Long non-coding RNAs (lncRNAs) are key regulators of major biological processes and their functional modes are dictated by their subcellular localization. Relative nuclear enrichment of lncRNAs compared to mRNAs is a prevalent phenomenon but the molecular mechanisms governing their nuclear retention in cells remain largely unknown. Here in this study, we harness the recently released eCLIP data for a large number of RNA-binding proteins (RBPs) in K562 and HepG2 cells and utilize multiple bioinformatics methods to comprehensively survey the roles of RBPs in lncRNA nuclear retention. We identify an array of splicing RBPs that bind to nuclear-enriched lincRNAs (large intergenic non-coding RNAs) thus may act as trans-factors regulating their nuclear retention. Further analyses reveal that these RBPs may bind with distinct core motifs, flanking sequence compositions, or secondary structures to drive lincRNA nuclear retention. Moreover, network analyses uncover potential co-regulatory RBP clusters and the physical interaction between HNRNPU and SAFB2 proteins in K562 cells is further experimentally verified. Altogether, our analyses reveal previously unknown factors and mechanisms that govern lincRNA nuclear localization in cells.

It is well known that functional modes of lncRNAs are intimately associated with their subcellular localization1and relative nuclear enrichment of lncRNAs compared to mRNAs is a prevalent phenomenon2. As RBPs control the production, maturation, localization, translation, and degradation of cellular RNAs3, we reason that it is important to uncover partner RBPs that can bind and facilitate lncRNA nuclear localization. In this study, we thus harnessed the recently released large scale of eCLIP data4and subcellular RNA-seq data5available in K562 and HepG2 cell lines to characterize lncRNA-RBP interactome and uncovered potential factors and associated mechanisms determining lncRNA nuclear retention. Analyses of the subcellular RNA-seq data identified nuclear enriched lncRNAs (nuc-lncRNAs) and confirmed that lncRNAs and eRNAs (enhancer associated lncRNAs) are relatively nuclear enriched in both cells. By integrating the RBP binding profiles, we next generated RBP/nuc-lncRNA interaction map to identify RBPs associated with nuc-lncRNAs including HNRNPU, SAFB2, KHSRP and KHDRBS1 in K562 and HRNPNPC as well as HNRNPL in HepG2 cell lines. To further confirm the above findings, HNRNPU was knocked down and which led to nuclear retention of a panel of lncRNAs.

Motivation: Thousands of long noncoding RNAs (lncRNAs) were newly identified from high throughput RNA-seq data. Functional annotation and prioritization of these lncRNAs for further experimental validation as well as the functional investigation is the bottleneck step for many noncoding RNA studies. Results: Here we describe lncFunTK that can run either as standard application or webserver for this purpose. It integrates high throughput sequencing data (i.e. ChIP-seq, CLIP-seq and RNA-seq) to construct the regulatory network associated with lncRNAs. Through the network, it calculates the Functional Information Score (FIS) of each individual lncRNA for prioritizing and inferring its functions through Gene Ontology (GO) terms of neighboring genes. In addition, it also provides utility scripts to support the input data preprocessing and the parameter optimizing. We further demonstrate that lncFunTK can be widely used in various biological systems for lncRNA prioritization and functional annotation. Availability and implementation: The lncFunTK standalone version is an open source package and freely available at http://sunlab.cpy.cuhk.edu.hk/lncfuntk under the MIT license. A webserver implementation is also available at http://sunlab.cpy.cuhk.edu.hk/lncfuntk/runlncfuntk.html. Supplementary information: Supplementary data are available at Bioinformatics online.