Drosophila Heterochromatin Protein 1 (HP1) variant Rhino permits transcription of PIWI-interacting RNA (piRNA) precursors within transposon-rich heterochromatic loci in germline cells. Current models propose that Rhino’s specific chromatin occupancy at piRNA source loci is determined by histone marks and maternally inherited piRNAs, but also imply the existence of other, undiscovered specificity cues. Here, we identify a member of the diverse family of zinc finger associated domain (ZAD)-C2H2 zinc finger proteins, Kipferl, as critical Rhino cofactor in ovaries. By binding to guanosine-rich DNA motifs and interacting with the Rhino chromodomain, Kipferl recruits Rhino to specific loci and stabilizes it on chromatin. In kipferl mutant flies, Rhino is lost from most of its target chromatin loci and instead accumulates on pericentromeric Satellite arrays, resulting in decreased levels of transposon targeting piRNAs and impaired fertility. Our findings reveal that DNA sequence, in addition to the H3K9me3 mark, determines the identity of piRNA source loci and provide insight into how Rhino might be caught in the crossfire of genetic conflicts.RNA interference systems depend on the synthesis of small RNA precursors whose sequences define the target spectrum of these silencing pathways. The  The genes within our DNA encode the essentials of our body plan and how each task in the body is achieved. However, our genome also contains many repetitive regions of DNA that do not encode functional genes. Some of these regions are genetic parasites known as transposons that try to multiply and spread around the DNA of their host. To prevent transposon DNA from interfering with the way the body operates, humans and other animals have evolved elaborate defense mechanisms to identify transposons and prevent them from multiplying.In one such mechanism, known as the piRNA pathway, the host makes small molecules known as piRNAs that have sequences complementary to those of transposons, and act as guides to silence the transposons. The instructions to make these piRNAs are stored in the form of transposon fragments in dedicated regions of host DNA called piRNA clusters. These clusters thereby act as genetic memory, allowing the host to recognize and silence specific transposons in other locations within the host’s genome. In fruit flies, a protein called Rhino binds to piRNA clusters that are densely packed to allow piRNAs to be made. However, it remained unclear how Rhino is able to identify and bind to piRNA clusters, but not to other similarly densely packed regions of DNA.Baumgartner et al. used a combination of genetic, genomic, and imaging approaches to study how Rhino finds its way in the fruit fly genome. They found that another protein called Kipferl interacts with Rhino and is required for Rhino to bind to nearly all piRNA clusters. Since Kipferl can by itself bind to the sequences that Rhino needs to find, the results suggest that Kipferl acts to recruit and initiate Rhino binding within densely packed piRNA clusters. Further experiments found that, in flies lacking Kipferl, Rhino binds to regions of DNA called Satellite repeats, hinting that these selfish sequences may compete for Rhino for their own benefit.The finding that Kipferl and Rhino work together to define the memory system of the piRNA pathway strongly advances our understanding of how a sequence-specific defense system based on small RNAs can be established. Eukaryotic genomes are littered with transposable elements. By constantly aiming to increase their copy number, transposons pose a major threat to genomic integrity and must therefore be tightly controlled. An important transposon silencing mechanism in animals is the PIWI-interacting small RNA (piRNA) pathway . Its losDrosophila gonads, two general types of piRNA clusters are distinguished based on their mode of transcription: Uni-strand clusters are transcribed on one genomic strand from canonical RNA polymerase II promoters, giving rise to long, single stranded, and poly-adenylated transcripts  but not ‘self’ (genic loci).The molecular identity of dual-strand piRNA clusters is conferred by Rhino, a germline-specific variant of the canonical Heterochromatin Protein 1 a (Su(var)2–5) . While SDespite an increasing understanding of how Rhino orchestrates piRNA cluster expression, little is known about how cells control the specific deposition of Rhino onto chromatin. Current models involve a role of maternally inherited Piwi-piRNA complexes in the specification of piRNA cluster identity through Piwi-dependent deposition of H3K9 methylation, which is postulated to define dual-strand clusters at the chromatin level . In thisAT-chX cluster attracts most of the cellular Rhino pool in differentiating spermatocytes but not at earlier developmental stages  and in iso1, the strain underlying the D. melanogaster reference genome (1118w strain) showed greater than 4-fold enrichment  for Rhino over input.As a foundation for studying how Rhino’s chromatin binding specificity is defined molecularly, and how variable it is in fly strains with different transposon insertion profiles, we determined Rhino’s chromatin occupancy genome-wide in two commonly used laboratory strains 2–5 ChIP-seq data) . The thrferences . Many Rhferences  panel 1 ster 80F  panel 2:ster 80F  right. B8 strain  panel 3)8 strain . Based ot piRNAs  panel 4.t piRNAs , the piRt piRNAs . The poot piRNAs . AltogetConsistent with a reported role of H3K9 methylation in dual-strand piRNA cluster biology, Rhino domains in both pericentromeric heterochromatin and within chromosome arms consistently showed enrichment in H3K9me3 . HoweverSequence-specific DNA binding proteins can guide HP1 family proteins to chromatin. HP1b and HP1c, for example, depend on the zinc finger proteins Woc and Row for chromatin binding to thousands of sites that lack signal for Su(var)2–5 and even H3K9me2/3 . ConsideCG2678 encodes a predicted DNA binder whose two annotated protein isoforms carry one or two arrays of C2H2 zinc fingers (ZnFs)  . Based os (ZnFs) . Only a cription , genome cription , and hetcription . As a prcription . We genecription . Togetherhino, CG2678 mRNA levels were detected primarily in ovaries  (cluster 80F), Rhino domains in pericentromeric heterochromatin, stand-alone transposon insertions in chromosome arms as well as loci like headcase that lack transposon sequences and piRNA output  , E. ClosA output .Notably, 647 genomic 1-kb tiles exhibited significantly higher enrichment for CG2678 than for Rhino (Z-score >3) and were not enriched for Rhino  . At thesCG2678 locus  in control flies remained Rhino-bound in ster 38C . Fluores mutants . In lineenvelope . We ther Kipferl . In contd testes , maintaid testes .kipferl mutant ovaries compared to wildtype controls (Rsp and 1.688 transcripts in kipferl mutants overlapped precisely with the prominent Rhino accumulations at the nuclear envelope. Rsp and 1.688 Satellites form large repetitive tandem arrays in pericentromeric heterochromatin (359 bp array (1.688 family) on the X-chromosome which extends over several megabasepairs, likely explains why Rhino foci in kipferl mutants are so prominent , Rhino-independent germline source loci (e.g. uni-strand cluster 20A) and Rhino-dependent germline source loci   . kipferlrce loci . A promi ovaries . At many ovaries .kipferl depletion on germline-specific, Rhino-dependent piRNA source loci was also reflected when piRNAs mapping in antisense orientation to transposon consensus sequences were analyzed  were not affected in rhino- or kipferl-depleted ovaries. Among the transposons with dominating germline piRNA populations, nine are targeted by Rhino- and Kipferl-independent piRNAs originating from Rhino-independent source loci like cluster 20A . All other transposons were targeted by Rhino-dependent piRNAs. For most of these elements, piRNA pools were reduced in kipferl-depleted ovaries   . Thus, ikipferl-depleted ovaries  and within chromosomal arms (3334 peaks), and generally coincided with regions bound by Kipferl and Rhino in wildtype ovaries, demonstrating that Kipferl binds the same sites in both genotypes, yet in different patterns , the extended Rhino domain in wildtype flies encompassed only very weak or no putative Kipferl nucleation sites  closely matched the in silico predictions for Kipferl’s ZnF arrays  or the N-terminal ZAD (KipferlΔZAD) (Δ1st-array showed strongly reduced chromatin binding capabilities (Δ2nd-array) instead had only mild impacts. The N-terminal ZAD, which is characteristic for the >90 ZAD-ZnF family members in D. melanogaster, promotes anti-parallel homodimerization but has not been directly linked to DNA binding (The yeast two-hybrid results  and ChIPerlΔZAD) . Kipferlbilities . Deletio binding . Yeast t binding . Deletio binding , consist binding . TogetheΔ1st-array and KipferlΔZAD, failed to colocalize with Rhino . Thus, neither the ZAD, nor the second ZnF array are critical for binding to Rhino, suggesting that Kipferl’s first ZnF array, besides its central role in chromatin binding, might also enable Rhino binding.To dissect the interaction of Kipferl with Rhino, we first examined the characteristic colocalization of Kipferl and Rhino in nurse cell nuclei. This revealed that both chromatin binding defective variants, Kipferlth Rhino . In bothth ZnF  . Exceptions were telomeric transposons and a few elements contained in clusters 42AB or 38C; these transposons were occupied by Rhino in wildtype ovaries despite no baseline Kipferl binding, and they maintained Rhino binding in kipferl-depleted ovaries, supporting the previous notion that Kipferl-independent chromatin recruitment mechanisms for Rhino likely exist . The correlation between GC-content and Rhino enrichment was abolished in ovaries depleted for Kipferl, indicating that Rhino’s preferential binding to GC-rich transposons is due to Kipferl-mediated recruitment and/or stabilization of Rhino on DNA sequences with Kipferl motifs  . For mosly exist . As the Burdock, HMS-Beagle, 3S18) were strongly dependent on Rhino for their silencing, and were deregulated also upon loss of Kipferl, although to a weaker extent  argues in favor of additional specificity factors. An intriguing observation from our work is that Kipferl’s spatio-temporal expression profile differs from that of Rhino: Kipferl levels in ovarian germline stem cells and cystoblasts are very low, and the protein is absent in testes. Kipferl’s ovary-specific expression is therefore a likely contributor for the pronounced differences in Rhino landscapes between ovaries and testes . For ovary dissections, flies were aged for 2–6 days, and put on apple juice plates with fresh yeast paste for 2 days.All fly stocks were kept at 25 °C with 12 hr dark/light cycles. Fly strains with genotypes, identifiers, and original sources are listed in the Key Resource Table and strains generated for this study are available from VDRC (CG2678 and rhino were generated in isogenised white embryos after co-injection of plasmids pBS-Hsp70-Cas9 (Addgene #46294) and pU6-BbsI-chiRNA (Addgene #45946) modified to express sgRNAs. Whole locus CG2678 deletion for RMCE was achieved following co-injection into ZH-2A(Act5C-Cas9) embryos (derived from Bloomington stock #58492) of plasmids pXZ13 . Transgenic flies harboring GFP tagged wildtype or engineered Rhino constructs were generated via insertion of desired tag sequences under the control of the rhino promoter region and the vasa 3’UTR into the attP40 landing site (rhino frame shift mutation on the same chromosome. Overexpression constructs for CG2678 were injected as pUASz plasmids (CG2678 into the attP2 landing site (eggless enhancer were obtained through integration of a mini-white containing plasmid into the attP40 landing site (CG2678 rescue constructs were introduced into the CG2678 whole locus deletion flies through co-injection of pRVV578 plasmid (Addgene #108279) harboring the endogenous CG2678 locus flanked by attB sites, with pBS130 (Addgene #26290) for expression of phiC31 integrase. Successful cassette exchange was monitored via loss of dsRed in eyes and the orientation of the inserted construct was verified by PCR. Rescue constructs harbor an internal 3xFLAG tag at residue S161, as neither amino- nor carboxy-terminal tagging of CG2678 yielded fully functional protein. Exceptions are KipferlΔZAD and KipferlGCN4 constructs, which harbor an N-terminal 3xFLAG tag. Of note, we found that the CG2678 locus harbors an additional 25 amino acid exon, flanked by 109 and 31 nucleotides of intronic sequence up and downstream, respectively, in several laboratory fly strains. Comparison with protein sequences annotated for other Drosophila species confirm the presence of the additional in frame coding sequence, inserted between P347 and K348 of the annotated melanogaster CG2678 protein  expression cassettes for germline knockdown were generated by cloning shRNA sequences into the Valium-20 vector  modifieding site  in fliesplasmids  containiing site . Transgeing site . CG2678 see also . PCR anaHP1b and HP1c was achieved through co-injections of pU6-BbsI-chiRNA together with pBS donor plasmids containing 1 kb homology arms into embryos from vas-Cas9; attP2 flies or ZH-2A(Act5C-Cas9) embryos for HP1b and HP1c, respectively. sgRNA sequences are listed in the Key Resource Table.Generation of endogenously tagged lines for E. coli, and purified using Ni-NTA resin (QIAGEN) according to standard protocols. Polyclonal antibodies were raised against a CG2678 peptide (aa R171-I190) at Eurogentec.Mouse monoclonal antibodies against His-tagged CG2678 (aa M2-K188) and His-tagged Rhino (full length) were generated by the Max Perutz Labs Antibody Facility. Antigens were cloned in pET-15b, transformed in BL21(DE3) Drosophila ovarian somatic cells (OSC) cells were cultured as previously described  using a plastic pestle. Protein concentration of whole ovary lysate was determined via Bradford assay to allow equal loading, and SDS-PAGE gel electrophoresis was performed according to standard procedures. Primary antibodies were incubated at 4 °C overnight, secondary antibodies for 1 hr at RT and the blots were developed using ECL (BioRad). Antibodies and dilutions are listed in the Key Resource Table.Five pairs of ovaries were mechanically disrupted in lysis buffer . Probe sequences are listed in 1.688 and Rsp Satellites was performed using a single fluorescent oligo or an in-house labelled probe set of 48 oligos, respectively  for 5 min at RT and washed two times 5 min with 2 x SSC. Finally, ovaries were mounted on microscopy slides using DAKO mounting medium (Agilent) and equalized at RT for at least 24 hr prior to imaging on a Zeiss LSM 880 inverted Airyscan microscope. Images are given as Z-stack across a maximum of 2 micrometers.RNA FISH for piRNA clusters ectively . FISH waFive to 10 ovary pairs or testes were dissected into ice cold PBS and subsequently incubated in fixation solution  for 20 min at room temperature with rotation. Fixed ovaries were washed 3 x 5 min in PBX  and blocked with BBX  for 30 min, all at room temperature with rotation. Primary antibody incubation was performed by incubation at 4 °C overnight with antibodies diluted in BBX followed by three 5-min washes in PBX. Ovaries were then incubated overnight at 4 °C with fluorophore-coupled secondary antibodies, washed three times in PBX including DAPI in the first wash to stain DNA . The final wash buffer was carefully removed before addition of ∼40 μL DAKO mounting to each sample. The samples were imaged on a Zeiss LSM-880 Axio Imager confocal-microscope and image processing was done using FIJI/ImageJ . Images https://www.hybrigenics-services.com). The coding sequence for full-length Rhino was cloned into pB27 (derived from pBTM116)   as an N-Yeast strains were grown in YPD or SC selective medium at 30 °C. pOAD and pOBD used as backbone for cloning were described in 2, 3  mM CaCl2, 0.5% NP40, 10% glycerol, cOmplete Protease Inhibitor Cocktail (Roche)) and incubated at 4  °C with nutation for 15 min followed by centrifugation for 5  min at 20,000 g at 4 °C for mild pre-extraction of cytoplasmic contaminants. The nuclear-enriched fraction was resuspended in lysis buffer ) and homogenized using first an electric plastic pestle tool (20 s on ice), followed by further douncing (20 times) and incubation at 4  °C with nutation for 2 hr. The lysate was cleared twice by centrifugation at 18,000 g for 10 min before mixing with 100 µL magnetic Pierce Streptavidin beads (ThermoFischer) pre-equilibrated in lysis buffer, followed by overnight incubation at 4  °C. The beads were washed once with lysis buffer for 10  min at 4  °C, with 2% SDS (10 min at room temperature), and 10 minutes each with wash buffers 1  and 2  for a minimum of 10  min at 4  °C, followed by five washes without detergent  before further downstream mass spectrometry analysis.Flies expressing GFP-Rhino or GFP fused to a nuclear localization signal were crossed to flies expressing low levels of TurboID biotin ligase fused to GFP-nanobody and the progeny was kept in cages on apple juice plates and fed with yeast paste containing 100 µM biotin (Sigma) for 16 hr prior to ovary dissection , as percentage of hatched eggs from total. Plates with less than 50 eggs were disregarded in the analysis. Wildtype females were included in every experiment as control.To measure female fertility, 10 virgin females were collected and aged for 2–3 days with at least 24 hr of mating with three Drosophila melanogaster genome (dm6 assembly) were split into non-overlapping 1-kb tiles. The tiles were annotated by intersection with genomic annotations for piRNA clusters. Tile mappability was determined by intersection with genomic blocks of continuous mappability using bedtools coverage. Tiles with mappability below 25% were excluded from all analyses (2761 1-kb tiles). Further exclusion criteria included a more than threefold deviation from median values for representative input libraries for either of the three wildtype genotypes used in this study , as well as tiles showing strong residual Rhino or CG2678 signal in ChIP-seq libraries prepared from the respective knock out ovaries .The four assembled chromosomes of the 42AB, 38C, and 80F were not included into either category, but were analyzed separately as reference loci.We used ovary H3K9me3 ChIP-seq data to define the extent of pericentromeric heterochromatin and euchromatic chromosome arms. The heavily H3K9me3 covered pericentric regions of the assembled chromosomes, as well as the entire chromosome 4 were classified as heterochromatic, while the rest was annotated as euchromatic. Detailed coordinates can be found in . Small genome contigs not assembled into the four major chromosomes were excluded from all analyses. For 1-kb tile analyses, piRNA clusters ChIP was performed as previously described . In brieStrand-specific RNA seq was performed as described previously . In brieSmall RNA cloning was performed as described in --best --strata --sam), allowing up to three mismatches. Genome unique reads were mapped to 1-kb tiles and a pseudocount of 1 was added after normalization to library depth, before enrichment over input values were determined. Each ChIP-seq sample was adjusted with a correction factor determined from median input levels and median background levels to reach median background enrichment of 1 to correct for unequal ChIP efficiency. To classify genomic regions into Rhino domains and non-Rhino domains, we used a binary cutoff of 4-fold enrichment calculated from two independent replicate experiments of the relevant wildtype genotypes. This cutoff corresponds to a p-value of <0.05. Kipferl-only 1-kb tiles were those that had no Rhino enrichment (below 4-fold) and that were significantly enriched in Kipferl over Rhino (Z-score=3). Replicates were averaged for genomic 1-kb tile analyses.ChIP-seq reads were trimmed to remove the adaptor sequences and to adjust all reads to 50 bp irrespective of sequencing mode. Reads were mapped to the dm6 genome using Bowtie  and 30 . Kipferl-Rhino shared versus Kipferl-only peaks were distinguished by intersection of narrow peaks called for Kipferl in two independent replicate experiments of rhino mutant ovaries, with broad peaks called for Rhino in two independent replicate experiments of 1118w ovaries using bedtools intersect with -u -f 0.75 for shared domains and -v for Kipferl-only domains. Rhino-independent Kipferl peaks that were detected independently in two replicate experiments were grouped into heterochromatic and euchromatic by intersection with heterochromatin coordinates outlined above. Kipferl DNA binding motifs were recovered from the top ~3000 summits of Rhino-independent Kipferl peaks (achieved through a score cutoff of 7 on summits) using HOMER (We used MACS2  with --bng HOMER . Heatmapng HOMER .--best --strata --sam) allowing up to three mismatches. Reads mapping to multiple elements were assigned to the best mapping position. Reads mapping to multiple positions were randomly distributed. Library depth normalized ChIP and input reads, respectively, were averaged over all nucleotide positions of each element to give one value per element. ChIP-seq enrichment was calculated after adding a pseudo count of 1 and adjusted using sample-specific correction factors determined from background 1-kb tiles to reach median background enrichments of 1. Corrected per-base enrichment was calculated for TE ChIP-seq profiles and replicates were averaged.Genome mapping reads longer than 23 nucleotides were mapped to TE consensus sequences  using boOccurrences of Kipferl DNA binding motifs were determined using PWMScan (ccg.epfl.ch/pwmtools /pwmscan.php) on the dm6 genome and on TE consensus sequences. For display in heatmaps, cumulative motif counts on both genomic strands were intersected with non-overlapping 100-bp windows.Drosophila melanogaster genome (dm6), using Bowtie  with 0 mismatch allowed. Genome mapping reads were intersected with Flybase genome annotations (r6.40) using Bedtools to allow the removal of reads mapping to rRNA, tRNA, snRNA, snoRNA loci and the mitochondrial genome. For TE mappings, all genome mappers were used allowing no mismatches. Reads mapping to multiple elements  and germline  piRNAs mapping to each tile or TE  were randomized in order and quantified using Salmon . For the analysis we used the FlyBase transcriptome (r6.40) which has been masked for sequences similar to transposons. To include both strands of transposons in the analysis, TE-consensus sequences were added to the FlyBase transcriptome in sense and antisense orientation. For gene expression visualization Salmon results were further processed to GeTMM values using edgeR (v3.34.0). For differential gene expression analysis Salmon results were processed using DeSeq2 (v1.32.0).For the RNAseq analysis, genome matching reads  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:Drosophila ZAD zinc finger protein Kipferl guides Rhino to piRNA clusters" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Kevin Struhl as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Zhao Zhang (Reviewer #2).Thank you for submitting your article "The The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:The reviewers have greatly enjoyed reading the manuscript and recognized the quality, novelty, and impact of the findings, supported by a large breadth of complementary approaches. How active piRNA-producing loci can emerge from heterochromatin and how they can be distinguished from other heterochromatic loci is an essential question, which more generally touches upon the question of the transcriptional activity of supposedly inert heterochromatin. Importantly, the identification of Kipferl may lead the path for the identification of other sequence-specific determinants of piRNA-producing loci, as H3K9me3-enrichment seems unlikely to be the sole feature allowing piRNA loci to be transcribed.We identified five points that need further attention and are listed below. Most of them involve toning down some of the claims and /or integrating prior knowledge in the novel model, such as the epistatic position of Kipferl towards maternally deposited piRNAs in the specification of piRNA-producing loci. Please address them in a revised version of your manuscript. In addition, answer the individual comments of the reviewers in a detailed rebuttal letter.1 – Kipferl was identified as a Rhino partner by yeast two-hybrid. However, independent confirmation by co-IP experiments seems to be missing. Please provide such experimental validation.melanogaster and more particularly, in simulans, and if so, whether some rapidly evolving amino-acids could be identified between the two species.2 – Regarding the potential position of Kipferl at the interface of a genetic conflict, reviewers were interested to know whether Kipferl was conserved outside of 3 – The claim that Kipferl directly binds to DNA should be toned down, as recruitment to Rhino-occupied loci through protein/protein interactions is equally possible at that stage, unless biochemical evidence is provided.4 – Please integrate the role of maternally deposited piRNAs into the model of Rhino recruitment to piRNA loci, and where they stand regarding Kipferl. For example, are these two modes of recruitment temporally separated ? Do maternal piRNAs and Kipferl activate different piRNA clusters? Or do they act redundantly? No specific experiments are required here, but the view(s) of the authors on that matter would be very useful to share, as maternal piRNA-dependent specification of piRNA-producing loci is a well-accepted model.5 – The authors propose that Kipferl acts upstream from Rhino but the hierarchy between Kipferl and Rhino seems more complicated than this simple model, as Kipferl binding at piRNA loci can also be reduced or even completely gone in Rhino mutants. The authors indeed discuss that Rhino is required for ectopic Kipferl spreading to Satellite loci, but it seems that Rhino is also required to some extent for Kipferl to bind at canonical piRNA loci. The authors are not required to carry out a tethering experiment-as suggested by reviewer #2- but the text should be modulated accordingly.Figure revisions:– Please provide statistics in violin and box plots. Currently, the reader may have the impression that changes may be present but non-significant.– Genomic coordinates have to be provided on UCSC genome tracks.– Please homogenize your scales in figures of genome tracks, particularly when comparing different genotypes for a given locus .Reviewer #1 (Recommendations for the authors):1. Without Kipferl, H3K9me3 seems to mildly (but visibly) increase  while H3K9me2 seems to mildly (but visibly) decrease . Is there a statistically significant difference between the two genotypes for K9me2/3? Is there a correlation between change in H3K9me2/3 and change in Rhino binding and what is their correlation coefficient?kipferl-/- could reflect a conflict. Given that Rhino binds satDNA in wildtype, what about the alternative possibility that it is merely a passive consequence of Rhino having to re-localize to Kipferl-independent loci in kipferl-/-? Or is there a disproportionate, higher-than-expected enrichment onto satDNA, with respect to 42AB and 38C, upon loss of Kipferl? Is there an increase in 42AB and 38C transcripts in kipferl-/- by RNA FISH? What makes up the remaining 79% piRNAs when 21% are satDNA piRNAs at line 307?2. In Abstract, Intro and Discussion, authors describe the exciting possibility that Rhino enrichment on satDNA in 3. It is unclear which sequencing experiment is done with replicates and how many replicates, which should be clearly described. For replicated experiment, is a representative dataset OR the average of replicates shown? In sequencing experiments without replicates, how could one be convinced of the result?kipferl, nxf3 double mutants restore Rhino foci that are absent in kipferl-/-.4. In all confocal images, is only one z-slice shown? Other than Figure 3C, Rhino seems to have additional foci besides the pastry domain at the absence of Kipferl . Showing the z-projection for single nurse cells might make statements about Rhino localization more convincing, especially the claim that 5. The correlation coefficient with respective p-value should be reported whenever a correlation argument is made throughout the manuscript. e.g., lines 161, 347 and throughout Figures7 and S7.42AB and 38C, as well as telomeric transposons, are all Kipferl-independent, one wonders if Kipferl really controls most/majority of Rhino domains. Since there are many Rhino-only and Kipferl-only domains/1kb tiles, listing actual numbers helps readers contextualize the relationship between Rhino and Kipferl as well as the prevalence of exceptions.6. Text description should not exaggerate the result. e.g., "every" at line 556. Text often says Kipferl is important for most/majority of Rhino domains, could it be supported by actual numbers ? Given that most active piRNA loci Reviewer #2 (Recommendations for the authors):Drosophila ovarian genome. I enjoyed reading and considering it in the past few days. I felt that there are some questions still unanswered--particularly how Rhino is recruited to 42AB. However, given the authors already generated massive amount of data and the complexity of Rhino recruiting mechanisms, I don't think it would be fair to ask for an answer here. Here are a few suggestions:This is a compelling story to establish the function of Kipferl in recruiting Rhino to the piRNA-producing loci in Drosophila species. Rhino appears to be rapidly evolving, even simulans Rhino could not be compatible with the melanogaster piRNA machineries. Does Kipferl homolog exist in simulans. If yes, how different it is with the melanogaster Kipferl, especially the amino acids involving in Rhino and DNA binding. Answering these questions could potentially broaden the impact of this story.1. I think the authors should comment on the potential homolog of Kipferl in other 2. Line159, H3K9me3 or H3K9me2?3. I am confused about the message from Line 365-367. Do you mean Rhino peaks towards the periphery?4. Line 452: motives or motifs.5. The last paragraph is way too long. It is good to only deliver one message per paragraph.Reviewer #3 (Recommendations for the authors):The manuscript, although well written, is very dense with a lot of figures which sometimes makes it difficult to follow.kipferl KD are compared. As the manuscript now stands, the reader can have the impression that the changes might be important but non-significant.– In all the manuscript there is no mention of significance in the violin and box plots when the control KD and the – For each UCSC genome browser track, the genomic coordinates of the presented region should be added either on the figure or in the legend.Figure Sup 3:– Based on the figure legend, there are some inversions the E is the B, the B◊D, the D◊E.42AB and 38C are not really convincing. Furthermore, it is surprising not to see any 80F FISH experiments.– The picture of the FISH experiments of the 42AB, 38C is very informative. It should also be added for the Kipferl dependent TE piRNAs. The presence in the cluster 80F should also be described.– Figure 4D. the name of the TEs and the presence in cluster Cluster 80F, the Rhino ChIP-seq track is at 300 for the w1118 and MTD-Gal4 genotypes whereas it is at 150 for the Iso1 genotype. It is the same problem in Figure 5C for the Kipferl ChIP-seq results on the 42AB cluster.– The scale of the UCSC genome browser tracks are variable. For a given locus it is important to keep the same scale between different genotypes. Figure 1C, considering the heterochromatic region and the  Essential revisions:1 – Kipferl was identified as a Rhino partner by yeast two-hybrid. However, independent confirmation by co-IP experiments seems to be missing. Please provide such experimental validation.We have attempted this experiment several times using various buffer conditions. A co-IP of Rhino and Kipferl has proven to be very challenging. In our experience, most of Kipferl is tightly associated with chromatin and cannot be solubilized under buffer conditions that are compatible with the Rhino interaction. Moreover, co-expression of the two proteins in S2 cells for pull-down Western experiments did not allow any conclusions, as we observed background binding of Kipferl to the beads. However, we have conducted a complementary experiment that strongly supports that Rhino and Kipferl do not only interact in a heterologous yeast two-hybrid assay, but that the two proteins are also in very tight physical proximity in vivo. Briefly, a TurboID experiment (taking advantage of GFP-tagged Rhino in flies co-expressing the TurboID Biotin ligase fused to a GFP nanobody) recovered the complete set of known Rhino interactors, and in addition CG2678/Kipferl. As a control, we used flies expressing nuclear GFP alone alongside TurboID-GFP_nanobody (in a triplicate experiment coupled to label free quantitative mass spectrometry). We added this important complementary evidence for a Rhino-Kipferl interaction to Figure 2.simulans, and if so, whether some rapidly evolving amino-acids could be identified between the two species.2 – Regarding the potential position of Kipferl at the interface of a genetic conflict, reviewers were interested to know whether Kipferl was conserved outside of melanogaster and more particularly, in D. simulans. We added the protein sequence alignment of Kipferl paralogs to Figure 2 Figure supplement 1. As far as we can tell, there are no obvious signs for rapidly evolving amino acids in the region that underlies the Rhino interaction (ZnF#4). We also did not find clear signs of positive selection within Kipferl when analyzing D. melanogaster natural strains. The absence of signs for positive selection has been mentioned in the previous Discussion and we slightly expanded that statement (last paragraph). In the future, it will be interesting to perform genetic swap experiments with Kipferl orthologs from other species.Based on sequence conservation and gene synteny, Kipferl orthologs can be confidently identified in several other Drosophilid species, including 3 – The claim that Kipferl directly binds to DNA should be toned down, as recruitment to Rhino-occupied loci through protein/protein interactions is equally possible at that stage, unless biochemical evidence is provided.We agree that this is formally possible. We do, however, like to point out that Kipferl does bind chromatin also independently of Rhino (at Kipferl-only loci), foremost based on the data where we expressed Kipferl in the OSC line that does not express detectable Rhino. We toned down the argumentation in the manuscript accordingly throughout the text relating to Figures 5 and 6.4 – Please integrate the role of maternally deposited piRNAs into the model of Rhino recruitment to piRNA loci, and where they stand regarding Kipferl. For example, are these two modes of recruitment temporally separated ? Do maternal piRNAs and Kipferl activate different piRNA clusters? Or do they act redundantly? No specific experiments are required here, but the view(s) of the authors on that matter would be very useful to share, as maternal piRNA-dependent specification of piRNA-producing loci is a well-accepted model.This is indeed an important point. Our data do not allow strong conclusions at this point. We extended this part of the discussion to reflect our current view on this topic (second paragraph).5 – The authors propose that Kipferl acts upstream from Rhino but the hierarchy between Kipferl and Rhino seems more complicated than this simple model, as Kipferl binding at piRNA loci can also be reduced or even completely gone in Rhino mutants. The authors indeed discuss that Rhino is required for ectopic Kipferl spreading to satellite loci, but it seems that Rhino is also required to some extent for Kipferl to bind at canonical piRNA loci. The authors are not required to carry out a tethering experiment-as suggested by reviewer #2- but the text should be modulated accordingly.kipferl mutant) or in early stages of oogenesis where Kipferl is barely detectable. We added specific comments to this in the text .The reviewers are correct in pointing out that our data is not consistent with a strict Kipferl -> Rhino hierarchy. This is as Rhino has a clear impact on Kipferl’s chromatin binding capacity as well. As we do find Kipferl-only loci but not any clear Rhino-only loci, we argue that Kipferl is capable of binding DNA/chromatin in the absence of Rhino, but that Rhino has a strong stabilizing and spreading impact on Kipferl at sites that are within an H3K9-methyl domain. It is, however, important to point out that Rhino is capable of binding chromatin also independently of Kipferl in situations, where Kipferl is experimentally deleted , as well as the statistical significance as calculated via student’s t-test (for ChIP-seq enrichments) or Wilcoxon signed-rank test (for sRNA-seq fold changes).– Genomic coordinates have to be provided on UCSC genome tracks.We added the coordinates of each screenshot below the locus name in the various figures.– Please homogenize your scales in figures of genome tracks, particularly when comparing different genotypes for a given locus .iso1 has the same pattern, but at some loci shows Rhino ChIP-seq signal at a lower level”. We speculate that this might be due to the fact that the kipferl locus in iso1 differs from the kipferl locus in all examined experimental fly strains as it lacks part of the linker between the two zinc finger arrays . For Figure 5, we made the y-axis scale consistent between genotypes. We also added panels with a zoom-in to show the remaining Kipferl enrichment pattern in better detail.We agree with this overall comment. For Figure 1, however, we think it makes more sense to leave the data display as is and to add a comment in the text saying that “Reviewer #1 (Recommendations for the authors):1. Without Kipferl, H3K9me3 seems to mildly (but visibly) increase  while H3K9me2 seems to mildly (but visibly) decrease . Is there a statistically significant difference between the two genotypes for K9me2/3? Is there a correlation between change in H3K9me2/3 and change in Rhino binding and what is their correlation coefficient?The reviewer is right. Our data indicate a small shift from H3K9me2 to me3 upon depletion of Kipferl. However, this was not limited to Rhino-bound genomic tiles. In the absence of more quantitative experiments using spike-in normalization approaches, we refrain from making a strong conclusion from this. Our key argument is that Rhino chromatin occupancy is lost at most sites despite high levels of remaining H3K9-methylation.kipferl-/- could reflect a conflict. Given that Rhino binds satDNA in wildtype, what about the alternative possibility that it is merely a passive consequence of Rhino having to re-localize to Kipferl-independent loci in kipferl-/-? Or is there a disproportionate, higher-than-expected enrichment onto satDNA, with respect to 42AB and 38C, upon loss of Kipferl? Is there an increase in 42AB and 38C transcripts in kipferl-/- by RNA FISH? What makes up the remaining 79% piRNAs when 21% are satDNA piRNAs at line 307?2. In Abstract, Intro and Discussion, authors describe the exciting possibility that Rhino enrichment on satDNA in 42AB or 38C (whose levels remain unchanged or decrease slightly), piRNAs derived from several Satellites increase strongly (up to 10-fold) although total piRNA levels in kipferl mutant ovaries decrease by about 40%. We did not observe increased RNA FISH signal for 42AB and 38C in the kipferl mutants compared to wildtype, while a strong increase in RNA FISH signal is seen for Satellites. This argues in favor of sequestration of the piRNA cluster machinery to Satellites, rather than just a distribution of free Rhino to remaining loci capable of binding Rhino. The remaining 80% piRNAs in kipferl mutants are somatic piRNAs (~20%) and piRNAs derived from Rhino dependent source loci where Kipferl is less important .Other than piRNAs originating from clusters 3. It is unclear which sequencing experiment is done with replicates and how many replicates, which should be clearly described. For replicated experiment, is a representative dataset OR the average of replicates shown? In sequencing experiments without replicates, how could one be convinced of the result?kipferl -/- and rhino -/-) to complement the missing replicates for the previous analysis. These results confirm our analysis from the RNAi-mediated knockdown samples.We specified the number of biological replicates in the figure legends throughout the manuscript and provide now additional data  for full mutant sRNA-seq data . Showing the z-projection for single nurse cells might make statements about Rhino localization more convincing, especially the claim that kipferl mutant nurse cells, the large peri-nuclear accumulations (the ‘Kipferl’) is the most prominent, but not the only visible Rhino domain. Regarding the Nxf3 comment: This might have been misleading in the text : In nxf3-kipferl double mutants, no Rhino domains are restored. It is just that the Kipferl-structure is not anymore at the nuclear periphery, but rather internalized.All confocal images are no more than 5-10 z-sections of 0.2 μm distance and are therefore displaying only a part of the entire nucleus. This is stated now in the methods section (relating to RNA FISH and immunofluorescence stainings). In 5. The correlation coefficient with respective p-value should be reported whenever a correlation argument is made throughout the manuscript. e.g., lines 161, 347 and throughout Figures7 and S7.We changed the wording in lines 161 (now 164) and 347 (now 404-405) and added coefficients throughout the text relating to Figure 7.42AB and 38C, as well as telomeric transposons, are all Kipferl-independent, one wonders if Kipferl really controls most/majority of Rhino domains. Since there are many Rhino-only and Kipferl-only domains/1kb tiles, listing actual numbers helps readers contextualize the relationship between Rhino and Kipferl as well as the prevalence of exceptions.6. Text description should not exaggerate the result. e.g., "every" at line 556. Text often says Kipferl is important for most/majority of Rhino domains, could it be supported by actual numbers ? Given that most active piRNA loci In our data, Kipferl in fact localizes to every Rhino domain in wildtype ovaries , even though it does not bind upstream of Rhino at all of these sites. When using binary cutoffs of peak-calling algorithms, 97% of all analyzable Rhino domains (in kb) are dependent on Kipferl. Of these, 65% are found within 5 kb up or downstream of a Rhino-independent Kipferl peak.Reviewer #2 (Recommendations for the authors):Drosophila ovarian genome. I enjoyed reading and considering it in the past few days. I felt that there are some questions still unanswered--particularly how Rhino is recruited to 42AB. However, given the authors already generated massive amount of data and the complexity of Rhino recruiting mechanisms, I don't think it would be fair to ask for an answer here. Here are a few suggestions:This is a compelling story to establish the function of Kipferl in recruiting Rhino to the piRNA-producing loci in Drosophila species. Rhino appears to be rapidly evolving, even simulans Rhino could not be compatible with the melanogaster piRNA machineries. Does Kipferl homolog exist in simulans. If yes, how different it is with the melanogaster Kipferl, especially the amino acids involving in Rhino and DNA binding. Answering these questions could potentially broaden the impact of this story.1. I think the authors should comment on the potential homolog of Kipferl in other Please see our response to the major point #2 above.2. Line159, H3K9me3 or H3K9me2?Here we are indeed talking about H3K9me2. We adjusted the text to make this more clear.3. I am confused about the message from Line 365-367. Do you mean Rhino peaks towards the periphery?42AB we observe a restriction of Rhino toward more central regions of the cluster upon loss of Kipfer, with a reduction of Rhino towards the boundaries of the cluster. It is at these more peripheral sites (where Rhino depends on Kipferl even at cluster 42AB) that we observe small Kipferl enrichment peaks in the rhino mutant Kipferl ChIP-seq data set. These low, but detectable Kipferl binding sites likely contribute to the stabilization of Rhino on these more “peripheral” regions of the cluster. We adjusted the text to convey this better (lines 427-436).Within the large piRNA cluster 4. Line 452: motives or motifs.All instances were changed to motifs.5. The last paragraph is way too long. It is good to only deliver one message per paragraph.We split this paragraph into two.Reviewer #3 (Recommendations for the authors):The manuscript, although well written, is very dense with a lot of figures which sometimes makes it difficult to follow.kipferl KD are compared. As the manuscript now stands, the reader can have the impression that the changes might be important but non-significant.– In all the manuscript there is no mention of significance in the violin and box plots when the control KD and the We added statistical significance parameters to the relevant plots as well as mean effect sizes.– For each UCSC genome browser track, the genomic coordinates of the presented region should be added either on the figure or in the legend.Coordinates have been added.Figure Sup 3:– Based on the figure legend, there are some inversions the E is the B, the B◊D, the D◊E.This has been corrected.42AB and 38C are not really convincing. Furthermore, it is surprising not to see any 80F FISH experiments.– The picture of the FISH experiments of the 42AB foci. We believe that the low levels of Rhino that remain at 42AB and especially at 38C are another reason why we don’t see stronger overlap. As cluster 80F lacks Rhino occupancy as well as piRNAs, we did not see the importance of a dedicated cluster 80F FISH experiment.The piRNA cluster FISH experiments aim to establish that the large Rhino accumulations observed in the absence of Kipferl do not correspond to the remaining Rhino-bound piRNA cluster loci. To that end the presented images were selected to depict the large Rhino-GFP domain in the same z plane with foci for both clusters. Due to photo bleaching effects we are not able to image deeper stacks, and likely miss weak Rhino signal that would be expected at 38C and 42AB, 38C is very informative. It should also be added for the Kipferl dependent TE piRNAs. The presence in the cluster 80F should also be described.– Figure 4D. the name of the TEs and the presence in cluster cluster 80F to the existing panel, as well as the cluster information about the top 20 elements losing antisense piRNA counts in the kipferl knock down.We added the information about 80F, the Rhino ChIP-seq track is at 300 for the w1118 and MTD-Gal4 genotypes whereas it is at 150 for the iso1 genotype. It is the same problem in Figure 5C for the Kipferl ChIP-seq results on the 42AB cluster.– The scale of the UCSC genome browser tracks are variable. For a given locus it is important to keep the same scale between different genotypes. Figure 1C, considering the heterochromatic region and the Cluster We changed this. Please also see our comment above (main revision requirements).


The relationships between chromosomal compartmentalization, chromatin state and function are poorly understood. Here by profiling long-range contact frequencies in HCT116 colon cancer cells, we distinguish three silent chromatin states, comprising two types of heterochromatin and a state enriched for H3K9me2 and H2A.Z that exhibits neutral three-dimensional interaction preferences and which, to our knowledge, has not previously been characterized. We find that heterochromatin marked by H3K9me3, HP1α and HP1β correlates with strong compartmentalization. We demonstrate that disruption of DNA methyltransferase activity greatly remodels genome compartmentalization whereby domains lose H3K9me3-HP1α/β binding and acquire the neutrally interacting state while retaining late replication timing. Furthermore, we show that H3K9me3-HP1α/β heterochromatin is permissive to loop extrusion by cohesin but refractory to CTCF binding. Together, our work reveals a dynamic structural and organizational diversity of the silent portion of the genome and establishes connections between the regulation of chromatin state and chromosome organization, including an interplay between DNA methylation, compartmentalization and loop extrusion. The authors find that silent chromatin is more diverse than just facultative and constitutive heterochromatin. These inactive types have distinct three-dimensional interaction characteristics that are transposable if the underlying chromatin state is altered. The best characterized chromosome-organizing process is loop extrusion. During interphase, cohesin complexes act as motors to extrude progressively growing chromatin loops. In vertebrates, the insulator protein CTCF serves as a directional barrier that halts loop-extruding cohesin9. Independent of loop extrusion, chromosomes are also spatially compartmentalized, with transcriptionally active chromatin located centrally and inactive chromatin more peripherally in the nucleus. As independent organizing processes, perturbing loop extrusion and its barriers does not eliminate compartmentalization14; however, the two processes act simultaneously and therefore can interfere with each other15.Chromosome organization within the nucleus is associated with vital cellular processes16. Heterochromatin is usually categorized into two types. Facultative heterochromatin, which is considered to be developmentally regulated, is enriched in H3K27me3  interaction profiles in HCT116 cells and to understand their relationship to the chromatin landscape  interactions as in ref. Evidence exists that some cell lines or cell types may have unique nuclear compartmentalization and that this may be linked to the structural differences of distinct states of chromatinape Fig. . Our met35. The positional component correlating strongly with E2 reflects the observation that pairs of centromere-proximal and centromere-distal regions show mildly elevated contact frequency throughout the genome , or a relationship between chromosomal and nuclear landmarks during interphase. As a result, we expected that the clustering of interaction profiles using trans Hi-C data would be influenced by chromosomal position independently of chromatin state. To test this idea, we examined subcompartment calls from GM12878  embedding of the leading eigenvectors Fig. . Furtherome Fig. 35. This 878 ref. . Indeed,878 ref. . Similar878 ref. .41. Indeed, several centromere-proximal and distal pairs of clusters showed similar functional profiles, so we consolidated the clusters into a total of five groups, described in detail below. Importantly, since not all interaction profiles imply the existence of spatially or phase-separated subnuclear compartments (see below), we will refer to our consolidated classification as interaction profile groups (IPGs) rather than (sub)-compartments. For simplicity, we have chosen a naming system similar to the one used for GM12878 trans interaction profile clusters (subcompartments), but below we discuss what correspondences can be made.We found that the data can be sensibly partitioned into eight clusters . To excl4. The first IPG, corresponding to cluster I, has the strongest self-interaction preference in trans, is enriched for the nuclear speckle marker SON and displays the greatest amount of transcriptional activity  at least as strongly as it does with itself (homotypic)  Fig. .1  and have the strongest preference for homotypic contacts in cis  with no equivalent in GM12878, whose loci share hallmarks of inactive chromatin Fig. . Despite1 being facultative heterochromatin, these loci are predominantly enriched for H3K27me3, with a mild enrichment in H3K9me2   exhibit the closest correspondence to known euchromatic and heterochromatic chromatin states, respectively. This can be observed using receiver operating characteristic (ROC) curves generated by using thresholded 50-kb binned signal tracks as binary classifiers for individual IPG assignments  suggests that the homotypic interactions are specific to each type  rather than a generic form of interaction common to all inactive chromatin.Our Ants Fig. . The A1 In summary, we discern three types of inactive chromatin by long-range contact frequencies in HCT116. Notably, none of these types appears to share an epigenetic similarity with the B2/B3 subcompartments described in GM12878  domains up to several megabases in length 10. We looked at the decay of contact probability with genomic separation, P(s), which is indicative of the underlying polymeric folding of the region51. We found that P(s) was affected by depletion of cohesin in all IPGs, including B4 domains, leading to the disappearance of the characteristic extrusion ‘shoulder’ in P(s)  derivatives suggest that A1 and A2 domains have more loops per kilobase than B4 and that B4 has a larger average loop size  Fig. 52. Moreoize Fig. .Fig. 4Al4 domains appearing relatively featureless in Hi-C maps, we find that extrusion-related stripes and dots (which disappear upon cohesin depletion) originating outside a domain can sometimes propagate through it, appearing along the periphery of the square , where H3K9me3-HP1α/β chromatin is lacking, we do not observe a similar reduction in number, occupancy or insulation of CTCF sites  cells) Fig. . We pred4/H3K9me3-HP1α/β is the result of low CTCF occupancy, and not because of an absence of extrusion. The density of extrusion barriers differs across IPG domains, resulting in different average extruded loop sizes 54 which has been shown to have defects in H3K9me3   Fig. . In our za) Fig. .Fig. 5In37. This shows that not all B4 domains are equally sensitive to DNMT1/DNMT3b loss. Interestingly, in the 5Aza-treated cells we find that all H3K9me3-HP1α/β domains show mild but uniform depletion of both Protect-seq signal, and HP1α and H3K9me3 levels  and those that retain it (persistent domains)  and a global weakening of B4 compartmentalization in 5Aza-treated cells  Fig. . Hi-C anlls Fig. . Next, wlls Fig. , reminisA.Z Fig. , which i4 domains can lose H3K9me3, HP1 and self-affinity. Replication timing has been proposed to maintain the global epigenetic state in human cells56. In turn, histone deposition, HP1 proteins and DNMT1 are associated with chromatin restoration at the replication fork58. Therefore, we hypothesized that the loss of H3K9me3-HP1α/β heterochromatin in DKO cells would be accompanied by a change in the timing of DNA replication at disrupted domains. To address whether replication timing is altered by the disruption of heterochromatin, we performed two-stage Repli-seq in HCT116 and DKO cells. Surprisingly, we observe similar replication timing profiles between HCT116 and DKO cells  or within regions where H3K9me3 and HP1 were gained in DKO  Repli-seq data from a recent study on the replication timing regulatory factor RIF1 (refs. 4 domains preserve very late replication timing (S12–S16 fractions) while B0 domains shift from being moderately late in the wild type to predominantly early  in H3K9me3-HP1α/β domains, compared with HCT116 Fig. . Moreove31, and genome-wide loss of DNA methylation has been shown to increase CTCF occupancy at CpG-containing motifs (termed reactivated CTCF sites)61. Hence, we hypothesized that new loop extrusion features seen in DKO and 5Aza-treated cells are due to reactivated CTCF sites. To confirm that loss of DNA methylation reactivates cryptic CTCF sites, we performed chromatin immunoprecipitation (ChIP) followed by sequencing (ChIP–seq) in HCT116, DKO and 5Aza-treated cells. To identify high-confidence reactivated CTCF peaks, we chose overlapping reactivated CTCF peaks from DKO (this study), DKO  /nucleosome occlusion model has been proposed to regulate CTCF binding in mouse embryonic stem cells66.To further investigate the genome-wide patterns of CTCF reactivation, we profiled DNA methylation, chromatin inaccessibility and histone modifications in relation to IPGs. To our surprise, reactivated CTCF motifs within BPGs Fig. . These d0 IPG in HCT116 cells which is not observed in GM12878 cells, forming large domains that do not display strong homotypic interactions. Yet another inactive chromatin state appears to underlie the B2/B3 subcompartments in GM12878 and remains poorly characterized. Notably, the features originally reported as enriched in B2 and B3 came from dissimilar cell types: HeLa67, HT1080 fibrosarcoma68 and skin fibroblasts69. Elucidating the molecular intermediates determining the behavior of known and novel IPGs will require a combination of unsupervised techniques and deep chromatin profiling72.Our study demonstrates a remarkable cell-type-related diversity in inactive chromatin and its relationship to 3D genome organization. In HCT116, each of the three inactive IPGs exhibits a distinct chromatin state, Protect-seq signal and DNA methylation status, and displays differences in homotypic affinity and the regulation of loop extrusion barriers (Table 4 domains transition to the B0-associated inactive chromatin state that lacks self-affinity yet maintains late replication timing. Whether this transition occurs via active chromatin remodeling or passive loss of heterochromatin remains unclear. It may be that the chromatin states underlying the B0 (poised) and B4 (constitutive) IPGs are generally transposable. Since disrupted B4 domains acquire H2A.Z, it is possible that the presence of this histone variant could function to interfere with heterochromatin deposition and spreading, as has been shown in budding yeast73. Moreover, since B0 selectively marks RIF1-sensitive late replicating chromatin in HCT116, it is possible that the B0-associated chromatin state itself depends on RIF1.Our results reveal striking connections between DNA methylation, H3K9me3 and HP1 deposition, and 3D chromosome organization at the level of chromosome compartmentalization and loop extrusion. We show that the heterochromatic state is integral to its nuclear compartmentalization. When DNA methylation is lost, H3K9me3-HP1/B15, our results represent a complementary phenomenon: strongly compartmentalizing heterochromatin suppressing the imposition of extrusion barriers (CTCF-bound sites) while remaining permissive to extrusion. These results highlight the two-way interplay between compartmentalization and extrusion.Loop extrusion and compartmentalization shape different aspects of genome organization. While the forces driving compartmentalization are believed to be global and intimately linked to the state of chromatin, the degree to which loop-extruding cohesins are influenced by the epigenome is not well understood. As loop extrusion has been shown to reduce the strength of compartmentalization and interfere with the segregation of short compartmental domains74 and later came to be associated with histone modifications75. We now have a more nuanced understanding of the molecular details, including several types of repressive histone modifications and associated proteins and their genomic distributions across cell types. Our work begins to unravel the diversity and plasticity in silent chromatin and its influence on genome compartmentalization, nuclear architecture and other chromosome-organizing processes.The classic definition of heterochromatin originated from staining mitotic chromosomes2. For drug treatment, HCT116 cells were treated with 5 µM 5Aza for 48 h, then washed with 1 × PBS before collection.HCT116 and DKO cells were cultured in McCoy5A medium. DKO cells were grown in the presence of G418, geneticin. All media were supplemented with 10% FBS at 37 °C and 5% COCells were grown to ∼75% confluency, collected with trypsin, washed in 1× PBS and frozen/stored at −80 °C. Thawed cells were fixed in 1% formaldehyde and quenched in 0.125 M glycine, then washed twice in 1 × PBS. Fixed cells were then resuspended in 500 μl of lysis buffer ) for 30 min on ice with periodic resuspension. Lysed cells were spun at 3,500 r.p.m. for 3 min and resuspended in 300 μl of 1 × NEB buffer 2, spun and resuspended in 198 μl of 1 × NEB buffer 2. Next, 2 μl of 10% SDS was added and incubated at 65 °C for 10 min. Afterwards, 400 μl of 1 × NEB buffer 2 and 60 μl of 10% Triton X-100 were added to quench the SDS. Samples were incubated at 37 °C for 15 min. Nuclei were spun at 3,500 r.p.m. for 3 min and resuspended in 300 μl of 1 × NEB buffer 2, and the wash step repeated.2+ , 5 μl of DNaseI (10 U), 5 μl of MNase  and 5 μl of RNase A (20 mg ml−1) were added . Cells plus the enzyme cocktail were incubated at room temperature  for 30 min. Digested cells were spun at 3,500 r.p.m. for 3 min and resuspended in 400 μl of 1 × NEB buffer 2, then rotated at room temperature for 15 min. Digested/wash no. 1 cells were spun at 5,000 r.p.m. for 3 min and resuspended in the same 200 μl of cocktail mix and incubated again at room temperature (or 37 °C) for 30 min. Digested cells no. 2 were spun at 10,000 r.p.m. for 3 min and resuspended in 400 μl of 1 × NEB buffer 2, then rotated at room temperature for 15 min . Then we spun digested cells no. 2 at 10,000 r.p.m. for 3 min and resuspended in 200 μl of 1 × NEB buffer 2, 20 μl of Proteinase K . They were digested overnight at 65 °C then purified using phenol/chloroform and ethanol precipitation (compatible with silica-bead purification).The Protect-seq protocol was performed as described in ref. DNA was quantified with Qubit (high-sensitivity) and sonicated using Covaris 50-µl, 300-bp protocol. Illumina libraries were prepared using the NEB Ultra II DNA library kit using the manufacturer’s protocol. We used 4–5 PCR cycles to amplify next-generation sequencing (NGS) libraries and index samples.The Hi-C protocol was performed similarly to ref. SimpleChIP Plus Enzymatic Chromatin IP Kit (Magnetic Beads) no. 9005 from Cell Signaling Technologies was used for all ChIP–seq experiments, using the manufacturer’s recommended protocol. We used 4 million cells per immunoprecipitation. Digested chromatin was pooled into a single tube for brief sonication to lyse nuclei. Supernatant was then split evenly between immunoprecipitations (minus 2% input). Antibodies and chromatin were incubated overnight at 4 °C, rotating. DNA was purified using spin columns and prepared using NEB Ultra II DNA Library Kit.Repli-seq was performed and analyzed as described in ref. 79 to remove low-quality reads and sequencing adapters. Hi-C datasets were processed using the distiller pipeline (https://github.com/open2c/distiller-nf) written for nextflow80. Briefly, we mapped Hi-C sequencing reads to the human reference assembly hg38 using bwa mem (ref. https://github.com/open2c/pairtools). Hi-C pairs were aggregated into contact matrices in the cooler format using the cooler package at multiple resolutions82. All contact matrices were normalized using the iterative correction procedure35 after bin-level filtering.Hi-C libraries were trimmed with the fastp packagemem ref.  with flahttps://github.com/ENCODE-DCC/chip-seq-pipeline2) with slight modifications using a simplified custom snakemake workflow. Briefly, reads were mapped to hg38 using bwa mem  generation was performed using MACS2 .To characterize long-range interaction profiles, 50-kb resolution Hi-C maps were dimensionally reduced by applying global eigendecomposition on m leading eigenvectors were rescaled and concatenated as columns, and k-means clustering was applied to the rows using scikit-learn. We produced cluster assignments for a range of k for Hi-C maps of GM12878  and related genomic and functional data proved to be indispensable for interpretability of clusters (see below).For visualization of the approximate manifold structure, further dimensionality reduction on the https://datashader.org) was used to generate scatter plot visualizations of points representing 50-kb genomic bins. The datashader pipeline is used to prevent overplotting dense point clouds by aggregating points onto a regular two-dimensional grid and either (1) color-mapping the resulting raster to associated quantitative values  or (2) displaying associated color-coded categorical values  via image compositing.The new matplotlib ref.  extensio93. Tracks were binarized at 50 kb using BinarizeBam and were modified to ignore bins filtered in Hi-C data. Models were trained using 50-kb bins (LearnModel -b 50000) for a range of state numbers. A seven-state model was chosen for HCT116. For DKO, a six-state model was able to qualitatively capture the same repressive states based on emission parameters .We ran ChromHMM ref.  to creatA gene quantification table for HCT116 was obtained from ENCODE and cross-referenced to GENCODE v29 basic gene annotations for hg38. Records were intersected against IPG labels using bioframe ref.  and grouHCT116 and DKO Whole Genome Bisulfite sequencing data (hg19) from ref. Functional profiles for spectral clusters . Unscaled stacked heatmaps were defined using the domain midpoints as a reference point flanked by a fixed genomic distance left and right, while rescaled stacked heatmaps were generated by independently partitioning the intradomain signal and flanking regions into a fixed number of bins. Metaplots were generated by averaging rescaled heatmaps vertically.IPG domain metaplots and stacked signal heatmaps were generated from BigWig files using the pybbi package  by applying a simple value-based discrimination threshold on the signal track. ROC curves and area under ROC for these classifiers were calculated using scikit-learn. Curves that dip below the diagonal indicate thresholds with predictive power for the complement of the target label .To assess the correspondence of individual signal tracks to IPG assignments derived from Hi-C data, we treated each mean-aggregated 50-kb resolution track as a binary classifier to predict a given IPG label  and bivariate summary maps of observed/expected contact frequency  using percentiles of either E1 or H3K9me3 signal as bins were also generated using cooltools.Expected contact frequency versus distance profiles were generated using cooltools ref.   were called using an HMM procedure. H3K9me3 ChIP–seq bigwigs were mean-aggregated at 25 kb, log-transformed and P as a function of genomic separation s were generated using cooltools by aggregating normalized contact frequency over valid pixels along diagonals of 10-kb-resolution cis contact maps limited to IPG domains, with diagonals grouped into geometrically increasing strata of genomic separation. Average contact frequency P(s) curves are displayed using log-log axes.Scaling curves of contact frequency 98 were calculated on 25-kb-resolution Hi-C maps with a 100-kb sliding window using the cooltools package. Additionally, an insulation minimum calling procedure based on peak prominence, described in ref. Diamond insulation scoresThe cooltools package was used to calculate aggregate observed-over-expected contact frequency maps (pileup maps) centered at CTCF sites and bounded by a fixed flanking genomic distance. Pileup maps are centered on the main diagonal at each feature’s midpoint.99. Viterbi state calls were made on a per bin basis and used for downstream analysis. Neighboring states were merged to create domains then converted to bed files (https://github.com/gspracklin/hmm_bigwigs).To identify early and late replicating domains, a 25-kb binned pandas dataframe was generated using bioframe. HCT116 and DKO replication timing signal tracks were imported into the binned dataframe using pybbi. Missing values were represented as Not a Number (NaN). Domains were identified with a two-state Gaussian HMM using Pomegranatez-score tracks. Differentially timed loci separated by up to 250 kb were then merged into larger intervals using bioframe.cluster to produce 199 differentially timed regions.Differential replication timing loci were identified by applying a cutoff of 0.75 on the difference between HCT116 and DKO 50-kb 100. The polymer simulations ran using the OpenMM engine for GPU-assisted molecular dynamics simulations101. Each simulation modeled 8–11 megabases (Mb) of chromatin fiber as a chain of 1-kb monomers, and included five copies of the system inside the same container. Each simulation was run for 500,000,000 molecular dynamics steps. Periodic boundary conditions were used to maintain a density of 0.2 monomers per cubic nanometer.Simulations were created using the Polychrom librarykT (the Boltzmann constant times absolute temperature), and distances are measured in terms of the diameter of the monomers, which is 20 nm. Adjacent monomers on the chain are connected by a harmonic bond with potential U = 100(r − 1)2, where r is the distance between the centers of the monomers. Polymer stiffness is modeled by U = S(1 − cos(α)), a force dependent on the angle α formed by three adjacent monomers, and S is a stiffness parameter equal to 1.5.The following energies are in terms of i will stay at i with probability ½ and move to i − 1 with probability ½ each step, unless i − 1 is occupied by an LEF or a CTCF. Similarly, a downstream leg at monomer j will stay at j with probability ½ and move to j + 1 with probability ½, unless j + 1 is occupied by an LEF or CTCF. CTCF sites were placed at fold-change peaks in HCT116 CTCF ChIP–seq (ENCODE ID ENCFF549PGC), with directionality according to CTCF motifs , where fc is the CTCF fold change and fcmed is the median CTCF fold change over the region. Legs were released from CTCFs with a probability of 0.006 each monomer step. Each LEF was unloaded with a probability of 1/100 each step of 1D dynamics, and LEFs were separated by an average of 600 monomers.To model loop extrusion, loop-extruding factors (LEFs) were probabilistically loaded onto the polymer chain at uniformly random positions. Each LEF is represented by a harmonic bond equivalent to the one that connects adjacent monomers on the chain. Each step of one-dimensional (1D) dynamics corresponded to 400 molecular dynamics steps. An LEF with an upstream leg at monomer Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41594-022-00892-7.Supplementary InformationSupplementary Note and Fig. 1.Reporting SummaryPeer Review FileSupplementary Table 1Supplementary Tables 1–3.


Evidence accumulated over the past decades shows that the number of identified antisense transcripts is continuously increasing, promoting them from transcriptional noise to real genes with specific functions. Indeed, recent studies have begun to unravel the complexity of the antisense RNA (asRNA) world, starting from the multidimensional mechanisms that they can exert in physiological and pathological conditions. In this review, we discuss the multiverse of the molecular functions of asRNAs, describing their action through transcription-dependent and RNA-dependent mechanisms. Then, we report the workflow and methodologies to study and functionally characterize single asRNA candidates. It has now become evident that a significative fraction of the transcriptome comprises RNAs that are transcribed in the antisense direction with respect to known genes. Despite being considered transcriptional noise during the past years, antisense transcription has now been recognized as one of the main mechanisms of gene expression regulation in all kingdoms of life . This prAlthough antisense transcripts were discovered in bacteria more than 30 years ago , only thFunctional studies showed that antisense transcripts are not a uniform category of regulatory RNAs but comprise different groups of RNAs sharing some common features. A possible way to categorize them is by their position and relationship with their sense gene . First oIn metazoan cells, asRNAs  exert multiple physiological roles  is upregulated by splicing and transcribing an RNA). Both promoter deletion and insertion of polyadenylation signals (PASs), which induces premature transcription termination at different lengths, were shown to result in a decrease of Sfmbt2 expression, a nearby gene located 5 kb upstream. Such a cis-activating effect is independent of Blustr sequence, as sequential deletions of downstream exons and introns do not impair Sfmbt2 expression; instead, it is correlated with transcription length. Additionally, impaired Blustr transcription affects the chromatin state of the Sfmbt2 promoter and decreases RNA polymerase occupancy at the transcription start site and within the gene body of Sfmbt2 [Upperhand (Uph) is an asRNA divergently transcribed from a bidirectional promoter that also produces Hand2, a heart development regulator. Although the Uph sequence is not much conserved in different species, production of a transcript from this locus spanning multiple well-conserved Hand2 enhancers is common across mammals. Insertion of PASs leading to premature transcription termination of Uph impairs Hand2 expression in cis, resulting in embryonic lethality in mouse. Instead, the insertion of a heterologous DNA sequence at the same site does not disrupt Hand2 expression. Accordingly, knockdown of the Uph mature transcript through Gapmer antisense oligonucleotides (ASOs) does not affect Hand2 expression. Specifically in this example, premature transcription termination reduces the enhancer chromatin signature at the Uph gene body and prevents binding of the transcription factor GATA4 to a Hand2 enhancer in this region, thus resulting in decreased Hand2 expression [In the case of non-overlapping asRNAs, sense and antisense genes are divergently transcribed and can act at the transcription initiation level by altering RNA polymerase II occupancy on neighboring promoters a. An exapression .Airn (antisense to Igf2r (insulin-like growth factor type 2 receptor) RNA) silences in cis the paternal alleles of Igf2r in the Igf2r imprinted cluster. Different shortened Airn transcripts created by insertion of PASs do not affect Igf2r silencing in mice. Instead, the transcriptional overlap with the Igf2r promoter interferes with the recruitment of RNA polymerase II, leading to Igf2r silencing [Per2AS, the asRNA of Period2 gene, regulates its sense gene exclusively through transcriptional interference, resulting in a negative feedback loop.On the other hand, in cases of overlapping asRNAs, sense and antisense genes are convergently transcribed. Also in this case, they can regulate transcription initiation by transcriptional interference b. Indeedilencing . Transcrilencing  demonstrIME4, encoding a meiosis key regulator, in budding yeast [IME4 and represses the transcription of the overlapping antisense RME2 (regulator of meiosis 2), allowing IME4 to be induced during meiosis. Instead, in haploid cells the expression of RME2 represses IME4. Accordingly, mutation in the α1-α2 binding site allows expression of RME2 and prevents IME4 expression in diploid cells. Moreover, promoter deletion of RME2 in haploid cells leads to an absence of RME2 expression and IME4 derepression. In addition, premature termination of RME2 transcription obtained by insertion of PASs alters its ability to repress IME4 in haploid cells. Concordant with this, deletion analyses reveal that a precisely oriented sequence within the IME4 ORF is necessary for RME2-mediated repression. Indeed, when such a region is deleted, IME4 transcription is derepressed in haploid cells. This suggests that, through transcriptional interference, RME2 transcription blocks polymerase elongation, but not initiation, at the IME4 locus [nAS25 is the asRNA encoded in exon 25 of the notch-1 locus [notch-1 transcription, defining a slow harmonic oscillation of notch-1 and nAS25 and coordinating cell-cycle dynamics. This event occurs probably due to the convergence of polymerization by RNA polymerase from template and non-template DNA strands. Indeed, in the absence of antisense transcription, processivity of RNA polymerase in the sense direction increases as there is a reduction of positive supercoiling of DNA.Antisense expression can also regulate gene expression after transcription initiation by transcriptional interference that occurs co-transcriptionally c. This cng yeast ,41. In d-1 locus . When itPer2AS and nAS25 are emblematic of the complexity and multilayered activities of asRNAs. Indeed, in addition to a transcription-level impact, they also show a transcript-level function by interacting with other targets  [notch-1 for nAS25) [In particular, the two examples of  Per2AS)  or even r nAS25)  through The most convincing demonstration that asRNAs are not only transcriptional noise, but real genes with specific functions, is derived from the discoveries of mechanisms of action based on their specific RNA sequences and/or structures . Indeed,In cis asRNAs can act within their genomic locus with multiple mechanisms directly binding to DNA, RNA or proteins.PDCD4-AS1 and PDCD4 in breast cancer [FOXC2-AS1 and FOXC2 in colorectal cancer progression [nAS25 asRNA which provides partial protection from RNA editing for notch-1 transcript by hybridizing to it and acting as a decoy target for the C➔U editing enzyme APOBEC-1  to mRNA, as was proposed for t cancer  and for gression , respectAPOBEC-1 a. The peduration .51A maps to the first intron of Sortilin-related receptor 1 (SORL1) and, by pairing with its pre-mRNA, drives a splicing shift of SORL1 from the canonical full-length protein form to an alternatively spliced shorter protein variant that is associated with an impaired processing of the amyloid-precursor protein (APP) [ZEB2) encodes for a transcriptional repressor of E-cadherin. Its antisense transcript ZEB2-AS1 hinders the processing of an intron in the 5′UTR, which harbors an internal ribosome entry site (IRES) on the ZEB2 mRNA, resulting in increased protein production [Antisense expression can also regulate the production of alternative transcript isoforms of the sense gene b. In thiin (APP) . asRNA–moduction .VIM-AS1, which regulates its sense VIM gene through the formation of an R-loop that is crucial to creating the optimal chromatin environment to favor the binding of transcription factors [Different asRNAs have been reported to interact with DNA by the establishment of R-loops which form when a G-rich RNA transcript invades the DNA duplex, annealing to the complementary strand and displacing the G-rich DNA strand to generate an RNA–DNA hybrid structure d. This m factors .Fendrr asRNA  is predicted to form a triple-helix with a complementary sequence within the FOXF1 promoter, thus attenuating FOXF1 expression [AS1DHRS4 was shown to negatively regulate the expression of its sense DHRS4 in cis exerting more than one of the mechanisms listed above. AS1DHRS4 forms an RNA duplex with its nascent sense transcript to mediate repressive histone modifications; moreover, AS1DHRS4 was also shown to interact with two histone methyltransferases to keep epigenetic silencing of DHRS4 locus and to recruit DNA methyltransferase to induce DNA methylation in the promoter region [Another way by which asRNAs can interact with other molecules is through their spatial conformation. Together with the DNA, asRNA can originate a triplex, in which a double-stranded DNA forms a triple helical structure by accommodating a single-stranded RNA in its major groove through Hoogsteen or reverse Hoogsteen base pairing  Figure f. Upon tpression . At the pression ,57. Thespression . For insr region .In trans-acting asRNAs are an expanding class of lncRNAs that leave their site of transcription to act both in the nuclear and in the cytoplasmic compartment. In trans effects can be mediated by antisense transcripts that interact with distant loci through the three-dimensional organization of chromatin and by the mechanisms of interaction listed above for in cis asRNAs (but in this case the target locus is not the asRNA originating locus). The existence of in trans functions somehow raises a nomenclature issue for asRNAs, which will be examined in the Conclusions section. Indeed, some of the so-called asRNAs are only casually transcribed in the opposite direction with respect to a given sense gene, without regulating it in cis or being the result of the same act of transcription . In cont(Uchl1)-AS1 is an example of asRNA–mRNA interaction. It is predominantly nuclear, but it can shuttle to the cytoplasm under stress conditions to bind to the 5′UTR of Uchl1 mRNA, thus triggering increased translation of the UCHL1 protein [BACE1-AS) is the antisense of BACE1, a crucial enzyme in the pathophysiology of AD which can lead to the formation of the APP plaques. It enhances the stability of the BACE1 mRNA by binding to a region of partial complementarity, masking a site for miR-485-5p, which would otherwise suppress BACE1 biosynthesis [Base pairing mechanisms are used also by in trans asRNAs to regulate the abundance or activity of other RNAs  far from their genomic locus, usually by sequestration or stabilization . The asR protein   sponging miRNAs c. There Fendrr, which, as stated above, acts in cis forming a RNA–DNA triplex with its sense gene (PITX2 [HOTAIR) in mammals, which is the antisense transcript to the homeobox C (HOXC) locus. Through a 5′ domain, HOTAIR binds to the Polycomb Repressive Complex 2 (PRC2), which induces histone H3 lysine 27 trimethylation, thus silencing HOXD locus in trans [HOTAIR is not the only asRNA that can exert its function in trans by interacting with PRC2; in fact, guidance of PRC2 by antisense transcripts is likely to be common, as the complex was shown to directly interact with more than 3000 asRNAs [HOTAIR also functions as a molecular scaffold for LSD1, which is involved in demethylation of histone H3 at lysine 4. HOTAIR binds to LSD1 through a 3′ domain coordinating its function in chromatin modification [BACE1-AS is also able to bind to the cytoplasmic RBP HuD (ELAVL4)  a, which AD brain .Thanks to recent advances in high-throughput and in-depth transcriptome sequencing technologies, RNA-seq is widely used for the identification of novel asRNAs ,72. As aGiven that the antisense and sense genes can overlap to different extents, a precise and correct annotation of both genes is a key step in the study of the asRNA. By exploiting different high-throughput sequencing data such as cap analysis of gene expression (CAGE-seq) ,76, 3′ eOnce a putative asRNA of interest is identified, the dissection of its possible mode of action is not trivial. It is important to discriminate whether the asRNA acts locally in cis, regulating the expression of the sense gene or nearby genes, or acts in trans, regulating the expression of distant genes. To distinguish all the possible modes of action, different loss- and gain-of-function strategies should be employed, as described in detail by Kopp et al. . Here, wA primary characterization is the study of the subcellular localization of the asRNA, which can be assessed by cell fractionation or immunofluorescence. This can be helpful to get hints concerning its nuclear function in cis or on its nuclear or cytoplasmic function in trans.To discriminate an in cis- from an in trans-acting function, it is possible to employ CRISPR/Cas9-based strategies to perform asRNA gain- and loss-of-function. For example, the deletion of the promoter or the entire locus can be exploited to abrogate the endogenous antisense transcription. The insertion of a PAS near the 5′ end of the asRNA locus can be used to induce premature transcription termination. CRISPR activation (CRISPRa) or inhibition (CRISPRi) can be employed to either activate or repress the endogenous transcription of the asRNA. In this regard, knowing the exact structure of the sense and antisense gene is crucial to design and adapt the strategy. Indeed, when the endogenous transcription of an antisense gene is manipulated to verify how this impacts on the expression of the sense gene, it is essential to exclude the fact that the used strategy may itself directly modulate the expression of the latter, for example, when relevant promoter/enhancer regions are edited. Afterward, expression/transcription of nearby and distant genes should be studied, as can be measured through quantitative PCR or whole transcriptomic analysis. In addition, as these strategies affect the endogenous asRNA transcription, nascent transcript analysis and ChIP to monitor polymerase occupancy can be performed to better evaluate the effect of these strategies on antisense transcription and consequently on sense and nearby gene transcription. In cases of an in cis-acting asRNA, an altered expression/transcription of such genes is expected. Moreover, the ectopic overexpression of the asRNA in knockout cells will not rescue the phenotype observed after antisense loss-of-function, as the asRNA will not be transcribed at its locus, thus further confirming its action in cis. In this case, the act of transcription of the asRNA is necessary for its activity or the RNA molecule may perform a regulatory function in a sequence/structure-specific manner. To discriminate transcription- and RNA-dependent mechanisms of action of in cis-acting asRNAs, it is possible to systematically introduce targeted mutations that disrupt splice sites and sequentially delete or replace sequences of the asRNA (such as exons). For instance, an asRNA that functions in a sequence-specific manner will be sensitive to removal of exons, while loci in which transcription is the key event will often tolerate sequence substitutions but will be inactivated by PAS insertion.On the other hand, in case of an in trans-acting asRNAs, an altered expression of distant genes is expected. In this case, the function in trans should be rescued by the ectopic overexpression of the asRNA in knockout cells. Indeed, the phenotype resulting solely from the ectopic overexpression of the asRNA is exclusively due to its function in trans. Inducible systems can be used to control expression/editing of asRNAs temporally and spatially. Vectors with Tetracyclin-/Cumate-/Tamoxifen-controlled system are some of the classical and most used tools . More reIn parallel, to study whether an asRNA locus produces a functional RNA molecule, ASOs or short interfering RNAs (siRNAs) can be used to knockdown the expression of the mature antisense transcript, as both siRNAs and ASOs target complementary RNA molecules. siRNAs can be employed to study RNA-dependent mechanisms in the cytoplasm as they exploit the RNAi pathway, which results in the cleavage of complementary target RNA in the cytoplasm . In contRegarding ASOs, it can be useful to consider the use of Gapmers, a chimeric class of ASOs. The central part of the Gapmer is a DNA antisense oligonucleotide able to bind to the complementary target RNA, thus forming an RNA–DNA-like duplex that induces the RNase-H-mediated cleavage of the target RNA. Instead, the two flanking regions of the Gapmer are locked nucleic acids (LNAs). The LNAs consist of special nucleotides in which the ribose moiety is modified with an extra bridge connecting the 2′ oxygen and the 4′ carbon forming a locked structure. This structure confers increased stability against enzymatic degradation and increased specificity and affinity for the complementary target RNA .It has been recently demonstrated that Gapmers can also target nascent transcripts and, when directed against regions in the proximity of the TSS of the target RNA, can trigger premature transcription inhibition . Indeed,Finally, the identification and study of proteins or nucleic acids (DNA and RNA) that interact with the asRNA is the next step in the functional study of RNA-dependent modes of action. Concerning this, different pulldown strategies can be exploited. Chromatin RNA immunoprecipitation (ChIRP) is used to study the RNA–DNA interactions , RNA antThe number of identified antisense transcripts is continuously increasing and the role of antisense transcription as an intrinsic mechanism for regulation of transcription itself is starting to be acknowledged and understood. However, the roles of antisense transcripts go far beyond the in cis regulation of sense genes through their act of transcription. Several examples of asRNAs that do not regulate their sense gene in cis, as well as asRNAs that are the result of independent transcriptional events , have indeed been reported in the literature. In addition, there is plenty of evidence of asRNAs that regulate their genes in cis through RNA-dependent mechanisms that rely on their sequence or structure, rather than through transcription-mediated events. Even more strikingly, the number of transcripts that, though being annotated as asRNAs, do not regulate their sense genes but control other genes in trans in the nucleus or in the cytoplasm is continuously growing. Among these, only a fraction of asRNAs  truly act as antisense of, i.e., pair to, other RNAs. In many cases, gene regulation occurs via the formation of complexes with the DNA and/or proteins.FOXC2-AS1 and FOXC2 [In view of this evidence, the definition of asRNA should be at least debated in the scientific community to distinguish RNAs that are the result of antisense transcription from a bidirectional promoter (transcription-based definition) and RNAs that work as antisense to other RNAs (mechanism-based definition). Though the two aspects can come together for asRNAs that are transcribed and overlap with their sense gene , most RAside from this lack of uniform classification and annotation, it is clear that asRNAs represent a multiverse of regulatory RNAs, the functions and mechanisms of action of which are very far from being completely understood. In this review, we reported the approaches that can be used to identify and functionally characterize novel asRNAs . SpecifiHOTAIR has been shown to be a factor of both metastasis and survival in primary breast tumors [BACE1-AS, which exerts a fundamental role in AD pathogenesis, can be detected in the plasma of AD patients and thus serves as a potential disease biomarker [ZEB2-AS1 [FOXC2-AS1 [In addition to it being interesting to understand more of the complexity of gene regulation, asRNAs are a rich environment for the discovery of novel biomarkers and therapeutic targets, in light of their deregulation in different human pathologies. For example, t tumors . BACE1-Aiomarker ,98. MoreZEB2-AS1  and FOXCOXC2-AS1 . In thisOXC2-AS1 .Overall, it is now evident that asRNAs are more than just the result of an act of non-coding transcription of the antisense strand that impacts locally on the expression of genes in cis. They are indeed a heterogeneous class of regulatory RNAs that exert specific functions in a variety of biological processes, through mechanisms that can also be dependent on their sequence/structure as well as alter the expression of distant genes in trans.


Spatial chromatin structure is intricately linked with somatic aberrations, and somatic mutations of various cancer-related genes, termed co-mutations (CoMuts), occur in certain patterns during cancer initiation and progression. The functional mechanisms underlying these genetic events remain largely unclear in thyroid cancer (TC). With discrepant differentiation, papillary thyroid cancer (PTC) and anaplastic thyroid cancer (ATC) differ greatly in characteristics and prognosis. We aimed to reveal the spatial gene alterations and regulations between the two TC subtypes.We systematically investigated and compared the spatial co-mutations between ATC (8305C), PTC (BCPAP and TPC-1), and normal thyroid cells (Nthy-ori-3–1). We constructed a framework integrating whole-genome sequencing (WGS), high-throughput chromosome conformation capture (Hi-C), and transcriptome sequencing, to systematically detect the associations between the somatic co-mutations of cancer-related genes, structural variations (SVs), copy number variations (CNVs), and high-order chromatin conformation.MAST-NSUN4, AM129B/TRUB2, COL5A1/PPP1R26, PPP1R26/GPSM1/CCDC183, and PRAC2/DLX4. For the majority of ATC and PTC cells, the HOXA10 and HIF2α signals close to the transcription start sites of CoMut genes within TADs were significantly stronger than those at the background. CNV breakpoints significantly overlapped with TAD boundaries in both TC subtypes. ATCs had more CNV losses overlapping with TAD boundaries, and noncoding SVs involved in intrachromosomal SVs, amplified inversions, and tandem duplication differed between ATC and PTC. TADs with short range were more abundant in ATC than PTC. More switches of A/B compartment types existed in ATC cells compared with PTC. Gene expression was significantly synchronized, and orchestrated by complex epigenetics and regulatory elements.Spatial co-mutation hotspots were enriched around topologically associating domains (TADs) in TC. A common set of 227 boundaries were identified in both ATC and PTC, with significant overlaps between them. The spatial proximities of the co-mutated gene pairs in the two TC types were significantly greater than in the gene-level and overall backgrounds, and ATC cells had higher TAD contact frequency with CoMuts > 10 compared with PTC cells. Compared with normal thyroid cells, in ATC the number of the created novel three-dimensional chromatin structural domains increased by 10%, and the number of shifted TADs decreased by 7%. We found five TAD blocks with CoMut genes/events specific to ATC with certain mutations in genes including Chromatin interactions and gene alterations and regulations are largely heterogeneous in TC. CNVs and complex SVs may function in the TC genome by interplaying with TADs, and are largely different between ATC and PTC. Complexity of TC genomes, which are highly organized by 3D genome-wide interactions mediating mutational and structural variations and gene activation, may have been largely underappreciated. Our comprehensive analysis may provide key evidence and targets for more customized diagnosis and treatment of TC.The online version contains supplementary material available at 10.1186/s11658-022-00409-6. Thyroid cancer (TC) remains one of the most common malignancies of the endocrine system, and is the fifth most common cancer in women , 2. In 2BRAF (V600E), TERT promoter, TP53, and RAS  , 65. ManRegulatory elements, such as CTCF, enhancers, and H3K4me1 signals were significantly enriched in de novo TAD changes in poorly differentiated TC, while loss events or shifted TADs had the weakest signals. These phenomena showed that the genomic characteristics of boundaries of TADs across discrepant human TC subtypes might dynamically regulate the specific gene activations or inhibitions related to poor prognosis in patients with ATC. These modifications correlated with closed or open conformations of chromatin and drove differential access of genes to regulatory proteins and transcription factors. Thus, cells can dynamically use histone alterations to regulate their gene expression. Furthermore, the switches between the A and B compartments were correlated with specific subtypes of gene expression profiles in ATC compared with PTC. A/B compartment switches between TC and normal cells exhibited specific gene expression patterns, which were in accordance with RNA-seq data. Inactivation of genes related to cell cycle arrest pathway may impact tumor cell proliferation. Reversely, genes related to cell proliferation and DNA replication were activated. These dysfunctional pathways involved with inactive/active genes might lead to the malignant transformation of ATC mediated by chromatin conformation.We tried to capture the landscape of genomic aberrations, including CNVs and SVs, in both TC cells compared with normal thyroid cells, to comprehensively understand the characteristics of genes correlated with the prognosis and recurrence risk of TC. By combining Hi-C with genome and RNA sequencing, we uncovered the relationships between TADs, SNVs, complex SVs, and CNVs in TC. We observed differential patterns of genomic aberrations surrounding the TADs and identified more newly established three-dimensional chromatin structural domains in poorly differentiated TC.In this study using data newly generated by ourselves, we comprehensively analyzed four representative cell lines derived from human PTC (BCPAP and TPC-1 cells), ATC (8305C cells), and normal thyroid tissues to reveal the spatial gene alterations and regulations in TC. Notably, the PTC cell line BCPAP is derived from a poorly differentiated PTC (PDPTC) . To furtWhile the use of cell lines was a limitation of our study, our findings provide important evidence for and highlight the need for further relevant investigations on mechanism of action and validation in human cancer tissues. The contributions of our findings to precision cancer medicine regarding individualized prediction of survival and treatment benefits also warrant further exploration.We presented a comprehensive analysis of the genomic and transcriptomic alterations associated with cancer heterogeneity and subtypes in TC Fig. G. Our fiAdditional file 1: Table S1. Key resourcesAdditional file 2: Figure S1. Summary of somatic mutations and distribution of somatic mutations in ATC and PTC cell lines. Figure S2. Hi-C interaction heat-maps in ATC and PTC cell lines. Figure S3. Motifs analysis for TAD regions with CoMut. Figure S4. TAD A/B compartment switches in ATC cells ordered in each chromosome.