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| Status |
Public on May 11, 2024 |
| Title |
Comparison of novel proteomic expression profiles for radiation exposure in male and female C57BL6 mice |
| Platform organism |
Homo sapiens |
| Sample organism |
Mus musculus |
| Experiment type |
Protein profiling by protein array
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| Summary |
Purpose: There is a need for point-of-care diagnostics for future mass casualty events involving radiation exposure. The development of radiation exposure and dose prediction algorithms for biodosimetry is needed for screening of large populations during these scenarios, and exploration of the potential effects which sex, age, genetic heterogeneity, and physiological comorbidities may have on the utility of biodosimetry diagnostics is needed. In the current study, proteomic profiling was used to examine sex specific differences in age matched C57BL6 mice on the blood proteome following radiation exposure and the usefulness of development and application of biodosimetry algorithms using both male and female samples. Methods: C57BL6 male and female mice between 9-11 weeks of age received a single total body radiation exposure of either 2, 4 or 8 Gy with plasma collection at days 1, 3 and 7 post-irradiation. Plasma was then screened using the SomaScan v4.1 assay for ~7000 protein analytes. A subset panel of protein biomarkers demonstrated significant (FDR<0.05 and |logFC|>0.2) changes in expression following radiation exposure. All proteins were used for feature selection to build predictive models of radiation exposure using different sample and sex specific cohorts. Both binary (prediction of any radiation exposure) and multidose (prediction of specific radiation dose) model series were developed using either female and male samples combined or only female or only male samples. The binary series (Models 1, 2 and 3) and multidose series (Models 4, 5 and 6) series included female/male combined, female only and male only respectively. Results: Detectable values were obtained for all ~7000 proteins included in the SomaScan assay for all samples. Each model algorithm built using a unique sample cohort was validated with a training set of samples and tested with a separate new sample series. Overall predictive accuracies in the binary model series was ~100% at the model training level and when tested with fresh samples 97.9% for Model 1(Female and Male) and 100% for Model 2 (Female only) and Model 3 (Male only). When sex specific Models’ 2 and 3 were tested with the opposite sex, the overall predictive accuracy rate dropped to 62.5% for Model 2 and remained 100% for Model 3. The overall predictive accuracy rate in the multidose model series was 100% for all models at the model training level and when tested with fresh samples 83.3%, 75% and 83.3% for Multidose Models 4-6 respectively. When sex specific Models’ 5 (Female only) and Model 6 (Male only) were tested with the opposite sex the overall predictive accuracy rate dropped to 52.1% and 68.8% respectively. Conclusion: These models represent novel predictive panels of radiation responsive proteomic biomarkers and illustrate the utility and necessity of considering sex specific differences in development of radiation biodosimetry prediction algorithms. As sex specific differences were observed in this study, and as use of point-of-care radiation diagnostics in future mass casualty settings will necessarily include persons of both sexes, consideration of sex specific variation is essential to ensure these diagnostic tools have practical utility in the field.
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| Overall design |
C57BL6 male and female mice between 9-11 weeks of age received a single total body radiation exposure of either 2, 4 or 8 Gy with plasma collection at days 1, 3 and 7 post-irradiation. Matched male and female cohorts were used with 20 untreated controls and 6 animals per dose/timepoint for each sex, totaling 148 animals for this study.
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| Contributor(s) |
Sproull M, Fan Y, Chen Q, Meerzaman D, Camphausen K |
| Citation missing |
Has this study been published? Please login to update or notify GEO. |
| Submission date |
May 09, 2024 |
| Last update date |
May 11, 2024 |
| Contact name |
Yu Fan |
| E-mail(s) |
yu.fan@nih.gov
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| Organization name |
NCI/CBIIT
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| Street address |
9609 Medical Center Dr
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| City |
Rockville |
| State/province |
MD |
| ZIP/Postal code |
20850 |
| Country |
USA |
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| Platforms (1) |
| GPL34462 |
SOMAscan human proteomic assay [SOMAscan version 4.1] SeqID version |
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| Samples (148)
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| Relations |
| BioProject |
PRJNA1109837 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE267131_non-normalized_data.txt.gz |
2.7 Mb |
(ftp)(http) |
TXT |
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