Identification of gene-based responses in human blood cells exposed to alpha particle radiation

Chauhan, Vinita; Howland, Matthew; Wilkins, Ruth

doi:10.1186/1755-8794-7-43

Cited by 34 publications

(24 citation statements)

References 35 publications

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“…Human lung fibroblasts are isolated from normal adult lung tissue. For all cell types the expression of TP53 has been observed with radiation exposure, however, the responses vary with cell‐type (Chauhan et al, ; Chauhan and Howland, ).…”

Section: Methodsmentioning

confidence: 99%

Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field

Chauhan

Kuo

McNamee

et al. 2016

Environ and Mol Mutagen

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Recent advances in “‐omics” technologies have simplified capacity to concurrently assess expression profiles of thousands of targets in a cellular system. However, compilation and analysis of “omics” data in support of human health protection remains a challenge. Benchmark dose (BMD) modeling is currently being employed in chemical risk assessment to estimate acceptable levels of exposure. Although typically applied to conventional endpoints, newer software has enabled this application to be extended to transcriptomic datasets. BMD analytical tools now have the capacity to model transcriptional dose‐response data to derive meaningful BMD values for genes, pathways and gene ontologies. In this report, radiation data obtained from the Gene Expression Omnibus (GEO) were analyzed to generate BMD values for transcriptional responses. The datasets comprised microarray analyses of human blood gamma‐irradiated ex vivo (0–20 Gy) and human‐derived cell lines exposed to alpha particle radiation (0.5–1.5 Gy). The distributions of BMDs for statistically significant genes and pathways in response to radiation exposure were examined and compared across studies. BMD modeling could identify pathway/gene sensitivities across wide radiation dose ranges, experimental conditions (time‐points, cell types) and radiation qualities. BMD analysis offered a new approach to examine transcriptional data. The results were shown to provide information on transcriptional thresholds of effects to support refined risk assessments for low dose ionizing radiation exposures, derive gene‐based values for relative biological effectiveness and identify pathways involved in radiation sensitivities across cell types which may extend to applications a clinical setting. Environ. Mol. Mutagen. 57:589–604, 2016. © 2016 Wiley Periodicals, Inc.

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Section: Methodsmentioning

confidence: 99%

Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field

Chauhan

Kuo

McNamee

et al. 2016

Environ and Mol Mutagen

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“…These include DNA repair genes ( Polk 23 and Pold1 26 ), inducers of apoptosis ( Ei24 30 , Bax 30 , and Phlda3 30 ), chaperonins ( Cct3 22 and Cct7 22 ), cell cycle regulators ( Ccng1 27 and Cdkn1a 30 ), B-cell development genes ( Cd79b 19 and Blnk 19 ), B-cell antigens ( Cd72 9 and Ms4a1 19 ), and a stress-response kinase that inhibits protein synthesis globally ( Eif2ak4 25 ).…”

Section: Murine Gene Signaturesmentioning

confidence: 99%

“…The functions associated with these and less frequently appearing genes are depicted in Figure 8. The pathways and functions represented include keratinocyte differentiation ( PRKCH 9 ), induction of apoptosis ( BCL2L 31 and BAX 30 ), DNA repair ( TP53BP1 23 , RAD17 24 , DDB2 19 , PRKDC 23 , and PCNA 27 ), actin nucleation ( ARPC1B 22 ), and regulation of JNK -p38 ( MAPK14 ) signalling ( GADD45A 27 and PPMD1 27 ). The four common genes belong to the DNA repair and regulating JNK -p38 ( MAPK14 ) pathways, which may imply particular significance to these functions in human response to radiation exposure.…”

Section: Human Gene Signaturesmentioning

confidence: 99%

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

2018

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Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2, PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2, CD8A, TALDO1, PCNA, EIF4G2, LCN2, CDKN1A, PRKCH, ENO1, and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

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“…However, there have been a limited number of studies that have compared gene expression profiles in mice or human blood cells after exposures involving heavy ions and α-particles, the latter representing isotopes likely to be used in a radiological dispersal device [9]. For example, using human peripheral blood mononuclear cells, Chauhan et al [10] found that the gene expression profile induced by α-particle radiation was very similar to the x-ray responses, despite the fact that α-particles are characterized by a higher linear energy transfer coefficient compared with x-rays [11]. In contrast, comparison of α-particle and x-ray irradiation impact on human tumor and endothelial cells [12], as well as human epidermal keratinocytes [13] produced distinct gene expression profiles.…”

Section: Introductionmentioning

confidence: 99%

Comparison of gene expression response to neutron and x-ray irradiation using mouse blood

Broustas

Harken

et al. 2017

BMC Genomics

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BackgroundIn the event of an improvised nuclear device detonation, the prompt radiation exposure would consist of photons plus a neutron component that would contribute to the total dose. As neutrons cause more complex and difficult to repair damage to cells that would result in a more severe health burden to affected individuals, it is paramount to be able to estimate the contribution of neutrons to an estimated dose, to provide information for those making treatment decisions.ResultsMice exposed to either 0.25 or 1 Gy of neutron or 1 or 4 Gy x-ray radiation were sacrificed at 1 or 7 days after exposure. Whole genome microarray analysis identified 7285 and 5045 differentially expressed genes in the blood of mice exposed to neutron or x-ray radiation, respectively. Neutron exposure resulted in mostly downregulated genes, whereas x-rays showed both down- and up-regulated genes. A total of 34 differentially expressed genes were regulated in response to all ≥1 Gy exposures at both times. Of these, 25 genes were consistently downregulated at days 1 and 7, whereas 9 genes, including the transcription factor E2f2, showed bi-directional regulation; being downregulated at day 1, while upregulated at day 7. Gene ontology analysis revealed that genes involved in nucleic acid metabolism processes were persistently downregulated in neutron irradiated mice, whereas genes involved in lipid metabolism were upregulated in x-ray irradiated animals. Most biological processes significantly enriched at both timepoints were consistently represented by either under- or over-expressed genes. In contrast, cell cycle processes were significant among down-regulated genes at day 1, but among up-regulated genes at day 7 after exposure to either neutron or x-rays. Cell cycle genes downregulated at day 1 were mostly distinct from the cell cycle genes upregulated at day 7. However, five cell cycle genes, Fzr1, Ube2c, Ccna2, Nusap1, and Cdc25b, were both downregulated at day 1 and upregulated at day 7.ConclusionsWe describe, for the first time, the gene expression profile of mouse blood cells following exposure to neutrons. We have found that neutron radiation results in both distinct and common gene expression patterns compared with x-ray radiation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3436-1) contains supplementary material, which is available to authorized users.

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Identification of gene-based responses in human blood cells exposed to alpha particle radiation

Cited by 34 publications

References 35 publications

Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field

Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Comparison of gene expression response to neutron and x-ray irradiation using mouse blood

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