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.

Figure 1.

Data production and analysis workflows. (A) Predictions of protein-lncRNA interactions (PRI) using catRAPID omics for the human proteome and long non-coding transcriptome. Interactions are further filtered by co-presence in the same GTEx tissue. The produced PRI network contains 6.02 million interactions. (B) Protein groups and lncRNAs are tested for enrichment in lncRNA protein's targets among groups of proteins. After noise filtering, a final list of scaffolding lncRNA candidates is produced. (C) Principle of the enrichment in lncRNA protein's targets among groups of proteins. Colors of nodes correspond to the ones used on the lncRNA association to protein groups box on (B).

Figure 2.

A global lncRNA–protein interaction network. Predicted protein-lncRNA interaction network composed by more than 6 millions interactions (grey circle) between 12629 proteins (pink circle) and 2799 lncRNAs (blue circle). The size of the network is compared to the human binary protein-protein interaction network (see Supplementary Methods). All circles are proportional to their components.

Figure 3.

Experimentally-determined lnc-405-interacting proteins are enriched as top catRAPID predictions. Gene Set Enrichment Analysis (GSEA) (69) of catRAPID predictions between lnc-405 lncRNA and 1459 human RBPs (Supplementary Material), using the RBPs identified as interactors in the MS experiment as a gene set. Note that only RBPs with catRAPID predictions (within size restrictions) were considered. P-value = 0.017 (10 000 simulations), normalized enrichment score = 1.59).

Figure 4.

Interactions between lncRNAs and protein groups. Boolean matrix representing enrichment between lncRNAs and protein groups on (A) non-redundant CORUM, (B) Wan 2015, (C) BioPlex and (D) Network modules. Blue color represents significant enrichments, white color represents non-significant enrichments. Only lncRNAs/protein groups with at least one significant enrichment are displayed. Matrix was clustered by hierarchical clustering with euclidean distance, dendrograms are not displayed due to the very high number of rows and columns. The PRC2 complex, as well as examples of promiscuous and specific enrichments are highlighted.

Figure 5.

Scaffolding lncRNA candidates display functional features. (A) Overlap between scaffolding lncRNA candidate gene (820 genes, 847 transcripts) and the following groups of functional or conserved lncRNA genes characterized in other studies: Hon2017 (36): lncRNAs displaying four features of functionality; Liu2016 (37): lncRNAs affecting cell growth according to CRISPRi experiments; Lnc2cancer (38): lncRNAs involved in cancer; LncRNADB (70): compendium of known functional lncRNAs; LncRNADisease (39): lncRNAs involved in human diseases; Mukherjee2017 (35): lncRNAs with a metabolic profile characteristic of functional transcripts; Necsulea2014 (40): lncRNAs conserved in therians; Smith2013 (41): lncRNAs containing at least one exonic conserved structural element (see Supplementary Material). Enrichment was tested with one-tailed Fisher's exact tests, background included all genes (12233 lncRNA genes, 15230 transcripts) analysed in this study. All P-values for the ‘Yes’ category are significant (P-value < 0.05), except for Necsulea2014. (B) Proportion of sequence covered with fitCons score above the threshold (x-axis), for different gene features (3′UTR, 5′UTR), lincRNA exons on scaffolding lincRNAs candidates and all other lincRNAs accessed in this study. Error bars: standard deviation of 100 subsampling experiments (with replacement) of 50 genes per category. ‘Scaffolding lincRNAs’ have a higher proportion of sequence covered above the threshold than ‘Other lincRNAs’ (one-tailed Kolmogorov–Smirnov test P-value = 0.008). As observed in other studies (35,42), lincRNA fitCons scores are lower than UTR regions of protein-coding genes.

Figure 6.

Disease-associated lncRNA network. Network representation of disease-associated lncRNAs (hexagonal nodes in yellow) potentially scaffolding protein groups (square colored nodes) containing at least one protein known to be involved in the same disease. Colors correspond to different diseases. Node size reflects the number of proteins in the group. Edges represent lncRNA–protein-group interactions. Edge width reflects the number of proteins interacting with the lncRNA. LncRNA transcripts were mapped to genes. Some disease names have been abbreviated for simplicity.

Figure 7.

SNHG1 lncRNA gene association to hepatocellular carcinoma through interaction with the TNFα/NF-κB signaling complex. Red nodes represent protein components of the TNFα/NF-κB signaling complex (non-redundant CORUM complex 10). Pink edges correspond to the identified protein-protein interactions between those proteins, downloaded from IntAct (71) on 22 May 2017. Interactions predicted in this study are represented by green dashed edges, experimentally determined ones (see Supplementary Material) by blue dashed edges. Negative regulatory interactions are shown in red and are taken from (53) and (52). ViennaRNA web services were used to predict the secondary structure of SNHG1 and miR-195 (72).