exposure in light of the anticipated use of radiological or nuclear materials by terrorists to produce ‘‘dirty bombs’’ or ‘‘improvised nuclear devices’’ [15–17]. We have introduced a method of screening humans for environmental exposure by showing that patterns of gene expression, or metagenes, can be identified in PB cells that accurately distinguish between irradiated and non- irradiated individuals [27]. As importantly, metagenes could be identified in the PB that distinguished different levels of exposure from each other with an accuracy of 96% [27]. In this study, we sought to evaluate the specificity of these PB signatures, as well as to determine the influence of genetic variation and time on the performance of the signature. We conclude that this approach represents a viable strategy for identifying environmental exposures and could be employed for screening populations of affected individuals. Methods Murine irradiation study Ten to 11 week old male and female C57Bl6 and female BALB/c mice (Jackson Laboratory, Bar Harbor, ME) were housed at the Duke Cancer Center Isolation Facility under regulations approved by the Duke University Animal Care and Use Committee. Between 5–10 mice/group were given total body irradiation (TBI) with a Cs137 source at an average of 660cGy/ min at doses of 50, 200, or 1000cGy as previously described [27]. Six hours, 24 hours, or 7 days post-TBI, approximately 500 ul peripheral blood was collected by retro-orbital bleed from both irradiated and control mice. PB mononuclear cells (PB MNCs) were isolated for total RNA extractions. Total RNA was extracted with Qiagen RNAeasy Mini Kits as previously described [27]. RNA quality was assayed using an Agilent Bioanalyzer 2100 (Agilent Technologies, Inc., Palo Alto, CA). Murine LPS study Ten C57Bl6 female mice were given intraperitoneal injections of a 100 mg of lipopolysaccharide endotoxin (LPS) from E. coli 055:B5 (Sigma-Aldrich, St. Louis, MO) to induce sepsis syndrome as previously described [28]. Peripheral blood was collected 6h later from treated and control mice, and RNA was processed as described in the irradiation studies. Human Irradiation and Chemotherapy Treatment Studies With approval from the Duke University Institutional Review Board (IRB), between 5–12 mL of peripheral blood was collected from patients prior to and 6 hrs following total body irradiation with 150 to 200 cGy as part of their pre-transplantation conditioning [27]. For additional comparison, peripheral blood was obtained from healthy volunteers and an additional cohort of patients prior to and 6 hrs following the initiation of alkylator-based chemotherapy alone (without radiotherapy). All patients and healthy volunteers who participated in this study provided written informed consent prior to enrollment, as per the Duke IRB guidelines. PB MNCs and total RNA were isolated from the blood using the identical methods as described for collection of murine cells and RNA. DNA Microarrays Mouse and human oligonucleotide arrays were printed at the Duke Microarray Facility using Operon’s Mouse Genome Oligo sets (version 3.0 and version 4.0) and Operon’s Human Genome Oligo set (version 3.0 and version 4.0). Data generated from Operon’s Mouse and Human version 3 was previously described [27]. Operon’s Mouse Genome Oligo set (version 4.0) (https:// www.operon.com/arrays/oligosets_mouse.php) contains 35,852 Radiation Signatures oligonucleotide probes representing 25,000 genes and approxi- mately 38,000 transcripts. Operon’s Human Genome Oligo set (version 4.0) (https://www.operon.com/arrays/oligosets_human. php) contains 35,035 oligonucleotide probes, representing approx- imately 25,100 unique genes and 39,600 transcripts. In order to compare across versions of the Operon oligo sets, Operon provided a map that matched the probes from both versions and only those oligonucleotides that overlapped between versions 3.0 and 4.0 were used in the analysis. RNA and Microarray Probe Preparation and Hybridization Briefly, MNCs were pelleted, and total RNA was isolated using the RNAeasy mini spin column [27]. Total RNA from each sample (mouse or human) and the universal reference RNA (Universal Human or Mouse Reference RNA, Stratagene, http:// www.stratagene.com) were amplified and used in probe prepara- tion as previously described [27]. The sample (mouse or human) was labeled with Cy5 and the reference (mouse or human) was labeled with Cy3. The reference RNA allows for the signal for each gene to be normalized to its own unique factor allowing comparisons of gene expression across multiple samples. This serves as a normalization control for two-color microarrays and an internal standardization for the arrays. Amplification, probe preparation and hybridization protocols were performed as previously described [27] and each condition examined had multiple replicates analyzed (n = 3–18 per mouse condition and n = 18–36 per human condition). Detailed protocols are available on the Duke Microarray Facility web site (http://microarray. genome.duke.edu/services/spotted-arrays/protocols). Data Processing and Statistical Analysis Genespring GX 7.3 (Agilent Technologies) was used to perform initial data filtering in which spots whose signal intensities below 70 in either the Cy3 or Cy5 channel were removed. For each analysis, only those samples in that analysis were used in the filtering process. To compare data from previously published results [27], we only used those probes that mapped to each other across the version 3.0 and version 4.0 arrays. To then account for missing values, PAM software (http://www-stat.stanford.edu/ ,tibs/PAM/) was used to impute missing values. k-nearest neighbor was used where missing values were imputed using a k- nearest neighbor average in gene space. In the analysis approach in which all samples were included, lowess normalization of the data followed by batch effect removal using 2-way mixed model ANOVA (Partek Incorporated) was performed. Gene expression profiles of dose response were used in a supervised analysis using binary regression methodologies as described previously [27]. Prediction analysis of the expression data was performed using MATLAB software as previously described [27]. When predicting levels of radiation exposure, gene selection and identification is based on training the data and finding those genes most highly correlated to response. Each signature summarizes its constituent genes as a single expression profile and is here derived as the first principal component of that set of genes (the factor corresponding to the largest singular value), as determined by a singular value decomposition. Given a training set of expression vectors (of values across metagenes) representing two biological states, a binary probit regression model is estimated using Bayesian methods. Bayesian fitting of binary probit regression models to the training data then permits an assessment of the relevance of the metagene signatures in within-sample classification, and estimation and uncertainty assessments for the binary regression weights mapping metagenes to probabilities of radiation exposure. To internally validate the predictive capacity of the metagene profiles, we PLoS ONE | www.plosone.org 2 April 2008 | Volume 3 | Issue 4 | e1912