eas圜LIP is applied to the top 33 most frequent missense mutations across 28 RBPs, identifying quantitative changes in RNA binding in specific RBPs that are recurrently mutated in cancer. Establishing a distribution for non-RBPs enables the definition of specific target RNAs for any RBP as those interactions with a frequency per protein are unlikely to occur with a randomly selected protein. eas圜LIP enables the calculation of the distribution of cross-linking for the average protein and we propose a quantitative threshold for whether a protein is an RBP. eas圜LIP quantifies RNA cross-links per protein and provides visual confirmation of each step. Here, we report a refinement of current CLIP protocols, termed eas圜LIP. More generally, recurrent mutations that are not exceptionally frequent are of unknown significance 14. Many cancer-associated genes are potentially RBPs and some RBPs contain recurrent missense mutations. There are widespread RBP expression changes in tumors, and the alternative splicing of tumor cells is predicted to affect cancer hallmarks, with some tumor types reverting to a more undifferentiated splicing pattern 13. Most tumors have aberrant splicing without apparent mutational cause 11, 12, indicating that there must be unknown mutations within or affecting RNA metabolism pathways that are collectively common. Defining RNA–protein interaction events per cell and per protein in absolute quantities, in contrast, may provide a framework for describing a global and widely reproducible view of RNA–protein interactions.Ī number of RBPs are mutated in human cancers however, the impact of such mutants on their association with RNA has not been quantitated. Determining the targets of an RBP by conventional approaches, such as enrichment over negative control immunopurification or by clustering of cross-links 10, are ultimately but indirectly determining if the absolute count of an RNA–protein complex in the cell is abnormally high. The frequencies of RNA–protein complexes, per-cell and per-interaction partner, would enable the fundamental characterization of RNA–protein interaction networks. Addressing this question for such proteins, and for additional potentially novel RBPs, has been hindered by the lack of a test that quantitates RNA interaction events per protein molecule to provide a global cutoff level of RNA binding to nominate a protein as an RBP.Ĭurrently, there is no general method to estimate absolute RNA–protein interaction frequencies and a quantitative test is needed to assess whether any nonrandom interaction with an RNA exists. For example, proteins important to cancer, such as BRCA1, SMAD3-4, SPEN, CHD2, and JUN, have been categorized as RBPs, yet are not generally studied as such, raising the question as to whether they actually act in that role. Many important proteins studied in a different context have been categorized as also binding RNA, yet few or no experiments have been published on their functions in RNA binding. Landmark proteomic efforts from multiple groups have identified many potential novel RBPs 2, 3, 4, 5, 6, 7 some, such as sequestosome-1 8, were subsequently verified and studied by cross-linking immunoprecipitation (CLIP), while the vast majority of which have not yet been evaluated by non-proteomic approaches 9. For example, only roughly half of the proteins either in the RBP census 1 or with an RNA-binding Gene Ontology (GO) term are considered RBPs by both sources. Quantitating RBP-RNA interactions can thus nominate proteins as RBPs and define the impact of specific disease-associated RBP mutations on RNA association.Īpproaches to quantify protein–RNA cross-links on a per-molecule basis are not widely available, leading to confusion both as to what constitutes an RNA-binding protein (RBP) and to the quantitative impact of disease-associated RBP mutations. We apply eas圜LIP to the 33 most recurrent cancer mutations across 28 RBPs, finding increased RNA binding per RBP molecule for KHDRBS2 R168C, A1CF E34K and PCBP1 L100P/Q cancer mutations. Measurement of >200 independent cross-link experiments across >35 proteins identifies an RNA cross-link rate threshold that distinguishes RBPs from non-RBPs and defines target RNAs as those with a complex frequency unlikely for a random protein. eas圜LIP provides absolute cross-link rates, as well as increased simplicity, efficiency, and capacity to visualize RNA libraries during sequencing library preparation. Here, we develop an ultraviolet (UV) cross-linking immunoprecipitation (CLIP)-sequencing method, eas圜LIP. Quantitative criteria to identify proteins as RNA-binding proteins (RBPs) are presently lacking, as are criteria to define RBP target RNAs.
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