{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T21:01:18Z","timestamp":1791234078959,"version":"4.1.0"},"reference-count":34,"publisher":"Radiation Research Society","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Radiation Research"],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>Development of biodosimetry diagnostic algorithms robust enough for successful application across the naturally occurring genetic diversity of a human population remains a challenge for medical countermeasure advancement. In the current study, a multi-sex, multi-strain approach was used to examine radiation response and algorithm-building in five murine strains that are commonly used as animal research models. For this study, blood samples were taken from male and female C57BL6, C3H\/HeJ and CD-1 and female CD2F1 and BALB\/c strains receiving a single 2, 4 or 8 Gy total-body irradiation exposure; blood was collected at days 1, 3 and 7 after irradiation. Proteomic analysis of 7K analytes using the Somalogic SomaScan v4.1 assay was used to characterize strain- and sex-specific changes to the global blood proteome in response to radiation. A binary exposure algorithm was also developed to differentiate exposed\/unexposed animals inclusive of both sexes and all five strains. Sex- and strain-specific differences in proteomic expression patterns were observed, with C57BL6 females being the most responsive to radiation exposure and CD1 females the least responsive, as measured by the total number of significantly altered proteins across all strains and sexes. Increased numbers of significantly altered proteins were seen across the five strains and both sexes at the higher dose and later timepoint of 8 Gy and day 7 postirradiation, respectively. The exposure algorithm successfully predicted radiation exposure irrespective of dose or time point with a predictive accuracy of 95.1%. The data indicate significant strain- and sex-specific differences in radiation sensitivity, as measured using the SomaScan 7K pro-teomics platform. Despite these baseline differences, successful multi-sex, multi-strain exposure prediction algorithms can be developed.<\/jats:p>","DOI":"10.1667\/rade-25-00171.1","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:01:52Z","timestamp":1761868912000},"page":"348-354","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-strain, Multi-sex Murine Model of Radiation Exposure"],"prefix":"10.1667","volume":"206","author":[{"given":"M.","family":"Sproull","sequence":"first","affiliation":[{"name":"Radiation Oncology Branch, National Cancer Institute, Bethesda, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Y.","family":"Fan","sequence":"additional","affiliation":[{"name":"Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics & Information Technology, National Cancer Institute, National Institutes of Health, Rockville, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Q.","family":"Chen","sequence":"additional","affiliation":[{"name":"Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics & Information Technology, National Cancer Institute, National Institutes of Health, Rockville, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Meerzaman","sequence":"additional","affiliation":[{"name":"Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics & Information Technology, National Cancer Institute, National Institutes of Health, Rockville, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Camphausen","sequence":"additional","affiliation":[{"name":"Radiation Oncology Branch, National Cancer Institute, Bethesda, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1243","reference":[{"key":"31","doi-asserted-by":"crossref","unstructured":"M Sproull, Y Fan, Q Chen, et al.  \"Radiat Res.\"  202, \"Organ-specific Biodosimetry Modeling Using Proteomic Biomarkers of Radiation Exposure.\" 697 (2024)","DOI":"10.1667\/RADE-24-00092.1"},{"key":"6","doi-asserted-by":"crossref","unstructured":"JD Down, MR Cornwall-Brady, W Huang, et al.  \"Radiat Res.\"  202, \"Selecting the most relevant mouse strains for evaluating radiation-induced multiple tissue injury after leg-shielded partial-body gamma irradiation.\" 510 (2024)","DOI":"10.1667\/RADE-24-00058.1"},{"key":"7","doi-asserted-by":"crossref","unstructured":"AJ Boria  and CJ Perez-Torres  \"Radiat Oncol.\"  15, \"Impact of mouse strain and sex when modeling radiation necrosis.\" 141 (2020)","DOI":"10.1186\/s13014-020-01585-5"},{"key":"8","doi-asserted-by":"crossref","unstructured":"L He, C Zhong, H Chang, et al.  \"Cell Genom.\"  3, \"Genetic architecture of the acute and persistent immune cell response after radiation exposure.\" 100422 (2023)","DOI":"10.1016\/j.xgen.2023.100422"},{"key":"13","doi-asserted-by":"crossref","unstructured":"R Hultborn, P Albertsson, S Ottosson, et al.  \"Radiat Res.\"  191, \"Radiosensitivity: Gender and order of administration of G-CSF, an experimental study in nice.\" 335 (2019)","DOI":"10.1667\/RR15038.1"},{"key":"16","doi-asserted-by":"crossref","unstructured":"S Martello, MA Bylicky, U Shankavaram, et al.  \"Radiat Res.\"  200, \"Comparative analysis of miRNA expression after whole-body irradiation across three strains of mice.\" 266 (2023)","DOI":"10.1667\/RADE-23-00007.1"},{"key":"19","doi-asserted-by":"crossref","unstructured":"NI Ossetrova, P Stanton, K Krasnopolsky, et al.  \"Health Phys.\"  115, \"Biomarkers for radiation biodosimetry and injury assessment after mixed-field (neutron and famma) radiation in the mouse total-body irradiation model.\" 727 (2018)","DOI":"10.1097\/HP.0000000000000938"},{"key":"24","doi-asserted-by":"crossref","unstructured":"HI Kohn  and RF Kallman  \"Radiat Res.\"  5, \"The influence of strain on acute X-ray lethality in the mouse: I. 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Grahn,  \"Genetics.\"  43, \"Acute radiation response of mice from a cross between radiosensitive and radioresistant strains.\" 835 (1958)","DOI":"10.1093\/genetics\/43.5.835"},{"key":"15","doi-asserted-by":"crossref","unstructured":"MM Satyamitra, DR Cassatt, BA Hollingsworth, et al.  \"Metabolites.\"  10, \"Metabolomics in radiation biodosimetry: current approaches and advances.\" (2020)","DOI":"10.3390\/metabo10080328"},{"key":"20","doi-asserted-by":"crossref","unstructured":"I Shuryak, L Nemzow, BA Bacon, et al.  \"Sci Rep.\"  13, \"Machine learning approach for quantitative biodosimetry of partial-body or total-body radiation exposures by combining radiation-responsive biomarkers.\" 949 (2023)","DOI":"10.1038\/s41598-023-28130-0"},{"key":"10","unstructured":"\"XGBoost R Package: XGBoost Developers Revision 21d95f3d.\"  (2023)"},{"key":"22","doi-asserted-by":"crossref","unstructured":"CG Broustas, I Shuryak, AJ Duval, et al.  \"Cytogenet Genome Res.\"  163, \"Effect of age and sex on gene expression-based radiation biodosimetry using mouse peripheral blood.\" 197 (2023)","DOI":"10.1159\/000530172"}],"container-title":["Radiation Research"],"original-title":[],"link":[{"URL":"https:\/\/radiation-research.kglmeridian.com\/view\/journals\/rare\/aop\/article-10.1667-RADE-25-00171.1\/article-10.1667-RADE-25-00171.1.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/meridian.allenpress.com\/radiation-research\/article-pdf\/doi\/10.1667\/RADE-25-00171.1\/3544086\/10.1667_rade-25-00171.1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/radiation-research.kglmeridian.com\/downloadpdf\/journals\/rare\/aop\/article-10.1667-RADE-25-00171.1\/article-10.1667-RADE-25-00171.1.xml","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/radiation-research.kglmeridian.com\/downloadpdf\/journals\/rare\/aop\/article-10.1667-RADE-25-00171.1\/article-10.1667-RADE-25-00171.1.xml","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T20:44:53Z","timestamp":1791233093000},"score":1,"resource":{"primary":{"URL":"https:\/\/bioone.org\/journals\/radiation-research\/volume-206\/issue-3\/RADE-25-00171.1\/A-Multi-strain-Multi-sex-Murine-Model-of-Radiation-Exposure\/10.1667\/RADE-25-00171.1.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,9,1]]}},"URL":"https:\/\/doi.org\/10.1667\/rade-25-00171.1","relation":{},"ISSN":["0033-7587","1938-5404"],"issn-type":[{"value":"0033-7587","type":"print"},{"value":"1938-5404","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,30]]}}}