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      Integrated analysis of transcriptomic and metabolomic profiling reveal the p53 associated pathways underlying the response to ionizing radiation in HBE cells

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          Abstract

          Background

          Radiation damage to normal tissues is a serious concern. P53 is a well-known transcription factor which is closely associated with radiation-induced cell damage. Increasing evidence has indicated that regulation of metabolism by p53 represents a reviving mechanism vital to protect cell survival. We aimed to explore the interactions of radiation-induced transcripts with the cellular metabolism regulated by p53.

          Methods

          Human bronchial epithelial (HBE) cell line was used to knockout p53 using CRISPR/cas9. Transcriptomic analysis was conducted by microarray and metabolomic analysis was conducted by GC–MS. Integrative omics was performed using MetaboAnalyst.

          Results

          326 mRNAs showed significantly altered expression in HBE p53-/- cells post-radiation, of which 269 were upregulated and 57 were downregulated. A total of 147 metabolites were altered, including 45 that increased and 102 that decreased. By integrated analysis of both omic data, we found that in response to radiation insult, nitrogen metabolism, glutathione metabolism, arachidonic acid metabolism, and glycolysis or gluconeogenesis may be dysregulated due to p53.

          Conclusions

          Our study provided a pilot comprehensive view of the metabolism regulated by p53 in response to radiation exposure. Detailed evaluation of these important p53-regulated metabolic pathways, including their roles in the response to radiation of cells, is essential to elucidate the molecular mechanisms of radiation-induced damage.

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          Most cited references57

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          TIGAR, a p53-Inducible Regulator of Glycolysis and Apoptosis

          The p53 tumor-suppressor protein prevents cancer development through various mechanisms, including the induction of cell-cycle arrest, apoptosis, and the maintenance of genome stability. We have identified a p53-inducible gene named TIGAR (TP53-induced glycolysis and apoptosis regulator). TIGAR expression lowered fructose-2,6-bisphosphate levels in cells, resulting in an inhibition of glycolysis and an overall decrease in intracellular reactive oxygen species (ROS) levels. These functions of TIGAR correlated with an ability to protect cells from ROS-associated apoptosis, and consequently, knockdown of endogenous TIGAR expression sensitized cells to p53-induced death. Expression of TIGAR may therefore modulate the apoptotic response to p53, allowing survival in the face of mild or transient stress signals that may be reversed or repaired. The decrease of intracellular ROS levels in response to TIGAR may also play a role in the ability of p53 to protect from the accumulation of genomic damage.
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            Tumor suppression in the absence of p53-mediated cell-cycle arrest, apoptosis, and senescence.

            Cell-cycle arrest, apoptosis, and senescence are widely accepted as the major mechanisms by which p53 inhibits tumor formation. Nevertheless, it remains unclear whether they are the rate-limiting steps in tumor suppression. Here, we have generated mice bearing lysine to arginine mutations at one (p53(K117R)) or three (p53(3KR); K117R+K161R+K162R) of p53 acetylation sites. Although p53(K117R/K117R) cells are competent for p53-mediated cell-cycle arrest and senescence, but not apoptosis, all three of these processes are ablated in p53(3KR/3KR) cells. Surprisingly, unlike p53 null mice, which rapidly succumb to spontaneous thymic lymphomas, early-onset tumor formation does not occur in either p53(K117R/K117R) or p53(3KR/3KR) animals. Notably, p53(3KR) retains the ability to regulate energy metabolism and reactive oxygen species production. These findings underscore the crucial role of acetylation in differentially modulating p53 responses and suggest that unconventional activities of p53, such as metabolic regulation and antioxidant function, are critical for suppression of early-onset spontaneous tumorigenesis. Copyright © 2012 Elsevier Inc. All rights reserved.
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              MetaboAnalystR 2.0: From Raw Spectra to Biological Insights

              Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.
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                Author and article information

                Contributors
                huangruixue@csu.edu.cn
                liuxiaodan1015@126.com
                heli128@126.com
                zhouyao12321@163.com
                birm4th@163.com
                Journal
                Cell Biosci
                Cell Biosci
                Cell & Bioscience
                BioMed Central (London )
                2045-3701
                15 April 2020
                15 April 2020
                2020
                : 10
                : 56
                Affiliations
                [1 ]GRID grid.216417.7, ISNI 0000 0001 0379 7164, Department of Occupational and Environmental Health, Xiangya School of Public Health, , Central South University, ; Changsha, Hunan 410078 China
                [2 ]Department of Radiation Biology, Beijing Key Laboratory for Radiobiology, Beijing Institute of Radiation Medicine, AMMS, Beijing, 100850 China
                [3 ]GRID grid.410737.6, ISNI 0000 0000 8653 1072, Institute for Chemical Carcinogenesis, State Key Laboratory of Respiratory, School of Public Health, , Guangzhou Medical University, ; Guangzhou, 511436 People’s Republic of China
                Article
                417
                10.1186/s13578-020-00417-z
                7160934
                32318262
                a53d306f-1044-4a40-b1fd-39a606196e83
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 22 January 2020
                : 4 April 2020
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Cell biology
                Cell biology

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