iDRiP for the systematic discovery of proteins bound directly to noncoding RNA

Hsueh-Ping Chu, Anand Minajigi, Yunfei Chen, Robert Morris, Chia-Yu Guh, Yu-Hung Hsieh, Myriam Boukhali, Wilhelm Haas, Jeannie T. Lee

Published: 2021-06-08 DOI: 10.1038/s41596-021-00555-9

Extended

Extended Data Fig. 1 Comparison of the results obtained with the iDRiP and ChIRP methods.

a , Scatterplots comparing various iDRiP-MS datasets. Each dot represents the protein abundance (log2 intensity) obtained from quantitative MS. Replicate 1 versus replicate 2 shown in blue boxes. Pearson correlation r is indicated. b , Scatterplots comparing various ChIRP-MS datasets. Each dot represents the protein abundance (log2 intensity) obtained from quantitative MS. Replicate 1 versus replicate 2 shown in blue boxes. Pearson correlation r is indicated. c , The number of twofold enriched targets (over luciferase control) and the number of significant biological processes (BP) sets from DAVID bioinformatics resources with FDR < 25% are indicated in each experiment. d , Agarose gel of DNase-treated iDRiP samples. UV-cross-linked samples were treated with or without DNase. DNA was purified by phenol/chloroform extraction. Lane 1, 100 bp marker. Lane 2, 1 kb marker. Lane 3, no DNase treatment. Lane 4, DNase treatment for 20 min. Lane 5, no DNase treatment (+RNase A, +proteinase K). Lane 6, DNase treatment (+RNase A, +proteinase K). e , Silver staining of the eluted proteins after iDRiP (using various probes: TERRA, sense, Luc and U1) in a polyacrylamide gel. The eluted proteins from U1-iDRiP show some specific bands compared with the negative control iDRiP (Luc).

Extended Data Fig. 2 Top TERRA interacting candidates revealed by iDRiP or ChIRP.

iDRiP proteomics (top panel) shows less background in controls (sense and U1) than ChIRP proteomics (bottom panel). Log2 enrichment of proteins captured by iDRiP or ChIRP (using various probes: TERRA, sense and U1) normalized using luciferase as a control.

Extended Data Fig. 3 U1 protein interactome identified by ChIRP-MS.

a , GO biological process (GO_BP_term) enrichment analysis for U1-ChIRP interactome. b , Scatterplot showing log2 enrichment of mitochondrial proteins found by TERRA-ChIRP versus U1-ChIRP. Pearson’s r is shown. c , The ranked list of expression data for all genes from RNA-seq data 13 was sorted from high expression to low expression. The target genes positions in the ranked list (FPKM value) are used to calculate an enrichment score for the entire set of targets (observed data). To see if the observed value is random noise, the position of gene labels was randomly ordered relative to the ranked FPKM list 10,000 times. The observed enrichment score ( Ob_ES ) is labeled with a red dotted line. The histogram is the distribution of randomized enrichment scores (RandES). The results show that in general the protein targets from iDRiP and ChIRP are clustered significantly near the low end of the transcriptome (1- P -value < 0.05).

Extended Data Fig. 4 Statistics of U1-iDRiP and U1-ChIRP proteomics.

a , Histogram of the probability density of the log2 abundance for U1-iDRiP-MS. Red line, fitted using the Gaussian formula. Blue lines, cutoffs for P < 0.05. b , Histogram of the probability density of the log2 abundance for U1-ChIRP-MS. Red line, fitted using the Gaussian formula. Blue lines, cutoffs for P < 0.05. c , Scatter plot of log2 abundance versus −log10 ( P values) for U1-iDRiP-MS. U1 snRNP genes are indicated. Red dots, genes with P < 0.05. d , Scatterplot of log2 abundance versus −log10 ( P values) for U1-ChIRP-MS. U1 snRNP genes are indicated. Red dots, genes with P < 0.05.

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More than 90% of the human genome is transcribed into noncoding RNAs, but their functional characterization has lagged behind. A major bottleneck in the understanding of their functions and mechanisms has been a dearth of systematic methods for identifyin
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ACKNOWLEDGMENTS

The ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007).
Kung, J. T., Colognori, D. & Lee, J. T. Long noncoding RNAs: past, present, and future. Genetics 193, 651–669 (2013).
Kapranov, P. et al. The majority of total nuclear-encoded non-ribosomal RNA in a human cell is ‘dark matter’ un-annotated RNA. BMC Biol. 8, 149 (2010).
Rinn, J. L. & Chang, H. Y. Genome regulation by long noncoding RNAs. Annu. Rev. Biochem. 81, 145–166 (2012).
Lee, J. T. & Bartolomei, M. S. X-inactivation, imprinting, and long noncoding RNAs in health and disease. Cell 152, 1308–1323 (2013).
Tay, Y., Rinn, J. & Pandolfi, P. P. The multilayered complexity of ceRNA crosstalk and competition. Nature 505, 344–352 (2014).
Anderson, K. M. et al. Transcription of the non-coding RNA upperhand controls Hand2 expression and heart development. Nature 539, 433–436 (2016).
Kotzin, J. J. et al. The long non-coding RNA Morrbid regulates Bim and short-lived myeloid cell lifespan. Nature 537, 239–243 (2016).
Chillon, I. & Marcia, M. The molecular structure of long non-coding RNAs: emerging patterns and functional implications. Crit. Rev. Biochem. Mol. Biol. 55, 662–690 (2020).
Uroda, T. et al. Conserved pseudoknots in lncRNA MEG3 are essential for stimulation of the p53 pathway. Mol. Cell 75, 982–995 e9 (2019).
Uroda, T. et al. Visualizing the functional 3D shape and topography of long noncoding RNAs by single-particle atomic force microscopy and in-solution hydrodynamic techniques. Nat. Protoc. 15, 2107–2139 (2020).
Kim, D. N. et al. Zinc-finger protein CNBP alters the 3-D structure of lncRNA Braveheart in solution. Nat. Commun. 11, 148 (2020).
Chu, H. P. et al. TERRA RNA antagonizes ATRX and protects telomeres. Cell 170, 86–101 e16 (2017).
Minajigi, A. Et al. Chromosomes. A comprehensive Xist interactome reveals cohesin repulsion and an RNA-directed chromosome conformation. Science 349 (2015).
McHugh, C. A. et al. The Xist lncRNA interacts directly with SHARP to silence transcription through HDAC3. Nature 521, 232–236 (2015).
Chu, C. et al. Systematic discovery of Xist RNA binding proteins. Cell 161, 404–416 (2015).
Simon, M. D. et al. High-resolution Xist binding maps reveal two-step spreading during X-chromosome inactivation. Nature 504, 465–469 (2013).
Simon, M. D. Capture hybridization analysis of RNA targets (CHART). Curr. Protoc. Mol. Biol. Chapter 21, Unit 21 25 (2013).
Aeby, E. et al. Decapping enzyme 1A breaks X-chromosome symmetry by controlling Tsix elongation and RNA turnover. Nat. Cell Biol. 22, 1116–1129 (2020).
Yi, W. et al. CRISPR-assisted detection of RNA-protein interactions in living cells. Nat. Methods 17, 685–688 (2020).
Disteche, C. M. Dosage compensation of the sex chromosomes. Annu. Rev. Genet. 46, 537–560 (2012).
Azzalin, C. M., Reichenbach, P., Khoriauli, L., Giulotto, E. & Lingner, J. Telomeric repeat containing RNA and RNA surveillance factors at mammalian chromosome ends. Science 318, 798–801 (2007).
Azzalin, C. M. & Lingner, J. Telomere functions grounding on TERRA firma. Trends Cell Biol. 25, 29–36 (2015).
Zhang, L.-F. et al. Telomeric RNAs mark sex chromosomes in stem cells. Genetics 182, 685 (2009).
Schoeftner, S. & Blasco, M. A. Developmentally regulated transcription of mammalian telomeres by DNA-dependent RNA polymerase II. Nat. Cell Biol. 10, 228–236 (2008).
Brieno-Enriquez, M. A., Moak, S. L., Abud-Flores, A. & Cohen, P. E. Characterization of telomeric repeat-containing RNA (TERRA) localization and protein interactions in primordial germ cells of the mousedagger. Biol. Reprod. 100, 950–962 (2019).
Sunwoo, H., Wu, J. Y. & Lee, J. T. The Xist RNA-PRC2 complex at 20-nm resolution reveals a low Xist stoichiometry and suggests a hit-and-run mechanism in mouse cells. Proc. Natl Acad. Sci. USA 112, E4216–E4225 (2015).
Ule, J. et al. CLIP identifies Nova-regulated RNA networks in the brain. Science 302, 1212–1215 (2003).
Edwards, A. & Haas, W. Multiplexed quantitative proteomics for high-throughput comprehensive proteome comparisons of human cell lines. Methods Mol. Biol. 1394, 1–13 (2016).
Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937–940 (2011).
McAlister, G. C. et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150–7158 (2014).
Huang, D. W. et al. Extracting biological meaning from large gene lists with DAVID. Curr. Protoc. Bioinformatics. https://doi.org/10.1002/0471250953.bi1311s27 (2009).
Chang, F. T. et al. PML bodies provide an important platform for the maintenance of telomeric chromatin integrity in embryonic stem cells. Nucleic Acids Res. 41, 4447–4458 (2013).
Seraphin, B. & Rosbash, M. Identification of functional U1 snRNA-pre-mRNA complexes committed to spliceosome assembly and splicing. Cell 59, 349–358 (1989).
Pomeranz Krummel, D. A., Oubridge, C., Leung, A. K., Li, J. & Nagai, K. Crystal structure of human spliceosomal U1 snRNP at 5.5 A resolution. Nature 458, 475–480 (2009).
Konarska, M. M. & Sharp, P. A. Association of U2, U4, U5, and U6 small nuclear ribonucleoproteins in a spliceosome-type complex in absence of precursor RNA. Proc. Natl Acad. Sci. USA 85, 5459–5462 (1988).
Hall, K. B. & Konarska, M. M. The 5′ splice site consensus RNA oligonucleotide induces assembly of U2/U4/U5/U6 small nuclear ribonucleoprotein complexes. Proc. Natl Acad. Sci. USA 89, 10969–10973 (1992).
Shibata, S. & Lee, J. T. Characterization and quantitation of differential Tsix transcripts: implications for Tsix function. Hum. Mol. Genet. 12, 125–136 (2003).
Sarma, K. et al. ATRX directs binding of PRC2 to Xist RNA and Polycomb targets. Cell 159, 869–883 (2014).
Jegu, T. et al. Xist RNA antagonizes the SWI/SNF chromatin remodeler BRG1 on the inactive X chromosome. Nat. Struct. Mol. Biol. 26, 96–109 (2019).
Sysoev, V. O. et al. Global changes of the RNA-bound proteome during the maternal-to-zygotic transition in Drosophila. Nat. Commun. 7, 12128 (2016).
Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).
Ong, S. E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteom. 1, 376–386 (2002).
Rauniyar, N. & Yates, J. R. Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13, 5293–5309 (2014).
Li, J. M. et al. TMTpro reagents: a set of isobaric labeling mass tags enables simultaneous proteome-wide measurements across 16 samples. Nat. Methods 17, 399–404 (2020).
Braun, C. R. et al. Generation of multiple reporter ions from a single isobaric reagent increases multiplexing capacity for quantitative proteomics. Anal. Chem. 87, 9855–9863 (2015).
McAlister, G. C. et al. Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal. Chem. 84, 7469–7478 (2012).
Lapek, J. D. et al. Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities. Nat. Biotechnol. 35, 983–989 (2017).
Kreuzer, J., Edwards, A. & Haas, W. Multiplexed quantitative phosphoproteomics of cell line and tissue samples. Post-Transl. Modif. That Modulate Enzym. Act. 626, 41–65 (2019).
Lee, J. T. & Lu, N. Targeted mutagenesis of Tsix leads to nonrandom X inactivation. Cell 99, 47–57 (1999).
Breitling, R., Armengaud, P., Amtmann, A. & Herzyk, P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 573, 83–92 (2004).
Del Carratore, F. et al. RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets. Bioinformatics 33, 2774–2775 (2017).
Flynn, R.A. et al. Systematic discovery and functional interrogation of SARS-CoV-2 viral RNA-host protein interactions during infection. Preprint at bioRxiv https://doi.org/10.1101/2020.10.06.327445 (2020).
Munschauer, M. et al. The NORAD lncRNA assembles a topoisomerase complex critical for genome stability. Nature 561, 132–136 (2018).
Schmidt, N. et al. The SARS-CoV-2 RNA–protein interactome in infected human cells. Nat. Microbiol. 6, 339–353 (2021).
West, J. A. et al. The long noncoding RNAs NEAT1 and MALAT1 bind active chromatin sites. Mol. Cell 55, 791–802 (2014).

iDRiP for the systematic discovery of proteins bound directly to noncoding RNA

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