RNA-RNA interactions play a crucial role in regulating gene expression and various biological processes, but identifying these interactions on a transcriptomic scale remains a challenge. To address this, we have developed a new biochemical technique called pCp-biotin labelled RNA hybrid and ultraviolet crosslinking and immunoprecipitation (lhCLIP) that enables the transcriptome-wide identification of intra- and intermolecular RNA-RNA interactions mediated by a specific RNA-binding protein (RBP). Using lhCLIP, we have uncovered a diverse landscape of intermolecular RNA interactions recognized by hnRNPK in human cells, involving all major classes of noncoding RNAs (ncRNAs) and mRNA. Notably, hnRNPK selectively binds with snRNA U4, U11, and U12, and shapes the secondary structure of these snRNAs, which may impact RNA splicing. Our study demonstrates the potential of lhCLIP as a user-friendly and widely applicable method for discovering RNA-RNA interactions mediated by a particular protein of interest and provides a valuable tool for further investigating the role of RBPs in gene expression and biological processes. RBPs play a crucial role in post-transcriptional regulation of RNAs by modulating RNA-RNA interactions, encompassing processes like pre-mRNA splicing and mRNA degradation. Existing techniques for exploring RNA interactions associated with specific RBPs require some troublesome experimental procedures, including the use of radioisotopes to visualize RBP-RNA complexes and the introduction of exogenous expression of the target protein. In this context, we introduce lhCLIP, a novel method designed for capturing RNA-RNA interactions mediated by a specific protein. Diverging from other approaches reliant on radioisotopes, lhCLIP eliminates the need for their use. Moreover, its heightened sensitivity enables the capture of RNA-RNA interactions mediated by endogenously expressed RBPs, making it an intuitive and competitive option for researchers. We utilized lhCLIP to investigate RNA hybrids that are directly bound by hnRNPK, unveiling a complex array of intra- and intermolecular RNA interactions with the potential to exert influence over RNA splicing. Genomic studies have revealed that over 85% of the human genome is transcribed into RNA ,2. HowevHowever, identifying RNA interactions on a transcriptomic scale remains a significant challenge. Recently, several techniques have been developed to map the RNA interactome, including MARIO (Mapping RNA interactome in vivo) , LIGR-seHere, we present lhCLIP (pCp-biotin labelled RNA hybrid and ultraviolet crosslinking and immunoprecipitation), a novel method for capturing RNA-RNA interactions mediated by a specific protein. lhCLIP incorporates pCp-biotin (biotinylated cytidine (bis) phosphate) to label the RBP-RNA complexes, enabling easy detection of the biotin moiety using streptavidin conjugated with horseradish peroxidase (HRP) in a chemiluminescent assay. Comparing to other methods, lhCLIP does not require the radioisotopes, furthermore, its heightened sensitivity enables the capture of RNA-RNA interactions mediated by endogenously expressed RBPs, making it a user-friendly and competitive option for researchers. We utilized lhCLIP to investigate RNA hybrids that are directly bound by hnRNPK, a DNA/RNA-binding protein . hnRNPK 2 of UV-C (254 nm) irradiations and resuspended in cell lysis buffer; (2) HeLa cells lysate is immunoprecipitated with antibody-coupled beads; (3) protein-RNA complexes are cut with micrococcal nuclease randomly and dephosphorylated at their 3′ overhangs on beads; (4) the RNA 3′ ends are labelled with pCp–biotin and subsequently treated with FastAP alkaline phosphatase to remove the 3′ phosphate group from ‘Cp–biotin’, and the 5′ overhangs are phosphorylated with T4 polynucleotide kinase (PNK); (5) the resulting RNA fragments in close proximity are ligated; (6) Separation of samples into one-third and two-thirds parts, which are then resolved by SDS–PAGE using NuPAGE 4–12% Bis-Tris Gels respectively. After resolving RNP complexes, they were transferred onto nitrocellulose for further analysis  HeLa cells are crosslinked with 0.3 J/cmanalysis . One-thianalysis . The remanalysis  and S1C.hnRNPK was detected as a 60 KDa band in the immunoprecipitation (IP) followed by Western blot assay using denatured SDS-PAGE gel electrophoresis . During MALAT1 and CCAT1  RNAs are known to be involved in RNA splicing by modifying snRNAs . Additioructures ,26. Our ructures ,26 indicNEAT1 [MALAT1 [NEAT1-MALAT1 interaction was found from the IgG control  between NEAT1 and its interacting transcripts   , four spRNVU1-7) , which mMALAT1-NEAT1 interaction, but also facilitates the generation of three-dimensional (3D) interaction maps of RNA and identification of novel RNA-RNA interactions mediated by specific protein in human cells.Our study has demonstrated that the lhCLIP method is capable of detecting both intra- and intermolecular RNA-RNA interactions associated with the specific protein of interest. This method not only recapitulates known RNA-RNA interactions, such as hnRNPK mediated snRNA secondary structure and Previous research has demonstrated that hnRNPK plays a role in regulating splicing . In thisMALAT1 depletion can affect alternative splicing [MALAT1-preRNA interaction and influenced RNA processing, which may require further recruitment or modification of other proteins localized to these sites. Our research identified 58 RNA alternative splicing events regulated by hnRNPK, of which only 4 alternative splicing events were found to bind with lncRNA and one splicing event was found to bind with snRNA. This indicated that hnRNPK may not directly recruit ncRNA to the specific alternative splicing events; Instead, hnRNPK may cooperate with multi-proteins and ncRNAs in the regulation of RNA alternative splicing. These RNA-RNA interactions that mediate by multi-proteins were not captured by lhCLIP. In other words, lhCLIP captures RNA-RNA interactions mediated directly by target protein.LncRNA also participates in the regulation of RNA splicing. splicing  and redusplicing . This suTaken together, lhCLIP provides an accurate method to reveal RNA-RNA interactions mediated by a particular protein and the unique functions of the RNA binding protein. Our technique eliminates the use of radioisotopes while retaining the ability to visualize protein-RNA complexes, and does not require the introduction of exogenous expression of the target protein, thus broadening the application of lhCLIP-based assays in the scientific community.The UV crosslinking used in lhCLIP selectively forms covalent bonds only at sites of direct protein-RNA interactions, leading to the exclusion of most indirect RNA-RNA interactions mediated by hnRNPK and other proteins from the analysis. As a result, the coverage of interacting RNAs remains insufficient, particularly for lowly expressed ncRNAs, which poses challenges in detecting their interactions. To address this limitation, researchers could explore options to enhance the experimental procedure or adopt alternative cross-linking methods, such as formaldehyde crosslinking or formaldehyde plus disuccinimidyl glutarate crosslinking.2. For lhCLIP library construction, 3 × 15-cm dishes of HeLa cells were utilized. The cells were washed once by ice-cold PBS, and 4 ml ice-cold PBS was added. The cells were then subjected to 0.3 J/cm2 of UV-C (254 nm) irradiations on ice, and collected by scraping. The cells from 3 dishes were transferred to a 15 mL tube and centrifugated at 500 g for 5 min at 4°C, and the supernatant was removed.HeLa  cells were grown in MEM  containing 10% fetal bovine serum and 0.5 mg/mL primocin  on a 15-cm dish at 37 °C in 5% CO100 μL Protein G Dynabeads  was blocked with 500 μL 5 mg/mL BSA/PBS at room temperature. Next, 10 μg anti-hnRNPK antibody  was conjugated to pre-blocked Protein G Dynabeads for 4 h at 4°C in 200 μL cell lysis buffer , 0.1% SDS, 0.5% sodium deoxycholate (protect from light)). Prior to immunoprecipitation, beads were washed twice with cell lysis buffer.The cell pellet was resuspended in 1 mL cell lysis buffer Complete (cell lysis buffer supplemented with 1:100 Protease Inhibitor Cocktail (add freshly), and 10 μL SUPERaseIn ) and put on ice for 15 min. Next, 2 μL of Turbo DNase  was added, and the sample was incubated in a Thermomixer at 1200 rpm and 37°C for 5 minutes. After centrifuging at 15,000 g and 4°C for 10 minutes, the supernatant was carefully transferred to a new tube.2, 0.2% NP-40).The cell lysis buffer was removed from the beads, which were then added to the cell lysate. The bead/lysate mixture was incubated at 4°C overnight with rotation. The supernatant was discarded using a magnetic stand and the beads were washed twice with high salt wash buffer . The beads were then wash twice with 1 × PNK buffer  in an Eppendorf Thermomixer for 10 min at 37°C, 15 s 1,400 r.p.m., 90 s rest. The reaction was stopped by 20 mM EGTA in 1 × PNK buffer.The RNAs attached to the beads were digested with 6 U micrococcal nuclease  in 200 μL of 1 × MN buffer  in an Eppendorf Thermomixer at 37 °C, 15 s 1,400 r.p.m., 90 s rest for 15 min. The RNA was then washed twice with 1 × PNK 20 mM EGTA buffer, twice with high salt wash buffer, and twice with 1 × PNK buffer. To label the RNA 3’-terminal, 2μL of 1 mM pCp–biotin  was added and the labelling was performed according to the manufacturer’s instructions. After 3 times washing with 1 × PNK buffer, the phosphate groups of RNA·Cp–biotin were removed with 10 U of FastAP alkaline phosphatase. After thorough washing with 1 × PNK + 20 mM EGTA buffer, high salt wash buffer and 1 × PNK buffer successively, the RNA 5’ ends were phosphorylated with T4 polynucleotide kinase . After twice washing with 1 × PNK + EGTA buffer and twice 1 × PNK buffer (0.05% NP-40), the proximity ligation was accomplished with 50 U T4 RNA ligase  in an Eppendorf Thermomixer overnight at 16°C, 15 s 1,400 r.p.m., 90 s rest.2, 0.2% Tween-20), 7.5 μL of 4 × NuPAGE sample buffer, 3 μL 1 M DTT) and heated in a Thermomixer, 1200 rpm, 10 min at 70°C. The samples were divided into one-third and two-thirds parts and respectively resolved using NuPAGE 4–12% Bis-Tris Gels (1.0 mm × 12 well) with SDS-PAGE at 160 V for 45 minutes. The resulting RNP complexes were then wet transferred onto nitrocellulose at 400 mA for 60 min at 4°C.The next day, the RNAs bound to beads were washed 2 times with 1 × PNK buffer and resuspended in elution/loading master mix  was added to the membrane. The membrane was then incubated in the conjugate/blocking buffer solution for 15 minutes with gentle shaking. Next, the membrane was transferred to a new container and briefly rinsed with 20 mL of 1X wash solution. The membrane was washed four times for 5 minutes each in 20 mL of 1X wash solution with gentle shaking. After washing, the membrane was transferred to a new container and 30 mL of Substrate Equilibration Buffer was added. The membrane was incubated in the buffer for 5 minutes with gentle shaking. Carefully blotting an edge of the membrane on a paper towel to remove excess buffer, the membrane was then placed in a clean container and the Substrate Working Solution was poured onto the membrane so that it completely covered the surface. The membrane was incubated in the substrate solution for 5 minutes without shaking. Finally, the membrane was removed from the Working Solution and the hnRNPK-RNA complex was visualized using an ImageQuant Las4000mini.The one-third of the samples that have been transferred onto a nitrocellulose membrane were analyzed using the LightShift Chemiluminescent RNA EMSA Kit  following the manufacturer’s instructions. Briefly, Membrane was crosslink at 120mJ/cmThe two-thirds of the samples that have been transferred onto a nitrocellulose membrane were placed on pre-wetted Whatman filter paper and the lane containing hnRNPK-RNA complex was cut using scalpels. Membranes were cut into 0.5–1 mm narrow strips that easily come to rest in the bottom of a siliconized 1.5-ml Eppendorf tube. 0.2 mL of proteinase K reaction buffer  containing 10 μL of proteinase K  was added to each tube and incubated for 60 min at 50°C in an Eppendorf Thermomixer, 15 s 1,000 r.p.m., 30 s rest. After 60 min, tubes were removed from the thermomixer and briefly centrifuged. 200 μL of saturated-phenol-chloroform, pH, 6.7, was added to each tube and incubated for 10 min at 37°C in an Eppendorf Thermomixer, 1,400 r.p.m. Tubes were briefly centrifuged and the entire contents were transferred to a 2-mL Heavy Phase Lock Gel tube. After 2 min centrifugation at >13,000 r.p.m., the aqueous layer was re-extracted with 1 mL of chloroform  in the same 2-ml Heavy Phase Lock Gel tube and centrifuged for 2 min at >13,000 r.p.m., the aqueous layer was transferred to a siliconized 1.5-mL Eppendorf tube and precipitated overnight at −20°C by addition of 18 μl 5 M NaCl, 3 μL Linear Polyacrylamide , and 0.8 mL of 100% ethanol. The next day, RNA was pelleted at >13,000 r.p.m. for 45 min at 4°C, washed once with 1 mL of ice old 75% ethanol, and air dried. Pellets were resuspended in 8 μL RNase-free water.2O) and incubated at 20°C for 15 min. The ligated DNA product were purified using 0.5 × VAHTS DNA Clean Beads  following the manufacturer’s protocol and eluted in 20 μL of EB buffer. The adaptor-ligated cDNA was mixed with 1 μL of PCR Primer Mix 4, 1 μL of 50 mM MgSO4, 25 μL of 2 × HF Amplification Mix, and 3 μL of USER enzyme . The mixture was incubated at 37 °C for 15 min to allow the USER enzyme to digest the dUTP strand, then at 94 °C for 2 min. PCR was performed with the following program: 98 °C for 45 s, 17 to 19 cycles of 98 °C for 15 s, 60 °C for 30 s and 72 °C for 30 s, final extension 72°C for 1 min. The 180–400-bp PCR products were selected using 1 × VAHTS DNA Clean Beads. The DNA concentration was measured by Qubit  and sequenced using Illumina NovaSeq.Strand-specific library was prepared with VAHTS Universal V8 RNA-seq Library Prep Kit for Illumina  according to manufacturer’s instructions with several modifications. For fragmentation, 8 μL of total RNA was mixed with 8 μL of 2 × Frag/Prime Buffer and incubated at 94 °C for 5 min, then quickly put on ice. To synthesize the first-strand cDNA, 7 μL 1st Strand Buffer 3 and 2 μL1st Strand Enzyme Mix 3 was added to the RNA fragments and incubated at 25°C for 10 min, 42°C for 15 min, 70°C for 15 min, followed by 4°C Hold. We next created dUTP second-strand cDNA by adding the reaction mixture (2nd Strand Buffer 2 (5 μL with dNTP and 20 μL with dUTP), 15 μL 2nd Strand Enzyme Super Mix 2) to the first-strand cDNA and incubating at 16 °C for 30 min, 65°C for 15 min, 4°C Hold. Illumina Y-shaped adaptors were ligated to the dsDNA by adding the reaction mixture , 0.1% SDS, 0.5% sodium deoxycholate (protect from light)) put on ice for 15 min. Extracts were cleared by centrifugation and incubated with anti-hnRNPK antibody  or mouse IgG Isotype control , immobilized on protein G magnetic beads (Invitrogen) overnight at 4°C. Immunocomplexes were then washed with cell lysis buffer 3 times, resuspended in 30 μL 1 × NuPAGE sample buffer + reducing agent, incubated at 70°C for 10 minutes, and subjected to 4–12% SurePAGE precast gel  and then transferred to nitrocellulose membranes . After blocking with 5% BSA in Tris-buffered saline containing 0.1% Tween-20 (TBST) for 1 hour at room temperature, transferred membranes were incubated overnight at 4°C with primary anti-hnRNPK antibody (1:1000 dilution). After horseradish peroxidase-conjugated secondary antibodies incubation, bands were visualized using a ImageQuant Las4000mini.For each IP, 5×10The lhCLIP sample processing involved UV crosslinking, immunoprecipitation, pCp-biotin labeling, ligation, and elution from beads using the elution/loading master mix  and 7.5 μL of 4 × NuPAGE sample buffer without reducing agents), followed by heating at 70°C for 10 minutes. The native protein marker , the prestained protein marker , the lhCLIP sample, and the input sample (which underwent UV cross-linking and subsequent treatment with loading buffer) were then subjected to 4–13% Blue native PAGE gel  and electrophoresed in cathode buffer B  at 100 V until blue running front has moved about one-third of the desired total running distance. The cathode buffer B was then replaced with cathode buffer B/10  and electrophoresis was continued for another 1 h with the current limited to 15 mA. The gel was then soaked in Tris-glycine Western transfer buffer for 10 min and transferred to 0.45-μm PVDF. Filters were destained in methanol, washed in water, blocked, and probed with anti–hnRNPK antibody, followed by horseradish peroxidase-conjugated secondary antibodies.MALAT1 probes with Cy3 dye were purchased from RiboBio (lnc1100063), digoxigenin-labeled human PID1 intron probes and RABGGTB probes, and biotin-labeled TERC probes were synthesized by Exonbio. RNA FISH was performed with D-T-G type mRNA in situ hybridization detection kit  according to the manufacturer’s instructions. The photographs were taken using a Nikon Eclipse Ni-E laser scanning confocal microscope.Human 7 cells were washed with PBS and cross-linked by 1% formaldehyde for 10 min with gentle agitation on a rotator at room temperature. Fixation was stopped by the addition of glycine (125 mM) and agitation for 5 min at room temperature. Fixed cells were washed twice in ice-cold PBS and lysed with 0.8mL lysis buffer. The cell lysate was mixed with an equal volume of 2 × Hybridization Buffer and denatured at 65°C for 10 minutes. Biotinylated antisense probes (40 pmol/probe) were denatured at 85°C for 3 minutes and added to the RAP system. Hybridization was carried out at 37°C for 30 minutes, followed by a 5-minute incubation at 50°C, and another round of hybridization at 37°C for 90–180 minutes. Next, 100 μL of streptavidin-coated magnetic beads were added and agitated for 30 min at room temperature. Non-specifically bound RNAs were removed by washing, and Trizol reagent was used to recover RNAs specifically pulled down by beads. RT-qPCR was used to analyze binding strength after reverse transcribing the RNAs. The probe and RT–qPCR primer sequences are listed in RNA Antisense Purification (RAP) was performed with a RAP Kit  according to the supplier’s protocol. Briefly, 2×10Mapping and filtering of lhCLIP data Because the structure of lhCLIP library is the same as that of RIC-seq, and both are used to obtain chimeric reads, lhCLIP data were processed according to the protocol of RIC-seq data processing [P values lower than 0.05 were used for downstream analysis.ocessing . The anaocessing , and PCRocessing . The polocessing . After focessing . The parocessing . Gapped ocessing . RNA–RNAVisualization of lhCLIP data in Integrative Genomics Viewer Intramolecular chimeric reads were stored and sorted in a bam format file and displayed using the Integrative Genomics Viewer (IGV) visualization tool (v.2.14.0).Visualization of lhCLIP data in Juicebox The Juicebox tool (v. 1.11.08) was applied to construct RNA–RNA interaction maps (http://aidenlab. org/juicebox). The chimeric read information was stored in a.short format file, in which each row recorded the genomic coordinates of two tags for a chimeric read. After sorting by chromosome names, this file was converted into.hic format using the pre command in Juicer tools [er tools  and furtRNA-seq data were extracted from previous report   from the ENCODE portal. This eCLIP data were displayed using IGV tool (v.2.14.0).In order to identify the binding sites of hnRNPK with The RNA-seq data of WT an hnRNPK knock down HeLa cell were downloaded from SRA database, accession SRP111756. HeLa cell RNA-seq data was analysed using MISO v.0.5.4 with default parameters comparing the hnRNPK knockdown samples to controls using differential splicing annotations obtained from the MISO website (miso_annotations_hg19_v2). After filtering for significant events (–num-inc 1 –num-exc 1–num-sum-inc-exc 10 –delta-psi 0.20 –bayes-factor 10), 58 splicing events were significant.The genomic positions of splicing events obtained from the MISO analysis were compared with RNA-RNA interaction sites detected by lhCLIP. A splicing event was considered as bound with an ncRNA if the exon or intron of the alternative splicing event bound with at least one ncRNA. ncRNAs which binding sites closest to alternative splicing events were also listed in S1 FigA) The mapping pipeline for lhCLIP data. (B) The relationship of intra- and intermolecular chimeric reads to reference transcripts. (C) The lhCLIP libraries were quantified using a 2100 Bioanalyzer. (D) Western blot detected the immunoprecipitated hnRNPK. (E) Analysis of the hnRNPK monomer and dimer captured in IhCLIP by blue native gel electrophoresis. The gel image illustrates the Coomassie Blue G-250 stained complex of the hnRNPK monomer along with its antibody. It also displays a native protein mark at 300 KDa, alongside a visible denatured pre-stained protein marker ladder. (F) Analysis of IhCLIP input sample by blue native gel electrophoresis and blotting with anti-hnRNPK antibody. In the left panel, the bands developed through ECL are displayed, while the right panel exhibits a denatured protein marker ladder captured under white light. Notably, the hnRNPK monomer band is evident around 60 KDa. (G) Correlation analysis of the non-chimeric reads between the replicates of lhCLIP.((TIF)Click here for additional data file.S2 FigA) The percentage of chimeric reads that are shared between the IgG and hnRNPK samples. (B) hnRNPK binding sites at MALAT1 and CCAT1 RNA revealed by lhCLIP. (C) Comparing the binding signals of hnRNPK with ncRNAs expressed at different levels. Up, The IGV shows the NEAT1, SNHG12, HSP90B2P, and NKAPP1 RNA-seq reads. Bottom, The IGV shows the NEAT1, SNHG12, HSP90B2P, and NKAPP1 lhCLIP non-chimeric reads. (D), (E) and (F) Comparison of intramolecular interactions of RNU12 (D), SNORD3B-1(E) and SNORD3A(F) identified by lhCLIP to their know structure.((TIF)Click here for additional data file.S3 FigA) RNA 3D map showing RNA-RNA interactions across all chromosomes. (B) No NEAT1-MALAT1 interaction was detected from IgG sample. hnRNPK sample is shown at the bottom left of the Fig. IgG sample is shown at the top right of the Fig. The blue boxes denote the position of NEAT1-MALAT1 interactions (C) No NEAT1-MALAT1 interaction was detected from control in which pCp-biotin labeling and ligation was omitted. hnRNPK sample is shown at the bottom left of the Fig. Control sample is shown at the top right of the Fig. The blue boxes denote the position of NEAT1-MALAT1 interactions (D) Overlaps of MALAT1 and NEAT1 interacting sites identified by lhCLIP and RIC-seq. Hypergeometric test was used to calculate the P value. (E) Positions of hybridization PID1 probes labelled with digoxigenin. (F) The colocalization of MALAT1 and PID1 pre-mRNA in HeLa cells was detected by smFISH.((TIF)Click here for additional data file.S4 Fig(A) lncRNA RP11-118E18.4 binds to the RNA alternative splicing region of CCT4. Top, Sashimi plots illustrating alternative splicing event of CCT4 in WT HeLa cells and hnRNPK knock down cells. Bottom, Schematic representations of RP11-118E18.4—CCT4 interaction in the RNA alternative splicing region mediated by hnRNPK.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file. 11 Jun 2023Dear Dr Jia,Thank you very much for submitting your Methods entitled 'lhCLIP reveals the in vivo RNA–RNA interactions recognized by hnRNPK' to PLOS Genetics.The manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important problem, but raised some substantial concerns about the current manuscript. Based on the reviews, we will not be able to accept this version of the manuscript, but we would be willing to review a much-revised version. Should you decide to revise the manuscript for further consideration here, your revisions should address the specific points made by each reviewer. 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All points should be backed up by data provided with the submission.Please be aware that our Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool.  PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.While revising your submission, please upload your figure files to the Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, use the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.We are sorry that we cannot be more positive about your manuscript at this stage. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Guanzheng LuoGuest EditorPLOS GeneticsQuanjiang JiSection EditorPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Hu et al. have established lhCLIP technology to analyze the RNA-RNA interactions mediated by a specific RNA binding protein (RBP), which provides a useful approach for investigating the roles of specific RBP in RNA metabolism. This approach shows its advances in omitting the application of radioisotopes and detecting only the direct interactions of RNA to specific RBP (crosslinked by UV light). However, two small points need to be clarified to improve the manuscript for publication in PLoS Genetics.1. The authors claimed that dimerization of HNRNPK could explain the intermolecular RNA-RNA interactions mediated by hnRNPK. However, the co-IP result  showed that dimerized HNRNPK are barely co-precipitated with the procedure. If the WB was run under denatured condition, the authors at least ran a gel with native condition  to show that the dimer of hnRNPK was indeed captured in IhCLIP.2. Why did the authors not detect the interaction of TERC with RABGGTB using smFISH? Or using RAP-qPCR for PID1 with MALAT1 to confirm the interaction?Reviewer #2: In this manuscript, the authors developed a novel method for capturing RNA-RNA interactions mediated by a specific RBP, named as lhCLIP. It’s useful and helpful for other researchers, but there are some questions should be addressed before the manuscript can be published:https://doi.org/10.1016/j.molcel.2023.03.001, published in 2023) depending on RIC-seq, and CRIC-seq can capture RNA-RNA interactions mediated by a specific RBP, so the authors should present the difference between ilCLIP and CRIC-seq.1. The authors said that the structure of ilCLIP library is the same as that of RIC-seq, while the authors of RIC-seq developed a CRIC-seq  in detail, for example, the library time, the chimeric rate, and so on.3. In the manuscript, the authors said that both CLASH and hiCLIP require overexpression of RBP, how can the authors get this conclusion? Sometimes, while there is no effective antibody of target RBP, or the expression of target RBP is low in target cell line, maybe some other causes, the author may overexpress the target RBP with FLAG or GFP label and use the label’s antibody to capture the target RBP’s RNA substrates, but that does not mean that both these methods require overexpression. At the same time, the authors said lhCLIP does not require the introduction of any exogenous protein-coding genes, so while there is not the target RBP’s antibody, how can you using lhCLIP to capture the target RBP’s RNA substrates?4. The authors should present the detailed messages for their analysis, for example, how to annotate the chimeric or non-chimeric reads, how to define the cluster in Figure 2C, and so on; the detailed parameters for each software; for some figures, the authors should write the figure legend in detail, for example, the Figure1B.5. There are some wrong spelling, for example, in Figure 3A, should be “contacts”, in line 193, should be “number”, and so on.Reviewer #3: The manuscript “IhCLIP reveals the in vivo RNA-RNA interactions recognized by hnRNPK”, Hu et al., developed IhCLIP that can investigate RNA-RNA interactions and the mediated specific proteins with UV crosslinking and proximity ligation and immunoprecipitation techniques. Big advantage of the method is that it provides both information of RNA-RNA and mediated specific protein complex at same time. I believe that the IhCLIP is important for further interactome analysis study, however I have several comments that authors should address.In this study, authors selected hnRNPK to investigate their IhCLIP technology. hnRNPK is one of most famous RNA binding proteins that is known to bind many types of RNAs for splicing function, nucleocytoplasmic function and mRNA translation. I wonder whether the technology can be broadly used for other RBPs.In addition, authors focus on very highly expressed nuclear transcripts such as NEAT1, MALAT1, snRNA, snoRNA etc. in this study. I would imagine that capturing and sequencing of hnRNPK binding on such RNAs are much easier than lowly expressed ncRNAs and lncRNAs. Authors must investigate lowly expressed RNA-RNA interactions and mention the limitation of the technology.The advantage of this method is that the RNA-RNA interactions are captured after crosslinking and proximity ligation. So that we can capture many RNAs that are not only directly bind hnRNPK but also bind hnRNPK binding proteins. However, I am afraid if we can understand whole interacted molecules of such splicing complexes with this technology because other proteins than hnRNPK are missing from the analysis. Authors should discuss and suggest further possible analysis or validation of interactome study with the technology.CLIP technologies are depending on antibody affinity to target protein to capture protein-RNA complexes. Even though we select two different types of antibodies to same target protein, results can be different with different antibodies. Can authors suggest how we can select good and proper antibodies for the specific results of IhCLIP and how we can understand that the results are not false positive?**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: NoneReviewer #2: NoneReviewer #3: Yes**********what does this mean?). 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I believe that the work should be published.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: NoneReviewer #2: NoneReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article , one way to make that data available is to deposit it in the The following link will take you to the Dryad record for your article, so you won't have to re‐enter its bibliographic information, and can upload your files directly: http://datadryad.org/submit?journalID=pgenetics&manu=PGENETICS-D-23-00429R1http://www.datadryad.org/depositing. 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PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  13 Oct 2023PGENETICS-D-23-00429R1  lhCLIP reveals the in vivo RNA–RNA interactions recognized by hnRNPK Dear Dr Jia, We are pleased to inform you that your manuscript entitled " lhCLIP reveals the in vivo RNA–RNA interactions recognized by hnRNPK " has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. 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The small nuclear RNAs 4.5SH and 4.5SI were characterized only in mouse-like rodents; their genes originate from 7SL RNA and tRNA, respectively. Similar to many genes transcribed by RNA polymerase III (pol III), the genes of 4.5SH and 4.5SI RNAs include boxes A and B, forming an intergenic pol III-directed promoter. In addition, their 5′-flanking sequences have TATA-like boxes at position −31/−24, also required for efficient transcription. The patterns of the three boxes notably differ in the 4.5SH and 4.5SI RNA genes. The A, B, and TATA-like boxes were replaced in the 4.5SH RNA gene with the corresponding boxes in the 4.5SI RNA gene to evaluate their effect on the transcription of transfected constructs in HeLa cells. Simultaneous replacement of all three boxes decreased the transcription level by 40%, which indicates decreased promoter activity in a foreign gene. We developed a new approach to compare the promoter strength based on the competition of two co-transfected gene constructs when the proportion between the constructs modulates their relative activity. This method demonstrated that the promoter activity of 4.5SI is 12 times that of 4.5SH. Unexpectedly, the replacement of all three boxes of the weak 4.5SH promoter with those of the strong 4.5SI gene significantly reduced, rather than enhanced, the promoter activity. Thus, the strength of a pol III-directed promoter can depend on the nucleotide environment of the gene. These elements, largely from 150 to 300 bp in length, originate from tRNA species and more rarely from 5S rRNA or 7SL RNA [In contrast to RNA polymerase II (pol II) largely synthesizing mRNA and various long non-coding RNAs, RNA polymerase III (pol III) synthesizes small RNAs. These include 5S rRNA, all tRNA species, 7SL RNA , 7SK RNA (a factor of pol II transcription elongation), U6 RNA (a component of spliceosomes), Y RNA (a factor of initiation of DNA replication), and vault RNA (a regulator of cellular autophagy), etc. ,2,3,4,5.Three types of pol III-directed promoters are known ,3,8. TheS. pombe) have a TATA box or a very similar sequence in their 5′-flanking regions at position −30/−24 [In addition to boxes A and B, the tRNA genes of plants and some yeasts  possessing type 2 promoters ,25. The The genomes of mice and rats have three 4.5SI RNA genes located 4–40 kb from one another . There aA large number of 4.5SH and 4.5SI RNA molecules can be found in various cells and tissues of mouse-like rodents, localized predominantly in the cell nuclei ,36. 4.5SPreviously, we have identified a TATA-like box at position −31/−24 required for the efficient transcription of these genes by transfecting HeLa cells with the 4.5SH and 4.5SI RNA genes with deletions in the 5′-flanking sequences ,23. The The cloning of the murine 4.5SI (the Mmu’ copy) and 4.5 SH RNA genes into the pGEM-T vector was described elsewhere ,23,28. THeLa cells  were grown to an 80% confluent monolayer in 60 mm Petri dishes using DMEM with 10% fetal bovine serum. Cells on one dish were co-transfected with 4 μg of plasmid DNA  and 1 μg of the control plasmid pU1 mixed with 10 μL of TurboFect transfection reagent  according to the manufacturer’s protocol. Transcriptional competition experiments were conducted in a similar way but without pU1. Cells on each plate were co-transfected with different quantities (specified in figures in 32P)dATP, Taq-polymerase, and reverse primers. The labeling conditions, primers, and 4.5SH and 4.5SI RNA probes, as well as the Northern hybridization conditions, were described elsewhere [RNA samples (10 μg) obtained from each transfection were separated via electrophoresis in a 6% polyacrylamide gel with 7 M urea, transferred to a nylon membrane  using semi-dry electroblotting, and hybridized with probes labeled using . The b values were determined from experimental data presented in plots in The promoter strength of different genes and constructs was evaluated as the ratio between the target plasmid quantity in transfection (a) and the competitor plasmid quantity (b) that suppressed target transcription by 50% (Khttp://rna.tbi.univie.ac.at) (accessed on 1 December 2022). The bend.it program [http://pongor.itk.ppke.hu/dna/bend_it.html#/bendit_intro) (accessed on 14 December 2022).The secondary RNA structure was predicted using the ViennaRNA Package 2.0  at Vienn program  was usedIt is imperative to use a suitable control to normalize the quantitative expression data in experiments on cell transfection with genetic constructs. Previously, we used a shrew SOR SINE construct as such a control in cell transfection with 4.5SI or 4.5SH RNA genes ,23. SincThe constructs of the 4.5SH RNA gene with Cs at positions 10 and 11 of box A  were repA series of 4.5SH RNA gene-based constructs was generated with the A, B, and TATA-like boxes replaced with their counterparts of the 4.5SI RNA gene. All combinations were made with single, double, and triple box substitutions  of 20–30 min, while 4.5SI RNA is a relatively long-lived one (T1/2 ≈ 20 h). 4.5SI RNA stability in the cell is due to the complementarity of its terminal regions making a long 16 bp double-stranded structure [1/2 = 20–30 min) of small nuclear RNAs lacking long double-stranded terminal regions or not protected by specific proteins [It has to be explained why 4.5SH RNA gene-based constructs were prioritized over 4.5SI RNA ones 9 vs. 3, . Previoutructure . 4.5SH Rtructure . We obseproteins . It is hproteins . In all 0.5 = 4/0.3 ≈ 13). In the reverse experiment with SIwt as the target and SHwt as the competitor, a 50% decrease in the transcription of 0.25 µg SIwt required about 2.8 µg of SHwt . Still, the B2_2 promoter was weaker (2.4 times) than that of the 4.5SI RNA gene, which can be attributed to the nucleotide sequence imperfections in the –31 to –24 region (boxes A and B in B2_2 perfectly match the consensus sequences).To evaluate the potential of the approach, we conducted experiments with the 4.5SI RNA gene as the target and B1 or B2 SINEs as competitors. As mentioned previously, the 4.5SH and 4.5 SI RNA genes originated from B1 and B2 SINEs, respectively. As a result, the A and B boxes are identical in 4.5SH RNA and B1 and highly similar in 4.5SI RNA and B2 genes . Previou0.5 = 0.25/1.8 ≈ 0.14) , boxes A and B (SH5), and all three pol III promoter boxes (SH9) replaced with their counterparts of the 4.5SI RNA gene as competitors. About 1.8 µg of SH3 suppressed the target (SIwt) transcription by 50% (K ≈ 0.14) . Thus, t ≈ 0.14) . The TAT ≈ 0.14) . Notice  ≈ 0.14) .These results were surprising because we expected the boxes of a strong promoter from the 4.5SI RNA gene to create a strong promoter when transferred to the 4.5SH RNA gene. This was not the case, and slight promoter enhancement was observed only after the replacement of box A alone. The pronounced promoter reduction after the simultaneous substitution of all three boxes was especially unexpected. Apparently, the boxes of the 4.5SI RNA gene within another gene (4.5SH) with “foreign” nucleotide sequences proved to be less efficient ligands of TFIIIB and TFIIIC and thus weaker promoters. Accordingly, the nucleotide context can modulate the function of pol III-directed promoters, and the nucleotide sequences of all three boxes were optimized for the surrounding gene sequences in evolution.5) between boxes A and B with very low bendability . Third, we used a new approach based on transcriptional competition between genes to evaluate their promoter strength.The 4.5SH RNA gene has Cresidues at positions 10 and 11 of box A, which is uncommon for the pol III-directed promoter. Their replacement with the Gs found in the box A consensus sequence decreased rather than increased the transcription efficiency in transfected cells. Box B of the 4.5SI RNA gene has a non-canonical G at position 7, and its replacement with the canonical A also clearly reduced the gene transcription efficiency. These data indicate that specific nucleotides in boxes A and B can increase the efficiency of the pol III-directed promoter in particular genes. Replacements of A, B, or TATA-like boxes alone or boxes A and B together in the 4.5SH RNA gene with those of the 4.5SI RNA gene either had no effect or decreased the transcription efficiency. This indicates promoter activity dependence on the gene nucleotide context. A method was developed to compare promoter activities based on transcriptional competition between co-transfected genes. The 4.5SH RNA gene promoter proved to be 12 times weaker than that of the 4.5SI RNA gene. The murine B1 SINE promoter also proved to be weak, while that of B2 SINE was strong. Simultaneous replacement of the three boxes of a weak promoter of the 4.5SH RNA gene with those of the strong 4.5SI RNA gene promoter decreased, rather than increased, its activity. One can speculate that promoters of a gene transferred to a different gene with heterologous nucleotide sequences largely lost their capacity to bind the transcription machinery and thus their promoter strength. Consequently, the nucleotide context of genes can modulate the function of internal pol III-directed promoters. In all likelihood, the promoter efficiency also depends on the distance between boxes A and B, as well as DNA bendability in the TATA-like box area. In addition, certain nucleotides outside of the three boxes can also contribute to promoter strength. So far, the transcription of genes with type 2 promoters by pol III was considered to be governed solely by A, B, and TATA-like boxes. The data obtained in this work indicate that the function of these boxes is not self-sufficient and depends on the nucleotide environment.


RNA modification plays important role in the occurrence and development of hepatocellular carcinoma. The best characterized RNA modification is m6A, while other kinds of RNA modifications have not been fully investigated in hepatocellular carcinoma (HCC). In the current study, we investigated the roles of one hundred RNA modification regulators belonging to eight different types of cancer-related RNA modifications in HCC. Expression analysis revealed that nearly 90% RNA regulators exhibited significantly higher expression in tumors than normal tissues. By consensus clustering, we identified two clusters with distinct biological characteristics, immune microenvironment, and prognostic pattern. An RNA modification score (RMScore) was constructed and stratified patients into high- and low-risk group, which showed significantly different prognosis. Moreover, a nomogram including clinicopathologic features and the RMScore could well predict the survival in HCC patients. This study indicated the important role of eight types of RNA modification in HCC and develop a RMScore, which will be a new method to forecast the prognosis of HCC patients. Hepatocellular carcinoma (HCC) is one of the leading cause of cancer-related deaths worldwide, with few effective therapeutic options . Most pa6A), N1-methyladenosine (m1A), and 5-methylcytosine (m5C), 2′-O-methylation (Nm), m7G N7-methylguanosine (m7G), pseudouridylation (Ψ), adenosine-to-inosine (A-to-I), 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U)  could predict the prognosis of HBV-related HCC  was remarkably associated with HCC patient prognosis (6A-related genes was reported to be able to predict the prognosis of HCC(19). However, these studies ignored the relationship and crosstalk between different types of RNA regulators. It is very necessary to systematically characterize the relationship and crosstalk of other kinds of RNA modification regulators in HCC.RNA modification is a critical posttranscriptional regulators of gene expression program . With thmcm5s2U) . Among tized one . For exaized one . ALKBH5 of LYPD1 . METTL3 of SOCS2 . Other mn of HCC . TRMT6/Tn of HCC . Howeverated HCC . A risk rognosis . A risk In the current research, we profiled one hundred RNA regulators from eight types of RNA modifications in HCC for the first time. We clustered HCC patients into two RNA modification-related subtypes with different prognosis and cancer hallmarks. An RNA modification score was constructed to serve as an independent prognosis predictor for HCC patients. These results would provide insight into the prognosis prediction and therapeutic management of HCC in the future.The gene mutation, expression and clinical information of HCC patients was downloaded from the TCGA and ICGC database.6A, m1A, m5C, Nm, m7G, Ψ, A-to-I, and mcm5s2U) were included in our study. They were listed in There were huge number of RNA regulators identified in eukaryotes. However, we only choose RNA regulators which were proved to play critical roles in HCC and other cancers . As a ret-test were used to compare the expression of 100 RNA regulators between tumor and adjacent normal tissues. The “Maftools” R package was used to analyze the mutation and interaction of 100 RNA modification genes. The protein-protein interaction between 100 RNA modification regulators were analyzed by the STRING database and visualized by using Cytoscape software  was performed by utilizing the R package “GSVA”. The annotated gene set “h.all.v7.4.symbols.gmt” was used for GSVA analysis. The enrichment scores of immune cells in sample from different groups were determined with single-sample gene set enrichment analysis (ssGSEA).p < 0.001). Then LASSO regression analysis was conducted to select survival-associated DEGs. Multivariate cox regression analysis was conducted to construct the final prognostic model. The RMScore was calculated by the following equation: RMScore = Exp1* Coe 1 + Exp2* Coe 2 + … Exp N* Coe N.To construct the RMScore, we first identified 1,427 differentially expressed genes (DEGs) in cluster 1 and cluster 2 groups. We divided TCGA-LIHC into training set and validation set. In the training set, univariate cox regression analysis revealed that 266 DEGs were identified to be significantly related to the prognosis of HCC patients  in individual patients. The R package “ggplot2” was utilized to generate the plots.The stage and RMScore, both of which were identified to be an independent prognostic value by multivariate Cox proportional hazards analysis, were included to construct the nomogram. The calibration curve was plotted to observe the prediction probabilities of the nomogram against the observed rates. The prognostic ability of the nomogram was evaluated by receiver operating (ROC) curves and decision curve analysis (DCA) curves.We choose a total of one hundred RNA modification regulators which were reported to play roles in at least one kind of cancer to perform following analysis .t-test was conducted to compare the gene expression between tumor and normal tissues. Notably, nearly 90% RNA regulators exhibited significantly higher expression in tumors than normal tissues  showed RNA modification regulator mutations. Mte of 1% . Geneticand CTU2 .p-value <0.05 and |r| ≥ 0.6 were shown in 6A and m5C, showed close correlations with each other.To reveal the interactive relationships between proteins encoded by 100 RNA regulators, we constructed a PPI network using STRING and Cytoscape software. Correlations with adjusted Results above collectively demonstrated that there were huge interaction and crosstalk between these 100 RNA regulators. They may form a complicated network to synergistically mediate the development and progression of HCC.K = 2–9) according to the expression of 100 RNA modification genes via an unsupervised consensus clustering analysis. The cumulative distribution function (CDF) curve and the area under the CDF curve was plotted, which indicated that the cohort was well distributed when K = 2 (p < 00.001)  . GSVA anResults above collectively suggested that 100 RNA modification regulators defined two RNA modification patterns in HCC, which exhibited different malignancy, immune microenvironment, and prognosis.To further investigate the potential genetic alterations between two RNA modification patterns, differential expression analyses were carried out between two clusters . A totalp < 0.001) (p = 0.033) and the ICGC cohort (p < 0.001)  . The sam< 0.001) . The tim< 0.001) . These rp < 0.001) (p < 0.001)  . Multiva< 0.001) . In the < 0.001) . We expl< 0.001) .We investigated the association between the RMScore and the sensitivity to chemotherapeutics in HCC patients. As a result, significant differences in the estimated IC50 between the two risk groups were observed for common chemotherapy or targeted drugs including paclitaxel , sorafenTo facilitate the clinical applicability and availability of the RMScore, a predictive nomogram for 1-, 3-, 5-year OS combined with tumor stage and RMScore was developed . The calThe current interventions for HCC are not satisfactory, and more precise prognostic indicator and promising strategies need to be explored. Recent studies point out that RNA modifiers play vital roles in HCC cell proliferation, exacerbation, and metastasis . HoweverHere, we systematically analyzed the expression, mutation, and the prognostic value of a total of one hundred RNA modifiers from eight types of RNA modifications in HCC. We found that most of the RNA modifiers were highly expressed in the tumor tissues compared to normal tissues. At the genetic level, nearly half of genes showed a least one kind of mutation. Most of the mutations were positively interacted with each other. These results suggested that eight types of RNA modifications might play important role in HCC.Two RNA modification patterns with distinct prognosis were identified in HCC patients.Then, we constructed RMScore to calculate the modification status of HCC patient. Patients with low RMScore showed better prognosis than patients with high RMScore. Similar to the pathway enrichment results in Cluster 1, pathway related to the cell cycle and proliferation, such as “DNA repair,” “G2M checkpoint,” “E2F targets” were all significantly enriched in the low RMScore group. All these results implied that the RMScore could well represent the modification status of the HCC patients.There were some studies which documented different RNA modification patterns in various type of tumors, including HCC. For example, Qi et al. identified two distinct m6A modification patterns, one of which showed higher expression of m6A modification regulators and poor prognosis . Similar6A writer METTL3 was documented to regulate sorafenib resistance though FOXO3-mediated autophagy in HCC , whether the RMScore is also applicable to non-Caucasian is unknown. However, our study was the first study to give an insight into the mutation and expression of eight types of RNA modifications in HCC. This study paves the way for further investigations of the roles of eight types of RNA modifications in the HCC in the future.Several limitations for our study should be recognized. Firstly, there were several cancer-related RNA modification regulators, such as m6Am regulators PCIF1 and METTL4, were not included in our analysis. This may lead to subtle deviations in our results, but does not affect the main conclusions. Secondly, due to the limited sample size, we still need more patients to verify the reliability of our conclusions. Thirdly, the potential mechanism of the role of genes contained in the RMScore was not based on experimental evidence, and required further


Transcription factors (TFs) regulate eukaryotic transcription through selective DNA-binding, can also specifically interact with RNA, which may present another layer of transcriptional control. The mechanisms of the TFs-DNA recognition are often well-characterised, while the details of TFs-RNA complexation are less understood. Here we investigate the dual recognition mechanism of the glucocorticoid receptor (GR), which interacts with similar affinities with consensus DNA and diverse RNA hairpin motifs but discriminates against uniform dsRNA. Using atomic molecular dynamics simulations, we demonstrate that the GR binding to nucleic acids requires a wide and shallow groove pocket. The protein effectively moulds its binding site within DNA major groove, which enables base-specific interactions. Contrary, the GR binding has little effect on the grooves geometry of RNA systems, most notably in uniform dsRNA. Instead, a hairpin motif in RNA yields a wide and shallow major groove pocket, allowing the protein to anchor itself through nonspecific electrostatic contacts with RNA backbone. Addition of a bulge increases RNA hairpin flexibility, which leads to a greater number of GR-RNA contacts and, thus, higher affinity. Thus, the combination of structural motifs defines the GR-RNA selective binding: a recognition mechanism, which may be shared by other zinc finger TFs. Multiple experiments suggest that transcription factors (TFs) also specifically interact with RNA, which may present an additional layer of transcription regulation4. RNA molecules are constantly present at the actively transcribed sites9. As our understanding of the role of TFs-RNA interactions still evolves, the available data suggest that TF-RNA interactions can facilitate the association of TFs with the chromatin fibre and the search for their corresponding DNA binding sites. Examples include, nuclear TF Y (NF-YA) interactions with PANDA long noncoding RNA10, which titrates the TF from the target genes to reduce their expression; Yin Yang 1 TF (YY1) interactions with nascent transcripts to retain its presence at actively transcribed promoters11; heat shock factor 1 (HSF1) that employs RNA as a scaffold for the TFs assembly on DNA12. Furthermore, a recent study by Oksuz et al. reports that nearly half (48%) of TFs identified in human K562 cells appear to interact with RNA13. As TF proteins do not possess the characteristic structural domains of well-studied RNA binding proteins15, little is known on the structural basis of RNA-TF associations.Transcription factor proteins, via selective binding to DNA, regulate genetic transcription18. The glucocorticoid receptor (GR) is a ubiquitous TF, which regulates the transcription of thousands of genes associated with development, immune responses, metabolism, inflammation, apoptosis, etc21. To regulate a transcriptional response, GR needs a glucocorticoid binding, which induces the factor translocation from the cytoplasm to the nucleus to enable interactions with DNA. GR is widely dysregulated in disease, and thus is targeted by synthetic glucocorticoids to combat a range of autoimmune disorders and many cancers, as a part of combinatorial therapy24. GR has a modular structure25, including two well-characterised domains: a ligand-binding domain and a DNA-binding domain. The GR DNA-binding domain (DBD) consists of two Zn-fingers of Cys4-type, and associates on DNA in several configurations that lead to distinct transcriptional responses29. The GR dimer-DNA association, in a head-to-head orientation, leads to the transcription induction. The GR monomer-DNA association, collaboratively or tethered to other TFs, e.g., from AP-1 or COUP families, leads to the transcription repression. The GR-DBD-DNA binding is highly sequence-specific. The protein recognises the AGAACA sequence, known as the glucocorticoid response element (GRE).Here we aim to provide atomic level insights into what drives the TF-RNA association, using as a model system the glucocorticoid receptor that can interact with RNA18. It has been proposed that GR binds the GRE-like elements within RNA molecules that act as molecular decoys for the GR-DBD, but details on the mechanism and selectivity remain elusive. An NMR study by Parsonnet et al.4, focusing on the mechanism of Gas5 lncRNA-GR interactions in vitro, reported intriguing results. Firstly, GR-DBD interacts with RNA as a monomer and adopts a distinct RNA-bound state, different from the DNA-bound and free states. Secondly, the protein recognises a range of RNA hairpin motifs, both synthetic and biologically derived, but discriminates against uniform dsRNA. And finally, the experiments suggested that GR recognises RNA in a structure-specific and not in sequence-specific manner, as is in the DNA case.GR interactions with RNA are much less explored. Several reports describe GR associations with tRNA, mRNA, and Gas5 long noncoding RNA (lncRNA)Motivated by these experimental insights, we want to further explore the mechanistic details of the GR-RNA recognition, using all-atom molecular dynamics simulations. As target systems for the GR binding, we employ several RNA structures: a fully paired hairpin, a hairpin with a bulge, and uniform dsRNA; and compare those to the GR interactions with its native DNA target. All studied nucleic acids contain the GRE-site. We observe that the presence of a hairpin and a bulge in an RNA molecule creates the most suitable pocket for the GR binding, which results in a similar interaction energy as is in the DNA-GR monomer case. However, contrary to GRE-DNA-GR recognition, which requires the formation of several specific contacts, the GR binding to RNA is secured by nonspecific electrostatic interactions of long-chain positively charged residues to RNA backbone. Furthermore, our data suggest that it is rather the 3D structural motif of an RNA molecule than its sequence that determines the stable binding by GR.4. We derive the 3D structures for the GRE_RNA and Gas5_RNA hairpins through the secondary structure prediction with RNAFold webserver31 (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) followed by the 3D structure prediction with 3dRNA webserver33 (https://bio.tools/3dRNA). We create the A-form dsRNA structure in USCF Chimera34. To derive GR-RNA complexes, we perform the macromolecular docking in HDOCK35, where GR (PDB ID: 6CFN36) is assigned as the receptor and the RNA molecule—as the ligand. The modelling of Gas5_RNA provides a larger structural variation compared to GRE_RNA .We simulate four different nucleic acids systems where the glucocorticoid receptor (GR) interacts with DNA (CCAGAACAGAGTGTTCTGA), dsRNA (GGAGAACAAAAUGUUCUUU), a fully paired RNA hairpin “GRE_RNA” (GGCAAAAUGUUCUUUCGAGAACAUUUUGCC) and a bulge-containing hairpin “Gas5” (GGCCCAGUGGUCUUUGUAGACUGCCUGAUGGCC) Fig. , and theA Figure , thus wes Figure . Experim38, using amber14SB39, parmbsc140 and parmbsc0 + chiOL342 forcefields for the protein, DNA and RNA, respectively. GR contains two Zn-Cys4 clusters, which we treat with the ZAFF parameters43. We solvate DNA and GR-DNA systems with 15 Å SPC/E44 water in cubic boxes and neutralise with K+. We add the K+ and Cl− ions to reach a physiological concentration of 150 mM KCl. We employ a different setup for all RNA simulated systems, which we solvate with 15 Å TIP3P water44 in cubic boxes and neutralise with Na+. We add Na+ and Cl− ions to reach a concentration of 100 mM NaCl. We treat the monovalent ions with the Joung-Cheatham parameters45. We select the KCl instead of NaCl environment for DNA since the conformational transitions such as the sequence-specific bimodality of shift, twist, and other helical parameters, are more accurately captured by the presence of K+ ions46. Conversely, when it comes to RNA, the NaCl environment is preferred as it provides the greater structural stability47. The origin of the greater structural stability lies in the differences in the charge density-related binding modes of the two cations. The smaller hydrated sodium ions preferentially bind to the highly charged phosphates that have high surface area. In contrast, the larger potassium ions can also reside in the grooves. As a result, more cations condense around the phosphate groups in the case of sodium ions, which reduces electrostatic repulsion between the backbones48. We proceed with energy minimization with 5000 steps of steepest decent, followed by 500 ps equilibration-runs with weak position restraints on heavy atoms of the solute (1000 kJ/mol) in the NVT and NPT ensembles, adjusting temperature and pressure to 300 K and 1 atm50, in periodic boundary conditions. For all RNA systems, both unbound and GR-bound, we perform an additional equilibration run with position restraints on heavy atoms of the solute (1000 kJ/mol) in the NPT ensemble for 20ns47. Releasing the restraints, for each of the systems we carry out 600 ns MD simulations at constant pressure and temperature (1 atm and 300 K).We perform all molecular dynamics simulations with the GROMACS MD engine version 2020.551 program from AMBERTOOLS16 software package. We distinguish between intermolecular specific (hydrogen bonding and hydrophobic contacts formed between atoms of protein side chains and nucleic acids bases) and nonspecific contacts (that involve interactions with either DNA/RNA or protein backbones). For every pair of protein-DNA residues we sum up all hydrogen bonds, salt bridges, and hydrophobic (apolar) interactions. We set the contribution of each type of contact to 1. This is done for simplicity, since the energy cost of hydrogen bonds, salt bridges, and hydrophobic interactions varies greatly, depending on the nature of the atoms involved, the bond geometry and the surrounding environment. We consider only the contacts that are present for longer than 10% of the trajectory, see previous publications for further details53. Subsequently, we apply Curves+ and Canal programs54 to derive the helical parameters, backbone torsional angles and groove geometry parameters of the nucleic acid systems for each trajectory snapshot extracted every ps. For the RNA hairpin systems, we perform the analysis for the fully paired GRE-site region. We use the GROMACS energy tool to calculate the protein-DNA/RNA interaction energies, which operates with the GROMACS force field functional terms i.e., the short range (within a cut-off distance ~ 10 Å) electrostatic (Coulomb) and Lennard–Jones (vdW) interactions for every atom pair within the selected groups are calculated and summed up to obtain the total interaction energy. To separate interaction energies into specific and nonspecific, we calculate interactions for several atom groups. The specific interactions involve the DNA/RNA bases and the protein side chains. While the non-specific interactions involve the DNA/RNA backbone and the protein residues, as well as the protein backbone and the DNA/RNA bases. We also perform free energy analysis and estimate the solvation effects using the MMPBSA/MMGBSA method included in the AMBER CPPTRAJ module. We employ the GROMACS “covar” and “anaeig” tools to calculate the configurational entropies, using two subsets of atoms: the backbone “P” atoms of the entire DNA/RNA sequences and of only the GR-response element. From the obtained eigenvectors, we calculate the configurational entropies at 300 K using the Schlitter’s formula55. To derive standard deviations, we calculate the configurational entropies for the entire 500 ns trajectory and for each consecutive 100 ns windows of the trajectory. We perform the principal component analysis (PCA) for every MD trajectory using the GROMAS “covar” tool. We perform PCA for the unbound and the GR-bound systems. For the GR-bound systems, we use two different PCA protocols: (1) for the heavy atoms of the protein-DNA/RNA complexes with the superposition on the nucleic acids; (2) for the heavy atoms of DNA/RNA with the superposition on the nucleic acids.For each of the generated MD trajectories, we discard the first 100 ns as equilibration. To analyse the protein-nucleic acids contacts, we use the CPPTRAJ34 for all molecular graphics.We use MatLab software for the post-processing and plotting of all data and USCF Chimera18, recognising RNA hairpin motifs within and discriminating against dsRNA4. To rationalize the experimental observations and gain further insights into the mechanism of GR association with RNA, we conduct a computational study. As target systems for the GR binding, we employ B-DNA containing the protein native target sequence and several RNA structures: a fully paired hairpin, a hairpin with a bulge, and uniform dsRNA  associates with high affinity with Gas5 lncRNA31 followed by the 3D structure prediction in 3dRNA33. We build the A-form dsRNA molecule using USCF Chimera34. To derive the GR-RNA complexes we perform the protein-nucleic acids docking in HDOCK35, specifying GR (PDB ID: 6CFN36) as a "receptor" and RNA as a "ligand". The modelling of the 3D structure of Gas5_RNA provides larger structural variations in the hairpin region compared to GRE_RNA . Furthermore, the comparison of the chemical shift data for the RNA systems, including Gas5_RNA, GRE_RNA, and dsRNA, indicated similar binding interfaces of GR-DBD, despite different affinities. However, the stoichiometry of the binding differs: GR binds DNA as a dimer, whereas RNA as a monomer. The docking in HDOCK results in GR-Gas5_RNA, GR-GRE_RNA, and GR-dsRNA complexes matching these criteria among the top-ten scored decoys. We build the GR dimer-DNA complex in USCF Chimera from the crystal structure of the complex (PDB ID: 5CBX37) where we exchange each GR monomer with the GR crystal structure (PDB ID: 6CFN) that includes helix 4, which was shown to be important for the GR-RNA association. We subsequently subject the derived five GR-nucleic acids complexes as well as all unbound RNA and DNA systems to 600 ns all-atomistic molecular dynamics (MD) simulations.We derive the models for Gas5_RNA and GRE_RNA through the secondary structure prediction in RNAFoldA Figure . The difA Figure : in modeWe first compare the trajectory-averaged structures for the bound and unbound DNA and RNA molecules, to see if the binding of GR contributes to any significant conformational changes in nucleic acids Fig. A. DNA, GWe next analyse the evolution of the RMSD values of heavy atoms of DNA/RNA with respect to the average structure along the MD trajectories. As seen in Fig. 53, where we follow the evolution of the number and strength of specific and nonspecific contacts . Lys442 interacts with the AG step (AGAACA); and Val443 and Arg447 form hydrophobic contacts with the GT step and the last T nucleotide, respectively, on the opposite DNA strand (AGAACA/TGTTCT). Arg447 also forms nonspecific electrostatic interactions with DNA backbone. For GR monomer 2, the residues exhibit nearly identical DNA-contacts as the monomer 1, with a few differences: His432 forms no specific contacts with the response element flanking sites while Lys442 exhibits stronger specific contacts. Alanine scanning experiments4 provide the contribution order of the positively charged residues of the GR-DBD domain for the DNA binding: R447 > K471 > K480≈R479 > K446. Based on our simulations, we conclude that R447 plays the major role for the GR-DNA binding and recognition: the residue interacts with the GRE-site through specific and nonspecific contacts. The K471 and K446 residues interact with DNA backbone, while K480 and R479 may play a structural stability role as these are not observed to directly interact with DNA. Comparing to the crystal structure of the GR-DNA complex (PDB ID: 5CBX37), we notice the only defence that Arg447 interacts through hydrogen bonds with the GT step (AGAACA/TGTTCT) instead of making hydrophobic and nonspecific interactions. However, in another crystal structure (PDB ID: 4HN5), Arg477 interacts similarly to what we observe in the MD simulations. This is because Arg477 is a so-called flickering residue which can fluctuate between different conformational substates.In case of the GR–DNA binding, each GR monomer specifically interacts with the GRE-site (AGAACA) employing mainly the residues of the recognition helix: His432, Lys442, Val443, and Arg447  that interact specifically with DNA, form nonspecific contacts with RNA. Also, most of the residues involved in nonspecific contacts with RNA, exhibit nonspecific interactions with DNA . Our simulations show that nonspecific interactions exploited by the recognition helix (Lys442 and Arg470) but also residues outside this region  are lost toward the end of the GR-dsRNA MD trajectory Figure . The rec4 show the following GR binding preference order: DNA > Gas5_RNA > GRE_RNA >  >  > dsRNA. To explore if our simulations capture the same trend, we next analyse the intermolecular interaction energies . To estimate the standard deviations, we calculate the configurational entropies for the entire 500 ns trajectory and for all consecutive 100 ns windows within. Upon the GR binding to DNA/RNA, we expect the configurational entropy to decrease if the two molecules are structurally complementary and form a stable complex. As if the protein makes the nucleic acid molecule more rigid for global motions. Looking at the configurational entropies for the entire sequences , adjacent to the bulge, have the least difference in twist, shift, tilt, and x-displacement values ce Table A.For DNA, dsRNA, and GRE_RNA the first three components describe similar motions, where the first two components correspond to sideward and upward bending, and the third component describes the twisting rotation Fig. -S9. The For Gas5_RNA systems the motion described by the first component dominates. It involves a bending motion that changes the size of the major groove pocket, going from an open to a closed and bent conformation Fig.  and S13,We continue with the PCA analysis of the trajectories for the protein-bound systems  can bind both DNA and RNA, but the association mechanisms are different.Using all-atomistic MD simulations, we explore the mechanistic details of transcription factor associations with RNA. As the model system we employ the glucocorticoid receptor (GR) bound to several nucleic acids model systems, which include the native DNA target and three structurally different RNA molecules containing the glucocorticoid response element (GRE) and GRE-like sequences. The studied RNA systems include dsRNA, a fully paired RNA hairpin, and an RNA hairpin with a bulge. The latter system is derived from long-noncoding Gas5 RNA, which was shown to interact with GR 64, explaining the observed positive cooperativity for the GR-DNA association28.The DNA recognition by a GR dimer involves formation of several specific contacts between the residues of the GR-DBD recognition helixes and the bases of the GRE-sites. These specific contacts anchor each recognition helix deep within DNA major groove, leading to the significant widening of the major groove of both GRE-sites Movie . It is eContrary, the GR-DBD-RNA complexation depends predominantly on nonspecific electrostatic contacts. Though, the number and strength of these nonspecific contacts depend on the structural motif of the RNA molecule Figs. , S3-S6. 4: DNA > Gas5_RNA > GRE_RNA >  >  > dsRNA. In addition, computationally we can estimate the binding energies for each GR monomer in the GR-DNA complex, which further reveals that a GR monomer can bind Gas5_RNA even stronger than DNA. This is possible due to the combination of two factors. The first being the above-mentioned GR-Gas5_RNA nonspecific interactions, because of the increased flexibility of the off-GRE stem. The second being the unfolding of helix 4, the residues of which then interact specifically with the bases of the terminal loop region. Crystal structures support that helix 4 can exist as both folded65 and unfolded66. In our computational experiments, we observe the unfolding of helix 4 for the GR-DNA, GR-GRE_RNA, and GR-Gas5_RNA_model2 systems. Though, we believe that the unfolding of helix 4 in the GR-DNA MD trajectory is the consequence of GRE-flanking regions being too short. The unfolding of helix 4 may present a mechanism for the GR-nucleic acids structural recognition—a hypothesis that we plan to investigate further in the future.The binding energies and the configurational entropies analyses of the GR-DNA/RNA complexes show the same binding affinities relations, as the binding experimentsTaken together, we demonstrate with atomic level detail that the glucocorticoid receptor employs the sequence recognition mechanism when binding DNA and the shape recognition mechanism when binding RNA. The protein interacts with either DNA or RNA using nearly same residues, yet the nature of the formed protein-nucleic acids contacts and the binding interfaces differ. We believe that the described dual recognition mechanism may be shared by other zinc finger transcription factors.−8 to 10−7 M)28, carries significant biological implications. This phenomenon might constitute a finely tuned evolutionary mechanism that enables organisms to promptly respond to conditions such as low blood-glucose levels and various stress conditions. Studies indicate that upon activation, GR is rapidly recruited to transcribed sites67.This could be attributed to tethered RNA transcripts functioning as molecular beacons, actively attracting the GR protein. Moreover, RNA transcripts could serve as reservoirs, enabling the protein to maintain heightened local concentrations to sustain transcriptional activity. The competitive binding of GR-DBD to DNA and RNA may play a pivotal role in modulating DNA-GR equilibrium binding constants, potentially leading to the dynamic regulation of gene transcription rates. The DNA/RNA binding duality offers a mechanism for the protein to dissociate from high-affinity GRE-sites. Indeed, experimental studies underscore that GR exhibits a more rapid turnover on DNA compared to other transcription factors69. However, further characterization is imperative to establish the in vivo role of the glucocorticoid receptor-nucleic acids binding duality.The ability of the glucocorticoid receptor to selectively interact with both DNA and RNA, demonstrating comparable binding affinities within the nanomolar range (10Supplementary Information 1.Supplementary Information 2.