Applicability of Gene Expression in Saliva as an Alternative to Blood for Biodosimetry and Prediction of Radiation-induced Health Effects

. 2024 May 01 ; 201 (5) : 523-534.

Jazyk angličtina Země Spojené státy americké Médium print

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid38499035

Grantová podpora
U19 AI067773 NIAID NIH HHS - United States

As the great majority of gene expression (GE) biodosimetry studies have been performed using blood as the preferred source of tissue, searching for simple and less-invasive sampling methods is important when considering biodosimetry approaches. Knowing that whole saliva contains an ultrafiltrate of blood and white blood cells, it is expected that the findings in blood can also be found in saliva. This human in vivo study aims to examine radiation-induced GE changes in saliva for biodosimetry purposes and to predict radiation-induced disease, which is yet poorly characterized. Furthermore, we examined whether transcriptional biomarkers in blood can also be found equivalently in saliva. Saliva and blood samples were collected in parallel from radiotherapy (RT) treated patients who suffered from head and neck cancer (n = 8) undergoing fractioned partial-body irradiations (1.8 Gy/fraction and 50-70 Gy total dose). Samples were taken 12-24 h before first irradiation and ideally 24 and 48 h, as well as 5 weeks after radiotherapy onset. Due to the low quality and quantity of isolated RNA samples from one patient, they had to be excluded from further analysis, leaving a total of 24 saliva and 24 blood samples from 7 patients eligible for analysis. Using qRT-PCR, 18S rRNA and 16S rRNA (the ratio being a surrogate for the relative human RNA/bacterial burden), four housekeeping genes and nine mRNAs previously identified as radiation responsive in blood-based studies were detected. Significant GE associations with absorbed dose were found for five genes and after the 2nd radiotherapy fraction, shown by, e.g., the increase of CDKN1A (2.0 fold, P = 0.017) and FDXR (1.9 fold increased, P = 0.002). After the 25th radiotherapy fraction, however, all four genes (FDXR, DDB2, POU2AF1, WNT3) predicting ARS (acute radiation syndrome) severity, as well as further genes (including CCNG1 [median-fold change (FC) = 0.3, P = 0.013], and GADD45A (median-FC = 0.3, P = 0.031)) appeared significantly downregulated (FC = 0.3, P = 0.01-0.03). A significant association of CCNG1, POU2AF1, HPRT1, and WNT3 (P = 0.006-0.04) with acute or late radiotoxicity could be shown before the onset of these clinical outcomes. In an established set of four genes predicting acute health effects in blood, the response in saliva samples was similar to the expected up- (FDXR, DDB2) or downregulation (POU2AF1, WNT3) in blood for up to 71% of the measurements. Comparing GE responses (PHPT1, CCNG1, CDKN1A, GADD45A, SESN1) in saliva and blood samples, there was a significant linear association between saliva and blood response of CDKN1A (R2 = 0.60, P = 0.0004). However, the GE pattern of other genes differed between saliva and blood. In summary, the current human in vivo study, (I) reveals significant radiation-induced GE associations of five transcriptional biomarkers in salivary samples, (II) suggests genes predicting diverse clinical outcomes such as acute and late radiotoxicity as well as ARS severity, and (III) supports the view that blood-based GE response can be reflected in saliva samples, indicating that saliva is a "mirror of the body" for certain but not all genes and, thus, studies for each gene of interest in blood are required for saliva.

Zobrazit více v PubMed

Chaudhry MA. Biomarkers for human radiation exposure. Vol. 15, J Biomed Sci. 2008. p. 557–63. PubMed

Badie C, Kabacik S, Balagurunathan Y, Bernard N, Brengues M, Faggioni G, et al. Laboratory intercomparison of gene expression assays. Radiat Res. 2013;180(2):138–48. PubMed PMC

Ostheim P, Coker O, Sch€ule S, Hermann C, Combs SE, Trott KR, et al. Identifying a diagnostic window for the use of gene expression profiling to predict acute radiation syndrome. Radiat Res. 2021;195(1):38–46. PubMed

Paul S, Amundson SA. Development of Gene Expression Signatures for Practical Radiation Biodosimetry. Int J Radiat Oncol Biol Phys. 2008;71(4). PubMed PMC

Cruz-Garcia L, O’Brien G, Sipos B, Mayes S, Love MI, Turner DJ, et al. Generation of a Transcriptional Radiation Exposure Signature in Human Blood Using Long-Read Nanopore Sequencing. Radiat Res. 2020;193(2):143–54. PubMed PMC

Port M, Ostheim P, Majewski M, Voss T, Haupt J, Lamkowski A, et al. Rapid High-Throughput Diagnostic Triage after a Mass Radiation Exposure Event Using Early Gene Expression Changes. Radiat Res. 2019;192(2):208–18. PubMed

Schulz BL, Cooper-White J, Punyadeera CK. Saliva proteome research: current status and future outlook. Crit Rev Biotechnol. 2013; 33(3):246–59. PubMed

Yoshizawa JM, Schafer CA, Schafer JJ, Farrell JJ, Paster BJ, Wong DTW. Salivary biomarkers: toward future clinical and diagnostic utilities. Clin Microbiol Rev. 2013; 26(4):781–91. PubMed PMC

Schafer CA, Schafer JJ, Yakob M, Lima P, Camargo P, Wong DTW. Saliva diagnostics: utilizing oral fluids to determine health status. Monogr Oral Sci 2014; 24:88–98. PubMed

Cuevas-Córdoba B, Santiago-García J. Saliva: a fluid of study for OMICS. OMICS 2014; 18(2):87–97. PubMed

Ghizoni JS, Nichele R, de Oliveira MT, Pamato S, Pereira JR. The utilization of saliva as an early diagnostic tool for oral cancer: microRNA as a biomarker. Vol. 22, Clinical and Translational Oncology. 2020. p. 804–12. PubMed

Li Y, John MAR, Zhou X, Kim Y, Sinha U, Jordan RCK, et al. Salivary transcriptome diagnostics for oral cancer detection. Clin Cancer Res. 2004;10(24):8442–50. PubMed

Kaczor-Urbanowicz KE, Martin Carreras-Presas C, Aro K, Tu M, Garcia-Godoy F, Wong DTW. Saliva diagnostics – Current views and directions. Vol. 242, Experimental Biology and Medicine. 2017. p. 459–72. PubMed PMC

Chen W, Cao H, Lin J, Olsen N, Zheng SG. Biomarkers for Primary Sjögren’s Syndrome. Vol. 13, Genomics, Proteomics and Bioinformatics. 2015. p. 219–23. PubMed PMC

Michael A, Bajracharya SD, Yuen PST, Zhou H, Star RA, Illei GG, et al. Exosomes from human saliva as a source of microRNA biomarkers. Oral Dis. 2010; 16(1):34–8. PubMed PMC

Maron JL, Johnson KL, Rocke DM, Cohen MG, Liley AJ, Bianchi DW. Neonatal salivary analysis reveals global developmental gene expression changes in the premature infant. Clin Chem. 2010; 56(3):409–16. PubMed PMC

Calouius PEB. The leukocyte count in saliva. Oral Surgery, Oral Medicine, Oral Pathology 1958. Jan 1 [cited 2023 Aug 21]; 11(1):43–6. PubMed

Segal A, Wong DT. Salivary diagnostics: Enhancing disease detection and making medicine better. European Journal of Dental Education. 2008; 12(SUPPL. 1):22–9. PubMed PMC

Yoshizawa JM, Schafer CA, Schafer JJ, Farrell JJ, Paster BJ, Wong DTW. Salivary biomarkers: Toward future clinical and diagnostic utilities. Vol. 26, Clinical Microbiology Reviews. 2013. p. 781–91. PubMed PMC

Pernot E, Cardis E, Badie C. Usefulness of saliva samples for biomarker studies in radiation research. Cancer Epidemiology Biomarkers and Prevention. 2014; 23(12):2673–80. PubMed

Lacombe J, Brooks C, Hu C, Menashi E, Korn R, Yang F, et al. Analysis of Saliva Gene Expression during Head and Neck Cancer Radiotherapy: A Pilot Study. Radiat Res. 2017; 188(1):75–81. PubMed PMC

Laiakis EC, Strawn SJ, Brenner DJ, Fornace AJ. Assessment of saliva as a potential biofluid for biodosimetry: A pilot metabolomics study in mice. Radiat Res. 2016; 186(1):92–7. PubMed PMC

Laiakis EC, Nishita D, Bujold K, Jayatilake MM, Bakke J, Gahagen J, et al. Salivary Metabolomics of Total Body Irradiated Nonhuman Primates Reveals Long-Term Normal Tissue Responses to Radiation. Int J Radiat Oncol Biol Phys. 2019; 105(4):843–51. PubMed PMC

Ostheim P, Tichý A, Sirak I, Davidkova M, Stastna MM, Kultova G, et al. Overcoming challenges in human saliva gene expression measurements. Sci Rep. 2020; 10(1). PubMed PMC

Ostheim P, Alemu SW, Tichý A, Sirak I, Davidkova M, Stastna MM, et al. Examining potential confounding factors in gene expression analysis of human saliva and identifying potential housekeeping genes. Sci Rep 2022; 12(1). PubMed PMC

Port M, Herodin F, Valente M, Drouet M, Lamkowski A, Majewski M, et al. First generation gene expression signature for early prediction of late occurring hematological acute radiation syndrome in baboons. Radiat Res. 2016; 186(1):39–54. PubMed

Port M, Hérodin F, Drouet M, Valente M, Majewski M, Ostheim P, et al. Gene Expression Changes in Irradiated Baboons: A Summary and Interpretation of a Decade of Findings. Radiat Res. 2021; 195(6):501–21. PubMed

Tichy A, Kabacik S, O’Brien G, Pejchal J, Sinkorova Z, Kmochova A, et al. The first in vivo multiparametric comparison of different radiation exposure biomarkers in human blood. PLoS One. 2018; 13(2). PubMed PMC

Cancer Institute N. Common Terminology Criteria for Adverse Events (CTCAE) Version 4.0. 2009. [cited 2023 Aug 21].

Cox JD, Stetz JA, Pajak TF. Toxicity criteria of the Radiation Therapy Oncology Group (RTOG) and the European Organization for Research and Treatment of Cancer (EORTC). Int J Radiat Oncol Biol Phys 1995. Mar 30 [cited 2023 Aug 21]; 31(5):1341–6. PubMed

Part I-Laboratory preparation and storage of a saliva/Orage-ne•RNA sample Purification steps Notes. 2017; (3):3–5.

Life Technologies. mirVanaTM miRNA Isolation Kit. 2011; 33.

Applied Biosystems. High Capacity cDNA Reverse Transcription Kits for 200 and 1000 Reactions Protocol (Rev E). Manual. 2010; (06):1–29.

Fisher T, July S. TaqMan PreAmp Master Mix User Guide. 2018; (4384557).

Kabacik S, MacKay A, Tamber N, Manning G, Finnon P, Paillier F, et al. Gene expression following ionising radiation: Identification of biomarkers for dose estimation and prediction of individual response. Int J Radiat Biol. 2011; 87(2):115–29. PubMed

Manning G, Kabacik S, Finnon P, Bouffler S, Badie C. High and low dose responses of transcriptional biomarkers in ex vivo X-irradiated human blood. Int J Radiat Biol. 2013; 89(7):512–22. PubMed

Paul S, Barker CA, Turner HC, McLane A, Wolden SL, Amundson SA. Prediction of in vivo radiation dose status in radiotherapy patients using ex vivo and in vivo gene expression signatures. Radiat Res. 2011; 175(3):257–65. PubMed PMC

Port M, Majewski M, Herodin F, Valente M, Drouet M, Forcheron F, et al. Validating Baboon Ex Vivo and in Vivo Radiation-Related Gene Expression with Corresponding Human Data. Radiat Res. 2018; 189(4):389–98. PubMed

Agbenyegah S, Abend M, Atkinson MJ, Combs SE, Trott KR, Port M, et al. Impact of Inter-Individual Variance in the Expression of a Radiation-Responsive Gene Panel Used for Triage. Radiat Res. 2018; 190(3):226–35. PubMed

Rothkamm K, Beinke C, Romm H, Badie C, Balagurunathan Y, Barnard S, et al. Comparison of established and emerging biodosimetry assays. Radiat Res. 2013; 180(2):111–9. PubMed PMC

Amundson SA. Transcriptomics for radiation biodosimetry: progress and challenges. Int J Radiat Biol. 2021. PubMed PMC

Ostheim P, Amundson SA, Badie C, Bazyka D, Evans AC, Ghandhi SA, et al. Gene expression for biodosimetry and effect prediction purposes: promises, pitfalls and future directions–key session ConRad 2021. Int J Radiat Biol. 2022; 98(5):843–54. PubMed PMC

Abend M, Ostheim P, Port M. Radiation-induced gene expression changes used for biodosimetry and clinical outcome prediction: challenges and promises. Cytogenet Genome Res 2023. May 12; 1–8. (doi: 10.1159/000530947) PubMed DOI

Amundson SA, Do KT, Shahab S, Bittner M, Meltzer P, Trent J, et al. Identification of potential mRNA biomarkers in peripheral blood lymphocytes for human exposure to ionizing radiation. Radiat Res. 2000; 154(3):342–6. PubMed

Dressman HK, Muramoto GG, Chao NJ, Meadows S, Marshall D, Ginsburg GS, et al. Gene expression signatures that predict radiation exposure in mice and humans. PLoS Med. 2007; 4(4):690–701. PubMed PMC

Aro K, Wei F, Wong DT, Tu M. Saliva liquid biopsy for point-of-care applications. Vol. 5, Frontiers in Public Health. 2017. PubMed PMC

Schwanke D, Fatanmi O, Wise S, Schüle S, Wiegel T, Singh VK, et al. Validating a four-gene set for H-ARS severity prediction in peripheral blood samples of irradiated Rhesus macaques. Radit Res. In press. 2024. PubMed

Lee SY, Choe YH, Han JH, Hwang G, Choi MY, Thakur G, et al. HPRT1 Most Suitable Reference Gene for Accurate Normalization of mRNA Expression in Canine Dermal Tissues with Radiation Therapy. Genes (Basel) 2022; 13(11):1928. PubMed PMC

Ostheim P, Majewski M, Gluzman-Poltorak Z, Vainstein V, Basile LA, Lamkowski A, et al. Predicting the radiation sensitivity of male and female rhesus macaques using gene expression. Radiat Res. 2021; 195(1):25–37. PubMed

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...