RNA methylation plays an important role in functional regulation of RNAs, and has thus attracted an increasing interest in biology and drug discovery. Here, we collected and collated transcriptomic, proteomic, structural and physical interaction data from the Harmonizome database, and applied supervised machine learning to predict novel genes associated with RNA methylation pathways in human. We selected five types of classifiers, which we trained and evaluated using cross-validation on multiple training sets. The best models reached 88% accuracy based on cross-validation, and an average 91% accuracy on the test set. Using protein-protein interaction data, we propose six molecular sub-networks linking model predictions to previously known RNA methylation genes, with roles in mRNA methylation, tRNA processing, rRNA processing, but also protein and chromatin modifications. Our study exemplifies how access to large omics datasets joined by machine learning methods can be used to predict gene function. Machine learning of multi-modal data, including transcriptomic, proteomic, structural and physical interaction, predicts genes and molecular pathways involved in RNA methylation in humans. Since then, the number of identified modifications has grown to over 150, found on both coding and non-coding RNAs across all three kingdoms of life2. Technological advances in the field have established that RNA modifications are widespread, reversible and dynamically regulated1. Methylation is the most abundant type, with methyl-groups decorating multiple RNA species, such as messenger RNA (mRNA), ribosomal RNA (rRNA) and tRNA, at different nucleosides and positions. So far, N6-methyladenosine (m6A) is the most studied modification, commonly detected in mRNA, rRNA, long intergenic non-coding RNA (lincRNA), primary microRNA (pri-miRNA), and small nuclear RNAs (snRNA). Other methyl-marks include 5-methylcytosine (m5C), N1-methyladenosine (m1A), 7-methylguanosine (m7G), 2’-O-dimethyladenosine (m6Am) and 5-hydroxymethylcytosine (hm5C)5.RNA modifications have been known since the 1960s, when the sequencing of the first transfer RNA (tRNA) from yeast revealed 10 chemically modified ribonucleosides, including pseudouridine (Ψ)6. Most enzymes use S-adenosyl-methionine (SAM) as a methyl donor to the RNA substrate, while many also recruit accessory proteins, which are often essential for substrate binding, localisation, and stability. The most well-studied examples of RNA methylation writers are by far the complex METTL3-METTL14 complex responsible for the deposition of m6A, followed by a NOL1/NOP2/Sun (NSUN) domain-containing family of tRNA-modifying enzymes depositing m5C on tRNAs7.Deposition of methyl-marks on RNA is catalysed by writer enzymes, known as RNA methyltransferases. To date, there are 57 RNA methyltransferases identified in the human genome. Of these, five methylate mRNAs, six small RNAs, 14 rRNAs, and 22 tRNAs, whereas 12 remain with unknown substrates8. This is because multiple lines of evidence suggest that RNA methylation plays a far more important role in cell functioning than previously thought. In line with this, several studies have shown that RNA methylation is a key modulator of transcript stability, gene expression, splicing and translation efficiency11. Furthermore, a growing body of data has demonstrated that changes in RNA methylation processes can be linked to a range of cancers, neurological disorders and various other diseases12. Surprisingly, despite this critical role in cellular homoeostasis and disease, RNA methylation pathways in general remain understudied7. Our current understanding of RNA modifications is also highly fragmentary, with an estimated 20% or more of RNA modifying enzymes still remaining unknown or unidentified13.Dynamic regulation of RNAs via chemical modifications has recently attracted a rising interest in RNA modifying enzymes as new potential therapeutic targets14.Conventional approaches for studying novel gene functions include a range of labour-intensive wet-lab techniques, including mutagenesis, gene disruption or gene depletion (knocking-down/-out) for characterising gene-specific phenotypic effects, and chromatography and mass spectrometry for identifying molecular interactions. However, over the last two decades, access to large-scale omics data has enabled the use of “dry” computational methods for understanding biological functions. A wide array of bioinformatic tools have been developed under the umbrella of functional genomics, ranging from methods used to identify homologous genes with similar functionalities across species to genome-wide screens for specific sequence motifs and functional domains. Today, machine learning techniques are emerging as a powerful approach to harness the increasing wealth of large-scale biological data, allowing the discovery of hidden patterns and more reliable statistical predictions15 and built a large machine learning dataset for supervised binary classification. We trained and evaluated five ensembles of predictive models: Logistic Regression (LR), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting (GB) models. We employed the best models to predict genes functionally associated with RNA methylation pathways in the human genome.Here, we aimed to better understand the molecular pathways involved in RNA methylation in human using machine learning. To this end, we used publicly available human transcriptomic, proteomic, structural and protein–protein interaction data6 and their partner proteins in protein complexes), or enzymes previously annotated as putative RNA methyltransferases (see Methods). Genes encoding for these proteins constituted our positive class (Class 1) in machine learning analyses. To frame our predictive modelling as a binary classification problem, we assembled multiple stratified training and test datasets by randomly sampling a number of genes equal to our positive set from the remaining genome, ensuring that all genes of our initial dataset were sampled exactly once , Gene Ontology17 (GO: 59), GTEx18 , Human Protein Atlas19 (HPA: 107), InterPro (1), Pathway Commons (PathCommons: 150) and TISSUES20 (40) datasets.We initially pooled 50,176 features collected from publicly available and previously curated transcriptomic, proteomic, functional annotation, structural and physical interaction datasets  and trained each on training sets from the full and the reduced feature set, creating an ensemble of models per classifier and feature set. Overall, all five model ensembles showed very similar performance based on cross-validation Table . Among cThe performance of the five classifiers for the reduced feature set without GO/InterPro annotations was diminished compared to the full dataset Table . The modTo evaluate results from different models and feature sets, we first compared the distribution of probability scores for all human genes, as predicted by models trained on the full feature set Fig.  and modeSecond, we assessed the degree of overlap among genes predicted with high confidence to be involved in RNA methylation by different machine learning models. Here, we defined as high confidence all genes in the top 1% of the probability distribution for Class 1. For most classifiers this fraction encompassed ~270 genes, except from GNB models that ambiguously assigned the same high probability score to a large number of genes, hence a larger set was considered . When comparing top predictions from different models, their relative concordance was high for both feature sets , we were particularly interested in obtaining an estimate of the false positives in our predictions. To this goal, we pooled together the entire hold-out data  and averaged the predicted probability score of each gene as estimated by each model in the GB ensemble. This allowed us to estimate the false positives, by counting the number of negative genes (Class 0) that were wrongfully predicted by our models as positive. Of the 5368 negative genes, 425 were erroneously classified, resulting to an estimated false positive rate of 0.079, defined as the number of False Positives (FP) divided by the sum of False Positives (FP) and True Negatives (TN).With regards to novel predictions, we first selected the top hundred genes predicted by the GB models to associate with RNA methylation pathways as candidates for further validation (Supplementary Data 21 for independent Protein–Protein Interaction (PPI) information on known RNA methylation genes and other genes of the human genome. We built a PPI network based on interactions with a confidence score of 400 or above, and performed Random Walks starting from proteins known to mediate methylation of RNAs (Class 1). This allowed us to weigh all other proteins in the network and rank them by their importance relative to our positive gene set. To evaluate whether genes predicted by our models were highly ranked among important interactors, we performed Gene Set Enrichment Analysis (GSEA) using the PageRank score as an input. We obtained a strong positive enrichment  for the model predictions , tRNA methylation , and tRNA processing . Indeed, four predictions in the cluster, CTU2, MOCS3, PUS1 and PUS3, are RNA modifying enzymes mediating tRNA modifications. CTU2 and MOCS3 are involved in 2-thiolation of mcm5S2U at wobble positions of tRNAs, whereas PUS1 and PUS3 belong to the tRNA pseudouridine synthase TruA family and mediate the formation of pseudouridine at positions 27/28 and 38/39 of certain tRNAs, respectively13. Among other members of the same community, the gene TRNT1 encodes the mitochondrial CCA tRNA nucleotidyltransferase 1 responsible for the addition of the conserved 3′-CCA sequence to tRNAs. It has been previously reported that the presence of the 3′-CCA tail on tRNA is required for target recognition by the tRNA methyltransferase NSUN624, which could underlie the functional link of TRNT1 with RNA methylation genes in our analyses.Community 1 C1, Fig.  groups mKARS encodes a Lysyl-tRNA ligase. Although, we have not found any orthogonal evidence linking FARS2 to RNA methylation, KARS has been previously inferred to physically interact with the RNA methyltransferase TRMT1, based on co-fractionation data (source BioGRID25).Likewise, two aminoacyl-tRNA synthetases, FARS2 and KARS, were also predicted to be closely associated with RNA methylation pathways and were part of Community 1. FARS2 is a mitochondrial Phenylalanine-tRNA ligase, responsible for the charging of tRNA(Phe) with phenylalanine. 26. N6AMT1 methylates the eukaryotic translation termination factor 1 (eRF1) on Gln-185. Notably, it has been reported that N6AMT1 forms the catalytic subunit of a heterodimer with the RNA methyltransferase TRMT11227, suggestive of a functional interplay between RNA methylation and post-translational modifications of translation factors.The same sub-network also included two HemK methyltransferases, HEMK1 and N6AMT1. The former is a N5-glutamine methyltransferase responsible for the methylation of the glutamine residue in the GGQ motif of the mitochondrial translation release factor MTRF1LMTO1 is a gene functionally associated with RNA methylation pathways. Previous studies have shown that MTO1 encodes for a mitochondrial protein which is indeed involved in the 5-carboxymethylaminomethyl modification (mnm5s2U34) of the wobble uridine base in mitochondrial tRNAs, with a crucial role in translation fidelity28.Our models also predicted that C7orf60, HENMT1, RRNAD1 and RSAD1. The gene C7orf60 or BMT2 encodes a probable S-adenosyl-L-methionine-dependent methyltransferase. Recent studies have suggested that BMT2  acts as an inhibitor of mTOR complex 1 (mTORC1) signalling in human, a SAM sensor signalling methionine sufficiency29. In yeast, BMT2 is responsible for the m1A2142 modification of 25S rRNA30. Two other methyltransferase genes in the same cluster were RRNAD1 and HENMT1. The former encodes for ribosomal RNA adenine dimethylase domain containing 1, but little is known about its function. HENMT1 is a small RNA methyltransferase that adds a 2′-O-methyl group at the 3′-end of piRNAs, contributing to the maintenance of Transposable Element (TE) repression in adult germ cells31. Functional annotation of this community indicated an enrichment in peptidyl-lysine methylation function , albeit this was based on only four proteins out the 23 forming this cluster . Among members of this community, we identified proteins with a role in methylation of other substrates. For example, FAM86A catalyses the trimethylation of the elongation factor 2 (eEF2) at Lys-52532. METTL13 is also a methyltransferase responsible for the dual post-translational methylation of the elongation factor 1-alpha (eEF1A) at two positions (Gly-2 and Lys-55), modulating mRNA translation in a codon-specific manner33. Both genes are involved in modifying translation elongation factor residues, same as N6AMT1 mentioned above. Our results hence suggest that post-translational modifications of translation factors and epitranscriptomic changes on RNAs could be interconnected in modulating translational efficiency.Community 2 C2, Fig.  consistsP = 6.79E−40) and rRNA processing . This finding is consistent with previous computational approaches aiming to predict the functional role of m6A modification sites, which have independently shown a strong connection between RNA methylation and RNA processing34. Among Community 3 members for instance, our predictions include five genes encoding for members of the nuclear RNA exosome, DIS3, EXOSC2, EXOSC5, EXOSC8 and EXOSC9. The exosome is known to participate in a wide variety of cellular RNA processing and degradation events preventing nuclear export and/or translation of aberrant RNAs. Exosome function is thus likely to be interlinked with epitranscriptomic marks on RNAs.Community 3 C3, Fig.  compriseP = 5.44E−36). EMG1 encodes for an RNA methyltransferase that methylates pseudouridine at position 1248 in 18S rRNA35. Pathway annotation data further suggest that EMG1 together with eight new predictions  are required in pre-18S rRNA processing and ribosome biogenesis. Of these, the NOL11 gene encodes a nucleolar protein contributing to pre-rRNA transcription and processing36. Partial evidence furthermore suggests that NOL11 interacts with the rRNA 2′-O-methyltransferase fibrillarin, FBL, which is involved in pre-rRNA processing by catalysing the site-specific 2′-hydroxyl methylation of pre-ribosomal RNAs36. FBL together with RRP9 and NOP56 are part of the box C/D RNP complex catalysing the ribose-2′-O-methylation of target RNAs.We also identified a sub-cluster within the community connecting DIMT1, EMG1, FBL and NOP2 with 15 proteins predicted by our models. All members of the sub-cluster are RNA-binding proteins involved in rRNA modification in the nucleus . A noteworthy case is RRP8, which in human is reported to bind to H3K9me2 and to probably act as a methyltransferase, yet studies in yeast have shown that the RRP8 homologue is responsible for installing m1A in the peptidyl transfer centre of the ribosome (m1A645 in 25S)37.Finally, three novel gene predictions within this community, P = 8.74E−29), or are associated in general with chromatin organisation  and histone modification . Previously known RNA methylation genes in this community were mainly involved in RNA-capping pathways, e.g., RNMT, CMTR1, CMTR2, FAM103A1, TGS1 and RNGTT. Recent studies have suggested that there is indeed extensive crosstalk between RNA modifications and epigenetic mechanisms of gene regulation39.Community 4 C4, Fig.  constituP = 1.91E−17) and mRNA methylation, in particular . Notably, this community captures proteins involved in the m6A pathway, including the m6A writer complex of METTL3-METTL14 with co-factor WTAP, METTL16 and ZC3H13, as well as the m6Am writer METTL440. Community 6 is the smallest of all communities with only four protein members, two previously annotated RNA methylation genes, HSD17B10 and KIAA0391, and two predicted genes POP1 and POP4. Functional analysis suggests that all four proteins contribute to tRNA processing  and three of them are involved in tRNA 5′-end processing . The HSD17B10 gene encodes the 3-hydroxyacyl-CoA dehydrogenase type-2, which is involved in mitochondrial fatty acid beta-oxidation. HSD17B10 is involved in tRNA processing as it also forms a subcomplex of the mitochondrial ribonuclease P together with TRMT10C/MRPP141. This subcomplex, named MRPP1-MRPP2, catalyses the formation of N1-methylguanine and N1-methyladenine at position 9  in tRNAs. KIAA0391, also known as PRORP, encodes a catalytic ribonuclease component of mitochondrial ribonuclease P. It appears that POP1 and POP2 are also components of ribonuclease P and contribute to tRNA maturation via 5′-end cleavage.Community 5 (C5) and Community 6 (C6) encompass fewer members than the other communities. Community 5 consists of 10 proteins creating a small sub-network of RNA methyltransferases and partner proteins involved in RNA methylation  samples to use for machine learning. Even though model performance based on test data was good, the small sample sizes may have hampered how well our models generalise. To better illustrate this limitation, we revisit our model predictions on test data, comprised of 18 positive and 5,368 negative genes in total. When considering the top 25 predictions , the False Positive Rate is very low at 0.002 , precision (90%) and recall (92%). A priori gene knowledge  together with expression data constituted the most informative data types in predictive modelling. Notably, in certain tissues, such as blood, heart, pancreas and brain, genes mediating RNA methylation seemed to show an up- or down-regulated expression profile.Using independent PPI data, we orthogonally validated top model predictions by corroborating close functional links to previously known RNA methylation genes. Community detection delineated six molecular subnetworks, with distinct roles in tRNA processing , rRNA processing (C3), mRNA methylation (C5), but also protein (C2) and chromatin modifications (C4). Network analyses suggested that deposition of methyl marks on tRNAs is co-orchestrated with other modification processes, such as 2-thiolation and pseudouridine formation. Similarly, rRNA methyltransferases appeared functionally linked to several genes involved in rRNA processing and ribosomal biogenesis. Intriguingly, RNA-capping enzymes were clustered with chromatin modifiers, raising the hypothesis of a crosstalk between the two processes. Our results further indicate that post-translational modifications of translation factors and epitranscriptomic changes on RNAs are intertwined in modulating translational efficiency. Overall, our study exemplifies how access to omics datasets joined by machine learning methods can be used to infer molecular pathways and novel gene function.https://enzyme.expasy.org/), InterPro (https://www.ebi.ac.uk/interpro/) and the GO Resource (http://geneontology.org/), in conjunction with a comprehensive literature review for annotated RNA methyltransferases following up on the pioneering paper of Schapira6. This allowed us to identify 92 proteins involved—or putatively involved—in RNA methylation to use for machine learning modelling  from over 70 major online resources. In particular, data were initially standardised as such continuous-valued datasets to range from 0 to 1, or −1 to 1, where 1 indicates strong positive gene-feature association, 0 indicates no observed gene-feature association, and -1 indicates strong negative gene-feature association (e.g. down-regulation for gene expression datasets). Binary and tertiary datasets were then derived by retaining the top 10% of the strongest gene-feature associations. We selected 15 of these one-hot-encoded datasets from four broad categories: (i) transcriptomics; (ii) proteomics; (iii) structural or functional annotations; and (iv) physical interactions . In addition, we compiled a second dataset of reduced dimensionality, by excluding all 5,148 GO and InterPro annotation features (“reduced feature set”).To obtain informative features for classifying gene functions, we interrogated the Harmonizome databasen = 74) and (ii) 20% kept unseen for model testing (n = 18). We considered the remaining genes of the human genome as negative samples (Class 0) and performed an analogous 80/20 split into training/cross-validation  and test sets (n = 5368). The underlying assumption here is that the vast majority of genes in the human genome serve other functions, thus the number of false negatives in the training data should be very small.To estimate the probability of a gene being associated with RNA methylation, we used standard machine learning approaches for binary classification. We labelled the 92 previously known RNA methylation genes as positive samples (Class 1), and split them into two sets comprising: (i) 80% of the data for training and cross-validation  were further divided into sets of 74—equal to the number of positive samples for training. We thus generated 290 training sets, where the positive class remained fixed and the negative class was represented by a random draw of an equal number of genes from the rest of the genome, sampling each gene once.To produce balanced sets of training samples, and to later reduce the variance of our final models through averaging, negative genes kept for training  zero values in more than 70% of the samples in each training set, or (ii) less than 16% variance in at least one training set. The selected features for each of the 290 training sets were then merged into a final list of features for model training and testing. We followed the exact same selection process for the reduced feature set as well.We next considered five types of machine learning models for binary classification: Logistic Regression (LR), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting (GB) models. We used grid search and 3-fold cross-validation on each training set for the SVM hyperparameter tuning of the kernel function (linear or RBF), cost parameter, and kernel bandwidth (RBF kernel only). For RF, we used grid search to determine the optimal number of trees in the forest, followed by a randomised search to select the best parameters for maximum number of features considered for splitting a node, maximum number of levels in each decision tree, minimum number of data points placed in a node before the node is split, and minimum number of data points allowed in a leaf node. Likewise, for the GB model, we performed grid search to optimise the learning rate and number of trees in the forest, and subsequently performed a randomised search to tune the remaining decision tree parameters (see RF). We trained all five predictive models on each of the training sets from the full and reduced feature sets, respectively. The performance of all classifiers was estimated using 10-fold cross-validation using standard performance metrics: accuracy, precision, recall (sensitivity), F1 score and Area Under the Receiver Operating Characteristic Curve (AUROC). Finally, we used GB feature ranking to determine the top 100 most informative features across the ensemble of training sets for the full and reduced feature sets, respectively.Once the best model ensemble was selected based on cross-validation, we tested its performance on unseen data. Analogous to the procedure described above for training data, we generated 298 testing datasets, by splitting the negative genes kept for testing into equal sets of 18 genes, and combining them with the 18 of positive samples previously retained. Each model from the classifier ensemble was evaluated on each of the test datasets using accuracy, precision, recall, F1 score and AUROC. Overall performance was calculated by averaging results of all models across test sets.Likewise, the prediction probability of each human gene was calculated by averaging probability scores for Class 1 across all models of the ensemble. Most non-Class 1 genes  were part of the negative samples in the training data of exactly one model in the ensemble; however, due to the high number of models (290) the effects of this on the final predictions is expected to be negligible.42. A heatmap of known and predicted RNA methylation genes across all features used for machine learning was generated using the R package pheatmap. Further in silico validation of model predictions was performed using GO enrichment analyses of predicted genes within the domain “Biological Process” using the package clusterProfiler43. Protein–Protein Interaction (PPI) data for human were obtained from STRING (v.11.0)21 and filtered to interactions with a combined score of 400 and above. All network analyses were performed using the igraph R package44. Functional annotation of PPI communities was performed using EnrichR23.All visualisations and meta-analyses were performed using the R software environment (v. 4.0.5)Sample sizes and statistical parameters used in each analysis are indicated in the relevant methods and results sections, as well as in the figure legends when applicable. All statistical analyses were performed in R (v4.0.5).Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary DataSupplementary Data 1-5Reporting summary


Circular RNA (circRNA) exists extensively and plays essential roles in serving as microRNA (miRNA) or protein sponges and protein scaffolding in many organisms. However, the profiles and potential functions of the virus-encoded circRNA, including human cytomegalovirus (HCMV)-encoded circular RNAs, remain unclear. In the present study, HCMV-encoded circRNAs profile in human embryonic lung fibroblasts (HELF) with lytic infection was investigated using RNA deep sequencing and bioinformatics analysis. In total, 629 HCMV-encoded circRNAs were identified with various expression patterns in our results. The full sequences and alternative splicings of circUS12, circUL55, and circUL89 were verified by reverse transcriptase-PCR (RT-PCR) with divergent primers followed and Sanger sequencing. Transcription of circUL89 was validated by Northern blot. The HCMV-encoded circRNA-miRNA network analyses revealed the potential function of HCMV-encoded circRNAs during HCMV infection in HELFs. Collectively, HCMV infection deduced abundant HCMV-associated circRNAs during infection, and the HCMV-encoded circRNAs might play important roles in benefiting HCMV infection. Human cytomegalovirus (HCMV) is a ubiquitous beta-herpesvirus that can infect a range of essential organs, including the brain, heart, lungs, liver, kidney, blood, bone marrow, and salivary gland . AlthougCircular RNAs (circRNAs) are a kind of ncRNA characteristic of a covalently closed loop without a polyadenylated tail by joining the 3’ donor site to the 5’ acceptor site, which is called back-splicing . This feVarious functions have been identified for circRNAs, such as, to serve as microRNA (miRNA) sponges as a competitor with the linear counterpart gene , or as avia serving as DNA-dependent protein kinase (DNA-PK) sponge  sponge . Recentl) sponge . CircE7 ) sponge . HoweverIn this study, the HCMV-encoded circRNA profile was investigated in human embryonic lung fibroblasts (HELFs) with lytic infection. Viral circRNAs with high transcriptional levels were validated by reverse transcription-polymerase chain reaction (RT-PCR) and Northern blot. Subsequently, putative target miRNAs of validated HCMV-encoded circRNAs were predicted and further analyzed to speculate the functions of viral circRNAs in infection.2.HELF cells were obtained from the Shanghai Institute for Biological Sciences, Chinese Academy of Sciences (CAS). The HELF cells were cultured in minimum essential medium (MEM) (HyClone) supplemented with 10% fetal bovine serum (FBS) (Gibco), 100 units/mL penicillin, and 100 μg/mL streptomycin at 37°C with an atmosphere of 5% COHCMV low-passage strain Han labeled with a green fluorescent protein (GFP) was used for virus preparation. Viral stocks were generated following standard ultracentrifugation procedures described previously .HELFs were infected with HCMV at a multiplicity of infection (MOI) of 3. After 24 hours of incubation, the culture medium was replaced with MEM containing 2% FBS, 100 units/mL penicillin, and 100 μg/mL streptomycin. Meantime, PBS-treated HELFs were prepared as a mock-infected control. Infection was confirmed at 72 hours post infection (hpi) . The integrity of the total RNAs was evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies), and the concentration of the RNAs was measured using the NanoDrop 1000 (Thermo Scientific). Ribo-Zero rRNA Removal Kits (Epicentre) were used to remove ribosomal RNA from the total RNAs. Then, the rRNA-depleted samples were treated with RNase R treatment (Epicentre) to digest linear RNAs according to the manufacturer’s instructions.The treated RNAs were fragmented and reversely transcripted into cDNA using SuperScript™ II Reverse Transcriptase (ThermoFisher) according to its protocol. RNA libraries were constructed using the TruSeq RNA LT Sample Prep Kit v2 (Illumina). Paired-end RNA sequencing was performed on an IlluminaHiSeq 2500 at Biotechnology Co. Ltd., Shanghai, China. The RNA sequencing data were deposited in the Gene Expression Omnibus database under accession number GSE138836.https://github.com/lh3/seqtk). The filtered reads were first mapped to the human reference genome (GRCH37/HG19). The remaining unmapped reads were then mapped to the HCMV genome (NC_006273.2) with BWA-MEM (version 0.7.13)  . The map 0.7.13) . The lenPredicted circRNAs with more than two backspliced junctions (BSJ) reads were identified as candidate HCMV-encoded circRNAs. Candidate circRNAs with BSJ reads of more than 50 were determined as prominent circular transcripts of HCMV in our research. The expression of the circRNA candidates was estimated by spliced reads per billion mappings .Divergent primers targeting the BSJ sequences of HCMV-encoded circRNAs  were designed with Primer Premier 6.00 . Sequences covering BSJ and flanking regions were obtained from RNA sequencing data and subjected to primer design. The amplicons should contain their junction sites and be preferably 200 ~300 bp in size . For RNase R treatment, 4μg of total RNA was incubated with RNase R (5 units/μg RNA) or RNase-free water as a control for 30 minutes at 37 ˚C. RT-PCR analysis was performed with PrimeScript RT-PCR Kit (TaKaRa) according to the manufacturer’s protocol. Convergent primers of glyceraldehyde 3-phosphate dehydrogenase  and HCMV UL123 were used as controls.PCR products were detected by electrophoresis on 1.5% agarose gel containing ethidium bromide and visualized under UV light. PCR products with expected sizes were purified using Wizard Plus SV Minipreps DNA Purification System (Promega) and cloned into pCR2.1vector using TA Cloning Kit (Thermo Fisher). Sanger sequencing was performed in ThermoFisher Scientific Co. Ltd., Shanghai, China.For each sequence obtained from the TA clone, about 90bp sections around the 5’ and 3’ junction sites were analyzed using DNAClub and compared using BioEdit to identify homologous sequences in the HCMV genome (NC_006273.2).DIG-labeled sense and antisense probes targeting circUL89 BSJ site were designed and synthesized with DIG Northern Starter Kit (Roche) according to instructions. Antisense probes targeting β-actin (NM_001101) were also synthesized to detect β-actin, which were used as the loading controls. Primers for generating these probes are listed in A total of 20 μg RNA for each detection was separated on agarose containing 2% formaldehyde and transferred to IMMOBILON-NY+ nylon membranes (MILLIPORE), and the membrane was rinsed and baked as previously . HybridiTo gain insights into the relationship between miRNAs and HCMV circRNAs, putative targeted miRNAs of the selected HCMV-encoded circRNAs, including circUS12, circUL89, and circUL55, were predicted by miRanda . MiRNAs http://www.geneontology.org/).The circRNA-miRNA network was constructed using Cytoscape (v3.4.0) . To asseTotal RNAs of HELFs with HCMV lytic infection were treated with RNase R before RNA sequencing. The filtered reads were firstly mapped to the human reference genome, and the remaining unmapped reads were then mapped to the HCMV genome. A total number of 629 potential HCMV-encoded circRNAs were identified in our results  for each viral circRNA were calculated to address the relative expression levels of HCMV-encoded circRNAs. The SRPBM of HCMV-encoded circRNAs ranged from 0.086 to 10.569 . In contrast, the rest circRNAs were mainly transcribed from the intergenic region  of circUS12 by sequencing, the products of which were unvisualized due to their lower abundances. By calculation, circUS12_3 was predicted to be 285nt in length, while circUS12_4 was 799nt in length  generated by splicing. To address whether circUL89 was produced from existing linear mRNA or primary RNA independently, we designed two more divergent primer pairs (circUL89A and circUL89B) overlapping in products’ terminals to acquire the entire sequence of circUL89. Theoretically, the divergent primer circUL89A and circUL89B amplify a product ~2000nt in length, respectively , to cope with the host’s antiviral mechanism. For instance, RNA2.7, a lncRNA encoded by HCMV, was identified to serve as an anti-apoptosis factor and maintain high ATP levels in cells with lytic infection or inhibit RNA polymerase II serin-2 phosphorylation, thus resulting in the success of the viral replication cycle in host cells .As newly recognized ncRNAs, circRNAs are usually generated by back-splicing of exons, introns, or both . BenefitIn recent years, some circRNAs encoded by herpesvirus have been identified and investigated in infected cells. Kaposi’s sarcoma herpesvirus (KSHV)-encoded circvIRF4 is found noticeably expressed in the KSHV-positive patient sample and infected primary vascular and lymphatic endothelial cells . CircBARIn this study, a total number of 629 HCMV-encoded circRNAs were experimentally revealed in HELFs infected with HAN strain. These uncovered circRNAs noticed a novel transcription manner of the HCMV clinical strain genome. Similar to the change in the host circRNAs in HCMV-infected HELFs , alternaThe BSJ site information and the entire sequence of HCMV-encoded circUS12, circUL55, and circUL89 were then achieved, respectively. Some circRNA isoforms bearing BSJ different from RNA-seq were discovered in our validation. These circRNAs have lower abundance and might be circular byproducts of linear splicing . HCMV-enThe HCMV US12 gene belongs to the US12 gene family, including US12 to US21, which encode GPCR (G-protein-coupled receptor)-like proteins . SimilarCircUL89 was proved to be a circular RNA with 3890nt in length, which is longer than known HCMV-encoded circRNAs. HCMV UL89 gene has two CDS post-splicing, while no splicing was found for circUL89 by mapping to the genome. It is speculated that virus-encoded circRNA might be derived from primary RNA independent of the production of mRNA. pUL89 is a component of terminase, whose other components are pUL56 and pUL51. Terminase facilitates viral replication by genome packaging. Previous research suggested aspartate 463 and arginine 544 are vital domains for pUL89 . Surprisin situ hybridization (FISH) with probes specific to miRNAs and to circRNA BSJs might be carried to distinguish miRNA interacted circRNAs from linear mRNAs and/or lncRNAs.CircRNAs could inhibit gene expression by serving as miRNA sponges . The putThe sequence of HCMV-encoded circUS12 and UL55 were identical to their linear counterparts. Some circRNAs have been discovered to be able to produce protein products . HCMV pUIn summary, this study experimentally delineated the profile of HCMV-encoded circRNAs in HELFs with lytic infection, laying a primary foundation for further elucidating the mechanisms of HCMV pathogenesis. However, more research is needed to investigate the functions of HCMV-encoded circRNAs further.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/JD, YH, and QR conceived and designed the experiments. JD, JZ, QW, YL, and ZL performed the experiments. YM and YQ analyzed the data. JD wrote the manuscript and prepared the figures. QR and YH checked and finalized the manuscript. All authors contributed to the article and approved the final manuscript.This work was supported by the National Natural Science Foundation of China under Grant No. 82071664.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Dead End (DND1) is an RNA-binding protein essential for germline development through its role in post-transcriptional gene regulation. The molecular mechanisms behind selection and regulation of its targets are unknown. Here, we present the solution structure of DND1’s tandem RNA Recognition Motifs (RRMs) bound to AU-rich RNA. The structure reveals how an NYAYUNN element is specifically recognized, reconciling seemingly contradictory sequence motifs discovered in recent genome-wide studies. RRM1 acts as a main binding platform, including atypical extensions to the canonical RRM fold. RRM2 acts cooperatively with RRM1, capping the RNA using an unusual binding pocket, leading to an unusual mode of tandem RRM-RNA recognition. We show that the consensus motif is sufficient to mediate upregulation of a reporter gene in human cells and that this process depends not only on RNA binding by the RRMs, but also on DND1’s double-stranded RNA binding domain (dsRBD), which is dispensable for binding of a subset of targets in cellulo. Our results point to a model where DND1 target selection is mediated by a non-canonical mode of AU-rich RNA recognition by the tandem RRMs and a role for the dsRBD in the recruitment of effector complexes responsible for target regulation. The authors report an unusual mode of AU-rich RNA recognition by the RNA recognition motifs of DND1, a protein essential for germline development, in a 27.5 kDa NMR structure and provide additional insight on DND1 function from cell-based experiments. Understanding how RBPs specifically recognize their mRNA targets and how this is connected to their downstream fate is therefore crucial to understand the complex PTGR networks involved in health and disease.Post-transcriptional gene regulation (PTGR) is orchestrated by an interplay between mRNA sequence and structure, and their dynamic interactions with RNA-binding proteins (RBPs). RBPs instantly cover mRNA transcripts as they are transcribed and are essential for all aspects of RNA metabolism like maturation, transport, cellular localization, and turnover. Differential gene expression patterns depend on tissue-specific RBP levels and their combined interactions with the transcriptome. Misregulation of this process, due to mutations in the RBPs or RNA to which they bind, is at the origin of a plethora of genetic diseases3. Dead End (DND1) is a germline-specific RBP. Conserved in vertebrates, it is essential for the development and migration of primordial germ cells (PGCs), pluripotent germline precursors, to the future reproductive organs. These processes occur early in embryogenesis by blocking the expression of somatic genes, controlled by extensive post-transcriptional regulation4. DND1 deletion causes loss of PGCs by reactivation of somatic gene expression patterns in zebrafish6. In mice, truncations of DND1  lead to male sterility and the formation of testicular teratomas8.The germline is composed of highly specialized cells which must preserve a totipotent genome through generations. It succeeds in this through a highly specialized RNA metabolism regulating a complex transcriptome9. DND1 binding to conserved U-rich regions close to miRNA seed sequences in the 3’ untranslated regions (3’UTRs) of these targets potentially blocked their accessibility to the miRNA-induced silencing complex (miRISC) which rescued translation. Second, on a transcriptome-wide level, DND1 was shown to have an opposite effect and a wider role in germ cell PTGR by destabilizing a set of transcripts that must be cleared from developing germ cells to ensure survival, through non-miRISC mediated recruitment of the CCR4-NOT deadenylase complex11.DND1 performs a dual role in regulating gene expression: stabilizing some mRNA transcripts while destabilizing others. First, a direct role for DND1 in promoting RNA stability by inhibiting the action of miRNAs was shown for specific candidate mRNAs (p27/CDKN1B and LATS2 in a human tumor cell line and Nanos/TDRD7 in zebrafish embryos)7.Transcriptome sequencing of the Ter mouse line, possessing a truncation in the DND1 gene, prior to the formation of teratomas, showed two groups of DND1 targets either up- or downregulated compared to wild-type, involved in pluripotency and in differentiation, respectively, in a sequential fashion, suggesting a dual and biphasic role for DND1. No enrichment for RNAs harboring a signature for miRNA regulation was found in the wild-type versus Ter cell line, although previously described DND1 targets were re-identified11. An additional digestion optimized-RIP-seq (DO-RIP-seq) approach described [A/G/U]AU[C/G/U]A[A/U] as RNA sequence motif enriched in DND1 targets7. Overall, these functional and genome-wide motif identification studies using high-throughput methods are partly contradictory. Thus, the molecular mechanisms of DND1 function and details on how DND1 achieves target specificity remain elusive.Photoactivatable-ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) assays revealed that targets cross-linked to DND1 are enriched in a UUU/UUA triplet and are associated with apoptosis, inflammation, and signaling pathways13. DND1 has a unique domain structure, carrying two RNA recognition motifs (RRMs) connected by a remarkably short, four-residue interdomain linker preceding a double-stranded RNA-binding domain (dsRBD)  in the first and third β-strands  Fig. . This coTo address the question of how DND1 recognizes and represses its cellular targets at the molecular level, we first set out to understand the contribution of the three RNA-binding domains of DND1 to target recognition. This demonstrated a crucial role for the RRMs. We then determined the solution structure of the DND1 tandem RRMs in complex with an AU-rich RNA. Our structure reveals an unusual mode of cooperative binding by tandem RRMs and explains the degeneracies in the motifs enriched in DND1 targets in recent genome-wide studies. Using luciferase-based assays, we validated not only the role of the tandem RRM-AU-rich RNA interaction, but in addition also could pinpoint an essential role for the dsRBD, in gene expression regulation by DND1. Finally, immunoprecipitation followed by liquid chromatography-tandem mass spectrometry analysis of the interactome of DND1 targets in HEK293T reveals enrichment for proteins involved in the stabilization of mRNAs like ELAVL1 and proteins localized in RNP granules. These results provide the first mechanistic and structural insights into the molecular mechanisms by which DND1 regulates a subset of mRNAs, thereby stabilizing the fate of germ cells.9. Later, genome-wide PAR-CLIP studies defined a much shorter UUU/UUA triplet motif11 and very recently a Digestion Optimized-RIP approach revealed [A/G/U]AU[C/G/U]A[A/U] (DAUBAW) as motif enriched in DND1 targets7. A single RRM usually binds 4–6 nucleotides15. To understand how the two RRMs of DND1 cooperate with the dsRBD to engage an RNA target, we set out to define the contributions of each domain to RNA binding. We first validated published DND1 targets using RNA immunoprecipitation (RIP) from HEK293T cells transiently expressing either FLAG-tagged DND1 wild-type (WT) or mutants thereof. Mutant 1-235 lacks the dsRBD but includes the extended RRM2, making it longer than the Ter-mutation truncation8, which is located in the middle of RRM2 α-helix 2  is a member of the SR protein family that is involved in both constitutive and alternative mRNA splicing. It had the third highest number of normalized 3’UTR read counts in the PAR-CLIP dataset11 and is expressed at the same level as PMAIP. It contains 15 UAUU motifs in its 3’UTR and was previously identified by DO-RIPs7. Survival of Motor Neuron (SMN) protein is a small nuclear ribonucleoprotein (snRNP) assembly factor. Its pre-mRNAs SMN1 and SMN2 are expressed at an order of magnitude lower level than PMAIP1 in HEK293, contain several UAUU motifs in the 3’UTR, depending on the transcript variant and are enriched in the DO-RIP dataset7. They had no reads in the PAR-CLIP dataset11. As a negative control, we used solute carrier family 25 member 6 (SLC25A6), the mRNA of which is expressed at similar levels as PMAIP1 in HEK293T cells11 and had no reads in either DO-RIP7 or PAR-CLIP11 datasets, even though it contains one UAUU motif in its 3’UTR. Interestingly, in five independent RNA-IP followed by RT-qPCR experiments, the SMN1/2 mRNAs showed the highest enrichment over the input when pulled down by WT DND1, even though the expression in HEK293 is an order of magnitude lower than PMAIP and SRSF2 and the SMN targets are missing in the PAR-CLIP dataset11. All experiments were normalized to the enrichment of SMN by DND1 WT pulldown  in the vicinity of miRNA seed sequences in the 3’UTR of the CDKN1B/p27 tumor suppressor mRNAx 2 Fig. ; R98A co9 using isothermal titration calorimetry (ITC) to monitor thermodynamic changes upon ligand binding  when titrated with the tandem RRM domains showing the importance of RRM2 for RNA binding  and UUUUUCC (>100 μM KD). The in vitro binding measurements are summarized in Supplementary Table To identify high-affinity RNA targets for the individual RRMs, we recombinantly expressed and purified the separate RRM domains, including their conserved N- and C-terminal extensions Fig. . We thening Fig. . In addiing Fig. . We founing Fig.  and thating Fig. . The bining Fig. . Surprising Fig. . Additioing Fig. . Large cons Fig.  showed t13C-ribose units, was key to resolving the spectral overlap of critical residues and aided the assignments of resonances and intermolecular NOEs  fold is conserved in all members of the hnRNPR-like family of RNA-binding proteins  packed on top of an N-terminal α-helix (α0). This structural element is tightly packed against the side of the RRM1 to create an extended surface Fig. , in an iUAUUUG RNA  from sidechains and main chains of the highly conserved interdomain linker   of CURNA Fig. . The intner Fig. . This is35) Fig. . We obse5 onwards, the protein–RNA interactions deviate from canonical binding. While in a canonical RRM the U5 base would be expected to stack onto RNP2 Y102, here U5 rather stacks on A4, as evidenced by the strong NOEs between A4 H1’ and U5 H5 and H6 resonances, as well as weaker NOEs between A4 H8 and the U5 H6 resonance . Altogether this structure suggests recognition of a N2Y3A4Y5U6N7N8 consensus sequence (Y is a pyrimidine) by the tandem RRMs of DND1 as cytidines in the positions of U3 and U5 would likely be accepted without significant disruption of the hydrogen-bonding network.The Uate Fig. . The U7 The binding topology on the eRRM1 and RRM2 is unusual with the involvement of a nucleotide-binding pocket in α-helix 2 of an RRM. As the β-hairpin extension and RNA-binding residues on eRRM1 are conserved in the hnRNPR-like family of RNA-binding proteins . Pleasingly, 29 of these 45 differentially present proteins were also found in ref. 27 30. One of its many functions is the regulation of mESC differentiation31. Just like DND1, it also has been reported to increase mRNA stability by blocking microRNA-targeting32. The RNA helicase MOV10, also enriched in our DND1 WT immunoprecipitations over the R98A RNA-binding mutant, is expressed in the cytoplasm and exhibits diffused staining there similar to WT and R98A DND1  for their formation35. In addition, several other proteins enriched in the WT DND1 target HEK293T interactome are also enriched in stress granules and/or P-bodies  analysis on three biological replicates, for identification and quantification of the pulled-down interactome . This suggested that the dsRBD might contribute to the binding of a subset of RNA targets regulated by these RBPs, or it recruits them to modulate the target specificity of DND1. Interestingly, TNRC6B is known to recruit CCR4-NOT. Finally, a surprising finding from the analysis by GO-term was enrichment of the “ATPase activity” classification. DDX17 is an RNA helicase, while HSPA1A, HSPA6, and TRAP1 are chaperones belonging to the heat shock protein family that assists protein folding upon ATP hydrolysis; these proteins recognize hydrophobic surfaces of unfolded proteins and partially folded intermediates, thereby preventing protein aggregation39 and Human Antigen D . In all these structures, RNA stretches of 8–10 nucleotides are bound in a canonical fashion, with higher affinity and specificity than if a single domain is used. In all aforementioned tandem RRM structures, the bound RNA adopts an extended conformation. The RRM-RRM orientation and RNA-binding topology are rather different in our DND1–RNA structure, which is a result of four structural features found so far only in this complex: the lack of a canonical RNA-binding surface for RRM2, an ultra-short inter-RRM linker, an extended RNA-binding surface of eRRM1, and the presence of an RNA-binding pocket on α-helix 2 of RRM2. The complex embeds only a short canonical RNA-binding stretch (U3-A4), which is followed by binding of U5-U7 in a highly unusual manner. Indeed, U6 is bound by RRM2 α-helix 2, resulting in a 120° rotation of U6 compared to A4 and the U6 phosphate. U7 ribose and G8 phosphate are bound by the tip of the eRRM1 extension. Binding by the linker residues supports the RNA in this unique conformation, its short length being likely crucial to bridge nucleotides specifically recognized by RRM2 and eRRM1. The path of the RNA backbone is reversed, and the RNA is more compacted than in previously determined tandem RRM-RNA complexes; the U3-G8 phosphates span ~21–23 Å HuD—Fig. 22, or an3 Å Fig. , while f3 Å Fig. . Such ba12, the DND1 N-terminal extension of a β-hairpin packed on a third α-helix is so far restricted to the hnRNPR-like family of proteins  found in the SYNCRIP structure. Our structure reveals that this N-terminal extension is essential for increasing the affinity to the RNA by fixing the backbone of U6 and G8 on the eRRM1 via Q39 and N37, respectively  was the only mutant outside of RRM1 causing loss of function26. Most other loss-of-function mutants in this study can be explained using our structure. We have already described that the zebrafish Y104 RNP mutant  is unstable in vitro. Even if it would be stable in vivo, interactions with U5 would be lost. The equivalents of Y72, F89, and H121 in zebrafish dnd1 are Y70, F87, and H119 in human DND1, which are important residues stabilizing the RRM fold. Y70 is particularly important for the interaction between α-helices 0 and 1 in eRRM1, linking the core RRM fold and the N-terminal eRRM extension. Mutation of these residues most likely disrupts the fold of eRRM1. The only loss-of-function mutant that is not that easily explained is N94K, a mutant of the equivalent T92 in the human protein, situated in the β2–β3 loop. This residue is in close proximity to G8 in our structure, but not well enough defined to interpret a specific interaction with the RNA. In the context of a longer RNA it could be involved in such specific binding. Finally, it should be mentioned that the Ter mutation, causing germ cell loss and testicular teratomas in mice8, is a truncation at the equivalent of R190 in α-helix 2, further confirming that RRM2 and the dsRBD are essential domains for DND1 function. The atypical binding pocket in RRM2 increases affinity and specificity to the readout of eRRM1 and creates a remarkable turn in the RNA backbone.We have shown that the primary RNA interaction interface of DND1 lies on eRRM1. It is the proximity of the second RRM, lacking a canonical RNA-binding interface, but presenting an atypical pocket for stabilization of this primary binding, that makes the RNA-binding topology by the tandem RRMs unusual. Structures of several types of RRM domains without aromatic residues on the β-sheet surface have been described11 was used in our structure determination as the RNA target. The motif Y3A4Y5U6N7N8 derived from our structure also fits with the [A/G/U]AU[C/G/U]A[A/U] motif enriched in RIP targets7. Moreover, this motif may be interpreted as a repetition of the UAU motif, containing 2 adenosines that are specifically recognized in our structure. We have not tested binding to an oligonucleotide containing two spaced adenosines, but the increased affinity (avidity) of RBPs binding to repetitions of high-affinity motifs has been demonstrated for several other RRM-containing proteins: hnRNPG45, PTB41, hnRNP C46, and more recently ELAVL1/HuR47. Our structure also provides some insight into how the residues outside of the YAY motif could be recognized. For example, the binding pocket on RRM2 specifically recognizing U6 in our structure , while RRM2 locks the uridine (U6) in its hrRRM binding pocket which can then initiate downstream processes from a fixed position  are rather less conserved  have been identified, that control the fate of ARE-containing mRNAs55. Because DND1 CLIP and RIP-enriched targets do not necessarily contain the canonical ARE AUUUA pentamer target sequence, DND1 can be classified as a germline-specific AU-rich RBP (AUBP) targeting class III AREs. The recruitment of degradation machineries to mRNAs for their destruction is a unifying mechanism between several AUBPs58, which could be shared by DND1 in one of its dual roles. The opposing, stabilizing role might depend on a network of other stabilizing RNA-binding proteins such as ELAVL1/HuR that is present in our pulldown. Interestingly, HuR shares with DND1 a possible function in stabilizing mRNA targets by inhibiting miRNA activity. Precisely how DND1 functions is likely target- and context-dependent as it may depend on the presence and concentration of competing factors. As multiple AUBPs may modulate the stability and translation of a single ARE-mRNA, questions of functional redundancy and additivity or antagonism arise. It is likely that variations in the relative amounts of mRNAs, in the AUBPs present within a certain cell type or developmental stage and in the binding affinities, determine both the identity of the targeted mRNAs and their fate48. On top of that, binding- and unbinding equilibria are altered in RNP-granular environments, where RNAs and RBPs are concentrated in a small volume. The sole fact that DND1 is specifically expressed in the germline will be a major contributing factor to its target specificity. Because of this highly complex context- and target dependence, the composition of the DND1 interactome found in our pulldowns cannot be confidently extrapolated to the process in developing germ cells. Thus, the interpretation of DND1 function based on reporter assays in non-native cell types should be addressed with caution. Similarly, the relevance of recognition motif derivation in large-scale DND1–RNA interaction studies performed in non-native cell types with transcriptomes differing from developing germ cells and using cross-linking that might overrepresent low-affinity motifs should also be interpreted with caution. The structural and biophysical work in this study raises our understanding of the requirements for a high-affinity RNA-binding motif in DND1. In turn, this helps to reinterpret the results from previous studies in order to better understand the complex gene-regulation networks during germline development and maintenance.DND1 binds UAUU which is contained in AU-rich sequence elements (AREs) in 3’UTRs that have long been known to target mRNAs for rapid degradation in mammals. AREs are divided into three classes with respect to the copy number of the canonical pentamer AUUUA sequence: several copies are dispersed between U-rich regions in class I, are clustered in class II, while class III are predominantly U-rich and lack these canonical pentamers1”). This contrasts with the RRM3 of another AUBP, HuR, that recognizes both AU-rich and U-rich sequences with similar affinities, the latter slightly higher due to an avidity effect47. Adenosines are bound by HuR RRM3 in two different positions: either on the periphery, or β-strand 3 using RNP1 (position “N2”). They are important to localize the protein at a precise position within the 3’UTR. Such a “locking” mechanism has been also proposed for the cytoplasmic polyadenylation element (CPE)-binding (CPEB) family of RNA-binding proteins. These RBPs bind the CPE sequence UUUUAU, which activates translation by cytoplasmic polyadenylation. The CPEB4 and CPEB1 tandem RRMs bind a UUUUA pentanucleotide sequence-specifically17. While the uridines are bound by RRM1, the adenosine is specifically recognized by RRM2 using RNP2 (position “N1”). RRM1 in isolation has a low affinity for U-rich RNAs and RRM2 does not bind U-rich sequences. Therefore, it is proposed that the protein is recruited to U-rich motifs through RRM1, after which it scans the 3’UTR in an open conformation until it encounters an adenosine in a consensus CPE and locks the protein onto this sequence. This is a similar mechanism as we now propose for DND1, although in our case the scanning for a high-affinity motif likely happens in a closed rather than open conformation since the isolated RRM1 does not bind U-rich sequences. This original mode of RNA target selection therefore could be a general mechanism for cytoplasmic RNA-binding proteins regulating RNA via their 3’ UTR.We have demonstrated here that DND1 prefers AU-rich over U-rich RNAs and that a central adenosine is required for high-affinity binding. The adenosine is specifically recognized by the eRRM1 binding pocket involving RNP2 and the interdomain linker , a germ-granular structure in mouse spermatocytes61. The remarkable and reproducible presence of TRAP1 as its folding chaperone suggests that DND1 could be targeted to mitochondria in HEK293T and perhaps by a similar mechanism in developing germ cells62. Taken together with the other results from our differential interactome maps, this suggests DND1 might be part of germ granules localized near mitochondria regulating gene expression post-transcriptionally.Although we found that overexpressed DND1 likely is not localized to RNP granules in HEK293T cells, the presence of many proteins found in such condensates in its interactome raises the question if DND1-dependent target regulation could occur in an RNP granule-like environment in its native environment in developing germ cells. There is considerable overlap between stress, P-body, and germ granules (RNP granules unique to the germline), although the latter are thought to be maintained as distinct RNP assemblies in germ cellsIn conclusion, we provide here the first structural and mechanistic insight into the molecular mechanisms by which the RNA-binding protein DND1 regulates a subset of mRNAs and in turn might stabilize the fate of germ cells. Our results hint at a specialized function of the individual RNA-binding domains of DND1, where the tandem RRMs are mainly responsible for target binding and the dsRBD for binding of a subset of targets and target regulation. This possibly occurs through the recruitment of regulatory factors, other RBPs that modulate the specificity of DND1, or displacement of competing RBPs. Our structure unifies DND1–RNA recognition elements recently found enriched in genome-wide interaction studies and facilitates understanding of loss-of-function mutants previously described in the literature. In addition, we have demonstrated an additional means by which an RNA recognition motif can recognize RNA, extending the repertoire of this versatile and abundant RNA-binding domain.A table of resources and reagents is supplied as Supplementary Table E. coli BL21(DE3) cells (Novagen) in Studier-medium P-5052 supplemented with 15NH4Cl or P-50501 supplemented with 15NH4Cl and 13C-glycerol (CIL). Precultures were grown in PA-0.5G medium64. Random fractionally deuterated protein for the recording of triple-resonance spectra for backbone assignment was expressed in 100% D2O (CIL) in which the media components were directly dissolved. Protein was expressed for 60 h at 15 °C in the presence of 100 µg/mL Kanamycin. Cells were harvested by centrifugation at 4 °C, 15 min at 2600×g, and the cell pellet was resuspended in lysis buffer , 10 mM imidazole, and 2 mM 2-mercaptoethanol). Cells were lysed with two freeze-thaw cycles and three passes through the Emulsiflex cell cracker (Avestin). Before lysis 0.5 mg/ml lysozyme, 25 μg/ml DNAseI and 1 mM Pefabloc SC (Sigma-Aldrich) was added. After centrifugation at 4 °C for 20 min at 43,000 × g, the cleared supernatant was sterile-filtered and loaded onto 2 mL Ni-NTA beads (Qiagen), equilibrated with lysis buffer, per liter of bacterial culture. The column was washed with 10 column volumes of lysis buffer, 20 columns of lysis buffer without Triton and 5 column volumes of the same buffer with 30 mM Imidazole, before the protein was eluted with elution buffer (lysis buffer without Triton and with 330 mM imidazole). For cleavage of the His6 tag, the pooled fractions were dialyzed against lysis buffer (1 M NaCl and no imidazole) in the presence of in-house purified TEV protease (1:100 w/w TEV:protein) at 4 °C overnight. Next day the TEV cleavage reaction was reloaded three times over a fresh Ni-NTA column to remove the His6-TEV protease, the His6-tag fusion and contaminating proteins. The proteins were concentrated with Vivaspin 20-mL centrifugal devices with 5000 or 10,000 MWCO (Sartorius) and buffer-exchanged into NMR buffer over PD-10 gel-filtration columns .DNA fragments encoding human Dnd1 RRM1 (12–139), RRM2 (136–227) or the tandem RRM12 (12–235) were PCR amplified from the source plasmid pCMV-SPORT6-hsDnd1, an IMAGE cDNA clone  purchased from Source BioScience (Nottingham UK) with the primers listed in Supplementary Table O-TOM protected ribonucleoside phosphoramidites and solid supports containing [13C5]-labeled ribose moieties were synthesized as described, followed by their sequence-specific introduction into the CUUAUUUG oligonucleotide65.Unlabeled RNA oligonucleotides were purchased from Dharmacon, deprotected according to the manufacturer’s instructions, lyophilized, and resuspended twice in water for large-scale protein–RNA complex production, NMR buffer for titrations or ITC buffer. For the solid phase synthesis of selectively ribose-labeled oligos 2′-260nm/A280nm and concentration was estimated using A280nm and a theoretical extinction coefficient of 18140 M−1 cm−1 for RRM1, 5930 M−1 cm−1 for RRM2, and 23470 M−1 cm−1 for RRM12. RNA concentrations were estimated using OligoCalc66. In the final buffer exchange step the RRM constructs were added dropwise to a 10% molar excess of RNA in the presence of 10ul of SuperaseIn RNase inhibitor (Ambion) per sample, concentrated, and further purified by size exclusion chromatography on a Sephadex 75 10/30 column  in 100 mM K2HPO4/KH2PO4 pH 6.6, 1 mM DTT. The fractions containing the protein–RNA complex were concentrated to 400–700 uM with Vivaspin 5-mL centrifugal devices with 10,000 MWCO (Sartorius). Before the measurements, a 10% molar excess of RNA was added to saturate the protein as well as 10% v/v D2O. Complexes were lyophilized before resuspending in D2O for NMR experiments that are conducted in deuterated solvent.Final protein was analyzed for nucleic acid contamination using A18 and M-MuLV Reverse Transcriptase RNaseH− (Finnzymes). The 3’UTR of TERT and fragments corresponding to positions 183–282 (according to Ensembl transcript ENST00000228872) of the 3’UTR of p27, including the predicted miR-221 binding sites, were amplified from the cDNA templates using the appropriate primers in Supplementary Table Total RNA was extracted from cultured human fibroblasts . In all, 1 µg was then used for reverse transcription reaction using Oligo(dT)2 at 37 °C.The cells were transfected with Lipofectamine 2000 Reagent (Invitrogen) after seeding them 16 h prior at 70,000 cells per well (24-well plate) or 2.8 × 106 cells per 10 cm dish for immunoblotting analysis and RNA immunoprecipitation. For transfections in 24-well plates, Lipofectamine 2000 Reagent was diluted in serum-free medium  and incubated for 5 min at room temperature. Plasmid DNA (0.5 μg per plasmid as indicated) and/or 50 nM final miR-221-3p miRNA mimic  or control mimic was then added, vortexed, and incubated for 20 min at room temperature while cell culture media was exchanged to DMEM containing 10% FBS without antibiotics. Finally, the transfection complexes were added to the cell culture vessel in a dropwise manner while swirling. Transfection media were changed 6 h later to regular culture media. Luciferase activity was measured 48 h after transfection using the Dual-Glo Luciferase Assay System  on a GloMax® Discover Multimode Microplate Reader . The results are represented as means and standard deviation (SD) from three independent experiments.HEK293T cells (Homo sapiens) were obtained from ATCC (CRL-3216). No further authentication of cell lines was performed. Cells were routinely checked for mycoplasma contamination. They were cultured in Dulbecco’s Modified Eagle Medium  containing 10% fetal bovine serum  including antibiotics ) in a humidified incubator  with 5% CO5 HEK293T cells using a RIPA buffer  complemented with EDTA-free protease inhibitor cocktail (Roche) followed by brief sonication. Protein concentrations were determined by DC Assay (BioRad). For each sample, 14 µg of total cellular protein was separated on 12% SDS-PAGE gels and transferred on PVDF membranes. The following antibody was used: FLAG-M2-HRP . Immunoblots were developed using the Clarify TM Western ECL substrate (BioRad) kit and were detected using an imaging system (ChemiDocTM MP—BioRad). All membranes were stained using a coomassie blue staining solution to ensure equal loading. The analysis was performed in triplicate.Total cellular protein was extracted from 6 × 1067. Briefly, subconfluent cells from one 10-cm dish were harvested 48 h after transfection, washed in PBS 1×, and cross-linked with 1% formaldehyde. Glycine  was added to quench the formaldehyde. Cells were pelleted by centrifugation and washed with PBS 1×. Immunoprecipitation (IP) lysis buffer , 1 mM DTT, 0.5% Triton X-100, 10% glycerol) containing 1 mM PMSF, protease inhibitors (Roche), and RNase inhibitor (Thermo Scientific) was added to the cell pellet. After sonication , cell lysates were precleared with IP-Lysis buffer containing 1% BSA. 40 µl of magnetic FLAG-M2 beads  were added to the precleared cell lysate and incubated on a rotary wheel at 4 °C overnight. FLAG-M2 beads were washed with IP lysis buffer five times and pelleted by centrifugation. RIP buffer  containing RNase inhibitor was added to the pellet and incubated 1 hour at 70 °C to reverse the cross-links. After centrifugation, the supernatant was used for RNA extraction using TRIzol reagent (Life Technologies) followed by a DNase-I treatment and a subsequent reverse transcription with oligo d[T]18 using the GoScript RT kit (Promega). One-step RT-qPCR was performed using the SYBR FAST Mix optimized for LightCycler 480  with primers listed in Supplementary Table −ΔCt). ΔCt is an average of (Ct [RIP] – (Ct [Input]) of technical triplicates with SD < 0.3. Three independent RIP experiments were performed.The RNA-immunoprecipitation (RIP) procedure was adapted from Vogt and Taylor6 HEK293T cells were seeded the night prior to transfection per 10-cm dish. In total, 24 µg of DNA per sample was transfected using 50 µl of Lipofectamine 2000. Cells were harvested, cell pellet washed with 1×PBS and resuspended in 1 ml IP-Lysis buffer  supplemented with PMSF and complete (Roche) protease inhibitors. Samples were incubated on ice for 10 min and sonicated. Lysates were centrifuged at 14,000 × g at 4 °C. Protein was quantified using the DC protein assay (BioRad) and normalized to 1 mg/ml total protein. 40 µl of FLAG-M2 magnetic beads (SIGMA) (50% slurry) per IP were washed 3× using 750 µl IP-Lysis buffer. Lysate was added and incubated overnight at 4 °C.In all, 1.2 × 10For western blotting, after removal of the supernatant, the beads were washed gently twice in IP-Lysis buffer, followed by five times in 1× TBS (pH 7.4). Protein was eluted by adding 100 µl 0.1 M glycine pH 3.0 to the beads, followed by shaking for 6 min at 23 °C and 600 rpm. In total, 10 µl 0.5 M Tris-HCl, pH 7.4 was added to the supernatant and 10 µl of elution was used for WB analysis.4CO3 and once with 100 µl acetonitrile. In total, 45 µl of digestion buffer  and 5 µl trypsin (100 ng/µl in 10 mM HCl) were added. Digestion for 30 min at 60 °C was assisted by microwave. The supernatant was collected and peptides were extracted from the beads with 150 µl of 0.1% trifluoroacetic acid/50% acetonitrile. Supernatants were combined and dried, dissolved in 20 µl 0.1% formic acid, further diluted 1:10 in 0.1% formic acid of which 2 µl were injected.For shotgun LC-MS/MS analysis, the beads were washed twice with 100 µl 50% acetonitrile/NHm/z) at a resolution of 70,000 at 200 m/z after accumulation to a target value of 3,000,000, followed by HCD (higher-energy collision dissociation) fragmentation on the twelve most intense signals per cycle. HCD spectra were acquired at a resolution of 35,000 using a normalized collision energy of 25 and a maximum injection time of 120 ms. The automatic gain control (AGC) was set to 50,000 ions. Charge state screening was enabled and singly and unassigned charge states were rejected. Only precursors with intensity above 25000 were selected for MS/MS. Precursor masses previously selected for MS/MS measurement were excluded from further selection for 40 s, and the exclusion window was set at 10 ppm. The samples were acquired using internal lock mass calibration on m/z 371.1010 and 445.1200. Database searches were performed using the Mascot search engine against SwissProt. Scaffold (4.8.9) was used to filter sequence assignments with a protein threshold set to 1% false discovery rate (FDR), minimum number of peptides 2 and peptide threshold set to 0.1% FDR. Enrichment of pulled-down proteins was calculated by dividing the number of exclusive unique spectral counts found for that protein by the total number of exclusive unique spectral counts in that sample. To compare enrichment for WT DND1 to the mutants, multiple t-tests were performed on the median enrichment values of three independent pulldowns using GraphPad Prism 7.04. Discoveries were determined using the Two-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli, with Q/FDR = 10%. Proteins with less than two exclusive unique spectral counts in the DND1 WT pulldown (median of the triplicate experiment) were excluded from the analysis  connected to a Q Exactive mass spectrometer (Thermo Scientific) equipped with a Digital PicoView source (New Objective). Solvent composition at the two channels was 0.1% formic acid for channel A and 0.1% formic acid, 99.9% acetonitrile for channel B. For each sample, 2 µl were injected. Peptides were trapped on a Symmetry C18 trap column  and separated on a BEH300 C18 column  at a flow rate of 300 nL/min by a gradient from 5 to 35% B in 90 min, 60% B in 5 min and 80% B in 1 min. The mass spectrometer was operated in data-dependent mode (DDA), acquiring a full-scan MS spectrum (350−1500 HEK293T cells were transfected with Lipofectamine 2000 Reagent (Invitrogen) after seeding them 16 h prior at 200,000 cells per well (six-well plate). For transfections in a six-well plate, 2.0 μg of respective plasmid was used. The transfection protocol was as described for 24-well plates. Coverslips in a six-well plate were incubated with Poly-L-Lysine (Sigma P4707) diluted in PBS 1× to a final concentration of 1 µg/ml for 1 h at 37 °C, following this they were washed three times with PBS 1×. Twenty-four hours post transfection, cells were trypsinized and passaged onto Poly-L-Lysine coated coverslips to be processed for indirect immunofluorescence 48 h post transfection.2, 300 mM Sucrose) for 7 min at room temperature. Cells were next blocked in PBS 1× supplemented with 1% BSA and 0.1% Tween-20 for 20 min at room temperature. Samples were incubated with primary antibodies diluted in blocking solution HuR , MOV10 , FLAG-M2  for 1 h at room temperature. Washes in PBS 1× + 0.1% Tween-20 were repeated three times and samples were incubated with secondary antibodies diluted in blocking solution (Alexa fluor 488 donkey anti-mouse IgG (IF 1:2000) Life Technologies A21202; Alexa fluor 546 donkey anti-rabbit IgG (IF 1:2000) Life Technologies A10040) for 1 h at room temperature. Samples were again washed three times with PBS 1× + 0.1% Tween-20 and incubated with DAPI at a final concentration of 0.1 μl/ml diluted in PBS 1× for 4 min at room temperature to counterstain nuclei. After a final wash in PBS 1×, coverslips were mounted on slides on a drop of antifade mounting media vecta shield (Vector labs H1000). Slides were imaged using a DeltaVision Multiplexed system with an Olympus IX71 inverse microscope equipped with a 60×1.4NA DIC Oil PlanApoN objective and a pco.edge 5.5 camera. Images were deconvolved using softWoRx (Applied Biosystems) and further processed using FIJI.After aspirating media, cells were washed with PBS 1× and fixed with 3.7% formaldehyde solution for 10 min at room temperature, cells were washed with PBS 1× and permeabilized in ice-cold CSK buffer  and NMRPipe69 and analyzed with NMR-FAM-SPARKY70. Sequence-specific backbone assignments were 93% complete for non-proline residues and were obtained from 2D 1H-15N HSQC, 2D 1H-13C-HSQC, 3D 1H-15N-NOESY (tmix = 120 ms) and a suite of 3D TROSY-based backbone experiments CO, HNCA, HN(CO)CA, HNCACB and HN(CO)CACB)72 run on a random fractionally deuterated 13C,15N-labeled (1:1.1) tandem RRM-CUUAUUUG complex. Sidechain protons were assigned to 80% completeness using 3D 1H-15N-NOESY (tmix = 120 ms), 3D 1H-13C-HMQC-NOESY (tmix = 70 ms), 3D 1H-13C-HSQC-aromatic-NOESY (tmix = 80 ms), 3D (H)CCH- (tmix = 21.7 ms) and HC(C)H-TOCSY (tmix = 23 ms) and 3D H(C)CH-COSY on a sample of fully protonated 13C,15N-labeled (1:1.1) tandem RRM-CUUAUUUG complex. Sidechains in the free 13C,15N-labeled RRM2 were assigned using H(C)(CCCO)NH-TOCSY (tmix = 17.75 ms) and (H)C(CCCO)NH-TOCSY (tmix = 17.75 ms) and transferred to the RRM12-CUUAUUUG complex where this was possible. RNA was assigned using the following set of spectra: 2D TOCSY (tmix = 60 ms), 2D NOESY (tmix = 150 ms) recorded on a 1:1 complex of unlabeled tandem RRM-CUUAUUUG complex. 2D 1H-13C-HSQC recorded on 1:1 complexes between unlabeled tandem RRMs and selectively 13C-ribose-labeled C*UU*AU*UU*G or CU*UA*UU*UG* RNA where an asterisk after the nucleotide represents a 13C-labeled ribose moiety. 2D F2 filtered NOESY (tmix = 120 ms) and 2D F1-filtered, F2-filtered NOESY recorded on 13C,15N-labeled tandem RRMs in 1:1 complex with 1) unlabeled CUUAUUUG RNA 2) selectively 13C-ribose-labeled C*UU*AU*UU*G RNA 3) selectively 13C-ribose-labeled CU*UA*UU*UG* RNA. Sugar puckers in the complex were identified from 2D 1H-1H- TOCSY (tmix = 60 ms). Strong H1’-H2’ and weak H3’-H4’ cross-peaks defined all puckers as C2’-endo. All χ dihedral angles were restrained to anti conformations based on lack of strong intraresidue H1’-H6/H8 NOEs. Intermolecular NOEs were identified using 2D 13C F2 filtered 2D NOESY (tmix = 60 ms) and 3D F3 filtered, F2-edited 13C HMQC-NOESY (tmix = 70 ms) in D2O. Intramolecular NOEs of RNA were identified using 2D 13C F1 filtered, F2 filtered NOESY (tmix = 150 ms) in D2O.All NMR spectra were recorded at 298 K on Bruker AVIII600 MHz, AVIII700 MHz, and Avance 900 MHz spectrometers equipped with cryoprobes and a Bruker AVIII750MHz spectrometer using standard NMR experiments if not mentioned otherwise15N-labeled protein (0.1–0.2 mM) in NMR buffer  and monitored by 1H-15N-HSQC. To monitor the chemical shift perturbations of the tandem RRM mutants upon addition of RNA, 1:1 complexes were directly prepared in NMR buffer  as described under “NMR sample preparation”.NMR titrations were performed by adding unlabeled concentrated RNA (1–5 mM) to 13C,15N-labeled protein with unlabeled RNA on a Bruker AVANCE 750 MHz spectrometer at 298 K. The heteronuclear 15N–{1H} NOE values were measured with reference and NOE experiments recorded in an interleaved fashion, employing water flip-back pulses. Heteronuclear NOE values are reported as the ratio of peak heights in paired spectra collected with and without an initial period (4 s) of proton saturation during the 5-s recycle delay. 15N T1 and T2 values were measured using TROSY-based pseudo-3D experiments employing flip-back pulses and gradient selection73. T1 spectra were acquired with delays, T = 40, 150, 300, 500, 900, 1500, 2200, and 3000 ms, T2 spectra were acquired with CPMG delays, T = 17, 34, 51, 68, 103, 137, 188, and 239 ms. 15N T1 and T2 values were extracted by plotting the decay of HN volumes and fitting the curves with standard exponential equations using the nlinLS lineshape fitting program within the NMRPipe package69. Residual dipolar coupling (RDC) restraints were extracted using 1H-15N TROSY run on a fully protonated 15N-labeled (1:1.1) tandem RRM-CUUAUUUG complex in NMR buffer (isotropic dataset) and NMR buffer mixed with 4.2% C12E5 polyethylene glycol/hexanol medium74 (anisotropic dataset). RDCs were derived by subtracting the isotropic from anisotropic 1H chemical shift differences between TROSY and anti-TROSY spectra recorded in an interleaved manner. Only un-overlapped peaks were analyzed and RDC restraints were employed only for structured residues with 15N het-NOE values larger than 0.6. The RDC rhombicity and anisotropy components were determined in CYANA by grid-search using an initial protein structure and further refined in subsequent structure calculations.Backbone dynamics data of the tandem RRMs in complex with CUUAUUUG were recorded on a 1:1.1 complex of random fractionally deuterated 1H–15N NOESY (tmix = 80 ms) and 3D 1H–13C HMQC-NOESY (tmix = 70 ms), 3D 1H-13C HSQC-aroNOESY (tmix = 80 ms) and 2D NOESY (tmix = 80 ms). The protein resonance assignments of the tandem RRM-CUUAUUUG complex and a list of manually assigned protein core NOEs were used as input for automatic peak picking and NOESY assignment using ATNOSCANDID76 in a two-step procedure. First, intra-RRM NOEs were assigned by including only resonance assignments for one individual RRM in two separate runs. Second, an ATNOSCANDID NOE assignment was performed using all resonance assignments. In this run, a list of upper limit distance restraints combining the restraints obtained in the runs performed with assignments for the individual RRMs was included. This procedure was found to be necessary to obtain the correct global topology for the two RRMs. The resulting peak lists were then checked and supplemented manually with additional picked peaks and several critical manual NOE assignments. The optimized NOESY peak lists from this procedure were re-assigned with the NOEASSIGN module of CYANA 3.9677 while iteratively adjusting and keeping key manual assignments fixed during iterative refinement of the structure. Intraprotein hydrogen bonds were identified for HN resonances which were protected during hydrogen–deuterium exchange by reference to intermediate structures and added as restraints in further rounds of structure calculation. Following the determination of the protein structure in the bound state, the structure of the complex was determined. Intra-RNA and intermolecular NOESY peaks were picked and assigned manually and calibrated using known distances of H5-H6 cross-peaks of pyrimidines. In structure calculations including the RNA, unambiguous intermolecular NOEs were included first for the initial positioning of the nucleotides. Intermolecular NOEs with ambiguous assignments were then included as ambiguous restraints in CYANA and assigned unambiguously based on preliminary calculations. To further confirm the intermolecular restraints, we back-calculated short intermolecular distances from our final structures and inspected the spectra for the completeness of intermolecular NOEs. Final structure calculations in CYANA included intraprotein, intra-RNA and intermolecular NOEs, protein dihedral backbone restraints, intraprotein hydrogen bond restraints, and restraints for sugar pucker and syn or anti conformations identified from NOE patterns of H6 or H8 resonances. Protein dihedral backbone restraints derived from TALOS+78 and additional manually defined β-hairpin turn restraints were used for the N-terminal β-hairpin extension. In the final structure calculation, 500 structures were calculated with CYANA, and the 50 lowest energy structures were selected for refinement with the SANDER module of AMBER1279 using the ff12SB force field with implicit solvent, and 20 were selected based on the criteria of lowest amber energy and lowest intermolecular restraint violations.Intramolecular protein distance restraints were derived from 3D 2HPO4/KH2PO4 pH 6.6, 1 mM 2-mercaptoethanol). RNA (100–400 µM) was dissolved in ITC buffer and titrated into protein (3.5–11 µM) in 2 µL followed by 8 µL (RRM12) or 10 µL (RRM1) steps every 300 s at 25 °C with a stirring rate of 307 rpm. Raw data were integrated, normalized for the molar concentration, and analyzed using the Origin 7.0 software according to a 1:1 RNA:protein ratio binding model. N was set to 1 for weak interactions.ITC experiments were performed on a VP-ITC microcalorimeter . Freshly purified protein was buffer-exchanged by gel filtration into ITC buffer (100 mM KFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary