BACKGROUND: The growing use of imaging procedures in medicine has raised concerns about exposure to low-dose ionising radiation (LDIR). While the disastrous effects of high dose ionising radiation (HDIR) is well documented, the detrimental effects of LDIR is not well understood and has been a topic of much debate. Since little is known about the effects of LDIR, various kinds of wet-lab and computational analyses are required to advance knowledge in this domain. In this paper we carry out an "upside-down pyramid" form of systems biology analysis of microarray data. We characterised the global genomic response following 10 cGy (low dose) and 100 cGy (high dose) doses of X-ray ionising radiation at four time points by analysing the topology of gene coexpression networks. This study includes a rich experimental design and state-of-the-art computational systems biology methods of analysis to study the differences in the transcriptional response of skin cells exposed to low and high doses of radiation. RESULTS: Using this method we found important genes that have been linked to immune response, cell survival and apoptosis. Furthermore, we also were able to identify genes such as BRCA1, ABCA1, TNFRSF1B, MLLT11 that have been associated with various types of cancers. We were also able to detect many genes known to be associated with various medical conditions. CONCLUSIONS: Our method of applying network topological differences can aid in identifying the differences among similar (eg: radiation effect) yet very different biological conditions (eg: different dose and time) to generate testable hypotheses. This is the first study where a network level analysis was performed across two different radiation doses at various time points, thereby illustrating changes in the cellular response over time.
BACKGROUND: The growing use of imaging procedures in medicine has raised concerns about exposure to low-dose ionising radiation (LDIR). While the disastrous effects of high dose ionising radiation (HDIR) is well documented, the detrimental effects of LDIR is not well understood and has been a topic of much debate. Since little is known about the effects of LDIR, various kinds of wet-lab and computational analyses are required to advance knowledge in this domain. In this paper we carry out an "upside-down pyramid" form of systems biology analysis of microarray data. We characterised the global genomic response following 10 cGy (low dose) and 100 cGy (high dose) doses of X-ray ionising radiation at four time points by analysing the topology of gene coexpression networks. This study includes a rich experimental design and state-of-the-art computational systems biology methods of analysis to study the differences in the transcriptional response of skin cells exposed to low and high doses of radiation. RESULTS: Using this method we found important genes that have been linked to immune response, cell survival and apoptosis. Furthermore, we also were able to identify genes such as BRCA1, ABCA1, TNFRSF1B, MLLT11 that have been associated with various types of cancers. We were also able to detect many genes known to be associated with various medical conditions. CONCLUSIONS: Our method of applying network topological differences can aid in identifying the differences among similar (eg: radiation effect) yet very different biological conditions (eg: different dose and time) to generate testable hypotheses. This is the first study where a network level analysis was performed across two different radiation doses at various time points, thereby illustrating changes in the cellular response over time.
Authors: Daria Prilutsky; Nathan P Palmer; Niklas Smedemark-Margulies; Thorsten M Schlaeger; David M Margulies; Isaac S Kohane Journal: Trends Mol Med Date: 2013-12-24 Impact factor: 11.951
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Authors: Smrithi S Menon; Medha Uppal; Subeena Randhawa; Mehar S Cheema; Nima Aghdam; Rachel L Usala; Sanchita P Ghosh; Amrita K Cheema; Anatoly Dritschilo Journal: Front Oncol Date: 2016-02-02 Impact factor: 6.244
Authors: Christine E Hellweg; Luis F Spitta; Kristina Koch; Arif A Chishti; Bernd Henschenmacher; Sebastian Diegeler; Bikash Konda; Sebastian Feles; Claudia Schmitz; Thomas Berger; Christa Baumstark-Khan Journal: Int J Mol Sci Date: 2018-07-30 Impact factor: 5.923