Zhang Hao, Li Zhi, Li Yanpu, Liu Yuanning, Liu Junxin, Li Xin, Shen Tingjie, Duan Yunna, Hu Minggang, Xu Dong
BMC Syst Biol. 2013;7 Suppl 2(Suppl 2):S3. doi: 10.1186/1752-0509-7-S2-S3. Epub 2013 Oct 14.
While it has been suggested that host microRNAs (miRNAs) may downregulate viral gene expression as an antiviral defense mechanism, such a mechanism has not been explored in the influenza virus for human flu studies. As it is difficult to conduct related experiments on humans, computational studies can provide some insight. Although many computational tools have been designed for miRNA target prediction, there is a need for cross-species prediction, especially for predicting viral targets of human miRNAs. However, finding putative human miRNAs targeting influenza virus genome is still challenging.
We developed machine-learning features and conducted comprehensive data training for predicting interactions between H1N1 genome segments and host miRNA. We defined our seed region as the first ten nucleotides from the 5' end of the miRNA to the 3' end of the miRNA and integrated various features including the number of consecutive matching bases in the seed region of 10 bases, a triplet feature in seed regions, thermodynamic energy, penalty of bulges and wobbles at binding sites, and the secondary structure of viral RNA for the prediction.
Compared to general predictive models, our model fully takes into account the conservation patterns and features of viral RNA secondary structures, and greatly improves the prediction accuracy. Our model identified some key miRNAs including hsa-miR-489, hsa-miR-325, hsa-miR-876-3p and hsa-miR-2117, which target HA, PB2, MP and NS of H1N1, respectively. Our study provided an interesting hypothesis concerning the miRNA-based antiviral defense mechanism against influenza virus in human, i.e., the binding between human miRNA and viral RNAs may not result in gene silencing but rather may block the viral RNA replication.
虽然有人提出宿主微小RNA（miRNA）可能通过下调病毒基因表达作为一种抗病毒防御机制，但在人类流感研究的流感病毒中尚未探索到这种机制。由于难以在人体上进行相关实验，计算研究可以提供一些见解。尽管已经设计了许多计算工具用于miRNA靶标预测，但仍需要进行跨物种预测，特别是用于预测人类miRNA的病毒靶标。然而，找到靶向流感病毒基因组的假定人类miRNA仍然具有挑战性。
我们开发了机器学习特征，并进行了全面的数据训练，以预测H1N1基因组片段与宿主miRNA之间的相互作用。我们将种子区域定义为从miRNA的5'端到3'端的前十个核苷酸，并整合了各种特征，包括10个碱基的种子区域中连续匹配碱基的数量、种子区域中的三联体特征、热力学能量、结合位点处凸起和摆动的罚分以及用于预测的病毒RNA二级结构。
与一般预测模型相比，我们的模型充分考虑了病毒RNA二级结构的保守模式和特征，大大提高了预测准确性。我们的模型鉴定了一些关键的miRNA，包括hsa-miR-489、hsa-miR-325、hsa-miR-876-3p和hsa-miR-2117，它们分别靶向H1N1的HA、PB2、MP和NS。我们的研究提供了一个关于人类中基于miRNA的抗流感病毒防御机制的有趣假设，即人类miRNA与病毒RNA之间的结合可能不会导致基因沉默，而是可能阻断病毒RNA复制。
