated mouse colon. Cancer Biol Ther 2004;3:1290–7.[Medline]

This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Zhao, W. Articles by Gius, D. Search for Related Content PubMed PubMed Citation Articles by Zhao, W. Articles by Gius, D.