Lemler David J, Brochu Hayden N, Yang Fang, Harrell Erin A, Peng Xinxia
Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC 27607, USA.
Bioinformatics Graduate Program, North Carolina State University, Raleigh, NC 27695, USA.
Vaccines (Basel). 2017 Oct 20;5(4):37. doi: 10.3390/vaccines5040037.
Research over the past decade has clearly shown that long non-coding RNAs (lncRNAs) are functional. Many lncRNAs can be related to immunity and the host response to viral infection, but their specific functions remain largely elusive. The vast majority of lncRNAs are annotated with extremely limited knowledge and tend to be expressed at low levels, making ad hoc experimentation difficult. Changes to lncRNA expression during infection can be systematically profiled using deep sequencing; however, this often produces an intractable number of candidate lncRNAs, leaving no clear path forward. For these reasons, it is especially important to prioritize lncRNAs into high-confidence "hits" by utilizing multiple methodologies. Large scale perturbation studies may be used to screen lncRNAs involved in phenotypes of interest, such as resistance to viral infection. Single cell transcriptome sequencing quantifies cell-type specific lncRNAs that are less abundant in a mixture. When coupled with iterative experimental validations, new computational strategies for efficiently integrating orthogonal high-throughput data will likely be the driver for elucidating the functional role of lncRNAs during viral infection. This review highlights new high-throughput technologies and discusses the potential for integrative computational analysis to streamline the identification of infection-related lncRNAs and unveil novel targets for antiviral therapeutics.
过去十年的研究清楚地表明，长链非编码RNA（lncRNA）具有功能。许多lncRNA可能与免疫以及宿主对病毒感染的反应有关，但其具体功能在很大程度上仍不清楚。绝大多数lncRNA的注释知识极为有限，且往往低水平表达，这使得专门的实验变得困难。感染期间lncRNA表达的变化可以通过深度测序进行系统分析；然而，这通常会产生数量多得难以处理的候选lncRNA，没有明确的前进方向。由于这些原因，利用多种方法将lncRNA优先列为高可信度的“命中靶点”尤为重要。大规模扰动研究可用于筛选参与感兴趣表型（如对病毒感染的抗性）的lncRNA。单细胞转录组测序可量化在混合样本中丰度较低的细胞类型特异性lncRNA。当与迭代实验验证相结合时，有效整合正交高通量数据的新计算策略可能会成为阐明lncRNA在病毒感染过程中功能作用的驱动力。本综述重点介绍了新的高通量技术，并讨论了综合计算分析在简化感染相关lncRNA鉴定以及揭示抗病毒治疗新靶点方面的潜力。
