RNP-MaP: In-cell analysis of protein interaction networks defines functional hubs in RNA
RNAs interact with networks of proteins to form complexes (RNPs) that govern many biological processes, but inter-protein networks on RNA are currently impossible to examine in a comprehensive way.  We developed a live-cell RNP-MaP (RNP network analysis by mutational profiling) chemical probing strategy for mapping simultaneous binding by and cooperative interactions among multiple proteins with single RNA molecules at nucleotide resolution. RNP-MaP revealed that two structurally related, but sequence-divergent noncoding RNAs, RNase P and RMRP, share nearly identical RNP networks and, further, that protein-mediated structural communication identifies function-critical network hubs in these RNAs. RNP-MaP identified previously unknown protein interaction networks within the XIST long noncoding RNA that are conserved between mouse and human RNAs and defined silencing, compartmentalization and splicing communities of proteins whose binding sites are networked together on XIST. The XIST E region contains a dense network of protein interactions, and including PTBP1, MATR3, and TIA1 proteins , which RNP-MaP revealed to each bind the XIST E region via two distinct interaction modes.;  Depletion of PTBP1 and MATR3 caused native XIST particles to disperse and disappear in a human cell line. the The highly networked XIST E region was sufficient to mediate XIST RNA-like foci formation in cells. RNP-MaP enables discovery and prioritization of in-cell protein interaction networks critical for function in long RNAs, in the absence of pre-existing knowledge about protein binding sites. Overall design: RNA within cells, extracted from cells, or reconstituted in solution was exposed to varying chemical probes to assess engagement by proteins (RNP-MaP) or structural conformation (SHAPE). Reconstituted solutions included PBS buffer supplemented with magnesium, T7-transcribed RNA, and recombinant proteins. Total RNA or specific RNAs (enriched by antisense pulldown or gene-specific amplificaiton) were subjected to MaP reverse transcription, which encodes the position of chemical adducts in RNA as non-templated nucleotides or deletions within product cDNA. Sequencing libraries were generated using TruSeq or Nextera XT workflows and sequenced on either a MiSeq or NextSeq 500 instrument. Locations of adducts were found using Shapemapper 2 software. Software analysis pipelines were employed to create SHAPE and RNP-MaP profiles, find in-cell reactivity perturbations, locate correlated mutations, and model RNA structures.