We applied FusionInspector to thousands of tumor and normal transcriptomes and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico and helps characterize numerous understudied fusions in tumor and normal tissue samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and facilitates transparent explanation and interpretation of machine-learning predictions and their experimental sources.Here, we present FusionInspector for   •FusionInspector software for supervised analysis of candidate fusion transcripts•Clustering of recurrent fusion transcripts resolves biologically relevant fusions•Distinguishing characteristics of fusion transcripts in tumor and normal tissues cis- or trans-splicing events, experimental artifacts during RNA sequencing, or computational errors of transcriptome reconstruction methods. Understanding how to discern, interpret, categorize, and verify predicted fusion transcripts is essential for consideration in clinical settings and prioritization for further research.Gene fusions play a key role as driver oncogenes in tumors, and their reliable discovery and detection are important for cancer research, diagnostics, prognostics, and guiding personalized therapy. While discovering gene fusions from genome sequencing can be laborious and costly, the resulting fusion transcripts can be recovered from RNA-sequencing data of tumor and normal samples. However, alleged and putative fusion transcripts can also arise from multiple sources other than chromosomal rearrangements, including  in silico characterization and interpretation of candidate fusion transcripts identified via RNA sequencing. Applied to thousands of tumor and normal transcriptomes, FusionInspector identified numerous likely biologically relevant fusions with potential roles in cancer or normal cellular biology.Haas et al. present FusionInspector, a method for  In cancer, gene fusions typically result from chromosomal rearrangements, including well-known drivers of cancer, such as BCR::ABL1 in chronic myelogenous leukemia (CML),,,,,,,cis- or trans-spliced products.The structural rearrangements leading to gene fusions can be detected or inferred through whole-genome sequencing (WGS) or from the presence of “fusion transcripts” in whole-transcriptome RNA sequencing (RNA-seq).,,trans-splicing of questionable functional relevance. Thus, there is an urgent need to understand the features that drive fusion detection and to generate high-quality catalogs of well-supported fusions.Nevertheless, predicting fusions from RNA-seq data is challenging, and the various methods developed to predict fusion products from RNA-seq vary tremendously in their accuracy for fusion detection, leading to both false positives and false negatives.,,,,,in silico assess fusions for their likely validity and assist researchers in reasoning about the quantity and quality of the evidence supporting predicted fusions, to differentiate likely artifacts from fusions with characteristics similar to biologically relevant fusions known to occur in tumor and normal tissues. While potential oncogenicity and structural rearrangements may often be implied by tumor vs. normal sample prevalence and chromosomal loci for fusion gene partners, respectively, FusionInspector makes no assertions regarding oncogenicity or mechanism (genome rearrangement vs. cis- or trans-splicing) by which biologically relevant fusions may be derived.Here we describe FusionInspector , a methohttps://github.com/FusionInspector/FusionInspector/wiki.We applied FusionInspector to assess recurrently predicted fusions in tumor and normal tissues, gathering insights into fusion transcript diversity and devising machine-learning methods for fusion classification A. We firin silico evaluation of a specified set of candidate fusions, predicted from either RNA-seq data or from a user-defined panel. FusionInspector captures all read alignments in the RNA-seq that provide evidence for the specified fusions or for the unfused partner genes, and further explores the candidate fusion genes for regions of microhomology , and the proximity of microhomologies to putative fusion breakpoints.FusionInspector  performsTo capture evidence supporting candidate fusions, FusionInspector identifies those reads that align concordantly between fusion genes as juxtaposed in their fused orientation and provide concordant alignments that span the two genes in this rearranged context. There are two types of fusion-supporting alignments: (1) split reads that define the fusion breakpoint, and (2) spanning fragments, where each paired-end read aligns to an opposite partner gene and the fragment bridges the fusion breakpoint . FusionINext, FusionInspector computes several features that are associated with the fusion based on these alignments to assist in their evaluation. First, it uses the normalized number of reads exclusively supporting each fusion variant as a proxy for the expression of the fusion transcript . Second, it computes the fusion allelic ratio (FAR) for the fusion with respect to each (5′ or 3′) partner transcript (5′-FAR and 3′-FAR) as the ratio of mutually exclusive reads supporting the fusion variant vs. each unfused partner gene  and S1C.For clarity, we will use the following definitions when reporting on fusions cataloged among samples. “Fusion” refers to two genes, geneA and geneB, where there exists a fusion transcript involving exons from geneA and geneB, ordered respectively, and written as geneA::geneB according to the Human Genome Organization’s (HUGO) Gene Nomenclature Committee (HGNC) nomenclature for fusion genes.By evaluating alignments to the modeled fusion contigs, FusionInspector demonstrates high sensitivity and specificity in supervised fusion detection, as we show for validated fusions in four well-studied breast cancer cell lines: BT474, KPL4, MCF7, and SKBR3. In our earlier study, 46 experimentally validated fusions were correctly predicted by at least one of 24 different fusion prediction methods.in silico validated fusion variant candidate  is likely to be biologically relevant or alternatively has features consistent with experimental or bioinformatic fusion artifacts.Using sequence and expression attributes of fusions and known characteristics of biologically relevant fusions (see below), FusionInspector further predicts whether each ,We illustrate these features in the context of two contrasting examples of fusion types . Fusion ,We first applied FusionInspector to examine recurrent fusion transcripts based on poly(A)-stranded RNA-seq from tumors  from 628 TCGA and 530 GTEx representative samples  from experimental or computational artifacts, or from low levels of  samples . For eacTo distinguish fusion artifacts from those with features consistent with biologically impactful fusions, we clustered fusion variants by their feature profiles . Cluster−90, Fisher’s exact one-sided test) (One fusion cluster (C4) was significantly enriched with COSMIC fusions, harboring 57% of our detected instances of COSMIC fusion occurrences among these samples, but only 4% of all fusion occurrences (p < 10ed test) B. Fusioned test) M. Sixteecis- or trans-splicing from the highly expressed partner genes.Conversely, other fusion clusters, spanning 3% of all fusion occurrences and no COSMIC fusion occurrences, had features indicative of experimental or computational artifacts, especially enrichment in microhomologies that could confound alignment or contribute to reverse transcriptase mispriming. We thus consider those fusion variants as putative artifacts. Of the 3% of all fusion occurrences encompassed by these clusters, two-thirds  had moderately to highly expressed partner genes, suggesting origination from reverse transcriptase mispriming, and the remainder had little evidence for partner gene expression, suggesting read misalignment artifacts. The low portion of such presumed artifacts is a testament to STAR-Fusion’s rigorous filtering.,,We reasoned that the set of 1,511 predicted fusion occurrences (835 distinct fusions) that were members of the COSMIC-enriched cluster C4 are likely enriched for fusions of functional significance and should be prioritized for further study. Some are already known to be relevant to cancer but not yet included in the COSMIC database, such as EGFR::SEPT14,in silico validations and attributes for 37,211 additional fusion occurrences  . Evaluatcis- or trans-splicing of more highly expressed normal fusion partner genes. Only nine fusions had any occurrences predicted as artifacts, found in both TCGA and GTEx as pancreas-specific fusions (further discussed below).FusionInspector-screened occurrences of known COSMIC fusions were mostly tumor enriched with few to no normal samples identified with evidence . All but 31 of the 236 fusions had occurrences classified as C4. Only seven fusions had at least 10% of their occurrences predicted to clusters with high counter-evidence (C49 or C51), suggesting the fusion transcripts may reflect low levels of This analysis highlighted intriguing, well-supported fusions for further study. For example, while the top-ranked tumor-enriched fusion, FGFR3::TACC3  is a known oncogenic driver,Because our initial selection of samples for FusionInspector exploration of recurrent fusion transcripts did not strictly require COSMIC fusions, only 31 of the 58 known COSMIC fusions initially discovered by our comprehensive survey with STAR-Fusion were further characterized above. We thus further applied FusionInspector to 46 additional samples with instances of the remaining 27 COSMIC fusions. Over 80% of these COSMIC fusion occurrences were predicted to other COSMIC fusion-containing clusters, none as likely artifacts, and 11 (41%) of these had occurrences predicted to the COSMIC-enriched cluster C4 , includitrans-splicing of neighboring transcripts.Approximately one-third (86) of the 236 C4+ targeted fusions in our analysis were robustly detected in normal tissues ; these may not be particularly relevant to cancer biology but may play a role in normal biological processes. Of these, 61 fusions are broadly expressed across at least five tissues, involve intrachromosomal pairs of genes, and can be largely explained by read-through transcription, local rearrangements, or Some other putative fusions that are prevalent in normal tissues may in fact represent normal structural variation in the human genome, which is not accounted for when performing read alignment to a single human reference. For example, Fusion KANSL1::ARL17, which would require a local rearrangement in the human reference genome, is prevalent across both tumor and normal tissues  have high fractions of occurrences predicted to “high-counter-evidence” clusters or “artifact-like” types , is also detected in six normal prostate samples (five TCGA and one GTEx) A. Each o,,In another example, COSMIC fusion PVT1:MYC was originally identified by STAR-Fusion in 20 samples . Interactions between PVT1 and MYC including their fusion are well-known contributors to tumorigenesis.COSMIC fusion VTI1A::TCF7L2, originally identified as an oncogenic fusion in colorectal cancer,,Interestingly, COSMIC fusion BCR::ABL1 was not tumor enriched in our analysis, likely due to paucity of the relevant tumors in TCGA. In particular, BCR::ABL1 occurs in >95% of CML cases,To highlight the utility of FusionInspector, we applied it to the 1,366 RNA-seq datasets from 1,230 participants across seven pediatric cancer projects in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) resource.in silico supported by FusionInspector, yielding 182,807 fusion occurrences with 217,614 fusion variants involving 88,307 distinct fusion gene pairs , containing 3,091 occurrences of 910 fusions, was significantly enriched for COSMIC fusions   A and S9Dct test) B. The fug 5′-FAR A.Figure TARGET fusions to identify other fusions potentially relevant to pediatric cancer. Of 910 fusions, 108 recurrent fusion candidates were present in at least three samples and absent from normal samples  fusions given the large IGH locus size -stranded RNA-seq, the supervised fusion variant identification could leverage alternative RNA-seq methodologies.,de novo reconstruction to optionally reconstruct more full-length fusion transcripts from RNA-seq data aligned to each fusion contig. FusionInspector is available as a stand-alone application for screening lists of candidate fusion transcripts and is also incorporated into STAR-Fusion for in silico validation or visualization of STAR-Fusion-predicted fusion transcripts. This facilitates analysis of fusions from both bulk and single-cell RNA-seq, as we have recently demonstrated.Because short reads remain limited in their capacity to represent full-length fusion transcripts, FusionInspector further integrates Trinityde novo reconstruction of full-length fusion transcripts.,Long-read transcriptome sequencing may eventually obviate short-read sequencing for fusion detection, thus removing the need for Machine learning is likely to play an increasingly important role in biomedical science and its clinical applications. In this paper we emphasize an important companion direction to machine learning, namely generating transparent and interpretable predictions, loosely referred to as explanations. The area of explanations and causal interpretation is growing rapidly in artificial intelligence.We hope that the practical applicability of FusionInspector will help drive transparency and other explanatory efforts in predictive areas in genomics and personalized medicine more generally, including screening and reassessment of evidence supporting fusion predictions, and visualization of the evidence via interactive reports  and S1B.FusionInspector as implemented here requires read alignment overlap with exons of reference gene structure annotations, and hence fusions restricted to intronic regions of either fusion partner or neighboring intergenic regions presently go undetected. Because genome-level fusion events often occur outside of exons, FusionInspector is largely ineffective with whole-genome or whole-exome sequencing data, instead necessitating RNA-seq.bhaas@broadinstitute.org).Further information and requests for resources or code should be directed to lead contact Brian Haas . First, the STAR (v2.6.1a) aligner was used to align RNA-seq reads from each sample to the human genome as follows:The resulting Chimeric.out.junction files generated by STAR containing candidate chimeric reads were then analyzed by STAR-Fusion like so "STAR-Fusion -J Chimeric.out.junction -O $output_dir/STARF --genome_lib_dir $ctat_genome_lib --min_FFPM 0 --no_annotation_filter" leveraging CTAT genome library GRCh38_gencode_v22_CTAT_lib_Sept032019.The parameters used here eliminated any filtering of fusions according to fusion expression levels or based on fusion annotations, to retain any fusions known to frequently occur in normal samples in both the normal and the tumor samples for further study. All STAR-Fusion predictions are provided in Fusion tumor enrichment was computed for each fusion according to ( (# tumor with fusion + 1) / ) /  /  ).FusionInspector takes as input a list of candidate fusions and RNA-seq files in fastq format, with each fusion formatted as "geneA::geneB" indicating a candidate fusion between geneA (5’) and geneB (3). Leveraging the companion CTAT genome library set of genomic resources , FusionInspector constructs fusion contigs by extracting the genomic sequences for each geneA and geneB, and concatenating each geneA and geneB pair into a single contig in collinear transcribed orientation. Gene structure annotations for fusion genes are similarly restructured to match the position and orientation of the corresponding genes in the fusion contigs. By default, long introns are shrunk to 1 kb in length by removing central regions of intron sequences, reducing the alignment search space and simplifying downstream visualizations.RNA-seq reads are aligned to the fusion contigs along with the whole reference genome by running STAR with both inputs, including the pre-indexed whole genome and a fasta file containing the fusion contigs. STAR first loads the whole reference genome index into RAM, then builds an index for the fusion contigs, and incorporates the fusion contig index into the whole genome index. Only those reads that align concordantly to the fusion contigs, while considering all alignments to the combined targets, are reported. Note, that in the fusion context, all fusion-supporting reads are aligned concordantly, but will align partially to one gene and partially to the adjacent gene. This functionality was implemented in STAR since version 2.5.0a to support FusionInspector functionality. STAR-Fusion directly executes STAR to align reads like so " STAR --runThreadN 4 --genomeDir ctat_genome_lib_build_dir/ref_genome.fa.star.idx --outSAMtype BAM SortedByCoordinate --twopassMode Basic --alignSJDBoverhangMin 10 --genomeSuffixLengthMax 10000 --limitBAMsortRAM 47271261705 --alignInsertionFlush Right --alignMatesGapMax 100000 --alignIntronMax 100000 --readFilesIn reads_1.fastq.gz reads_2.fastq.gz --genomeFastaFiles finspector.fa --outSAMfilter KeepAllAddedReferences --sjdbGTFfile finspector.gtf --alignSJstitchMismatchNmax 5 -1 5 5 --scoreGapNoncan −6 --readFilesCommand 'gunzip -c' ", where 'finspector.fa' and 'finspector.gtf' correspond to the fusion contigs sequence and structure annotation files.FusionInspector examines the aligned reads output by STAR and identifies read alignments supporting fusions between gene pairs represented by the fusion contigs. Candidate fusion breakpoints are identified by split read alignments having partial alignments that anchor to exons of the neighboring fusion genes. Spanning fragments are identified as paired-end reads having each read mapping entirely on opposite sides of the breakpoint. Alignments must meet minimum criteria to be counted as evidence, requiring at least 98% sequence identity and no more than 10 bases unaligned at their ends (soft- or hard-clipped bases). For split reads, at least 10 bases must align adjacent to each breakpoint (anchor), and each anchor region must have sufficient sequence complexity, requiring entropy ≥ 1.2. For spanning fragments, each paired-end read sequence must have sufficient complexity, requiring entropy ≥ 1.2. Preliminary fusion predictions are defined based on candidate fusion breakpoints and sets of compatible spanning fragments. RNA-seq fragments that span a candidate breakpoint but support transcription from an unfused partner gene are captured, stored as counter-evidence, and used to compute the partner gene counter FFPM and fusion allelic ratio.There is often evidence for multiple fusion variants, and while the split reads are unique to and define each breakpoint, the spanning fragments are often compatible with multiple breakpoints and assigned ambiguously. We implemented an expectation maximization (EM) algorithm based on that described in kallistoFusion candidates are then filtered according to defined minimum evidence requirement, with defaults set as requiring at least one split read to define the junction breakpoint, and at least 25 aligned bases supported by at least one read on both sides of the fusion breakpoint (termed ‘long double anchor support’ or ‘LDAS’). If the breakpoint involves non-consensus dinucleotide splice sites, then at least three split reads are required to support the breakpoint. A final filter of fusion predictions to exclude those containing overly promiscuous fusion partners (maximum 10) or those involving paralogs of more dominantly supported fusions is applied identically as previously described.de novo assembly,de novo reconstruct candidate fusion transcripts based on reads aligning to the fusion contigs. When employed, Trinity-reconstructed fusion transcripts are identified in the final FusionInspector report, and the assembled transcripts are available for further study. In addition, FusionInspector integrates IGV-reportsOptionally, Trinity Fusion Clustering and Class Prediction section below).FusionInspector was run on TCGA v11, GTEx v8, and TARGET via Terra/AnVIL,Second, FusionInspector was run in fusion screening mode to explore instances of defined COSMIC-peak-enriched fusions (Leiden cluster 4 (C4) of the 61 fusion clusters found to be heavily enriched for COSMIC fusions). There were 231 instances of C4 fusions selected according to the following criteria: found in at least 3 samples, at least one fusion occurrence found clustered to C4, and at least 30% of occurrences found localized to clusters containing at least two known COSMIC fusion occurrences. These were further supplemented with five recurrent COSMIC fusions that are not members of C4 , to a total of 236 fusion gene pairs . The 236Using RNA-seq data for breast cancer cell lines BT474, MCF7, KPL4, and SKBR3 as in,k-mers with k = 10 were identified between candidate fusion gene pairs as represented in the FusionInspector-constructed fusion contigs . The Euclidean distance of each candidate fusion breakpoint to the nearest site of microhomology was determined in the FusionInspector fusion contig coordinate system. Attributes of interest for clustering fusions were: (1) the fusion expression level (FFPM),  partner gene fusion allelic ratios (5′-FAR and 3′-FAR),  the left and right unfused partner gene expression levels expressed as 5′- and 3'-counter-FFPM and computed based on the number of counter-reads observed as aligned at each corresponding gene breakpoint site,  indicators for consensus dinucleotides and agreement with reference gene structure exon boundaries at the fusion breakpoints, and (8) the number of microhomologies and (9) distance of the breakpoint to the nearest microhomology. These numerical values were centered and scaled to Z-scores, truncated within the interval  to remove outliers, and then rescaled so each attribute numerical vector would fill the interval  simplifying our evaluation of metrics using a consistent low-to-high range for each attribute type.All 53,240 fusion variants surveyed by FusionInspector from our initial subset of TCGA and GTEx samples were clustered according to sequence and expression characteristics. Microhomologies defined as exact k-nearest-neighbor graph (k=50) of fusions, and clustered the graph by Leiden clusteringWe calculated the distance between fusions based on vectors with these values, constructed a e.g., "COSMIC-like") are assigned a prediction according to that fusion cluster annotation term. Such fusion attribute cluster predictions are now incorporated into FusionInspector (v2.8.0).A random forest classifier was built to predict Leiden cluster membership based on each fusion variant’s scaled feature attributes. The classifier was constructed by randomly selecting a maximum of 300 fusions (median cluster size) from each cluster, and leveraging 2/3 of fusions for training and 1/3 for testing, all performed using Ranger.Statistical tests were performed using R as indicated, with P-values adjusted by Benjamini-Hochberg procedure to control the false discovery rate. Wilcoxon rank-sum test was restricted to comparisons involving at least three members of each set. All statistical analyses performed are included within the Supplementary Code .


Drosophila melanogaster Y loops which represent huge, several megabases long, single transcription units. The Y loops provide a particularly amenable model system for transcriptionally active chromatin. We find that, although these transcribed loops are decondensed they are not organised as extended 10nm fibres, but rather they largely consist of chains of nucleosome clusters. The average width of each cluster is around 50nm. We find that foci of active RNA polymerase are generally located off the main fibre axis on the periphery of the nucleosome clusters. Foci of RNA polymerase and nascent transcripts are distributed around the Y loops rather than being clustered in individual transcription factories. However, as the RNA polymerase foci are considerably less prevalent than the nucleosome clusters, the organisation of this active chromatin into chains of nucleosome clusters is unlikely to be determined by the activity of the polymerases transcribing the Y loops. These results provide a foundation for understanding the topological relationship between chromatin and the process of gene transcription.While the biochemistry of gene transcription has been well studied, our understanding of how this process is organised in 3D within the intact nucleus is less well understood. Here we investigate the structure of actively transcribed chromatin and the architecture of its interaction with active RNA polymerase. For this analysis, we have used super-resolution microscopy to image the  Drosophila primary spermatocytes have large nuclei with easily visualised transcriptionally active regions of the Y chromosome known as ‘Y loops’. Using super-resolution imaging on Y loops, we show that this active chromatin has a specific organisation as a chain of nucleosome clusters. We also find that actively elongating RNA Polymerase is considerably less prevalent along the loops than the clusters. We conclude that the organisation of active chromatin into nucleosome clusters is unlikely to be caused by transcription elongation. This finding is an important step towards understanding the complex topological relationship between chromatin structure and regulation of transcription.Transcription is a highly regulated process enabling the production of different cell types during development and the ability to change gene expression in response to external signals. When transcription regulation goes wrong it can lead to a variety of disease states including cancer. In eukaryotes, transcription operates on chromatin, a polymer of repeating nucleosomes formed by packaging DNA around histone proteins. Chromatin organisation is linked to transcription regulation, as it is thought to control the access of transcription machinery to the DNA. However, directly imaging chromatin within the nucleus is traditionally very difficult due to the dense packaging.  Considerable advances have been made recently in understanding the organisation of chromatin in the nucleus and its relevance for the function and regulation of the genome (for a recent review see ). GenomiDrosophila primary spermatocytes which show an ordered arrangement of separate chromosomes and a large nucleoplasmic space [Imaging chromatin structure within the nucleus is, however, challenging as in most nuclei chromatin is densely packed. Here we take advantage of the exceptionally large nuclei of ic space ,21. Muchic space –22. Thesic space ,24. For ic space ,22–24. Tic space .In this study, we have focused on the use of super-resolution microscopy to investigate the chromatin structure of Y loops and its relationship to transcription. We find that the chromatin fibre in these transcriptionally active loops is not simply extended as a 10nm fibre but rather is largely organised as a chain of nucleosomal clusters. RNA polymerase is associated with the periphery of these clusters. Comparison of the nucleosome cluster versus RNA polymerase prevalence suggests that the chromatin clustering is not generated by RNA polymerase activity between the clusters. It appears that the chain of nucleosomal clusters forms the basic structure of decondensed chromatin upon which RNA polymerase acts.Drosophila spermatogenesis, after the last spermatogonial mitosis, the primary spermatocytes enter a G2 phase that lasts for several days. In this period they expand over 20-fold in volume and activate the spermatogenesis transcription program. As part of this program, three individual Y chromosome genes decondense from the Y chromosome mass, which is located close to the nucleolus, and spill out into the nucleoplasm forming the Y loops. These actively transcribed loops can be easily visualised by DNA labelling or anti-histone immunolabelling and in confocal microscopy, the loops appear as chromatin ribbons that follow convoluted paths within the nucleus  representing actively elongating RNA polymerase, together with anti-histone labelling for chromatin. We anticipated that elongating RNA polymerase might occupy the linking regions between the Y loop chromatin clusters however the labelling was surprising in two ways; first, the RPol-PSer2 labelling was sparse relative to the occurrence of cluster linking regions and second, the RPol-PSer2 labelling was not on the main chromatin fibre axis, rather the RPol-PSer2 localisation was peripherally associated with chromatin clusters .The first observation suggests that the cluster chain organisation is not dictated by the distribution of active polymerase and the second indicates a specific novel topological relationship between the cluster chain and the organisation of foci of active transcription. We confirmed that the observed displacement of the RPol-PSer2 localisations from the fibre axis was not due to camera misalignment see . The disThe chromatin clusters are often associated with sparse anti-histone localisations surrounding the dense cluster core; these may represent decondensed loops emanating from the clusters and active polymerase complexes associated with the periphery of clusters may be engaged with these loop regions . We noteAs the RPol-PSer2 phosphorylated form of RNA polymerase may not represent all polymerase engaged on the Y loop chromatin, we also investigated the localisation of RNA polymerase II with the phospho-Ser5 modification on the C-terminal domain repeats (RPol-PSer5). The RPol-PSer5 phosphorylated form is associated with the initiation complex, paused polymerase and polymerase interacting with splicing complexes ,35. As sSome labelling is associated with the Y loops but, as with RPol-PSer2, this is sparse relative to the clusters. We then investigated the combined occurrence of RPol-PSer2 and RPol-PSer5 using an antibody that labels both modified polymerase forms. The distribution of RPol-PSer2/PSer5 supports the above results , confirmIn order to further assess the role that active transcription plays in the generation of clustered chromatin along the Y loops, transcription inhibition using α-amanitin and triptolide for 20 hours was undertaken. For this we used cultured larval testes to ensure optimal penetration of the drugs into the testes, alongside control larval testes with no drug exposure. The larval testes were used to create a spermatocyte preparation in the same manner as adult testes, the histones were immunolabelled and the nuclei imaged using STORM as before . DespiteThe Y loops represent single transcription units whose large size provides an opportunity to investigate the organisation of nascent transcription in an active transcription loop. We have used the EU-Click-iT assay to image, by laser scanning confocal microscopy (LSCM), the nascent transcription occurring during a short time window (20min) of exposure of the testes to the EU nucleotide. This analysis was undertaken using confocal microscopy, as opposed to using STORM, as the confocal system is more suitable to provide the larger 3D depth needed to understand the distribution of nascent transcription along the Y loops that span almost the entire nucleoplasm. We observe nascent transcription from Y loops as a series of blobs of EU incorporation distributed along extended lengths of the Y loop fibres . This diThe confocal images of RPol-PSer2 and RPol-PSer5  provide Drosophila as a tractable model system for the super-resolution imaging of chromatin in intact nuclei. We have focussed on the organisation of transcriptionally active chromatin using the Y loop genes which are activated in spermatocytes and extend out from the Y chromosome into the nucleoplasm as huge loops. We find that these active chromatin loops do not simply extend as 10nm “beads-on-a string” nucleosomal fibres but instead have a more complex structure and generally adopt an organisation as chains of nucleosome clusters. We have examined the relationship between this structure and the arrangement of RNA polymerase transcribing the loops and the organisation of nascent transcription.We have exploited the large size of the primary spermatocyte nuclei in The chromatin clusters have a relatively tight size distribution with a median width of 52nm and an interquartile range from 44-61nm. To estimate the width we have used the FWHM which provides a robust estimate of width, however it is an underestimate of the full width as the clusters do not have sharp perimeters. The lack of sharpness of the outer edge of clusters may be due to loosening of the compaction at the periphery or the formation of small loops of 10nm fibre extending from the compact clusters. In addition some of the uncertainty of the edge of the clusters will be due to the localisation precision of the STORM and also the labelling method, as the positions of the histones are visualised with an anti-histone primary antibody and a fluorophore-conjugated secondary antibody. Although this estimate is not a direct measurement, it provides a starting point for interpretation of the observed labelling in terms of underlying chromatin structure. As presented above in the Results, a chromatin cluster with a width of 52nm could accommodate 54 nucleosomes using a packing density (CVC of 35%) derived from EM analysis of nucleosome density in intact nuclei. In addition to the clusters, in the STORM images we also see sparse labelling between clusters indicating that the clusters are linked by nucleosomal chromatin and we also see localisations extending from the clusters supporting the occurrence of small chromatin loops emanating from the compact clusters. Our interpretation of the overall structure is illustrated in the schematic in To investigate the mechanism of cluster formation we asked whether the cluster linking regions might be sites of elongating polymerase as we considered that chromatin disruption or polymerase-dependent supercoiling might underpin the chromatin cluster chain organisation. However, elongating polymerase recognised by phosphorylation on Ser2 in the CTD repeats was too sparsely distributed on the loops to account for the chromatin cluster organisation. Even though the modified polymerase form associated with paused polymerase (RPol-PSer5) was more abundant than RPol-PSer2, neither its distribution nor the combined distributions of RPol-PSer2 and RPol-PSer2 indicated a fixed relationship between active polymerase and chromatin cluster formation. Thus our evidence does not support the idea that the cluster chain organisation is generated by transcription in the intervening regions. A similar conclusion was reached by Castells-Garcia et al.  who suggExamination of elongating polymerase (RPol-PSer2) in association with the chromatin fibre of the Y loops revealed an interesting topological relationship. The RPol-PSer2 is not simply associated with the central axis of the chromatin fibre but often is located off the fibre axis, on the periphery of a chromatin cluster . In someThe spermatocyte Y loop system also enabled us to examine the progression of the two phosphorylated forms of RNA polymerase (RPol-PSer2 and RPol-PSer5) on transcription units in intact nuclei. RPol-PSer2 is distributed over the Y loops and is associated with nascent transcript as expected for active elongating polymerase. RPol-PSer5 on the other hand is concentrated on the periphery of the nucleolus presumably where Y loop transcription is initiated, however it is also seen extending a short way out from the nucleolus  in a posPrimary antibodies were: mouse anti-histone , 1:1000; rabbit anti-RPol-PSer2 , 1:500; rabbit anti-RPol-PSer5 , 1:500 and rabbit anti-RPol-PSer2/5 ; 1:500. Secondary antibodies were from Invitrogen and used at 1:1000; goat anti-mouse Ig-Alexa Fluor 647 (A-21235), goat anti-rabbit Ig Alexa Fluor 488 (A-11008) and goat anti-rabbit Ig Alexa Fluor 568 (A-11011).Drosophila melanogaster were dissected in PBS. The primary spermatocytes were isolated via gentle pipetting following collagenase digestion  of the testes sheath, and fixed in 4% formaldehyde/PBS for 20min at 37°C. The primary spermatocytes were filtered (Partec 04-004-2327) and seeded onto 35 mm high μ-dishes (Ibidi) for 30min at room temperature. The cells were blocked overnight (1% Roche Western Blotting Reagent (WBR), Merck; 0.5% Triton X-100 in PBS). Following immunolabelling, the cells were fixed  for 20min at room temperature and stored in PBS at 4°C. To control for under-labelling of structures, we tested a range of antibody concentrations and found no significant difference in the observed nucleosome or RPol distribution.Testes of 0–5 day old male w1118 2 laser power throughout. The 488 laser was used at increasing power throughout image acquisition to recover fluorophores from the triple dark state, using ~ 70–100 W/cm2 power. We found the 488nm laser performed better on our microscope system as it gave a higher signal to noise than the commonly used 405nm activatory laser.Cells were imaged using a Zeiss Elyra 7, at 30°C using a BP490-560/LP640 filter. The STORM imaging buffer was as described in Peters et al., 2018 , adapted2 (Sigma M8266) 4 mM, EGTA (Bioserv BS-7249E) 2 mM, Glycerol (Sigma G5516) 30% v/v in dH2O (pH 8.4). 250U Pyranose Oxidase enzyme (Sigma P4234) was added to 1.25 ml of enzyme storage buffer, mixed well then aliquoted into 75 μl tubes for storage at -20°C.Pyranose Oxidase Enzyme Storage buffer: PIPES (Sigma P-6757) 24 mM, MgCl2O.Pyranose Oxidase Imaging Base buffer: Tris buffer pH 8.5 (Jena Bioscience BU-124L-84) 50 mM, NaCl (Fisher Scientific 7647) 10 mM, Glucose (Sigma G8270) 0.56 M in dHPyranose Oxidase Final Imaging buffer: to 2784 μl Imaging Base Buffer was added 75 μl Pyranose Oxidase in Enzyme Storage Buffer (5 U/ml), 150 μl MEA (Sigma 30070) 1M (50 mM), 6 μl Catalase (Sigma C30)(40 μg/ml) and 30 μl COT (Sigma 138924) 200 mM in DMSO (2 mM).Resulting.czi files were processed into localisation files using Zeiss ZEN Black software. The reconstructions were filtered based on precision , photon number , and point spread function (PSF) width (80 – 220nm) to improve the accuracy of the images and filter out noise. Drift correction was completed using the model-based drift correction, allowing an automatic selection of references points, a maximum of 21. The images were visualised using the “molecule density” option, which displays the localisations according to the density of neighbouring points, as well as precision value. The final images were then converted into.tif files and imported into ImageJ for the addition of scale bars. The FIJI plug-in Template Matching was used to correct any remaining misalignment of stack images  was recorded for downstream cluster analysis. In order to identify and describe individual clusters, a mode-finding clustering algorithm, MeanShift was applied [To mitigate the particular issue of cluster clumping caused by histones following along a Y loop fibre , a double cluster model was considered: the origin points are not randomly distributed but form a modified Thomas process as well. The PCF in this case became: applied ,29 using applied .The crucial parameter of MeanShift is the search radius, which was set to 2σ, as suggested in  and calc3. The maximum number of nucleosomes within a given sphere volume for each cluster was then calculated by dividing the cluster volume by the nucleosome volume.The volume of a single nucleosome was calculated as a disc of radius 5.5nm, and height 5nm, giving a nucleosome volume of 475.2nmDrosophila melanogaster larvae, and placed into either the transcription inhibition medium consisting of 200 μg/ml α-amanitin (Sigma), 1 μM triptolide (ApexBio A3891) in Schneider’s medium (Sigma) + 10% FBS (Biosera), or control medium containing 0.01% DMSO. After 19h 15min incubation in a humidity chamber at 25°C, EU was added to 5 testes per condition. After 20h of total incubation time, testes were either processed for EU-Click-chemistry and confocal imaging of whole-mount testes or for STORM imaging of single spermatocytes.20 testes per condition were dissected from male w1118 Drosophila melanogaster (w1118) larval testes were dissected into ice-cold Schneider’s medium (Gibco) supplemented with 10% FBS (Biosera) and 1x Penicillin/Streptomycin (Sigma). For EU labelling and click chemistry the Click-iT RNA Imaging Kit  was used according to the manufacturer’s protocol with minor modifications. Briefly, testes were incubated in medium containing 1 mM 5-ethynyl uridine (EU) for 20min (45min for transcription inhibition experiment) at 25°C and fixed with 3.7% Formaldehyde (Sigma), 1% Triton X-100 (Sigma) in PBS (Oxoid). For detection, the incorporated EU was ligated to Alexa Fluor 647 azide via the click reaction. For immunofluorescence, testes were blocked and permeabilized with 1% Western Blocking Reagent , 0.5% Triton X-100 in PBS at 4–8°C over night, incubated with primary antibodies in PBS containing 1% WBR, washed with 0.1% Tween 20 (Sigma-Aldrich) in PBS, incubated with secondary antibodies, washed with 0.1% Tween 20 in PBS, post-fixed for 20min with 3.7% Formaldehyde at room temperature and mounted on microscopy slides with AF1 (Citifluor).Imaging was performed on a Leica TCS SP8 confocal microscope with an HC PL APO CS2 63x/1.40 OIL objective, 1 AU pinhole size, voxel size of 42x42x140 or 71x71x299nm, 4x line averaging using 488 Argon and 633nm HeNe lasers, and GaAsP Hybrid Detectors (HyD). The Alexa Fluor 488 channel was acquired sequentially with the 647 channel. Data were denoised with Noise2Void (3D)  using ZeS1 Information(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S1 Data(CSV)Click here for additional data file. 20 Oct 2022Attachment129126_1_rebuttal_2494637_rk05mv.pdfSubmitted filename: Click here for additional data file. 29 Oct 2022Dear Dr White,Thank you very much for submitting your Research Article entitled 'The anatomy of transcriptionally active chromatin loops in Drosophila primary spermatocytes using super-resolution microscopy' to PLOS Genetics, through Review Commons.The manuscript was fully evaluated at the editorial level, taking into account the Review Commons peer review reports and your Revision Plan.  We will be happy to consider a revised manuscript, and we generally agree with the revision plan that you propose.  We cannot, of course, promise publication at that time. We do concur with reviewer #2 that the data in figures 9 and 10 need to be generated by STORM rather than by conventional confocal microscopy. Although not explicitly mentioned by the reviewers, it also seems to us that the conclusions drawn from these experiments need to be backed up by quantitative image analysis, rather than by anecdotal images alone. Additional experiments that study the effect of transcription inhibition on the cluster organisation (as suggested by Reviewer #1) would also strengthen the manuscript significantly. Minor point: in the legend of Fig 3B, please explain what the three colors are.plosgenetics@plos.org.Should you decide to revise the manuscript for further consideration here, please update the list of your responses to the review comments and a description of the changes you have made in the manuscript.  If you decide to revise the manuscript for further consideration at PLOS Genetics, please aim to resubmit within the next 60 days, unless it will take extra time to address the concerns of the reviewers, in which case we would appreciate an expected resubmission date by email to Submission Checklist.You can use this link to log into the system when you are ready to submit a revised version, having first consulted our https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocolsTo enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. 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If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.While revising your submission, please upload your figure files to the Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, use the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Bas van SteenselAcademic EditorPLOS GeneticsGregory P. CopenhaverEditor-in-ChiefPLOS Genetics 4 Jan 2023AttachmentResponse_to_reviewers_revised.docxSubmitted filename: Click here for additional data file. 4 Feb 2023Dear Dr White,We are pleased to inform you that your manuscript entitled "The anatomy of transcriptionally active chromatin loops in Drosophila primary spermatocytes using super-resolution microscopy" has been editorially accepted for publication in PLOS Genetics. Congratulations!Before your submission can be formally accepted and sent to production you will need to complete our formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. 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Inform journal staff as soon as possible if you are preparing a press release for your article and need a publication date.Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Genetics!Yours sincerely,Bas van SteenselAcademic EditorPLOS GeneticsGregory P. CopenhaverEditor-in-ChiefPLOS Geneticswww.plosgenetics.orgTwitter: @PLOSGenetics----------------------------------------------------Comments from the reviewers (if applicable):Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors have fully addressed my comments. This work is of high interest and perform in a robust manner. Overall, this manuscript will be of high interest to the community.Reviewer #2: The authors addressed the majority of the comments satisfactorily. Some of the suggestions were not addressed probably after an agreement with the editor. I would like just to make some minor remarks.In response to this comment “Given the availability of broadly used methods for clustering such as DBScan …The authors could justify the choice or compare results using other more broadly used methods.”The authors answered “…In selecting this approach we compared various approaches and we found Meanshift to perform best with respect to identifying clusters closely positioned along a fibre. (Example data comparing the performance of Meanshift with DBscan in cluster identification with varying radius input parameter was provided to reviewers).”I would like to mention that I was not provided any example data.In the response to Reviewer #1 – Comment 1, the authors stated “we tried a range of antibody concentrations which did not change the observations” referring to labeling density of nucleosomes and RNA Pol II. Since they included a related statement in the Methods section it would be appropriate to include supplementary panels supporting the statement or at least show the data to the reviewers.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: None**********what does this mean?). 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PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  21 Feb 2023PGENETICS-D-22-01212R1 The anatomy of transcriptionally active chromatin loops in Drosophila primary spermatocytes using super-resolution microscopy Dear Dr White, We are pleased to inform you that your manuscript entitled "The anatomy of transcriptionally active chromatin loops in Drosophila primary spermatocytes using super-resolution microscopy" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. 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Disruption of H3K36 methylation in somatic cells leads to transposon transcription and activation of key genes in the PIWI-interacting RNA pathway together with RNA production from piRNA clusters. Drosophila melanogaster brains and that there is division of labour for the repression of transposable elements between the different methyltransferases Set2, NSD, and Ash1. Furthermore, we show that disruption of methylation leads to somatic activation of key genes in the PIWI-interacting RNA pathway and the preferential production of RNA from dual-strand piRNA clusters.Transposable elements constitute a substantial portion of most eukaryotic genomes and their activity can lead to developmental and neuronal defects. In the germline, transposon activity is antagonized by the PIWI-interacting RNA pathway tasked with repression of transposon transcription and degrading transcripts that have already been produced. However, most of the genes required for transposon control are not expressed outside the germline, prompting the question: what causes deleterious transposons activity in the soma and how is it managed? Here, we show that disruptions of the Histone 3 lysine 36 methylation machinery led to increased transposon transcription in  Transposable elements make up a large part of the eukaryotic genome and come in a multitude of variants. They have been shown to drive gene regulation and to exhibit sophisticated ways of selective insertion in the genome , therebyDrosophila melanogaster, three evolutionary conserved proteins methylate H3K36. SET domain containing 2 (Set2) is the only histone methyltransferase to trimethylate H3K36 and Set2 mutants show a 10-fold reduction of H3K36me3, whereas bulk mono- and dimethylated H3K36 remain largely unaffected (Drosophila Nuclear receptor-binding SET domain protein (NSD) but closely related mammalian orthologs  have been shown to mono and dimethylate H3K36  has been extensively studied and is foremost associated with transcriptional activity. However, the role of H3K36 methylation is complex and it has been shown to be a key player in preventing run-away cryptic transcription , DNA repaffected . Absent,affected . Less iste H3K36 . NSD losed H3K36 .D. melanogaster larvae causes increased transcription of transposable elements  also target, yet unknown, non-histone proteins. The loss of Set2 impairs dosage compensation of the male X chromosome independent of H3K36 methylation  and found that the bulk of the transcriptional activation takes place in the pericentromeric regions of all chromosomes and the entire sequenced part of the Y chromosome  on transposon repression, we analysed transcript levels of transposable elements in dissected brains from third instar male larvae with the lysine-to-arginine replacement mutant 2xH3K36R . We haveR larvae . The ∆Hif H3K36M . Neverth2xH3K36K . In our  repeats  and S1     . NSDds46 < 0.05) . The ash < 0.05) . To bettsferases . Analysisferases  comparedtivation . Interesromosome .H3K36 methyltransferase-specific transposon superfamily transcription. Transposon classifications and abundance for histone methyltransferase mutant-specific clusters described in Fig 2D.Table S1. ∆HisC; 12xH3K36R is to a large extent recapitulated in Set21, indicating that disrupted methylation of H3K36 causes increased transposon transcription. We next asked if this activation can be linked to one of the three methylation states. To this end, we analysed ChIP-seq data for mono-, di-, and trimethylated H3K36 from adult fly heads to test if different transposons vary in their H3K36 methylation levels. We observe a significant difference between LINE and LTR compared with simple repeats or genome average where both LINE and LTR transposons have higher relative levels of H3K36me2 but lower levels of H3K36me1 and H3K36me3, whereas simple repeats show a slight but significant elevation of the more ubiquitous H3K36me1       and in S < 0.05) . Similar < 0.05) . In ash1 < 0.05) , which semplates . Corresppe = 5.1 . OverallD. melanogaster are defined as two general types based on their mode of transcription in the germline , proposed to aid the transcription of piRNA source loci via read-through from flanking genes  in ∆HisC; 12xH3K36R showed significant enrichment for genes related to piRNA metabolic processes (Table S2). For Set21, we observe significant up-regulation of AGO3 and aub together with genes encoding the Tudor domain proteins, fs(1)Yb and BoYb (2 fold up-regulation of CG15930 (Tudor domain containing protein 5-like) in ∆HisC; 12xH3K36R. CG15930 has been implicated in the piRNA metabolic process and promoting male sexual identity in the germline . Kots is a germline-specific Tudor domain containing protein essential for maintaining high piRNA levels produced from uni-strand and dual-strand piRNA clusters  in both sexes are: AGO3, piwi, krimp, moon, rhi, fs(1)Yb, papi, egg, qin, SoYb, tud and vas (Table S3). In addition, female larvae also exhibit significant up-regulation (P < 0.05) of: del, BoYb, CG15930, and Kots (Table S3). The only piRNA biosynthesis gene specifically up-regulated in males is squash (Table S3). To summarise, the impact of impaired H3K36 methylation on increased transposable element transcription and transcription of piRNA clusters can be detected in multiple tissues, sexes, and by independent experimental measurements.Finally, we asked if the observed effects on transposon activity, piRNA biosynthesis, and dual-strand cluster activation also occurred outside of eye discs and larval brains. To this end, we reanalysed the results of sequencing nuclear RNA from th sexes . We obseth sexes  with simth sexes , and S1.th sexes  and femath sexes . When loth sexes . However cluster  and, in clusters . This is2 fold change values for differentially transcribed genes related to the PIWI/piRNA biosynthesis machinery in females (left) and males (right). ns = P > 0.05, * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001, **** = P ≤ 0.0001.Differential transcription analysis results for PIWI/piRNA biosynthesis genes in whole larvae. Table showing logTable S3. ∆HisC; 12xH3K36R is already lowered by the induced PIWI/piRNA system. The mechanism to increase the transcription of transposable elements remains elusive but does not depend on DNA methylation because D. melanogaster lacks 5-methylcytosine.We show here that compromised H3K36 methylation, likely through the loss of H3K36me2, causes increased transcription of transposable elements. The activation of transposable elements in turn triggers transcriptional activation of the PIWI machinery followed by transcriptional activation of dual-strand piRNA clusters. As the PIWI/piRNA system has a dampening effect on transposable-element transcription, we speculate that the observed effect seen in D. melanogaster, has evolved to efficiently target runaway transcription of transposons in germ cells. In recent years, however, the transcription of retrotransposons in somatic cells has shown to be tightly connected to cancer progression, neuronal defects, and brain development  also showing increased transposon activity in Set21 . The transposon-activity profile of ash122/ash19011 is the least biased but interestingly has the biggest relative increase in LTR/Copia repeats with 6.25% compared with Set21 (5.03%) and NSDds46 (1.67%) (Table S1). We note that because of multi-mapping of reads, we cannot determine the exact origin of the transcribed transposable elements. However, we show that the bulk of transposable elements originate from intergenic regions and the observed up-regulation of transposable elements is not a result of transcriptional activation or increased transcription of transposon harbouring genes (The clustering of transcribed transposons shows patterns for ng genes .The largely overlapping increase in transcription of repeated elements after knock-out of H3K36-specific methyltransferases (particularly Set2) and H3K36R replacement (replication-dependent H3.2K36R) argues that it is the methylation of H3K36 that keeps transposable elements transcriptionally inactive in somatic cells. However, the histone replacement approach has limitations. First, H3K36R replacement effectively abolishes all lysine modifications including acetylation. It also may affect posttranslational modifications of neighbouring amino acids. Thus, H3K36R is less effectively methylated at lysine 27 (H3K27) by the Polycomb Repressive Complex 2 (PRC2) in vitro . LikewisD. melanogaster  were quantified within Repeatmasker (v.4.0.6) annotations . For ∆HisC; 12xH3K36R, the number of transposons filtered out was 1,694. For Set21, NSDds46, ash122/ash19011, and ∆H3.3B; ∆H3.3A the number of filtered out transposons were 2,354, 3,641, 2,586, and 3,799, respectively. The whole larvae nuclear RNA-seq results are presented unfiltered because of insufficient number of biological and technical replicates. ChIP-seq data were assigned to repeats using Repeatmasker (v.4.0.6) annotations (∆HisC; 12xH3K36R and ∆HisC; 12xH3K36K.The RNA sequencing data were processed as described in otations . For anaotations  alongsidotations . Stringeotations  in SeqMootations  on the t


Plasmodium falciparum has a complex life cycle during which it needs to differentiate into multiple morphologically distinct life forms. A key process for transmission of the disease is the development of male and female gametocytes in the human blood, yet the mechanisms determining sexual dimorphism in these haploid, genetically identical sexual precursor cells remain largely unknown. To understand the epigenetic program underlying the differentiation of male and female gametocytes, we separated the two sexual forms by flow cytometry and performed RNAseq as well as comprehensive ChIPseq profiling of several histone variants and modifications.The protozoan malaria parasite We show that in female gametocytes the chromatin landscape is globally remodelled with respect to genome-wide patterns and combinatorial usage of histone variants and histone modifications. We identified sex specific differences in heterochromatin distribution, implicating exported proteins and ncRNAs in sex determination. Specifically in female gametocytes, the histone variants H2A.Z/H2B.Z were highly enriched in H3K9me3-associated heterochromatin. H3K27ac occupancy correlated with stage-specific gene expression, but in contrast to asexual parasites this was unlinked to H3K4me3 co-occupancy at promoters in female gametocytes.P. falciparum.Collectively, we defined novel combinatorial chromatin states differentially organising the genome in gametocytes and asexual parasites and unravelled fundamental, sex-specific differences in the epigenetic code. Our chromatin maps represent an important resource for future understanding of the mechanisms driving sexual differentiation in The online version contains supplementary material available at 10.1186/s12915-023-01568-4. Plasmodium parasites, the infectious agents causing malaria, have a complex life cycle, in which they undergo asexual replication in the human host and sexual replication in the Anopheles mosquito. For successful transmission from the human to the vector, the parasites need to differentiate into male and female gametocytes in a process referred to as gametocytogenesis. During this process that takes around 10 to 12 days in Plasmodium falciparum, the parasites sequester in the bone marrow [Malaria poses a global health burden with more than 400,000 deaths each year, even though progress towards the elimination of malaria has been made in the past decades . The Plae marrow  and drame marrow . Upon mae marrow . On the e marrow . Therefoe marrow .P. berghei and the human malaria parasite P. falciparum showed that the majority of gametocyte specific proteins are found pre-dominantly either in male or in female gametocytes, reflecting the fundamentally different biological functions of the two sexes [Male and female gametocytes can be morphologically distinguished from each other around day seven of development, when they are classified as stage IV gametocytes. Female gametocytes are characterised by condensed hemozoin, an accumulation of osmiophilic bodies which are involved in gamete emergence, a high density of ribosomes and a relatively small nucleus with a nucleolus, consistent with their need to engage in protein synthesis important for the emerging macrogamete and zygote . mRNAs cwo sexes , 12.P. falciparum is regulated. It has been shown that epigenetic regulation plays a critical role for commitment to gametocytogenesis by de-repression of the transcription factor AP2-G, which is silenced by H3K9me3 and its associated factor heterochromatin protein 1 (HP1) in asexual parasites [Little is known about how the sexual dimorphism in arasites –16. Furtarasites –21. Howevar gene family encoding the major virulence factor PfEMP1 [var gene silencing [Plasmodium parasites H2A.Z occupies intergenic regions across the euchromatic genome irrespective of their activity, however H2A.Z enrichment levels positively correlate with transcription levels suggesting it may define promoter strength [var genes, as it is deposited in the promoter of the transcribed var gene together with the histone variant H2B.Z [P. falciparum co-occurs in the same nucleosomes with H2A.Z throughout the genome [P. falciparum [Plasmodium [P. falciparum [T. brucei, acetylated H2A.Z is enriched at TSS nucleosomes of polycistronic transcription units where its presence correlates with gene expression, possibly by being involved in RNA PolII recruitment [In asexual parasites, the chromatin is principally divided into the transcriptionally permissive euchromatin and the transcriptionally silent heterochromatin. The latter is marked by H3K9me3 and its associated protein HP1 and restricted to subtelomeric regions and internal islands, where it is involved in the repression of virulence-associated multigene families, such as genes of the r PfEMP1 , and othr PfEMP1 –28. Thisr PfEMP1 . Anotherilencing .Converseilencing  and is iilencing . In yeasilencing . In asexstrength –36. H2A.nt H2B.Z , 37. Thent H2B.Z  and in Pe genome . The hise genome , 40, 41.e genome . H3K4me3lciparum . In addilciparum . Acetylaasmodium , also poasmodium  and is blciparum . In the ruitment .P. falciparum gametocytes characterised by H3K9me3 and HP1 indicated that several gametocyte specific genes as well as the erythrocyte remodelling machinery are differentially marked in gametocytes, implicating these processes in gametocyte maturation [P. berghei investigated the differences of HP1 and H3K9ac coverage separately in mature male and female gametocytes, revealing differences in their heterochromatin and euchromatin landscape pointing to a complex epigenetic regulation underlying the different gene expression patterns observed [P. falciparum, no data is available on how the sex-specific transcriptomes change over gametocyte differentiation and how the chromatin landscape organises the genome to regulate sex-specific gene expression patterns.Few studies have addressed how chromatin structure impacts gene regulation during sexual development. It has been demonstrated using bulk gametocyte cultures that the transcriptome changes dynamically during the 10 day gametocyte maturation process , 47, 48 turation , 50. Morturation . Importaturation , 52 and turation , 54. Howobserved . In P. fP. falciparum gametocytes during their maturation and to investigate the underlying epigenetic landscape. To this end we profiled several histone modifications using chromatin immunoprecipitation followed by high-throughput sequencing (ChIPseq) in asexual parasites, female gametocytes, and for some modifications also in male gametocytes, and correlated these with the transcriptional data. Finally, we also analysed the combinatorial patterns of these modifications in female gametocytes and ring stage parasites, generating comprehensive chromatin state maps for the two parasite stages.To close some of the gaps in our knowledge of sexual differentiation in malaria parasites, we set out to study the transcriptome of separated male and female ABCG2 gene was used [R2 = 0.83; D6 vs D10 R2 = 0.84; D4 vs D10 R2 = 0.64) than the female gametocytes , suggesting that the male gametocytes transcriptome is more dynamic during sexual maturation  and later (day 6 and day 10) female gametocytes, respectively. The 969 genes (290 (30%) of unknown function) with peak expression in early female gametocytes (Cluster 3) contained mostly genes related to “mitochondrion”, which is maternally inherited during fertilisation , and “TCarasites , 66. Thietocytes .In the male gametocytes, three clusters could be clearly distinguished (Cluster 5–7). Early male gametocytes (day 4 – stage II/III) showed highest expression of 579 genes (118 (20%) of unknown function). The predominant processes were related to proteolysis and proteasome complex, consistent with the critical function of proteostasis during metazoan spermatogenesis , 68. In It is known that the transcriptome of mature gametocytes is fundamentally different from asexual parasites , 5, 12 aPlasmodium [Toxoplasma gondii, TgCARM1 mediated H3R17me2 is implicated in parasite differentiation and is located in promoters of stage specifically expressed genes [Tri-methylation of histone 3 lysine 4 (H3K4me3) is well-studied in asexual parasites, and is typically associated with promoters , 35. Di-asmodium  but has ed genes .R2 = 0.93 and R2 = 0.97 in female day 6 gametocytes and ring stage parasites, respectively), suggesting that H3K4me3 and H3R17me2 globally co-occur , indicating global remodelling of the H3K4me3/H3R17me2 modification landscape in sexual stages , which has upstream peaks in ring stage parasites as well as female day 6 gametocytes and is expressed in both stages . Peaks that would intersect both features were called “overlapping” peaks. In asexual parasites, most peaks were found to be overlapping , or located in intergenic regions , with a smaller proportion localising in coding regions . In contrast in gametocytes the majority of peaks were intragenic  or overlapping , with only 1.2% accumulating in intergenic regions  , Pf3D7_1478000 (gexp17)) [geco did not correlate with H3K9me3 patterns, as highest transcription was observed in ring stage parasites, followed by male and then female gametocytes. As geco transcription was reported to peak in rings and early gametocytes [geco expression later during the life cycle in salivary gland sporozoites [Generally, H3K9me3 was highly conserved at telomeres and subtelomeres and over intrachromosomal islands covering variant surface antigen genes as described previously , 50, 55.ly) Fig. A. Howevely) Fig. B, especily) Fig. C. Intrigly) Fig. B. This wgexp17)) , which hgexp17)) , 50. Of Fig. Pfg1-748, Pf3ap2-g gene is sufficient for its activation [Consistent with a function during commitment but not during gametocyte maturation, the gene coding for the sex-specific transcription factor AP2-G was covered with H3K9me3 in all stages. However, in gametocytes only the gene body was heterochromatic, whereas in ring stage parasites H3K9me3 stretched over the upstream region as well  Fig. A. To do To interrogate how global chromatin indexing differs between ring stage parasites and female gametocytes, the bins in the female day 6 gametocyte UMAP were coloured according to their corresponding state in ring stage parasites Fig. B. The revar gene introns [To better understand the epigenetic code in asexual and sexual parasites, we analysed the distribution of the chromatin states in relation to different genetic elements including exons, introns, and intergenic regions Fig. C. To cap introns , 87, 88.p-value < 0.01)  of at least 300 bp in length (representing one to two nucleosomes) within 2 kb upstream of their ATG gene start  in their promoter in gametocytes or ring stage parasites showed significantly higher expression in gametocytes than in ring stage parasites, which may indicate the presence of an activating mark that was not studied in our set of histone modifications, or lower nucleosomes occupancy, which was previously linked to high transcriptional activity in gametocytes , 90. GenState 7 (220 genes) and state 8 (78 genes) only existed in gametocytes and were predominantly characterized by H2A.Z, either unmodified (state 8) or featuring low levels of acetylation (state 7). Interestingly, these states were also enriched upstream of many genes related to parasite invasion and associated with significantly lower expression levels in female gametocytes than in ring and schizont parasites  was prevalent upstream of 778 genes including genes associated with cell cycle, IMC, and parasite invasion. Consistent with their function in later developmental stages, these genes showed significantly lower expression in ring stage parasites compared to schizonts and gametocytes , showed a broad range of expression. Interestingly, the most highly expressed genes in this group were associated with protein export and include the major genes coding for the nd Pf113 –96. Of nIn conclusion, we identified chromatin states that are stage specifically enriched in genes linked to particular biological functions and may act as the anchor for recruiting specific chromatin remodelling complexes which fine tune the expression of these genes. In particular we found that H3K27ac occupancy was highly correlated with stage specific gene transcription. In gametocytes, the presence of unacetylated H2A.Z seems to be associated with transcriptional repression.In this study, we reveal fundamental differences in the transcriptional regulation and epigenetic code of developing male and female gametocytes in comparison to asexual parasites. When analysing genes with peak expression in each stage  in its C-terminal tail, which suggests it may also fulfil an architectural role in heterochromatin formation and maintenance. The heterochromatin association of H2A.Z could be facilitated by post-translational modifications that would distinguish heterochromatin associated H2A.Z from euchromatin associated H2A.Z, and indeed we observed that H2A.Z was less acetylated in heterochromatic than in euchromatic nucleosomes. In differentiated mouse cells, H2A.Z is not associated with heterochromatin, however its monoubiquitinated form is enriched on the silenced X-chromosome, which is also marked with the repressive mark H3K27me3 [P. falciparum [In terminus . InteresH3K27me3 . Ubiquitlciparum , but itsT. brucei, H2A.Zac localizes to nucleosomes around the TSS and may be involved in recruiting RNA-Pol II to activate transcription [P. berghei H3K9ac correlated with transcription in asexual parasites as well as male gametocytes and ookinetes but not female gametocytes [P. falciparum sporozoites but not in oocysts [Of note, acetylated H2A.Z co-occurs with H3K4me3 at active promoters and with H3K27ac at enhancers, while being depleted from bivalent, silent enhancers in embryonic stem cells . In T. bcription . This isetocytes . Convers oocysts . These o oocysts , and ali oocysts . UltimatP. falciparum and reveal fundamental differences not only between sexual and asexual parasites, but in particular also between male and female gametocytes. Clearly, chromatin modifications regulating transcription in gametocytes extend beyond the functionally known combinatorial patterns of histone modifications, and we need to take other, recently identified posttranslational modifications into consideration to better understand the processes regulating sexual differentiation. Asexual and sexual parasites differ significantly in their cellular metabolism, and recent publications increasingly demonstrate how changes in metabolites can govern differential gene expression programs by causing alternative chromatin modifications [P. falciparum.In conclusion, our data demonstrate the complexity of the histone code in genome organization of the malaria parasite ications . SeveralTo isolate male and female gametocytes, the previously described 3D7::ABCG2-GFP parasite line was used . For det2, 5% CO2, and 94% N2 at 37 °C.Parasites were cultivated as described previously , at 5% hTo induce gametocytogenesis, a tightly synchronised ring stage culture at about 6–8% parasitemia was stressed by adding 50% fresh medium to the partially spent medium, thereby decreasing the haematocrit (day -2) . At abou6 parasites per µl), heated for 5 min at 95 °C and subsequently spun at 14,000 × g for 10 min. Finally, the supernatant was loaded on a 4 – 12% Bis–Tris gel (Invitrogen) in NuPAGE MES SDS running buffer (Thermo Fisher Scientific) and run for 1.25 h at 120 V. Thereafter, proteins were transferred onto Nitrocellulose membranes . The blotting was conducted at 400 mA for 1 h in transfer buffer  in a Mini-PROTEAN Tetra Vertical Electrophoresis Cell (Biorad) blotting chamber. After blotting, the membranes were blocked for 2 h in blocking solution (5% non-fat milk powder in TBS with 0.05% Tween). The first antibody was applied in blocking solution with 0.02% sodium azide at 4 °C overnight. After washing the membrane in TBS-T, horse radish peroxidase (HRP) conjugated secondary antibodies in blocking solution were applied to the membrane for 2 h at RT. Detection was done using chemiluminescent HRP substrate (Immobilon ® Western Millipore) and imaging with a ChemiLux Imager (Intas Science Imaging).For the preparation of complete cell lysates, the erythrocytes were lysed on ice by resuspension in 0.075% Saponin (Sigma Aldrich) in PBS. Upon lysis, the erythrocytes were centrifuged at 2300 × g for 10 min at 4 °C. The pellet was washed twice with PBS, resuspended with 2 × Laemmli-Buffer  at least two days before sorting and then further cultivated in complete culture medium. As outlined in Additional File For RNA extraction, FACS sorted gametocytes or culture pellets of ring and schizont stage infected erythrocytes were resuspended in 10 times the pellet volume TRIzol® Reagent (Life Technologies). RNA was extracted by addition of 0.2 volumes chloroform followed by 30 min centrifugation at full speed and 4 °C. The aqueous layer containing the RNA was purified using the RNeasy Mini Kit . DNA was removed by on-column digest and an additional in solution digest with DNase I . The concentration of the resulting RNA was measured by NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific) and the purity was determined by qPCR with primers specific for SBP1.8 parasites. The mix was transferred to a prechilled 2 ml tissue grinder (Kimble) and incubated on ice for 30 min. Subsequently, IGEPAL CA-630 was added to a final concentration of 0.25% and the parasites were lysed with 100 strokes. The lysed parasites were then centrifuged for 10 min at 20,000 × g, 4 °C to pellet the nuclei.Sorbitol synchronized cultures of ring stage parasites (4–14 hpi), schizont stage parasites (38–48 hpi) or purified gametocytes were crosslinked at a final concentration of 1% PFA (16% PFA Thermo Fisher Scientific) for 10 min at 37 °C. The reaction was quenched for 5 min on ice with 125 mM glycine. The infected erythrocytes were then pelleted (5 min at 800 × g at 4 °C) and permeabilized in 0.075% saponin in PBS on ice to release haemoglobin. The parasite pellets were then washed twice with PBS (2300 × g at 4 °C for 5 min) and resuspended in 2 ml cold lysis buffer ) per 108 nuclei and sonication for 2 times 8 cycles of 30 s ON and OFF in a chilled Bioruptor® Sonication System (Diagenode). The sheared material was spun  and the supernatant was then transferred and diluted 1:10 with ChIP Dilution Buffer . The diluted chromatin was then pre-cleared with 30 µl Protein G Sepharose 4 Fast Flow  for 1 h at 4 °C under agitation. The beads were prepared for the immunoprecipitation (IP) by blocking in 0.1% BSA (Sigma Aldrich) in ChIP Dilution Buffer for 10 min. After two washing steps with ChIP Dilution Buffer, the beads were preincubated with the respective antibodies  per 10Libraries were prepared from the ChIP and RNA samples. For ChIP sequencing the Accel-NGS™ 2S Plus DNA Library Kit for Illumina platforms (SWI Swift Bioscience) was used according to the manufacturer’s guidelines, including the Accel-NGS™ 2S Indexing Kit (SWI Swift Bioscience) until the PCR amplification step. The RNA sequencing libraries were prepared using the NEBNext Ultra II directional RNA Library Prep Kit for Illumina (NEB) with the NEBNext Poly(A) mRNA Magnetic Isolation module (NEB) and the NEBNext Multiplex Oligos for Illumina (NEB). For both library types, Ampure XP beads (Beckman Coulter) were used for the purification steps and the library amplification step was performed with 12–15 cycles using KAPA Polymerase  with the dsDNA HS Assay Kit (Thermo Fisher Scientific Q32854) or the RNA HS Assay Kit (Thermo Fisher Scientific Q32855) to determine the concentration, and subsequently on a TapeStation (Agilent Technologies) using the D5000 kit according to the manufacturers protocol to determine molarity from the average fragment size. The analysis was done using the TapeStation Analysis Software 3.1.1., with a size range of 150 bp to 2000 bp. The libraries were subsequently pooled to a final concentration of 5 nM and sequenced as 100–150 bp paired end reads for RNAseq and 100–150 bp paired end reads for ChIP-Seq on an Illumina HiSeq 2500 instrument . The files were then trimmed to remove remaining adaptors and low quality reads using TrimGalore 0.6.4 . If necessary, the files were further trimmed using Cutadapt 2.8 [Plasmodium falciparum (PlasmoDB 3D7 version 28) using Bowtie2 version 2.3.4.3 with default settings for paired-end reads [p-value < 0.01. The list of ookinete specific genes was retrieved from PlasmoDB using the following settings: > fivefold overexpressed in ookinetes compared to all other stages on a floor of 100 reads [The raw data was processed on the Spartan HPC (University of Melbourne). ChIPseq data in fastq format was first quality checked using FastQC 0.11.8  Parasites expressing GFP-tagged ABCG2 [TM Deep Red and FACS-sorted according to their GFP signal. The mitotracker/GFP double positive (G+M+) female gametocyte population and the mitotracker positive, GFP negative (G-M+) male gametocyte population were then cultivated and reanalysed after two days by A) live microscopy and B) FACS. The GFP fluorescence intensity of the mitotracker positive cells was determined and significant differences between the populations determined by t-test. C) Heatmap showing the z-score transformed sense expression of several previously described stage specific marker genes (colour coded) determined by RNAseq to validate the cellular identity of the harvested parasite populations. D4 = Day 4, D6 = Day 6, D10 = Day 10, M = male gametocyte, F = female gametocyte. Male gametocytes: day 4: n=3, day 6: n=2, day 10: n=1; female gametocytes: day 4: n=2, day 6: n=4, day 10: n=4; ring stage parasites: n=2, schizont stage parasites: n=1. E) Heatmap of the spearman correlation coefficients between the RNAseq data sets from the different stages. Male gametocytes: day 4: n=3, day 6: n=2, day 10: n=1; female gametocytes: day 4: n=2, day 6: n=4, day 10: n=4; ring stage parasites: n=2, schizont stage parasites: n=1. Figure S2. Western blot of histone variants and modifications in bulk gametocyte cultures. H3 served as a loading control and Pfs16 as gametocyte specific marker. HP1 = heterochromatin protein 1.Figure S3. Overview over log2-transformed ChIP/Input coverage across all 14 P. falciparum chromosomes in female gametocytes and ring stage parasites. H3K4me3 , H3R17me2 , H3K27ac , H2A.Z , H3K9me3 . Figure S4. Heatmaps showing the log2-transformed ChIP/Input coverage of all genes and their 2 kb up/downstream region for each modification sorted into groups according to their expression. The black bar in the silent genes represents silent mitochondrial and apicoplast genes, that carry no histone modifications. A) Day 6 female gametocytes. B) Day 6 male gametocytes. C) Ring stage parasites. H3K4me3 , H3R17me2 , H3K27ac , H2A.Z , H2A.Zac , H3K9me3 . Figure S5. Global ChIPseq and RNAseq profiles for regions of particular interest in female gametocytes and ring stage parasites. Genes are marked depending on their genomic orientation in either green (+ genes) or orange (- genes). Introns are shaded in light green or dark red. The position of the ATG is marked by a dashed line. Transcripts (FPKM) are shown in black. Regions shown: SBP1 from 65.5 kbp to 72 kbp, AP2-G from 904.7 kbp to 918 kbp, Pfs230 from 368.4 kbp to 381.2 kbp, PUF1 from 773.2 kbp to 783.5 kbp. H3K4me3 , H3R17me2 , H3K27ac , H2A.Z , H3K9me3 . Figure S6. H3K4me3 and H3R17me2 coverage in genes with peak expression in asexual or sexual parasites. Average log2-transformed ChIP/Input coverage plots from two biological replicates for H3K4me3 and H3R17me2 in day 6 female gametocytes and ring stage parasites over genes with peak expression in early (cluster 3) and late (cluster 4) female gametocytes and rings (cluster 1). Figure S7. H3K4me3/H3R17me2 coverage in gametocytes is significantly increased in ookinete and oocyst specific genes. Log2-transformed ChIP/Input coverage plots from two biological replicates for H3K4me3 and H3R17me2 in day 6 female gametocytes and ring stage parasites over ookinete genes upregulated after JIB-04 treatment , region shown from 2167 kbp to 2170 kbp. RNAseq data: Male gametocytes: day 4: n=3, day 6: n=2, day 10: n=1; female gametocytes: day 4: n=2, day 6: n=4, day 10: n=4; ring stage parasites: n=2; ChIPseq data: H3K9me3 . Figure S9. H2A.Z is associated with heterochromatic areas in day 6 female gametocytes. Overview over chromosome 7 showing log2-transformed ChIP/Input coverage for H2A.Z  and H3K9me3  in all stages, H2A.Zac in male (n=1) and female gametocytes (n=2), and H2B.Z in female gametocytes (n=1). Zoom into internal heterochromatic cluster on chromosome 7. Genes are marked in either green (+ genes) or orange (- genes). Introns are marked either in light green or dark red, depending on gene orientation. Figure S10. H3K27ac and H2A.Zac coverage is unchanged in translationally repressed genes. Average log2-transformed ChIP/Input coverage plots for H2A.Zac  in day 6 male and female gametocytes and H3K27ac in female gametocytes (n=3) over all genes in comparison to genes ranking in the top expressed tercile (excluding DOZI-repressed genes) and DOZI-repressed genes in day 6 female gametocytes. Figure S11. Heat maps of chromatin states discovered in female day 6 gametocyte and ring stage parasites. Heatmap showing the log2-transformed ChIP/Input coverage of the studied histone modifications over all 50 bp bins represented in each of the A) female day 6 gametocyte states and B) ring stage parasite states. Figure S12. Length distribution of chromatin states. Neighbouring bins with identical chromatin states were fused and the lengths of the continuous regions with the same chromatin state were plotted as violin plots for female day 6 gametocytes and ring stage parasites.ed ABCG2  were tigeatment  and gene clusters including GO analyses.Additional file 4: Table S3. Comparison of heterochromatic loci with published data from Bunnik et al. 2018 [al. 2018  and Frasal. 2018 .Additional file 5: Table S4. List of genes associated with different chromatin states upstream in female gametocytes and ring stage parasites including Gene Ontology analyses.Additional file 6: Table S5. Antibodies used for western blot (WB), immunofluorescence analysis (IFA), and chromatin immunoprecipitation (ChIP).Additional file 7. Uncropped western blots shown in Figure S