IMR90 radiation bystander time-course experiment 0.5Gy alpha particle

The radiation bystander effect is an important component of the overall biological response of tissues and organisms to ionizing radiation. Little is known about the contribution of genome level changes in neighboring bystander cells to tissue and organ stress after irradiation. The timing of these changes is critical in the physiological context and these questions can only be answered by studying signaling and global transcriptomics in a chronological way. Here, we present a strategy to identify different biologically important signaling modules that act in concert in the radiation and bystander responses. We used time series gene expression analysis of normal human fibroblast cells measured at 0.5 hour, 1 hour, 2 hours, 4 hours, 6 hours and 24 hours after exposure to radiation coupled with a novel clustering method targeted to short time series, Feature Based Partitioning around medoids Algorithm (FBPA), to look for genes that were potentially co-regulated. This method uses biologically meaningful features of the expression profile and dimension augmentation to address the analysis of sparse data sets such as ours. We applied FBPA and Short Time series Expression Miner (STEM) to the same datasets and present the results of our comparisons using computational metrics as well as biological enrichment. Enrichment showed that gene expression in irradiated cells fell into broad categories of signal transduction, cell cycle/cell death and inflammation/immunity; but only FBPA clustered functions well. In bystander cells, the gene expression response was also broadly categorized into functions associated with cell communication and motility, signal transduction and inflammation; but neither STEM nor FBPA separated biological functions as well as in irradiated samples. Network analysis revealed that p53 and NF-kappaB were central players in gene expression in both irradiated and bystander gene clusters. Analysis of individual clusters also suggested new regulators of gene expression in the radiation and bystander response that may act at the epigenetic level such as histone deacetylases (HDAC1 and HDAC2) and methylases (KDM5B) that can act as strong transcription repressors. Based on these results, we propose a novel time series clustering method, FBPA, as a powerful approach that can be applied to sparse data sets (including genomic profiling data), where the choice of features selected for clustering and stringent statistical outcome analysis can augment our knowledge of the underlying cellular mechanisms in biological processes. There are 72 total samples, 4 corresponding biological replicates of IMR90 cells that were not irradiated (control=C), irradiated (alpha=A) and bystander (B), cells were harvested at 0.5 hour, 1 hour, 2 hours, 4 hours, 6 hours and 24 hours after treatment

辐射旁效应（radiation bystander effect）是组织与生物体对电离辐射产生整体生物学应答的关键组成部分。目前学界对受照后邻近旁细胞的基因组水平变化在组织及器官应激中的贡献仍知之甚少。此类变化的发生时序在生理情境中具有关键意义，而相关问题的解答唯有通过按时间序列系统性研究信号通路与全局转录组学（global transcriptomics）方可实现。
本研究提出一种策略，用以识别在辐射与旁效应应答中协同发挥功能的不同生物学重要信号模块。我们对正常人类成纤维细胞开展时序基因表达分析，分别在暴露于辐射后的0.5小时、1小时、2小时、4小时、6小时及24小时采集样本，并结合一种针对短时序数据的新型聚类方法——基于中心点的特征划分算法（Feature Based Partitioning around medoids Algorithm, FBPA），以筛选潜在共调控的基因。
该方法借助表达谱的生物学特征与维度增强策略，可有效处理类似本研究这类稀疏数据集的分析任务。我们将FBPA与短时序表达挖掘器（Short Time series Expression Miner, STEM）应用于同一数据集，并通过计算指标与生物学富集分析对比了二者的结果。
富集分析结果显示，受照细胞的基因表达可归为信号转导、细胞周期/细胞死亡及炎症/免疫等宽泛功能类别；但唯有FBPA的聚类效果优异。在旁效应细胞中，基因表达应答同样可大致划分为细胞通信与运动、信号转导及炎症相关功能类别，但STEM与FBPA均未能像在受照样本中那样有效区分生物学功能。
网络分析揭示，p53与NF-κB是受照与旁效应细胞基因簇中基因表达的核心调控因子。对单个聚类的分析还发现了辐射与旁效应应答中可能在表观遗传层面发挥调控作用的新型基因表达调控因子，例如可作为强效转录抑制因子的组蛋白去乙酰化酶（histone deacetylases, HDAC1与HDAC2）及甲基化酶KDM5B。
基于上述结果，我们提出新型时序聚类方法FBPA，其可作为一种高效手段应用于稀疏数据集（包括基因组谱分析数据），鉴于为聚类选择的特征与严格的统计结果分析可加深我们对生物学过程中潜在细胞机制的认知。本数据集总计包含72份样本，来自未受照的IMR90细胞（对照组=C）、受照α辐射组（A）与旁效应组（B），每组均设置4份平行生物学重复，样本分别于处理后的0.5小时、1小时、2小时、4小时、6小时及24小时收获。