文章基本信息

标题：Deep models of integrated multiscale molecular data decipher the endothelial cell response to ionizing radiation
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作者：Ian Morilla ; Philippe Chan ; Fanny Caffin 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：1
页码：1-31
DOI：10.1016/j.isci.2021.103685
语种：English
出版社：Elsevier
摘要：SummaryThe vascular endothelium is a hot spot in the response to radiation therapy for both tumors and normal tissues. To improve patient outcomes, interpretable systemic hypotheses are needed to help radiobiologists and radiation oncologists propose endothelial targets that could protect normal tissues from the adverse effects of radiation therapy and/or enhance its antitumor potential. To this end, we captured the kinetics of multi-omics layers—i.e. miRNome, targeted transcriptome, proteome, and metabolome—in irradiated primary human endothelial cells culturedin vitro. We then designed a strategy of deep learning as in convolutional graph networks that facilitates unsupervised high-level feature extraction of important omics data to learn how ionizing radiation-induced endothelial dysfunction may evolve over time. Last, we present experimental data showing that some of the features identified using our approach are involved in the alteration of angiogenesis by ionizing radiation.Graphical abstractDisplay OmittedHighlights•Irradiation promotes cell death, senescence, and impaired angiogenesis in HUVECs•Multi-omics analyses enable tracking pathways from early to late dysfunction•Dynamic modeling allows accurate learning of essential dysfunction checkpoints•Loss-/gain-of-function experiments identify key players of angiogenesisin vitroRadiation biology; Systems biology; Omics; Proteomics; Metabolomics; Transcriptomics