Developing Human Radiation Biodosimetry Models: Testing Cross-Species Conversion Approaches Using an Ex Vivo Model System

In the event of a large-scale radiation exposure, accurate and quick assessment of radiation dose received would be critical for triage and medical treatment of large numbers of potentially exposed individuals. Current methods of biodosimetry, such as the dicentric chromosome assay, are time consuming and require sophisticated equipment and highly trained personnel. Therefore, scalable biodosimetry approaches, including gene expression profiles in peripheral blood cells, are being investigated. Due to the limited availability of appropriate human samples, biodosimetry development has relied heavily on mouse models, which are not directly applicable to human response. Therefore, to explore the feasibility of using non-human primate (NHP) models to build and test a biodosimetry algorithm for use in humans, we irradiated ex vivo peripheral blood samples from both humans and rhesus macaques with doses of 0, 2, 5, 6 and 7 Gy, and compared the gene expression profiles 24 h later using Agilent human microarrays. Among the dose-responsive genes in human and using non-human primate, 52 genes showed highly correlated expression patterns between the species, and were enriched in p53/DNA damage response, apoptosis and cell cycle-related genes. When these interspecies-correlated genes were used to build biodosimetry models with using NHP data, the mean prediction accuracy on non-human primate samples was about 90% within 1 Gy of delivered dose in leave-one-out cross-validation. However, tests on human samples suggested that human gene expression values may need to be adjusted prior to application of the NHP model. A “multi-gene” approach utilizing all gene values for cross-species conversion and applying the converted values on the NHP biodosimetry models, gave a leave-one-out cross-validation prediction accuracy for human samples highly comparable (up to 94%) to that for non-human primates. Overall, this study demonstrates that a robust NHP biodosimetry model can be built using interspecies-correlated genes, and that, by using multiple regression-based cross-species conversion of expression values, absorbed dose in human samples can be accurately predicted by the NHP model.

[1]  Caroline H. Johnson,et al.  Radiation Metabolomics. 5. Identification of Urinary Biomarkers of Ionizing Radiation Exposure in Nonhuman Primates by Mass Spectrometry-Based Metabolomics , 2012, Radiation research.

[2]  H. Dressman,et al.  A Translatable Predictor of Human Radiation Exposure , 2014, PloS one.

[3]  Albert J Fornace,et al.  Radiation Metabolomics. 1. Identification of Minimally Invasive Urine Biomarkers for Gamma-Radiation Exposure in Mice , 2008, Radiation research.

[4]  John N Weinstein,et al.  Integrating global gene expression and radiation survival parameters across the 60 cell lines of the National Cancer Institute Anticancer Drug Screen. , 2008, Cancer research.

[5]  L. Zhao,et al.  Identification of Radiation-Induced Expression Changes in Nonimmortalized Human T Cells , 2010, Radiation research.

[6]  Suzanne L Wolden,et al.  Development of a Metabolomic Radiation Signature in Urine from Patients Undergoing Total Body Irradiation , 2014, Radiation research.

[7]  Suzanne L Wolden,et al.  Radiation-induced micro-RNA expression changes in peripheral blood cells of radiotherapy patients. , 2011, International journal of radiation oncology, biology, physics.

[8]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[9]  Suzanne L Wolden,et al.  Prediction of In Vivo Radiation Dose Status in Radiotherapy Patients using Ex Vivo and In Vivo Gene Expression Signatures , 2011, Radiation research.

[10]  Gaetan Gruel,et al.  Biological Dosimetry by Automated Dicentric Scoring in a Simulated Emergency , 2013, Radiation research.

[11]  John E Moulder,et al.  The urine proteome as a radiation biodosimeter. , 2013, Advances in experimental medicine and biology.

[12]  Marco Mernberger,et al.  Characterization of the p53 Cistrome – DNA Binding Cooperativity Dissects p53's Tumor Suppressor Functions , 2013, PLoS genetics.

[13]  Amanda G Paulovich,et al.  Antibody-Based Screen for Ionizing Radiation-Dependent Changes in the Mammalian Proteome for Use in Biodosimetry , 2009, Radiation research.

[14]  Michael C Joiner,et al.  Accurate gene expression-based biodosimetry using a minimal set of human gene transcripts. , 2014, International journal of radiation oncology, biology, physics.

[15]  Waylon Weber,et al.  Gene Expression Response of Mice after a Single Dose of 137Cs as an Internal Emitter , 2014, Radiation research.

[16]  A. Nakamura,et al.  Q(γ-H2AX), an analysis method for partial-body radiation exposure using γ-H2AX in nonhuman primate lymphocytes. , 2011, Radiation measurements.

[17]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[18]  Sally A. Amundson,et al.  Identification of Potential mRNA Biomarkers in Peripheral Blood Lymphocytes for Human Exposure to Ionizing Radiation , 2000, Radiation research.

[19]  N. Heintz,et al.  Regulation of human histone gene expression: kinetics of accumulation and changes in the rate of synthesis and in the half-lives of individual histone mRNAs during the HeLa cell cycle , 1983, Molecular and cellular biology.

[20]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[21]  Gene H. Golub,et al.  Missing value estimation for DNA microarray gene expression data: local least squares imputation , 2005, Bioinform..

[22]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[23]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[24]  David J. Sandgren,et al.  Protein biomarkers for enhancement of radiation dose and injury assessment in nonhuman primate total-body irradiation model. , 2014, Radiation protection dosimetry.

[25]  Joseph R. Nevins,et al.  Gene Expression Signatures of Radiation Response Are Specific, Durable and Accurate in Mice and Humans , 2008, PloS one.

[26]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[27]  Shi-wei Chen,et al.  Radiation dose effect of DNA repair-related gene expression in mouse white blood cells , 2011, Medical science monitor : international medical journal of experimental and clinical research.

[28]  Sally A Amundson,et al.  Gene expression signatures of radiation exposure in peripheral white blood cells of smokers and non-smokers , 2011, International journal of radiation biology.

[29]  T. Hankemeier,et al.  Metabolic Phenotyping Reveals a Lipid Mediator Response to Ionizing Radiation , 2014, Journal of proteome research.

[30]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[31]  A. Nakamura,et al.  The Use of Gamma-H 2 AX as a Biodosimeter for Total-Body Radiation Exposure in Non-Human Primates , 2010 .

[32]  M. Mori,et al.  Transcriptional response to ionizing radiation in lymphocyte subsets , 2005, Cellular and Molecular Life Sciences CMLS.

[33]  Stein Aerts,et al.  iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections , 2014, PLoS Comput. Biol..

[34]  Eric Grégoire,et al.  Strategy for Population Triage Based on Dicentric Analysis , 2009, Radiation research.

[35]  Eric Grégoire,et al.  Broad Modulation of Gene Expression in CD4+ Lymphocyte Subpopulations in Response to Low Doses of Ionizing Radiation , 2008, Radiation research.

[36]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[37]  Robert M. White,et al.  Nanosensor dosimetry of mouse blood proteins after exposure to ionizing radiation , 2013, Scientific Reports.

[38]  S. Amundson,et al.  p53-Independent Downregulation of Histone Gene Expression in Human Cell Lines by High- and Low-LET Radiation , 2011, Radiation research.

[39]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[40]  Maite Huarte,et al.  Genome-wide analysis of the human p53 transcriptional network unveils a lncRNA tumour suppressor signature , 2014, Nature Communications.

[41]  Tytus D. Mak,et al.  Metabolomic and Lipidomic Analysis of Serum from Mice Exposed to an Internal Emitter, Cesium-137, Using a Shotgun LC–MSE Approach , 2014, Journal of proteome research.

[42]  M. O’Reilly,et al.  DNA damage induces downregulation of histone gene expression through the G1 checkpoint pathway , 2004, The EMBO journal.

[43]  Anushya Muruganujan,et al.  PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the Gene Ontology Consortium , 2009, Nucleic Acids Res..

[44]  T. Brown,et al.  Identification of Urinary Biomarkers from X-Irradiated Mice Using NMR Spectroscopy , 2011, Radiation research.

[45]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[46]  Tero Aittokallio,et al.  Dealing with missing values in large-scale studies: microarray data imputation and beyond , 2010, Briefings Bioinform..

[47]  William F Blakely,et al.  Multiple blood-proteins approach for early-response exposure assessment using an in vivo murine radiation model , 2009, International journal of radiation biology.

[48]  M. Kruszewski,et al.  Toward the development of transcriptional biodosimetry for the identification of irradiated individuals and assessment of absorbed radiation dose , 2015, Radiation and environmental biophysics.

[49]  H. Fathallah-Shaykh,et al.  Gene Expression Analysis in Radiotherapy Patients and C57BL/6 Mice as a Measure of Exposure to Ionizing Radiation , 2011, Radiation research.

[50]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[51]  Jian Zhang,et al.  THE RABIT: A RAPID AUTOMATED BIODOSIMETRY TOOL FOR RADIOLOGICAL TRIAGE , 2010, Health physics.

[52]  D. Fu,et al.  Circulating Interleukin-18 as a Biomarker of Total-Body Radiation Exposure in Mice, Minipigs, and Nonhuman Primates (NHP) , 2014, PloS one.

[53]  R. Wilkins,et al.  QUICKSCAN DICENTRIC CHROMOSOME ANALYSIS FOR RADIATION BIODOSIMETRY , 2010, Health physics.

[54]  A. Turtoi,et al.  Early gene expression in human lymphocytes after gamma-irradiation–a genetic pattern with potential for biodosimetry , 2008, International journal of radiation biology.

[55]  G. Auner,et al.  Gene Expression-Based Detection of Radiation Exposure in Mice after Treatment with Granulocyte Colony-Stimulating Factor and Lipopolysaccharide , 2012, Radiation research.

[56]  M. B. Grace,et al.  Human In vivo Radiation-Induced Biomarkers , 2004, Cancer Research.

[57]  V. Meineke,et al.  Gene Expression Comparisons Performed for Biodosimetry Purposes on In Vitro Peripheral Blood Cellular Subsets and Irradiated Individuals , 2012, Radiation research.

[58]  H. Dressman,et al.  Gene Expression Signatures That Predict Radiation Exposure in Mice and Humans , 2007, PLoS medicine.

[59]  T. C. Hsiang,et al.  A Bayesian View on Ridge Regression , 1975 .

[60]  Sally A Amundson,et al.  Development of gene expression signatures for practical radiation biodosimetry. , 2008, International journal of radiation oncology, biology, physics.

[61]  H. Dressman,et al.  Diagnosis of Partial Body Radiation Exposure in Mice Using Peripheral Blood Gene Expression Profiles , 2010, PloS one.

[62]  J. Rinn,et al.  Integrative genomic analysis reveals widespread enhancer regulation by p53 in response to DNA damage , 2015, Nucleic acids research.

[63]  Susumu Goto,et al.  Data, information, knowledge and principle: back to metabolism in KEGG , 2013, Nucleic Acids Res..

[64]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[65]  R. Weichselbaum,et al.  Ionizing radiation down-regulates histone H1 gene expression by transcriptional and post-transcriptional mechanisms. , 1993, Radiation research.