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 MS2 coat protein to >107 RNA targets generated on a flow-cell surface by in situ transcription and inter-molecular tethering of RNA to DNA. We decompose the binding energy contributions from primary and secondary RNA structure, finding that differences in affinity are often driven by sequence-specific changes in association 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 (RNAMaP) relationships across molecular variants.

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 nucleasedead 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.DNA | molecular biophysics | kinetics | sequencing | CRISPR C RISPR-associated protein 9 (Cas9) is programmed to bind its target DNA by a guide RNA (gRNA) that, once loaded, forms a ribonucleoprotein (RNP) complex. The Streptococcus pyogenes CRISPR system, the most extensively studied and applied system to date, targets a 23-bp DNA sequence containing (i) an "NGG" protospacer adjacent motif (PAM) element downstream of the single-guide RNA (sgRNA) target DNA (1) and (ii) a 20-bp sequence upstream of the PAM bearing complementarity to the gRNA (2). Genome engineering applications leverage the nuclease activity of the Cas9 RNP, but Cas9 engineered to lack the residues required for cleavage [dCas9 (nuclease-dead Cas9)] has proven valuable by enabling the creation of customizable and programmable DNA binding elements that can activate and repress gene expression with high precision (CRISPRa and CRISPRi) (3).The biophysical underpinnings of the Cas9 target search have been investigated both by directed biochemical assays (4, 5) and through measurements of off-target Cas9 activity (6-11). These studies have led to a model for binding wherein Cas9 proceeds through a series of steps starting with PAM recognition, followed by DNA melting, RNA strand invasion, and heteroduplex formation dependent on complementarity with a 5-10-bp seed. Structural data have further suggested that conformational changes in the HNH domain reposition catalytic residues and permit allosteric regulation of the RuvC domain. This conformational gating ensures that cleavage occurs only in the context of substantial homology between gRNA and target (12, 13).The specificity of Cas9 DNA binding is crucial for all potential applications of Cas9's RNA-programmable targeting. Localization of dCas9...

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 transcriptomewide 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.RNA | next-generation sequencing | systems biochemistry | RNA binding proteins | Vts1 R NA-binding proteins (RBPs) constitute 5-10% of the eukaryotic proteome (1-3) and collectively govern the localization, translation, and decay of virtually every transcript (4-6). Despite the ubiquity of RBPs and their central importance in gene regulation, decoding the links between RNA primary sequence and its cadre of regulators remains a major unresolved challenge (7). Current approaches for characterizing RBP function generally involve trade-offs between throughput, comprehensiveness, and quantitative precision. Biophysical measurements can be made with targeted biochemical approaches such as electrophoretic mobility shift assays (EMSAs) or fluorescence polarization (FP) (8, 9), but these methods can only interrogate known RNA-protein interactions and are inherently lowthroughput. Selection-based approaches [e.g., in vitro selection, high-throughput sequencing of RNA, and sequence-specificity landscapes (SEQRS)/RNA bind-n-seq (RBNS)] achieve higher throughput, but these techniques remove binding sites from their natural sequence context and identify "winners" based on more than simple affinity (10). Transcriptome-wide methods, which often use cross-linking and immunoprecipi...

Visual sensitivity can be tuned by differential expression of opsin genes. Among African cichlid fishes, seven cone opsin genes are expressed in different combinations to produce diverse visual sensitivities. To determine the genetic architecture controlling these adaptive differences, we analysed genetic crosses between species expressing different complements of opsin genes. Quantitative genetic analyses suggest that expression is controlled by only a few loci with correlations among some genes. Genetic mapping identifies clear evidence of trans‐acting factors in two chromosomal regions that contribute to differences in opsin expression as well as one cis‐regulatory region. Therefore, both cis and trans regulation are important. The simple genetic architecture suggested by these results may explain why opsin gene expression is evolutionarily labile, and why similar patterns of expression have evolved repeatedly in different lineages.

Metabolic flux analysis is a vital tool used to determine the ultimate output of cellular metabolism and thus detect biotechnologically relevant bottlenecks in productivity. 13C-based metabolic flux analysis (13C-MFA) and flux balance analysis (FBA) have many potential applications in biotechnology. However, noteworthy hurdles in fluxomics study are still present. First, several technical difficulties in both 13C-MFA and FBA severely limit the scope of fluxomics findings and the applicability of obtained metabolic information. Second, the complexity of metabolic regulation poses a great challenge for precise prediction and analysis of metabolic networks, as there are gaps between fluxomics results and other omics studies. Third, despite identified metabolic bottlenecks or sources of host stress from product synthesis, it remains difficult to overcome inherent metabolic robustness or to efficiently import and express nonnative pathways. Fourth, product yields often decrease as the number of enzymatic steps increases. Such decrease in yield may not be caused by rate-limiting enzymes, but rather is accumulated through each enzymatic reaction. Fifth, a high-throughput fluxomics tool hasnot been developed for characterizing nonmodel microorganisms and maximizing their application in industrial biotechnology. Refining fluxomics tools and understanding these obstacles will improve our ability to engineer highlyefficient metabolic pathways in microbial hosts.