Yeles Constantinos, Vlachavas Efstathios-Iason, Papadodima Olga, Pilalis Eleftherios, Vorgias Constantinos E, Georgakilas Alexandros G, Chatziioannou Aristotelis
Department of Biochemistry and Molecular Biology, Faculty of Biology, National and Kapodistrian University of Athens, Zografou Campus, 15701 Athens, Greece
Metabolic Engineering and Bioinformatics Research Team, Institute of Biology Medicinal Chemistry & Biotechnology, National Hellenic Research Foundation, 11635 Athens, Greece
Cancers (Basel). 2017 Nov 25;9(12):160. doi: 10.3390/cancers9120160.
Ionizing radiation-induced bystander effects (RIBE) encompass a number of effects with potential for a plethora of damages in adjacent non-irradiated tissue. The cascade of molecular events is initiated in response to the exposure to ionizing radiation (IR), something that may occur during diagnostic or therapeutic medical applications. In order to better investigate these complex response mechanisms, we employed a unified framework integrating statistical microarray analysis, signal normalization, and translational bioinformatics functional analysis techniques. This approach was applied to several microarray datasets from Gene Expression Omnibus (GEO) related to RIBE. The analysis produced lists of differentially expressed genes, contrasting bystander and irradiated samples versus sham-irradiated controls. Furthermore, comparative molecular analysis through BioInfoMiner, which integrates advanced statistical enrichment and prioritization methodologies, revealed discrete biological processes, at the cellular level. For example, the negative regulation of growth, cellular response to Zn-Cd, and Wnt and NIK/NF-kappaB signaling, thus refining the description of the phenotypic landscape of RIBE. Our results provide a more solid understanding of RIBE cell-specific response patterns, especially in the case of high-LET radiations, like α-particles and carbon-ions.
电离辐射诱导的旁观者效应(RIBE)包括多种效应,这些效应有可能对相邻的未受辐射组织造成大量损伤。分子事件的级联反应是在暴露于电离辐射(IR)时启动的,这种情况可能发生在诊断或治疗性医疗应用过程中。为了更好地研究这些复杂的反应机制,我们采用了一个统一的框架,该框架整合了统计微阵列分析、信号归一化和转化生物信息学功能分析技术。这种方法应用于来自基因表达综合数据库(GEO)的几个与RIBE相关的微阵列数据集。分析得出了差异表达基因列表,将旁观者样本和受辐射样本与假辐射对照进行对比。此外,通过BioInfoMiner进行的比较分子分析,该分析整合了先进的统计富集和优先级排序方法,揭示了细胞水平上离散的生物学过程。例如,生长的负调控、细胞对锌 - 镉的反应以及Wnt和NIK/NF - κB信号传导,从而完善了对RIBE表型景观的描述。我们的结果为RIBE细胞特异性反应模式提供了更深入的理解,特别是在高传能线密度辐射的情况下,如α粒子和碳离子。