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. Researchers from North Carolina State University highlight new high-throughput technologies and discuss the potential for integrative computational analysis to streamline the identification of infection-related lncRNAs and unveil novel targets for antiviral therapeutics.
A proposed workflow of three major phases for identifying host lncRNAs
that are involved in viral infections
Considering the lack of functional information for lncRNAs in general, the first step will be to survey lncRNAs associated with infection of interest (Discovery phase). Unbiased genome scale approaches like a transcriptome deep sequencing (RNA-seq) analysis of infected samples collected in vitro or in vivo is widely employed. Large-scale lncRNA screening is also emerging as a powerful alternative, but is less applicable due to technical constraints. Once a set of infection-related lncRNAs are identified, the next step is to narrow down the list to a small set of high interest lncRNAs (Prioritization phase), an extremely challenging task. There are multiple computational strategies for the annotation and prioritization of identified lncRNAs based on orthogonal information as indicated. Though not covered here, other types of information, like regulatory elements uncovered by Chip-Seq experiments, histone modification marks, and curated molecular interaction networks can all facilitate the prioritization. However, analytical methods for the quantitative integration of information from different sources need to be developed. This advancement may require community-based collaborative efforts. The last step is to experimentally validate specific candidate lncRNAs (Validation phase), while accounting for the unique characteristics of lncRNAs.