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Inter-Strain Differences in LINE-1 DNA Methylation in the Mouse Hematopoietic System in Response to Exposure to Ionizing Radiation.

ABSTRACT: Long Interspersed Nuclear Element 1 (LINE-1) retrotransposons are the major repetitive elements in mammalian genomes. LINE-1s are well-accepted as driving forces of evolution and critical regulators of the expression of genetic information. Alterations in LINE-1 DNA methylation may lead to its aberrant activity and are reported in virtually all human cancers and in experimental carcinogenesis. In this study, we investigated the endogenous DNA methylation status of the 5' untranslated region (UTR) of LINE-1 elements in the bone marrow hematopoietic stem cells (HSCs), hematopoietic progenitor cells (HPCs), and mononuclear cells (MNCs) in radioresistant C57BL/6J and radiosensitive CBA/J mice and in response to ionizing radiation (IR). We demonstrated that basal levels of DNA methylation within the 5'-UTRs of LINE-1 elements did not differ significantly between the two mouse strains and were negatively correlated with the evolutionary age of LINE-1 elements. Meanwhile, the expression of LINE-1 elements was higher in CBA/J mice. At two months after irradiation to 0.1 or 1 Gy of ¹³⁷Cs (dose rate 1.21 Gy/min), significant decreases in LINE-1 DNA methylation in HSCs were observed in prone to radiation-induced carcinogenesis CBA/J, but not C57BL/6J mice. At the same time, no residual DNA damage, increased ROS, or changes in the cell cycle were detected in HSCs of CBA/J mice. These results suggest that epigenetic alterations may potentially serve as driving forces of radiation-induced carcinogenesis; however, future studies are needed to demonstrate the direct link between the LINE-1 DNA hypomethylation and radiation carcinogenesis.

SUBMITTER: Miousse IR

PROVIDER: S-EPMC5535921 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundBreast tissue is among the most sensitive tissues to the carcinogenic actions of ionizing radiation and epidemiological studies have linked radiation exposure to breast cancer. Currently, molecular understanding of radiation carcinogenesis in mammary gland is hindered due to the scarcity of in vivo long-term follow up data. We undertook this study to delineate radiation-induced persistent alterations in gene expression in mouse mammary glands 2-month after radiation exposure.MethodsSix to eight week old female C57BL/6J mice were exposed to 2 Gy of whole body ? radiation and mammary glands were surgically removed 2-month after radiation. RNA was isolated and microarray hybridization performed for gene expression analysis. Ingenuity Pathway Analysis (IPA) was used for biological interpretation of microarray data. Real time quantitative PCR was performed on selected genes to confirm the microarray data.ResultsCompared to untreated controls, the mRNA levels of a total of 737 genes were significantly (p<0.05) perturbed above 2-fold of control. More genes (493 genes; 67%) were upregulated than the number of downregulated genes (244 genes; 33%). Functional analysis of the upregulated genes mapped to cell proliferation and cancer related canonical pathways such as 'ERK/MAPK signaling', 'CDK5 signaling', and '14-3-3-mediated signaling'. We also observed upregulation of breast cancer related canonical pathways such as 'breast cancer regulation by Stathmin1', and 'HER-2 signaling in breast cancer' in IPA. Interestingly, the downregulated genes mapped to fewer canonical pathways involved in cell proliferation. We also observed that a number of genes with tumor suppressor function (GPRC5A, ELF1, NAB2, Sema4D, ACPP, MAP2, RUNX1) persistently remained downregulated in response to radiation exposure. Results from qRT-PCR on five selected differentially expressed genes confirmed microarray data. The PCR data on PPP4c, ELF1, MAPK12, PLCG1, and E2F6 showed similar trend in up and downregulation as has been observed with the microarray.ConclusionsExposure to a clinically relevant radiation dose led to long-term activation of mammary gland genes involved in proliferative and metabolic pathways, which are known to have roles in carcinogenesis. When considered along with downregulation of a number of tumor suppressor genes, our study has implications for breast cancer initiation and progression after therapeutic radiation exposure.

Project description: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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Inter-Strain Differences in LINE-1 DNA Methylation in the Mouse Hematopoietic System in Response to Exposure to Ionizing Radiation.

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