Elucidating Genes and Transcription Factors of Human Peripheral Blood Lymphocytes Involved in the Cellular Response upon Exposure to Ionizing Radiation for Biodosimetry and Triage: An In Silico Approach

Abstract

Background Gene expression–based biodosimetry is a promising method for estimating radiation dose following exposure. A panel of highly radio-responsive genes in human peripheral blood was used in the current investigation to create and evaluate a unique gene expression–based radiological biodosimetry method.

Methodology In human cellular research, we reviewed the literature on genes and proteins correlating to radiation response in vivo and in vitro. We looked at two publicly accessible independent radiation response gene expression profiles (GSE1977 and GSE1725) and identified the differentially expressed genes (DEGs).

Results The obtained data exhibited 42 genes with substantial differential expression, 25 of which were upregulated and 17 of which were downregulated in ionizing radiation exposure groups compared with control groups. The gene ontology enrichment analysis revealed that the hub genes are significantly involved in the regulation of the mitotic cell cycle phase transition, regulation of the mitotic cell cycle, and mitotic cell cycle checkpoint signaling. Out of the 42 DEGs, four top genes (CDK1, CCNB1, UBC, and UBB) were obtained through network centrality features. However, the multicomponent filtering procedure for radiation response genes resulted in cyclin-dependent kinase 1 (CDK1) as a critical gene in the dataset curated.

Conclusion Our findings suggest the possibility of discovering novel gene connections involved in the cellular response of human peripheral blood lymphocytes upon exposure to ionizing radiation.

Keywords

radiation - differentially expressed genes - CDK1 - gene ontology - peripheral blood

Publication History

Article published online:
04 June 2024

• References

• 1 Vaiserman A, Koliada A, Zabuga O, Socol Y. Health impacts of low-dose ionizing radiation: current scientific debates and regulatory issues. Dose Response 2018; 16 (03) 1559325818796331
• 2 Hall EJ, Brenner DJ. Cancer risks from diagnostic radiology. Br J Radiol 2008; 81 (965) 362-378
• 3 Jen KY, Cheung VG. Transcriptional response of lymphoblastoid cells to ionizing radiation. Genome Res 2003; 13 (09) 2092-2100
• 4 Velegzhaninov IO, Shadrin DM, Pylina YI. et al. Differential molecular stress responses to low compared to high doses of ionizing radiation in normal human fibroblasts. Dose Response 2015; 13 (01) 14-058
• 5 Knops K, Boldt S, Wolkenhauer O, Kriehuber R. Gene expression in low- and high-dose-irradiated human peripheral blood lymphocytes: possible applications for biodosimetry. Radiat Res 2012; 178 (04) 304-312
• 6 Dressman HK, Muramoto GG, Chao NJ. et al. Gene expression signatures that predict radiation exposure in mice and humans. PLoS Med 2007; 4 (04) e106
• 7 Kultova G, Tichy A, Rehulkova H, Myslivcova-Fucikova A. The hunt for radiation biomarkers: current situation. Int J Radiat Biol 2020; 96 (03) 370-382
• 8 Visweswaran S, Joseph S, Dhanasekaran J. et al. Exposure of patients to low doses of X-radiation during neuro-interventional imaging and procedures: dose estimation and analysis of γ-H2AX foci and gene expression in blood lymphocytes. Mutat Res Genet Toxicol Environ Mutagen 2020; 856-857: 503237
• 9 Abend M, Badie C, Quintens R. et al. Examining radiation-induced in vivo and in vitro gene expression changes of the peripheral blood in different laboratories for biodosimetry purposes: first RENEB gene expression study. Radiat Res 2016; 185 (02) 109-123
• 10 Lacombe J, Sima C, Amundson SA, Zenhausern F. Candidate gene biodosimetry markers of exposure to external ionizing radiation in human blood: a systematic review. PLoS One 2018; 13 (06) e0198851
• 11 Lucas J, Dressman HK, Suchindran S. et al. A translatable predictor of human radiation exposure. PLoS One 2014; 9 (09) e107897
• 12 Albanese J, Martens K, Karanitsa LV, Schreyer SK, Dainiak N, Dainiak N. Multivariate analysis of low-dose radiation-associated changes in cytokine gene expression profiles using microarray technology. Exp Hematol 2007; 35 (4, Suppl 1): 47-54
• 13 Manning G, Macaeva E, Majewski M. et al. Comparable dose estimates of blinded whole blood samples are obtained independently of culture conditions and analytical approaches. Second RENEB gene expression study. Int J Radiat Biol 2017; 93 (01) 87-98
• 14 Visweswaran S, Joseph S, S VH, O A, Jose MT, Perumal V. DNA damage and gene expression changes in patients exposed to low-dose X-radiation during neuro-interventional radiology procedures. Mutat Res Genet Toxicol Environ Mutagen 2019; 844: 54-61
• 15 Ainsbury E, Badie C, Barnard S. et al. Integration of new biological and physical retrospective dosimetry methods into EU emergency response plans - joint RENEB and EURADOS inter-laboratory comparisons. Int J Radiat Biol 2017; 93 (01) 99-109
• 16 Li S, Lu X, Feng JB. et al. Developing gender-specific gene expression biodosimetry using a panel of radiation-responsive genes for determining radiation dose in human peripheral blood. Radiat Res 2019; 192 (04) 399-409
• 17 Oh JH, Deasy JO. Inference of radio-responsive gene regulatory networks using the graphical lasso algorithm. BMC Bioinformatics 2014; 15 (Suppl. 07) S5
• 18 Oh JH, Wong HP, Wang X, Deasy JO. A bioinformatics filtering strategy for identifying radiation response biomarker candidates. PLoS ONE 2012; 7 (06) e38870
• 19 Sekaran TSG, Kedilaya VR, Kumari SN, Shetty P, Gollapalli P. Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach. Radiat Oncol J 2021; 39 (01) 48-60
• 20 Zhao Y, Wong L, Goh WWB. How to do quantile normalization correctly for gene expression data analyses. Sci Rep 2020; 10 (01) 15534
• 21 Ritchie ME, Phipson B, Wu D. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43 (07) e47-e47
• 22 Ashburner M, Ball CA, Blake JA. et al; The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nat Genet 2000; 25 (01) 25-29
• 23 Ge SX, Jung D, Yao R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 2020; 36 (08) 2628-2629
• 24 Chen EY, Tan CM, Kou Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 2013; 14 (01) 128
• 25 Lu H, Zhou Q, He J. et al. Recent advances in the development of protein-protein interactions modulators: mechanisms and clinical trials. Signal Transduct Target Ther 2020; 5 (01) 213
• 26 Petrakis S, Andrade-Navarro MA. Editorial: protein interaction networks in health and disease. Front Genet 2016; 7: 111
• 27 Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 2014; 8 (Suppl. 04) S11
• 28 Vidal M, Cusick ME, Barabási AL. Interactome networks and human disease. Cell 2011; 144 (06) 986-998
• 29 Lambert SA, Jolma A, Campitelli LF. et al. The human transcription factors. Cell 2018; 172 (04) 650-665
• 30 Clarke DJB, Kuleshov MV, Schilder BM. et al. eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks. Nucleic Acids Res 2018; 46 (W1): W171-W179
• 31 Berger SI, Posner JM, Ma'ayan A. Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases. BMC Bioinformatics 2007; 8 (01) 372
• 32 Lachmann A, Ma'ayan A. KEA: kinase enrichment analysis. Bioinformatics 2009; 25 (05) 684-686
• 33 Xu K, Bezakova I, Bunimovich L, Yi SV. Path lengths in protein-protein interaction networks and biological complexity. Proteomics 2011; 11 (10) 1857-1867
• 34 Delprato A. Topological and functional properties of the small GTPases protein interaction network. PLoS ONE 2012; 7 (09) e44882
• 35 Embar V, Handen A, Ganapathiraju MK. Is the average shortest path length of gene set a reflection of their biological relatedness?. J Bioinform Comput Biol 2016; 14 (06) 1660002
• 36 Gollapalli P, G TS, H M, Shetty P. N SK. Network topology analysis of essential genes interactome of Helicobacter pylori to explore novel therapeutic targets. Microb Pathog 2021; 158: 105059
• 37 Bononi A, Agnoletto C, De Marchi E. et al. Protein kinases and phosphatases in the control of cell fate. Enzyme Res 2011; 2011: 329098
• 38 Bhullar KS, Lagarón NO, McGowan EM. et al. Kinase-targeted cancer therapies: progress, challenges and future directions. Mol Cancer 2018; 17 (01) 48
• 39 Fält S, Holmberg K, Lambert B, Wennborg A. Long-term global gene expression patterns in irradiated human lymphocytes. Carcinogenesis 2003; 24 (11) 1837-1845
• 40 Turtoi A, Sharan RN, Srivastava A, Schneeweiss FHA. Proteomic and genomic modulations induced by γ-irradiation of human blood lymphocytes. Int J Radiat Biol 2010; 86 (10) 888-904
• 41 Marchetti F, Coleman MA, Jones IM, Wyrobek AJ. Candidate protein biodosimeters of human exposure to ionizing radiation. Int J Radiat Biol 2006; 82 (09) 605-639
• 42 Liao H, Ji F, Ying S. CDK1: beyond cell cycle regulation. Aging (Albany NY) 2017; 9 (12) 2465-2466
• 43 Diril MK, Ratnacaram CK, Padmakumar VC. et al. Cyclin-dependent kinase 1 (Cdk1) is essential for cell division and suppression of DNA re-replication but not for liver regeneration. Proc Natl Acad Sci U S A 2012; 109 (10) 3826-3831
• 44 Satyanarayana A, Kaldis P. Mammalian cell-cycle regulation: several Cdks, numerous cyclins and diverse compensatory mechanisms. Oncogene 2009; 28 (33) 2925-2939
• 45 Howe O, White L, Cullen D. et al. A 4-gene signature of CDKN1, FDXR, SESN1 and PCNA radiation biomarkers for prediction of patient radiosensitivity. Int J Mol Sci 2021; 22 (19) 10607
• 46 El-Deiry WS. p21(WAF1) mediates cell-cycle inhibition, relevant to cancer suppression and therapy. Cancer Res 2016; 76 (18) 5189-5191
• 47 Qin L, Fan M, Candas D. et al. CDK1 enhances mitochondrial bioenergetics for radiation-induced DNA repair. Cell Rep 2015; 13 (10) 2056-2063
• 48 Candas D, Fan M, Nantajit D. et al. CyclinB1/Cdk1 phosphorylates mitochondrial antioxidant MnSOD in cell adaptive response to radiation stress. J Mol Cell Biol 2013; 5 (03) 166-175
• 49 Orlando DA, Lin CY, Bernard A. et al. Global control of cell-cycle transcription by coupled CDK and network oscillators. Nature 2008; 453 (7197) 944-947