Capturing the interactome of newly transcribed RNA

Bao, Xichen; Guo, Xiangpeng; Yin, Menghui; Tariq, Muhammad; Lai, Yiwei; Kanwal, Shahzina; Zhou, Jiajian; Li, Na; Lv, Yuan; Pulido-Quetglas, Carlos; Wang, Xiwei; Lu, Junyan; Khan, Muhammad Jadoon; Xihua, Zhu; Luo, Zhiwei; Shao, Chunli; Lim, Do Hwan; Xiao, Lei; Li, Nan; Wang, Wei; He, Minghui; Liu, Yu Lin; Ward, Carl; Wang, Tong; Zhang, Gong; Wang, Dongye; Yang, Jianhua; Chen, Yiwen; Zhang, Chaolin; Jauch, Ralf; Yang, Yun; Wang, Yangming; Qin, Baoming; Änkö, Minna‐Liisa; Hutchins, Andrew P.; Sun, Hao; Wang, Huating; Fu, Xiang Dong; Zhang, Biliang; Esteban, Miguel A.

doi:10.1038/nmeth.4595

Cited by 191 publications

(171 citation statements)

References 47 publications

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“…This technique was an important advance but, like RIP, identifies the species involved but not the site of interaction, and is limited to mature mRNAs. To identify the total RNA‐bound proteome, the approach of 5‐ethynyluridine (EU) labelling of RNAs followed by biotin ligation using the click reaction (RICK) was recently developed (Bao et al , ; Huang et al , ) as well as approaches based on phase separation, in which RNA–protein conjugates are recovered from an aqueous/phenol interface (Queiroz et al , ; Trendel et al , ). In addition, MS analyses have been developed to identify the precise amino acid at the site of RNA–protein crosslinking (Kramer et al , ).…”

Section: Introductionmentioning

confidence: 99%

Defining the RNA interactome by total RNA ‐associated protein purification

Shchepachev

Bresson

Spanos

et al. 2019

Molecular Systems Biology

130

139

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The RNA binding proteome ( RBP ome) was previously investigated using UV crosslinking and purification of poly(A)‐associated proteins. However, most cellular transcripts are not polyadenylated. We therefore developed total RNA ‐associated protein purification ( TRAPP ) based on 254 nm UV crosslinking and purification of all RNA –protein complexes using silica beads. In a variant approach ( PAR ‐ TRAPP ), RNA s were labelled with 4‐thiouracil prior to 350 nm crosslinking. PAR ‐ TRAPP in yeast identified hundreds of RNA binding proteins, strongly enriched for canonical RBP s. In comparison, TRAPP identified many more proteins not expected to bind RNA , and this correlated strongly with protein abundance. Comparing TRAPP in yeast and E. coli showed apparent conservation of RNA binding by metabolic enzymes. Illustrating the value of total RBP purification, we discovered that the glycolytic enzyme enolase interacts with tRNA s. Exploiting PAR ‐ TRAPP to determine the effects of brief exposure to weak acid stress revealed specific changes in late 60S ribosome biogenesis. Furthermore, we identified the precise sites of crosslinking for hundreds of RNA –peptide conjugates, using iTRAPP , providing insights into potential regulation. We conclude that TRAPP is a widely applicable tool for RBP ome characterization.

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Section: Introductionmentioning

confidence: 99%

Defining the RNA interactome by total RNA ‐associated protein purification

Shchepachev

Bresson

Spanos

et al. 2019

Molecular Systems Biology

130

139

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“…This result could suggest that regulating splicing would be a common function which various modifications possess. We also discovered that 55 of the 72 RBPs identified by this analysis overlaps with the proteins isolated by the RICK experiment [48], which systematically captures proteins bound to a wide range of RNAs ( Supplementary Table 3). This indicates that our unsupervised learning captures RBPs that are biologically meaningful and repeatedly identified by other related studies.…”

Section: Features Learned By Mr-gan Confirmed Known Modification Sequmentioning

confidence: 76%

Predicting sites of epitranscriptome modifications using unsupervised representation learning based on generative adversarial networks

Salekin

Mostavi

Chiu³

et al. 2020

Preprint

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Epitranscriptome is an exciting area that studies different types of modifications in transcripts and the prediction of such modification sites from the transcript sequence is of significant interest. However, the scarcity of positive sites for most modifications imposes critical challenges for training robust algorithms.To circumvent this problem, we propose MR-GAN, a generative adversarial network (GAN) based model, which is trained in an unsupervised fashion on the entire pre-mRNA sequences to learn a low dimensional embedding of transcriptomic sequences. MR-GAN was then applied to extract embeddings of the sequences in a training dataset we created for eight epitranscriptome modifications, including m 6 A, m 1 A, m 1 G, m 2 G, m 5 C, m 5 U, 2′-O-Me, Pseudouridine (Ψ) and Dihydrouridine (D), of which the positive samples are very limited. Prediction models were trained based on the embeddings extracted by MR-GAN. We compared the prediction performance with the one-hot encoding of the training sequences and SRAMP, a state-of-the-art m 6 A site prediction algorithm and demonstrated that the learned embeddings outperform one-hot encoding by a significant margin for up to 15% improvement. Using MR-GAN, we also investigated the sequence motifs for each modification type and uncovered known motifs as well as new motifs not possible with sequences directly. The results demonstrated that transcriptome features extracted using unsupervised learning could lead to high precision for predicting multiple types of epitranscriptome modifications, even when the data size is small and extremely imbalanced.Keywords: N6-methyladenosine (m 6 A), epitranscriptome, RNA modification site prediction, generative adversarial networks (GAN), unsupervised representation learning, methylated RNA immunoprecipitation sequencing (MeRIP-Seq).

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“…High throughput RNA association annotations were primarily collected from Hentze et al 2018supplemental table S2 (Hentze et al, 2018. In addition, we gathered more recent high throughput datasets from (Bao et al, 2018;Huang et al, 2018;Queiroz et al, 2019;Trendel et al, 2019).…”

Section: Rna-associated Annotations Overlap Comparisons and Score Pementioning

confidence: 99%

“…Further, RNPs are strongly implicated in human diseases including amyotrophic lateral sclerosis (ALS) (Scotter et al, 2015), spinocerebellar ataxia (Yue et al, 2001), and autism (Voineagu et al, 2011). Accordingly, substantial recent effort has been focused on systematic identification of RNA-associated proteins (Baltz et al, 2012;Bao et al, 2018;Brannan et al, 2016;Castello et al, 2012Castello et al, , 2016He et al, 2016;Huang et al, 2018;Queiroz et al, 2019;Treiber et al, 2017;Trendel et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Systematic discovery of endogenous human ribonucleoprotein complexes

Mallam

Sae-Lee

Schaub

et al. 2018

Preprint

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RNA-binding proteins (RBPs) play essential roles in biology and are frequently associated with human disease. While recent studies have systematically identified individual RBPs, their higher order assembly into Ribonucleoprotein (RNP) complexes has not been systematically investigated. Here, we describe a proteomics method for systematic identification of RNP complexes in human cells. We identify 1,428 protein complexes that associate with RNA, indicating that over 20% of known human protein complexes contain RNA. To explore the role of RNA in the assembly of each complex, we identify complexes that dissociate, change composition, or form stable protein-only complexes in the absence of RNA. Importantly, these data also provide specific novel insights into the function of well-studied protein complexes not previously known to associate with RNA, including replication factor C (RFC) and cytokinetic centralspindlin complex. Finally, we use our method to systematically identify cell-type specific RNA-associated proteins in mouse embryonic stem cells. We distribute these data as a resource, rna.MAP (rna.proteincomplexes.org) which provides a comprehensive dataset for the study of RNA-associated protein complexes. Our system thus provides a novel methodology for further explorations across human tissues and disease states, as well as throughout all domains of life. ! 3! ! 4! upon CF-MS by comparing chromatographic separations of cellular lysate under control and RNA degrading conditions ( Figure 1A). A statistical framework is then applied to discover RNP complexes by identifying concurrent shifts of known protein complex subunits upon RNA degradation ( Figure 1A).Analysis of DIF-FRAC data answers important questions as to the role of RNA plays in macromolecular complexes. Specifically, we identify RNP complexes that 1) dissociate, 2) form stable protein-only complexes, and 3) change composition in the absence of RNA suggesting specific roles for RNA in each of these cases. Because DIF-FRAC is independent of UV crosslinking, nucleotide incorporation, genetic manipulation or poly(A) RNA capture efficiency, it can therefore be used to investigate a wide variety of cell types, tissues and species. To demonstrate this versatility, we apply DIF-FRAC to mouse embryonic stem cells (mESCs), identifying 1,165 RNA-associated proteins, to show the method is highly adaptable and can be extended to discover RNP complexes in diverse samples.Finally, we created a system-wide resource of 1,428 RNP complexes, many of which are previously unreported as having an RNA component, representing 20% of known human protein complexes. We provide our resource, rna.MAP, to the community as a fully searchable web database at rna.proteincomplexes.org. Results and Discussion Differential fractionation (DIF-FRAC) identifies RNP complexesThe DIF-FRAC strategy builds upon our previous strategy of Co-Fractionation Massspectroscopy (CF-MS) for identifying protein complexes in cellular lysate (Havugimana et al., 2012;Wan et al., 2015). CF-MS chromatograp...

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Capturing the interactome of newly transcribed RNA

Cited by 191 publications

References 47 publications

Defining the RNA interactome by total RNA ‐associated protein purification

Defining the RNA interactome by total RNA ‐associated protein purification

Predicting sites of epitranscriptome modifications using unsupervised representation learning based on generative adversarial networks

Systematic discovery of endogenous human ribonucleoprotein complexes

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