Systematic discovery of endogenous human ribonucleoprotein complexes

Abstract

RNA-binding proteins (RBPs) play essential roles in biology and are frequently associated with human disease. While recent studies have systematically identified individual RBPs, their higher order assembly into Ribonucleoprotein (RNP) complexes has not been systematically investigated. Here, we describe a proteomics method for systematic identification of RNP complexes in human cells. We identify 1,428 protein complexes that associate with RNA, indicating that over 20% of known human protein complexes contain RNA. To explore the role of RNA in the assembly of each complex, we identify complexes that dissociate, change composition, or form stable protein-only complexes in the absence of RNA. Importantly, these data also provide specific novel insights into the function of well-studied protein complexes not previously known to associate with RNA, including replication factor C (RFC) and cytokinetic centralspindlin complex. Finally, we use our method to systematically identify cell-type specific RNA-associated proteins in mouse embryonic stem cells. We distribute these data as a resource, rna.MAP (rna.proteincomplexes.org) which provides a comprehensive dataset for the study of RNA-associated protein complexes. Our system thus provides a novel methodology for further explorations across human tissues and disease states, as well as throughout all domains of life. ! 3! ! 4! upon CF-MS by comparing chromatographic separations of cellular lysate under control and RNA degrading conditions ( Figure 1A). A statistical framework is then applied to discover RNP complexes by identifying concurrent shifts of known protein complex subunits upon RNA degradation ( Figure 1A).Analysis of DIF-FRAC data answers important questions as to the role of RNA plays in macromolecular complexes. Specifically, we identify RNP complexes that 1) dissociate, 2) form stable protein-only complexes, and 3) change composition in the absence of RNA suggesting specific roles for RNA in each of these cases. Because DIF-FRAC is independent of UV crosslinking, nucleotide incorporation, genetic manipulation or poly(A) RNA capture efficiency, it can therefore be used to investigate a wide variety of cell types, tissues and species. To demonstrate this versatility, we apply DIF-FRAC to mouse embryonic stem cells (mESCs), identifying 1,165 RNA-associated proteins, to show the method is highly adaptable and can be extended to discover RNP complexes in diverse samples.Finally, we created a system-wide resource of 1,428 RNP complexes, many of which are previously unreported as having an RNA component, representing 20% of known human protein complexes. We provide our resource, rna.MAP, to the community as a fully searchable web database at rna.proteincomplexes.org. Results and Discussion Differential fractionation (DIF-FRAC) identifies RNP complexesThe DIF-FRAC strategy builds upon our previous strategy of Co-Fractionation Massspectroscopy (CF-MS) for identifying protein complexes in cellular lysate (Havugimana et al., 2012;Wan et al., 2015). CF-MS chromatograp...

hu.MAP 2.0: Integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies

“…We also observe another uncharacterized protein, CCDC9, as being a member of the exon-exon junction complex (EJC) ( Figure 4C ). Since the EJC is a ribonucleoprotein complex involved in RNA splicing, we searched our previously collected RNA DIF-FRAC mass spectrometry data 42 for evidence of CCDC9 as being both a member of the EJC and also associated with RNA. The DIF-FRAC experiment identifies ribonucleoprotein complexes by comparing elution profiles of protein complexes with and without RNAseA treatment.…”

“…19 were downloaded from the Pride web resource 46 (PXD004193) and reprocessed using the MSBlender pipeline 47 . Full details are described in Mallam et al 42 .…”

Systematic Identification of Protein Phosphorylation-Mediated Interactions

“…CF-MS identifies protein complexes by separating proteins in their native assemblies along a biochemical gradient using non-denaturing separations and utilizes the tendency of proteins in a complex to co-elute, as detected by protein mass spectrometry on the resulting biochemical fractions, in order to identify protein interactions and complexes. CF-MS was more recently adapted in the form of DIFFRAC to identify RNA-binding protein (RBP) complexes by looking for changes in elution patterns between a control and RNase-treated sample when both samples are separated by size on a size-exclusion chromatography (SEC) column 22 . The DIFFRAC approach has been effective in different biological systems, including different cell types and embryonic tissues 25 , and we realized that the method could be applied to identify regulatory mechanisms of protein interactions.…”

“…Confident that the experimental component of the method was working, we then turned towards systematically scoring the interaction changes we identified. In the previous work identifying RNA-binding proteins a single "DIFFRAC score" was estimated using a normalized L1-norm ( equation 1 and 2 ) 22 , treating each complete elution trace as a single vector. Due to the global nature of the DIFFRAC score, it tends to be somewhat less sensitive to proteins which have significant abundance changes within only a single fraction.…”

“…To this end, we sought to develop an approach to specifically identify protein phosphorylation that regulates protein interactions. This method uses dif ferential frac tionation (DIFFRAC 22 ) of chromatographically separated control and phosphatase-treated native (non-denatured) cell extracts, as measured by protein mass spectrometry on each chromatographic fraction, in order to detect changes in protein assemblies. Additionally, we developed and used orthogonal scoring methods for ranking proteins with the most notable phosphorylation-dependent changes in their separation behaviour.…”