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Transcriptomic Responses in Mouse Blood During the First Week After In www.nature.com/scientificreports OPEN Transcriptomic responses in mouse blood during the frst week after in vivo gamma irradiation Sunirmal Paul1, Norman J. Kleiman 2 & Sally A. Amundson 1* Due to limitations of available human models for development of gene expression based radiation biodosimetry, many such studies have made use of mouse models. To provide a broad view of the gene expression response to irradiation in the mouse, we have exposed male C57BL/6 mice to 0, 1.5, 3, 6 or 10 Gy of gamma rays, sacrifcing groups of the mice at 1, 2, 3, 5, or 7 days after exposure. We then profled global gene expression in blood from individual mice using Agilent microarrays. In general, we found increasing numbers of genes diferentially expressed with increasing dose, with more prolonged responses after the higher doses. Gene ontology analysis showed a similar pattern, with more biological processes enriched among the genes responding to higher doses, and at later times after exposure. Clustering the timecourse expression data using maSigPro identifed four broad patterns of response, representing diferent gene ontology functions. The largest of these clusters included genes with initially decreased expression followed by increased expression at later times, a pattern of expression previously reported for several genes following neutron exposure. Another gene cluster showing consistent down regulation suggests genes useful for biodosimetry throughout the frst week after exposure can be identifed. Tere continues to be worldwide concern about potential human health risks to large populations from radiation exposure afer a nuclear accident or terrorist incident; e.g., detonation of a “dirty bomb” or an improvised nuclear device. Current research is directed at developing both radiation countermeasures and methods using biomark- ers to better quantify which individuals might best beneft from therapeutic interventions1–4. Biodosimetry approaches range from improving and automating classical cytogenetic methods5,6 to developing novel methods based on high-content screening, such as transcriptomics, proteomics, and metabolomics7–10. Our group, and others, have focused on the development of transcriptomic signatures derived from whole blood, a highly radiation responsive tissue requiring minimally invasive collection methods. Some studies have used human material, mainly blood from patients undergoing total body irradiation (TBI) prior to bone mar- row transplantation11–14, or blood from healthy donors irradiated ex vivo15–20. Te TBI model is limited by clin- ical irradiation protocols, which provide a narrow range of doses, usually delivered in several fractions, and by concerns that at a molecular level, the radiation response of extremely ill patients does not accurately refect that of healthy individuals. While ex vivo irradiation of blood drawn from healthy donors provides fexibility in the doses studied, this model only partially recapitulates the in vivo response11,13, and the blood has a limited useful lifetime in culture21. Te elapsed time afer exposure is an important consideration for the development of transcriptomics-based radiation signatures, as gene expression changes in response to irradiation are highly dynamic22. Much biodosimetry signature development has therefore used mouse models to provide in vivo responses at a range of doses and exposure times12,14,23–27, however, most such studies reporting whole-genome expression data have focused on limited post-irradiation time points. Here we report the frst broad analysis of whole-genome gene expression in the blood of mice during the frst week afer exposure to gamma radiation doses from 0–10 Gy. Te study covers the initiation of biological responses and the time period of most interest for radiation biodosimetry or triage. While the data themselves will be useful for signature development, this paper focuses on the temporal patterns of gene expression response and the nature of the biological and molecular functions implied by the patterns of gene expression change. Te 1Center for Radiological Research, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, 10032, USA. 2Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University Irving Medical Center, New York, NY, 10032, USA. *email: [email protected] SCIENTIFIC REPORTS | (2019) 9:18364 | https://doi.org/10.1038/s41598-019-54780-0 1 www.nature.com/scientificreports/ www.nature.com/scientificreports Days post irradiation 1.5 Gy 3 Gy 6 Gy 10 Gy 1 97 (75%)* 1337 (65%) 1462 (56%) 1308 (45%) 2 271 (18%) 4482 (42%) 5671 (35%) 5526 (40%) 3 86 (42%)* 4950 (39%) 7506 (44%) 8181 (45%) 5 488 (92%) 1648 (50%) 5706 (43)% 8763 (42%) 7 14 (29%)* 540 (34%) 3623 (39%) 8871 (40%) Table 1. Diferentially expressed genes by dose and time. Te number of genes signifcantly diferentially expressed relative to time matched controls at the level of p < 0.001 at each dose and time is listed along with the percentage of up-regulated genes, which is given in parentheses. *All genes had a false discovery rate below 5%, with the exception of the groups marked with an asterisk. See Supplementary File 1 for full gene lists. Figure 1. Heatmap of the expression of the 381 genes (rows) that were signifcantly diferentially expressed throughout the frst week afer exposure to any of the doses of radiation tested. Intensity of expression in individual mice (columns) was measured by microarray and is indicated according to the color scale, with light cyan representing the lowest level of expression, and dark blue the highest level of expression. Grey indicates an expression level not signifcantly above the microarray background. Te annotated list of genes in clustered order is available in Supplementary File 2. study provides a foundation for further studies of the role of gene expression changes during the frst week of response afer exposure to a range of sub-lethal and lethal doses of ionizing radiation. Results Diferential gene expression in mouse blood after irradiation. Te numbers of genes diferentially expressed at each sacrifce time (1, 2, 3, 5, or 7 days) and γ-ray dose (1.5, 3, 6, and 10 Gy), along with their false discovery rate (FDR), are summarized in Table 1. Te full gene lists with statistics and fold-change data are availa- ble in Supplementary File 1. Of these genes, we found that 381 were diferentially expressed at all times measured within a week afer exposure to at least one of the doses tested (Fig. 1). Since gene expression measurements made from whole blood refect the response of multiple cell types with diferent radiation sensitivities, some researchers have isolated specifc lymphocyte subtypes for the develop- ment of radiation biodosimetry signatures28,29. We have chosen to work with whole blood in part to reduce the pre-processing steps that would be needed to bring an assay to the point of care. Tis does mean that the gene expression changes we fnd may be partially driven by changes in the relative mixture of cell types in circulation afer diferent times and exposure doses. We used Cibersort30 to deconvolute our gene expression data and esti- mate the relative abundance of diferent cell subtypes. Te cell-type signatures used in Cibersort are based only on gene expression data from non-irradiated cells, however, and cannot distinguish between changes in cell numbers and functional changes during the response to radiation. Nonetheless, Cibersort indicated a dose-dependent depression in the representation of naïve B cells throughout the experiment, compensated to some extent by an increase in the inferred number of macrophages afer exposure (Supplementary File 1). As biomarkers of radi- ation efects are developed it may be of interest to further pursue the cellular source of the signals through cell sorting experiments. Gene ontology analysis. We used the PANTHER Statistical Overrepresentation Test31 to identify biological processes that were signifcantly enriched (p < 0.05 with the fairly stringent Bonferroni correction for multiple comparisons) within the sets of up- or down-regulated genes at the various times afer irradiation. Te patterns of signifcant enrichment of biological processes among genes responding to 3, 6 or 10 Gy γ-rays are summa- rized in Fig. 2 as a function of time since exposure, with the actual processes listed in Supplementary File 2. No SCIENTIFIC REPORTS | (2019) 9:18364 | https://doi.org/10.1038/s41598-019-54780-0 2 www.nature.com/scientificreports/ www.nature.com/scientificreports Figure 2. Gene Ontology analysis of diferentially expressed genes. PANTHER was used to identify signifcantly over-represented GO-Slim biological processes (rows) among (A) up- or (B) down-regulated genes at 1, 2, 3, 5, or 7 days afer exposure to 3, 6, or 10 Gy gamma radiation. Each column represents a dose and time combination. To illustrate the patterns of response among biological processes, the cells are colour coded by p-value as shown in the key. Related processes have been grouped together in the marked regions: (a) metabolic processes, (b) transport/localization, (c) adhesion/movement, (d) mitochondrial processes, (e) signalling/ communication, (f) immune response, (g) cell cycle, (h) cell death, (i) diferentiation/development. Details including the process names and p-values are available in Supplementary File 2. signifcantly enriched biological processes were found among the genes responding to 1.5 Gy γ-rays. We found many of the same processes enriched among the genes responding to the three higher doses, but with a shif in the timing of the perturbation dependent on dose. Tis included many processes related to metabolism among the up-regulated genes, and functions such as signal transduction, immune system process, and cell cycle among down-regulated genes. In general, the perturbation of processes was more quickly resolved afer lower doses and more prolonged afer the higher doses. Functions related to cell death only appeared as signifcantly enriched afer doses of 6 Gy and above, possibly due to the stringency of the multiple comparison correction applied in the analysis.
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