Alu elements and long flanking introns to be highly deregulated upon SFPQ knockdown. Moreover, SFPQ depletion leads to increased intron retention with concomitant induction of cryptic splicing, premature transcription termination, and polyadenylation, particularly prevalent for long introns. Aberrant splicing in the upstream and downstream regions of circRNA producing exons are critical for shaping the circRNAome, and specifically, we identify missplicing in the immediate upstream region to be a conserved driver of circRNA biogenesis. Collectively, our data show that SFPQ plays an important role in maintaining intron integrity by ensuring accurate splicing of long introns, and disclose novel features governing Alu-independent circRNA production.Circular RNAs (circRNAs) represent an abundant and conserved entity of non-coding RNAs; however, the principles of biogenesis are currently not fully understood. Here, we identify two factors, splicing factor proline/glutamine rich (SFPQ) and non-POU domain-containing octamer-binding protein (NONO), to be enriched around circRNA loci. We observe a subclass of circRNAs, coined DALI circRNAs, with distal inverted  Gene expression is the output of multiple tightly coupled and controlled steps within the cell, which are highly regulated by a variety of factors and processes. Among these are the physical and functional interactions between the transcriptional and splicing machineries that are of great importance for the generation of both canonical and alternative isoforms of RNA transcripts. This includes a novel class of unique, closed circular RNA (circRNA) molecules.Alu repeats are found to be significantly enriched in the flanking introns of circRNAs  family with highly conserved RNA recognition motifs (RRMs)   and theys (RRMs) . The pros (RRMs) , where ts (RRMs) , pre-mRNs (RRMs)  and 3’ens (RRMs)  to nucles (RRMs)  and nucls (RRMs) . Recentls (RRMs)  represens (RRMs) .Alu elements. Moreover, we show that long introns in particular are prone to intron retention and alternative splicing with concomitant premature termination. While premature termination is not the main driver of circRNA deregulation, we provide evidence for a complex interplay between upstream (acting positively on circRNA production) and downstream features (acting negatively) that collectively govern the production of individual circRNAs in the absence of SFPQ. This not only elucidates a conserved role for SFPQ in circRNA regulation but also identifies upstream alternative splicing as an approach toward circRNA production.Here, we show that SFPQ depletion leads to specific deregulation of circRNAs with long flanking introns devoid of proximal inverted Alu element dependencies, we characterized the circRNAome in two of the main ENCODE cell lines, HepG2 and K562 (Alu elements (IAEs) are important for the biogenesis of a subset of circRNAs  qPCR in HepG2  and HEK2SFPQ mRNA appeared unaffected by SFPQ knockdown and greatly elevated upon NONO depletion  residing in intron eight of this gene .As an upstream termination impacts downstream elements, we determined the relative genic position of up- and downregulated PASs. This showed a clear and general 5’region tendency of upregulated vs downregulated intronic PASs , suggestCollectively, this suggests that a notable fraction of genes exhibit alternative splicing and premature termination upon SFPQ knockdown with increased probability for longer introns, underscoring, once again, the importance of SFPQ in gene expression. Similarily, U1 snRNP has been shown to be important for repression of upstream, cryptic PAS usage in introns, known as U1 telescripting . This raIf SFPQ depletion results in wide-spread increase in premature termination, the observed deregulation of circRNAs in our dataset could simply be explained by incomplete transcription and not as a biogenesis effect per se. This notion is consistent with the fact that circRNAs in general and DALI circRNAs in particular associate with long flanking introns prone to alternative splicing and premature termination. However, not all circRNAs were depleted upon SFPQ knockdown and particularly in mouse brain, the DALI circRNAs were affected in both directions (i.e. up- and downregulated). To test whether the deregulation of circRNAs is driven by premature termination, we stratified circRNAs by their host gene clusters. This showed that while most circRNAs derive from kc1, 2, and 4, roughly the same expression profile is observed across all clusters . In addiBut what is then the underlying explanation for the deregulated expression of DALI circRNAs upon SFPQ depletion? As no single feature captures the circRNA deregulation accurately, we turned to multivariate regression analysis. Here, we collected a number of genic features , and differential expression data upon SFPQ depletion  . PairwisSplitting the quantified circRNAs into train and test sets (80:20 ratio), we trained a generalized linear model (GLM) against the observed circRNA log2foldchange. As all features were standardized, the resulting coefficients serve as a proxy for feature importance. Here, ranking features by coefficient, it is evident for both HepG2 and mouse brain that 5’ features generally correlate positively with circRNA production, whereas 3’ features (and IAE distance) correlate negatively . As seenAlu elements are frequently found close to circRNA exons , that the absence of SFPQ results in aberrant splicing with extensive induction of cryptic splice acceptor sites, particularly in long introns. This correlates with a similar increase in intron retention, suggesting that these two phenotypes are closely coupled. Consistently, recent studies have shown SFPQ to associate with long introns  and HepG2  were cultured in Dulbecco’s modified Eagle’s media (DMEM) with GlutaMAX (Thermo Fisher Scientific) supplemented with 10% foetal bovine serum (FBS) and 1% penicillin/streptomycin sulphate (P/S). All cells were kept at 37°C in a humidified chamber with 5% COOne μg of DNase-treated total RNA was reverse transcribed using the M-MLV Reverse Transcriptase kit (Thermo Fisher Scientific) according to manufacturer’s protocol with the use of random hexamers to prime the reaction. In case of RT-PCR, the reaction was conducted with 30 cycles of PCR with or without RT enzyme (Primers listed in Poly(A) enrichment was performed using NEBNext Poly(A) mRNA Magnetic Isolation Module (New England BioLabs Inc) according to manufacturer’s protocol with five μg total RNA from CTRL-KD or SFPQ-KD in HEK293T cells used as input.For total RNA sequencing of HEK293T, RNA from SFPQ, NONO, and CTRL KD  were rRNA depleted using RiboCop rRNA Depletion Kit V1.2 (Lexogen) according to manufacturer’s protocol. Subsequent cDNA libraries were prepared using SENSE Total RNA-Seq Library Prep Kit (Lexogen) following manufacturer’s protocol.For 3’end sequencing, cDNA libraries from HEK293T were prepared using QuantSeq 3' mRNA-Seq Library Prep Kit (Lexogen). For both methods, RNA quality was determined using the BioAnalyzer RNA nanochip (Agilent) and library concentration was quantified with KAPA Library Quant KIT RT-qPCR (Roche). Total RNAseq was done as 100nt paired-end sequencing and performed using the Illumnia platform , while for 3’end sequencing, 75nt single-end sequencing was performed at MOMA  on a NextSeq500. For total RNA sequencing of HepG2 cells, library preparation and sequencing was performed at BGI (Copenhagen) using BGIseq.2 dishes. Cells were harvested by trypsinization and resuspended in 2 ml PBS, 2 ml nuclear isolation buffer  and 6 ml water on ice for 20 min (with frequent mixing). Nuclei were pelleted by centrifugation at 2,500G for 15 min. Nuclear pellet was resuspended in 1 ml RIP buffer  and 5 mM dithiothreitol (DTT) supplemented with Ribolock (Thermo Fisher Scientific) and proteinase inhibitor cocktail (Roche). Resuspended nuclei were split into two fractions of 500 μl each (for Mock and IP). Nuclear membrane and debris were pelleted by centrifugation at 13,000 RPM for 20 min. Antibody to SFPQ (P2860 Sigma), NONO (ab70335 Abcam), or FLAG epitope  was added to supernatant (2.5 μg) and incubated for 4 hr at 4°C with gentle rotation. Of protein A/G beads, 20 μl were added and incubated for 1 hr at 4°C with gentle rotation. Beads isolated using magnetic, the supernatant were removed and beads were resuspended in 500 μl RIP buffer and repeated for a total of 5 RIP washes. Beads were divided into two fractions for protein (30%) and RNA (70%). Protein fraction was resuspended in 2xSDS loading buffer  and boiled at 95°C for 5 min. RNA fraction was resuspended in 1 ml TRIzol Reagent (Thermo Fisher Scientific).RIP was performed as previously described  with somCells were harvested in 1xPBS and centrifuged at 1200 rpm at 4°C for 5 min. For cell lysis, the cell pellet was collected and resuspended in 2xSDS loading buffer  and briefly boiled at 95°C for 5 min before loading 1% on a 10% Tris-Glycine SDS-PAGE gel (Thermo Fisher Scientific) and run for app. 1.5 hr at 125 V. The proteins were transferred to an Immobilon-P Transfer Membrane (EMD Millipore) by wet-blotting ON at 4°C at 25 V. Subsequently, the membrane was pre-blocked for 1 hr at RT with 10% skim milk, followed by 1 hr incubation with primary antibody  and 1 hrReads were mapped onto hg19 and mm10 for human and mouse data, respectively, with STAR v2.7,  and quanhttps://github.com/marvin-jens/find_circ) adhering to default settings, and only the shared predictions with ciri2 quantification were kept for analysis. circRNAs were annotated using annotate_circ.py . Flanking intron lengths were based on the mean total distance to the flanking exons based on gencode annotation , and IAE distance is the shortest possible Alu-mediated inverted repeat distance based on RepeatMasker (UCSC Genome Browser). For mouse, the IAE-distance is the shortest distance involving B1, B2 or B4 elements possible. DALI and PASI circRNAs were classified based on the median flanking intron lengths and median IAE distance in the sample. If no flanking introns were annotated, the circRNA was classified as ‘other’. Furthermore, circRNAs were classified as conserved if both splice sites coincide exactly with previously detected mouse circRNAs  but with [–minOverlap 5] to avoid quantification of any overlapping spliced reads.encodeproject.org  with parameters [--nomodel --shift 0 –g 2.9e9], quantseq peaks were extracted. Each peak was then subsequently quantified using featureCounts and analysed for the presence of polyA signal (A[AU]UAAA) and the presence of polyA-stretches within the locus or in the immediate flanking regions (+/- 50 nts). Based on gencode annotation, each peak was assigned as exonic, intronic, ambiguous, or intergenic.Quantseq reads were mapped onto hg19 using STAR as described above. The resulting bam-files were merged and divided into plus and minus-strand alignments. Then, using MACS2 peakcall  and visualizations were done in R using ggplot2 (v3.3.0) and GraphPad.2 and then pre-hybridized in Church buffer  at 37°C for 1 hr. For visualization, the membrane was probed with 5’ radioactively labeled DNA oligonucleotides at 37°C ON and washed twice in washing buffer  for 10 min at 25°C prior to exposure on a phosphoimager screen, and finally scanned using a Typhoon imager (Amersham).PAGE northern was conducted as previously described . Brieflygithub.com/ncrnalab/pyutils.Sequencing data has been deposited on GEO (accession no GSE157622), and scripts for RNAseq data processing are available at github:  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:We believe that your paper provides relevant mechanistic insights about splicing of long introns, the generation of circular RNAs and the interplay between these two processes.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, including Juan Valcárcel as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by James Manley as the Senior Editor.Thank you for submitting your article "The RNA-binding protein SFPQ preserves long-intron splicing and regulates circRNA biogenesis" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:Some circRNAs are flanked by inverted repeat elements, but many are not and this paper investigates the factors that control this latter class of RNAs. The authors find, using ENCODE circRNA and eCLIP data, as well as their own RIP results, that SFPQ is highly enriched in the introns flanking these circRNAs. siRNA depletion of SFPQ or its interacting partner NONO indeed regulate circRNA levels, concomitant with activation of alternative/cryptic splice sites, upregulation of intron retention and premature polyadenylation. Multivariate regression analyses indicate that upstream intron length and aberrant splicing in the immediate upstream region impact on circRNA biogenesis. The authors propose that a combination of aberrant splicing, premature termination and other elements control the production of circRNAs of the DALI  type.The manuscript provides new insights into circRNA biosynthesis and efficient splicing of long introns, showcases the complexity of the interplay between various mechanisms for proper RNA processing and provides original leads for the field to pursue. The manuscript however falls short of demonstrating direct effects of SFPQ on circRNA production and of proving the cause-effects of the interplay between RNA processing steps.Essential revisions:1) The authors should try to rescue the effects of the knock down by expressing wild type and mutant forms of SFPQ and thus rigorously prove that SFPQ is responsible for these activities and identify critical protein domains involved. They should also use minigenes to show that exon circularization depends on the presence of the SFPQ sites and test the contribution of intronic polyadenylation sites. SFPQ could help avoiding the use of premature cleavage and polyA sites, which are known to alter circRNA formation . The authors should discuss this in the context of the role of U1 snRNP on usage of intronic PAS (the telescripting mechanism uncovered by Dreyfuss and colleagues). The authors should also determine if U1 snRNA levels are altered as this may be part of the reason for the widespread transcription termination effects that are observed.2) Removal of SFPQ has very different effects on circRNA levels in HepG2 and HEK293T cells (mostly downregulation of DALI circRNAs) compared to in mouse brain . The authors should address this, to start with by looking into the CLIP-seq data from Takeuchi et al., 2018 and explore the binding of SFPQ to the introns flanking the down-regulated vs. up-regulated circRNAs. They should acknowledge the previous work by Takeuchi et al., 2018 on the function of SFPQ in transcription and discuss the drop-off phenotype showing that mice loss of SFPQ impairs transcriptional elongation accompanied by a decrease in RNA Pol II density. Essential revisions:1) The authors should try to rescue the effects of the knock down by expressing wild type and mutant forms of SFPQ and thus rigorously prove that SFPQ is responsible for these activities and identify critical protein domains involved.We thank the reviewers for this suggestion. Regarding a SFPQ rescue experiment, we have pursued several approaches. First, we tried targeting SFPQ in the 3’UTR using four different siRNAs followed by co-introduction of a SFPQ WT expression plasmid. However, we were unable to obtain any successful depletion of endogenous SFPQ by this method. Then, we designed a Myc-tagged SFPQ WT expressing plasmid resistant to one of the functional siRNAs (SFPQ siRNA#1) used in this paper. Unfortunately, effective co-transfection of SFPQ WT in conjunction with a 5-day knockdown protocol was challenging due to low transfection efficiency. Instead, we established a short-term 2-day, one-hit rescue setup. Here, western blotting demonstrated successful KD of endogenous SFPQ and Myc-SFPQ WT expression in our two day, one hit KD protocol in HEK293T cells . We observed a substantial rescue of our cryptic spliced transcript, DENND1A, supporting a direct role of SFPQ in regulating cryptic splicing. However, the two-day protocol is insufficient to examine the effects on DALI circRNA expression, due to their high level of stability.Regarding rescue-experiments with SFPQ domain mutants, this is definitely an interesting future avenue of research, however, in the current manuscript, we have not focused on the individual domains of SFPQ and therefore we argue that this is currently beyond the scope of this paper.They should also use minigenes to show that exon circularization depends on the presence of the SFPQ sites and test the contribution of intronic polyadenylation sites. SFPQ could help avoiding the use of premature cleavage and polyA sites, which are known to alter circRNA formation . The authors should discuss this in the context of the role of U1 snRNP on usage of intronic PAS (the telescripting mechanism uncovered by Dreyfuss and colleagues). The authors should also determine if U1 snRNA levels are altered as this may be part of the reason for the widespread transcription termination effects that are observed.We agree that the use of minigenes to show that exon circularization depends on the presence of the SFPQ sites, if successful, would be an important addition to this study. While SFPQ is known to have enriched binding in long introns of target RNAs , SFPQ lacks a defined RNA-binding consensus sequence . This suggests that SFPQ binding specificities may be regulated by a more complex mechanism, where the recruitment mechanism, binding proportion, and co-binding partners may affect SFPQs roles in transcription, splicing or stabilization of pre-mRNAs. As a consequence of SFPQs undefined binding specificity we are unable to generate a mini-gene system where we could confidently establish that exon circularization is dependent on SFPQ binding.Furthermore, we agree that SFPQ’s ability to preserve long gene transcription through repression of intronic PAS usage is similar to the U1 telescripting mechanism uncovered by Dreyfuss and colleagues.As suggested by the reviewers, we have conducted northern blotting and qRT-PCR analysis to determine the effect of SFPQ depletion on U1 snRNA levels. Here, U1 snRNP levels were not found to be significantly affected by SFPQ knockdown for both HepG2 and HEK293T cells . Based on these observations, we cannot exclude any changes in U1 occupancy or localization upon SFPQ depletion, and the relationship between SFPQ and U1 could prove to be an interesting direction to pursue further. However, our data suggest that the effect of SFPQ depletion on intronic PAS usage cannot solely be explained by U1 shortage. This is now also included in the Discussion:“Moreover, U1 snRNP abundancy has been described to be important for transcriptional elongation of long, neuronal genes by protecting against aberrant PAS usage . However, in many cases the observed premature termination upon SFPQ depletion co-appears with an upstream cryptic SA, and the U1 snRNA levels seem unaffected by SFPQ depletion suggesting that the SFPQ phenotype is not mediated entirely by U1 telescripting.”2) Removal of SFPQ has very different effects on circRNA levels in HepG2 and HEK293T cells (mostly downregulation of DALI circRNAs) compared to in mouse brain . The authors should address this, to start with by looking into the CLIP-seq data from Takeuchi et al., 2018 and explore the binding of SFPQ to the introns flanking the down-regulated vs. up-regulated circRNAs. They should acknowledge the previous work by Takeuchi et al., 2018 on the function of SFPQ in transcription and discuss the drop-off phenotype showing that mice loss of SFPQ impairs transcriptional elongation accompanied by a decrease in RNA Pol II density.We thank the reviewers for this suggestion, and we have now included the eCLIP data from Takeuchi (GSE96081), shown in Figure 3—figure supplement 3G. Consistent with the SFPQ eCLIP from HepG2 cells, DALI circRNAs in the mouse brain data show highly enriched SFPQ binding in the flanking regions. Furthermore, to better grasp the impact of SFPQ on circRNA biogenesis, we now stratify the eCLIP analysis by upstream and downstream binding. Interestingly, this shows that particularly for circRNA with increased expression upon SFPQ repression, SFPQ binds solely to the upstream region, whereas downregulated circRNAs generally show enriched SFPQ binding both up- and downstream . Additionally, we also now include the eCLIP data in the final GLM model. Here, most particularly for eCLIP signal in the downstream region, enrichment correlates negatively with circRNA expression upon SFPQ depletion, in mouse and HepG2, emphasizing that SFPQ binding in the flanking regions (mostly downstream) support circRNA expression .Reference:1) Van Nostrand EL, Freese P, Pratt GA, Wang X, Wei X, Xiao R, et al. A large-scale binding and functional map of human RNA-binding proteins. Nature. 2020 Jul 29;583(7818):711–9.


IRF4. Next, we exploit DeepMEL to analyze enhancer architectures and identify accurate transcription factor binding sites for the core regulatory complexes in the two different melanoma states, with distinct roles for each transcription factor, in terms of nucleosome displacement or enhancer activation. Finally, DeepMEL identifies orthologous enhancers across distantly related species, where sequence alignment fails, and the model highlights specific nucleotide substitutions that underlie enhancer turnover. DeepMEL can be used from the Kipoi database to predict and optimize candidate enhancers and to prioritize enhancer mutations. In addition, our computational strategy can be applied to other cancer or normal cell types.Deciphering the genomic regulatory code of enhancers is a key challenge in biology because this code underlies cellular identity. A better understanding of how enhancers work will improve the interpretation of noncoding genome variation and empower the generation of cell type–specific drivers for gene therapy. Here, we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable enhancer models. We apply this strategy to decipher the enhancer code in melanoma, a relevant case study owing to the presence of distinct melanoma cell states. We trained and validated a deep learning model, called DeepMEL, using chromatin accessibility data of 26 melanoma samples across six different species. We show the accuracy of DeepMEL predictions on the CAGI5 challenge, where it significantly outperforms existing models on the melanoma enhancer of  Deciphering gene regulatory programs entails mapping the network of TFs and cis-regulatory regions that govern the identity of a given cell type, as well as understanding how the specificity of such a network is encoded in the DNA sequence of genomic enhancers. Profiling accessible chromatin via DNase I hypersensitive sequencing (DNase-seq) or via the assay for transposase-accessible chromatin using sequencing (ATAC-seq) represents a useful approach for identifying putative enhancers  to nhancers . Indeed,nhancers . By dispnhancers .Because the presence and architecture of TF binding sites within enhancers determines which TFs can bind with high affinity, understanding this “enhancer logic” can help interpret the functional role of enhancers within a gene regulatory network. Several techniques exist to study the enhancer code, including (1) motif discovery tools ; (2) comDCT, TYR, PMEL, and MLANA; and the mesenchymal-like (MES) state, in which the cells are more invasive and therapy resistant, expressing high levels of genes involved in TGFB signaling and epithelial-to-mesenchymal transition (EMT)-related genes , and four melanoma lines established from zebrafish  . The difference in the number of peaks across the samples is attributable, on the one hand, to genome size , and on the other hand, to data quality (measured as the fraction of reads in peaks [FRiP]) .We profiled chromatin accessibility using ATAC-seq on a collection of melanoma cell lines across six species, for a total of 26 samples A. These P-121-3) , 2011. PSupplemental Fig. S1D), which correspond to the two main cell states in human melanoma, that is, the melanocytic state (MEL) and mesenchymal-like state (MES), as was further confirmed for most of the cell lines by previously generated RNA-seq data , whereas they are closed in MES melanoma lines  .Unsupervised clustering of the 16 human lines revealed two distinct groups (d MM165) B. As in Supplemental Fig. S1F). Accordingly, we identified 303,392 alignable and 10,592 conserved accessible regions across all mammalian species. This number decreases when including zebrafish, to 29,619 alignable regions and, only 116 conserved accessible regions. Nearly half of the 10,592 conserved accessible mammalian regions were promoters within 1 kb of a transcription start site . Indeed, high conservation of proximal promoters has previously been reported  between species using the liftOver tool (at least 10% of bases must remap) . When sureported . In eachSupplemental Fig. S1K), or of all samples (including zebrafish) using the 29,619 alignable regions .Next, we examined whether the MEL and MES melanoma states are conserved in the other species of our cohort. Clustering all mammalian samples based on the accessibility of the 303,392 alignable regions , followed by motif enrichment using HOMER . The enriched motifs of the TFAP2 family can most likely be linked to TFAP2A because this is a master regulator in human melanocytes and melanoma . In the 113 conserved accessible regions across the MEL cell lines across all six species, BLS scoring again revealed SOX, ETS, MITF, and TFAP2A motifs among the most conserved motifs   containid motifs E.In conclusion, two independent strategies of motif analysis suggest conservation of TF binding sites for known melanoma master regulators, with conserved SOX10, MITF, TFAP2A, and ETS TF family motif enrichment in MEL enhancers across all six studied species.Supplemental Fig. S2A,B). This provided a nuanced classification, with topic 4 and topic 7 representing the MEL- and MES-specific enhancers, respectively, being accessible across all MEL or MES samples . In addition, we found two topics with regions that are generally accessible across all cell lines (topic 1 and topic 19) . These ubiquitously accessible regions are highly enriched for proximal promoters  and for known promoter-specific TF binding motifs linked to SP and NFY TF families  and neural crest cell differentiation (FDR = 4.26 × 10−7), whereas genes near topic 7 regions were enriched for GO terms involved in cell–cell adhesion (1.56 × 10−13). Motif discovery on the top regions assigned to each topic confirmed enrichment of SOX, ETS, TFAP2A, and MITF motifs in the MEL topic regions (topic 4) and AP-1 in the MES topic (topic 7) . An example topic 4 region in the promoter of the SOX10 target gene MIA  model on the human ATAC-seq data. First, to construct an unsupervised training set, we clustered all 339,099 human ATAC-seq peaks using cisTopic—a probabilistic framework to analyze scATAC-seq data that can also be applied to bootstrapped bulk ATAC-seq data ; Methodsgene MIA  is shownmelanoma .Supplemental Fig. S3; Supplemental Note). After successful training of DeepMEL—area under the receiver operating characteristic curve (auROC) = 0.863 and area under the precision recall curve (auPR) = 0.374 on test data for topic 4 regions —we used the weights of the neurons from the convolutional filters to extract local patterns learned by the model. We transformed these convolution filters into PWMs and found the importance of each filter for each topic (Methods). Filters that represent SOX, MITF, TFAP2A, and RUNX motifs were most relevant for the MEL-specific topic 4; filters that represent AP-1, TEAD, and RUNX binding sites were assigned to the MES-specific topic 7 , but were also accessible in human melanocytes , indicating that MEL-specific melanoma regions are not cancer-specific but already accessible in their cell of origin, that is, the melanocytes. As a consequence, we can potentially extrapolate the observations on this topic to normal melanocyte enhancers. Although in the remainder of this work we will score accessible regions to identify functional enhancers, it is also possible to score the entire genome, without filtering for ATAC-seq peaks .Using the 24 topics as classes, we trained a multiclass, multilabel classifier using a neural network, called “DeepMEL” C. As inp topic 7 F. Thus, IRF4, nucleotides important for classifying this enhancer as topic 4 emerge as motifs for SOX10, MITF, TFAP2A, and RUNX factors .To examine the TF binding site architecture within enhancers, we used a model interpretation tool, DeepExplainer . For a MIRF4 enhancer as case study. For this enhancer, Supplemental Fig. S4G).It is known that enhancer accessibility does not directly translate to enhancer activity . To testIn conclusion, DeepMEL, trained on topics of human coaccessible regions, is performant in classifying melanoma regulatory regions into different classes based on purely the DNA sequence. Features learned by DeepMEL correspond to TF binding motifs of master regulators of specific classes. These motifs can also be located and visualized within regions using a model interpretation tool, allowing examination of the motif architecture within specific enhancers, and predicting the effect of mutations on enhancer accessibility.cis-regulatory module (CRM) scoring with PWMs . Of note, predicted MEL enhancers in the pig melanoma cells (MeLiM) were similarly accessible in pig melanocytes , again indicating that MEL melanoma enhancers can be used as a model for melanocyte enhancers.Next, we asked whether the human-trained model DeepMEL can be used to predict MEL and MES enhancers in other species. We started with the dog genome as a test case, because the differential ATAC-seq peaks between the MEL  and MES (Dog-IrisMel-14205) dog cell lines can serve as true positives A. DeepMEith PWMs A. Havingith PWMs B. Betweeith PWMs B. The maSupplemental Fig. S5G). Only a small subset of these enhancers could have been found using liftOver . Of these genes, 32 form a core set of conserved MEL-specific genes throughout all species including zebrafish, each having a MEL enhancer nearby. Examples of genes in the core set are MITF, PMEL, and TYRP1, genes known to be involved in melanocyte development, melanosome formation, and melanin production . Of these 379 genes, 217 (67%) had at least one MEL-predicted enhancer within 200 kb upstream of and downstream from the gene. Between 70% and 85% of the orthologous MEL genes in other species had at least one MEL enhancer 200 kb upstream of and downstream from the gene . This altogether supports the idea that MEL enhancers near orthologous genes are indeed orthologous enhancers.A long-standing question in enhancer studies is how to compare enhancers with each other, if their sequences do not align . Here, wERBB3. DeepMEL predicts a MEL enhancer upstream or intronic of ERBB3 in each of the mammalian species, which were also found by liftOver of the human ERBB3 enhancer , suggesting that both enhancers are orthologous to the human ERBB3 enhancer. Applying DeepExplainer to the multiple-aligned sequences revealed a conserved motif architecture in the orthologous mammalian ERBB3 enhancers containing each three SOX motifs and one TFAP2A motif , for each of the predicted core regulatory factors . To achieve this, we devised an optimized motif scoring method that obtains precise positions of TF binding motifs by multiplying DeepMEL activation scores of convolutional filters  with the DeepExplainer profile of each enhancer .Supplemental Fig. S6A), and the regions that close after SOX10-KD are highly enriched for SOX motifs (NES = 28.5), possibly revealing a pioneering-role of SOX10 in MEL enhancers. Next to SOX motifs, a combination of one or multiple TFAP2A, MITF, or RUNX-like motif hits were present in 84% of the MEL-predicted enhancers , which could be in line with the previously described role of TFAP2A as a stabilizer of nucleosome-depleted regions , suggesting a repressive role of RUNX factors. The presence of a MITF site did not seem to alter the accessibility of enhancers compared to SOX-only enhancers but did increase H3K27ac signal , possibly indicating that MEL enhancers bound by MITF are more active.The first observation was that each MEL enhancer contains at least one SOX10 motif hit, and often two or more B. This snhancers B. Next, nhancers C. We obtnhancers C. As valnhancers . All clu regions . The oppSupplemental Fig. S6D). In addition, liftOver of the clusters showed that the regions of a human cluster correspond more to the same cluster in the other species , indicating conservation of the MEL enhancer clusters across species. For instance, the dog orthologs of two human MEL enhancers belonging to either the [SOX10 + MITF] cluster (intronic enhancer of CD9) or to the cluster containing [SOX10 + TFAP2A + RUNX] (intronic enhancer of STIM1)  E were paf STIM1) F.Altogether, these data suggest a Core Regulatory Complex (CoRC)  of SOX10Supplemental Fig. S6A,B).Because previous results suggested a pioneering and stabilizer function for SOX10 and TFAP2A, respectively, we wanted to further investigate these putative roles and how they are mechanistically affecting chromatin accessibility. First, we analyzed the location of binding sites relative to the position of the nucleosome, focusing on a human and dog MEL enhancer that contain a combination of one SOX10 and one TFAP2A site A,B. We pSupplemental Fig. S7A,B). We did not observe strong positional preferences of MITF and RUNX motifs relative to the nucleosome start or middle point .These example enhancers raised an interesting positional preference of SOX10 and TFAP2A. To assess whether this occurs globally, we centered human MEL enhancers on the SOX10 and TFAP2A motif hits and calculated the aggregated location of the nucleosome start and middle point C–E. SOX1Altogether these data suggest that SOX10 functions as a pioneer in the CoRC of MEL enhancers, leading to their accessibility by binding to the central nucleosome, near the nucleosome start point. Conversely, TFAP2A appears to act as stabilizer of SOX-dependent nucleosome-depleted regions by binding around the nucleosome middle point, possibly going in competition with the central nucleosome.Supplemental Fig. S8A). For example, an enhancer upstream of APPL2 is predicted as a MEL enhancer in the dog line Dog-OralMel-18249 (topic 4 DL score of 0.35), whereas the orthologous enhancer in human is not accessible . Among the four studied regulators, mostly the disruption or gain of one or more SOX10 binding sites between orthologous enhancers quantitatively altered the ATAC-seq signal in a concordant way , indicating that SOX10 mutations are most causal for changes in MEL enhancer accessibility, and possibly also in enhancer activity, as was the case in the APPL2 enhancer above. However, concordance between accessibility and activity was not always observed . Furthermore, luciferase assays of six human or dog MEL-predicted enhancers suggested that enhancers with at least one MITF motif (n = 3) are significantly more active compared to enhancers without any MITF motif (n = 3) , indicates that MITF could function as an activator in MEL enhancers. Indeed, MITF has been shown to activate genes involved in pigmentation by recruitment of cofactors and chromatin remodeling complexes  are highly predictive for differences in accessibility (Spearman's correlation of 0.43) ( (n = 3) G. Althouomplexes  and was omplexes . SOX10 bse study G.In conclusion, DeepMEL provides a suitable platform to study the effect of evolutionary mutations on MEL enhancer accessibility and, in some cases, activity across species. Together, these results validate that SOX10 is crucial for enhancer accessibility in MEL enhancers, and necessary but insufficient for MEL enhancer activity, because activity appears to be mainly dependent on MITF binding.Here, we present an in-depth study of melanoma enhancer logic, especially in enhancers specific to the melanocytic (MEL) state, by exploiting both cross-species data and machine learning. Although the MEL and MES melanoma cell states have been studied extensively on a transcriptomic and epigenomic level, the combinatorial code of binding sites of their regulatory factors in state-specific enhancers had not yet been explored. Understanding the enhancer logic and the mechanism by which TFs bind and direct active enhancers will become increasingly important, because it will be essential for the development of new therapies that influence cell state-specific enhancer functions in a targeted way  , or to pPredicting enhancers and determining their functional role within gene regulatory networks has been an active field for years. Despite the well-established power of cross-species approaches in this field, to our knowledge, a large comparative epigenomics study in melanoma has not yet been conducted, although several non-human models are commonly used in melanoma research  and haveDespite their proven advantages, sequence-based comparative approaches have limited power to identify orthologous regulatory regions in distant species, in part because of the rapid evolution of distal enhancers . MethodsIt is well established that distinct functional classes of TFs exist, with respect to enhancer binding. Pioneer TFs, such as POU5F1, SOX2, Grh-like TFs, and FOXA1, are able to bind nucleosomal DNA, leading to displacement of the nucleosome and facilitating the binding of other TFs to the accessible enhancer . SOX2 anIRF4 MEL enhancer, where DeepMEL outperformed existing methods tested in IRF4 enhancer had a strong effect on activity, probably owing to the reappearance of the central nucleosome.Besides classifying accessible (orthologous) regions and predicting important TF motifs within them, DeepMEL is an accurate predictor of the effect of mutations on enhancer accessibility and, for some enhancers, also the activity. This was for instance the case for the cis-regulatory variation.In conclusion, the combination of comparative epigenomics with deep learning allowed us to perform an in-depth analysis of the melanoma enhancer logic. This work presents an overall framework that can be applied to decipher the enhancer logic in a cell type or cell state of interest, starting from the generation of an extensive cell type–specific (cross-species) epigenomics data set, all the way through the training and exploitation of a deep neural network to decode enhancer features across species, and to utilize it to assess the impact of 2 and were maintained in Ham's F10 nutrient mix (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum  and 100 µg mL−1 penicillin/streptomycin (Thermo Fisher Scientific).Human melanoma cultures (MM lines) are short-term cultures derived from patient biopsies . Cells w−/−; mitf−/− zebrafish embryos. Embryos were scored for melanocyte rescue at 48–72 h post-fertilization, and equal numbers were raised to adulthood (15–20 zebrafish per tank) and scored weekly (from 8 to 12 wk post-fertilization) or biweekly (>12 wk post-fertilization) for the emergence of raised melanoma lesions .Experiments were performed as previously outlined . Briefly lesions . For in  lesions , 2011, a lesions . All cel2 in Roswell Park Memorial Institute (RPMI) medium (Thermo Fisher Scientific) supplemented with 10% FBS (Thermo Fisher Scientific) and 1% penicillin/streptomycin (Thermo Fisher Scientific).The horse cell lines HoMel-L1 and HoMel-A1 are melanoma cell lines derived from a Lipizzaner stallion and Shagya-Arabian mare, respectively, and were established in 2 in MEM medium supplemented with 1× MEM nonessential amino acids (Thermo Fisher Scientific), 1 mM Na pyruvate, 2 mM glutamine, 100 units/mL penicilin/streptomycin (Thermo Fisher Scientific), 10% FCS and 3.7 g/mL Na bicarbonate. MeLiM cells were cultured in DMEM high glucose (Thermo Fisher Scientific), 10% FCS, Pen/Strep, and 5% CO2.The immortal line of pigmented melanocytes (PigMel) was previously derived , and the2 in Ham's F-12 Nutrient Mixture medium (Thermo Fisher Scientific) supplemented with 10% FBS (Thermo Fisher Scientific) and 1% penicillin/streptomycin (Thermo Fisher Scientific).The dog cell lines Dog-IrisMel-14205 and Dog-OralMel-18249 were established by Aline Primot, and were derived from an uveal melanoma from a beagle crossed dog and an oral melanoma from the palate of a Shih Tzu, respectively. Cells were cultured at 37°C with 5% CO2 in Dulbecco's Modified Eagle Medium (DMEM) (Thermo Fisher Scientific) supplemented with 10% FBS (Thermo Fisher Scientific) and 1% penicillin/streptomycin (Thermo Fisher Scientific).The mouse melanoma cell line was generated as described . Cells wSOX10 , TFAP2A , and a negative control pool  at a concentration of 20 nM for SOX10-KD, and 40 nM for TFAP2A-KD and the control using as medium Opti-MEM (Thermo Fisher Scientific) and omitting antibiotics. The cells were incubated for 72 h before processing.SOX10, TFAP2A, and the control knockdown (KD) were performed in MM001 using a SMARTpool of four siRNAs against, respectively, Omni-assay for transposase-accessible chromatin using sequencing (OmniATAC-seq) was performed as described previously . After tFifty thousand cells per line were lysed and subjected to a tagmentation reaction and library construction as described in https:// hgdownload.soe.ucsc.edu/downloads.html)  and by applying the parameters ‐‐alignIntronMax 1 and ‐‐alignIntronMin 2. For the human data, we used hg19 as genome assembly instead of the more recent GRCh38 assembly because i-cisTarget  ; or made by bamCoverage  (6), in which RIP stands for the number of reads in peaks.Paired-end or single-end reads were mapped to the human genome (hg19-GENCODE v18) using Bowtie 2 (v2.2.6)  or STAR (v2.1.2)  callpeak(v2.1.2) , providi v3.3.1) , using a2FC higher than 2.5 and a PAdj lower than 0.0005. HOMER (Count matrices were produced by featureCounts (v1.6.5)  for five5. HOMER  was perfATAC-seq regions of non-human species were defined as alignable regions when they could be converted to hg19 coordinates using liftOver   by proviPer species, a count matrix was made on the alignable union ATAC-seq regions by featureCounts (v1.6.5) . The couhttp://iregulon.aertslab.org/collections.html)  score per conserved accessible sequence was used to calculate the branch length score (BLS) across species according to http://hgdownload.cse.ucsc.edu/goldenpath/hg19/phyloP100way/ (UCSC). The sum of the BLSs for all the conserved accessible sequences across the mammalian or all six species was used as a total score for each motif. We normalized these scores by performing BLS on a shuffled variant of all sequences by shuffleseq , keeping the same base-pair compositions and sequence lengths, and subtracting the shuffled BLS from the true BLS per motif.Conserved accessible ATAC-seq regions were identified as described above, and for each of the species, the set of conserved accessible regions was converted to the coordinate system per species and FASTA sequences were retrieved. All sequences were scored with the cisTarget motif collection (v8)  model, DeepMEL, was trained on the binarized regions of the 24 topics obtained from the cisTopic analysis explained above. To increase the amount of training data, the 500-bp regions in the merged BED file of all 339,099 ATAC-seq regions across the 16 human cell lines (see “Data processing of ATAC-seq and OmniATAC-seq samples”), were augmented by extending them to 700 bp around the summit and sliding a 500-bp window over these elongated regions with a 10-bp stride. This augmented master region BED file was intersected with each topic BED file separately (using BEDTools) , and a raverage function) of the neurons in the final Dense layer . The model was compiled using the Adam optimizer with the default learning rate, which is 0.001. To calculate the loss, the binary cross entropy (binary_crossentropy) was used. The model was trained for two epochs with a batch size of 128, which took 67 min. Keras 2.2.4 (https://keras.io) with tensorflow 1.14.0 , MaxPooling1D layer (with the pool_size 10 and strides 10), TimeDistributed Dense layer together with Bidirectional LSTM layer (with 128 unit and setting the dropout as 0.1 and the recurrent_dropout as 0.1), and Dense layer (with 256 units and setting the activation as relu). After MaxPooling1D, Bidirectional LSTM, and Dense layer, a Dropout layer was used each time with the fraction of dropout set as 0.2, 0.2, and 0.4, respectively. For each region in the training data, DeepMEL takes the one-hot encoded (500 bp × 4 nt) forward and reverse strand and passes them separately through the model. To make the final prediction, DeepMEL takes the average activation , test (regions on Chr 2), and label-shuffled regions.http://iregulon.aertslab.org/collections.html) .Filters of the convolution layer were converted to position-weight matrices (PWMs) by the following strategy: (1) 4,000,000 unique 20-bp-long (size of the filters) sequences were randomly generated; (2) the activation score of each filter for each sequence was calculated and the top 100 sequences were selected; (3) a count matrix was generated from these 100 sequences obtained for each filter; and (4) finally, the count matrices were converted into PWMs. To assign the filters to topics, a similar strategy that is mentioned in Basset  was usedFrom the 35,940 topic regions that were obtained after binarization of the 24 topics within the selected cisTopic model , 500 regions were randomly selected to initialize the DeepExplainer pipeline . A hypotIRF4 enhancer case, the actual IRF4 enhancer sequence used in the in vitro saturation mutagenesis assay  overlapped with a predicted MEL enhancer in human MEL cell lines in our cohort . The delta prediction score of topic 4 (MEL topic) was calculated following an in silico saturation mutagenesis on this region, and a Pearson's correlation was calculated on the overlapping nucleotides between the in silico and in vitro assays (451 bp).In silico saturation mutagenesis of a region was performed by separately changing each nucleotide on the 500-bp sequence into the three other nucleotides and scoring these mutated sequences with DeepMEL. The delta prediction score for each mutation was calculated for each of the 24 topics by comparing the prediction score of the mutated sequence relative to the prediction score for the initial sequence. For the We designed an optimized motif scoring method, in which activation scores of the filters on each sequence are multiplied by the DeepExplainer importance scores of the sequence. Then, after the output of this multiplication was normalized, a threshold was calculated for each motif by comparing MEL and MES enhancers. This approach yielded significant motif hits with their precise location.Nucleosome start and middle point predictions were calculated by using the executable nucleosome prediction tool Kaplan_v3  that takFootprints of the Tn5 were determined by inferring Tn5 cut sites from the start point of each ATAC-seq read in a BAM file using a custom script.The performance of DeepMEL to discriminate between MEL and MES regions in human and dog was calculated by scoring the top 5000 differential MEL and MES regions in human and dog (described above) with DeepMEL and calculating the precision of correct assignment . The performance of DeepMEL was compared with the motif scoring tool Cluster-Buster  by scori2FC > 2.5 and PAdj < 0.005). We converted the gene symbols to Ensembl gene IDs using biomaRt    on RNA-s v3.5.2)  and foun v3.5.2) . For the v3.5.2) . liftOve v3.5.2)  was usedConserved accessible MEL enhancers in the extended loci of conserved MEL-specific genes across the six species (see above) were scored by DeepMEL. A matrix was generated consisting of a score for each of the 256 nodes in the Dense layer for each of the regions. A Pearson's correlation matrix was generated to calculate the pairwise similarity between each of the regions.The first chromosome of the human genome (hg19) was tiled with a sliding window of 500 bp and a 100-bp shift using BEDTools makewindows (v2.28.0) . Tiles cWe defined highly probable orthologous MEL enhancers between human and another species as regions that were predicted as MEL in one species and for which there was a stringent liftOver (-minMatch = 0.995)  and highRenilla plasmid (Promega) using lipofectamine 2000 (Thermo Fisher Scientific). Luciferase activity of each construct was measured using the Dual-Luciferase Reporter Assay (Promega) according to the manufacturer's instructions. Enhancer luciferase activity was normalized against the Renilla luciferase activity.Six MEL-predicted enhancers  were synthetically generated and cloned into a pTwist ENTR plasmid (Twist Bioscience) via Twist Bioscience. Regions were transferred from the Gateway entry clone into the destination vector  via a LR reaction by mixing 2 µL of the entry clone (100 ng/µL) with 1 µL of the destination plasmid (150 ng/µL), 1 µL TE buffer, and 1 µL LR enzyme , and incubating this mixture at 25°C. Afterwards, 1 µL of Proteinase K (Thermo Fisher Scientific) was added and reactions were incubated for 1 h at 37°C for 10 min. Then, 3 µL of each LR reaction was transformed into 50 µL of Stellar competent cells (Takara Bio) via heat shock. Next, 200 µL of SOC medium was added and the cells were incubated for 1 h in a shake incubator at 37°C, before plating the transformed cells on LB agar plates with 1/1000 carbenicillin and incubation overnight at 37°C. The next day, one colony per construct was picked and grown overnight in 5 mL of LB medium with 1/1000 carbenicillin in a shake incubator at 37°C before plasmid extraction using the NucleoSpin Plasmid Transfection-grade kit (Macherey-Nagel). For each construct, three biological replicates were performed by transfecting the plasmids into 80% confluent cells of MM001 in a 24-well plate. Per transfection, 400 ng of the construct was transfected together with 40 ng of https://www.ncbi.nlm.nih.gov/geo/) through accession number GSE61965  under accession number GSE142238. This includes OmniATAC-seq data of human melanoma cell lines , two dog melanoma cell lines, two horse melanoma cell lines, one pig melanoma sample, one pig melanocyte cell line, and one mouse melanoma cell line; ATAC-seq data of four zebrafish cell lines; and OmniATAC-seq data of SOX10 and TFAP2A knockdown in the human melanoma cell line MM001. The DeepMEL model was deposited in Kipoi (http://kipoi.org/models/DeepMEL/). Code and custom scripts for training DeepMEL, DeepMEL predictions, DeepExplainer usage, and BLS scoring are provided in GitHub (https://github.com/aertslab/DeepMEL) and as Supplemental Code.All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; in Kipoi  (http://The authors declare no competing interests.


Genomic imprinting is a parent-of-origin dependent phenomenon that restricts transcription to predominantly one parental allele. Since the discovery of the first long noncoding RNA (lncRNA), which notably was an imprinted lncRNA, a body of knowledge has demonstrated pivotal roles for imprinted lncRNAs in regulating parental-specific expression of neighboring imprinted genes. In this Review, we will discuss the multiple functionalities attributed to lncRNAs and how they regulate imprinted gene expression. We also raise unresolved questions about imprinted lncRNA function, which may lead to new avenues of investigation. This Review is dedicated to the memory of Denise Barlow, a giant in the field of genomic imprinting and functional lncRNAs. With her passion for understanding the inner workings of science, her indominable spirit and her consummate curiosity, Denise blazed a path of scientific investigation that made many seminal contributions to genomic imprinting and the wider field of epigenetic regulation, in addition to inspiring future generations of scientists. H19 RNA was the first long noncoding RNA (lncRNA) within the mammalian genome to be discovered, followed shortly by the X-inactive-specific transcript (Xist) lncRNA . Si. SiSfmbtectively ,62  and repressive modifications  .,87.cis; e domain . In the romoters . For thefunction . The higotif III . 3D modeThe function of lncRNAs serving as guides leaves open many outstanding questions. Are there lncRNA specific sequences or 2D and/or 3D structural requirements for generating triplex formation and for modifier complex interactions? Four PRC2 proteins  have known RNA-binding capacities –95 with Gtl2 imprinted domain, the Gtl2 lncRNA may function as a decoy . AFF3 is a core component of the superelongation complex that facilitates an active chromatin state and promotes transcription of the polycistronic Gtl2lt transcript [Gtl2 lncRNA, in turn, may act as a decoy for the Gtl2 ICR, maintaining its active chromatin state [Gtl2 lncRNA  [Gtl2 promoter deletion, Gtl2 lncRNA expression is abolished, leading to increased EZH2 binding at the Gtl2 ICR [Ezh2, Eed, and Jarid2 deletion, the maternal Gtl2 ICR is occupied by DNMT3A and DNMT3L and becomes de novo methylated [Gtl2 lncRNA interacts with JARID2 and PRC2 to mitigate EZH2 methyltransferase activity and DNMT3A/3L recruitment at the maternal Gtl2 ICR.Another function attributed to lncRNAs is that of a molecular decoy. Decoy function is described by lncRNAs acting as a sink or sponge to sequester proteins away from a target site . This co a decoy . In ES canscript ,97. The in state . This istl2 ICR) ,99. UponGtl2 ICR . Furtherthylated . TogetheThere are many outstanding questions about lncRNA decoy function. As a molecular decoy, lncRNAs may maintain the ICR in an active state and at the same time, the active ICR directs lncRNA expression. What controls this apparent feedback loop? How susceptible is the active ICR to fluctuations in lncRNA levels? Is decoy function mediated via RNA-binding proteins? What is the relationship between weak and strong chromatin modifier interactions with chromatin and lncRNAs? If lncRNAs act as decoys to titrate away positive and negative regulatory proteins, what mechanisms are in place to balance decoy versus other lncRNA function such as scaffold function?cis to regulate expression of imprinted genes within their respective domains, imprinted lncRNAs also regulate other genes or domains in trans [H19 lncRNA function alter expression of imprinted genes in the Kcnq1ot1, Airn, Nespas, and Gtl2 domains [IPW lncRNA (from the SNHG14 lncRNA) lead to changes in GTL2 lncRNA, microRNA, and snoRNAs in the GTL2 domain [trans function is that imprinted domains can colocalize to the same 3D nuclear space [Gtl2 and H19 lncRNAs are involved in the regulation of the signaling transforming growth factor β (TGF-β) pathway [trans-actions of these lncRNAs point to either lncRNA scaffold or guide function. As a scaffold function, the H19 lncRNA interacts with the epigenetic modifier methyl-CpG-binding domain protein 1 (MBD1) and targets MBD1 to imprinted genes in trans, modulating repression through H3K9me3 [IPW interacts with EHMT2, conferring H3K9me3 to the Gtl2 ICR [Gtl2 lncRNA interacts with JARID2 and PRC2, directing H3K27me3 to gene targets outside of the Gtl2 domain [H19 lncRNA–DNA triplex binding sites in humans and mice identified promoter target sites at multiple imprinted genes, including Igf2r, Gnas, Dlk1, Peg1, and Cdkn1c [GTL2 lncRNA has DNA triplex binding sites that map to multiple genome-wide target sites in both human and mouse cell lines [H19 lncRNA regulation of gene targets in trans remain to be fully elucidated, the Gtl2 lncRNA–DNA triplex structures may act together with its PRC2-binding motifs to modulate gene targets in trans [In addition to imprinted lncRNAs operating in in trans . Loss an domains , while l2 domain . One facar space ,104, genar space , where ear space , imprint pathway ,107, the pathway ,109, and pathway ,111. Ove H3K9me3 . In a siGtl2 ICR . The Gtl2 domain . As lncRd Cdkn1c , while tll lines . While ttrans function, what mechanisms operate for imprinted lncRNAs to find their targets? Is lncRNA trans function at imprinting gene networks and other potential chromosomal interactions left to random associations or are there active mechanisms to target imprinted lncRNAs to single gene promoters or to chromosomal networks?As stated above, many outstanding questions regarding lncRNA scaffold and guide function remain to be resolved. More specifically for lncRNA GTL2 lncRNA function [GTL2 lncRNA has shed new light on the importance of higher-order tertiary structures [GTL2 lncRNA interact to generate pseudoknot structures, with specific loops imparting a stable 3D structure (“kissing loops”) [GTL2 lncRNA function in trans, which is independent of the ability of the GTL2 lncRNA to recruit proteins, such as PRC2. To the best of our knowledge, imprinted genes within the Gtl2 imprinted domain have not been assessed for the effects of altered Gtl2 secondary and tertiary structure on their expression. Specific conserved motifs within Kcnq1ot1 lncRNA also contribute to folding and secondary stem-loop structure of the lncRNA [Kcnq1ot1 lncRNA and leads to a relaxation of paternal allelic silencing of imprinted genes within the domain [Kcnq1ot1 lncRNA forms tertiary structures to carry out its function remains to be determined.lncRNAs are able to fold into secondary and tertiary higher-order 3D structures, confining them to the nucleus and influencing their regulatory function . While tfunction , the recructures . Here, h loops”) . Surprise lncRNA . Mutatioe domain . WhetherThe mechanism of how lncRNA higher-order structure contributes to its ability to regulate expression remains unknown. It has been postulated that spatial organization of lncRNA pilots their function by directing protein positioning and localizing lncRNAs to target sites ,116. ThuH19 imprinted domain. CCCTC-binding factor (CTCF) and the cohesin complex generate insulator activity and chromatin looping between the maternal H19 ICR and downstream enhancer, which leads to upstream maternal Igf2 silencing  and neonatal heart [Kcnq1 and upstream Cdkn1c genes [Kcnq1ot1 lncRNA in directing chromatin interactions that may generate an active loop for the Kcnq1ot1 lncRNA gene while exclusion of paternal upstream and downstream alleles may lead to their repression. This role may be developmentally dependent, as posttranscriptional depletion of Kcnq1ot1 lncRNA did not impact paternal allelic silencing of imprinted genes in ESCs and TSCs [Kcnq1ot1 lncRNAs in TSCs [At the paternal al heart ,124. In 1c genes . These dand TSCs , and CTC in TSCs .Airn domain, chromatin-chromatin interactions are observed at the maternal Airn domain between the Airn gene body and the Slc22a3 promoter in visceral yolk sac endoderm [Airn lncRNA disrupts these chromatin interactions, as demonstrated by premature truncation of the Airn lncRNA. Since cohesin complex and CTCF binding had little enrichment at the paternal domain, and CTCF had little to no interaction with the Airn lncRNA in TSCs [Airn lncRNA lacks a role as an architectural component.At the endoderm . At the Many unresolved questions remain for lncRNAs as architectural components. Does CTCF bind (a subset) of imprinted lncRNAs? If so, do lncRNAs play a role in recruiting or biasing CTCF binding to lncRNA promoters and ICRs? Is there also a role for lncRNAs in inducing chromatin looping? What other lncRNA-protein or lncRNA-chromatin interactions could be involved in chromatin looping? Do imprinted lncRNA have chromatin-templating roles on which chromatin-chromatin interactions are mediated?Airn lncRNA gene and its transcript act through transcriptional interference to silence the paternal Igf2r allele, as a host for small interfering RNAs, in long-range silencing of upstream paternal alleles through a lncRNA scaffold, and possibly as a guide to direct triplex formation (Gtl2 lncRNA functions as a scaffold, a decoy, and a host for microRNAs and snoRNAs. It may also act through higher-order structure and as an architectural component upstream in cis, and as a guide in trans (Since the discovery of the first imprinted lncRNA, significant progress has been made in understanding the functional roles of imprinted lncRNAs in imprinted domain regulation. Compelling evidence described above supports multiple functions for how lncRNAs regulate long-range repressive function. Perhaps the most remarkable feature is that these regulatory mechanisms are not mutually exclusive, with a single lncRNA and its transcription regulating different genes within an imprinted domain. For example, the paternal ormation . By compin trans . Howevercis and trans targets as well as the stoichiometric relationships between lncRNAs, protein partners, and their target sites [Moving forward, more in-depth, comprehensive studies are required to understand the properties of imprinted lncRNAs as well as to delve deeper into the actual mechanisms pertaining to imprinted lncRNA function and their mode of action. Case in point, to establish the plausibility of posttranscriptional lncRNA functions, it is essential to determine how imprinted lncRNAs are specifically localized to their et sites ,126. Wit


In the male germ cells of placental mammals, 26–30-nt-long PIWI-interacting RNAs (piRNAs) emerge when spermatocytes enter the pachytene phase of meiosis. In mice, pachytene piRNAs derive from ~100 discrete autosomal loci that produce canonical RNA polymerase II transcripts. These piRNA clusters bear 5′ caps and 3′ poly(A) tails, and often contain introns that are removed before nuclear export and processing into piRNAs. What marks pachytene piRNA clusters to produce piRNAs, and what confines their expression to the germline? We report that an unusually long first exon (≥ 10 kb) or a long, unspliced transcript correlates with germline-specific transcription and piRNA production. Our integrative analysis of transcriptome, piRNA, and epigenome datasets across multiple species reveals that a long first exon is an evolutionarily conserved feature of pachytene piRNA clusters. Furthermore, a highly methylated promoter, often containing a low or intermediate level of CG dinucleotides, correlates with germline expression and somatic silencing of pachytene piRNA clusters. Pachytene piRNA precursor transcripts bind THOC1 and THOC2, THO complex subunits known to promote transcriptional elongation and mRNA nuclear export. Together, these features may explain why the major sources of pachytene piRNA clusters specifically generate these unique small RNAs in the male germline of placental mammals. The pachytene piRNA loci are transcribed by RNA polymerase II in the male germline of placental mammals. Here the authors show that a long first exon or a long unspliced transcript correlates with germline-specific production of piRNA precursor transcripts and mature piRNAs. Eutherian mammals produce three distinct types of germline piRNAs during spermatogenesis. In mice, piRNAs first appear in the fetal testis; these piRNAs initiate transposon silencing via DNA methylation5. A second set of piRNAs, pre-pachytene piRNAs, emerges in the neonatal testis2. The function of pre-pachytene piRNAs, which typically derive from mRNAs, remains unknown. Finally, when spermatocytes enter the pachytene phase of meiosis, male mice produce a third type of piRNAs: pachytene piRNAs9. Unique to placental mammals, the 26–30 nt long pachytene piRNAs derive from ~100 discrete autosomal loci that contain fewer transposon sequences than the genome as a whole12. Pachytene piRNAs are both highly diverse—a single spermatocyte produces hundreds of thousands of different piRNA species—and highly abundant, reaching a peak intracellular concentration (7.2 µM) that rivals the abundance of ribosomes13. Yet the biological functions and target RNAs of pachytene piRNAs remain to be defined. Some studies suggest that pachytene piRNAs silence mRNAs during spermiogenesis18, while others support the view that they have no sequence-specific function19.In animal germ cells, 21–35 nt long PIWI-interacting RNAs (piRNAs) guide PIWI proteins to repress transposons and regulate gene expression11. Mouse pachytene piRNAs derive from 100 well-defined loci. An additional 84 annotated loci generate pre-pachytene piRNAs, while 30 “hybrid” loci produce piRNAs with characteristics from both classes11. Human adult and juvenile testes produce piRNAs from at least 83 pre-pachytene, 10 hybrid, and 89 pachytene loci12. In both mice and humans, the pachytene piRNA clusters and the genes encoding the proteins required for piRNA production are coordinately activated by the transcription factor A-MYB, which is first expressed as spermatogonia enter meiosis I12. A-MYB also activates transcription of other genes required for meiosis, but not implicated in piRNA biogenesis or function20.The genomic regions that produce piRNAs, referred to as piRNA clusters or piRNA source loci, resemble canonical mRNA- and long noncoding RNA- (lncRNA) producing genes: they are transcribed by RNA polymerase II (RNA pol II); bear 5′ caps and 3′ poly(A) tails; their transcription start sites (TSS) are marked with histone H3 trimethylated on lysine 4 (H3K4me3); and their transcripts often contain introns that are removed before nuclear export and processing into piRNAs21, a DNA modification normally associated with silencing rather than active transcription. These atypical promoters tend to be inactive in the soma. High levels of 5-hydroxymethylcytosine (5hmC) and active histone marks, such as H3K4me3 and histone H3 acetylated on lysine 9 (H3K9ac) or lysine 27 (H3K27ac) have been proposed to facilitate expression of pachytene piRNA clusters in mouse and human testis21. In mice, roughly half of pachytene piRNA clusters bind the transcription elongation factor BTBD18, and Btbd18 mutant male mice are sterile and show decreased transcription and piRNA production from these loci22.Like many genes transcribed in the germline during spermatogenesis, the promoters of pachytene piRNA clusters contain fewer CG dinucleotides than protein-coding genes expressed in the soma, and most of the CGs are cytosine methylated11, 41 are divergently transcribed from a central promoter. These include 15 pairs that produce piRNAs from both arms, 7 that produce piRNAs from one arm and an mRNA from the other, and 4 that produce piRNAs from one arm and a lncRNA from the other. Similarly, among the 89 well-annotated human pachytene piRNA clusters12, 46 are divergently transcribed from a central promoter, including 18 pairs that produce piRNA precursors from both arms, 7 that produce an mRNA from the other arm, and 3 that generate a lncRNA from the other arm. What genic or epigenetic features direct the transcripts of pachytene piRNA clusters into the piRNA pathway, what determines which arm of these divergently transcribed loci makes piRNAs, and what prevents the expression of pachytene piRNAs outside the testis remain unknown.Among the 100 well-annotated mouse pachytene piRNA clusters25. In Drosophila, the THO complex is essential for piRNA biogenesis, and tho mutants are female sterile28. Our integrative analysis of transcriptome, piRNA, and epigenome datasets across multiple species reveals that a long first exon is an evolutionarily conserved feature of pachytene piRNA clusters. Finally, comparison of testis germ cells with a variety of somatic tissues and cell types suggests that a highly methylated promoter, often containing a low or intermediate level of CG dinucleotides, correlates with germline expression and somatic silencing of pachytene piRNA clusters.Here, we report that an unusually long (≥10 kb) first exon or long unspliced transcript correlates with germline-specific production of piRNA precursor transcripts from mouse pachytene piRNA clusters. We further report that pachytene piRNA precursors from long-first-exon or long unspliced transcripts are preferentially bound by THOC1 and THOC2, subunits of the THO complex, which is required for transcription elongation and nuclear export of mRNAs30. Our analysis of transcriptome data31 shows that the primary precursor transcripts of pachytene piRNAs, but not pre-pachytene or hybrid piRNAs, are also restricted to male gonads. Pachytene piRNA precursor transcripts were abundant in the adult mouse testis, but not in adult brain, colon, liver, skeletal muscle, heart, kidney, lung, or spleen. In contrast, the precursor transcripts of pre-pachytene and hybrid piRNAs, which often also function as mRNAs, were found in all tissues examined  behave like somatic tissues: most pachytene piRNA clusters are not expressed, whereas hybrid and pre-pachytene piRNA clusters are actively transcribed . Consistent with their testis-specific expression, pachytene piRNA clusters showed higher RNA pol II occupancy in the testis than in somatic tissues. Active histone marks—the promoter mark H3K4me3; the enhancer marks H3K4me1, H3K4me2, and H3K27ac; and the transcriptional elongation mark H3K36me3—were also substantially higher for these 90 loci in the testis than the soma  was expressed in all tissues, while Mov10l1 (Mov10-like RISC complex RNA helicase 1) was also expressed in the heart, where it was originally discovered32. Consistent with their high expression in testis, the 15 genes had substantially higher levels of active histone marks and RNA pol II binding, and significantly lower levels of the repressive histone mark H3K27me3 in adult testis compared with the panel of somatic tissues . Thus, most pachytene piRNA clusters are expressed exclusively or specifically in the mouse testis, and generally show greater tissue specificity than genes encoding piRNA pathway proteins.Sixty nine of the 100 annotated pachytene piRNA clusters, but only one of the 17 piRNA pathway genes, were exclusively expressed in the testis  and low level of CG dinucleotides (defined in “Methods”) at their promoters Fig. , featurence Fig. .Fig. 2Lo37. In contrast, half of mouse pachytene piRNA clusters are either long and intronless  or possess a long first exon . Moreover, for pachytene piRNA clusters, first-exon length (or gene length for unspliced genes) correlates with piRNA abundance . Among the 21,956 annotated protein-coding and 3496 lncRNA-producing genes in mice, all have short first exons . Furthermore, for two of these pre-pachytene piRNA clusters, the long-first-exon isoforms produced more piRNAs than the short-first-exon isoforms . The third pre-pachytene piRNA cluster, 7-qD2-40.1, only makes a long-first-exon isoform, and it makes abundant piRNAs . Together, our data indicate that a long first exon (or a long unspliced transcript) is a specific feature of pachytene piRNA clusters, which may distinguish them from protein-coding and lncRNA genes.Most protein-coding and lncRNA genes have short exons, including first exons (median length of annotated first exons = 229 nt); a long first exon delays splicing and hinders transcriptional elongationons Fig.  except fp value < 2.2 × 10−16 for high-CG vs. low-CG and intermediate-CG combined). In somatic cells, the promoter O/E CG of a protein-coding gene is anticorrelated with its methylation level and correlated with its expression level: low-CG promoters are typically methylated and repressed, while high-CG promoters are unmethylated and expressed39. Similarly, for protein-coding genes in the germline, promoter O/E CG correlates with expression level , while both promoter O/E CG and expression level anticorrelate with promoter methylation level . In pachytene spermatocytes, promoter O/E CG and methylation level of pachytene piRNA clusters are similarly anticorrelated ; however, expression anticorrelates with promoter O/E CG  and correlates with promoter methylation level  or intermediate CG , whereas the promoters of most pre-pachytene (78/84) or hybrid (26/30) piRNA clusters are high CG . Moreover, many pachytene piRNA clusters were broadly marked with acylated histones, extending far into the gene body  transcripts: 39 of the 52 unspliced genes have transcripts longer than 10,000 nt; 5 of the 45 intron-containing loci have long first exons; two genes produce both long unspliced and long-first-exon transcripts from the same locus; and a single locus, nes Fig. . Notably22. BTBD18 binds the promoters of 51 pachytene piRNA clusters, and for 48 of these loci, the steady-state levels of both precursor transcripts and piRNAs decrease ≥2-fold in Btbd18Null homozygous testes, compared with heterozygous littermates. These 48 BTBD18-dependent pachytene piRNA-producing loci include 46 of the 47 pachytene piRNA clusters that produce long transcripts—39 long intronless genes, 5 long-first-exon genes, and 2 genes producing both long intronless and long-first-exon transcript isoforms . For 33 of the 49 genes, BTBD18 binding extends at least to the midpoint of the first exon -seq data10-qB4-6488.1 is transcribed from a 475-bp long, bidirectional promoter that generates the Ddx50 mRNA from the opposite arm . In contrast, THOC1 and THOC2 bound less RNA from short intronless or short-first-exon pachytene, pre-pachytene, or hybrid piRNA precursor transcripts . Remarkably, for all seven spliced piRNA clusters with long first exons, the RIP signals of THOC1 and THOC2 were largely confined to long first exons of the piRNA precursor transcripts, suggesting that the THO complex binds to transcripts from the 5′-end to the first 5′ splice site  and THOC2—from whole adult testes and sequenced the co-immunoprecipitated RNA (RIP-seq); immunoprecipitation with IgG served as a control  and BTBD18 dependence . Unlike protein-coding genes with low-CG promoters, the low-CG pachytene piRNA clusters maintain high histone acylation levels in adult testis. They also exhibit high 5hmC levels, which may aid their transcriptional activation49.Nearly half (49/100) of pachytene piRNA clusters have low-CG promoters Fig. ; these gConsistent with the known correlation between the expression level of a gene and the O/E CG of its promoter, protein-coding genes with low-CG promoters had significantly lower steady-state mRNA abundance in the testis than those with intermediate-CG or high-CG promoters , we found that the promoter 5hmC levels of low-CG pachytene piRNA clusters were significantly higher than those of control genes  in cells where pachytene piRNAs are abundant—pachytene spermatocytes  and round spermatids —but not in hepatocytes, which lack piRNAs . Similarly, the median methylation level for the testis-specific protein-coding genes with intermediate-CG promoters was 17% in pachytene spermatocytes vs. 71–81% in the soma . These results suggest that DNA methylation may be an essential mechanism for silencing these intermediate-CG genes in the soma.The promoters of both pachytene piRNA clusters and testis-specific protein-coding genes were highly methylated (>50%) in the neonatal forebrain, heart, and liver—representative somatic tissues. Yet a subset of these two classes of genes showed lower levels of promoter methylation in pachytene spermatocytes, representative of male germline cells Fig. . These sIndeed, most low-CG promoters of pachytene piRNA clusters (40/49) were highly methylated in both pachytene spermatocytes and forebrain, and most high-CG promoters 17/18) showed low levels of methylation in both pachytene spermatocytes and the forebrain. In contrast, most intermediate-CG (23/33) and some low-CG (9/49) promoters had low methylation in pachytene spermatocytes, but high methylation in the forebrain  or intermediate- (n = 3) CG promoters. For these genes, H3K27me3 rather than DNA methylation may repress somatic expression: the median H3K27me3 level on the promoters of these genes was 1.9 times higher in the forebrain than in pachytene spermatocytes . For the remaining 12 pachytene piRNA clusters expressed in forebrain, which constituted all of the pachytene piRNA clusters with forebrain transcript abundance ≥0.1 RPKM, promoter H3K27me3 levels in forebrain and pachytene spermatocytes do not significantly differ . Similar results were observed in neonatal heart and liver  are expressed in somatic tissues (696 of 930 with >1 RPKM), despite low levels of A-Myb transcript in somatic cells (0.54 RPKM). The A-Myb paralogs B-Myb and C-Myb bind similar sequence motifs to A-Myb, but they are expressed in only a subset of the somatic tissues ; therefore, they are unlikely to be responsible for the expression of A-MYB target genes in somatic tissues. We conclude that the absence of A-MYB in the soma is not sufficient to silence pachytene piRNA clusters in somatic tissues. Thus, three mechanisms—low CG and the associated high hypermethylation, high H3K27me3, and long first exons—may explain the somatic silencing of pachytene piRNA clusters. We performed the same analysis for testis-specific protein-coding genes, and observed that high DNA methylation and high H3K27me3, but not long first exons , promoter elements, and exon–intron structure of the major pachytene clusters are preserved among placental mammals12 for their evolutionary conservation with the 100 mouse pachytene piRNA clusters. We determined whether the genes were syntenic and whether the syntenic genes also produced piRNAs. For 21 mouse pachytene piRNA clusters, the syntenic loci in at least three other eutherian species produced piRNAs at similar levels (<5-fold change from mouse). For 45 other mouse pachytene piRNA clusters, the syntenic loci in rat produced similar levels of piRNAs. These data define three groups of pachytene piRNA clusters: eutherian-conserved, murine-conserved, and mouse-specific . In each of the six eutherian mammals examined, eutherian-conserved pachytene piRNA clusters had longer first exons than other pachytene piRNA clusters , suggesting that a long first exon promotes pachytene piRNA production across eutherian mammals.We analyzed the 89 pachytene piRNA clusters in human, 133 in rhesus, 132 in marmoset, 114 in rat, 117 in cow, 194 in opossum, and 89 in platypusRhesus macaque, which diverged from mouse 90 million years ago. H3K27ac ChIP-seq in adult Rhesus testis revealed that H3K27 acetylation levels were higher at pachytene piRNA-producing genes than other gene types, and the H3K27ac signal spread broadly from the TSS as far as 40 kbp . MBD proteins, in turn, recruit histone deacetylases (HDACs), especially class III HDACs, such as sirtuins, which remove lysine acetyl, butyryl, and crotonyl modificationsThree additional features likely act to repress pachytene piRNA clusters in the soma. First, both low-CG and intermediate-CG promoters show high levels of DNA methylation in the soma. Second, the subset of pachytene piRNA clusters with high- or immediate-CG promoters show a high density of the repressive histone mark H3K27me3. Third, pachytene piRNA clusters tend to have long first exons or long unspliced transcripts. Pachytene piRNA clusters with intermediate-CG promoters may particularly rely on DNA methylation and H3K27me3 for their somatic silencing: just one-third (11/33) of intermediate-CG pachytene piRNA clusters are unspliced with long transcripts or spliced with long first exons; nearly two-thirds (30/49) of low-CG pachytene piRNA clusters display these features.56. Long first exons delay the recruitment of the splicing machinery and hence hinder transcriptional elongation. Co-transcriptional splicing has been proposed to be required for the recruitment of the THO complex, which is essential for transcriptional elongation and nuclear export of spliced RNA57. Consistent with this model, deletion of the first intron of a protein-coding gene markedly decreases its expression58. How then do long unspliced and long-first-exon pachytene piRNA transcripts recruit the THO complex in the male germline? Our analyses identify several features that may promote THO complex binding in the absence of splicing: broad histone acylation and binding of the transcriptional elongation factor BTBD18. Perhaps BTBD18 recruits histone acyl transferases to open the chromatin of long-first-exon or unspliced pachytene piRNA clusters. We do not yet know whether the efficient transcriptional elongation that results from BTBD18 binding and histone acylation suffices to allow co-transcriptional loading of the THO complex or if BTBD18 also participates in the direct recruitment of THO. Once bound to the piRNA precursor transcript, the THO complex may enhance transcriptional elongation and facilitate export of the piRNA precursor transcripts to the perinuclear nuage, where piRNA processing initiates  were used as wild-type controls.68. ChIP-seq libraries were sequenced as 79-nt paired-end reads using NextSeq500 (Illumina).To perform ChIP, small pieces of frozen testis tissue were cross-linked with 2% (w/v) formaldehyde at room temperature for 30 min using end-over-end tumbler. We next crushed fixed tissues in the presence of ChIP lysis buffer (1% (w/v) SDS, 10 mM EDTA, 50 mM Tris-HCl, pH 8.1) by 40 strokes with a B pestle in a Dounce homogenizer . Thereafter, lysate was sonicated using Covaris ultrasonicator  to shear the chromatin to 150–200 bp. Lysate was then diluted 1:10 with ChIP dilution buffer (0.01% (w/v) SDS, 1.1% (w/v) Triton X-100, 1.2 mM EDTA, 16.7 mM Tris-HCl, pH 8.1, 167 mM NaCl). We performed immunoprecipitation using 6 µg rabbit polyclonal anti-H3K27Ac antibody . Following the immunoprecipitation, we extracted DNA with phenol:chloroform:isoamyl alcohol  and prepared libraries for anti-H3K27ac, and input DNA as previously described2, 0.5% (v/v) NP-40, 1 mM phenylmethylsulphonyl fluoride; 1× Proteinase Inhibitor cOmplete, EDTA-free Protease Inhibitor Cocktail (Roche), 0.4 U/µl RNAseOUT (Invitrogen)) in 10 strokes with pestle A and 20 strokes with pestle B in a 2 ml Dounce homogenizer (Kimble-Chase). Lysate was sonicated  at medium strength, 15 s on and 1 min off for 10 min at 0 °C. Lysate was cleared by centrifugation for 15 min at 16,050 × g at 4 °C. Lysate (100 µl) was used for input RNA extraction using RNeasy Mini Kit (Qiagen). A total of 10 µl rabbit anti-THOC2 and anti-IgG, or mouse anti-THOC1 and anti-IgG antibody was conjugated to the 100 µl Dynabeads (Invitrogen) protein A or protein G, respectively, in citric phosphate (CP) buffer  for 2 h at room temperature with rotation. Antibody-conjugated beads were washed three times with CP buffer containing 0.1% (v/v) Tween-20 and incubated with mouse testis lysate overnight at 4 °C with rotation. Beads were washed three times in cold (4 °C) lysis buffer. The immunoprecipitated RNA was recovered from the antibody beads using the RNeasy Mini kit (Qiagen). The extracted RNA was used to generated RNA-seq libraries as described68.Testis from 4-month-old C57BL/6 J mice were frozen in liquid nitrogen and stored at −80 °C until use. One testis was lysed in 1 ml lysis buffer , and ATAC-seq73 data. We also reanalyzed ovary RNA-seq data  from GEO (accession: GSE119411). Public ChIP-seq data of H3K4me1, H3K4me3, H3K27me3, H3K27ac, and RNA pol II in six adult mouse tissues  were obtained from the mouseENCODE consortium74. We also included H3K4me2 ChIP-seq data for the adult mouse testis, liver, and brain69, but we did not find ChIP-seq data in other somatic tissues to fully complement the RNA-seq data. We analyzed the WGBS data generated by the ENCODE consortium on fetal mouse tissues at eight embryonic time points until birth (E10.5 to P0), along with published WGBS data on mouse round spermatids21.We reanalyzed previously published RNA-seqGene annotations and ribosomal RNA (rRNA) sequences were from Ensembl (Release 82). To determine whether Ensembl Release 100 might yield more lncRNAs with long first exons, we reanalyzed the data using the latest Ensembl gene annotation. Indeed, Ensembl Release 100 contains 924 more lncRNAs than Release 82 , but just one of the 924 lncRNAs (AC160336.1) has long first exon. RNA-seq data indicate that this lncRNA is not expressed in adult mouse testis. Thus, our conclusions remain unchanged using Ensembl Release 100.75. Unmapped reads were then mapped to the mm10 genome from the UCSC genome browser using STAR (version 020201) with default parameters 76. SAMtools (version 1.8) was used to transform the alignment results from sam format to bam format throughout this study77. HTSeq (version 0.9.1) was used with default parameters to count uniquely mapping reads78. For each protein-coding or lncRNA gene (Ensembl Release 82) and piRNA-producing locus, we then calculated and normalized the number of uniquely mapped reads as Reads Per Million mapped reads (RPM) and the number of uniquely mapped reads normalized by the total transcript length of each gene as RPKM.To analyze RNA-seq and RIP-seq data, rRNA was first removed from RNA-seq and RIP-seq data by mapping to rRNAs using Bowtie2 (version 2.2.5) with default parameters79. The H3K27ac ChIP-seq data in primary spermatocytes and round spermatids (accession GSE107398) were not provided with input, so we calculated read density normalized by sequencing depth. Furthermore, we normalized read density by sequencing depth for all ATAC-seq data. To calculate the level of each epigenetic mark for the various groups of genes , we averaged the enrichment or read density at the promoter (TSS ± 2 kb) or gene body (TSS + 2 kb till the 3′-end) as indicated.ChIP-seq data and ATAC-seq data were mapped to the mouse reference genome mm10 using Bowtie2 with the parameter–very-sensitive. Enrichment of ChIP signal over input (.bedGraph) was computed from alignment files (.bam) across the mouse genome using the MACS2 callpeak and bdgcmp modules with default parametersWe used a control gene set to normalize ChIP-seq signal levels across different datasets. We first identified 1230 genes with similar expression levels  in three cell types . We then used the median levels of each histone mark for these 1230 control genes in three cell types to normalize the levels of all histone marks, chromatin accessibility , and RNA pol II binding in the corresponding cell types. We used the same strategy to normalize the H3K27me3 levels in testis and somatic tissues. We identified a set of control genes whose expression levels differed by <1.5-fold between testis and each somatic tissue: 1132, 913, and 817 control genes for comparing testis with forebrain, heart, and liver, respectively. This normalization method reduced the bias caused by the difference in antibody specificity across different batches or different tissue or cell types.80. Only C in CG positions was used for computing methylation levels. Methylation level was computed as mCG/CG averaged over all detected CG sites in a region. Each WGBS dataset had four replicates, and we calculated the methylation level for each region in each replicate and then averaged the levels across the four replicates.We applied quality control and adapter trimming to all WGBS data using Trim Galore! (version 0.4.1). Sequence alignment and methylation calls were performed using Bismark , normalizing to uniquely mapping or uniquely mapping plus multiply mapping reads result in similar piRNA abundance and the same conclusions.Small RNA sequencing data were mapped to the mouse genome (mm10) using Bowtie (version 1.1.0) after removing rRNA (from Ensembl) and miRNA (from miRBase) with parameters -a–best–strata5hMe-DIP (5hmC) data were mapped to the mouse genome (mm10) using Bowtie2 with the parameter–very-sensitive. The number of aligned reads were processed into enrichment relative to input in the bedGraph format. The 5hmC level for each gene was calculated by averaging the enrichment in the TSS ± 500 bp window.A-Myb mutant and heterozygous mice. We first identified the genes whose TSSs were within 500 bp of a A-MYB ChIP-seq peak called by MACS2 with parameters -q 0.05–keep-dup all -B (ref. 79). We then filtered out the genes with less than threefold enrichment of A-MYB ChIP-seq signal over input in the TSS ± 500 bp window. Keeping only those genes with >2-fold decrease in A-Myb mutant testis compared to heterozygotes at both 14.5 and 17.5 dpp yielded 837 A-MYB-regulated genes, including 791 protein-coding and 46 lncRNA genes , where the expected number = [(Fraction of C + Fraction of G)/2]2, i.e.,Normalized CG dinucleotide content in a promoter was calculated as described previouslyPromoters were classified into three groups: low-CG, O/E CG < 0.25; intermediate-CG, 0.25 < O/E CG < 0.5; and high-CG, O/E CG > 0.5.n denotes the total number of tissues examined, Expi denotes the expression levels in a particular tissue, and Expts denotes the expression level in testis (we set the ts-score to 0 if any Expi > Expts). We used ts-score to rank piRNAs in Fig. Testis-specific genes corresponded to those genes with expression levels in testis >4-fold higher than the maximal expression in any somatic tissues that we examined. We also calculated a tissue-specific score (ts-score),11 of several piRNA clusters (Supplementary Data 12-qE-23911.1 because it is in a low mappability region and not supported by uniquely mapping reads. (2) We removed the intron in 5-qF-14224.1 which is 59 nt long with a noncanonical splice site motif CC-CT and supported by few reads. (3) We changed the TSS of 17-qC-59.1 from chr17: 50,237,659 to chr17:50,239,160 based on A-MYB ChIP-seq, RNA-seq, and small RNA-seq data. (4) We changed the main TSS of 4-qB3-639.1 from chr4: 62,230,936 to chr4: 62,228,511 based on A-MYB ChIP-seq, RNA-seq, small RNA-seq, and BTBD18 ChIP-seq data. (5) We added a long and intronless isoform  for Gm13817, which is divergently transcribed from 2-qE1-35981.1. Gm13817 is an unannotated pachytene piRNA-producing gene and produces >100 piRNAs per million unique mapped reads. (6) We removed the long-first-exon isoform of pi-Zfp652.1 (a hybrid piRNA clusters), which is not supported by RNA-seq or small RNA-seq reads. (7) We changed the main TSS of 10-qC-875.1 from chr10: 86,617,011 to chr10: 86,591,510 based on A-MYB and H3K4me3 ChIP-seq, RNA-seq, small RNA-seq, and BTBD18 ChIP-seq data. We also separated 10-qC-875.1 from a spliced lncRNA Gm48485, which is primarily expressed in round spermatid.Based on the RNA-seq and small RNA-seq data produced after 2013, we made small modifications to our previous annotationsBtbd18 mutant mice than Btbd18 heterozygous mice, and whose piRNA abundance was also ≥2-fold lower in the 18-day postpartum testis tissue of Btbd18 mutant mice than that of Btbd18 heterozygous mice.We defined BTBD18-dependent pachytene piRNA clusters as those whose expression level was ≥2-fold lower in pachytene spermatocytes of 12. We refined the previously annotated piRNA clusters in rhesus, marmoset, rat, cow, opossum, and platypus12 to delineate exons and introns  could produce similar amounts of piRNAs (change < 5) to mouse in the syntenic regions. The remaining pachytene piRNA clusters were defined as murine-conserved when the syntenic region in rat produced similar amount of piRNAs to mouse, mouse-specific if otherwise.We performed evolutionary conservation analysis on mouse pachytene piRNA clusters with a similar approach as described before for human piRNA clustersIn order to quantify how much the signals of histone modification, chromatin accessibility, or factor binding extend into the gene body, we first cut the genome into 200-bp bins and then computed the average enrichment of each type of signal over input or average read count per one million reads over these 200-bp bins. Bins far downstream pachytene piRNA clusters (TSS + 80 kb to TSS + 100 kb) were considered background bins and bins with signal higher than 95% quantile of the background bins were regarded as signal-enriched bins. Transposon copies reside in pachytene piRNA clusters can hinder the signal identification and lead to signal gaps. To avoid this, we identified the furthest continually enriched bins for each pachytene piRNA clusters allowing 3800-bp gaps (19 bins). The extension index was defined as the distance from the TSS to the furthest continually enriched bin. The relative extension index was defined as the extension index divided by the relative first-exon length, with the maximum set to 1.Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4