School of Biology, University of St Andrews, St Andrews, KY16 9TH, Fife, UK.
EaStCHEM School of Chemistry and Biomedical Sciences Research Complex, University of St Andrews, St Andrews, KY16 9ST, Fife, UK.
Sci Rep. 2022 May 6;12(1):7482. doi: 10.1038/s41598-022-11633-7.
Differences in the expression patterns of genes have been used to measure the effects of non-stress or stress conditions in poultry species. However, the list of genes identified can be extensive and they might be related to several biological systems. Therefore, the aim of this study was to identify a small set of genes closely associated with stress in a poultry animal model, the chicken (Gallus gallus), by reusing and combining data previously published together with bioinformatic analysis and Bayesian networks in a multi-step approach. Two datasets were collected from publicly available repositories and pre-processed. Bioinformatics analyses were performed to identify genes common to both datasets that showed differential expression patterns between non-stress and stress conditions. Bayesian networks were learnt using a Simulated Annealing algorithm implemented in the software Banjo. The structure of the Bayesian network consisted of 16 out of 19 genes together with the stress condition. Network structure showed CARD19 directly connected to the stress condition plus highlighted CYGB, BRAT1, and EPN3 as relevant, suggesting these genes could play a role in stress. The biological functionality of these genes is related to damage, apoptosis, and oxygen provision, and they could potentially be further explored as biomarkers of stress.
基因表达模式的差异已被用于衡量禽类非应激或应激条件的影响。然而，所鉴定的基因列表可能很广泛，它们可能与多个生物系统有关。因此，本研究的目的是通过重新使用和组合先前发表的数据以及生物信息学分析和贝叶斯网络，在一个多步骤的方法中，从一个禽类动物模型——鸡（Gallus gallus）中确定一小部分与应激密切相关的基因。从公共可用的存储库中收集了两个数据集并进行了预处理。进行了生物信息学分析，以鉴定两个数据集之间具有差异表达模式的共同基因，这些基因在非应激和应激条件下表现不同。使用 Banjo 软件中实现的模拟退火算法学习了贝叶斯网络。贝叶斯网络的结构由 19 个基因中的 16 个以及应激条件组成。网络结构显示 CARD19 直接与应激条件相连，同时突出了 CYGB、BRAT1 和 EPN3 作为相关基因，表明这些基因可能在应激中发挥作用。这些基因的生物学功能与损伤、细胞凋亡和氧气供应有关，它们可能作为应激的生物标志物进一步探索。
