Comprehensive analysis of RNA-protein interactions by high-throughput sequencing–RNA affinity profiling

Tome, Jacob M.; Ozer, Abdullah; Pagano, John M.; Gheba, Dan; Schroth, Gary P.; Lis, John T.

doi:10.1038/nmeth.2970

Cited by 127 publications

(167 citation statements)

References 41 publications

Supporting

Mentioning

164

Contrasting

Order By: Relevance

“…Our approach directly harnessed the throughput of Illumina sequencing, using the MiSeq sequencing flow cell itself as a platform for high-throughput biochemistry. Although the flow cell provides an ideal substrate for massively parallel experiments, current Illumina instruments are not amenable to customization (28,29). Previous methods such as RNA on a massively parallel array (RNA-MaP) and high-throughput sequencing-RNA affinity profiling (HiTS-RAP) overcame this issue by operating on the now antiquated Genome Analyzer II.…”

Section: Resultsmentioning

confidence: 99%

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

She

Chakravarty

Layton

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

108

View full text Add to dashboard Cite

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...

show abstract

Section: Resultsmentioning

confidence: 99%

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

She

Chakravarty

Layton

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

108

View full text Add to dashboard Cite

show abstract

“…S2). A new generation of high-throughput RNA biochemistry platforms (42)(43)(44) offers the prospect of both training these next-generation energetic prediction algorithms and carrying out blind tests with many thousands of measurements.…”

Section: Discussionmentioning

confidence: 99%

Blind tests of RNA nearest-neighbor energy prediction

Chou

Kladwang

Kappel

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

The predictive modeling and design of biologically active RNA molecules requires understanding the energetic balance among their basic components. Rapid developments in computer simulation promise increasingly accurate recovery of RNA's nearest-neighbor (NN) freeenergy parameters, but these methods have not been tested in predictive trials or on nonstandard nucleotides. Here, we present, to our knowledge, the first such tests through a RECCES-Rosetta (reweighting of energy-function collection with conformational ensemble sampling in Rosetta) framework that rigorously models conformational entropy, predicts previously unmeasured NN parameters, and estimates these values' systematic uncertainties. RECCES-Rosetta recovers the 10 NN parameters for Watson-Crick stacked base pairs and 32 single-nucleotide dangling-end parameters with unprecedented accuracies: rmsd of 0.28 kcal/mol and 0.41 kcal/mol, respectively. For setaside test sets, RECCES-Rosetta gives rmsd values of 0.32 kcal/mol on eight stacked pairs involving G-U wobble pairs and 0.99 kcal/mol on seven stacked pairs involving nonstandard isocytidine-isoguanosine pairs. To more rigorously assess RECCES-Rosetta, we carried out four blind predictions for stacked pairs involving 2,6-diaminopurine-U pairs, which achieved 0.64 kcal/mol rmsd accuracy when tested by subsequent experiments. Overall, these results establish that computational methods can now blindly predict energetics of basic RNA motifs, including chemically modified variants, with consistently better than 1 kcal/mol accuracy. Systematic tests indicate that resolving the remaining discrepancies will require energy function improvements beyond simply reweighting component terms, and we propose further blind trials to test such efforts.RNA helix | ensemble prediction | simulated tempering | thermodynamics | blind prediction R NA plays central roles in biological processes, including translation, splicing, regulation of genetic expression, and catalysis (1, 2), and in bioengineering efforts to control these processes (3-5). These critical RNA functions are defined at their most fundamental level by the energetics of how RNA folds and interacts with other RNAs and molecular partners, and how these processes change upon naturally occurring or artificially introduced chemical modifications. Experimentally, the folding free energies of RNA motifs can be precisely measured by optical melting experiments, and a compendium of these measurements have established the nearest-neighbor (NN) model for the most basic RNA elements, including double helices with the four canonical ribonucleotides (6). In the NN model, the stability of a base pair is assumed to only be affected by its adjacent base pairs, and the folding free energy of a canonical RNA helix can be estimated based on NN parameters for each stacked pair, an initialization term for the entropic cost of creating the first base pair, and corrections for different terminal base pairs. Although next-NN effects and tertiary contacts are not treated in the NN mo...

show abstract

“…More recent techniques couple the extensive mutagenesis of a target with selection for function and high-throughput sequencing [2][3][4] . These 'select-and-sequence' approaches have been applied mutational interference mapping experiment (mime) for studying rnA structure and function to a wide range of biological phenomena, including protein function 5 , catalysis 6 and the identification of DNA-and RNAbinding motifs 3,7,8 .…”

mentioning

confidence: 99%

“…These 'select-and-sequence' approaches have been applied mutational interference mapping experiment (mime) for studying rnA structure and function to a wide range of biological phenomena, including protein function 5 , catalysis 6 and the identification of DNA-and RNAbinding motifs 3,7,8 . Indeed, two recently developed techniques, termed RNA-Map 4 and high-throughput sequencing-RNA affinity profiling (HiTS-RAP) 2 , quantitatively measure RNA-protein interactions on millions of RNAs tethered to a DNA sequencing machine. However, these techniques require specialized equipment not present in many laboratories 2,4 .…”

mentioning

confidence: 99%

Mutational interference mapping experiment (MIME) for studying RNA structure and function

et al. 2015

View full text Add to dashboard Cite

RNA regulates many biological processes; however, identifying functional RNA sequences and structures is complex and time-consuming. We introduce a method, mutational interference mapping experiment (MIME), to identify, at single-nucleotide resolution, the primary sequence and secondary structures of an RNA molecule that are crucial for its function. MIME is based on random mutagenesis of the RNA target followed by functional selection and next-generation sequencing. Our analytical approach allows the recovery of quantitative binding parameters and permits the identification of base-pairing partners directly from the sequencing data. We used this method to map the binding site of the human immunodeficiency virus-1 (HIV-1) Pr55(Gag) protein on the viral genomic RNA in vitro, and showed that, by analyzing permitted base-pairing patterns, we could model RNA structure motifs that are crucial for protein binding.

show abstract

Comprehensive analysis of RNA-protein interactions by high-throughput sequencing–RNA affinity profiling

Cited by 127 publications

References 41 publications

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

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

Blind tests of RNA nearest-neighbor energy prediction

Mutational interference mapping experiment (MIME) for studying RNA structure and function

Contact Info

Product

Resources

About