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Radiat Res. 2019 Oct 16. doi: 10.1667/RR15442.1. [Epub ahead of print]

Comparison of Proteomic Biodosimetry Biomarkers Across Five Different Murine Strains.

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Radiation Oncology Branch, National Cancer Institute, Bethesda, Maryland.


In the event of a radiological or nuclear attack, advanced clinical countermeasures are needed for screening and medical management of the exposed population. Such a population will represent diverse heterogeneity in physiological response to radiation exposure. The current study seeks to compare the expression levels of five previously established proteomic biodosimetry biomarkers of radiation exposure, i.e., Flt3 ligand (FL), matrix metalloproteinase 9 (MMP9), serum amyloid A (SAA), pentraxin 3 (PTX3) and fibrinogen (FGB), across multiple murine strains and to test a multivariate dose prediction model based on a single C57BL6 strain against other murine strains. Female mice from five different murine strains (C57BL6, BALB/c, C3H/HeJ, CD2F1 and outbred CD-1 mice) received a single whole-body dose of 1-8 Gy from a Pantak X-ray source at a dose rate of 3.59 Gy/min. Plasma was collected by cardiac puncture at days 1, 2, 3 and 7 postirradiation. Plasma protein levels were determined via commercially available ELISA assay. Significant differences were found between radiation-induced expression levels of FL, MMP9, SAA, PTX3 and FGB among the C57BL6, BALB/c, C3H/HeJ, CD2F1 and CD-1 strains (P < 0.05). The overall trends of dose-dependent biomarker elevation, however, were similar between strains, with FL and PTX3 showing the highest degree of correlation. Application of a previous C57BL6 multivariate dose prediction model using additional murine strains showed the limitations of a model based on a single strain and the need for data normalization for variance generated by technical assay variables. Our findings indicate that strain specific differences do exist between expression levels of FL, MMP9, SAA, PTX3 and FGB in C57BL6, BALB/c, C3H/HeJ, CD2F1 and CD-1 murine strains and that use of multiple biomarkers for dose prediction strengthens the predictive accuracy of a model when challenged with a heterogeneous population.


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