Comprehensive and quantitative mapping of RNA-protein interactions across a transcribed eukaryotic genome.

RNA-binding proteins (RBPs) control the fate of nearly every transcript in a cell. However, no existing approach for studying these posttranscriptional gene regulators combines transcriptome-wide throughput and biophysical precision. Here, we describe an assay that accomplishes this. Using commonly available hardware, we built a customizable, open-source platform that leverages the inherent throughput of Illumina technology for direct biophysical measurements. We used the platform to quantitatively measure the binding affinity of the prototypical RBP Vts1 for every transcript in the Saccharomyces cerevisiae genome. The scale and precision of these measurements revealed many previously unknown features of this well-studied RBP. Our transcribed genome array (TGA) assayed both rare and abundant transcripts with equivalent proficiency, revealing hundreds of low-abundance targets missed by previous approaches. These targets regulated diverse biological processes including nutrient sensing and the DNA damage response, and implicated Vts1 in de novo gene "birth." TGA provided single-nucleotide resolution for each binding site and delineated a highly specific sequence and structure motif for Vts1 binding. Changes in transcript levels in vts1Δ cells established the regulatory function of these binding sites. The impact of Vts1 on transcript abundance was largely independent of where it bound within an mRNA, challenging prevailing assumptions about how this RBP drives RNA degradation. TGA thus enables a quantitative description of the relationship between variant RNA structures, affinity, and in vivo phenotype on a transcriptome-wide scale. We anticipate that TGA will provide similarly comprehensive and quantitative insights into the function of virtually any RBP.

High-throughput biochemical profiling reveals sequence determinants of dCas9 off-target binding and unbinding.

The bacterial adaptive immune system CRISPR-Cas9 has been appropriated as a versatile tool for editing genomes, controlling gene expression, and visualizing genetic loci. To analyze Cas9's ability to bind DNA rapidly and specifically, we generated multiple libraries of potential binding partners for measuring the kinetics of nuclease-dead Cas9 (dCas9) interactions. Using a massively parallel method to quantify protein-DNA interactions on a high-throughput sequencing flow cell, we comprehensively assess the effects of combinatorial mismatches between guide RNA (gRNA) and target nucleotides, both in the seed and in more distal nucleotides, plus disruption of the protospacer adjacent motif (PAM). We report two consequences of PAM-distal mismatches: reversal of dCas9 binding at long time scales, and synergistic changes in association kinetics when other gRNA-target mismatches are present. Together, these observations support a model for Cas9 specificity wherein gRNA-DNA mismatches at PAM-distal bases modulate different biophysical parameters that determine association and dissociation rates. The methods we present decouple aspects of kinetic and thermodynamic properties of the Cas9-DNA interaction and broaden the toolkit for investigating off-target binding behavior.

Quantitative analysis of RNA-protein interactions on a massively parallel array reveals biophysical and evolutionary landscapes.

RNA-protein interactions drive fundamental biological processes and are targets for molecular engineering, yet quantitative and comprehensive understanding of the sequence determinants of affinity remains limited. Here we repurpose a high-throughput sequencing instrument to quantitatively measure binding and dissociation of a fluorescently labeled protein to >10(7) RNA targets generated on a flow cell surface by in situ transcription and intermolecular tethering of RNA to DNA. Studying the MS2 coat protein, we decompose the binding energy contributions from primary and secondary RNA structure, and observe that differences in affinity are often driven by sequence-specific changes in both association and dissociation rates. By analyzing the biophysical constraints and modeling mutational paths describing the molecular evolution of MS2 from low- to high-affinity hairpins, we quantify widespread molecular epistasis and a long-hypothesized, structure-dependent preference for G:U base pairs over C:A intermediates in evolutionary trajectories. Our results suggest that quantitative analysis of RNA on a massively parallel array (RNA-MaP) provides generalizable insight into the biophysical basis and evolutionary consequences of sequence-function relationships.