Wageningen University & Research, Food & Biobased Research, Bornse Weilanden 9, 6708 WG, Wageningen, The Netherlands.
Alanya Alaaddin Keykubat University, Faculty of Engineering, Food Engineering Department, Kestel-Alanya, 07450, Antalya, Turkey.
Sci Rep. 2017 Jul 28;7(1):6778. doi: 10.1038/s41598-017-06355-0.
Intestinal epithelial cells, like Caco-2, are commonly used to study the interaction between food, other luminal factors and the host, often supported by microarray analysis to study the changes in gene expression as a result of the exposure. However, no compiled dataset for Caco-2 has ever been initiated and Caco-2-dedicated gene expression networks are barely available. Here, 341 Caco-2-specific microarray samples were collected from public databases and from in-house experiments pertaining to Caco-2 cells exposed to pathogens, probiotics and several food compounds. Using these datasets, a gene functional association network specific for Caco-2 was generated containing 8937 nodes 129711 edges. Two in silico methods, a modified version of biclustering and the new Differential Expression Correlation Analysis, were developed to identify Caco-2-specific gene targets within a pathway of interest. These methods were subsequently applied to the AhR and Nrf2 signalling pathways and altered expression of the predicted target genes was validated by qPCR in Caco-2 cells exposed to coffee extracts, known to activate both AhR and Nrf2 pathways. The datasets and in silico method(s) to identify and predict responsive target genes can be used to more efficiently design experiments to study Caco-2/intestinal epithelial-relevant biological processes.
肠上皮细胞,如 Caco-2,常用于研究食物、其他腔室因素与宿主之间的相互作用,通常通过微阵列分析来研究暴露后基因表达的变化。然而,从未启动过针对 Caco-2 的综合数据集,并且几乎没有专门针对 Caco-2 的基因表达网络。在这里,从公共数据库和内部实验中收集了 341 个 Caco-2 专用微阵列样本,这些实验涉及到暴露于病原体、益生菌和几种食物化合物的 Caco-2 细胞。使用这些数据集,生成了一个包含 8937 个节点和 129711 个边的 Caco-2 特异性基因功能关联网络。开发了两种计算方法,一种是改进的双聚类版本和新的差异表达相关分析,用于识别感兴趣通路中的 Caco-2 特异性基因靶标。随后将这些方法应用于 AhR 和 Nrf2 信号通路,并通过 qPCR 验证了在暴露于已知激活 AhR 和 Nrf2 通路的咖啡提取物的 Caco-2 细胞中预测靶基因的表达变化。用于识别和预测响应性靶基因的数据集和计算方法可用于更有效地设计实验来研究与 Caco-2/肠上皮相关的生物学过程。