Radiation Type- and Dose-Specific Transcriptional Responses across Healthy and Diseased Mammalian Tissues


Creative Commons License

Sagkrioti E., Biz G. M., Takan I., Asfa S., Nikitaki Z., Zanni V., ...More

ANTIOXIDANTS, vol.11, no.11, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 11
  • Publication Date: 2022
  • Doi Number: 10.3390/antiox11112286
  • Journal Name: ANTIOXIDANTS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, Food Science & Technology Abstracts, Directory of Open Access Journals
  • Keywords: radiation response, bioinformatics, oxidative stress, transcriptomics, radiobiology database, gene signature, GENE-EXPRESSION RESPONSES, HIGH-LET RADIATION, OXIDATIVE STRESS, ION IRRADIATION, GAMMA-RAYS, CELL-DEATH, KAPPA-B, CANCER, RADIOTHERAPY, PROTON
  • Dokuz Eylül University Affiliated: Yes

Abstract

Ionizing radiation (IR) is a genuine genotoxic agent and a major modality in cancer treatment. IR disrupts DNA sequences and exerts mutagenic and/or cytotoxic properties that not only alter critical cellular functions but also impact tissues proximal and distal to the irradiated site. Unveiling the molecular events governing the diverse effects of IR at the cellular and organismal levels is relevant for both radiotherapy and radiation protection. Herein, we address changes in the expression of mammalian genes induced after the exposure of a wide range of tissues to various radiation types with distinct biophysical characteristics. First, we constructed a publicly available database, termed RadBioBase, which will be updated at regular intervals. RadBioBase includes comprehensive transcriptomes of mammalian cells across healthy and diseased tissues that respond to a range of radiation types and doses. Pertinent information was derived from a hybrid analysis based on stringent literature mining and transcriptomic studies. An integrative bioinformatics methodology, including functional enrichment analysis and machine learning techniques, was employed to unveil the characteristic biological pathways related to specific radiation types and their association with various diseases. We found that the effects of high linear energy transfer (LET) radiation on cell transcriptomes significantly differ from those caused by low LET and are consistent with immunomodulation, inflammation, oxidative stress responses and cell death. The transcriptome changes also depend on the dose since low doses up to 0.5 Gy are related with cytokine cascades, while higher doses with ROS metabolism. We additionally identified distinct gene signatures for different types of radiation. Overall, our data suggest that different radiation types and doses can trigger distinct trajectories of cell-intrinsic and cell-extrinsic pathways that hold promise to be manipulated toward improving radiotherapy efficiency and reducing systemic radiotoxicities.