It is postulated that secreted soluble factors are important contributors of bystander effect and adaptive responses observed in low dose ionizing radiation. Using multidimensional liquid chromatography-mass spectrometry based proteomics, we quantified the changes of skin tissue secretome – the proteins secreted from a full thickness, reconstituted 3-dimensional skin tissue model 48 hr after exposure to 3, 10 and 200 cGy of X-rays. Overall, 135 proteins showed statistical significant difference between the sham (0 cGy) and any of the irradiated groups (3, 10 or 200 cGy) on the basis of Dunnett adjusted t-test; among these, 97 proteins showed a trend of downregulation and 9 proteins showed a trend of upregulation with increasing radiation dose. In addition, there were 21 and 8 proteins observed to have irregular trends with the 10 cGy irradiated group either having the highest or the lowest level among all three radiated doses. Moreover, two proteins, carboxypeptidase E and ubiquitin carboxyl-terminal hydrolase isozyme L1 were sensitive to ionizing radiation, but relatively independent of radiation dose. Conversely, proteasome activator complex subunit 2 protein appeared to be sensitive to the dose of radiation, as rapid upregulation of this protein was observed when radiation doses were increased from 3, to 10 or 200 cGy. These results suggest that different mechanisms of action exist at the secretome level for low and high doses of ionizing radiation.
References
[1]
Royal HD (2008) Effects of low level radiation-what's new? Semin Nucl Med 38: 392–402. doi: 10.1053/j.semnuclmed.2008.05.006
[2]
Hendee WR, O'Connor MK (2012) Radiation risks of medical imaging: separating fact from fantasy. Radiology 264: 312–321. doi: 10.1148/radiol.12112678
[3]
BIER V (2006) Phase 2, Health risks from exposure to low levels of ionizing radiation.
[4]
Dauer LT, Brooks AL, Hoel DG, Morgan WF, Stram D, et al. (2010) Review and evaluation of updated research on the health effects associated with low-dose ionising radiation. Radiat Prot Dosimetry 140: 103–136. doi: 10.1093/rpd/ncq141
[5]
Brenner DJ, Doll R, Goodhead DT, Hall EJ, Land CE, et al. (2003) Cancer risks attributable to low doses of ionizing radiation: assessing what we really know. Proc Natl Acad Sci U S A 100: 13761–13766. doi: 10.1073/pnas.2235592100
[6]
Yang F, Waters KM, Webb-Robertson BJ, Sowa MB, von Neubeck C, et al. (2012) Quantitative phosphoproteomics identifies filaggrin and other targets of ionizing radiation in a human skin model. Exp Dermatol 21: 352–357. doi: 10.1111/j.1600-0625.2012.01470.x
[7]
Hu ZP, Kim YM, Sowa MB, Robinson RJ, Gao X, et al. (2012) Metabolomic response of human skin tissue to low dose ionizing radiation. Mol Biosyst 8: 1979–1986. doi: 10.1039/c2mb25061f
[8]
Snijders AM, Marchetti F, Bhatnagar S, Duru N, Han J, et al. (2012) Genetic differences in transcript responses to low-dose ionizing radiation identify tissue functions associated with breast cancer susceptibility. PLoS ONE 7: e45394. doi: 10.1371/journal.pone.0045394
[9]
Knops K, Boldt S, Wolkenhauer O, Kriehuber R (2012) Gene expression in low- and high-dose-irradiated human peripheral blood lymphocytes: possible applications for biodosimetry. Radiat Res 178: 304–312. doi: 10.1667/rr2913.1
[10]
Chaudhry MA, Omaruddin RA, Kreger B, de Toledo SM, Azzam EI (2012) Micro RNA responses to chronic or acute exposures to low dose ionizing radiation. Mol Biol Rep 39: 7549–7558. doi: 10.1007/s11033-012-1589-9
[11]
Albrecht H, Durbin-Johnson B, Yunis R, Kalanetra KM, Wu S, et al. (2012) Transcriptional response of ex vivo human skin to ionizing radiation: comparison between low- and high-dose effects. Radiat Res 177: 69–83. doi: 10.1667/rr2524.1
[12]
Makridakis M, Vlahou A (2010) Secretome proteomics for discovery of cancer biomarkers. J Proteomics 73: 2291–2305. doi: 10.1016/j.jprot.2010.07.001
[13]
Morgan WF, Sowa MB (2007) Non-targeted bystander effects induced by ionizing radiation. Mutat Res 616: 159–164. doi: 10.1016/j.mrfmmm.2006.11.009
[14]
Varnum SM, Springer DL, Chaffee ME, Lien KA, Webb-Robertson BJ, et al. (2012) The effects of low-dose irradiation on inflammatory response proteins in a 3D reconstituted human skin tissue model. Radiat Res 178: 591–599. doi: 10.1667/rr2976.1
[15]
Mothersill C, Antonelli F, Dahle J, Dini V, Hegyesi H, et al. (2012) A laboratory inter-comparison of the importance of serum serotonin levels in the measurement of a range of radiation-induced bystander effects: overview of study and results presentation. Int J Radiat Biol 88: 763–769. doi: 10.3109/09553002.2012.715795
[16]
Piehowski PD, Petyuk VA, Orton DJ, Xie F, Moore RJ, et al. (2013) Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis. J Proteome Res 12: 2128–2137. doi: 10.1021/pr301146m
[17]
Zimmer JS, Monroe ME, Qian WJ, Smith RD (2006) Advances in proteomics data analysis and display using an accurate mass and time tag approach. Mass Spectrom Rev 25: 450–482. doi: 10.1002/mas.20071
[18]
Mayampurath AM, Jaitly N, Purvine SO, Monroe ME, Auberry KJ, et al. (2008) DeconMSn: a software tool for accurate parent ion monoisotopic mass determination for tandem mass spectra. Bioinformatics 24: 1021–1023. doi: 10.1093/bioinformatics/btn063
[19]
Kim S, Gupta N, Pevzner PA (2008) Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases. J Proteome Res 7: 3354–3363. doi: 10.1021/pr8001244
[20]
Petritis K, Kangas LJ, Yan B, Monroe ME, Strittmatter EF, et al. (2006) Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information. Anal Chem 78: 5026–5039. doi: 10.1021/ac060143p
[21]
Jaitly N, Mayampurath A, Littlefield K, Adkins JN, Anderson GA, et al. (2009) Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data. BMC Bioinformatics 10: 87. doi: 10.1186/1471-2105-10-87
[22]
Monroe ME, Tolic N, Jaitly N, Shaw JL, Adkins JN, et al. (2007) VIPER: an advanced software package to support high-throughput LC-MS peptide identification. Bioinformatics 23: 2021–2023. doi: 10.1093/bioinformatics/btm281
[23]
Webb-Robertson BJ, McCue LA, Waters KM, Matzke MM, Jacobs JM, et al. (2010) Combined statistical analyses of peptide intensities and peptide occurrences improves identification of significant peptides from MS-based proteomics data. J Proteome Res 9: 5748–5756. doi: 10.1021/pr1005247
[24]
Matzke MM, Waters KM, Metz TO, Jacobs JM, Sims AC, et al.. (2011) Improved quality control processing of peptide-centric LC-MS proteomics data. Bioinformatics.
[25]
Webb-Robertson BJ, Matzke MM, Jacobs JM, Pounds JG, Waters KM (2011) A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors. Proteomics 11: 4736–4741. doi: 10.1002/pmic.201100078
[26]
Shi T, Zhou JY, Gritsenko MA, Hossain M, Camp DG 2nd, et al. (2012) IgY14 and SuperMix immunoaffinity separations coupled with liquid chromatography-mass spectrometry for human plasma proteomics biomarker discovery. Methods 56: 246–253. doi: 10.1016/j.ymeth.2011.09.001
[27]
Nanashima N, Ito K, Ishikawa T, Nakano M, Nakamura T (2012) Damage of hair follicle stem cells and alteration of keratin expression in external radiation-induced acute alopecia. Int J Mol Med 30: 579–584. doi: 10.3892/ijmm.2012.1018
[28]
Mhawech P (2005) 14-3-3 proteins–an update. Cell Res 15: 228–236. doi: 10.1038/sj.cr.7290291
[29]
Wang Z, Nesland JM, Suo Z, Trope CG, Holm R (2011) The prognostic value of 14-3-3 isoforms in vulvar squamous cell carcinoma cases: 14-3-3beta and epsilon are independent prognostic factors for these tumors. PLoS ONE 6: e24843. doi: 10.1371/journal.pone.0024843
[30]
Feng XP, Yi H, Li MY, Li XH, Yi B, et al. (2010) Identification of biomarkers for predicting nasopharyngeal carcinoma response to radiotherapy by proteomics. Cancer Res 70: 3450–3462. doi: 10.1158/0008-5472.can-09-4099
[31]
Liu Y, Chen Q, Zhang JT (2004) Tumor suppressor gene 14-3-3sigma is down-regulated whereas the proto-oncogene translation elongation factor 1delta is up-regulated in non-small cell lung cancers as identified by proteomic profiling. J Proteome Res 3: 728–735. doi: 10.1021/pr034127+
[32]
Chaker S, Kashat L, Voisin S, Kaur J, Kak I, et al. (2013) Secretome proteins as candidate biomarkers for aggressive thyroid carcinomas. Proteomics 13: 771–787. doi: 10.1002/pmic.201200356
[33]
Wang F, Bing Z, Zhang Y, Ao B, Zhang S, et al. (2013) Quantitative proteomic analysis for radiation-induced cell cycle suspension in 92-1 melanoma cell line. J Radiat Res 54: 649–662. doi: 10.1093/jrr/rrt010
[34]
Datta K, Hyduke DR, Suman S, Moon BH, Johnson MD, et al. (2012) Exposure to ionizing radiation induced persistent gene expression changes in mouse mammary gland. Radiat Oncol 7: 205. doi: 10.1186/1748-717x-7-205
[35]
Barjaktarovic Z, Anastasov N, Azimzadeh O, Sriharshan A, Sarioglu H, et al. (2013) Integrative proteomic and microRNA analysis of primary human coronary artery endothelial cells exposed to low-dose gamma radiation. Radiat Environ Biophys 52: 87–98. doi: 10.1007/s00411-012-0439-4
[36]
Pluder F, Barjaktarovic Z, Azimzadeh O, Mortl S, Kramer A, et al. (2011) Low-dose irradiation causes rapid alterations to the proteome of the human endothelial cell line EA.hy926. Radiat Environ Biophys 50: 155–166. doi: 10.1007/s00411-010-0342-9
[37]
Amundson SA, Bittner M, Meltzer P, Trent J, Fornace AJ Jr (2001) Biological indicators for the identification of ionizing radiation exposure in humans. Expert Rev Mol Diagn 1: 211–219. doi: 10.1586/14737159.1.2.211
[38]
Laiakis EC, Hyduke DR, Fornace AJ (2012) Comparison of mouse urinary metabolic profiles after exposure to the inflammatory stressors gamma radiation and lipopolysaccharide. Radiat Res 177: 187–199. doi: 10.1667/rr2771.1
