Protein complex scaffolding predicted as a prevalent function of long non-coding RNAs

Abstract The human transcriptome contains thousands of long non-coding RNAs (lncRNAs). Characterizing their function is a current challenge. An emerging concept is that lncRNAs serve as protein scaffolds, forming ribonucleoproteins and bringing proteins in proximity. However, only few scaffolding lncRNAs have been characterized and the prevalence of this function is unknown. Here, we propose the first computational approach aimed at predicting scaffolding lncRNAs at large scale. We predicted the largest human lncRNA–protein interaction network to date using the catRAPID omics algorithm. In combination with tissue expression and statistical approaches, we identified 847 lncRNAs (∼5% of the long non-coding transcriptome) predicted to scaffold half of the known protein complexes and network modules. Lastly, we show that the association of certain lncRNAs to disease may involve their scaffolding ability. Overall, our results suggest for the first time that RNA-mediated scaffolding of protein complexes and modules may be a common mechanism in human cells. INTRODUCTION More than 60% of the human genome is transcribed into tens of thousands of RNAs with low coding potential (1). Long non-coding RNAs (lncRNAs) are a subset of those transcripts longer than 200 nt, transcribed by RNA polymerase II, often capped, spliced and polyadenylated (2). The possible function of most of the > 26 000 GENCODE annotated lncRNAs is yet to be addressed (3), and many are thought to be transcription errors or noise. However, thousands of lncRNAs have been found to be differentially expressed in distinct cell types, with dozens shown to be implicated in transcription regulation (4), stress responses (5) and disease (6). Indeed, lncRNAs are versatile molecules able to perform numerous tasks in the cell through binding of proteins, DNA or other RNA molecules (2). All cellular functions are performed by interactions between molecules, such as interaction between proteins and RNAs. These interactions can be stable, leading to ribonucleoprotein (RNP) complexes such as the ribosome, the spliceosome or the telomerase complex, or transient such as those involved in transport and degradation of nuclear transcripts. Similarly, components of complexes or pathways need to be physically close to each other (either transiently or permanently) in order to perform their function. One way to achieve this, while attaining selectivity in a crowded cell, is to employ platform or scaffold molecules that piece together components of a complex or a pathway (7). Although proteins can and do serve as scaffolds for other proteins (8), the use of RNA scaffolds would present several advantages, since ‘one protein comprising 100 amino acids can capture only one or two proteins, whereas one RNA molecule comprising 100 nt can capture around 5–20 proteins’, simultaneously (9). Moreover, lncRNAs can act immediately after transcription, while protein scaffolds require at least the step of translation before being functional (2). Several ncRNAs have been found to function as scaffolds for RNP complexes such as TERC (Telomerase RNA Component), SRP (Signal Recognition Particle RNA) and LINP1 (LncRNA In Nonhomologous End Joining Pathway 1) (2,10,11) or found to transiently assemble groups of proteins as in the case of XIST (X-inactive specific transcript) and both the granule-forming NEAT1 (Nuclear Paraspeckle Assembly Transcript 1) and MALAT1 (Metastasis Associated Lung Adenocarcinoma Transcript 1) (5,12). Although known scaffolding lncRNAs carry out important cellular functions, only a few dozen cases have been uncovered so far (7), many while studying the protein complexes rather than the lncRNAs. We therefore hypothesize that other yet uncharacterized lncRNAs may act as scaffolds. Recently, with the development of RNA interactome capture methodologies, the repertoire of RNA-binding proteins (RBPs) has greatly expanded (13), leading to the discovery of hundreds of novel RNA-interacting proteins, many of which contain no known RNA-binding domain (RBD). In addition, studies using high-throughput methods to detect RNAs bound by RBPs including iCLIP, PAR-CLIP and recently eCLIP (14), demonstrate that most RBPs bind thousands of different RNA molecules depending on the cell line. However, these investigations have been limited to a set of ∼140 RBPs containing known RBDs (14,15) and do not cover the full extent of the protein–RNA interaction space. Furthermore, only one fraction of the RNAs targeted by the RBPs are found in common by independent replicate experiments, suggesting that the interaction maps of the studied RBPs are far from complete (14). Computational prediction of protein–RNA interactions can therefore help fill the gap in our knowledge of protein–RNA interactions and be applied to large-scale analyses. In this paper, we study for the first time the prevalence of protein complex scaffolding as a function of lncRNAs. By exploiting a computed protein–RNA interaction network, we developed and applied an original large-scale approach to identify candidate lncRNAs possibly acting as scaffolding molecules for protein complexes and network functional modules. We discovered hundreds of scaffolding lncRNA candidates, suggesting that RNA scaffolding is a prevalent and widespread mechanism in the cell. In addition, we found that more than half of the protein complexes and network modules in the cell may be scaffolded by lncRNAs, reinforcing the widespread nature of their action. MATERIALS AND METHODS LncRNA–protein interaction predictions The catRAPID omics protein–RNA interaction predictor (16) was used to predict interactions between the human long non-coding RNA transcriptome (Ensembl v82) and the human canonical proteome, leading to ∼243 million predictions. Predictions with interaction propensity score ≥50 were kept for further analyses (∼30.8 million interactions). See Supplementary Material for details. Tissue expression filtering To create a set of high confidence protein–RNA interaction predictions, we restricted the analysis to pairs of lncRNA–proteins that are likely to be found together in at least one tissue. Human tissue expression data from the GTEx v6.0 project (17) was used. We downloaded RPKM (Reads Per Kilobase of transcript per Million mapped reads) information from 8555 samples across 53 tissues, already mapped to human transcripts (GENCODE v19). RPKM values of samples coming from the same tissue were averaged after a step of removing outlier values (below or above 1.5-times the interquartile range). Protein expression was derived from their coding mRNA expression, by selecting the highest RPKM value among the protein's mRNAs for each tissue. Only protein–RNA interactions where both the RNA and the protein have a minimum RPKM value of 1.58 in at least one of the 53 tissues, were retained. This cutoff was determined as the optimal expression cutoff (maximizing the sum of specificity and sensitivit (...truncated)