The combination of high-throughput sequencing and in vivo crosslinking approaches leads to the progressive uncovering of the complex interdependence between cellular transcriptome and proteome. Yet, the molecular determinants governing interactions in protein-RNA networks are not well understood. Here we investigated the relationship between the structure of an RNA and its ability to interact with proteins. Analysing in silico, in vitro and in vivo experiments, we find that the amount of double-stranded regions in an RNA correlates with the number of protein contacts. This relationship —which we call structure-driven protein interactivity— allows classification of RNA types, plays a role in gene regulation and could have implications for the formation of phase-separated ribonucleoprotein assemblies. We validate our hypothesis by showing that a highly structured RNA can rearrange the composition of a protein aggregate. We report that the tendency of proteins to phase-separate is reduced by interactions with specific RNAs. Interactions between protein and RNA occur on a large scale, and are often studied from a proteincentric point of view. Here the authors show that the number and strength of RNA-protein interactions correlate with the amount of RNA structure, which impacts the biological activity of the transcript. During the past decade many efforts were made to develop methods to study RNA isoforms: sequencing has been essential for detection of RNA species3 and recent developments provided a great deal of data on polymorphisms4, expression5 and half-lives6 of all types of RNAs, generating a valuable resource to understand their cellular functions and regulation. Although a number of techniques identified biological characteristics such as cellular location7 and secondary structure9, the characterization of the interaction network remains one of the most urgent challenges11. To this aim, computational methods are being developed to identify physicochemical features of the transcripts10, their conservation between species12 and, most importantly, binding partners13 that are also active in the cellular environment14.Since the central dogma was proposed in 1950, the main role attributed to RNA has been to act as the intermediate between DNA and protein synthesis. Yet, more than 70% of the genome is transcribed and just a small part codes for proteins15. Its ability to act as a hub of cellular networks is at the centre of an active research field and has already led to the discovery of diverse ribonucleoprotein (RNP) assemblies17. A number of membrane-less organelles contain specific mixtures of RNAs and RBPs (RNA-binding proteins) that, due to their intrinsic lability, are difficult to characterize10. In most cases, liquid-like RNP assemblies, or condensates, such as P-bodies and stress granules18, exchange components with the surrounding content and adapt to the environmental condition in a dynamic way. Within these phase-separated assemblies RNA plays a central role19: whereas a polypeptide of 100 amino acids can interact with one or two proteins, a chain of 100 nucleotides is able to bind to 5–20 proteins, thus providing an ideal platform or scaffold for interactions21. Not surprisingly, changes in the interactions within RNP granules leading to liquid-to-solid phase transition are associated with the development of several human diseases, including neurological disorders and different types of cancer17. In RNP condensates such as stress granules, regulation of protein and RNA contacts is primarily controlled by HSP70 and co-chaperones17 that act as versatile elements promoting assembly and disassembly of complexes22.RNA is involved in many cellular processes such as control of gene expression, catalysis of various substrates, scaffolding of complex assemblies, and molecular chaperoning12. Known complexes in which the structure regulates protein binding include transfer RNAs (tRNAs) whose three-dimensional conformation facilitates the codon/anticodon interaction23 and the ribosomal RNA (rRNA) scaffold that sustains the ribosome24. Importantly, the structure of a messenger RNA (mRNA) defines its lifecycle25, recruitment of ribosomes and response against environmental changes25. There are several cases of nucleotide chains of non-coding RNAs acting as scaffolds for protein complexes21: structured domains in NEAT1 attract paraspeckle components26 and repeat regions in XIST sequester proteins to orchestrate X-chromosome inactivation27. By contrast, poorly structured snoRNAs have been shown to facilitate the assembly of other transcripts28.In this large spectrum of activities, RNA structure controls the precise binding of proteins by creating spatial patterns and alternative conformations for the  interactions to occur 29 and are classified as single-stranded RNA (ssRNA) and double-stranded RNA (dsRNA), depending on their binding preferences. Here we investigated the relationship between RNA structure and ability to interact with RBPs. At the transcriptome level, we find that the amount of RNA secondary structure correlates with the number of protein interactions. We propose several possible implications of this relationship: a link to RNA types and biological roles; a connection to regulatory networks; and the ability to modulate phase separation. Based on our observations, we also demonstrated that this RNA property can be exploited in vitro to tune the contact network of a protein aggregate.Of both coding and non-coding transcripts, RBPs are known as the major regulators8 8 . PARS is an experimental technique that distinguishes double- and single-stranded regions of RNA using the catalytic activity of two enzymes, RNase V1 (able to cut double-stranded nucleotides) and S1 (able to cut single-stranded nucleotides) and for which positive scores indicate double-stranded regions  and compared the interaction scores of different groups  , allowing identification of binding partners with high confidence. Indeed, as reported in a recent analysis of about half a million of experimentally validated interactions31, the algorithm is able to separate interacting vs. non-interacting pairs with an area under the curve (AUC) receiver operating characteristic (ROC) curve of 0.78  significantly below 0.25 when the Z-score values are >2). Comparison of RNA groups with different structural content shows a consistent trend in which higher structural content in RNA molecules results in higher protein interaction scores  and random forest (RPISeq-RF). Due to specific computational requirements, we applied RPISeq to an ensemble of RBPs  against the HS and LS set from the tails of the structural content distribution (100 transcripts) to estimate the binding probabilities  test; Supplementary Fig. 1b–c), in agreement with catRAPID analysis . eCLIP provides protein contacts on target RNAs at individual nucleotide resolution through ligation of barcoded single-stranded DNA adapters30. In agreement with catRAPID predictions31 , which indicates that our results are not biased by the protein types used in our analysis.To match our predictions with experimental data, we investigated all RBP–RNA interactions revealed by enhanced CrossLinking and ImmunoPrecipitation, eCLIPs31 Fig. , eCLIP bs31 Fig. . We note9. This method of assessing RNA structure employs deep sequencing to detect unpaired adenosine and cytidine nucleotides. Once more, the analysis shows that the RNA secondary structure of the human transcripts is tightly correlated with protein-binding abilities.To further corroborate that the trend is genuine and not only intrinsic to PARS measurements, we analysed the protein-interacting potential of the entire human transcriptome against the RNA secondary structure measured with the dimethyl sulfate modification (DMS) technique  to retrieve the RNA-binding preferences of human proteins  measured with PAR-CLIP, high-throughput sequencing-CLIP (HITS-CLIP) and individual nucleotide resolution CLIP (iCLIP)10. Due to intrinsic differences in the CLIP approaches , each experiment reports different protein–RNA interactions10. Yet 77% of the RBPs have preference for highly structured RNAs for at least one of the experimental methods . In parallel, we estimated the structural content of each transcript using the CROSS algorithm that was previously trained on SHAPE data38 to predict the double-stranded propensity at nucleotide level resolution. Our results presented in Fig. Given possible technical biases of high-throughput experiments, we decided to verify the reproducibility of the trend by investigating the correlation between RNA structure and protein interactions in low-throughput analyses. We first studied the interactome of eight large (>1000 nt) RNAs whose protein partners have been identified by microarray, a crosslinking-free approachWe corroborated our observations through the study of RNP complexes deposited in the Protein Data Bank (PDB) database , the algorithms employed (catRAPID and RPISeq or CROSS to mimic SHAPE data) or organism (PDB database), we found a correlation between number of protein interactions and RNA structural content.39 (http://cd-hit.org/). With a threshold of 85% similarity, we found 22 clusters  with at least one RBP contact revealed by eCLIP. We then calculated the correlation between DMS signal and eCLIP protein interactions for each cluster and obtained a negative correlation in 64% of cases. This finding indicates that between two similar transcripts the one with higher structural content is more likely to have a larger number of protein interactions.We next investigated if the tight link between secondary structure and number of protein interactions is a property of specific RNA types Fig. . To thisp value = 0.003; KS test; Fig. The two transcripts sharing the highest similarity (99.31%) are the γ-globins HBG1 and HBG2 (haemoglobin subunits γ1 and γ2) that are expressed in fetal liver, spleen and bone marrow (NCBI Gene ID: 3048). The γ-globin variant with higher structure (HBG1) has a significantly larger number of protein interactors 28, show the highest amount of structure, whereas RNAs targeting complementary regions in nucleic acids such as antisense, miRNAs and a number of long intergenic non-coding RNAs (lincRNAs)42 feature the smallest amount of structure43 , we note that some of them, such as for instance NEAT144 and XIST27, are able to scaffold protein assembly through structured domains. As there is an ongoing debate on the structural differences between coding and non-coding transcripts46 and our analysis of DMS and PARS data reveals contradicting results for specific RNA types, we suggest further investigations in future studies  is not associated with specific semantic similarity clusters , the HS set  using the cleverGO47. Thus, our analysis suggests a ‘recursive’ property: highly contacted transcripts code for highly contacting proteins  and α-helical propensity  are removed  as either exclusively ssRNA binders (453 proteins) or dsRNA binders ent Fig. . The com56 and organize the assembly of phase-separate RNP assemblies57, thus fulfilling the ‘recursive’ property presented in Fig. 30 show that most of the RNAs coding for human chaperones are involved in interactions with multiple proteins  or function (e.g. coding for chaperones) share similar structural characteristics Fig. . Thus, iHSP70 has a larger number of partners (30 RBPs identified by eCLIP) than BRaf , while the poorly structured BRaf has a protein product binding to a smaller set of molecules . Our observations suggest that an RNA with a large number of interactions is prone to act as a network regulator: we speculate that, because of the higher interactivity, HSP70 transcript could perform as a chaperone depending on the context.We found that Raf Fig. . Moreove60 that we incubated with either HS (HSP70) or LS (BRaf) transcripts  to induce formation of a liquid-to-solid phase transition of a protein assemblypts Fig. . We obsepts Fig. . Indeed,60 could be the result of either direct or indirect protein–RNA interactions  and S (able to cut single-stranded nucleotides)3. Nucleotides with a PARS score higher than 0 indicate double-stranded conformation, while values lower than 0 are considered single stranded69. Transcripts with all nucleotides undetermined were discarded from our analysis. To measure the PARS structural content, for each transcript, we normalized by length and computed the fraction of double-stranded regions over the entire sequence. Given the stepwise function ϑ(x) = 1 for x > 0 and ϑ(x) = 0 otherwise, we computed the fraction of structured domains as:V(i) and S(i) are the number of double- and single-stranded reads. The top (larger fraction of double-stranded nucleotides) and bottom (larger fraction of single-stranded nucleotides) transcripts are listed in Supplementary Data To profile the secondary structure of human transcripts, we used PARS dataTo measure the secondary structure content of the human transcripts without the 5′- and 3′- UTR, we retrieved the corresponding locations of the 5′- and 3′-UTR from Ensembl database (section Database of RNA types) and repeated the same procedure described above eliminating the PARS values corresponding to the UTRs.9. The number of reads of each transcript was normalized to the highest value . To circumvent this practical problem, we used CD-HIT (similarity clustering threshold of 85%) to select a representative group of 50 distinct RBPs out of our pool of 579 RBPs. Similarly, we built a set of 100 transcripts . Using both RPISeq approaches (RPISeq-SVM and RPISeq), we predicted that HS has higher interaction propensities . We processed the eCLIP data using bioconductor package for R (https://www.bioconductor.org/) and mapped the genomic locations to UCSC transcript Identifiers.We used human protein–RNA interactions that were identified through eCLIP experiments in two cell lines, K562 and HepG210 >5 and −log2(fold_enrichment) >3] as in a previous work30. The interactions retrieved after applying the above thresholds represent the top 5% of all interactions in the eCLIP database. The number of protein binders for the transcripts at the top and bottom PARS are listed in Supplementary Table To measure the fraction of protein binders for each transcript, we applied stringent cut-offs [−loghttp://lulab.life.tsinghua.edu.cn/postar/) using POSTAR’s thresholds  and mapped to human GENCODE release 27 transcripts (which directly correspond to Ensembl release 91). POSTAR provides the results of two alternative CLIP-seq data processing methods for each CLIP-seq method . We prioritized Piranha since it had been applied to all three experimental methods in POSTAR. In total, we retrieved data for 103 experiments on 85 distinct RBPs .iCLIP, PAR-CLIP, and HITS-CLIP interaction data were obtained from the POSTAR database  ‘low structured’ RNA preference when the median secondary structure content of its transcriptome is lower than that of the background with p value ≤0.01 and (iii) ‘no preference’ for the rest of the cases.To measure the RNA structure preference of the 85 RBPs, we used DMS data (see Methods: RNA secondary structure measured by DMS) and compared the secondary structural content of the interactome of each RBP with the transcriptome as background. An RBP has (i) high structured RNA preference when the median secondary structure content of its transcriptome is higher than that of the background with 38. CROSS was developed to perform high-throughput RNA profiling. The algorithm predicts the structural profile (single- and double-stranded state) at single-nucleotide resolution using sequence information only and without sequence length restrictions (scores >0 indicate double-stranded regions). In our analysis, we used the CROSS icSHAPE method that is trained on icSHAPE data8.We predicted the secondary structure of transcripts using CROSS . Then, we computed the percentage structural content of the list of RNAs using CROSS. We then defined the percentage structural content as the percentage of nucleotides having a positive score (double-stranded tendency).To find an alternative link between RNA secondary structure and the number of RBP contacts, we analysed protein-array dataFrom the PDB database we retrieved all structures that contain complexes composed of protein and RNA molecules, excluding those that contain DNA or RNA/DNA hybrid and with X-ray resolution ≤2.0 Å (exact Search Parameter: ‘Chain Type: there is a Protein and a RNA chain but not any DNA or Hybrid and Resolution is between 0.0 and 2.0’). These complexes account for 196 PDB identifiers .Using physicochemical properties, the multiclever MachineFor sets and results see Data availability section.http://weizhongli-lab.org/cd-hit/) with similarity threshold of 85% to cluster RNAs for which DMS (see Methods: RNA secondary structure measured by DMS) and eCLIP data are available. CD-HIT returned 22 clusters  meeting the criterion that a cluster should be populated with at least two transcripts. Pearson’s correlation was estimated for each individual cluster. HBG1 and HBG2 were the two transcripts sharing the highest similarity (99.31%). HBG1 was found to be significantly more structured (DMS score: 0.04) than HBG2 (DMS score: 0.07) and in agreement with our observations HBG1 has more eCLIP interactors than HBG2 .We used the CD-HIT algorithm (https://www.ensembl.org/index.html).The gene type information for all RNAs that PARS value was available was extracted from the Ensembl database version GRCh38.p12 (www.genecards.org) searching for keyword criteria: ‘HSP heat shock’. This returned as a result 1258 entries. Out of these results, we looked for the term ‘Heat Shock Protein’ in the description and removed entries referring to ‘DNA Heat Shock Proteins’, entries described as ‘Pseudogenes’ and ‘Heat Shock Transcription Factors’. After the above criteria, the dataset was reduced to 27 gene names, and for each of those we retrieved from Ensemble, the corresponding UCSC identifier. As described ), our compiled database for eCLIP is annotated with UCSC identifiers. We used the UCSC identifiers of the HSP family to retrieve the fraction of Proteins that they bind.We retrieved the heat-shock protein members from Genecards database . For each protein of interest we counted the number of unique interactors  as defined by their Gene Name.Protein–protein interaction information was retrieved from BioGRID, version 3.4.163, which contains 312,474 human non-redundant interactions (U test). Wilcoxon’s is a non-parametric test used to compare the mean of two distributions without any given assumption about them. To compare properties and measure the difference between RNA sets, we used the KS test. KS test is also a non-parametric test used to compare the distance between two cumulative distribution functions.To assess whether ssRNAs exhibit different trends from dsRNAs, we used the Wilcoxon’s test  was added to the lysate at 100 µM and the mixture was incubated at 4 °C for 1 h with gentle shaking. After 1 h, the lysate with b-isox was splitted into three tubes (3 × 120 µL) and 1 µg HSP70 mRNA, 1 µg BRaf mRNA or the same volume of water and incubated again at 4 °C for 1 h with gentle shaking. The aggregates were then collected by centrifugation at 10,000 × g at 4 °C for 10 min. The supernatant was collected and 2× sample buffer was added. The pellets were washed two times by being resuspended in 50 µL lysis buffer, vortexed, kept on ice for 10 min and centrifuged again at 10,000 × g, at 4 °C for 10 min. The washed pellets were resuspended in 50 µL MS buffer  and frozen before mass spectrometry analysis (or in 50 µL 2× sample buffer for sodium dodecyl sulfate-polyacrylamide gel electrophoresis and Coomassie blue staining). Subsequently, the samples were processed according to the FASP protocol using 30k filtration units  for data-dependent LC-MS/MS analysis with a Q Exactive Plus mass spectrometer.The MS data were analyzed using the software environment MaxQuant version 1.5.3.30. Proteins were identified by searching MS and MS/MS data against the human complete proteome sequences from UniProtKB, version of November 2015, containing 70075 sequences. Carbamido-methylation of cysteines was set as fixed modification. N-terminal acetylation and oxidation of methionines were set as variable modifications. Up to two missed cleavages were allowed. The initial allowed mass deviation of the precursor ion was up to 4.5 ppm and for the fragment masses it was up to 20 ppm. The ‘match between runs’ option was enabled to match identifications across samples within a time window of 2 min of the aligned retention times. The maximum false peptide and protein discovery rate was set to 0.01. Protein matching to the reverse database or identified only with modified peptides were filtered out. Relative protein quantitation was performed using the LFQ algorithm of the Maxquant with a minimum ratio count of 1. Bioinformatic data analysis was performed using Perseus (version 1.5.2.6). The proteins with minimum three valid LFQ values in at least one group (background/Hsp70 mRNA/B-Raf mRNA) of 4 biological replicates were considered as quantified and used for downstream analysis. Proteins significantly changed in the presence of Hsp70 or BRaf mRNA compared to the background control (water) were identified by two-sample t-test at a p cutoff of 0.05.For in vitro transcription, linearized plasmids were purified by phenol/chloroform/isoamylalcohol extraction and ethanol precipitation. Ampliscribe T7 high Yield kit  was used to transcribe 1 μg of template for 4 h at 37°C. The reaction mix was digested with DNaseI and RNA was purified using MegaClear transcription clean-up kit (Thermo Fisher Scientific). About 30 million HeLa cells were then  trypsinized and collected. They were washed two times with ice-cold phosphate-buffered saline, resuspended in 300 µL lysis buffer  1:100) and incubated at 4 °C for 20 min with gentle shaking. The lysate was centrifuged at 21,000 × p value) >1.3 and log(RNA/Background) <0) and not significantly released in the presence of LS mRNA  <1.3 or log(RNA/Background) >0); (ii) as negative set we used all the proteins that did not show any significant change in the presence of either HS or LS mRNAs  <1.3) in the b-isox precipitate.catRAPID omiXcore integrates the interaction propensities of individual protein and RNA fragments into a unique score that was used to compare predictions with experiments. To this aim, we computed the AUC ROC of two protein groups: (i) the positive set was comprised of all proteins that were significantly released in the presence of HS mRNA  <1.3 and |log(RNA/Background)| <0.15), (ii) medium stringent cases, those that show modest change  <1.3 and |log(RNA/Background)| <0.30) and (iii) low stringent cases, those that show greater change  <1.3 and |log(RNA/Background)| <1).We used a cut-off to define the experimental stringency: (i) highly stringent cases, all those proteins with minimum fold change in the presence of both the two mRNAs  and we observed higher performances when increasing the experimental stringency.Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5


N6-methyladenosine (m6A) is the most abundant internal RNA modification of cellular mRNAs. m6A is recognised by YTH domain-containing proteins, which selectively bind to m6A-decorated RNAs regulating their turnover and translation. Using an m6A-modified hairpin present in the Kaposi’s sarcoma associated herpesvirus (KSHV) ORF50 RNA, we identified seven members from the ‘Royal family’ as putative m6A readers, including SND1. RIP-seq and eCLIP analysis characterised the SND1 binding profile transcriptome-wide, revealing SND1 as an m6A reader. We further demonstrate that the m6A modification of the ORF50 RNA is critical for SND1 binding, which in turn stabilises the ORF50 transcript. Importantly, SND1 depletion leads to inhibition of KSHV early gene expression showing that SND1 is essential for KSHV lytic replication. This work demonstrates that members of the ‘Royal family’ have m6A-reading ability, greatly increasing their epigenetic functions beyond protein methylation. When a cell needs to make a protein, it reads from the master copy of the gene in the DNA and prints out temporary duplicates called mRNA. These duplicates then act as templates for protein production. Both DNA and mRNA can be further modified by adding on chemical tags that recruit specific proteins. While chemical modifications in DNA are known to control the activity of genes, their role in mRNA is only just being uncovered.6A. This methyl group has also been found in the mRNA of certain viruses, including the Kaposi’s sarcoma-associated herpesvirus (KSHV) which causes cancer. Recent work has shown that a family of proteins, known as the YTH family, can recognise and bind to this specific methyl group and regulate the rate at which mRNA degrades. To investigate whether other proteins can recognise m6A, Baquero-Pérez et al. mapped the m6A residues of mRNAs encoded by KSHV genes and looked at which proteins the methyl mark interacts with.One of the most common chemical modifications in mRNA is the addition of a methyl group called m6A. Baquero-Pérez et al. showed that a member of this family, SND1, can read the m6A methyl mark on mRNAs from both the virus and the host cell. Further experiments showed that SND1 binds to and stabilises a viral mRNA which provides the template for a protein that the virus needs to replicate. When SND1 was removed from human immune cells infected with KSHV, this caused the virus to replicate less efficiently.The experiments revealed that a family of proteins – nicknamed the ‘Royal family’ – that recognise methyl groups in proteins, can also bind to mRNA that contains mThe discovery that the Royal family of proteins can recognise methylated mRNA as well as methylated proteins suggests that there may be a common feature that allows proteins to read methylation. Understanding the shape of this feature could lead to new treatments that block viruses from making the proteins they need to replicate. Despite the identification of this modification over four decades ago, only recently have major breakthroughs in our understanding of this modification been made due to the development of m6A-seq, which allows immunoprecipitation of fragmented m6A-modified RNAs followed by next-generation sequencing (NGS) analysis. m6A-seq and subsequent enhanced versions of the technique led to transcriptome-wide maps of cellular m6A modification  domain-containing proteins. YTH readers directly bind m6A in target RNAs through an aromatic cage  is a DNA virus responsible for several Acquired Immuno Deficiency Syndrome-associated aggressive malignancies . KSHV ha6A epitranscriptome using a novel m6A peak-calling algorithm and characterise m6A readers that specifically interact with KSHV m6A-modified RNA sequences found in ORF50 and ORF37. Through the use of RNA affinity, in addition to YTH readers, eight members from the Tudor domain ‘Royal family’, including SND1 , were specifically enriched in m6A-modified KSHV sequences. The ‘Royal family’ is a well-characterised family that reads methylated residues in histones through the use of an aromatic cage  analysis using publically available datasets deposited in the ENCyclopedia Of DNA Elements (ENCODE) further confirmed that SND1 has a binding profile similar to other m6A reader proteins. Importantly, depletion of SND1 in KSHV-infected cells significantly reduced the stability of unspliced ORF50 RNA and led to markedly reduced levels of RTA protein together with a global impairment of KSHV lytic replication. Furthermore, we show that m6A-modification in ORF50 RNA regulates SND1 binding to this RNA, particularly to the unspliced form. These data identify SND1 as an essential m6A reader for KSHV lytic replication and implicate the ‘Royal family’ as a family which comprises m6A readers. This, considerably expands the landscape of m6A readers and the epigenetic functions of Royal members beyond protein methylation.Here we accurately decipher the KSHV mtic cage  which istic cage . Electro6A peak-calling algorithm that identifies high-confidence methylated residues with more precision than previously described approaches   and 3’ untranslated region (UTR) throughout KSHV infection  which implements a novel mproaches . Utilisiplicates . At 20 hSHV ORFs . The posplicates . 12 viraqRT-PCR) . m6A peaectively . Cellulashut-off   and an enrichment of 4-fold m6A-IP/input. After applying these cut-offs, our TREx BCBL1-Rta cells datasets contained twice as many m6A peaks as compared to the Tan dataset in the same cell line. ~17,000 and~10,000 cellular m6A peaks were identified in our dataset during latency and lytic replication, respectively, while the Tan dataset yielded ~9,000 m6A peaks during latency and ~4,500 m6A peaks during lytic replication . In such situations, many peaks cannot be mapped one-to-one, with multiple peaks often overlapping a single one when comparing datasets, which leads to overlap quantifications that are not easily interpretable. Therefore, we compared the overlap of m6A-modified transcripts identified between all three studies. In TREx BCBL1-Rta cells, 72% of our 5,830 m6A-modified transcripts in latent cells were also present in the same cell line during latency in the Tan dataset, while 52% of our 4,059 m6A-modified transcripts in lytic cells were common to the lytic Tan dataset  distinctly enriched SND1, a Tudor domain-containing protein. SND1 was in the top thirteen enriched protein hits, together with YTHDF1, YTHDF2 and YTHDF3. Further binding validation of YTHDF1 and SND1 in RNA affinity experiments demonstrated that YTHDF1 was greatly enriched in all three methylated baits while SND1 showed a modest but clear enrichment exclusively in m6A-ORF50-1 bait  is required for sDMA-binding , two distinct bands were visualised, one lower band which migrated at the same speed of A-ORF50-1 bait and another higher band migrating slower  with strong base-pairing throughout the stem , a protein with no RNA-binding properties, was also tested in EMSA showing no selectivity for m6A-ORF50-1 bait , directly overlapped with m6A peaks . In brief, both spliced transcripts and introns were segmented into a total of ~750,000 transcriptomic regions based on changes in their fold change RIP/input . Applyinnd 3’UTR . We then6A peaks . As a co6A peaks . SND1-en in UTRs  and lncR in UTRs  were als regions .6A peaks, a U-tract immediately followed by an m6A motif was identified  showed reduced overlap between m6A peaks and its binding sites. We investigated the repertoire of transcripts bound by SND1 and other reader proteins and found extensive binding to protein coding transcripts , to the second highest scoring motif, upon restricting the analysis to SND1 binding sites on exons which contain the m6A modification (7965 peaks), of which 58%  directly overlap an m6A site  was used as negative control antibody. The YTHDF1 RNA target SON  and a shRNA scramble cell line were generated. Surprisingly, following 24 hr of lytic replication there were no significant differences between the scramble and the knockdown cell lines in the amount of KSHV lytic transcripts produced , which leads to the expression of unspliced echanism . HoweverRF50 RNA , it therRF50 RNA , and RTARF50 RNA . These dRF50 RNA . These rORF50 RNA, we assessed whether SND1 regulated splicing events in cellular RNA processing, due to the previously reported role of SND1 in the regulation of splicing (Although SND1 did not affect splicing of the splicing . Thus wesplicing . The samORF50 promoter by carrying out chromatin immunoprecipitation (ChIP) with the ChIP-grade antibody we previously used for RIP-seq experiments. RNA polymerase II (RNAPII) antibody (clone CTD4H8) and non-specific immunoglobulin (IgG) were used respectively as positive and negative control antibodies. Whilst RNAPII was enriched at the GAPDH promoter and multiple viral promoters including ORF50, there was no significant enrichment for either SND1 or the non-specific IgG  ORF50 RNA was monitored in scramble and SND1-depleted TREx BCBL1-Rta cells that had been reactivated into the lytic cycle for 24 hr before addition of actinomycin D. Strikingly, native ORF50 RNA was significantly more unstable in SND1-depleted cells . The same METTL3 knockdowns were repeated in BCBL1 cells but transiently. After five days post-lentiviral transduction, cells were reactivated for 24 hr and viral and protein levels analysed. Here, one METTL3 knockdown cell line (METTL3 KD2) achieved a higher level of depletion of METTL3 protein and viral transcript and protein levels were both significantly reduced compared with the scramble cell line  FLAG-ORF50 -containing both ORF50 exons and the intron- and mutant plasmids were transfected into HEK-293 cells and m6A enrichment quantified by m6A-IP-qPCR , which inhibits m6A deposition by reducing levels of the methyl donor S-adenosylmethionine (SAM). HEK-293 cells were transfected with WT FLAG-ORF50 plasmid and 4 hr after transfection, control 0.25% (v/v) DMSO or 200 µM DAA was incubated for an additional 24 hr. Note that DAA did not result in cytotoxicity at this concentration after 26 hr post-treatment   and stabilises this mRNA leading to elevated protein AT1R levels  , thus SNR levels . IntriguR region . FinallyR region . Under sR region  and stabP2 mRNAs . SimilarP2 mRNAs . It willP2 mRNAs .We performed RNA-lifetime profiling in scramble and SND1-depleted cells using latent and lytic TREx BCBL1-Rta and BCBL1 cells; however the NGS data were extremely noisy and this precluded us from identifying a consistent phenotype between replicates.6A sites   and deng6A sites , thus it6A-decorated RNAs would interact with positively charged residues. This model would explain the RNA structure dependency observed for these proteins in binding m6A-modified RNA. In contrast, the aromatic cage of YTH readers resides in a hydrophobic pocket surrounded by a positively charged surface, rendering the YTH domain favourable to bind any methylated RNA sequence. Of note, plant agenet members contain two plant agenet domains (Agenet1 and Agenet2) in tandem, each harbouring an aromatic cage. Whilst the aromatic cage of Agenet2 is proposed to be the site that binds methylated lysines  proteins, AKAP1, SMN and SPF30 display methyl-arginine-binding and are involved in RNA metabolism  was within the top ten enriched proteins in m6A-ORF50-1 bait. The remainder members of the Tudor protein subfamily bind methyl-lysine residues and contain other domains related to chromatin biology, consequently, these proteins are unlikely to participate in RNA metabolism. The discovery of PSIP1, HDGFRP2 and MSH6 as putative m6A readers is surprising as the PWWP domain is a well-established nucleosome-binding domain and these proteins participate in DNA repair (6A-containing target RNAs (6A RNA-mediated response to UV-induced DNA damage was recently reported (6A reading ability of these proteins is the fact that mass spectrometry identification of PSIP1 short (p52) isoform, which includes the PWWP domain, revealed that ~95% of interactors function in pre-mRNA processing. Moreover this isoform co-localised with SRSF2 in nuclear speckles and modulated alternative splicing  (Merck Millipore) was used in m6A-immunoprecipitations. Antibodies used in RIP are as follows: anti-SND1 ; anti-FXR1 ; anti-FXR2 ; anti-PSIP1 ; anti-YTHDF1 ; anti-YTHDF3  and normal rabbit IgG . 3-deazaadenosine (DAA) was purchased from Cambridge Bioscience. For RIPs, either 2 µg of anti-SND1, FXR1, FXR2, PSIP1 or normal rabbit IgG were used per RNA immunoprecipitation. For YTHDF1 and YTHDF3, 1 and 5.8 µg were used per immunoprecipitation respectively. For ChIP experiments, 2 µg of α-RNAPII (clone CTD4H8) antibody (Merck Millipore), anti-SND1  or normal rabbit IgG  were used per chromatin immunoprecipitation. The same lot number (00020506) of SND1 antibody  was used for western blot, RIP-seq, RIP-qPCR and ChIP experiments.HEK-293T and HEK-293 cells were purchased from ATCC (American Type Culture Collection) and cultured in Dulbecco’s modified Eagle’s medium with glutamine  supplemented with 10% (v/v) fetal calf serum (FCS) (Gibco) and 1% (v/v) penicillin-streptomycin (P/S) (Gibco). TREx BCBL1-Rta cells, a BCBL1-based, primary effusion lymphoma (PEL) B cell line that has been engineered to inducibly express exogenous Myc-tagged RTA by the addition of doxycycline, were a gift of JU Jung . BCBL1 cells were a gift from Dr Andrew Hislop . BCBL1 cells were grown in RPMI1640 growth medium with glutamine (Gibco) supplemented with 10% (v/v) FCS (Gibco) and 1% (v/v) P/S (Gibco). TREx BCBL1-Rta cells were grown in RPMI1640 growth medium with glutamine (Gibco) supplemented with 10% (v/v) FCS, (Gibco), 1% P/S (v/v) (Gibco) and 100 μg/mL hygromycin B (Thermo Scientific). All cell lines were tested negative for mycoplasma. For virus reactivation, TREx BCBL1-Rta cells were induced using 2 μg/mL doxycycline hyclate (Sigma-Aldrich) and BCBL1 cells were induced using 2 mM sodium butyrate (Sigma-Aldrich). Antibodies used in Western blotting are listed below: anti-SND1 ; anti-SND1 ; anti-METTL3 ; anti-FTO ; anti-YTHDF1 ; anti-YTHDF2 ; anti-YTHDF3 ; anti-ORF57 ; anti-GAPDH ; anti-PARP , the rabbit polyclonal anti-RTA was a gift from Professor David Blackbourn  and used at 1:1000. The mouse monoclonal anti-ORF54 was a gift from Friedrich Grässer  and used at 1:1000. Rabbit anti-mfree DNA Removal Kit (Ambion) was used to remove any contaminating DNA from RNA samples. Isolated RNA was purified by standard ethanol precipitation and 100 μg of total RNA was fragmented with RNA fragmentation reagent (Ambion) according to the manufacturer’s protocol. Fragmented RNA was ethanol-precipitated and re-suspended in 10 μl of RNase-free water and stored at −80°C. 2 μg of total fragmented RNA was saved as input RNA for later use in cDNA library construction. For each m6A-immunoprecipitation (m6A-IP), 25 μl of slurry of Magna ChIP Protein A+G magnetic beads (Merck Millipore) were washed twice with IP/wash buffer . Beads were re-suspended in 100 μl of IP/wash buffer and coated with 5 μg of rabbit anti-m6A antibody (ABE572) (Merck Millipore) for 45 min at room temperature with rotation. Beads were then washed three times with IP/wash buffer and IPs were prepared by mixing the antibody-coated beads with 900 μl of IP/wash buffer, 35 μl of 0.5 M EDTA pH 8.0, 4 μl of RNasin Plus (Promega) and 100 μg of fragmented RNA. IPs were incubated overnight at 4°C with rotation. Beads were then washed six times with IP/wash buffer. IP samples were further incubated with 126 μl of IP/wash buffer, 15 μl of 10% SDS (v/v) and 9 μl of PCR-grade proteinase K (20 mg/mL) (Thermo Scientific) for 30 min at 55°C. After incubation, the supernatant containing the RNA was transferred to a new microcentrifuge tube and 250 μl of IP/wash buffer was added to each sample. RNA was purified with the use of phenol:chloroform:isoamyl alcohol (Sigma-Aldrich) and finally sodium acetate/ethanol-precipitated together with 1 µl of RNA-grade glycogen (Thermo Scientific) to allow visualisation of the RNA pellet. Several m6A-IPs (four to six) were pooled to provide enough sample for cDNA library construction and next-generation sequencing (NGS) as described below. 1.5 to 3 ng of RNA from input and m6A-IPs were used for NGS library production using NEBNext Ultra kit (NEB) according to the manufacturer’s protocol. Libraries for the first biological replicate were sequenced on a HiSeq 2500 platform (Illumina) with 101 bp paired-end lane. Libraries from the second biological replicate were sequenced on a HiSeq 3000 platform (Illumina) with 151 bp paired-end lane. For m6A-seq two independent biological replicates were prepared for each time point analysed .Total RNA from TREx BCBL1-Rta cells was extracted using TRIzol (Thermo Scientific) according to the supplier’s protocol. DNA-6 cells were used. Cells were fixed with 1% (v/v) formaldehyde  for 10 min at room temperature. Crosslinking was stopped by addition of glycine at a final concentration of 125 mM for 5 min. Cells were washed with PBS (Lonza) and re-suspended in 200 μl of ice-cold shearing buffer  supplemented with Complete, EDTA-free protease inhibitors (Roche) and 1 µl of murine RNase inhibitor (NEB). Samples were then sonicated with an EpiShear multi-sample sonicator (Active Motif) with pulses of 30 s of sonication followed by a 30 s rest for a total of 12 min at 30% amplitude. Polystyrene sonication tubes (Active Motif) were used to achieve efficient sonication. After sonication, samples were centrifuged at 12,000 x g for 10 min at 4°C and the supernatant was used immediately in RIPs. For each RIP, 25 μl of slurry of Magna ChIP Protein A+G magnetic beads (Merck Millipore) were coated with the antibody of interest as previously described for m6A-seq. RIPs were prepared by mixing the antibody-coated beads with 800 μl of IP/wash buffer , 35 μl of 0.5 M EDTA pH 8.0, 4 μl of murine RNase inhibitor (NEB) and 200 μl of lysate containing fragmented RNA. RIPs were incubated for 3 hr at 4°C with rotation. Beads were then washed six times as previously described  was used according to the manufacturer’s instructions. DNA-free DNA Removal Kit (Ambion) was used to remove any contaminating DNA from RIP RNA samples. Total RNA from input samples was isolated with the use of TRIzol (Thermo Scientific) according to the supplier’s protocol and treated with DNase I using the DNA-free DNA Removal Kit (Ambion). Input RNA was saved without applying sonication because we observed lower quality sequencing libraries when using sonicated input RNA than when using input RNA that was heat-fragmented. Saved input RNA was therefore heat-fragmented for 8 min at 94°C before proceeding with library construction. Due to the large size of RNA fragments observed after SND1 immunoprecipitation, isolated RNA from RIP samples was further fragmented for 7 min at 94°C before first strand synthesis. 100 to 200 ng of each input and RIP sample was used for NGS libraries which were made using the TruSeq Stranded Total RNA library production kit (Illumina) according to the manufacturer’s protocol. All samples were multiplexed and sequenced on two 151 bp paired-end lanes on a HiSeq 3000 instrument (Illumina). For RIP-seq, two independent biological replicates were prepared for each time point analysed . Additionally, for one biological replicate, two technical replicates were deep-sequenced for all RIP samples. A third biological replicate RIP sample at 0 hr was also deep-sequenced.TREx BCBL1-Rta cells remained unreactivated or were reactivated for 8 or 20 hr. At the desired time point a fraction of the cells was removed to serve as input RNA to control for RNA expression and stored in TRIzol (Thermo Scientific) at −80°C. For each RNA immunoprecipitation (RIP), 7 × 10escribed . RIP samNGS libraries were generated from two independent biological replicates using scramble and SND1 KD2 TREx BCBL1-Rta cells both during latency and after 24 hr of lytic reactivation. Two independent biological replicates from scramble and SND1 KD2 BCBL1 cells both during latency and after 24 hr of lytic reactivation were also deep-sequenced. Libraries were made using TruSeq Stranded Total RNA library preparation kit (Illumina) according to the manufacturer’s protocol. Libraries were sequenced on 151 bp paired-end lanes on a HiSeq 3000 instrument (Illumina).6A-IPs and RIPs were carried out as described above respectively, with the following modification. 1% input samples (10 μl from the 1 mL IP reaction) were removed before immunoprecipitation and stored at −80°C. Input samples were processed together with immunoprecipitated samples from the proteinase K treatment onwards. Purified input and immunoprecipitated RNA was resuspended in 10 μl of RNase-free water and reverse transcribed as described in the RT-qPCR analysis section. qPCR normalisation was performed similarly to chromatin immunoprecipitation (ChIP) coupled to detection by qPCR analysis using ΔΔCt method (relative quantification). An example for m6A-IPs follows. Each m6A-IP sample Ct value for each primer used was normalised to the corresponding input Ct value: ΔCt normalised m6A-IP = Ct m6A-IP – [Ct Input –log2 (Input dilution factor)]. Input dilution factor = (fraction of the input reaction saved)−1. As 1% input was saved, the dilution factor is 100 or 6.644 cycles (i.e. log2 of 100). Percentage of recovery from the initial reaction (% input) was then calculated for each primer as 100 x Amplification efficiency (AE) . m6A enrichment was finally calculated as the fold change between the % input for a region containing m6A peaks over the % input for a negative control region. SND1 enrichment was calculated as the fold change between the % input for a region of interest over the % input for a region of a non-target control RNA such as 18S rRNA. For HEK-293 cells, RIP-qPCR was performed the same way as described for TREx BCBL1-Rta cells with the exception that the sonication time was reduced to eight min and 50 μg of fragmented RNA were used per IP.m6A-seq, RIP-seq and RNA-seq data were generated at the next-generation sequencing facility of the University of Leeds, United Kingdom. Data were extracted and de-multiplexed using bcl2fastq Conversion software (Ilumina), which exports a matched pair (read 1 and read 2) of compressed fastq files per sample. Quality control of all sequence data, including publicly available datasets, was carried out using FastQC software  file containing genomic feature coordinates  was assembled manually using data from the KSHV 2.0 annotation dataset . The hum6A peaks were called using m6aViewer software version 1.6  was required in both biological replicates. Peaks positions were considered overlapping between replicates if the calls were within 100 nucleotides between corresponding positions. To determine the number of SND1 RNA targets identified by transcript-wide analysis that are m6A-modified, a more stringent m6A peak calling cut-off was used to compliment a more stringent SND1 RIP cut-off, with a minimum of 100 read paired at the tallest point in the m6A peak and a 2-fold enrichment of m6A-IP reads over input reads using a FDR < 1%. For target overlap between heterologously expressed YTH readers and SND1, high-confidence SND1-bound genes  were defined at a cut-off of FDR < 1% and a minimum of 2-fold RIP enrichment over input, while m6A peaks were used as before .msion 1.6  with defPeak motif discovery was performed by exporting the flanking 100 base of RNA sequence surrounding peaks in KSHV methylome to a FASTA file using m6aViewer software. Sequences containing repetitive viral sequence were removed. The remaining data was then used for enriched sequence motif detection using the MEME software , with scRsubread  and input (control) sample data that mapped to each RefSeq and KSHV transcripts. R package Rsubread  was usedRsubread  was usedDifferential expression analyses were performed in R using DESeq2 package. Functional enrichment analyses were performed using the clusterProfiler R package , using tAlternative splicing events were detected using Comprehensive AS Hunting (CASH) . In addi6A-seq data, four replicates . Two replicates of each eCLIP experiment were obtained from the ENCODE database as narrowPeak bed files: HepG2 FXR2 ; HepG2 SND1 ; IGF2BP1 ; IGF2BP3 ; TIAL1 .Data downloaded from public repositories were aligned as above, except without the addition of KSHV sequence to the reference genome. The following data were obtained from NCBI’s GEO database as raw FASTQ files: HeLa cell line YTHDF1 PAR-CLIP data, two replicates ; HeLa cell line YTHDF2 PAR-CLIP data, three replicates ; HeLa cell line YTHDF3 PAR-CLIP data, three replicates . HepG2 cell line m6A peaks, HepG2 RIP-seq data was processed with m6aViewer 1.6 software, using default settings, and overlapping peaks, containing a ≥ 1.5 fold increase of m6A-IP reads over input with a FDR < 5% across both replicates were kept. De novo motif analysis of HepG2 m6A-seq and eCLIP sites was performed by feeding peaks into the findMotifsGenome.pl function within the HOMER suite (version 4.9.1) using parameters ‘-rna -len 5’. For SND1 motif discovery over m6A exonic regions, exons containing m6A-seq peaks were identified and these whole exon sequences were screened for SND1 peaks. The SND1 peaks within this set of m6A-modified exons were used for motif discovery.Binding sites from eCLIP experiments were downloaded from ENCODE as narrowPeak bed files. Clusters from replicate one which directly overlapped clusters in replicate two were extracted and kept for downstream analyses. To look at overlaps between eCLIP sites and m6A peaks called in both biological replicates is supplied as Excel data sheet for all mAll deep-sequencing data discussed in this publication have been deposited in NCBI’s GEO Database, GEO accession number GSE119026. All identified peptides/PSMs for each RNA bait can be found in 6A peaks detected in ORF37 and ORF50 transcripts. For each oligo, one oligo was m6A-modified at the GGACU motif while the control bait remained unmodified. For the ORF37 bait the sequences used were: 6A-CUUCUUCUAUAGCAUUUCCA-3´5´-biotin-CGGAAAGCUGGCACUGAAGG-m and 5´-biotin-CGGAAAGCUGGCACUGAAGGACUUCUUCUAUAGCAUUUCCA-3´. Baits spanning an m6A consensus in the first m6A peak identified in ORF50 (ORF50-1) were: 6A-CUGUUGCCGGCUUCCAUGGUA-3´5´-biotin-UUUGCCAAUCCUGGAGCCAGG-m and 5´-biotin-UUUGCCAAUCCUGGAGCCAGGACUGUUGCCGGCUUCCAUGGUA-3´. The sequences for baits spanning an m6A consensus in the fourth m6A peak identified in ORF50 (ORF50-4) were: 6A-CUGUACCAGCUGGACACGCCA-3´5´-biotin-GUUGUCCAGUAUUCUGCAAGG-m and 5´-biotin-GUUGUCCAGUAUUCUGCAAGGACUGUACCAGCUGGACACGCCA-3´. Cropped versions of ORF50-1 (cORF50-1) were: 6A-CUGUUGCCGGCUUC-3´5´-biotin-GGAGCCAGG-m and 5´-biotin-GGAGCCAGGACUGUUGCCGGCUUC-3´. Stable versions of ORF50-1 (sORF50-1) were: 6A-CUGUUGCCGGCUUCCGGGGCC-3´5´-biotin-UUGGCCCAUCCCGGAGCCAGG-m and 5´-biotin-UUGGCCCAUCCCGGAGCCAGGACUGUUGCCGGCUUCCGGGGCC-3´.The following biotin-labelled RNA oligonucleotides were centred on the closest GGACU motif to the mAll baits were purchased from Integrated DNA Technologies (IDT).g to pellet cell debris and the supernatant was kept. 1000 µg of protein per pull-down in an approximate volume of 200 µl were supplemented with 40 units of RNasin Plus (Promega) and 50 µg of yeast tRNA (Sigma-Aldrich) and pre-cleared with 30 µl (resin volume) of streptavidin-conjugated agarose beads (Merck Millipore) for 3 hr at 4°C with rotation. The final volume of the binding reaction was topped to 1 mL with binding buffer  NP-40, 0.5 mM DTT, 10 mM Tris HCl, pH 7.4). While pre-clearing, 30 µl (resin volume) of streptavidin-conjugated agarose beads per pull-down were blocked with 1% (w/v) BSA (Sigma-Aldrich) in PBS (Lonza) and 50 µg of yeast tRNA (Sigma-Aldrich). Pre-cleared lysates were then mixed with the blocked beads and 4 µg of each biotinylated RNA oligo were added per pull-down. Input samples were immediately collected and stored at −80°C until further use. RNA baits were incubated for 2 hr at 4°C with rotation followed by five washes with binding buffer. Proteins were released from the beads by heating at 95°C for 5 min in 30 µl of 2 X Laemmli sample buffer. Input and pull-down samples were used for either Western blotting or LC-MS/MS analysis.TREx BCBL1-Rta cells were reactivated with doxycycline for 24 hr followed by lysis of the cells for 25 min in lysis buffer  containing complete protease inhibitor cocktail (Roche) and phosphatase inhibitor cocktail 2 (Sigma-Aldrich). Lysates were centrifuged at 4°C for 10 min at 12,000 x −1. Solvent A was 0.1% formic acid and Solvent B was aqueous 80% acetonitrile in 0.1% formic acid. Peptides were ionised by nano-electrospray ionisation at 2.1 kV using a stainless-steel emitter with an internal diameter of 30 μm (Thermo Scientific) and a capillary temperature of 250°C. Tandem mass spectra were acquired using an LTQ- Orbitrap Velos mass spectrometer controlled by Xcalibur 2.1 software (Thermo Scientific) and operated in data-dependent acquisition mode. The Orbitrap was set to analyse the survey scans at 60,000 resolution (at m/z 400) in the mass range m/z 300 to 2000 and the top twenty multiply charged ions in each duty cycle selected for MS/MS in the LTQ linear ion trap. Charge state filtering, where unassigned precursor ions were not selected for fragmentation, and dynamic exclusion  were used. Fragmentation conditions in the LTQ were as follows: normalised collision energy, 40%; activation q, 0.25; activation time 10 ms; and minimum ion selection intensity, 500 counts. The raw data files were processed and quantified using Proteome Discoverer software v1.4 (Thermo Scientific) and searched against the UniProt Human database  plus KSHV protein sequences using the SEQUEST algorithm. Peptide precursor mass tolerance was set at 10ppm, and MS/MS tolerance was set at 0.8 Da. Search criteria included carbamidomethylation of cysteine (+57.0214) as a fixed modification and oxidation of methionine (+15.9949) as a variable modification. Searches were performed with full tryptic digestion and a maximum of 1 missed cleavage was allowed. The reverse database search option was enabled and all peptide data was filtered to satisfy false discovery rate (FDR) of 5%.LC-MS/MS was performed at the proteomics facility of the University of Bristol, United Kingdom. Samples were separated using SDS-PAGE until the dye front had migrated approximately one centimetre into the separating gel. Each gel lane was then excised and subjected to in-gel tryptic digestion using a DigestPro automated digestion unit (Intavis Ltd.). The resulting peptides were fractionated using an Ultimate 3000 nano-LC system in line with an LTQ-Orbitrap Velos mass spectrometer (Thermo Scientific). In brief, peptides in 1% (v/v) formic acid were injected onto an Acclaim PepMap C18 nano-trap column (Thermo Scientific). After washing with 0.5% (v/v) acetonitrile 0.1% (v/v) formic acid, peptides were resolved on a 250 mm ×75 μm Acclaim PepMap C18 reverse phase analytical column (Thermo Scientific) over a 150 min organic gradient, using seven gradient segments  with a flow rate of 300 nl min6A readers, all proteins identified for a given methylated and control bait were sorted by total number of PSM’s for the m6A bait. Proteins were then classified as enriched in methylated baits if the number of PSM’s assigned to the protein was at least double in the methylated bait compared with the control bait. All identified unique peptides and PSMs for all RNA baits can be accessed on Comparative reports were produced between methylated and control RNA baits, the data were filtered at 5% FDR and had also removed any proteins that were only matched by a single peptide. In addition, the data was sorted based on the number total number of peptide spectrum matches (PSM’s) identified, such that those proteins at the top of the list were only identified in the methylated samples (e.g. no peptides matching that protein were detected in the non-methylated sample). Comparative reports for ORF50-1, ORF50-4 and ORF37 baits are supplied as Gene-annotation enrichment analysis were performed with The Database for Annotation, Visualisation and Integrated Discovery (DAVID) v6.8.RNA secondary structure prediction of baits was carried out using UNAfold web server .E. coli strain BL21-DE3 (Thermo Scientific). GST-recombinant proteins contained the FXR1 plant agenet domain (residues 2–132) which includes Agenet1 and Agenet2 in tandem, the PSIP1 PWWP domain (residues 3–100) or the CBX3 chromodomain (residues 29–86). SND1-C-terminus comprises of residues 548–910. Recombinant GST plasmid was commercially available . Recombinant proteins were produced by lowering the temperature to 18°C and inducing the culture with 0.2 mM IPTG for 20 hr. Bacteria pellet from 1 L culture was re-suspended with 30 mL of lysis buffer  and incubated on ice for 45 min. The lysate was then sonicated using a MSE Soniprep 150 sonicator (12 cycles of 20 s pulse-on and 20 s pulse-off). The lysate was centrifuged at 12,000 x g for 15 min at 4°C. The supernatant was incubated with glutathione agarose beads  for 2 hr at 4°C. The resin was washed twice with lysis buffer and once with 50 mM Tris HCl, pH 7.4. Protein was eluted from the beads using 50 mM Tris HCl with 20 mM reduced glutathione (Sigma-Aldrich) that had been adjusted to a final pH of 7.4.All recombinant proteins were gene synthesised (NovoPro Bioscience), cloned into pGEX-4T-1 vector (NovoPro Bioscience) and expressed in 2 and 1 mM DTT) supplemented with 5% (v/v) glycerol. For EMSAs in the presence of herring sperm DNA (Promega), DNA was mixed and incubated in the binding reaction. Note that this sperm DNA is provided after phenol-chloroform extraction, ethanol precipitation and sonication, which produces single-stranded fragments. Binding reactions were incubated with increasing amounts of recombinant protein which was freshly isolated (e.g. no more than two days after purification from bacteria and stored at 4°C). The same biotinylated baits used in the RNA affinity experiments were used for EMSAs. All baits were used at 3.75 nM oligo final concentration, except for cORF50-1 which was used at 16 nM. Binding reactions were incubated for 30 min at room temperature. 10 µl reactions were mixed with 2 µl of 5 X loading dye and 10 µl were loaded onto a 6% (w/v) non-denaturing polyacrylamide gel in 1 X TAE buffer . Gels were ran for 45 min at 100V and transferred to a nylon membrane  via wet transfer with 1 X TAE as transfer buffer for 45 min at 400 mA. RNA was crosslinked to the membrane at 120mJ/cm2 using a CL-1000 ultraviolet crosslinker (UVP). The rest of the protocol was performed as described in the kit manual. Biotinylated baits were visualised after exposure to an ultra-sensitive ECL  (Thermo Scientific) and exposed to Amersham hyperfilm ECL .EMSAs were carried out using LightShift chemiluminescent RNA EMSA Kit (Thermo Scientific) according to the manufacturer’s instructions. Binding reactions consisted of kit supplied 1 X binding buffer  according to the manufacturer’s instructions. All plasmids containing the desired mutation were confirmed by DNA sequencing (Eurofins Genomics). To mutate the GGACT motif present in ORF50-1 bait the following primers were used: TCCTGGAGCCAGGATTGTTGCCGG (forward), CCGGCAACAATCCTGGCTCCAGGA (reverse). To mutate the GGACT motif present in ORF50-4 bait these primers were used: TCCAGTATTCTGCAAGGATTGTACCAGCTGGACAC (forward), GTGTCCAGCTGGTACAATCCTTGCAGAATACTGGA (reverse).A FLAG tag g at room temperature, in the presence of 8 μg/mL of polybrene (Merck Millipore). Virus supernatant was removed after 7 hr post-spin inoculation and cells were maintained in fresh growth medium for 48 hr before undergoing 3 µg/mL puromycin (Sigma-Aldrich) selection. Stable cell lines were generated after 8 days post-selection when cell stocks were frozen. The following shRNAs were used in the experiments: SND1 KD1 (TRCN0000245143), SND1 KD2 (TRCN0000049656), METTL3 KD1 (TRCN0000289742), METTL3 KD2 (TRCN0000289812), FTO KD1 (TRCN0000246247), FTO KD2 (TRCN0000246250), YTHDF1 KD1 (TRCN0000062771), YTHDF1 KD2 (TRCN0000286871), YTHDF2 KD1 (TRCN0000254411), YTHDF2 KD2 (TRCN0000265510), YTHDF3 KD1 (TRCN0000365164) and YTHDF3 KD2 (TRCN0000167772).Lentiviruses were generated by transfection of HEK-293T cells seeded in 12-well plates using a three-plasmid system. Per 12-well, 4 µl of lipofectamine 2000 (Thermo Scientific) were used together with 1 µg of pLKO.1 plasmid expressing shRNA against the protein of interest, 0.65 µg of pVSV.G, and 0.65 µg psPAX2. pVSV.G and psPAX2 were a gift from Dr. Edwin Chen at the University of Leeds. 8 hr post-transfection, medium was changed into 1.5 mL of DMEM supplemented with 10% (v/v) FCS. Two days post-transfection viral supernatants were harvested, filtered through a 0.45 µm filter (Merck Millipore) and immediately used for transductions of TREx BCBL1-Rta or BCBL1 cells. One mL of each 12-well plate was used to infect 500,000 cells by spin inoculation for 60 min at 800 x All shRNA plasmids were purchased from either Sigma-Aldrich or Dharmacon. Scramble shRNA was a gift from Professor David Sabatini (Addgene plasmid # 1864).Western blots were performed as previously described . In briefree DNA Removal Kit (Ambion) was used to remove any contaminating DNA from RNA samples. Reverse transcription was performed with ProtoScript II (NEB), murine RNase inhibitor (NEB), random hexamers (Bioline) and 1 μg of total RNA. Quantitative PCR (qPCR) reactions (20 μl) included 1 X SensiMix SYBR green master mix (Bioline), 0.5 μM of each primer and 5 μl template cDNA. Cycling was performed in a RotorGene Q 2plex machine (Qiagen). The cycling programme was a 10 min initial preincubation at 95°C, followed by 40 cycles of 95°C for 15 s, 60°C for 30 s and 72°C for 20 s. After qPCR, a melting curve analysis was performed between 65°C and 95°C (with 0.2°C increments) to confirm amplification of a single product. To assess primer amplification efficiency (AE), for each gene of interest a standard curve was constructed using a pool of cDNA derived from unreactivated and reactivated cells. At least four different dilutions of pool cDNA were quantified to generate a standard curve. The slope of the standard curve was used to calculate the AE of the primers using the formula: AE = (10−1/slope). For gene expression analysis all genes of interest were normalised against the housekeeping gene GAPDH (ΔCT). A summary of all the primers used in this study is provided in qRT-PCR was performed as previously described . In briefree DNA Removal Kit (Ambion) was used to remove any contaminating DNA from RNA samples and qRT-PCR was carried out as described above. The gene of interest was normalised against 18S rRNA, as this RNA, but not GAPDH, was stable after 6 hr of actinomycin D treatment.TREx BCBL1-Rta cells were treated with 2.5 μg/ml of actinomycin D (Thermo Scientific) and samples were collected at the desired time points. Total RNA was extracted using TRIzol (Thermo Scientific) according to the supplier’s protocol. DNA-6 BCBL1 cells were used per immunoprecipitation and digested with 15 units of micrococcal nuclease (MNase) per 100 μl of MNase Digestion buffer in a 37°C water bath for 15 min. Nuclei were lysed for 30 min with lysis buffer two in addition to vortexing for 30 s every 5 min. Immunoprecipitations were carried out using EZ-ChIP kit (Millipore) according to the supplier’s instructions. Immunoprecipitations were done overnight at 4°C and contained 50 μl of digested chromatin (2 × 106 cells), 450 μl of ChIP dilution buffer and 2 µg of the antibody of interest. Primers for the KSHV promoter regions of ORF50, Ori-Lyt, PAN RNA, ORF59 and K12 have been previously reported  (Promega), according to the manufacturer's manual. 10,000 HEK-293 cells were seeded in quadruplicate in a flat 96-well culture plate (Corning). After 26 hr inhibitor exposure, 20 µl of CellTiter 96 AQueous One Solution Reagent was added and cells were incubated for 1 hr in a humidified incubator in 5% CO2 at 37°C. Absorbance was measured at 490 nm using a PowerWave XS2 (BioTek) plate reader.Determination of the cellular metabolic activity was performed using a non-radioactive CellTiter 96 AQ In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.6A RNA reader essential for KSHV replication" for consideration by eLife. Your article has been reviewed by Päivi Ojala as the Senior Editor, a Reviewing Editor, and three reviewers. The following individual involved in review of your submission has agreed to reveal their identity: Nara Lee (Reviewer #3).Thank you for submitting your article "The Tudor SND1 protein is a mThe reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:This is an interesting investigation into the landscape of m6A modification of KSHV transcripts and the readers of the modified RNAs. Seven members of the "Royal family" of TUDOR domain containing proteins were identified as new putative m6A readers, including SND1. SND1 associates with the KSHV ORF50 transcript in a manner dependent on the m6A modification and SND1 depletion leads to a global impairment of KSHV gene expression. Identification of members of the 'Royal family' as new m6A-readers greatly increases their epigenetic functions beyond protein methylation.Essential revisions:1) Reviewers 1 and 3 suggested the authors to map the SND1 binding sites by CLIP-seq rather than RNA-IP.2) Reviewer 2 suggested the authors to perform a ChIP-seq analysis of the viral genome to exclude the possibility that SND1 binds and activates the ORF50 promoter.The full reviews are listed below, in the hope that the comments can help to improve the quality of the paper.Reviewer #1:More evidence is needed to improve the quality of the manuscript.Essential revisions:1) I have several concerns for the gel shift assay and the data interpretation:(A) The components of the 1x binding buffer of the gel shift assay (subsection “Electrophoretic mobility shift assays (EMSAs)”) looked different from that is stated in the manual of the LightShift Chemiluminescent EMSA Kit from ThermoScientific in terms of salt concentrations. This could affect the secondary structure of the probes used in the assay. Also, it is not clear whether the authors used non-specific Poly (dI•dC) as instructed by the kit. Considering the function of SND1 as a transcriptional factor, does the presence of non-specific DNA affect the RNA binding of SND1 in gel shift assay?(B) In Figure 2, Figure 3—figure supplement 6 and Figure 3—figure supplement 7, the biotin signals from the probe were always overwhelming compared to the shifted probes, making it hard to calculate the ratio of free probe to bound probe and estimate the binding affinity (Kd). Why is this the case? Any way to optimize the imaging condition? Maybe fluorescent dye labelled probe could help.2) The authors saved the input sample of the RIP-seq saved before crosslinking the cells. Normally people process the input and RIP samples in parallel until before IP. The authors claimed the reason is that the sonicated input gave lower quality sequencing libraries maybe due to overshearing (subsection “RIP-seq”). I still believe that the input sample should be saved after sonication, otherwise it is hard to estimate artifacts introduced by the sonication step. And "overshearing" itself may already be a concern for high-quality RIP assays. Instead of "formaldehyde-crosslinked RIP-seq", the authors may consider performing "CLIP-seq" as a validation which also gives high-resolution binding sites of protein on RNA based on crosslinking-induced mutations in cDNA libraries.3) "SND1 binds symmetrically demethylated arginines (sDMA)" via its Tudor domain, as described on subsection “RNA affinity identifies putative m6A readers which belong to the Tudor domain ‘Royal family’”. I am wondering if the authors have tested affinity of the protein towards N6, N6-dimethyladenosine. It may worth checking if known ribosomal RNA N6, N6-dimethyladenosine sites were enriched in their SND1 RIP-seq data.Reviewer #2:The authors cannot demonstrate the final consequences of SND1 binding to the ORF50 RNA. Here the manuscript reveals conceptual deficits. Also, the work of another laboratory with similar findings is not discussed sufficiently.Essential revisions:1) Abstract: The authors provide interesting structural data, but the claim that all identified proteins recognize m6A in a structural-dependent manner is an overstatement . To verify this more mutants are necessary. The critical claim of the manuscript is that SND1 stabilizes the ORF50 RNA. However, the single data figure given in Figure 6 does not clarify this point (see below). The following sentence mentions a global impairment of KSHV gene expression, which is no surprise if the master switch of reactivation is blocked. This claim has to be toned down to "inhibits KSHV early gene expression".2) Subsection “Royal domains bind m6A-modified RNA hairpins in a RNA secondary structure-dependent manner”, Figure 2, Figure 3—figure supplement 4 and Figure 3—figure supplement 4: First, these data nicely show that SND1 binds the ORF50-1 sequence. However, the interpretation of the cORF50-1 shortened stem and the sORF50-1 mutant is not straightforward. To state "in structural-dependent manner" the authors need to increase the stability of the stem by erasing the bulges only. Also, the cORF50-1 sequence may be just too short to form the correct stem. The authors should extend the stem of cORF50-1 mutant by unrelated bases to provide more insights on the structural features needed by SND1.3) Subsection “SND1 is a m6A reader in KSHV-infected cells”, Figure 3: The authors should compare their obtained m6A frequency in the high-confidence SND1 targets to ratios available for other m6A reader proteins such as the YTHDF family members. Is there a similar correlation or do these readers contains even more m6A modified target sequences in their high-confidence interval? These data should be available from public resources.4) Subsection “SND1 is a m6A reader in KSHV-infected cells”: how SND1 recognizes its RNA target sequences is pure speculation and should be removed from the result section. In addition, the motif provided in Figure 4—figure supplement 3D looks like 3' splice site and may argue for SND1's role in splicing.ORF50 RNA and is essential for KSHV replication” and Figure 5: The authors provide an interesting discrepancy between the TREx BCBL-1 cells and naturally infected BCBL-1 cells. Their argument reads as follows: in naturally, unmodified BCBL-1 cells, the ORF50 RNA is decreased during SND1 knockdown. This effect is masked in the TREx BCBL-1 cells since they contain a chromosomally integrated doxycycline-inducible ORF50 gene. However, the authors fail short in the interpretation of their results. An alternative explanation would be that the DNA-binding and promoter-modifying capacity of SND1 comes into play. The major difference between the two ORF50 versions is the nature of the promoter driving their expression. The dox version harbors 6 to 7 tetracyclin-operators and a minimal CMV promoter. In the virus the ORF50 promoter is the sensor of the cellular status and is highly regulated to induced lytic reactivation only under certain conditions. To clarify the role of SND1 the authors need to perform a ChIP-seq analysis of the viral genome to exclude the possibility that SND1 binds and activates the ORF50 promoter. Thus, a lack of SND1 may cause an inhibition of ORF50 transcription and may explain all downstream effects. If this is the case the derived claims such as "SND1 stabilizes the ORF50 transcript" may not be corrected. In addition, the impairment of viral lytic gene expression could also be explained in a similar manner. The authors themselves mention this possibility indirectly in the Discussion section stating that SND1 functions as co-activator of the EBV protein EBNA-2.5) Subsection “SND1 stabilises 6) Figure 5D and G: The authors should include lytic markers such as K8.1 and ORF59.7) Figure 6B: Here, the authors need to provide the raw data. In addition, to an accelerated decay the SND1 knockdown may already reduce the amount ORF50 transcript. See also argument provided above. Also, the authors should distinguish between the decay of spliced vs. unspliced ORF50 RNA if possible.8) Supplementary Figure S11: I agree with the authors that a METTL3 knockdown might be difficult in BCBL-1 cells. Even though the increase in ORF50 RNA is not significant I am wondering if the authors here observe the same effect as the Glaunsinger laboratory . These authors were able to reduce the amount of METTL3 in BCBL-1 cells sufficiently and observed an increase in ORF50 protein, but not RNA. These findings are not discussed in the current manuscript! If METTL3 knockdown is too difficult, the authors should use the DAA treatment presented in Figure 6C,D to assess ORF50 RNA levels in the absence of m6A modifications.Reviewer #3:The most exciting part of this manuscript is the identification of novel m6A reader proteins, which were originally thought to bind methylated proteins only. While I am convinced of the specificity of SND1 towards m6A, my main criticism is directed towards the authors' choice of experimental approach to map SND1 binding sites on RNA. As detailed below, RNA-IP in this particular instance appears inappropriate to locate SND1 binding sites.Essential revisions:While different experimental approaches can be applied to identify RNA-protein interactions, such as RNA-IP or CLIP-based techniques, in the case of SND1 the use of RNA-IP appears inappropriate. As mentioned in subsection “SND1 is a m6A reader in KSHV-infected cells” and shown in Figure 4—figure supplement 1, the vast majority of RNA fragments was sheared to <200 nt (not "bp"), yet the enriched fragments were of considerably greater size, which the authors suggest may be due to the anti-SND1 antibody having higher affinity for longer fragments. This statement is not very plausible. On the other hand, given the affinity of SND1 for methylated arginines, it is possible that chromatin-associated nascent RNAs were indirectly precipitated through SND1 binding to other chromatin-associated factors . These complexed RNAs would greatly resist shearing by sonication as compared to free RNAs. According to Materials and methods section, no DNase treatment was included prior to IP and hence indirect RNA IP cannot be excluded. While bona fide SND1 targets may certainly be present within the deep sequenced RNAs, this cohort may be largely polluted by non-specific transcripts.Rather than RNA-IP, eCLIP or iCLIP would have the advantage that the crosslinked sites can be identified unambiguously, which would be particularly useful in overlapping m6A sites on RNA with SND1 binding sites. Essential revisions:1) Reviewers 1 and 3 suggested the authors to map the SND1 binding sites by CLIP-seq rather than RNA-IP.6A reader proteins and SND1. Although these datasets are from different cell lines, Figure 6A, shows that there is an extensive overlap of transcripts which contain m6A sites, showing that this analysis approach is valid. Therefore, comparing the overlap between transcripts which are both m6A-modified and bound by SND1 from RIP-seq and eCLIP datasets, we observe 1,166 transcripts bound by SND1 of which 88% contained m6A sites . In addition, using this eCLIP dataset it allowed the direct comparison of SND1 binding to other established m6A readers using existing eCLIP datasets for these respective proteins . Furthermore, we found comparable binding site overlap for SND1 and established readers with m6A peaks, whereas a control protein (TIAL1) showed reduced overlap between m6A peaks and its binding sites. Importantly, we also utilised HOMER to identify motifs bound by SND1 using the ENCODE eCLIP datasets, which excitingly revealed that SND1 has a high scoring binding motif which matches the core of the m6A consensus motif . We thank the reviewers for this suggestion, which has led to this important new discovery. These results of all the new eCLIP analysis has been incorporated as Figure 6, subsection “SND1 is an m6A reader in KSHV-infected cells” and in the Materials and methods section.To confirm our SND1 RIP analysis, we have analysed SND1 binding profiles using existing eCLIP datasets from the ENCODE consortium. This has allowed us to compare the binding profiles for established m2) Reviewer 2 suggested the authors to perform a ChIP-seq analysis of the viral genome to exclude the possibility that SND1 binds and activates the ORF50 promoter.ORF50 promoter in BCBL1 cells. Therefore, to conclusively address this query we have performed ChIP coupled to qPCR detection with the control antibody RNA polymerase II (RNAPII), the rabbit SND1 antibody and a non-specific isotype antibody. We tested the occupancy of these proteins at the viral ORF50 promoter and several other viral promoters in BCBL1 cells. Primers for the cellular GAPDH promoter were used as positive control for RNAPII antibody. Whilst RNAPII was present in 4 viral out of 5 viral promoters tested at different levels of occupancy, there was no specific enrichment for either SND1 or the negative control antibody when using 2 µg of antibody per ChIP (the same concentration used for RIP experiments). Note that the rabbit SND1 antibody is a ChIP-grade antibody as demonstrated by our formaldehyde-fixed RIP-seq experiments, in which cells are subjected to identical formaldehyde fixation conditions, and was able to efficiently immunoprecipitate SND1 protein. The same lot number (00020506) of SND1 antibody  was used for western blot, RIP and ChIP experiments. Consequently, we conclude that SND1 does not bind the ORF50 promoter. These data has been incorporated as Figure 7 —figure supplement 3 and discussed in the manuscript .We agree with the reviewer that this a critical check, however we consider a genomic-wide analysis unnecessary to address this concern, as our RNA-seq analysis on the SND1 knockdown cell line showed that KSHV replication is basically unchanged in TREx BCBL1-Rta cells in the absence of SND1, with no significant downregulation of any of the KSHV ORFs  suggesting that SND1 may not be necessary to promote transcription. Thus, the key question is addressing whether SND1 may bind the The full reviews are listed below, in the hope that the comments can help to improve the quality of the paper.Reviewer #1:More evidence is needed to improve the quality of the manuscript.Essential revisions:1) I have several concerns for the gel shift assay and the data interpretation:(A) The components of the 1x binding buffer of the gel shift assay (subsection “Electrophoretic mobility shift assays (EMSAs)”) looked different from that is stated in the manual of the LightShift Chemiluminescent EMSA Kit from ThermoScientific in terms of salt concentrations. This could affect the secondary structure of the probes used in the assay. Also, it is not clear whether the authors used non-specific Poly (dI•dC) as instructed by the kit.Thermo Scientific offers two different kits to perform EMSA. One kit is optimised to assess DNA/protein interactions LightShift Chemiluminescent EMSA Kit (cat no: 20148) in which the binding buffer composition is: 10X Binding Buffer: 100mM Tris, 500mM KCl, 10mM DTT; pH 7.5.The kit we used in this manuscript is optimised to assess RNA/protein interactions, the LightShift Chemiluminescent RNA EMSA Kit (cat no: 20158), in which the binding buffer composition is 10X REMSA Binding Buffer: 100mM HEPES (7.3), 200mM KCl, 10mM MgCl2, 10mM DTT.The use of Poly (dI•dC) is instructed for the DNA/protein interactions kit. We did not alter the RNA EMSA kit binding buffer composition as this has been optimised for RNA/protein interactions. In addition when we isolated SND1-C-terminus protein we noticed that this protein was particularly sensitive to salt concentrations, as when we dialysed the protein after isolation into 10mM HEPES (7.3), 200 mM KCl, 1mM MgCl2, 1 mM DTT and 0.1% triton X (v/v), SND1 precipitated out of solution.Considering the function of SND1 as a transcriptional factor, does the presence of non-specific DNA affect the RNA binding of SND1 in gel shift assay?6A-modified RNA bait (ORF50-1) was abolished, as shown by the disappearance of the shifted bait and full detection of the free ORF50-1 bait. This result is not surprising as SND1 is a known transcription factor and this result suggests that excess DNA may sequester SND1 away from RNA. This experiment has been incorporated to the manuscript as figure 3 —figure supplement 7C and discussed in the manuscript .This is an interesting question, therefore, we have repeated the EMSAs in the absence or presence of herring sperm DNA. In the presence of this non-specific DNA the interaction between SND1 and the m(B) In Figure 2, Figure 3—figure supplement 6 and Figure 3—figure supplement 7, the biotin signals from the probe were always overwhelming compared to the shifted probes, making it hard to calculate the ratio of free probe to bound probe and estimate the binding affinity (Kd). Why is this the case? Any way to optimize the imaging condition? Maybe fluorescent dye labelled probe could help.The overwhelming signal for the free probe is due to the fact that very small amount of RNA was shifted in comparison with the free probe . A shorter . Similarly, a weak shift has also been previously observed in EMSAs when using FMRP protein  and IGF2BP proteins .”This sentence has been included in the main manuscript : “Note that the membranes had to be overexposed to obtain a good shift signal due to the small amount of shifted RNA in comparison with the free bait, consistent with the modest enrichment of SND1 in m2) The authors saved the input sample of the RIP-seq saved before crosslinking the cells. Normally people process the input and RIP samples in parallel until before IP. The authors claimed the reason is that the sonicated input gave lower quality sequencing libraries maybe due to overshearing (subsection “RIP-seq”). I still believe that the input sample should be saved after sonication, otherwise it is hard to estimate artifacts introduced by the sonication step. And "overshearing" itself may already be a concern for high-quality RIP assays. Instead of "formaldehyde-crosslinked RIP-seq", the authors may consider performing "CLIP-seq" as a validation which also gives high-resolution binding sites of protein on RNA based on crosslinking-induced mutations in cDNA libraries.6A-seq libraries.See response in essential revisions 1 above. We have removed the sentence “The lower quality libraries may have been a result of overshearing” as we have no evidence to demonstrate this. We do have evidence that after sonication the input RNA was of equivalent size  to the one generated for m3) "SND1 binds symmetrically demethylated arginines (sDMA)" via its Tudor domain, as described on subsection “RNA affinity identifies putative m6A readers which belong to the Tudor domain ‘Royal family’”. I am wondering if the authors have tested affinity of the protein towards N6, N6-dimethyladenosine. It may worth checking if known ribosomal RNA N6, N6-dimethyladenosine sites were enriched in their SND1 RIP-seq data.6A. Further studies characterising these methyl-modifications and their corresponding readers will help explore this possibility.” Discussion section.Our SND1 RIP-seq samples were ribosomal depleted before cDNA library construction, thus we are unable to check this. However, it is possible that SND1 may have the ability to bind other RNA-modifications, this has been included in the discussion as follows: “Finally, it is worth pointing out that it exists the possibility that SND1 may be able to read other RNA methyl-modifications in addition to mReviewer #2:The authors cannot demonstrate the final consequences of SND1 binding to the ORF50 RNA. Here the manuscript reveals conceptual deficits. Also, the work of another laboratory with similar findings is not discussed sufficiently.Essential revisions:1) Abstract: The authors provide interesting structural data, but the claim that all identified proteins recognize m6A in a structural-dependent manner is an overstatement . To verify this more mutants are necessary. The critical claim of the manuscript is that SND1 stabilizes the ORF50 RNA. However, the single data figure given in Figure 6 does not clarify this point (see below). The following sentence mentions a global impairment of KSHV gene expression, which is no surprise if the master switch of reactivation is blocked. This claim has to be toned down to "inhibits KSHV early gene expression".The Abstract has been modified to address these comments.2) Subsection “Royal domains bind m6A-modified RNA hairpins in a RNA secondary structure-dependent manner”, Figure 2, Figure 3—figure supplement 4 and Figure 3—figure supplement 4: First, these data nicely show that SND1 binds the ORF50-1 sequence. However, the interpretation of the cORF50-1 shortened stem and the sORF50-1 mutant is not straightforward. To state "in structural-dependent manner" the authors need to increase the stability of the stem by erasing the bulges only. Also, the cORF50-1 sequence may be just too short to form the correct stem. The authors should extend the stem of cORF50-1 mutant by unrelated bases to provide more insights on the structural features needed by SND1.6A. The use of SHAPE or NMR would help addressing this question, but this is beyond the scope of this manuscript. Consequently, we have changed the abstract and the sections in which we stated the royal domains bound “in structural-dependent manner”. For example in the Introduction.We agree with the reviewer that to state “in a structural-dependent manner” may be an overstatement. However, the proposed experiments do not clarify either how the structure differs between the different RNA baits in the presence or absence of m3) Subsection “SND1 is a m6A reader in KSHV-infected cells”, Figure 3: The authors should compare their obtained m6A frequency in the high-confidence SND1 targets to ratios available for other m6A reader proteins such as the YTHDF family members. Is there a similar correlation or do these readers contains even more m6A modified target sequences in their high-confidence interval? These data should be available from public resources.6A methylated. We have now compared PAR-CLIP and m6A-seq datasets and shown that ~65% of total target RNAs bound by YTH readers are m6A methylated. See figure 4 —figure supplement 2 and subsection “SND1 is an m6A reader in KSHV-infected cells”.This information has been added. As stated in the original manuscript, we observe a 50% of SND1 high confidence targets are m4) Subsection “SND1 is a m6A reader in KSHV-infected cells”: how SND1 recognizes its RNA target sequences is pure speculation and should be removed from the result section. In addition, the motif provided in Figure 4—figure supplement 3D looks like 3' splice site and may argue for SND1's role in splicing.This has been removed.5) Subsection “SND1 stabilises ORF50 RNA and is essential for KSHV replication” and Figure 5: The authors provide an interesting discrepancy between the TREx BCBL-1 cells and naturally infected BCBL-1 cells. Their argument reads as follows: in naturally, unmodified BCBL-1 cells, the ORF50 RNA is decreased during SND1 knockdown. This effect is masked in the TREx BCBL-1 cells since they contain a chromosomally integrated doxycycline-inducible ORF50 gene. However, the authors fail short in the interpretation of their results. An alternative explanation would be that the DNA-binding and promoter-modifying capacity of SND1 comes into play. The major difference between the two ORF50 versions is the nature of the promoter driving their expression. The dox version harbors 6 to 7 tetracyclin-operators and a minimal CMV promoter. In the virus the ORF50 promoter is the sensor of the cellular status and is highly regulated to induced lytic reactivation only under certain conditions. To clarify the role of SND1 the authors need to perform a ChIP-seq analysis of the viral genome to exclude the possibility that SND1 binds and activates the ORF50 promoter. Thus, a lack of SND1 may cause an inhibition of ORF50 transcription and may explain all downstream effects. If this is the case the derived claims such as "SND1 stabilizes the ORF50 transcript" may not be corrected. In addition, the impairment of viral lytic gene expression could also be explained in a similar manner. The authors themselves mention this possibility indirectly in the Discussion section stating that SND1 functions as co-activator of the EBV protein EBNA-2.See response in essential revision 2 section above.6) Figure 5D and G: The authors should include lytic markers such as K8.1 and ORF59.ORF50 RNA and is essential for KSHV replication”.Markers for the lytic ORF54 protein have now been included, see updated Figure 7D and 7G. We have also quantified by qPCR lytic PAN levels in both TREX and BCBL1 cells . This has been included in the manuscript subsection “SND1 stabilises 7) Figure 6B: Here, the authors need to provide the raw data. In addition, to an accelerated decay the SND1 knockdown may already reduce the amount ORF50 transcript. See also argument provided above. Also, the authors should distinguish between the decay of spliced vs. unspliced ORF50 RNA if possible.ORF50 RNA was assessed with specific primers on the same samples used in Figure 8B, the result has been included in the manuscript, Figure 8B. The effect on decay was not as pronounced as for the observed for unspliced ORF50 RNA, consistent with our SND1 binding results , in which depletion of m6A in ORF50 RNA particularly abrogated SND1 binding to the unspliced form of ORF50 transcript.The decay of spliced 8) Supplementary Figure S11: I agree with the authors that a METTL3 knockdown might be difficult in BCBL-1 cells. Even though the increase in ORF50 RNA is not significant I am wondering if the authors here observe the same effect as the Glaunsinger laboratory . These authors were able to reduce the amount of METTL3 in BCBL-1 cells sufficiently and observed an increase in ORF50 protein, but not RNA. These findings are not discussed in the current manuscript! If METTL3 knockdown is too difficult, the authors should use the DAA treatment presented in Figure 6C,D to assess ORF50 RNA levels in the absence of m6A modifications.6A-methylation in the KSHV lytic replication, with reduced ORF50 mRNA levels following METTL3, YTHDF1 or YTHDF3, and increased ORF50 mRNA levels following FTO depletion. Our results are consistent with previous studies performed in the same BCBL1 cell line .We have now repeated the METTL3 knockdowns in BCBL1 transiently, instead of generating stable depleted cell lines. After five days post-lentiviral transduction cells were reactivated for 24hours and viral and protein levels analysed. In addition, we have also created stable cell lines with two different shRNAs targeting FTO, YTHDF1, YTHDF2 and YTHDF3. Our results are all supportive of a positive role of mORF50 RNA impairs SND1 binding to ORF50 RNA”.All these data have been incorporated as Figure 7—figure supplement 4 and Figure 7—figure supplement 5 and in the main manuscript, subsection “Removal of m6A in Reviewer #3:The most exciting part of this manuscript is the identification of novel m6A reader proteins, which were originally thought to bind methylated proteins only. While I am convinced of the specificity of SND1 towards m6A, my main criticism is directed towards the authors' choice of experimental approach to map SND1 binding sites on RNA. As detailed below, RNA-IP in this particular instance appears inappropriate to locate SND1 binding sites.6A reader proteins and shows that the m6A consensus sequence is a high scoring binding motif for SND1.See response in essential revisions 1 above. We have now included new analysis of SND1 eCLIP data which shows binding profiles similar to other mEssential revisions:While different experimental approaches can be applied to identify RNA-protein interactions, such as RNA-IP or CLIP-based techniques, in the case of SND1 the use of RNA-IP appears inappropriate. As mentioned in subsection “SND1 is a m6A reader in KSHV-infected cells” and shown in Figure 4—figure supplement 1, the vast majority of RNA fragments was sheared to <200 nt (not "bp"), yet the enriched fragments were of considerably greater size, which the authors suggest may be due to the anti-SND1 antibody having higher affinity for longer fragments. This statement is not very plausible. On the other hand, given the affinity of SND1 for methylated arginines, it is possible that chromatin-associated nascent RNAs were indirectly precipitated through SND1 binding to other chromatin-associated factors . These complexed RNAs would greatly resist shearing by sonication as compared to free RNAs. According to Materials and methods, no DNase treatment was included prior to IP and hence indirect RNA IP cannot be excluded. While bona fide SND1 targets may certainly be present within the deep sequenced RNAs, this cohort may be largely polluted by non-specific transcripts.Rather than RNA-IP, eCLIP or iCLIP would have the advantage that the crosslinked sites can be identified unambiguously, which would be particularly useful in overlapping m6A sites on RNA with SND1 binding sites.6A reader proteins and shows that the m6A consensus sequence is a high scoring binding motif for SND1.As mentioned above we have now included new analysis of SND1 eCLIP data which shows binding profiles similar to other mWe have additionally removed the sentence “suggesting the antibody had higher affinity for longer fragments”.