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Characterization of R-Loop–Interacting Proteins in Embryonic Stem Cells Reveals Roles in rRNA Processing and Gene Expression

Chromatin-associated RNAs have diverse roles in the nucleus. However, their mechanisms of action are poorly understood, in part because of the inability to identify proteins that specifically associate with chromatin-bound RNAs. Here, we address this problem for a subset of chromatin-associated RNAs that form R-loops—RNA–DNA hybrid structures that include a displaced strand of ssDNA. R-loops generally form cotranscriptionally and have important roles in regulation of gene expression, immunoglobulin class switching, and other processes. However, unresolved R-loops can lead to DNA damage and chromosome instability. To identify factors that may bind and regulate R-loop accumulation or mediate R-loop–dependent functions, we used a comparative immunoprecipitation/MS approach, with and without RNA–protein crosslinking, to identify a stringent set of R-loop–binding proteins in mouse embryonic stem cells. We identified 364 R-loop–interacting proteins, which were highly enriched for proteins with predicted RNA-binding functions. We characterized several R-loop–interacting proteins of the DEAD-box family of RNA helicases and found that these proteins localize to the nucleolus and, to a lesser degree, the nucleus. Consistent with their localization patterns, we found that these helicases are required for rRNA processing and regulation of gene expression. Surprisingly, depletion of these helicases resulted in misregulation of highly overlapping sets of protein-coding genes, including many genes that function in differentiation and development. We conclude that R-loop–interacting DEAD-box helicases have nonredundant roles that are critical for maintaining the normal embryonic stem cell transcriptome.


Characterization of R-Loop-Interacting Proteins in Embryonic Stem Cells Reveals Roles in rRNA Processing and Gene Expression
Tong Wu 1 , Jennifer Nance 2 , Feixia Chu 2 , and Thomas G. Fazzio 1,* Chromatin-associated RNAs have diverse roles in the nucleus. However, their mechanisms of action are poorly understood, in part because of the inability to identify proteins that specifically associate with chromatin-bound RNAs. Here, we address this problem for a subset of chromatin-associated RNAs that form R-loops-RNA-DNA hybrid structures that include a displaced strand of ssDNA. R-loops generally form cotranscriptionally and have important roles in regulation of gene expression, immunoglobulin class switching, and other processes. However, unresolved R-loops can lead to DNA damage and chromosome instability. To identify factors that may bind and regulate R-loop accumulation or mediate Rloop-dependent functions, we used a comparative immunoprecipitation/MS approach, with and without RNA-protein crosslinking, to identify a stringent set of Rloop-binding proteins in mouse embryonic stem cells. We identified 364 R-loop-interacting proteins, which were highly enriched for proteins with predicted RNA-binding functions. We characterized several R-loop-interacting proteins of the DEAD-box family of RNA helicases and found that these proteins localize to the nucleolus and, to a lesser degree, the nucleus. Consistent with their localization patterns, we found that these helicases are required for rRNA processing and regulation of gene expression. Surprisingly, depletion of these helicases resulted in misregulation of highly overlapping sets of protein-coding genes, including many genes that function in differentiation and development. We conclude that Rloop-interacting DEAD-box helicases have nonredundant roles that are critical for maintaining the normal embryonic stem cell transcriptome.
R-loops are found throughout the genome in both prokaryotes and eukaryotes (1)(2)(3)(4)(5). The majority of R-loops occur when nascent RNAs anneal with the DNA templates from which they are transcribed (6). The unique structure of an R-loop-consisting of an RNA-DNA hybrid (RDH) together with ssDNA-not only affects access of proteins that bind dsDNA but also creates potential sites of recruitment for RNA-, ssDNA-, or RDH-binding proteins.
Although the rules governing R-loop formation and stability are not completely understood, studies have shown that several nucleic acid features favor R-loop formation, including purine-rich (especially G-rich) sequences in transcripts (7), the formation of G-quadruplexes (G4s) (8), negative supercoiling (9), pausing of RNA polymerase (RNAP), and DNA nicks on nontemplate strand (10). For example, upon annealing with the template DNA strand, the G-rich transcripts will form stabilized RDHs because of the higher thermodynamic stability of GC pairs as well as the tendency of G-rich RNA/C-rich DNA of RDHs to exhibit higher stability than equivalent G-rich DNA/Crich DNA of dsDNA (11,12). Negative supercoiling, which stabilizes unwound DNA, promotes annealing of nascent RNA to form RDHs (13,14). In some cases, these features work coordinately: generation of G4s in the G-rich nontemplate ssDNA will induce and stabilize R-loops on the template strand (15), leading to significant colocalization of G4s and Rloops in the genome (16).
Methods of R-loop mapping have been developed to understand the distribution, occurrence, and dynamics of Rloops in the genome. Since the development of DNA-RNA immunoprecipitation (IP) sequencing, most early studies use the RDH-specific monoclonal antibody S9.6 to detect R-loops (17)(18)(19)(20), whereas in recent years, alternative methods have been carried out. An alternative approach uses a catalytic dead mutant RNase H1 that binds to but does not digest RDHs to map R-loops (21)(22)(23). Coupling of S9.6 mapping with bisulfite sequencing allows for single-nucleotide resolution mapping because of the fact that ssDNA occurs coordinately with RDHs (24). Although these studies all vary somewhat, they all find that a large fraction of R-loops colocalize with nascent transcripts, showing the highest enrichment at the promoter proximal, and to a lesser degree, transcription termination sites and gene bodies (5,21,25). In agreement with these findings, R-loops have been shown to have important regulatory roles in transcription initiation and termination (26)(27)(28). Although the roles of R-loops at coding genes have been the focus of many studies (4,5,18,28), Rloops are also prevalent at sites of RNAP I (rRNA) and RNAP III (tRNA) transcription (20,29). Moreover, R-loops have been shown to regulate epigenetic marks such as DNA methylation and histone modifications near gene promoters (4,19,30), as well as immunoglobulin class switch recombination (31,32), RNA splicing, and DNA replication (33,34). Despite these important roles, R-loops can promote genomic instability as ssDNA is prone to mutagenesis, and collisions of R-loops with DNA replication forks can cause DNA breaks (35)(36)(37). In addition, the accumulation of R-loops beyond normal levels is thought to play roles in amyotrophic lateral sclerosis, Friedreich's ataxia, and fragile X syndrome (38)(39)(40). Therefore, Rloop levels must be tightly regulated throughout the genome to maintain genome integrity.
R-loop levels are known to be regulated by several different mechanisms, including relaxation of negative supercoiling by Topoisomerases 1 and 2 (29), resolution of RDHs by helicases such as senataxin (28) and DDX21 (41), and degradation of the RNA portion of RDHs by nucleases, such as RNase H1 (42,43) and RNase H2 (44,45). Perhaps as a cellular response to the contributions of R-loops to genomic stress, R-loops often have a relatively short half-life of 10 to 20 min (5). This can be attributed, in part, to a number of helicases and nucleases known to remove or prevent R-loop formation (46). R-loops have also been shown to regulate local chromatin structure. For example, R-loops regulate the localization of the Tip60-p400 and PRC2 chromatin remodeling complexes (4) as well as the DNA methyltransferase 1 (47), DNA demethylase TET1 (48), and RNA methyltransferase METTL3 (27,49). Besides these examples, studies in mouse and human cells have identified additional proteins within R-loop-enriched chromatin fractions obtained by IP of RDHs by an RDH-specific monoclonal antibody named S9.6 (17,50,51). Although these studies have uncovered several new regulators of Rloop formation or stability, these approaches invariably suffer from a high background of chromatin proteins found within the same chromatin fragment as the R-loop. Consequently, no definitive list of bona fide R-loop-interacting proteins has been identified in any cell type.
Several studies have profiled the R-loop interactomes of human or mouse cell lines (50,51). For studies that focus on endogenous R-loops, an RDH-specific monoclonal antibody named S9.6 has been used to enrich for RDHs from a pool of fragmented chromatin (18). However, because of the fact that R-loop-proximal chromatin-encompassing hundreds of base pairs-is invariably copurified with RDHs, it is difficult to distinguish R-loop-binding proteins from nearby chromatin proteins using these approaches. Although more aggressive DNA fragmentation may reduce the fraction of chromatin proteins copurified with RDHs, RDHs are sensitive to extensive sonication and numerous nonsequence-specific nucleases. Therefore, a different strategy is required to profile R-loop-binding proteins with higher specificity.
Here, we describe two proteomics approaches for more stringent identification of R-loop-associated proteins in mouse embryonic stem cells (mESCs). We identify overlapping sets of R-loop-interacting proteins and many new potential regulators of R-loops. Interestingly, we show that Rloop-binding proteins identified by these approaches are highly enriched in nucleolar proteins, consistent with the high levels of R-loops found within this compartment (29,52,53). We find that several R-loop-binding helicase proteins appear to have highly overlapping roles in processing of rRNA, as well as expression of coding genes, suggesting they function in a common pathway. Finally, we show that RNA-protein crosslinking traps a set of proteins that are lost in the absence of crosslinking because of transient or weak association with the RDHs. These studies provide a resource of stringent R-loopassociating proteins in mESCs, including multiple new potential regulators of R-loop formation or stability. In addition, our studies reveal that introduction of selective RNA-protein crosslinking can identify R-loop-binding proteins that are missed by standard approaches.

Experimental Design and Statistical Rationale
For all IP/LC-MS/MS, we performed three biological replicates, with p values calculated by fitting the data to a linear model using ProStar (ProStar Software, Inc) (54) and using a moderated t test, which is robust for large datasets, including proteomics (55). For IP/ Western blotting, we performed two or three biological replicates, using mouse IgG2a (same isotype as the S9.6 antibody) as negative controls. For comparison of two proteomics datasets, p values were calculated by hypergeometric tests, which are appropriate for testing the overlaps of moderately sized lists of proteins or genes. For Gene Ontology (GO) term analysis, the PANTHER Classification System was used, with Bonferroni-corrected Fisher's exact tests used to calculate significance. The five GO pathways with the highest observed/expected ratio were shown. For RT-quantitative PCR (qPCR), we typically performed three biological replicates, each with three technical replicates, except where indicated, and Student's t tests were used to test for statistical significance, assuming a normal distribution of RT-qPCR measurements. Three biological replicates were performed for RNA-Seq. Quantitative Western blotting was performed in two biological replicates with both trials shown. Nonquantitative immunofluorescence experiments were performed 2 to 3 independent times per antibody, and representative results are shown.

S9.6 co-IP
For uncrosslinked IP, mESCs were resuspended in 10 mM Tris-HCl, pH 7.0, 150 mM NaCl, and 0.15% NP-40 with 1× Halt Protease Inhibitor Cocktail (Thermo Fisher Scientific; 78429), layered onto sucrose buffer (10 mM Tris-HCl, pH 7.0, 150 mM NaCl, 25% sucrose, and protease inhibitor) and centrifuged at 1000g. The pellet was resuspended in stringent wash buffer (10 mM Tris-HCl, pH 7.9, 1.5 mM MgCl 2 , 420 mM NaCl, 25% glycerol, 0.2 mM EDTA, 0.5 mM DTT, and protease inhibitor) and incubated on ice for 30 min. Nuclei were centrifuged at 7000g and resuspended in AM-150/0.1% NP-40 buffer (150 mM KCl, 20 mM Tris-HCl, pH 7.9, 5 mM MgCl 2 , 0.2 mM EDTA, 10% glycerol, 0.1% NP-40, with 1× Halt Protease Inhibitor Cocktail). The salt-extracted nuclei were then sonicated using a Bioruptor (Diagenode) for 15 cycles, 30 s on/30 s off with the intensity set at medium. Based on protein concentration, 1 mg of sonicated chromatin was mixed with 20 μg S9.6 or mouse IgG2a as control and incubated overnight at 4 • C. About 20 μg of sonicated chromatin was set aside untreated as input (2% of IP samples). Before mixing sonicated chromatin with antibody, 0.2 μl of RNase A (Thermo Fisher Scientific; EN0531) was added and treated at 37 • C for 15 min. For DNase I treatment, 10 μl of DNase I (New England Biolabs; M0303L) was added and treated at 37 • C for 2 h. The next day, the mixture was incubated with prewashed Protein G magnetic beads (New England Biolabs; S1430S), washed three times in AM-150/0.1% NP-40 buffer for 5 min each, and eluted in 1× SDS loading buffer (0.2 M Tris-HCl, pH 6.8, 277 mM SDS, 40% glycerol, and 6 mM bromophenol blue) by boiling for 10 min. For crosslinked IP, mESCs were cultured in stable isotope labeling by amino acids in cell culture (SILAC) medium (Thermo Fisher Scientific; A33972) either supplemented with standard lysine and arginine or 13 C 6 15 N 2 lysine and 13 C 6 15 N 4 arginine. About 500 μM 4-thiouridine (4SU) (Biosynth Carbosynth; NT06186) was added to heavy isotope-treated cells for 2 h while the light isotopecultured cells were treated with dimethyl sulfoxide vehicle. Both sets of cells were then UV treated at a wavelength of 312 nm for 1 J/cm 2 , cells were lysed and combined after sonication at 1:1 ratio based on protein amount, and immunoprecipitated as aforementioned. After washing, IP samples were subjected to on-beads RNase A treatment before elution as described previously. Both uncrosslinked and crosslinked S9.6 co-IP were performed with three biological replicates.

LC-MS/MS
S9.6 co-IP elution was run on a 10% SDS-PAGE gel, and gel slices were recovered with care to exclude the majority of the IgG heavy and light chains. Gel bands were in-gel digested and analyzed by LC-MS and LC-MS-MS as described previously (56,57). The digestion mixture was separated on a 75 μm × 25 cm PepMap Rapid Separation Liquid Chromatography column (100 Å; 2 μm) at a flow rate of~450 nl/ min, and the eluant was analyzed by an LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific). LC-MS data were acquired in a data-dependent acquisition mode, cycling between an MS scan (m/z 315-2000) acquired in the Orbitrap, followed by collision-induced dissociation analysis on the three most intensely multiply charged precursors acquired in the linear ion trap. The centroided peak lists of the collision-induced dissociation spectra were generated using PAVA searched against a database that is consisted of the Swiss-Prot protein database (version 2017. 11 . Protein hits were reported with a Protein Prospector protein score of ≥22, a protein discriminant score of ≥0.0, and a peptide expectation value of ≤0.01 (58). This set of thresholds of protein identification parameters did not return any substantial falsepositive protein hits from the randomized half of the concatenated database. Data are available via ProteomeXchange (59) with identifier PXD022697. For differential analysis of IP versus input, we used the R package ProStar (54) to calculate the normalized fold change of spectra counting and p value and to make volcano plots.
For crosslinked co-IP, cells were differentially labeled, combined, immunoprecipitated, and processed for MS as described previously, and fold-change values were normalized based on the SILAC ratio. For comparison of the crosslinked and uncrosslinked S9.6 co-IP, we first included proteins that appeared in either or both the input and immunoprecipitates in the uncrosslinked data (587 proteins), filtered this list to include only those proteins also found in the crosslinked dataset, and compared both datasets after Z-score normalization.

S9.6 Dot Blotting
mESCs were resuspended in ES cell lysis buffer (10 mM Tris, pH 7.5, 10 mM EDTA, 10 mM NaCl, and 0.5% sarkosyl) with 160 μg/ml Proteinase K (Bioline; BIO-37084) added freshly, 37 • C overnight. The next day, phenol/chloroform extraction was performed, the supernatant was mixed with NaOAc pH 5.2 to 0.3 M and then in 2.5 volume of cold ethanol, and kept at −80 • C for 30 min. Spin at maximum speed for 30 min at 4 • C, DNA pellet was washed with 70% ethanol and resuspended in 200 μl meter of water. For DNase I treatment, 1.2 μg of genomic DNA was resuspended in 1× DNase I buffer in a total volume of 20 μl, with 2.5 μl of DNase I. For RNase H treatment, the same amount of genomic DNA was resuspended in 1× RNase H buffer with 1 μl of RNase H (New England Biolabs; M0297L). For RNase A treatment, no buffer was supplied with 1 μl of RNase A. All the treatments were done at 37 • C for 2 h, then 500/250/125/62.5 ng of genomic DNA was spotted onto Amersham Hybond-N+ membrane (Cytiva; RPN303B) using the Bio-Dot Apparatus (Bio-Rad; 1706545). To achieve annealed RDHs for dot blotting, 3.5 μM of 45-nt ssRNA and ssDNA that are complementary to each other were mixed and slowly cooled down (~5 • C/min) from 95 to 25 • C. About 500 ng of annealed RDHs were treated using the same amount of DNase I/ RNase H/RNase A as aforementioned. The samples were loaded onto Hybond-N+ membrane at 200/100/50/25 ng, followed by UV treatment at a wavelength of 254 nm for 120 mJ/cm 2 . The genomic DNA blots and RDH blots were blocked with 5% milk for 1 h and then incubated with S9.6 monoclonal antibody (1:1000 dilution in PBS with Tween-20 [PBST] with 2.5% BSA) overnight. The next day, blots were washed three times with PBST, incubated with goat antimouse antibody conjugated with horseradish peroxidase (1:10,000 dilution in PBST with 2.5% BSA) for 1 h, and then washed three times. Blots were ready for development.

Northern Blotting
DNA probes were amplified using complementary DNA (cDNA) from mESCs as a template, radiolabeled by [α-32P]dCTP (PerkinElmer; 3000 Ci/mmol; 10 mCi/ml, BLU013H100UC) using Prime-a-Gene Labeling System (Promega; U1100). Unincorporated dCTP was removed by Bio-Spin P-30 columns (Bio-Rad; 7326231). Radioactivity was determined by a scintillation counter, 5 × 10 6 cpm of labeled probes were used for a Northern blotting assay. About 1 μg of total RNA was heated at 65 • C for 15 min, cooled on ice, and load into gel containing 1% agarose and 6% formaldehyde, ran in 1× Mops buffer for 100 V for 1.5 h. RNA was transferred from gel to Amersham Hybond-N+ membrane using 10× saline-sodium citrate (SSC) buffer (Invitrogen; 15557044) in a TurboBlotter (Cytiva; 10416316) overnight. The next day, blots were crosslinked at a wavelength of 254 nm for 120 mJ/cm 2 and then transferred into hybridization tubes. Blots were prehybridized with 10 ml PerfectHyb Plus hybridization buffer (MilliporeSigma; H7033-50ML) at 68 • C for 10 min. Radiolabeled probes were added and hybridized at 68 • C overnight with rotation. The next day, blots were quickly washed twice with 5 ml 2× SSC-0.1% SDS wash buffer, then washed with 10 ml 2× SSC-0.1% SDS wash buffer, and rotated at room temperature for 10 min. The blots were exposed to X-ray film in a Kodak BioMax cassette (MilliporeSigma; C4729) at −80 • C. Two biological replicates were performed with similar results.

Endoribonuclease-Prepared siRNA Preparation and Transfection
Endoribonuclease-prepared siRNAs were prepared as described (60). Briefly, cDNA from mESCs was used as a template to amplify cDNA that targets each gene with T7 anchor sequence added. In vitro transcription was then performed using T7 RNAP (New England Biolabs; M0251L). RNA was digested by ShortCut RNase III (New England Biolabs; M0245L) to generate a pool of small siRNAs, which was purified using a PureLink RNA Mini Kit (Thermo Fisher Scientific; 12183020). For transfection, 400 ng of endoribonuclease-prepared siRNA was mixed with 0.4 ml of serum-free medium and 4 μl of Lipofectamine 2000 (Thermo Fisher Scientific; 11668-019), and after 15 min of incubation, 2.8 × 10 5 mESCs were added and the mixture was plated in one well of a gelatinized 6-well plate. Media were replaced~16 h later, and cells were harvested 48 h after transfection.

RT-qPCR and RNA-Seq
RNA was extracted using an RNA Clean & Concentrator-25 Kit (Zymo Research; 11-353B) with on-column DNase I digestion for 1 h. For RT-qPCR, cDNA was synthesized using purified mouse mammary tumor virus reverse transcriptase. Quantification was performed using primers targeting cDNA, with Gapdh used as a loading control (supplemental Table S11). RNA-Seq libraries were prepared by BGI Genomics Company. mRNA was enriched from total RNA by oligo(dT)-attached magnetic beads, followed by fragmentation, firstand second-strand cDNA synthesis, end repair, add A, adapter ligation, and PCR amplification. PCR products were purified with Ampure XP beads (Beckman Coulter; A63881). The PCR products were denatured and circularized by the splint oligo sequence, and the singlestrand circle DNAs were formatted as the final library. Libraries were amplified with phi29 to make DNA nanoball, loaded into the patterned nanoarray, sequenced on BGISEQ-500 using 100-base paired-end sequencing. Reference genome mapping, transcript assignment, quantification, and differential analysis were done using RSEM (https://deweylab.github.io/RSEM/) (61). Heat maps were made by Java TreeView (http://jtreeview.sourceforge.net/). GO term analyses were done by PANTHER Classification System, and overlapping of RNA-Seq data was illustrated by UpSet (https://jku-vds-lab.at/tools/ upset/) (62).

Stringent Capture of R-Loop-Binding Proteins
To identify a stringent set of R-loop-associated proteins, we developed an S9.6 co-IP protocol that first uses high salt washes to remove most chromatin-associated proteins, followed by chromatin fragmentation and IP of RDHs (Fig. 1A). To validate this approach, we first tested whether S9.6 can pull down the RNAP II core subunit RPB1, RNA helicase DHX9, and the splicing factor SFPQ. DHX9 has been shown to bind to RDHs and R-loops in vitro (63), as well as associate with R-loops in vivo, where it regulates their accumulation (50,64). SFPQ is a splicing factor that was shown to reduce the accumulation of R-loops (64). We observed reproducible enrichment of these proteins (supplemental Fig. S1A), validating our approach. In addition, we verified that R-loops were enriched within S9.6 immunoprecipitates by qPCR, observing higher signals at genes known to form R-loops (4) in comparison to genomic regions that lack R-loops (supplemental Fig. S1B). As an additional control, we showed that addition of DNase I strongly reduced enrichment of coimmunoprecipitated proteins (supplemental Fig. S1C), further validating this approach. Finally, after confirming that RDHs were also highly sensitive to RNase A (supplemental Fig. S1, E and F), as demonstrated in two previous studies (65,66), we found that treatment of chromatin with RNase A was able to deplete endogenous R-loops, as expected (supplemental Fig. S1D). These studies demonstrate the high specificity with which R-loops and known interacting proteins are enriched by our approach.

Identification of R-Loop-Binding Proteins by MS
Our laboratory previously mapped the genomic locations of R-loops in mESCs and showed that they regulate the recruitment of two chromatin remodeling complexes, making mESCs a good platform for studying R-loop functions (4). To identify the R-loop interactome of mESCs in an unbiased manner, we performed S9.6 co-IP with or without RNase A treatment in three biological replicates. Samples were fractionated by SDS-PAGE (supplemental Fig. S2A) and subjected to tryptic digestion after isolating gel slices that excluded the majority of IgG heavy and light chains. Input and IP samples from each treatment were subjected to LC-MS/MS to identify the repertoire of proteins that interact with R-loops. A total of 709 proteins were detected in any of the three biological replicates in input or IP samples, with high correlation among the replicates (supplemental Fig. S2B). As expected, very few proteins were identified in RNase A-treated samples, demonstrating high specificity of the IP conditions (supplemental Table S1 and supplemental Fig. S2C). After filtering for proteins present in at least two IP replicates and removal of contaminating IgG peptides, 335 proteins were retained for downstream analysis (supplemental Table S2). Next we used the ProStar software package to identify the most significantly enriched R-loop-interacting proteins (54). We observed 76 proteins that were enriched more than twofold (relative to normalized input) with p value <0.003 (supplemental Table S3 and Fig. 1, B and C). One of the most highly enriched proteins is DHX9, which was previously shown to interact with RDHs in HeLa cells, further validating our approach (50). Other highly enriched proteins included SPB1 (gene name: Ftsj3), a nucleolar protein that regulates pre-rRNA processing (67) and NOG1 (gene name Gtpbp4), a nucleolar GTP-binding protein with crucial roles in 60S ribosome biogenesis (68). Another highly enriched protein was CHTOP, a component of the THO-TREX complex previously shown to inhibit the formation of R-loops (69,70). In addition, numerous known or predicted RNA-binding proteins were identified, including DDX18, DDX21, DDX27, DDX54, which belong to the DEAD-box family of RNA helicases (71)(72)(73). Another group of R-loop-interacting proteins, including PUM3 and HNRPU, have both DNA-and RNA-binding activities, suggesting possible roles in R-loop-dependent regulation of chromatin architecture or local epigenomic features. To validate our findings, we performed S9.6 co-IP followed by Western blotting and detected DDX18, DDX27, DDX54, with high IP efficiency (Fig. 1D). In all, 49% of proteins (164 of 335) identified in our S9.6 co-IP/MS analysis are known RNA-binding proteins, including several known regulators of R-loop formation, suggesting a high proportion of hits are bona fide R-loopinteracting proteins.
R-loops and other non-canonical DNA structures, such as G4s and cruciform DNA, have previously been shown to colocalize throughout the genome (74,75). We therefore investigated if R-loop-interacting proteins identified in this study are known to bind to and regulate other DNA or RNA structures. To this end, we took advantage of G4IPDB, a database of G4-binding proteins (76). We examined the overlap of 77 known G4-binding proteins from Homo sapiens (77) with the 335 R-loop-interacting proteins identified above, where one-to-one mouse to human homologs could be identified. We observed that 17 of the 77 G4-binding proteins overlap with the 335 mESC R-loop binding proteins (supplemental Table S4, observed/expected = 13.2; p value = 8.472 × 10 −15 ). Among the 17 overlapping proteins is the DDX21 helicase, consistent with in vitro studies showing that DDX21 resolves R-loops and G4s (41,78). Of the 17 known cruciform-binding proteins from H. sapiens (79), we found that two-TOP2A and YWHAB-overlapped with our set of 335 R-loop-binding proteins (supplemental Table S5, observed/ expected = 7.0; p value = 0.032). Yeast TOP2, a homolog of the mouse TOP2A (DNA topoisomerase 2-alpha), is known to regulate R-loops formed during rRNA synthesis (29). In contrast, the YWAHB protein has no known function in regulation of R-loops. The presence of YWAHB in both datasets could simply reflect the colocalization of cruciform structures and R-loops form at sites with negative supercoiling (14,79). We next examined the enrichment of GO categories among the 335 RDH-interacting proteins identified by our approach. As expected, multiple protein classifications associated with RNA processing were highly enriched (supplemental Fig. S3A). We observed similar enrichment by analyzing the 76 most prominent R-loop interacting proteins (enriched over two-fold, with p value < 0.003, data not shown). Notably, GO terms associated with rRNA processing were especially prominent, in agreement with the fact that R-loops are frequently observed in nucleoli (52,53).
To examine the cellular localization of several stringent Rloop-interacting proteins, we performed immunofluorescence staining in mESCs. Consistent with the GO term analysis, DDX18, DDX24, and DDX27 showed substantial overlap with the nucleolar marker FBRL, although nuclear localization outside the nucleolus was also observed, especially for DDX24 and DDX27 ( Fig. 2A). Conversely, two poorly enriched proteins, CTCF and SFPQ, exhibited largely diffuse nuclear staining (supplemental Fig. S3B). This raised the possibility that the strongest hits may be largely nucleolar, whereas hits with lower enrichment were more likely to interact with nuclear R-loops. Consistent with this possibility, R-loop-interacting proteins contributing to the enrichment of nucleolus-or rRNArelated GO terms exhibited stronger overall enrichment in our MS dataset (Fig. 2B). Finally, we compared our hits to a dataset describing the nucleolar proteome, which was previously measured by an independent group using isolated nucleoli from mouse fibroblasts followed by protein extraction and MS quantification (80). Of the 320 nucleolar proteins previously identified, we found that 122 overlapped with the 335 stringent R-loop-interacting proteins identified in our study (supplemental Table S6 and Fig. 2C, O/E: observed/ expected = 22.8; p = 3.957 × 10 −138 ). Furthermore, of the 76 most prominently enriched R-loop-associated proteins (described previously), 39 overlapped with the nucleolar proteome (supplemental Table S7 and Fig. 2C, O/E = 32.1; p = 3.367 × 10 −50 ). These data indicate that many of the most strongly enriched R-loop-interacting proteins function largely within the nucleolus, likely because of the high abundance of cotranscriptional R-loops during rRNA synthesis. Accordingly, proteins with important roles in the regulation or functions of R-loops at protein coding genes may be enriched to lower levels in the LC-MS/MS dataset.

Shared Functions of R-Loop-Associated DEAD-Box Proteins
The DEAD-box protein family, named after the conserved D-E-A-D amino acid sequence within the Walker B motif, consists of known or putative ATP-dependent RNA helicases that are conserved throughout eukaryotes (71)(72)(73). DEAD-box proteins have been found to contribute to RNA metabolism in vivo, including processes such as mRNA transcription and degradation, splicing, mRNA export, and ribosome biogenesis (71).

FIG. 2.
Many R-loop-binding proteins are enriched within the nucleolus. A, immunofluorescence staining of three R-loop-associated DEAD-box proteins, DDX18, DDX24, and DDX27, costained with FBRL to mark the nucleolus. DNA was stained by DAPI. The scale bar represents 10 μm. B, comparison of rRNA-related and rRNA-unrelated proteins enriched by S9.6. R-loop-associated proteins were classified as rRNA related or unrelated by GO term analysis, using the biological process term "ribosomebiogensis," "rRNAmetabolicprocess," and "rRNAprocessing." Boxes of the box plot represent the first and third quartiles, the band represents the median, and the whiskers depict 1.5 times the interquartile range. C, Venn diagram of the overlap between the R-loop interactomes (with and without filtering) and the nucleolar proteome from Kar et al. (80). The upper diagram includes the 335 proteins enriched in S9.6 co-IP samples with the 76 most enriched (corresponding to the orange dots in Fig. 1B) depicted in the lower diagram. p Values were calculated using hypergeometric tests. DAPI, 4 ′ ,6diamidino-2-phenylindole; FBRL, fibrillarin; GO, Gene Ontology; IP, immunoprecipitation.
Of the 335 stringent R-loop-associated proteins we identified from our MS, 13 belong to the DEAD-box protein family, including known R-loop regulators such as DDX5 (81) and DDX21 (41). To better understand the functions of this protein family in the regulation of R-loops, we used RNA interference to examine the cellular consequences of partial depletion of Rloop-associated proteins DDX10, DDX24, DDX27, and DDX54. Several DEAD-box family members were previously shown to localize to the nucleolus and/or function in ribosome biogenesis (82)(83)(84)(85)(86). For example, DDX5 has been shown to promote rRNA transcription (87), whereas DDX51 and DDX54 were shown to affect different steps of rRNA processing (88,89). We therefore examined the roles of DDX10, DDX24, DDX27, and DDX54 in the production and processing of rRNAs. To this end, we performed Northern blotting on total RNA isolated from mESCs, using probes specific to the 18S or 28S rRNAs. In addition to fully processed rRNAs, these probes also hybridize to the 45S pre-rRNA and multiple smaller rRNA processing intermediates (90)(91)(92), as outlined in Figure 3A. The 18S rRNA Northern probe, which also detects the 45S, 41S, and 34S pre-rRNAs, uncovered alterations in levels of 45S and 34S pre-rRNAs in Ddx24 and Ddx10 knockdown (KD), respectively (Fig. 3B). In contrast, the 28S probe revealed increased 36S pre-rRNA in Ddx24 KD and Ddx27 KD mESCs (Fig. 3C). In addition, Ddx54 KD cells exhibited increased 32S pre-rRNA relative to enhanced GFP KD control cells (Fig. 3C). Collectively, these data demonstrate that DDX10, DDX24, DDX27, and DDX54 contribute to production of mature 18S and 28S rRNAs at the level of rRNA processing, as previously observed for DEAD-box helicases DDX5, DDX17, and DDX51 (82,88,93).
In addition to their substantial nucleolar localization, several R-loop-interacting DEAD-box proteins exhibited some localization within non-nucleolar regions of the nucleus (Fig. 2A). To test these proteins for potential roles in expression of proteincoding genes, we performed mRNA-Seq upon KD of the same set of DEAD-box proteins. We observed very high correlation among replicates of each individual KD as well as high correlation among different Ddx KDs (supplemental Fig. S4A). Using a cutoff of twofold upregulated or downregulated in any KD relative to control and adjusted p value <0.05, we observed 737 genes that were differentially expressed, with more genes upregulated than downregulated, in one or more Ddx KD relative to controls (supplemental Table S8 and Fig. 4A). Consistent with the high correlation of their overall expression profiles, the sets of genes upregulated or downregulated by depletion of each factor were highly overlapping. Of the 603 genes significantly upregulated in any of the four KDs, 209 were shared among all four of the KDs (Fig. 4D), including transcripts, such as Cdkn1a, Cdkn2b, and Wnt5a. The observed changes in expression of these transcripts upon KD of each DEAD-box protein were further confirmed by RT-qPCR (supplemental Fig. S4, B-D). Of the 134 genes significantly downregulated in at least one KD, 19 were downregulated in all four KDs (Fig. 4E). This overlap was also significantly more than expected (p = 1.27 × 10 −133 ), albeit lower than observed for upregulated genes. The substantial overlap of genes misregulated by KD of each factor suggested either that DDX10, DDX24, DDX27, and DDX54 regulate mRNA levels through a shared pathway or that the genes observed to be misregulated were more sensitive to the alterations in rRNA levels observed upon KD of these proteins. Notably, GO term analysis of upregulated genes revealed enrichment for multiple terms related to cellular differentiation, including "neuronal differentiation," "cell fate commitment," and "cell migration" (Fig. 4B). Similarly, downregulated genes were also enriched for terms related to development and differentiation, including genes that regulate lipid and lipoprotein homeostasis, such as Adipoq (94) and Pcsk9 (95) (Fig. 4C and supplemental Fig. S4E). These findings raise the possibility that some alterations in gene expression upon depletion of these DEAD-box family helicases may be specific to ESCs,

Essential Roles of R-Loop-Binding Proteins in Stem Cells
Mol Cell Proteomics (2021) 20 100142 9 with potential implications for maintenance of the pluripotent state or regulation of ESC differentiation.

RNA-Protein Crosslinking Uncovers Transiently or Weakly Interacting R-Loop-Associated Proteins
Although proteins that bind to or near R-loops can be identified by co-IP using the S9.6 antibody, it is difficult to distinguish proteins that bind directly to RDHs from the proteins that bind to chromatin flanking the RDHs. In addition, while our stringent chromatin wash reduces background in our co-IP procedure, it likely also removes RDH-associated proteins that interact transiently or weakly with these structures. To systematically and specifically identify proteins that directly bind to the RDH structure of R-loop, we adapted a crosslinking protocol previously used to enrich for RNA-associated proteins (96) and coupled this procedure with S9.6 co-IP.
To this end, we cultured mESCs with 4SU to incorporate this nucleotide analog into RNA transcripts, followed by irradiation with intermediate wavelength (312 nm) UV light, which has been shown to induce crosslinks between 4SU-labeled RNA and their direct binding proteins (96,97). To precisely quantify the extent to which crosslinking affected enrichment of proteins, we introduced SILAC (98, 99) to directly compare protein abundance in the presence or the absence of 4SU addition. After 4SU or vehicle addition and UV treatment, cells were lysed and their chromatin was mixed in equal amounts, followed by S9.6 IP and LC-MS/MS for each of three independent replicates (Fig. 5A).
After removing proteins that appeared only in one replicate, 231 proteins were left for downstream analysis (supplemental Tables S9 and S10). Interestingly, although we observed enrichment of 116 proteins upon 4SU addition, another group of 115 proteins was reduced in the 4SU samples relative to non-4SU-containing samples. Whereas the enriched proteins were likely proteins that directly interact with the RNA part of R-loops, either transiently or weakly, those that were reduced in the crosslinked samples may represent proteins that interact very strongly with R-loops with multiple RNA contacts throughout each polypeptide. For proteins in this latter category, multiple protein-RNA crosslinks may render a substantial fraction of peptides "unreadable" by MS because of the covalent attachment of oligonucleotides of unknown mass that remain even after bulk digestion of RNA in the immunoprecipitates. Although 202 of 231 proteins identified in the crosslinking dataset overlapped with the 335 R-loop-interacting proteins identified in our initial studies, an additional 29 proteins were recovered in the crosslinked samples, for a total of 364 R-loop-binding proteins overall (supplemental Table S10). Based on GO term analysis, these 29 additional proteins included proteins that function in mRNA splicing or transport, raising the possibility that some of these proteins were only weakly or transiently associated with R-loops because of active roles in removing transcripts from contexts conducive to R-loop formation.
To explore the possibility that crosslinking may enrich weakly interacting proteins while depleting strongly interacting proteins, we directly compared the two datasets, hereafter referred to as "crosslinked" and "uncrosslinked." For this comparison, we included proteins that appeared in two or more replicates in the uncrosslinked data and further filtered this list to include only those proteins identified by both MS approaches, leaving 214 total proteins. Interestingly, we found that the proteins with higher enrichment in the presence of 4SU than in its absence were among the most poorly enriched in the uncrosslinked data relative to input (Fig. 5B, upper left  quadrant). Conversely, the proteins for which enrichment was reduced by crosslinking were often among the highly enriched in the uncrosslinked data (Fig. 5B, lower right quadrant). Consequently, we observed a negative correlation between the two datasets (Fig. 5B). To further validate these results, we performed S9.6 co-IP followed by quantitative Western blotting, with or without crosslinking, on a sample of proteins across the enrichment spectrum. We examined three proteins (CEBPZ, DDX18, and DDX27) reduced in the presence of 4SUdependent crosslinking and one (SFPQ) enriched in the presence of crosslinking (Fig. 5B). Consistent with the MS data, the quantitative Western blotting showed an anticorrelation pattern (Fig. 5C). Overall, these findings suggest that crosslinking improves enrichment of weakly or transiently RDH-interacting proteins that bind directly to RNA but reduces enrichment of some strongly associated proteins. Previous studies of RNA-protein interactions revealed that proteins and protein domains directly associated with RNA can be underrepresented in the spectra upon crosslinking because of the covalent attachment of RNA nucleotides (96). A similar phenomenon may explain the crosslinkingdependent reduction we observe for strongly enriched Rloop-associated factors. DISCUSSION In this study, we established approaches for rigorous identification of R-loop-associated proteins, using a modified IP approach that includes stringent extraction of chromatin proteins prior to pull down, with or without selective RNAprotein crosslinking. These approaches uncovered several hundred associated proteins, including several known R-loop regulatory factors and a number of novel R-loop-interacting proteins. Consistent with their potential roles in regulation of RDHs or association with R-loops once they form, many of these proteins were known or predicted RNA-binding proteins. One of the most enriched proteins was DHX9, which has previously been shown to bind R-loops and regulate their formation (50,64). Moreover, DHX9 is also known to bind Gquadruplexes within DNA (63), suggesting a possible role for DHX9 in coordinate regulation of R-loops and G-quadruplexes where they colocalize in the genome (16).
Upon examining the functions of R-loop-interacting proteins identified in this study, GO terms related to rRNA were enriched, and factors that localize within the nucleolus and function in rRNA processing were particularly prominent. The proteins most highly enriched by S9.6 tend to be nucleolar, often with known or predicted functions in rRNA production or processing, in contrast to the relatively lowly enriched proteins with no apparent nucleolar localization or functions in rRNA metabolism. These findings lend additional support to the idea that a significant fraction of R-loops within cells are located within nucleolus because of RNAP I-mediated transcription (29,52).
Members of the DEAD-box family of RNA helicases were overrepresented among the R-loop-associated RNA-binding proteins identified. DEAD-box helicases are known to be involved in RNA processing, including transcription, splicing, and RNA decay (100). Several DEAD-box proteins have established roles in regulation of rRNA (82). For example, DDX5 functions in rRNA transcription and processing (87,93), whereas DDX51 was shown to be necessary for normal cleavage of pre-rRNAs (88). Consistent with these roles, we found that R-loop-interacting proteins DDX18, DDX24, and DDX27 localized largely to the nucleolus, with lower levels of localization to non-nucleolar chromatin ( Fig. 2A). To further study the functions of several R-loop-interacting DEAD-box proteins, we tested the effects of DDX10, DDX24, DDX27, and DDX54 loss using RNA interference. Northern blotting revealed increased accumulation of different pre-rRNAs in different DEAD-box KD cells, suggesting these DEAD-box proteins impact rRNA maturation at several different steps. Each of these DEAD-box proteins has previously been implicated in aspects of rRNA production in multiple organisms. Human DDX10 was previously shown to reside within a complex that contributes to 18S rRNA processing (84). Consistent with this finding, we showed that Ddx10 KD caused accumulation of 34S pre-rRNA, a precursor of mature 18S rRNA. Mak5p, the yeast homolog of mouse DDX24, has been shown to facilitate processing of the yeast homolog of 28S rRNA (86), whereas human Ddx24 KD showed accumulation of 36S pre-rRNA (101), consistent with our results. KD of Ddx27 in U2OS cells was previously shown to reduce the abundance of 28S mature rRNA (85). Our results showed that Ddx27 KD caused accumulation of 36S, a precursor of 28S, providing a potential explanation for this earlier finding. Finally, DDX54 was previously shown to regulate levels of precursors of 18S rRNA (89), while we found that DDX54 regulates pre-rRNA precursors of 28S rRNA. These differences could potentially be due to differences in experimental system or cell type. In total, our findings confirm and add additional insights into the known roles of DEAD-box helicases in rRNA processing. These data suggest that resolution of R-loops by multiple RNA helicases acting nonredundantly may be necessary to efficiently process the pre-rRNA transcript into mature rRNAs.
In addition, by performing mRNA-Seq, we observed misregulation of a highly overlapping set of genes in all four KDs, including genes implicated in cellular differentiation and migration. These findings raise the possibility that DEAD-box helicases act in a common pathway to regulate the levels of hundreds of mRNAs. These effects could potentially occur through direct effects on a shared set of mRNA-encoding genes. Consistent with this possibility, DDX17, which was known to regulate rRNA processing, has also been shown to interact with the estrogen receptor-alpha transcription factor and regulate its target genes (102). Similarly, DDX21 has been shown to release pausing of RNAP II by resolving R-loops associated with estrogen-regulated genes (41). Alternatively, the shared set of genes we found to be misregulated upon KD of DDX10, DDX24, DDX27, or DDX54 in mESCs could be particularly sensitive to changes in rRNA levels.
Although stringent high salt extraction of chromatin prior to IP of R-loops may reduce contamination with general chromatinbinding proteins, this harsh treatment may also result in loss of dynamic or weakly binding proteins that nevertheless play roles in R-loop regulation or function. An example of one such protein was SFPQ, which was detectable by S9.6 IP/Western blotting but was only significantly enriched in our S9.6 IP/MS data upon crosslinking. To identify such proteins, we introduced a specific RNA-protein crosslinking step into our IP protocol and utilized SILAC labeling to increase sensitivity. By comparing the array of R-loop-interacting proteins identified with and without crosslinking, we observed that crosslinking increased enrichment of numerous proteins that were weakly enriched in the absence of crosslinking, as predicted, as well as a number of proteins that were not identified in the absence of crosslinking. 4SU is a "zero length" crosslinking reagent, suggesting proteins enriched upon crosslinking may directly interact with RDHs (97,103,104). One such protein was SFPQ, a splicing factor known to regulate R-loops (64). In addition, STRBP, a protein that binds to both DNA and RNA, along with HNRH1 and HNRH2 was also enriched by crosslinking. The latter two proteins belong to the heterogeneous nuclear ribonucleoprotein family that functions in various RNA-dependent processes, including regulation of R-loop abundance (105). Perhaps more interestingly, a large fraction of proteins highly enriched in the uncrosslinked dataset exhibited reduced enrichment in the presence of crosslinking, suggesting that many proteins that strongly interact with the RNA component of R-loops may become less easily detected by MS approaches, because of the covalent addition of RNA nucleotides of unknown size. These two methods in combination enabled us to identify 364 stringent R-loop-interacting proteins, including both known regulators of R-loops and proteins previously not shown to bind these structures. Taken together, these findings suggest that both uncrosslinked and crosslinked S9.6 co-IP offer advantages for identification of the R-loop interactome in vivo. These studies serve as a resource for uncovering the mechanisms by which R-loops are regulated as well as the means by which R-loops might affect regulation or processing of RNA. Given the key roles of R-loops in gene regulation, DNA damage, and genomic instability, a better understanding of the factors that bind and regulate R-loop formation will lead to a better understanding of developmental gene regulation and diseases.

DATA AVAILABILITY
Primary MS data are available via ProteomeXchange with identifier PXD022697. RNA-Seq data are deposited in Gene Expression Omnibus (GSE161890).
Supplemental data -This article contains supplemental data.
Acknowledgments -We thank J. Benanti, S. Gopalan, E. Hass, C.-H. Chao, and A. MacDonagh for critical comments on the article. We thank C. Song for a detailed S9.6 IP protocol and S. Wieczorek for advice on use of ProStar. We thank A. Luciano and M. Gura for their assistance with Northern blotting experiments.
Funding and additional information -These studies were supported by R01HD072122 (to T. G. F.) and R01HD093783 (to F. C. and T. G. F.). T. W. was supported in part by Li Weibo PhD Scholarship Fund. The content is solely the responsibility of the authors and does not necessarily represent the official