This article has Open Peer Review reports available.
Global gene expression analyses of bystander and alpha particle irradiated normal human lung fibroblasts: Synchronous and differential responses
© Ghandhi et al; licensee BioMed Central Ltd. 2008
Received: 29 September 2008
Accepted: 24 December 2008
Published: 24 December 2008
The existence of a radiation bystander effect, in which non-irradiated cells respond to signals from irradiated cells, is now well established. It raises concerns for the interpretation of risks arising from exposure to low doses of ionizing radiation. However, the regulatory mechanisms involved in the bystander response have not been well elucidated. To provide insight into the signaling pathways responding in bystanders, we have measured global gene expression four hours after bystander and direct alpha particle exposure of primary human lung fibroblasts.
Although common p53-regulated radiation response genes like CDKN1A were expressed at elevated levels in the directly exposed cultures, they showed little or no change in the bystanders. In contrast, genes regulated by NFκB, such as PTGS2 (cyclooxygenase-2), IL8 and BCL2A1, responded nearly identically in bystander and irradiated cells. This trend was substantiated by gene ontology and pathway analyses of the microarray data, which suggest that bystander cells mount a full NFκB response, but a muted or partial p53 response. In time-course analyses, quantitative real-time PCR measurements of CDKN1A showed the expected 4-hour peak of expression in irradiated but not bystander cells. In contrast, PTGS2, IL8 and BCL2A1 responded with two waves of expression in both bystander and directly irradiated cells, one peaking at half an hour and the other between four and six hours after irradiation.
Two major transcriptional hubs that regulate the direct response to ionizing radiation are also implicated in regulation of the bystander response, but to dramatically different degrees. While activation of the p53 response pathway is minimal in bystander cells, the NFκB response is virtually identical in irradiated and bystander cells. This alteration in the balance of signaling is likely to lead to different outcomes in irradiated cells and their bystanders, perhaps leading to greater survival of bystanders and increased risk from any long-term damage they have sustained.
The existence of a bystander effect in which cells not exposed to ionizing radiation respond to a stress signal from nearby irradiated cells is now well established. The recent report of tumor induction in an in vivo mouse bystander model  demonstrates that bystander responses may affect health outcomes. However, it is still not well understood how this indirect stress response may impact the overall risk from low dose radiation exposures. The sharply increasing use of radio-diagnostic procedures makes these questions especially timely.
Bystander studies have employed many diverse models. In some systems, direct cell-to-cell contact and the presence of gap junctions appear to be required [1, 2]. In other experiments, shared medium or the transfer of medium from irradiated to non-irradiated cells is sufficient to transmit an effect . Multiple endpoints have been studied as bystander effects. Most, such as sister chromatid exchanges , micronucleus formation , chromosome aberrations , mutation induction , and oncogenic transformation  are considered deleterious. However, bystander apoptosis  and terminal differentiation  also occur, possibly representing tissue protective mechanisms. Like responses to direct irradiation, bystander effects may represent a balance between protective and potentially harmful mechanisms. The factors contributing to this balance are currently unknown.
Although various signaling molecules, including cytokines , reactive oxygen species , nitric oxide , calcium , cyclooxygenase-2 (PTGS2)  and MAP kinases  have been implicated in the bystander process, the differential signal transduction pathways regulating the responses to bystander damage are not completely understood. In contrast to the prominent role of p53 in cellular responses to direct ionizing radiation exposure, p53 is not required for expression of the bystander effect . However, it has recently been shown that DNA-PKcs and ATM, a major upstream activator of p53, are required for generation of a bystander signal, but not for response to that signal . Thus, the central cellular radiation damage response pathway does appear to have some involvement in bystander signaling.
Many of the cellular responses to direct ionizing radiation exposure are mediated in part through modulation of gene expression. Although translational and post-translational effects are also important, much can be learned from global gene expression studies that compare transcript levels across the entire genome. Accordingly, gene expression profiling has been used to address many questions in radiation biology, including the prediction of radiation sensitivity in tumor cell lines [18, 19] or normal tissue  and predicting exposure dose for biodosimetry . More sophisticated network analyses of transcriptomic data are also starting to provide insight into signaling pathways and key transcription factors involved in radiation responses [19, 22]. Such an approach is well suited to the study of signaling mechanisms involved in cellular crosstalk and bystander responses.
Microarrays have also been used in some bystander studies. Screening of small numbers of genes on membrane-based arrays identified GJA1 (connexin-43)  and PTGS2 (cyclooxygenase-2)  as genes expressed at higher levels in bystanders. Whole transcriptome studies have reported differentially expressed genes after medium transfer from irradiated normal human diploid lung fibroblasts  and in normal human fibroblasts exposed to a small number of carbon ions targeted to defined sites in the culture .
In the present study, we have measured global gene expression in directly irradiated and bystander IMR-90 normal human lung fibroblasts at four hours after exposure to 0.5 Gy alpha particles. We have also monitored micronucleus formation in tandem with gene expression as a physiological indicator of bystander response in each experiment. We used an exposure system that shields half the cells on each dish, allowing both direct cell-to-cell communication and shared medium. Separation of the shielded and exposed sections of the culture allowed us to analyze responding bystanders and the directly irradiated cells generating the signal within the same experiment. We used quantitative real-time PCR to confirm differential expression of 37 genes, and describe a previously un-reported biphasic response of NFκB regulated genes, which is highly synchronous in irradiated and bystander cells. Pathway analysis of our microarray results revealed transcriptional networks centered on p53 and NFκB in directly irradiated cells. In bystander cells, the response of the p53 node was selectively abrogated. In contrast, the response of the NFκB node was nearly identical in bystanders and irradiated cells. Matrix metalloproteinase genes were also coordinately up-regulated in bystanders, indicating a possible tissue remodeling response in bystander cells. Our findings suggest that in fibroblasts, the bystander response centers on stress signaling and cytokines, rather than on classic radiation responses like proliferation, cell death and cell cycle checkpoints. Non-hit cells in proximity to irradiated cells may therefore be involved in regulation of cell and tissue defense in preference to cell fate decisions.
Cell culture, irradiation and RNA isolation
Early passage (population doubling < 35) IMR-90 human lung fibroblasts (Coriell Cell Repository, Camden, NJ) were sub-cultured in Dulbecco's modified Eagle's medium (Invitrogen, Carlsbad, CA) and Ham's F10 medium in a 1:1 mixture plus 15% fetal bovine serum. Mylar-bottomed culture dishes were prepared as described previously . An inner dish with a base of 38-micron-thick Mylar strips was inserted into a larger dish with a 6-micron Mylar base. The 38-micron Mylar completely shields the alpha particles so that only cells on the thinner Mylar areas of the dish were directly irradiated. Cells seeded in these dishes formed a contiguous layer. Cells were exposed to 0 (sham irradiated) or 0.5 Gy 4He ions (125 keV/μm) as simulated alpha particles using the track segment irradiation facility of the 5.5-MV Singletron accelerator at the Radiological Research Accelerator Facility of Columbia University. Four independent experiments were conducted.
Directly irradiated (outer dish) and bystander (inner dish) cells were separated at specified times after irradiation and RNA was isolated using Ribopure (Applied Biosytems, Foster City, CA). RNA concentration was measured using a NanoDrop-1000 spectrophotometer (Thermo Scientific, Waltham, MA) and RNA quality was monitored with the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). All RNA samples had RNA integrity numbers > 9.0  and 260 nm/280 nm absorbance ratios > 2.
Binucleate Micronucleus assay
Cells were incubated for 24 hours after irradiation in the original dishes, then separated, trypsinized, counted and seeded into chamber well slides with 1μg/mL cytochalasin B (Sigma-Aldrich, St. Louis, MO). Cells were fixed at 72 hours after irradiation and stained with 5% Giemsa (Invitrogen). Samples were blinded, and 500 binucleate cells in each were scored for the presence of micronuclei using established criteria .
Microarray Hybridization and Analysis
Cyanine-3 (Cy3) labeled cRNA was prepared from 0.3 μg RNA using the One-Color Low RNA Input Linear Amplification PLUS kit (Agilent). Dye incorporation and cRNA yield were monitored with the NanoDrop ND-1000 Spectrophotometer. 1.5 μg of cRNA (> 9 pmol Cy3 per μg cRNA) was fragmented, hybridized to Agilent Whole Human Genome Oligo Microarrays (G4112F) using the Gene Expression Hybridization Kit, and washed following Agilent's recommendations. Slides were scanned with the Agilent DNA Microarray Scanner (G2505B) and default parameters of Feature Extraction Software 9.1 (Agilent) were used for image analysis, data extraction, background correction, and flagging of non-uniform features.
Background corrected intensities were log2 transformed and median-normalized in BRB-Array Tools, Version 3.7.0 . Non-uniform outliers or features not significantly above background intensity in 20% or more of the hybridizations were filtered out, leaving 25800 features. A further filter requiring a minimum 1.5-fold change in at least 20% of the hybridizations was then applied yielding a final set of 7793 features that were used for subsequent analyses. The microarray data is available through the Gene Expression Omnibus database using accession number GSE12435.
BRB-Array Tools was used to identify genes that were differentially expressed in directly and bystander irradiated cells using a random-variance paired t-test, an improvement over the standard t-test that permits sharing information among genes about within-class variation without assuming that all genes have the same variance . The test compares the differences in mean log-intensities between classes relative to the expected variation in mean differences computed from the independent samples. Genes with p-values less than 0.005 were considered statistically significant. The false discovery rate (FDR) was also estimated for each gene  to control for false positives.
Quantitative Real-Time PCR (qRT-PCR)
Real time PCR assays used on Low-density arrays
Real time PCR sequences used in individual assays
5' CTG GAG ACT CTC AGG GTC GAA
5' CGG CGT TTG GAG TGG TAG AA
5' TCA TGC TGG TCT GCC GCC GT
Relative fold-inductions were calculated by the ΔΔCT method as previously used  and with SDS version 3 software (Applied Biosystems). We measured 7 housekeeping genes on the low-density arrays and applied Genorm  to determine the most appropriate genes for normalizing the results. The low-density array data was normalized to the geometric mean of PPIA and UBC. The individual gene assays in the time-course study were normalized to ACTB.
Gene ontology and pathway analysis
The genes responding significantly (FDR < 10%) to either direct alpha particle or bystander irradiation were imported into PANTHER  and the number of genes in each functional classification category was compared against the number of genes from the NCBI human genome in that category. The binomial test was used to statistically determine over-representation of PANTHER classification categories . Bonferroni corrected p-values less than 0.05 were considered significant.
The sets of genes significantly responding to direct or bystander irradiation (p < 0.005) were also imported into Ingenuity Pathways Analysis (IPA) (Ingenuity® Systems, http://www.ingenuity.com) to analyze network interactions between the genes. The imported genes were mapped onto a global molecular network developed from information contained in the Ingenuity Pathways Knowledge Base. Networks of these significant genes were then algorithmically generated based on their connectivity. The biological functions that were most significant to these networks were determined, and Fischer's exact test was used to calculate p-values determining the probability that each biological function assigned to a network was due to chance alone. We also identified the IPA canonical pathways that were most significant within the differentially expressed gene sets. Fischer's exact test was used to calculate a p-value for the probability that the association between the differentially expressed genes and the canonical pathway was explained by chance alone.
Micronucleus induction in bystander cells
Gene expression profiles in irradiated and bystander cells
In four independent experiments RNA was extracted 4 hours after exposure of IMR-90 human diploid fibroblasts to 0 or 0.5 Gy alpha particles. In each experiment, RNA from directly-, bystander- or mock-irradiated cells was hybridized to human whole genome microarrays using the Agilent one-color protocol. We used the class comparison feature of BRB-Array Tools  to identify genes with significantly different expression levels in IMR-90 fibroblasts 4 hours after direct or bystander exposure to 0.5 Gy alpha particles. In the directly irradiated cells, 300 genes were differentially expressed (p < 0.005; Additional File 1). Of these, 191 had a false discovery rate (FDR) < 10%. In IMR-90 bystander cells 305 genes were differentially expressed (p < 0.005; Additional File 2), 135 of which had a FDR < 10%. 165 genes responded to both direct and bystander irradiation, 73 with FDR < 10% in both conditions (Additional File 3).
Gene ontology analysis
Gene ontology analysis using PANTHER
Inflammation mediated by chemokine and cytokine signaling pathway
6.44 × 10-4
Apoptosis signaling pathway
3.29 × 10-3
3.46 × 10-4
Plasminogen activating cascade
5.92 × 10-3
5.61 × 10-3
2.90 × 10-2
Toll receptor signaling pathway
6.46 × 10-2
TGF-beta signaling pathway
6.51 × 10-2
3.87 × 10-3
Immunity and defense
3.42 × 10-10
2.30 × 10-5
1.54 × 10-9
5.41 × 10-6
Cell proliferation and differentiation
8.99 × 10-9
5.74 × 10-6
1.72 × 10-7
6.83 × 10-4
Intracellular signaling cascade
1.29 × 10-6
2.43 × 10-3
2.89 × 10-6
4.05 × 10-5
7.67 × 10-6
1.51 × 10-5
1.36 × 10-5
Cell surface receptor mediated signal transduction
2.40 × 10-5
1.68 × 10-5
2.73 × 10-5
2.23 × 10-5
Inhibition of apoptosis
2.81 × 10-4
2.51 × 10-4
5.77 × 10-4
4.14 × 10-3
8.92 × 10-4
Cytokine and chemokine mediated signaling pathway
2.58 × 10-3
3.76 × 10-4
Cell cycle control
4.60 × 10-4
1.65 × 10-3
6.54 × 10-11
2.29 × 10-6
3.22 × 10-5
3.32 × 10-4
8.39 × 10-5
1.68 × 10-2
We next used Ingenuity Pathways Analysis (IPA) to perform pathway analysis of the differentially expressed gene sets. The top interacting networks of radiation responsive genes were significantly enriched for functions of cell death, connective and skeletal development and function (p = 10-48), inflammatory and immunological disease, cell-to-cell signaling and function (p = 10-45) and cancer, cell death and tumor morphology (p = 10-29). The top bystander networks were significantly enriched for connective tissue disorder and inflammatory and immunological disease (p = 10-48), connective and skeletal development and function and cellular development (p = 10-35) and tissue and cellular growth and proliferation (p = 10-35).
Gene expression levels by real-time PCR
Comparison of relative gene expression by microarray and qRT-PCR
cyclin dependent kinase inhibitor, p21
Human mouse double minute, p53 binding
growth arrest & DNA damage inducible
Glial cell line-derived neurotrophic factor precursor
damage-specific DNA binding protein 2
activating transcription factor 3
TNF receptor superfamily, member 6
growth differentiation factor 15
interleukin 1 beta
interleukin 1 alpha
Chemokine CXC ligand 2
Chemokine CXC ligand 3
Chemokine CXC ligand 5
BCL2 related protein1
Leukemia inhibitory factor
fibroblast growth factor 2
POU domain, class 5, transcription factor 1
intercellular adhesion molecule 1 (CD54)
matrix metallopeptidase 1
matrix metallopeptidase 3
tumor necrosis factor, alpha-induced prot. 3
superoxide dismutase 2
Plasminogen activator inhibitor 2 precursor
laminin, beta 3
gap junction protein, alpha 1, 43 kDa (connexin 43)
gap junction protein, beta 2, 26 kDa (connexin 26)
dual specificity phosphatase 2
caspase recruitment domain fam., member 9
dickkopf homolog 1
Time course analysis of selected genes
Both direct and bystander exposure of human fibroblasts to alpha particle irradiation increased formation of micronuclei and altered the global gene expression profiles. Micronucleus formation, a measure of unrepaired DNA damage, was monitored in all experiments as a cellular indicator of bystander effects. Both direct and bystander irradiation consistently induced micronuclei (Figure 1). Despite carefully controlling parameters such as cell age, plating density and time on Mylar prior to irradiation, slight differences in parameters such as extent of cell-to-cell contact within the monolayers can still occur between experiments. It was not possible to maintain confluent cells for many days on the Mylar dishes as is often done to standardize fibroblast cultures, since the cells rapidly detach from the Mylar surface under these conditions. Such variations between expriments may have had a somewhat larger effect on gene expression in bystander cells, where the induction of some genes varied more between experiments than in directly irradiated cells. This is reflected in larger error bars for the bystanders at specific time points, as in Figure 3. This may be an effect of the indirect nature of the inducing signal in bystander cells combined with normal inter-experimental variations.
We identified 305 genes responding in the bystander cells, and 300 in the directly irradiated cells. qRT-PCR was used to confirm differential expression of 37 of these genes (Table 4). The microarray analysis overestimated relative changes of two genes by more than 20%, but both were still up-regulated by the qRT-PCR measurement. Nearly a third of the validated genes showed greater fold-changes by qRT-PCR than predicted by the arrays. Such ratio compression is often encountered in microarray experiments, and is thought to be sequence dependent [35, 36]. In all, our microarray analysis was well supported by the qRT-PCR results.
There was a broad similarity between the gene expression response to direct alpha particle exposure and previously reported responses of primary fibroblasts [37–39] and other cell types to x- and gamma rays, in that the major functional categories of responding genes were cell cycle regulation, apoptosis, damage response, and signaling pathways. The alpha particle response showed a strong contribution of p53-regulated genes similar to that commonly seen in other cell lines [19, 34, 37, 38] in response to x rays or gamma rays, and consistent with a prior study of gamma ray irradiated IMR-90 fibroblasts, in which the classic DNA damage response genes were found to be the most responsive . Genes such as GADD45A, BTG2, IL11, MT1G and DKK1 were responsive to both gamma rays  and direct alpha particle irradiation in IMR-90. Potential differences in responses due to differences in radiation quality cannot be inferred from comparisons of these studies, however. Differences in cell strains, experimental protocols, radiation dose and the timing of the gene expression assays, all of which vary among published reports, will result in different gene expression patterns, and preclude such direct comparisons.
Alternately, p53 may be differentially activated in bystander cells. It is known that different stresses and different cellular contexts can result in different patterns of p53-dependent gene expression . Differential regulation of p53 activity is also thought to help explain the switch between the cell protective and cell killing functions of p53 [41, 42]. Interestingly, among the p53 genes with apoptosis-related gene ontology annotations, 5 of 11 genes responding to direct irradiation are apoptosis-promoting and 6 of 11 are anti-apoptotic. In contrast, only six of these genes respond to bystander irradiation, including five annotated as anti-apoptotic, and only one annotated as pro-apoptotic. This may suggest activation of a more specialized protective response of the p53 pathway in bystander cells. The balance of pro- and anti-apoptotic signals in a cell is influenced by factors in addition to p53, however. Since apoptosis is not efficiently induced by radiation in primary fibroblasts, the up-regulation of pro-apoptotic genes by direct radiation does not appear to be sufficient to tip the overall balance toward programmed cell death in this cell type.
In contrast to p53, genes regulated by NFκB responded almost identically in irradiated and bystander cells (Figure 4b). Gene ontology analysis indicated that the NFκB cascade was a highly significant process in response to both treatments (Table 3). Activation of NFκB has been demonstrated in bystander cells, and its chemical inhibition has also been shown to reduce mutant fraction in bystander cells , consistent with a central role in bystander responses. Related processes of inflammation, immunity and cell signaling were also highly significant in bystander cells (Table 3). The products of many of the genes responding to both direct and bystander irradiation are signaling molecules that are expressed extracellularly (Figure 2), making them attractive candidates for soluble mediators of the bystander effect. Indeed, IL8, one of the most robust responders in both irradiated and bystander cells, was one of the first factors implicated in transmission of a bystander signal .
In addition to elevation of chemokine and cytokine signaling molecules, PANTHER gene ontology analysis also indicated a significant radiation and bystander response of the plasminogen activating cascade (Table 3). This pathway regulates remodeling of the extracellular matrix (ECM) and is a common marker of metastasis  and chronic inflammation . Genes from this pathway that were expressed at higher levels in both irradiated and bystander fibroblasts (Table 4, Additional File 3) included SERPINB2, and the matrix metalloproteinase (MMP) genes MMP1, MMP3 and MMP10, as well as ICAM1, a regulator of MMP levels . A previous bystander study also reported up-regulation of MMP1 and MMP3 in irradiated fibroblasts, although medium transfer did not elicit a response from these genes , perhaps indicating a requirement for direct cell-to-cell contact, or possibly even an ECM-mediated effect. MMP17 has also been reported as over-expressed in bystander, but not directly irradiated, fibroblasts . Although SERPINB2 is a negative regulator of tissue degradation, the MMPs are positive regulators and would be expected to promote tissue and matrix degradation. These opposing activities may represent a self-limiting activation of ECM remodeling, which represents a novel potential bystander response.
In contrast to results with fibroblasts, MMP2, MMP3 and MMP9 were reported to be sharply but transiently down-regulated by irradiation of human lens epithelial cells , although ICAM1 was up-regulated in these cells . This pattern of responses may be specific to lens epithelium, possibly contributing to radiation cataractogenesis. Bystander effects on the ECM might similarly be expected to be tissue specific.
Because the bystander signal must be generated in the irradiated cells, then transmitted to bystanders, it might be reasonable to expect a delay in the bystander response due to the time required to produce and transmit a signal from the irradiated cells. Indeed, a lag of four hours was recently reported between irradiated and bystander cells for p53 phosphorylation . To check for a possible lag effect on gene expression, we measured the response over time of one gene responding predominantly in the directly irradiated cells (CDKN1A) and three genes responding similarly in irradiated and bystander cells (IL8, PTGS2 and BCL2A1). The slight response of CDKN1A in bystanders was confirmed, and was not due simply to a difference in timing, as slightly elevated levels of this gene remained steady between 30 minutes and 24 hours after irradiation (Figure 3a).
The expression pattern of the other three genes showed a striking biphasic response, with nearly identical magnitudes of induction in irradiated and bystander cells at all times (Figure 3b–d). This suggests a common regulator activated by the same pathway in both irradiated and bystander cells. A similar biphasic pattern has been reported for FGF2 expression, with peaks at 0.5 and 3–4 hours after exposure of human lens epithelial cells to helium ions . This pattern also parallels that reported for NFκB binding in response to TNFα treatment of human T-cells and mouse fibroblasts . These waves of NFκB activity are thought to arise from oscillations between NFκB and its inhibitor IκB. A similar biphasic activation has been reported for receptor tyrosine kinases, which are activated within minutes of radiation exposure . For instance, phosphorylation of ERBB1 peaks at 5–10 minutes after irradiation, returns to baseline levels, then shows a second wave of activation, most likely in response to the release of factors such as TGF-α .
We found no lag of gene expression response in bystander cells, even at the earliest time measured (30 minutes). This virtually simultaneous response in bystander and irradiated cells implies a very fast-moving signal. One group, using γ-H2AX foci as a rapid indicator of bystander responses, has been able to time the movement of the bystander signal through a culture of fibroblasts. They reported significant increases in γ-H2AX foci 2.5 millimeters away from the irradiated cells within 2 minutes . Since in our system, no bystander is more than about 1 millimeter away from irradiated cells, this rate of signal transmission would be consistent with our observation of essentially no lag time between responses in irradiated and bystander cells.
Analysis of directly irradiated normal human fibroblasts and their non-irradiated bystanders has implicated two major regulatory hubs in the global gene expression response. Genes regulated by p53 respond preferentially in directly irradiated cells with only muted responses in bystanders. Conversely, the responses of genes regulated by NFκB are virtually identical in both irradiated and bystander cells. These genes show a distinctive biphasic response suggestive of complex regulatory pathways synchronized throughout the entire culture, in irradiated and bystander cells alike. The greater relative contribution of signaling through NFκB in bystander cells may tip the balance toward survival of these cells, even in the presence of persistent damage, possibly putting bystander cells at increased risk for long-term consequences of radiation damage.
We would like to thank Mr. Stephen Marino and the RARAF staff for assistance with irradiations. The Radiological Research Accelerator Facility (RARAF) is an NIH supported Resource Center through NIBIB grant EB-002033. This work was supported by NIH grant CA-49062.
- Mancuso M, Pasquali E, Leonardi S, Tanori M, Rebessi S, Di Majo V, Pazzaglia S, Toni MP, Pimpinella M, Covelli V, Saran A: Oncogenic bystander radiation effects in Patched heterozygous mouse cerebellum. Proc Natl Acad Sci U S A. 2008, 105 (34): 12445-12450. 10.1073/pnas.0804186105.View ArticlePubMedPubMed CentralGoogle Scholar
- Azzam EI, de Toledo SM, Little JB: Expression of CONNEXIN43 is highly sensitive to ionizing radiation and other environmental stresses. Cancer Res. 2003, 63: 7128-7135.PubMedGoogle Scholar
- Mothersill C, Seymour CB: Cell-cell contact during gamma irradiation is not required to induce a bystander effect in normal human keratinocytes: evidence for release during irradiation of a signal controlling survival into the medium. Radiat Res. 1998, 149: 256-262. 10.2307/3579958.View ArticlePubMedGoogle Scholar
- Hagelstrom RT, Askin KF, Williams AJ, Ramaiah L, Desaintes C, Goodwin EH, Ullrich RL, Bailey SM: DNA-PKcs and ATM influence generation of ionizing radiation-induced bystander signals. Oncogene. 2008Google Scholar
- Prise KM, Belyakov OV, Folkard M, Michael BD: Studies of bystander effects in human fibroblasts using a charged particle microbeam. Int J Radiat Biol. 1998, 74: 793-798. 10.1080/095530098141087.View ArticlePubMedGoogle Scholar
- Nagasawa H, Little JB: Induction of sister chromatid exchanges by extremely low doses of alpha-particles. Cancer Res. 1992, 52: 6394-6396.PubMedGoogle Scholar
- Zhou H, Randers-Pehrson G, Waldren CA, Vannais D, Hall EJ, Hei TK: Induction of a bystander mutagenic effect of alpha particles in mammalian cells. Proc Natl Acad Sci USA. 2000, 97: 2099-2104. 10.1073/pnas.030420797.View ArticlePubMedPubMed CentralGoogle Scholar
- Lewis DA, Mayhugh BM, Qin Y, Trott K, Mendonca MS: Production of delayed death and neoplastic transformation in CGL1 cells by radiation-induced bystander effects. Radiat Res. 2001, 156: 251-258. 10.1667/0033-7587(2001)156[0251:PODDAN]2.0.CO;2.View ArticlePubMedGoogle Scholar
- Sawant SG, Randers-Pehrson G, Geard CR, Brenner DJ, Hall EJ: The bystander effect in radiation oncogenesis: I. Transformation in C3H 10T1/2 cells in vitro can be initiated in the unirradiated neighbors of irradiated cells. Radiat Res. 2001, 155: 397-401. 10.1667/0033-7587(2001)155[0397:TBEIRO]2.0.CO;2.View ArticlePubMedGoogle Scholar
- Belyakov OV, Folkard M, Mothersill C, Prise KM, Michael BD: Bystander-induced differentiation: a major response to targeted irradiation of a urothelial explant model. Mutat Res. 2006, 597: 43-49.View ArticlePubMedGoogle Scholar
- Iyer R, Lehnert BE, Svensson R: Factors underlying the cell growth-related bystander responses to alpha particles. Cancer Res. 2000, 60: 1290-1298.PubMedGoogle Scholar
- Morgan WF, Hartmann A, Limoli CL, Nagar S, Ponnaiya B: Bystander effects in radiation-induced genomic instability. Mutat Res. 2002, 504: 91-100.View ArticlePubMedGoogle Scholar
- Han W, Wu L, Chen S, Bao L, Zhang L, Jiang E, Zhao Y, Xu A, Hei TK, Yu Z: Constitutive nitric oxide acting as a possible intercellular signaling molecule in the initiation of radiation-induced DNA double strand breaks in non-irradiated bystander cells. Oncogene. 2007, 26: 2330-2339. 10.1038/sj.onc.1210024.View ArticlePubMedGoogle Scholar
- Shao C, Lyng FM, Folkard M, Prise KM: Calcium fluxes modulate the radiation-induced bystander responses in targeted glioma and fibroblast cells. Radiat Res. 2006, 166: 479-487. 10.1667/RR3600.1.View ArticlePubMedGoogle Scholar
- Zhou H, Ivanov VN, Gillespie J, Geard CR, Amundson SA, Brenner DJ, Yu Z, Lieberman HB, Hei TK: Mechanism of radiation-induced bystander effect: role of the cyclooxygenase-2 signaling pathway. Proc Natl Acad Sci USA. 2005, 102: 14641-14646. 10.1073/pnas.0505473102.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhou H, Ivanov VN, Lien YC, Davidson M, Hei TK: Mitochondrial function and nuclear factor-kappaB-mediated signaling in radiation-induced bystander effects. Cancer Res. 2008, 68: 2233-2240. 10.1158/0008-5472.CAN-07-5278.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang Y, Zhou J, Held KD, Redmond RW, Prise KM, Liber HL: Deficiencies of double-strand break repair factors and effects on mutagenesis in directly gamma-irradiated and medium-mediated bystander human lymphoblastoid cells. Radiat Res. 2008, 169: 197-206. 10.1667/RR1189.1.View ArticlePubMedGoogle Scholar
- Torres-Roca JF, Eschrich S, Zhao H, Bloom G, Sung J, McCarthy S, Cantor AB, Scuto A, Li C, Zhang S, Jove R, Yeatman T: Prediction of radiation sensitivity using a gene expression classifier. Cancer Res. 2005, 65: 7169-7176. 10.1158/0008-5472.CAN-05-0656.View ArticlePubMedGoogle Scholar
- Amundson SA, Do KT, Vinikoor LC, Lee RA, Koch-Paiz CA, Ahn J, Reimers M, Chen Y, Scudiero DA, Weinstein JN, Trent JM, Bittner ML, Meltzer PS, Fornace AJ: Integrating global gene expression and radiation survival parameters across the 60 cell lines of the National Cancer Institute Anticancer Drug Screen. Cancer Res. 2008, 68: 415-424. 10.1158/0008-5472.CAN-07-2120.View ArticlePubMedGoogle Scholar
- Svensson JP, Stalpers LJ, Esveldt-van LRE, Franken NA, Haveman J, Klein B, Turesson I, Vrieling H, Giphart-Gassler M: Analysis of gene expression using gene sets discriminates cancer patients with and without late radiation toxicity. PLoS Med. 2006, 3: 1904-1914. 10.1371/journal.pmed.0030422.View ArticleGoogle Scholar
- Paul S, Amundson SA: Development of gene expression signatures for practical radiation biodosimetry. Int J Radiat Oncol Biol Phys. 2008, 71: 1236-1244.View ArticlePubMedPubMed CentralGoogle Scholar
- Kumaraswamy S, Chinnaiyan P, Shankavaram UT, Lu X, Camphausen K, Tofilon PJ: Radiation-induced gene translation profiles reveal tumor type and cancer-specific components. Cancer Res. 2008, 68: 3819-3826. 10.1158/0008-5472.CAN-08-0016.View ArticlePubMedPubMed CentralGoogle Scholar
- Chaudhry MA: Bystander effect: biological endpoints and microarray analysis. Mutat Res. 2006, 597: 98-112.View ArticlePubMedGoogle Scholar
- Iwakawa M, Hamada N, Imadome K, Funayama T, Sakashita T, Kobayashi Y, Imai T: Expression profiles are different in carbon ion-irradiated normal human fibroblasts and their bystander cells. Mutat Res. 2008, 642: 57-67.View ArticlePubMedGoogle Scholar
- Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, Lightfoot S, Menzel W, Granzow M, Ragg T: The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol. 2006, 7: 3-10.1186/1471-2199-7-3.View ArticlePubMedPubMed CentralGoogle Scholar
- Fenech M: Cytokinesis-block micronucleus cytome assay. Nat Protoc. 2007, 2: 1084-1104. 10.1038/nprot.2007.77.View ArticlePubMedGoogle Scholar
- Simon R, Lam A, Li M-C, Ngan M, Menenzes S, Zhao Y: Analysis of gene expression data using BRB-Array Tools. Cancer Informatics. 2007, 2: 11-17.Google Scholar
- Wright GW, Simon RM: A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics. 2003, 19: 2448-2455. 10.1093/bioinformatics/btg345.View ArticlePubMedGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Statist Soc B. 1995, 57: 289-300.Google Scholar
- Amundson SA, Grace MB, McLeland CB, Epperly MW, Yeager A, Zhan Q, Greenberger JS, Fornace AJ: Human in vivo radiation-induced biomarkers: gene expression changes in radiotherapy patients. Cancer Res. 2004, 64: 6368-6371. 10.1158/0008-5472.CAN-04-1883.View ArticlePubMedGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3: RESEARCH0034-10.1186/gb-2002-3-7-research0034.View ArticlePubMedPubMed CentralGoogle Scholar
- Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A: PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003, 13: 2129-2141. 10.1101/gr.772403.View ArticlePubMedPubMed CentralGoogle Scholar
- Thomas PD, Kejariwal A, Guo N, Mi H, Campbell MJ, Muruganujan A, Lazareva-Ulitsky B: Applications for protein sequence-function evolution data: mRNA/protein expression analysis and coding SNP scoring tools. Nucleic Acids Res. 2006, 34: W645-50. 10.1093/nar/gkl229.View ArticlePubMedPubMed CentralGoogle Scholar
- Amundson SA, Bittner M, Chen YD, Trent J, Meltzer P, Fornace AJ: cDNA microarray hybridization reveals complexity and heterogeneity of cellular genotoxic stress responses. Oncogene. 1999, 18: 3666-3672. 10.1038/sj.onc.1202676.View ArticlePubMedGoogle Scholar
- Yuen T, Wurmbach E, Pfeffer RL, Ebersole BJ, Sealfon SC: Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays. Nucleic Acids Res. 2002, 30: e48-10.1093/nar/30.10.e48.View ArticlePubMedPubMed CentralGoogle Scholar
- Draghici S, Khatri P, Eklund AC, Szallasi Z: Reliability and reproducibility issues in DNA microarray measurements. Trends Genet. 2006, 22: 101-109. 10.1016/j.tig.2005.12.005.View ArticlePubMedGoogle Scholar
- Ding LH, Shingyoji M, Chen F, Hwang JJ, Burma S, Lee C, Cheng JF, Chen DJ: Gene expression profiles of normal human fibroblasts after exposure to ionizing radiation: a comparative study of low and high doses. Radiat Res. 2005, 164: 17-26. 10.1667/RR3354.View ArticlePubMedGoogle Scholar
- Kis E, Szatmari T, Keszei M, Farkas R, Esik O, Lumniczky K, Falus A, Safrany G: Microarray analysis of radiation response genes in primary human fibroblasts. Int J Radiat Oncol Biol Phys. 2006, 66: 1506-1514.View ArticlePubMedGoogle Scholar
- Sokolov MV, Smirnova NA, Camerini-Otero RD, Neumann RD, Panyutin IG: Microarray analysis of differentially expressed genes after exposure of normal human fibroblasts to ionizing radiation from an external source and from DNA-incorporated iodine-125 radionuclide. Gene. 2006, 382: 47-56. 10.1016/j.gene.2006.06.008.View ArticlePubMedGoogle Scholar
- Hamada N, Ni M, Funayama T, Sakashita T, Kobayashi Y: Temporally distinct response of irradiated normal human fibroblasts and their bystander cells to energetic heavy ions. Mutat Res. 2008, 639: 35-44.View ArticlePubMedGoogle Scholar
- Ohki R, Kawase T, Ohta T, Ichikawa H, Taya Y: Dissecting functional roles of p53 N-terminal transactivation domains by microarray expression analysis. Cancer Sci. 2007, 98: 189-200. 10.1111/j.1349-7006.2006.00375.x.View ArticlePubMedGoogle Scholar
- Das S, Raj L, Zhao B, Kimura Y, Bernstein A, Aaronson SA, Lee SW: Hzf Determines cell survival upon genotoxic stress by modulating p53 transactivation. Cell. 2007, 130: 624-637. 10.1016/j.cell.2007.06.013.View ArticlePubMedPubMed CentralGoogle Scholar
- Narayanan PK, LaRue KE, Goodwin EH, Lehnert BE: Alpha particles induce the production of interleukin-8 by human cells. Radiat Res. 1999, 152: 57-63. 10.2307/3580049.View ArticlePubMedGoogle Scholar
- Kylmaniemi M, Oikarinen A, Oikarinen K, Salo T: Effects of dexamethasone and cell proliferation on the expression of matrix metalloproteinases in human mucosal normal and malignant cells. J Dent Res. 1996, 75: 919-926.View ArticlePubMedGoogle Scholar
- Le NT, Xue M, Castelnoble LA, Jackson CJ: The dual personalities of matrix metalloproteinases in inflammation. Front Biosci. 2007, 12: 1475-1487. 10.2741/2161.View ArticlePubMedGoogle Scholar
- Domeij H, Modeer T, Quezada HC, Yucel-Lindberg T: Cell expression of MMP-1 and TIMP-1 in co-cultures of human gingival fibroblasts and monocytes: the involvement of ICAM-1. Biochem Biophys Res Commun. 2005, 338: 1825-1833. 10.1016/j.bbrc.2005.10.137.View ArticlePubMedGoogle Scholar
- Chang PY, Bjornstad KA, Rosen CJ, Lin S, Blakely EA: Particle radiation alters expression of matrix metalloproteases resulting in ECM remodeling in human lens cells. Radiat Environ Biophys. 2007, 46: 187-194. 10.1007/s00411-006-0087-7.View ArticlePubMedGoogle Scholar
- McNamara MP, Bjornstad KA, Chang PY, Chou W, Lockett SJ, Blakely EA: Modulation of lens cell adhesion molecules by particle beams. Phys Med. 2001, 17 (Suppl 1): 247-248.PubMedGoogle Scholar
- Chang PY, Bjornstad KA, Chang E, McNamara M, Barcellos-Hoff MH, Lin SP, Aragon G, Polansky JR, Lui GM, Blakely EA: Particle irradiation induces FGF2 expression in normal human lens cells. Radiat Res. 2000, 154: 477-484. 10.1667/0033-7587(2000)154[0477:PIIFEI]2.0.CO;2.View ArticlePubMedGoogle Scholar
- Hoffmann A, Levchenko A, Scott ML, Baltimore D: The IkappaB-NF-kappaB signaling module: temporal control and selective gene activation. Science. 2002, 298: 1241-1245. 10.1126/science.1071914.View ArticlePubMedGoogle Scholar
- Schmidt-Ullrich RK, Contessa JN, Lammering G, Amorino G, Lin PS: ERBB receptor tyrosine kinases and cellular radiation responses. Oncogene. 2003, 22: 5855-5865. 10.1038/sj.onc.1206698.View ArticlePubMedGoogle Scholar
- Dent P, Reardon DB, Park JS, Bowers G, Logsdon C, Valerie K, Schmidt-Ullrich R: Radiation-induced release of transforming growth factor alpha activates the epidermal growth factor receptor and mitogen-activated protein kinase pathway in carcinoma cells, leading to increased proliferation and protection from radiation-induced cell death. Mol Biol Cell. 1999, 10: 2493-2506.View ArticlePubMedPubMed CentralGoogle Scholar
- Hu B, Wu L, Han W, Zhang L, Chen S, Xu A, Hei TK, Yu Z: The time and spatial effects of bystander response in mammalian cells induced by low dose radiation. Carcinogenesis. 2006, 27: 245-251. 10.1093/carcin/bgi224.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/1/63/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.