A global view of network of lncRNAs and their binding proteinsby Desi Shang, Haixiu Yang, Yanjun Xu, Qianlan Yao, Wenbin Zhou, Xinrui Shi, Junwei Han, Fei Su, Bin Su, Chunlong Zhang, Chunquan Li, Xia Li

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Molecular Biology / Biotechnology


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A global view of network of lncRNAs and their binding proteins

Desi Shang 1, a , Haixiu Yang 1, a , Yanjun Xu 1 , Qianlan Yao 1 , Wenbin Zhou 1 ,

Xinrui Shi, Junwei Han 1 , Fei

Su 1 , Bin Su 1 , Chunlong Zhang 1 , Chunquan Li 2,1,*

Xia Li 1,*

Received (in XXX, XXX) Xth XXXXXXXXX 20XX, Accepted Xth XXXXXXXXX 20XX

DOI: 10.1039/b000000x 5

Recently, the long non-coding RNAs (lncRNAs) had obtained wide attention because they had broad and crucial functions in regulating complex biological processes. Many lncRNAs exerted their functions by interfacing with corresponding RNA binding proteins and the complexity of lncRNAs’ function was attributed to multiple lncRNA-protein interactions. To gain insights into global relationship between lncRNAs and their binding proteins, here we constructed a lncRNA–protein network (LPN) based on 10 experimentally determined functional interactions between them. This network included 177 lncRNAs, 92 proteins and 683 relationships between them. Cluster analysis of LPN revealed that some proteins (such as AGO and IGFBP families) and lncRNA (such as XIST and MALAT 1) were densely connected, suggesting the potential co-regulated mechanism and functional cross-talk of different lncRNAs. We then characterized the lncRNA functions and found that lncRNA binding proteins (LBP) enriched in many 15 cancer or cancer-related pathways. Finally, we investigated the different topological properties of LBPs in

PPIs network. Compared with disease proteins and average ones, LBP tend to have significantly higher degree, betweenness, and closeness but relatively lower clustering coefficient, indicating their centrality and essentiality in the context of biological network.

Introduction 20

The appearance of next generation sequencing (NGS) revealed that a fraction of transcribed RNAs is non-coding 1. Among them, long non-coding RNAs (lncRNAs) had obtained wide attention in recent years because they had a crucial role in regulating complex biological processes including translation, splicing, nuclear 25 organization and potentially epigenetic regulation of gene expression 2-6. Moreover, they participated in many kinds of diseases ranging from neurodegeneration to cancers 1, 3-6. Yang et al. had systemically studied the lncRNA-disease associations from a network view and found that number of lncRNAs 30 associated with the disease had a broad distribution 7. The multiformity and complexity of lncRNAs’ function was attributed to multiple proteins the lncRNA interacted with, because almost all the lncRNAs exerted their functions by interfacing with corresponding RNA binding proteins 2, 4, 8. Meanwhile, one RNA 35 binding protein could interact with different target lncRNAs to regulate a variety of cellular processes 9, 10. Based on the complex relationships between lncRNA and their RNA binding proteins (LBPs), a large scale lncRNA–protein network should be established and network analysis should be utilized to study the 40 global characteristic of lncRNA interacted proteins which could shed new light into understanding complex lncRNAs’ function in cellular circuitry and disease progression 4.

The development of experimental and bioinformatics technologies had made it available to construct a global lncRNA–45 protein network (LPN). Some high-throughput biotechnology, such as cross-linking immunoprecipitation and high-throughput sequencing (CLIP-seq), had led the number of lncRNA–protein interactions to grow rapidly in recent years 11. Furthermore,

NPInter (http://www.panrna.org/NPInter/index.php), which was a 50 curated database, had collected experimentally determined functional interactions between noncoding RNAs (excluding tRNAs and rRNAs) and other biomolecules (proteins, RNAs and genomic DNAs) 11. Here, we generated a bipartite LPN in which nodes represent lncRNA and proteins if they had experimentally 55 determined functional interactions in the NPInter database. We then used network biology to (i) implement cluster analyses of

LPN; (ii) characterize the lncRNA functions by LBPs (iii) investigate the different topological peoperties of LBPs in (protein-protein interaction) PPIs network. Our results showed 60 that the LPN may offer insights into understanding underlying mechanisms of lncRNAs actions.

Results and discussion

Construction of lncRNA–protein network

We constructed a bipartite network consisting of two disjoint 65 kinds of nodes. One kind of nodes corresponded to human lncRNAs, whereas the other corresponded to proteins interacting to lncRNAs. A protein and a lncRNA were connected if there were experimentally verified functional interactions between them. The list of lncRNAs, proteins and.associations between 70 them was obtained from the NPInter v2.0. The NPInter was the first and almost the complete and up-to-date database that integrates experimentally verified functional interactions between noncoding RNAs (excluding tRNAs and rRNAs) and other