Insight into high-resolution three-dimensional genome organization and its effect on transcription remains largely elusive in plants. Here, using a long-read ChIA-PET approach, we map H3K4me3- and RNA polymerase II (RNAPII)-associated promoter–promoter interactions and H3K9me2-marked heterochromatin interactions at nucleotide/gene resolution in rice. The chromatin architecture is separated into different independent spatial interacting modules with distinct transcriptional potential and covers approximately 82% of the genome. Compared to inactive modules, active modules possess the majority of active loop genes with higher density and contribute to most of the transcriptional activity in rice. In addition, promoter–promoter interacting genes tend to be transcribed cooperatively. In contrast, the heterochromatin-mediated loops form relative stable structure domains in chromatin configuration. Furthermore, we examine the impact of genetic variation on chromatin interactions and transcription and identify a spatial correlation between the genetic regulation of eQTLs and e-traits. Thus, our results reveal hierarchical and modular 3D genome architecture for transcriptional regulation in rice. Three-dimensional genome organization and its effect on transcription remain elusive in rice. Here, the authors map promoter–promoter interactions and heterochromatin interactions using ChIA-PET and reveal spatial correlation between the genetic regulation of eQTLs and e-traits. CTCF/cohesin and other DNA-binding proteins, as well as RNAs, have been identified as key factors in the spatial organization of long-range chromatin interactions17. Chromatin loops mediated by CTCF/cohesin and other protein factors coalesce into chromatin contact domains/topologically associating domains (TADs) and further aggregate into active and inactive compartments and chromosome territories. In addition, our and other studies revealed that CTCF and RNAPII-organized chromatin interactions provide a topological basis for transcriptional regulation20. Recent investigations have also thoroughly explored 3D genome dynamics and functions during the cell cycle, development, and signaling in multiple cells and in single cells25. Overall, the mammalian genome is organized as a complex and delicate structure, which is divided into different TAD/compartment categories with distinct epigenetic features. In addition, this organization plays a critical role in gene transcription. Similarly, 3D genome maps for Arabidopsis, rice, and other plants have been generated using the Hi-C approach31. However, due to the limited resolution of Hi-C6, comprehensive high-resolution chromatin architecture, including both active and inactive elements/compartments, and its effects on transcriptional regulation remain unclear in plants.Revealing the structural and functional relationship of the three-dimensional (3D) genome is one of the fundamental questions in biology. Chromosome conformation capture-derived approaches, especially high-throughput chromosome conformation capture (Hi-C)cis-regulatory elements and heterochromatin regions using RNA polymerase II (RNAPII) and histone modifications (H3K4me3 and H3K9me2)-associated long-read ChIA-PET data in rice (Oryza sativa). By integrating transcriptome and epigenome data analysis, we provide details regarding 3D genome architecture and framework for transcriptional regulation. We also reveal the impact of genetic variation on chromatin interactions and transcription regulation. Furthermore, we demonstrate the spatial correlation between the genetic regulation of expression quantitative trait loci (eQTLs) and e-traits. Collectively, these results, along with recent reports on the long-range chromatin interaction maps in maize33, provide high-resolution views on 3D genome architecture and its role in transcriptional regulation in plants.In this study, we present comprehensive long-range chromatin interaction maps including active To investigate the genome architecture organization principle and its effects on transcriptional regulation in rice, we mapped active genes related chromatin architecture by capturing RNAPII and H3K4me3-associated chromatin interactions and present heterochromatin-engaged genome architecture using H3K9me2-associated interactions in young leaves of two rice varieties, Minghui 63 (MH63) and Zhenshan 97 (ZS97). We collected ~329 million uniquely mapped paired-end tags 2 × 150 bp) from 12 ChIA-PET libraries  than did the Hi-C data: the target protein-binding sites and chromatin interactions between binding sites mediated by the targeted protein  model Fig. . We idenWe further characterized the expression patterns of genes of the PPI model and the basal model. We analyzed RNA-seq data from 20 different rice tissues (Supplementary Table 34 core genes (including candidate core genes) occurred in >99% of 453 rice accessions, and distributed genes occurred in <99% of rice accessions. Compared with the BP model, the PPI model contained the highest proportion of core genes and the lowest proportion of distributed genes, whereas the non-H3K4me3-marked genes included the lowest proportion of core genes and the highest proportion of distributed genes  of RNAPII-binding sites were involved in interactions and tended to be with higher intensity than did basal sites  RNAPII anchor genes were also involved in H3K4me3-associated genome architecture. When compared with genes anchored by only H3K4me3 or RNAPII loops, genes engaged in both H3K4me3 and RNAPII loops had the highest transcriptional activity  of H3K9me2-marked regions acted as interaction anchors that show significantly longer peak breadth but equal peak intensities compared with those of basal sites Fig. . We alsotes Fig. , indicatFinally, we identify 11,590 high-confidence (FDR < 0.05) H3K9me2-associated heterochromatin interaction loops, with approximate half of these loops being intrachromosomal interactions  with a genomic span from 10 kb to 8.5 Mb, accounting for 37% of the rice genome Fig. . InteresWe aimed to comprehensively characterize the rice genome architecture with different chromatin interacting domains (CIDs), including HID, AID, mixed interacting domains (MID), TID, and gap regions Fig. . The HIDGypsy and Copia retrotransposons and the highest proportion of Harbinger and Mariner DNA transposons, as well as the highest density of expressed genes. By contrast, the opposite genomic properties were observed in HID regions compared with those in other regions. Consistently, AID regions comprised the lowest DNA methylation level and inactive mark H3K9me2 density and the highest densities of active marks H3K4me3 and H3K27ac and transcribed mark H3K4me1. HID regions, on the other hand, presented the opposite epigenomic properties . The inactive mark H3K9me2 showed a higher rate of differential peak than did the active mark H3K4me3 Fig. . InteresMH01g0584500 gene is present in MH63 but absent in ZS97, and there was a chromatin interaction between MH01g0584500 gene and MH01g0584200 gene in MH63. The expression level of the interacting gene MH01g0584200 was higher in MH63  trans-eQTLs   in riceits Fig. , we coll 36 Fig. . From thTLs Fig. . FurtherTLs Fig. , suggestne) Fig. . Togethe29. In addition, it has been recently reported that the dynamics of A/B compartments and TAD domains is associated with the gene expression changes in different tissues37. We speculate that active- and inactive-interacting modules identified in this study might also be dynamic between tissues. Previous study reported that the Rabl conformation of rice chromosomes only exist in xylem cells according to FISH experiments38, and was hard to observe in Hi-C contact maps in seedlings37. This type of chromosome conformation cannot be detected in our ChIA-PET data. This might be caused by the fact that the 3D genome architecture captured by Hi-C and ChIA-PET method only reflects ensemble structure from different cell types  in rice seedlings.In this study, we constructed the high-resolution 3D architecture of rice genomes using antibodies against RNAPII, active mark H3K4me3, and inactive mark H3K9me2. Approximately 82% of the rice genome was annotated and separated into relatively independent spatial interactive modules, while the spatial independence between active and inactive modules was further validated with an immunofluorescence assay. Notably, 35% of H3K9me2 peaks participated in the inactive modules. In view of the stability of H3K9me2 modification across tissues, the inactive modules may act as scaffolds of the 3D architecture and separate the active modules into different spaces. In the active module, anchor genes involved fundamental biological processes and were organized into different clusters. The distinct transcriptional potential for different module categories mainly depended on the density and relative ratio of basal and anchor genes. Based on these findings, we provide the high-resolution 3D genome organization principles within active and inactive segments, which are separately assigned to different modules or subcompartments with spaced arrangements in the rice high-order genome architecture19, we detected only extensive PPIs organized by RNAPII (and H3K4me3) in rice, suggesting that the function of RNAPII varies in higher-order chromatin structure between rice and mammals. One possibility is that the function of mammalian RNAPII to tether promoter–enhancer interactions may be replaced by other factors (such as specific histone modifications or transcription factors) in rice. Another possibility is that there are few typical promoter-enhancer interaction in rice. Consistent with previous studies in mammals8, the genetic evidence indicated that promoter fragments in H3K4me3 and/or RNAPII looping enhanced gene transcription. In addition, a higher interaction dimension of the promoters resulted in a higher level of gene expression. Thus, anchor promoters of H3K4me3 and RNAPII may be enhancer-like and play an important role in gene transcription regulation through constructing the rice 3D architecture.In contrast with both RNAPII-tethered promoter–enhancer and promoter–promoter interactions in mammalsTwo rice varieties, ZS97 and MH63  were used in this study. The germinating seeds were collected by soaking dry seeds in water for 72 h at 37 °C. The germinated seeds were grown in a phytotron with the day/night cycle set at 14 h/10 h and a temperature of 32 °C/28 °C. Young leaves of 2-week-old seedlings were collected and immediately cross-linked.6. Briefly, rice tissues were dual cross-linked with 1% formaldehyde  for 10 min and 1.5 mM ethylene glycol bis (succinimidylsuccinate)  for 30 min at room temperature. The tissues were transferred to 0.2 M glycine for stopping the cross-linking reaction and rinsed three times with ddH2O. Frozen tissues were kept at −80 °C until use. For each experiment, about 4 g of sample was ground in liquid nitrogen into fine powder and resuspended in 100 ml of EB1 buffer . The mixed solution was filtered through Miracloth, and the filtrate was centrifuged at 1800 × g for 10 min at 4 °C. The pellet was washed three times in 5 ml of EB2 buffer  and centrifuged at 2000 × g for 10 min at 4 °C. Subsequently, the pellet was resuspended in 2 ml of EB3 buffer  and centrifuged at 2000 × g for 1 h at 4 °C. The final pellet was resolved in 1 ml of NLB buffer . Sonicate chromatin solution into 1–3 kb  using a Bioruptor (Diagenode). Centrifuge the chromatin fragments at 2000 × g for 10 min at 4 °C and transfer the supernatant to a new tube for immunoprecipitation. To coat antibodies to magnetic beads, take 60–100 μg antibody  and mix with 800 μl suspended protein G (~1:100 dilution) magnetic beads at 4 °C for 8 h with rotation. Mix sheared chromatin solution to antibody-loaded beads in a new tube at 4 °C overnight with gentle rotation. The supernatant was discarded and the beads were sequentially washed with 5 mL 0.1% SDS FA cell lysis buffer  for three times, 5 mL High salt ChIP buffer  twice, 5 mL ChIP wash buffer  once and 5 mL 1 × TE buffer  twice. ChIP DNA was used for end-repair and A-tailing using T4 DNA polymerase  and Klenow enzyme . Proximity ligation of ChIP DNA was performed using biotinylated bridge-linker . Protein-DNA complex was reverse cross-linked and the ChIA-PET libraries were prepared by using Tn5 transposase . The libraries were sequenced (2 × 150 bp) using Illumina Hiseq X-Ten (Beijing AnnoRoad Company).Rice long-read ChIA-PET libraries were constructed with a modified ChIA-PET methodz-axis in the whole nuclei. Spot function of Imaris X64 software (www.bitplane.com) was applied to count the foci with an estimated diameter of 0.15 μm and a threshold on “quality”, allowing an accurate detection of visible foci without including the background signal. A total of 22 nuclei were analyzed and averaged for foci counts.Rice nuclei isolated from formaldehyde-fixed rice leaves were spread on microscope slides for specimen preparation. Rabbit anti-H3K4me3  and mouse anti-H3K9me2 antibodies , which were used at a 1:200 dilution, were detected with donkey anti-rabbit IgG-Alexa 488  and goat anti-mouse IgG-TRITC antibodies , respectively. The specimens were counterstained with vectashield antifade mounting medium  and observed under a Nikon N-SIM microscope with 3D SIM mode to scan all the immunofluorescence at different 39, a JAVA-based package for automatic processing of ChIA-PET sequence data, including linker filtering, read mapping, redundancy removal, protein-binding sites, and chromatin interaction identification. The genomes were from two rice varieties, MH63 and ZS97, which were downloaded from RIGW. In the ChIA-PET Tool pipeline, we chose ChIP-seq peaks extended by ±10% as the given anchors to call clusters. Considering the technical noise, we identified high-confidence clusters by FDR < 0.05 and a given PET count, which was decided by data sequencing depth. The precise statistics of ChIA-PET replicate data sets and combined data sets are summarized in Supplementary Table ChIA-PET data were processed with updated ChIA-PET Tool software40 with “--all --matrix-format complete” parameters. For matrix normalization, we processed the raw contact maps by iterative correction methods from HiC-Pro software41 with default settings to adjust the count of the contact matrix. ChIA-PET contact maps were visualized by HiCPlotter software42.Inter-ligation pets (Ipets: genome span of two tags per pet >8 kb) obtained from H3K4me3- and RNAPII-mediated ChIA-PET unique mapping reads were used to generate raw contact maps with bin size settings of 100, 50, and 10 kb. The raw contact matrix was calculated by the bedpe2Matrix program from ChIA-PET2 software43 to delineate A/B compartments in ChIA-PET data at 100, 50, and 10-kb resolution. During this study, each chromosome was divided into fixed windows at coarse resolution. The first principal component of the correlation matrix indicated the compartments.We used an eigenvector program from juicer software34 with “--dta-cufflinks” parameters. Mapped reads (.sam format) were further converted to bam format and sorted by read position through samtools44. FPKM values were calculated by Cufflinks45 with default parameters. The mean value of replicates is referred to as the gene expression level.RNA-seq data of 20 rice tissue samples were collected from the GEO dataset. The detailed information, including GEO accessions and tissue types, is summarized in Supplementary Table According to FPKM values from 20 tissues, we analyzed the co-expression correlation of H3K4me3- and RNAPII-mediated interacting gene pairs Fig. . The PCCAs for the co-expression study of subnetworks Fig. , we sele46 as shown in Supplementary Fig. Based on the connection of H3K4me3-associated PPIs, we obtained the top 100 complex subnetworks with intrachromosome pet count ≥4 and interchromosome pet count ≥6 and integrated them into a global network by using Cytoscape (version 3.5.1)47. The loop region for clustering was extended by ±10% loop length. According to the transcription factor modification patterns within H3K4me3 loops, we classified all loops into seven groups, as shown in Supplementary Fig. We performed k-means clustering for H3K4me3-associated intrachromosomal interactions by DeeptoolsWe first identified multiple chromatin loops that are connected to each other and clustered into candidate domains with continuous chromatin loop coverage. We further calculated the aggregate loop coverage along all chromosomes at base-pair resolution and subtracted these low-coverage regions from the candidate domains. Subsequently, the resulting domain regions were filtered by a genomic span. The domains more than 1 Mb in size (200 kb for AIDs) are excluded and yielded the final TIDs and HIDs. A CID means a genomic region spanned by continuous connected loops mediated by specific protein factor. Four categories of CIDs were defined: H3K9me2-associated HIDs, H3K4me3-associated AIDs, and MIDs covered by both H3K9me2 and H3K4me3, and only RNAPII-associated TIDs.48 to combine MH63 peaks (MH63 ChIP-seq data map to MH63) and ZS97 peaks (ZS97 ChIP-seq data map to MH63), which overlap by at least 1 bp. Differential peaks were calculated by the DESeq package49 based on the merged binding peaks. The parameters for differential peaks were adjusted p-value < 0.05, log2-fold-change >1 or <−1. The numbers of differential peaks are shown in Fig. We used a merge function from bedtools softwareFurther information on research design is available in the Supplementary informationReporting SummarySource data


Aspergillus fumigatus is the major filamentous fungal pathogen in humans. Infections associated with A. fumigatus are often treated with azole drugs, but resistance to these antifungal agents is increasing. Mortality from aspergillosis associated with azole-resistant fungi is extremely high. Previous work has identified transcriptional control of the azole drug target-encoding gene cyp51A as an important contributor to resistance in A. fumigatus. Here, we demonstrate that the transcription factor AtrR binds to a region in the cyp51A promoter that is associated with alleles of this gene conferring clinically important azole resistance. Using high-throughput genomic technologies, we also uncover a large suite of target genes controlled by AtrR. These data indicate that AtrR coordinately regulates many different processes involved in drug resistance, metabolism, and virulence. Our new understanding of AtrR function provides important new insight into the pathogenesis of A. fumigatus. Aspergillus fumigatus has a mortality rate that can approach 90% in certain patient populations. The best-understood avenue for azole resistance involves changes in the cyp51A gene that encodes the target of azole drugs, lanosterol α-14 demethylase. The most common azole resistance allele currently described is a linked change corresponding to a change in the coding sequence of cyp51A and a duplication of a 34-bp region in the promoter leading to a tandem repeat (TR). Our previous studies identified a positively acting transcription factor called AtrR that binds to the promoter of cyp51A as well as that of an important membrane transporter protein gene called abcG1. In this work, we characterize two different mutant alleles of atrR, either an overproducing or an epitope-tagged form, causing constitutive activation of this factor. Using an epitope-tagged allele of atrR for chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq), the genomic binding sites for AtrR were determined. Close to 900 genes were found to have an AtrR response element (ATRE) in their promoter regions. Transcriptome evaluation by RNA sequencing (RNA-seq) indicated that both alleles led to elevated transcription of a subset of target genes. An electrophoretic mobility shift assay and DNase I protection mapping localized the ATREs in both the abcG1 and cyp51A promoters. The ATRE in cyp51A was located within the 34-bp repeat element. Virulence in a murine model was compromised when AtrR was either deleted or overproduced, indicating that the proper dosage of this factor is key for pathogenesis.Aspergillosis associated with azole-resistant  Aspergillus fumigatus have a mortality rate that can approach 90% in particular patient populations  and contains a duplication of a 34-bp region in the promoter (tandem repeat of 34 bp [TR34])  and HapX  transporter-encoding gene abcG1  by two pieces of evidence. First, strains lacking atrR failed to drive normal transcription of either cyp51A or abcG1. Second, chromatin immunoprecipitation (ChIP) experiments demonstrated that AtrR bound to both the cyp51A and abcG1 promoter regions. However, the sequence of the AtrR response element (ATRE), its relationship to the SRE, and the genomic target sites of this factor remained among the gaps in our knowledge.A new participant in the transcriptional control of s oryzae . This faatrR, as well as identification of the suite of AtrR-responsive genes in vivo using chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq). The sequence of the ATRE was identified from these analyses and confirmed by a combination of DNase I footprinting, electrophoretic mobility shift assays (EMSA), and mutagenesis. AtrR and SrbA both bind to the critical 34-bp region of cyp51A and are likely to cooperate to drive the transcription of this key azole resistance determinant, while only AtrR appears to control abcG1 transcription.Here, we report the generation of two hypermorphic or hyperactive alleles of atrR corresponding to the wild-type gene containing a C-terminal insertion of a 3× hemagglutinin (3× HA) tag  tag . More respA gene . Based ospA gene , we esti alleles .hspA promoter upstream of atrR, either tagged with 3× HA or untagged, strongly elevated voriconazole resistance. Interestingly, even insertion of the 3× HA epitope tag alone was sufficient to elevate azole resistance. These data suggest that fusion of foreign sequences to the C terminus or overproduction of AtrR (see below) both represent means of overcoming the normal control of transactivation by this transcription factor.The presence of the abcG1, as being an important determinant in azole resistance  transporter-encoding gene, here called sistance , 17, as sistance . To exam strains .abcG1 strongly depressed voriconazole resistance in a strain overproducing AtrR. However, even in the absence of abcG1, overproduction of AtrR was still able to elevate azole resistance. This is consistent with abcG1 being an important but not exclusive target gene of AtrR impacting voriconazole resistance.A loss of atrRΔ strains were grown in minimal medium, along with transformants containing the epitope-tagged atrR allele (atrR-3× HA) or strains with the hspA promoter in place of the wild-type atrR promoter driving the native (hspA-atrR) or epitope-tagged (hspA-atrR-3× HA) allele. Whole-cell protein extracts were prepared from these strains and analyzed by Western blotting using anti-AtrR or anti-AbcG1 antiserum.To confirm that increased AtrR levels led to increased expression of AbcG1, we directly assayed the expression of these two proteins using polyclonal antibodies directed against recombinant forms of each. Isogenic wild-type and atrRΔ cells  was used to evaluate mRNA levels for atrR and abcG1.We next measured expression levels of the mRNA for atrR was increased in the presence of the hspA-atrR allele but was not altered by the insertion of the epitope tag at the C terminus of the native gene  analyses  using the atrR-3× HA and hspA-atrR-3× HA strains. Wild-type (negative control), atrR-3× HA, and hspA-atrR-3× HA strains were grown to mid-log phase and cross-linked chromatin prepared, as described previously  terms were enriched. The most enriched GO terms included sequence-specific DNA binding, plasma membrane, and several metabolic processes  Copyright © 2019 Paul et al.2019Paul et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.02563-18.3TABLE S1Aspergillus fumigatus. This table represents all known transcription factor-encoding genes that were found to have AtrR binding in their promoter regions via the ChIP-seq experiments reported here. The log2 expression values are from our RNA-seq data and refer to the ratio of the RNA levels from atrR-3× HA (3× HA)/wt, hspA-atrR (hspA)/wt, or atrRΔ (atrR null)/wt. Download Table S1, XLSX file, 0.1 MB.Sequence-specific transcriptional regulation bound by AtrR in Copyright © 2019 Paul et al.2019Paul et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the cyp51A has already been demonstrated to be a target gene for both SrbA and AtrR regulation  (hem13)  , and a h (hem13) . As the 10.1128/mBio.02563-18.2FIG S2FIG S2, PDF file, 0.8 MB.GO term enrichment for SrbA unique targets. Output of FungiFun2 of genes found in SrbA ChIP-seq analysis that are only predicted to contain a SrbA response element (SRE). Data for SrbA are from .Copyright © 2019 Paul et al.2019Paul et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the hspA overproduction derivative and the 3× HA-tagged form. Isogenic wild-type and atrRΔ, hspA-atrR, and atrR-3× HA mutant strains were grown in minimal medium. Total RNA was prepared and analyzed by RNA-seq, as described previously . We also compared the effects seen when atrR was deleted with results from our earlier study  had a stronger phenotypic response than did the presence of the atrR-3× HA allele, as evidenced by the larger number of genes that were induced in response to the hspA-atrR allele. These data support the view that much of the AtrR-responsive regulon occurs via indirect effects through the engagement of other transcription factor networks (like SrbA) and others identified in the ChIP-seq analysis described above. This also supports the suggestion made above that the effects of AtrR are amplified via induction of the large number of transcription factor-encoding genes containing ATREs.The overlap between upregulated genes in either hyperactive allele was compared along with the total number of ChIP-positive genes . Only 38in vivo processes. We employed genetic epistasis to determine if the hypermorphic hspA-atrR allele was capable of suppressing the well-described azole sensitivity of a srbAΔ null strain. Isogenic wild-type and atrRΔ, srbAΔ, hspA-atrR, and hspA-atrR srbAΔ mutant strains were grown and tested using a disk diffusion assay. Plates were incubated at 37°C and photographed . Both of these genes are required for normal azole resistance and driving high-level expression of AtrR cannot suppress the need for SrbA under these conditions.While overproduction of AtrR strongly elevated voriconazole resistance in an otherwise wild-type genetic background, the loss of cyp51A promoter as the binding site for AtrR, we wanted to precisely locate this element and support our identification of the ATRE. Additionally, we wanted to compare where SrbA bound to the 34-bp repeat with the ATRE and previous data using a plasmon resonance approach  for SrbA binding to SRE2 is 10-fold higher than for SRE1 , a 4-bp replacement mutant in the upstream CCG repeat (SphI), or with a mutant lacking both copies of the CCG repeat (DM). These unlabeled oligonucleotides were used as competitors to determine if they retained the ability to block AtrR binding to the biotinylated 34-bp probe. Recombinant proteins were added to the biotinylated probe in the absence or the presence of the indicated competitor DNAs. After binding, free probe and protein-probe complexes were resolved on nondenaturing acrylamide gels, transferred to nylon membranes, and visualized by blotting with an IRDye-labeled streptavidin  with the 34-bp region of ptavidin .AtrR formed a complex with the 34-bp probe that could be competed away using either a 42-bp version of this promoter fragment or the same 42-bp competitor containing the 6-bp mutation between the two CCG repeats. Importantly, mutant competitors lacking either one (SphI) or both CCG repeats (DM) could not compete. These data confirm the importance of these CCG repeats in normal DNA binding of the ATRE by AtrR.abcG1 gene and a homologue called abcG4 (Afu2g15130). The region 1,000 bp upstream of the ATG codon for both these genes was used to drive luciferase expression, and the resulting fusion constructs produced similar low-level luciferase expression  on fungal virulence. Mice were challenged with these strains along with an isogenic wild-type control and survival followed over an 11-day time course  or 600 (hspA-atrR) genes were respectively induced in hypermorphic atrR backgrounds, but a minority of these induced genes contained ATREs. This is most readily interpreted by the majority of the transcriptional induction seen in these strains resulting from indirect effects. We hypothesize that AtrR occupies a relatively upstream position in the network of transcription factors that impact gene regulation in A. fumigatus, as supported by the GO term enrichment for these factors in the ChIP-seq data (abcG1 and cyp51A) and a larger collection of genes that are activated via AtrR control of genes encoding other transcription factors .The large number of genes containing ChIP-seq peaks corresponding to AtrR was an unexpected observation. Earlier work identified approximately 120 genes as containing SREs and being transcriptionally responsive to SrbA , 22. Ourseq data . When At12CCG as being sufficient to function as an ATRE in vivo, this single consensus is not sufficient to explain the entire range of target genes bound by AtrR. As can be seen in in vivo ATREs is likely to be more complex and will require further characterization. Our finding of the important tandem ATREs located more than 1 kb upstream of the ATG in abcG1 illustrates the potentially increased complexity for A. fumigatus promoter organization compared to similar regions in more extensively characterized fungi, such as Saccharomyces cerevisiae (reviewed in reference While our data strongly support the ability of a sequence element with the CGG(N)atrR or overproduction of the wild-type factor illustrates the importance of the dosage of AtrR during pathogenesis. The normal disease process required a properly metered level of this protein and suggests that interventions targeting the activity of this transcriptional regulator might be effective in interfering with the pathogenicity of this important fungus. The essential role AtrR plays in azole resistance would also be impacted by interfering with normal function of this transcription factor. Recent experiments describing the successful identification of a drug preventing normal transcription factor activation . Selection of transformants was made in minimal medium ; trace elements, vitamins, and nitrate salts are as described in the appendix of reference All strains used in this study were derived from the AfS35 (FGSC #A1159) strain and are listed in hspA-atrR construct were made as follows: 4 DNA fragments consisting of 1,000 bp upstream of the atrR coding sequence, β-rec/six-ptrA blaster cassette (from pSK485), hspA promoter, and 1,000 bp corresponding to the atrR 5′ coding sequence were joined by Gibson assembly at the BamH1 site of pUC19 to form pSP99. This plasmid was then digested with SmaI and PmeI to release the 8.5-kb integration construct from the plasmid backbone and transformed into AfS35, SPF89, and SPF844 to generate strains SPF108, SPF112, and SPF110, respectively, under pyrithiamine selection.Derivatives containing the srbAΔ disruption strain was generated using a hygromycin split-marker strategy  targeting construct was initially made in a pRS316 vector backbone containing 1.2 kb DNA upstream of the hisB gene, the complete hygromycin cassette, firefly luciferase gene , and 1.2-kb DNA downstream of the hisB gene using Gibson assembly. This plasmid was named pSP62 and was digested with PmeI and AscI to insert either the 1-kb or 1.3-kb abcG1 promoter fragment just upstream of the luciferase reporter gene to generate pSP101a and pSP101b. The pSP62, pSP100a, and pSP100b plasmids were digested with NotI and KpnI to release the integration construct from the vector and transformed in SPF87, AfS35, and SPF108 strain backgrounds to generate the different transcriptional fusion reporter strains given in The A. fumigatus were suspended in 1× phosphate-buffered saline (PBS) supplemented with 0.01% Tween 20 (1× PBST). The spore suspension was counted using a hemocytometer to determine the spore concentration. Spores were then appropriately diluted in 1× PBST. For the drug diffusion assay, ∼106 spores were mixed with 10 ml soft agar (0.7%) and poured over 15 ml regular agar containing (1.5%) minimal medium. A paper disk was placed on the center of the plate, and 10 μl of either 1 mg/liter voriconazole or 20 mg/liter fluconazole was spotted onto the filter paper. For the spot assay, ∼100 spores (in 4 µl) were spotted on minimal medium with or without the drug. The plates were incubated at 37°C and inspected for growth every 12 h.Fresh spores of atrR were PCR amplified and cloned in frame as BamHI/SalI fragments downstream of the 6×-His tag in pET28a+  to form pGT3 and transformed into the bacterial strain Escherichia coli BL21(DE3). Two liters of transformed bacteria was grown to log phase and induced with isopropyl-β-d-thiogalactopyranoside (IPTG) for 90 min. Cell lysates were prepared using a French press. Protein purification was accomplished using Talon metal affinity resin  as described by the manufacturer. Protein fractions were analyzed by staining them with Coomassie blue and by Western blotting using His-specific antibodies. The purified proteins were then lyophilized and sent to Pacific Immunology  for injection into rabbits to generate polyclonal antibodies against AtrR. Antiserum generated from these rabbits was received and tested for immunoreactivity against A. fumigatus cell lysates. The antiserum was then purified using an AminoLink Plus coupling resin  according to the manufacturer’s instructions, and the affinity-purified antiserum was used to detect the AtrR protein from A. fumigatus cell lysates.A region of 597 bp  from Western blotting was performed as described in reference 6 spores in a petri dish containing 20 ml of Sabouraud dextrose broth and grown for 24 h at 37°C under nonshaking conditions. The threshold cycle (TC) value of the act1 transcript was used as a normalization control.Quantitative reverse transcription-PCR (qRT-PCR) was performed as described in reference A. fumigatus genome using Bowtie 2 (Chromatin immunoprecipitation (ChIP) was done as described in reference Bowtie 2 . The MACBowtie 2 . ChIP-seBowtie 2 . A fastaBowtie 2 . A summa10.1128/mBio.02563-18.4TABLE S22 values. Expression values are listed as ratios of RNA levels as in the table legend above. Column A has the designated peak names for each peak called by MACS2; columns B and C are the peak start and end values along the chromosome; column D is the width of the peak region; column E is the position of the top value of read counts (abs_summit); column F is the −log10 for each peak; column G is the fold enrichment of read counts in the HA-tagged strain compared to the untagged wild-type control; column H lists the length of each identified peak; column I is the −log10 corresponding to the fold enrichment for the peaks; columns J and K are the average and maximal −log10 for the peaks identified in a given length, respectively; columns L and M represent the start and stop positions of the nearby feature , respectively; column N indicates the strand location of a feature; column O is the relative location of a peak compared to the feature of interest; column P is the distance of the most upstream peak to the feature, while column Q is the distance from the peak closest to the feature; columns R and S are the systematic or common gene names for each feature; column T is the description of the function of the encoded product, and column U indicates if this feature has been verified for expression. AC to AG represent the RNA-seq data for the strains described above. The systematic name is in AC and the length of the related transcript in is column AD. Download Table S2, XLSX file, 0.9 MB.ChIP- and RNA-seq data sets from strains producing various forms of AtrR. This table summarizes both NGS data sets generated in the production of the manuscript. Columns A to U correspond to the ChIP-seq data that led to our identification of the AtrR target genes, and columns W to Y are the corresponding RNA-seq expression data as logCopyright © 2019 Paul et al.2019Paul et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the A. fumigatus genome using HISAT2 (RNA was prepared as described in reference g HISAT2 . Transcrg HISAT2 .cyp51A promoter was PCR amplified using the GAAAAAACTCATGAGTGAATAATCG (sense [S]) and CATGCTGTATTTTATATTCACCTACC (antisense [AS]) primer pair. This region contained the 34 bp that is duplicated in the cyp51A TR34 allele and is associated with clinically relevant azole-resistant strains. The atrR probe was amplified using the CTAGATCCTGGATTCATGGCTC (S) and CGACTAACCACAATCGACTGG (AS) primer pair, the abcG1 probe amplified using the TGTACGGAGTATATCCATAGCTG (S) and GTGACAAAAACTCCGCTAGCA (AS) primer pair, and the srbA probe amplified using the AGGGCAGGTAACGTTACAGCC (S) and GAATTGCTGCCTGTGCCTGAG (AS) primer pair. All of these probes were designed such that they corresponded to the AtrR ChIP-seq peak region for their respective promoters. Aliquots of each probe were end-labeled with [γ-32P]-ATP and T4 polynucleotide kinase. For competitive binding, 40-fold molar excess of the nonradiolabeled probe was added to the binding reaction.Radiolabeled EMSA was performed using the protocol described in reference cyp51A promoter that is duplicated (TR34) in many drug-resistant clinical isolates and were ordered from IDT . The sense strands of the competitor nonlabeled oligonucleotides are listed in 2, 10% glycerol, and 1 μg poly(dI-dC) in a 20-μl reaction volume. Twenty picomoles biotinylated probe was used per reaction. When nonlabeled probes were used, they were in 12-fold molar excess to the labeled probe. Incubation was done at room temperature for 30 min. DNA and protein-DNA complexes were resolved by electrophoresis at 100 V on a 5% native acrylamide gel at room temperature using a buffer containing 45 mM Tris (pH 8.0), 45 mM boric acid, and 1 mM EDTA (TBE). DNA was electrotransferred from the gel to a nylon membrane (Hybond-N + Amersham) in 0.5× TBE buffer at 100 V for 30 min. The membrane was then blocked in 1% SDS in Odyssey blocking buffer (PBS) for 30 min, replaced with fresh blocking buffer plus 1% SDS, and incubated in 1:10,000 streptavidin-680 nm IRDye (Li-Cor) for 30 min at room temperature. The membrane was then washed three times in 1× PBST for 5 min each, followed by scanning using a Li‐Cor Odyssey infrared imaging system, application software version 3.0, and quantified using the Image Studio Lite 4.0 software (Li‐Cor).Biotin-labeled EMSA was performed as follows: biotin-labeled oligonucleotides used in the reaction corresponded to the 34-bp sequence in the cyp51A probe, a PCR product corresponding to 453 to 191 bp upstream of the translation start site containing the 34 bp duplicated in TR34 was cloned into pCR2.1-TOPO. This plasmid was digested with SalI, treated with calf intestinal alkaline phosphatase, phosphorylated with T4 polynucleotide kinase and [γ-32P]-ATP, and finally cleaved with HindIII. For generation of the abcG1 probe, a PCR product of the abcG1 promoter containing 1,210 to 1,039 bp upstream of the abcG1 translation start site, including the ChIP-seq peak region, was cloned into pCR2.1-TOPO. This plasmid was digested with SalI, treated with calf intestinal alkaline phosphatase, phosphorylated with T4 polynucleotide kinase and [γ-32P]-ATP, and finally cleaved with MluI.DNase I footprinting assay was carried out as described in reference Firefly luciferase assays were done using the firefly luciferase assay kit procured from Biotium, Inc., as described in reference 9 conidia/ml. Immediately after infection, 3 mice from each group were sacrificed, and lungs were harvested, homogenized, and quantitatively cultured to verify the delivery of conidia to the lungs. The remaining mice were monitored twice daily for survival assessment. As a negative control, 8 mice were immunosuppressed but not infected.To investigate the virulence of various strains, we used a nonneutropenic mouse model of invasive aspergillosis . BrieflyAll NGS data are available as a linked GEO SuperSeries accession number GSE123446.


Global epigenetic reprogramming is vital to purge germ cell-specific epigenetic features to establish the totipotent state of the embryo. This process transpires to be carefully regulated and is not an undirected, radical erasure of parental epigenomes. The TRIM28 complex has been shown to be crucial in embryonic epigenetic reprogramming by regionally opposing DNA demethylation to preserve vital parental information to be inherited from germline to soma. Yet the DNA-binding factors guiding this complex to specific targets are largely unknown. Here, we uncover and characterize a novel, maternally expressed, TRIM28-interacting KRAB zinc-finger protein: ZFP708. It recruits the repressive TRIM28 complex to RMER19B retrotransposons to evoke regional heterochromatin formation. ZFP708 binding to these hitherto unknown TRIM28 targets is DNA methylation and H3K9me3 independent. ZFP708 mutant mice are viable and fertile, yet embryos fail to inherit and maintain DNA methylation at ZFP708 target sites. This can result in activation of RMER19B-adjacent genes, while ectopic expression of ZFP708 results in transcriptional repression. Finally, we describe the evolutionary conservation of ZFP708 in mice and rats, which is linked to the conserved presence of the targeted RMER19B retrotransposons in these species. Summary: Analysis of the function and targets of a maternal KRAB-zinc-finger protein, ZFP708, found to specifically mediate maintenance of DNA methylation at a subset of LTR retrotransposons during embryonic epigenetic reprogramming. The inheritance of selective epigenetic information from germline to soma is crucial for organismal development. This epigenetic memory is remarkably maintained during the near-complete erasure of the germ cell epigenomes that establishes the totipotent embryonic state. The mechanisms mediating this resistance to reprogramming are far from understood. The ‘maintenance’ DNA methyltransferase DNMT1 is essential to this process, creating the paradox of DNMT1 requirement for DNA methylation maintenance while simultaneously enabling global demethylation . In the KRAB-ZFPs make up the largest subtype of C2H2-type zinc-finger transcription factors with over 300 genes encoded in the mouse genome . While mZfp708  as a candidate of interest in two independently published RNA-sequencing datasets exploring oocyte and preimplantation embryo transcriptomes  and other public datasets show low to no expression levels at later stages or adult tissues.To shed light into the functions and targets of maternal TRIM28 during oocyte to embryo transition and early preimplantation development, we explored the expression patterns of potential TRIM28-interacting KRAB-ZFP transcription factors in mouse oocytes and at early embryonic stages. We identified riptomes . Zfp708 astocyst A. We conastocyst B. As oppZfp708 is located on mouse chromosome 13 and is part of a typical KRAB-ZFP cluster containing at least 23 other family members, first described as Rslcan (regulator of sex-limitation candidate) genes . The presence of intact C2H2 zinc-finger domains suggests DNA-binding potential; the KRAB-A box is a known docking platform for TRIM28.e) genes , 2005. IZfp708 transcript corresponding to the annotated isoform A (NM_001012325.2) from ovary cDNA. We failed in several attempts to derive specific antibodies for ZFP708. This is a frequent problem owing to the repetitive nature of the zinc-finger arrays and high KRAB domain conservation in KRAB-ZFPs. We therefore introduced an N-terminal (3x) FLAG-tag for downstream analyses . Immunofluorescence staining of FLAG-ZFP708 in mouse embryonic stem cells (mESCs) revealed robust expression and clear nuclear localization  disrupts TRIM28 interaction . The identified, high-confidence binding sites across three independent experiments showed a notable enrichment over long terminal repeat (LTR) EREs compared with the genome-wide distribution of these elements (Tables S2-S5). To validate our FLAG-tag KRAB-ZFP ChIP-seq approach, we compared the FLAG-ZFP708 targets with those of a published but unrelated FLAG-tagged KRAB-ZFP: ZFP809 .TRIM28 complex targeting through KRAB-ZFPs to specific sites crafts regional epigenetic changes or, during embryonic reprogramming, can preserve regional DNA methylation . We sougelements A. More selements A (Tables: ZFP809 . Althoug: ZFP809 B. In addFLAG ChIP-seq on control (wild-type) mESCs showed no enrichment at the newly identified FLAG-ZFP708-binding sites C. InsteaFig. S2E). We observed similar TRIM28 peak overlap frequency to RMER19B elements in our own ChIP-seq datasets from control or ZFP708-KΔ-overexpressing mESCs  . In mESCs overexpressing intact ZFP708, however, the RMER19B overlap is increased to 2.5% of all LTR-overlapping TRIM28 peaks . Ectopically expressed ZFP708 allows for efficient recruitment of TRIM28 to all FLAG-ZFP708-bound sites, whereas peak overlap with TRIM28 in control and ZFP708-KΔ-overexpressing cells is minimal . Similarly, minimal overlap is found with the aforementioned five published TRIM28 ChIP-seq datasets , which is in line with the marginal expression of endogenous ZFP708 in mESCs. The recruitment of TRIM28 to ZFP708 sites in the ZFP708 overexpression ESCs correlates with a specific increase of H3K9me3, suggesting the recruitment of not just TRIM28 but the whole KRAB-ZFP/TRIM28 machinery  (Detailed studies showing TRIM28 binding to and regulation of an extensive array of EREs in mESCs have not described RMER19B elements as TRIM28 targets to date . We re-eachinery E. As forachinery E or publig. S2H) . Finallyig. S2H) C-E.Fig. S3A). Comparing the RMER19B elements bound in all three experiments (66) with the 100 RMER19B elements showing least enrichment we can, however, confirm the clear correlation of TRIM28 and H3K9me3 recruitment to bound elements versus unbound elements .Our stringent ZFP708-binding analysis, achieved by overlapping three replicate ChIP-seq datasets, may result in underestimation of the number of RMER19B elements bound. Indeed, enrichment analysis of the individual data supports this notion . ChIP-qPCR analysis of three prominent targeted RMER19B elements show that ZFP708 binding to DNA is evidently DNA methylation independent . Our findings further suggest that DNA binding of ZFP708 is independent of pre-existing H3K9me3 or TRIM28/SETDB1, which are absent at identified ZFP708 target sites in mESCs prior to ZFP708 overexpression .As shown for ZFP57, KRAB-ZFP binding to target sites can be DNA methylation dependent . To testThus, by ectopically expressing ZFP708 in mESCs, we were able to identify RMER19B elements as its likely targets in early embryos. Moreover, we were able to show the KRAB domain-dependent, functional recruitment of TRIM28 and H3K9me3 to these sites. ZFP708 binding is highly specific and independent of pre-existing H3K9me3 and DNA methylation.Tables S4, S5). The best prediction, found in 71% and 74% of sequences, respectively, revealed a strong motif, which was furthermore similar between both peak sets . Among the closely related RMER19A and RMER19C elements, this motif is specific to RMER19B , presumably resulting in the exclusive targeting of these elements . Using this predicted motif, we addressed ZFP708 binding in a locus-specific manner. To this end, we focused on an intronic RMER19B insertion at the Uck2 gene locus on chromosome 1 (henceforth referred to as Uck2-RMER19B), for which ChIP-seq analysis revealed strong binding for ZFP708- and ZFP708-dependent TRIM28/H3K9me3 recruitment/enrichment . Using CRISPR/CAS9, we targeted the RMER19B-binding motif found at this site and identified a mESC clone carrying a homozygous 77 bp deletion, including the predicted binding site, hereafter named Uck2-ΔRMER19B . ChIP-qPCR confirmed strong enrichment of ZFP708 at the wild-type Uck2-RMER19B locus in FLAG-ZFP708 OE mESCs, as observed in our ChIP-seq findings. However, in Uck2-ΔRMER19B/FLAG-ZFP708 OE mESCs, this binding was significantly reduced . In addition, H3K9me3 enrichment was reduced significantly at the Uck2-ΔRMER19B locus compared with the non-targeted Uck2-RMER19B site (Fmnl2-RMER19B) (We used non-RMER19B-overlapping and RMER19B-overlapping peaks to independently predict a putative consensus-binding motif for ZFP708 (19B site C. On theRMER19B) C.Fig. 3Uck2-RMER19B retrotransposon into a PGK promoter-driven luciferase construct to test the repressive activity of ZFP708 on this element. Co-transfection of the reporter with Flag-Zfp708 resulted in significant reduction of luciferase activity, suggesting the ZFP708-mediated recruitment of the repressive TRIM28 machinery to the RMER19B element . We first performed unsupervised K-means clustering of H3K9me3 domains at all RMER19B elements in zygotes into two groups: group 1 with high levels of H3K9me3; group 2 with low levels of H3K9me3. Out of the almost 6000 RMER19B insertions annotated in the mouse genome (mm9), only a small fraction showed H3K9me3 enrichment in zygotes . Remarkably, ZFP708-unbound RMER19B elements and ZFP708-bound non-RMER19B regions do not recapitulate these dynamics and show little to no H3K9me3 enrichment and changes throughout preimplantation development . In conclusion, ZFP708-bound RMER19B elements identified with our overexpression approach in mESCs gain H3K9me3 transiently post-fertilization, a dynamic correlation with the ZFP708 expression in the preimplantation embryo.Global H3K9me3 dynamics have recently been described in preimplantation mouse embryos using ultra-low input ChIP-seq methods . This wo zygotes A. Notable detail B-D. No ae detail B-D. Thise detail . Yet, ine detail , the H3KZfp708 knockout mice using the CRISPR/CAS9 method targeting the KRAB-domain region to address ZFP708 function in vivo. We identified a heterozygous founder line with a four-nucleotide deletion producing a frame-shift mutation followed by an early translation termination site. The predicted resulting truncated protein lacks both KRAB and zinc-finger domains . Although qRT-PCR in mutant and control oocytes did not show nonsense-mediated decay (NMD) of Zfp708 mRNA, cloning and sequencing of RT-PCR products confirmed the deletion of four nucleotides at the CRISPR target site as identified at the genomic level (+/−Zfp708 mice gave rise to viable pups in expected mendelian ratios. The identified homozygous (Zfp708−/−) animals showed no overt physical abnormalities and displayed normal feeding and breeding behavior compared with control mice in our facility. Both male and female Zfp708−/− animals are fertile.We established ic level B,C. InteZfp708 knockout, we turned to investigate the molecular, epigenetic consequences of Zfp708 loss during early embryonic development. ZFP708 is not expressed in wild-type mESCs and ZFP708-targeted RMER19B elements are generally not H3K9me3 enriched . We then addressed their methylation state in mESC lines derived from control (+/+Zfp708), maternal-null (mat−/+Zfp708) and maternal/zygotic-null (mat−/−Zfp708) blastocysts under 2i conditions . Despite the prevalent global hypomethylated state under 2i conditions, the examined ZFP708-targeted RMER19B sites retained notable levels of DNA methylation with 44±6.9%, 53.6±3.5% and 88±5.2%, respectively, at the three tested loci in control lines . The Oct4 promoter was found to be fully hypomethylated in all cell lines . Absence of maternal as well as maternal/zygotic ZFP708 in the embryos from which the mESCs were derived, consistently resulted in substantial hypomethylation across all three examined loci  . In wild-type sperm, the Uck2-RMER19B locus is fully methylated and mutant-derived sperm show equally high methylation levels . In contrast to the global DNA demethylation in preimplantation embryos, elevated DNA methylation levels persist and are also found in control blastocysts (74.3±13.6%). Notably, however, lack of maternal ZFP708 results in a near-complete loss of DNA methylation at the Uck2-RMER19B in blastocysts . Hence, maternal ZFP708 is required for the maintenance of DNA methylation marks at its target sites in preimplantation embryos.It has been shown that KRAB-ZFP/TRIM28-mediated initial silencing through H3K9me3 in early embryos is epigenetically heritable through DNA methylation . ZFP57, enriched C-E. To aLoss of KRAB-ZFP/TRIM28-mediated repression can result in direct or indirect transcriptional activation of EREs  and/or neighboring genes, respectively . To asseFig. S13A,B, Table S6)  . The lacable S6) B,C. Finaable S6) C.Table S7). In mESCs derived from maternal/zygotic mutant blastocysts, we found that of 16 genes showing significant expression changes, 12 are upregulated, as expected (Table S8). Conversely, we found that overexpression of ZFP708 in mESCs can negatively impact on ZFP708-bound RMER19B adjacent genes (Table S9). In vivo, these transcriptional changes were less pronounced and variable across individual embryos (Tables S10-S13). No significant deregulation was detected among the genes in blastocysts and GV oocytes. In zygotes, of five significantly regulated peak-adjacent genes, two showed mildly increased expression . Although at the two-cell stage we do observe a number of differentially expressed genes adjacent to the analyzed peak regions, these embryos show overall highest variability in gene expression, possibly reflecting stage differences in relation to zygotic gene activation .We next examined potential collateral expression effects on genes neighboring ZFP708-bound RMER19B elements . Chr2q22 was previously described as one of two syntenic regions for the mouse Rsl locus in rat, yet the putative ZFP708 ortholog was not identified in that study  that specifically targets LTR class endogenous retroviral elements of the ERVK family: RMER19B. Our work thus expands the repertoire of KRAB-ZFP/TRIM28 targets and, more importantly, reveals a new transcription factor shaping the embryonic epigenome by mediating regionally restricted germline-to-soma inheritance of epigenetic marks.in vivo approach for target gene identification is a common problem in the field. Ectopic overexpression of tagged ZFP708  in mESCs has proven a suitable approach for identifying RMER19B elements as putative targets. However, the extent of these interactions is to be taken with caution. For example, only a small fraction of all RMER19B elements in the genome were identified as true targets by peak calling, yet levels of enrichment can be observed across a much wider repertoire of RMER19B insertions . Conversely, binding to ‘other’ (non-repeat) regions . Support for the selective targeting of a few RMER19B elements is provided by the H3K9me3 profiling in wild-type embryos via ultra-low ChIP-seq .The interplay of DNA methylation and H3K9me3 at ZFP708 targets is unclear. This is true for all TRIM28-targeted regions in very early embryos described to date. Clearly, wild-type embryos retain DNA methylation at ZFP708-bound RMER19B elements (as shown in blastocysts and derived mESCs). This DNA methylation maintenance is dependent on maternal ZFP708, the lack of which results in severe hypomethylation . In thisl2 locus . Gamete-Despite the epigenetic changes observed at ZFP708-bound RMER19B elements, the loss of ZFP708 itself does not affect viability or fertility, or cause any other overt phenotypes in mice. This lack of a phenotypic impact in ZFP708 knockout embryos, despite RMER19B hypomethylation, may suggest an archaic mechanism to repress an ERE family that has long since lost its mutagenic/regulatory potential. On the other hand, the lack of a detectable phenotype may merely reflect our limitations in gauging a possible transcriptional or evolutionary impact in a laboratory setting. Indeed, although the transcriptional effect of ZFP708 loss on RMER19B elements itself is not given in embryos, we find transcriptional changes of neighboring genes in mutant-derived mESCs and, to lesser extent, in embryos. These changes, in particular in embryos, are small and variable in individual embryos. On the other hand, the species-specific insertions of these RMER19B elements may drive the tissue-specific activation/repression of genes – an intriguing hypothesis to be tested in the future. Our current work however, provides insights into such potential regulatory functions, as ZFP708 overexpression in mESCs can alter RMER19B neighboring gene expression.in vivo either  plates. DNMT TKO mESCs  were provided by the RIKEN BRC through the National Bio-Resource Project of the MEXT, Japan.Pgk promoter was cloned into the pGL4.23 vector (Promega) to drive luciferase activity. The Uck2-RMER19B (amplified from wild-type mESCs) or the Uck2-ΔRMER19B (amplified from CRISPR mutated mESC clone #15) were then cloned upstream of the Pgk promoter. HEK293T cells were transfected with Uck2-RMER19B:Pgk-pGL4.23 or Uck2-ΔRMER19B:Pgk-pGL4.23 reporter constructs along with FLAG-ZFP708 or FLAG-ZFP708-KΔ, respectively. Renilla activity, expressed from co-transfected pGL4.75 (Promega), was used for internal normalization. 48 h after transfection, cells were lysed and assayed using the Dual-Luciferase reporter assay system (Promega).ZFP708 was cloned from ovary cDNA. N-terminal 3xFLAG-tagged ZFP708 or ZFP708-KΔ was produced by PCR amplification and cloning into the PJ549 piggy-bac vector (DNA2.0). 24 h after transfection, cells were selected with puromycin before GFP-positive cells were sorted by FACS. To test ZFP708-mediated repression, first the mouse mESCs transfected with FLAG-ZFP708 were seeded on glass slides in 12-well plates to be fixed 48 h later with 4% formaldehyde and permeabilized with 0.1% Triton X-100. After blocking with FBS (10%), cells were incubated with mouse anti-FLAG  and rabbit anti-TRIM28  antibodies. Secondary antibodies  were used at 1: 500 dilutions. Images were acquired on a Zeiss confocal laser-scanning microscope.Cells were lysed in RIPA buffer and protein was quantified using a Bradford protein assay (Bio-Rad). Lysates were analyzed by western blotting using the following antibodies: anti-Flag , anti-TRIM28  and anti-tubulin . For co-immunoprecipitation analysis, the lysates were immunoprecipitated using anti-Trim28 antibody and analyzed for association with ZFP708 by western blot using anti-Flag antibody.3. DNA was column purified with a QIAquick PCR Purification Kit (Qiagen).ChIP was performed as previously described . ChromatChIP DNA concentrations were measured using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific). ChIP-seq libraries were prepared with the NEBNext Ultra II DNA Library Preparation Kit for Illumina (NEB), following manufacturer's instructions. PCR conditions were adjusted to starting material concentrations and final elution volumes were reduced. Libraries were quantified using a Bioanalyzer  and PCR . ChIP-Seq libraries were pooled and sequenced on the NextSeq500, as a single-end (75 bp) run.Raw reads were aligned to the mm9 genome using Bowtie2 (default parameters), before sorting the bam files with samtools sort and indexing with samtools index. Bigwig files were generated with the bamCoverage function of deeptools2.0, using the options --normalizeUsing RPKM --binSize 25 --smoothLength 100. Peaks were called with Macs2 callpeak (default parameters). FLAG-ZFP708 peaks were called over a FLAG ChIP control (in wild-type mESCs) in three replicates and only peaks that were called in all three replicates were used for analysis. For heat maps, ChIP and input data were converted to bigwig files, normalized and corresponding inputs were subtracted. Heat maps, density profiles and violin plots were generated using Deeptools and GraphPad. The following published ChIP-seq datasets were used: FLAG-Zfp809 (SRX485302), TRIM28 (GSM1406445), SETDB1 (GSM440256), H3K9me3 (mESCs GSM1327148) and H3K9me3 (germ cells and embryos GSE97778). For peak overlap analysis TRIM28 ChIP-seq peaks in mESCs from ΔCt method) was calculated using the housekeeping gene actin to normalize the target genes or as otherwise indicated.Total RNA was isolated from cells and tissues using RNeasy Mini Kit (Qiagen) according to the manufacturer's instructions and 1 μg of total RNA was used for reverse transcription using the High-Capacity cDNA Kit (Applied Biosystems). Total RNA from pooled or individual embryos was isolated using PicoPure RNA Isolation Kit (Fisher Scientific) and all of the eluate was used for reverse transcription. Reverse transcription was performed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems) using random hexamer primers. qPCR assays for gene expression analysis were designed using the Assay Design Center Web Service . qPCR was performed using Taq-Man Fast Universal PCR Master Mix (Applied Biosystems) in combination with the universal probe library (Roche) on a CFX384 Touch Real Time PCR system (Bio-Rad). Relative gene expression  as a fasta file. This fasta file was then used to build the index on SalmonTE, using the --te_only option. The TEs expression was quantified with SalmonTE quant (default parameters) on the raw fastq files.RNA-seq libraries from mESCs were generated using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (NEB), after mRNA isolation using the NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB) following manufacturer's instructions. The resulting libraries were pooled and sequenced on a NextSeq500 instrument, at a 1×75 bp single read length to >14 M reads per sample. Raw reads were aligned to the mm9 genome using STAR 2.4.2a [using options --sjdbGTFfile Mus_musculus.Ensembl.NCBIM37.65.gtf --sjdbOverhang $((lng - 1)) --outSAMstrandField intronMotif --outSAMtype BAM Unsorted --twopassMode Basic], before sorting the bam files with samtools sort and indexing with samtools index. The number of reads mapping to each gene was quantified with htseq-count (using options --order=pos --format=bam --stranded=reverse) on features from the gtf file Mus_musculus.Ensembl.NCBIM37.65.gtf. The raw counts of reads were then imported to DEseq2 in R to perform the differential expression analyses. The expression of transposable elements (TEs) was quantified using SalmonTE. The list of repeats was downloaded from Repbase   and oligDNA methylation was analyzed by bisulfite conversion, cloning and sequencing as described previously . BrieflyTable S14. All information for antibodies used in this study is listed in Table S15.Oligonucleotides used in this study are listed in


Drosophila euchromatin and heterochromatin, which implies that heterochromatic compaction does not reduce MNase accessibility of linker DNA.Micrococcal nuclease (MNase) is widely used to map nucleosomes. However, its aggressive endo-/exo-nuclease activities make MNase-seq unreliable for determining nucleosome occupancies, because cleavages within linker regions produce oligo- and mono-nucleosomes, whereas cleavages within nucleosomes destroy them. Here, we introduce a theoretical framework for predicting nucleosome occupancies and an experimental protocol with appropriate spike-in normalization that confirms our theory and provides accurate occupancy levels over an MNase digestion time course. As with human cells, we observe no overall differences in nucleosome occupancies between The online version of this article (10.1186/s13059-019-1815-z) contains supplementary material, which is available to authorized users. Nucleosomes are the basic units of DNA compaction and the fundamental constituents of chromatin . They coDifferent cells can have different nucleosome organizational features , 13, andThe most frequently used method of mapping nucleosome positions and occupancy involves digestion of chromatin with micrococcal nuclease (MNase), an endo- and exo-nuclease that preferentially digests the naked DNA between nucleosomes, releases the nucleosomes from chromatin, and enriches the nucleosome-protected DNA fragments . To deteUnfortunately, MNase has a sequence bias, and it cleaves DNA about 30 times faster upstream of an A or T than it does 5′ of a G or C , 18. TheTherefore, a quantitative interpretation of the MNase-seq occupancy profiles is still problematic. While nucleosome occupancy should be understood as the precise fraction of cells that contain a nucleosome at a given genomic locus , the numN, as a function of the logarithm of the MNase enzyme concentration used in the digestion, E:Important efforts have been made recently to distinguish between nucleosome occupancy and chromatin accessibility , 22. Usia represents the slope, and b represents the y-intercept of the fit. Mieczkowski et al. have defined an MNase accessibility score, called MACC  cells contain a linker at position x in their genome. At the beginning of the MNase digestion reaction, all CO nucleosomes are part of intact chromatin fibers, and we denote the number of “bound” nucleosomes at time t = 0 by B(0). As digestion proceeds, after time t, we will have a number of nucleosomes, B(t), that are still bound to chromatin and a number of nucleosomes, N(t), that had already been released from chromatin as mononucleosomes. During digestion, nucleosomes are released from chromatin, increasing N(t) and decreasing B(t). At the same time, MNase also attacks free mononucleosomes from the sample, slowly decaying the number of these due to over-digestion and internal cleavages followed by nucleosome disassembly. These two processes can be represented by a simple reaction chain, B → N → Ø. The corresponding concentrations of nucleosomes from the sample satisfy the following differential equations:E] is the concentration of free MNase, which remains approximately constant in the experiment, and k1 and k2 represent the rate constants for the nucleosome release and decay reactions, respectively  = CO and N(0) = 0 are , starts to be higher than the rate of release of new nucleosomes from chromatin (k1[B][E]), and the overall level of intact mononucleosomes starts to decrease until it completely vanishes, when MNase has destroyed all nucleosomes by over-digestion.Therefore, after time mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimx are characterized by different nucleosome occupancies and accessibilities, and the nucleosome counts that are obtained from each locus will depend on four parameters: the level of digestion d, nucleosome occupancy O(x), and the two rate constants k1(x) and k2(x), corresponding to the nucleosome release and decay processes, respectively. The predicted dependence of the nucleosome counts obtained from any genomic locus on the corresponding parameters O, k1, k2, and on the level of digestion d, is shown in Additional file O affects the overall height and area of the nucleosome count profile as a function of the digestion level, acting as a simple rescaling factor when the other parameters  are kept constant. Additional file k1 mostly affects the initial slope of the apparent occupancy, N/C, while the decay rate constant k2 mostly affects the right tail of the apparent occupancy distribution. The fact that the three parameters O, k1, and k2 influence different aspects of the apparent occupancy .Different genomic loci O, k1, and k2, if the nucleosome amounts generated at multiple digestion levels are measured. For example, this non-linear fit can be obtained using the lsqcurvefit function in MATLAB, nls function in R, or scipy.optimize.leastsq function in Python.Although the solution given by Eq.  seems coO, k1, k2) for each nucleosome, as described next.Unfortunately, the traditional MNase-seq method is not appropriate for a rigorous comparison and fit of the nucleosomal counts that are obtained in different experiments. In general, when MNase-seq data are compared, these data are normalized such that the total number of reads in all experiments is set to a common value. This normalization is not valid for our purpose, as the total number of nucleosomes released from chromatin depends on the level of digestion. For this reason, we adopted a modified MNase-seq procedure, q-MNase-seq . Then, each sample consisting of a mix of fly and yeast DNA is used to prepare the sequencing library. As each sample already contains the spike-ins, it is acceptable to use different numbers of cycles of PCR amplification, if that is necessary in order to obtain similar amounts of total DNA for sequencing. After sequencing, the DNA from each sample is aligned both to the Drosophila genome (version dm6) and to the S. cerevisiae genome (version sacCer3) to obtain the numbers of fly mononucleosomes and spike-in reads. Then, we normalize the counts of fly mononucleosomes by the amount of spike-in DNA from each sample such that all samples have the same number of spike-in fragments. Using the normalized data, we can analyze any genomic region and plot the nucleosome counts as a function of the digestion level , 1 min, 2 min, 2.5 min, 5 min, 10 min, 15 min, 20 min, 40 min, 60 min, and 120 min. The extent of digestion was verified by gel electrophoresis in an agarose gel  for which we performed end-polishing, adapter ligation, PCR amplification, and paired-end sequencing of the undigested DNA fragments without size selection. As previously observed [We performed a q-MNase-seq experiment, by digesting The nucleosome counts that were obtained in the six experiments were then normalized by the spike-in DNA amounts from each sample. An IGV browser snapshot of a representative region of the genome illustrates some of the differences among the properly normalized nucleosome counts observed after different levels of digestion Fig. . The nucDrosophila genome, using a custom algorithm for computing the median position for all clusters of aligned nucleosomes . Therefore, we are confident that our nucleosome calling algorithm has detected most of the nucleosomes, including the poorly positioned ones.Using the combined nucleosome counts from the six experiments, we next identified the typical positions of nucleosomes along the In order to offer enough protection against MNase to result in a peak in MNase-seq occupancy profiles, a protein must be stably bound to DNA. The transient in vivo binding of fast transcription factors will not protect DNA during 5–60 min time course of MNase digestion. Such transient binding events may only give a significant peak in MNase-seq occupancy profiles obtained by digesting the chromatin for a very short period of time . As we describe below, the sites that were bound by “MNase-sensitive” complexes  accounted for fewer than 1% of the total number of identified peaks.CG3225. After we identified the well-positioned nucleosomes and normalized the nucleosome counts using the spike-in levels from each sample, we were able to study the evolution of the number of released nucleosomes as a function of the digestion level. Even in a small region of the genome . It was previously assumed that nucleosome gaps at promoters and transcription termination regions are largely nucleosome-free . We beliFrom Fig. Because A/T-rich nucleosomes are destroyed first Fig.  and are O and the two rate constants k1 and k2 characterizing the nucleosome release and decay processes.Using the spike-in normalized nucleosome counts, we can test the prediction of our theoretical model. Having identified all well-positioned nucleosomes, we analyzed the digestion time course for all ~ 700,000 regions of the genome where we identified a nucleosome. Figure Figure k1 close to 1 and represent the typical nucleosomes, which are released from chromatin at relatively slow rates. The second group of sites (< 1%) is characterized by relatively high values of the chromatin digestion rate k1(>10) and represents loci that are bound by relatively fragile complexes, which offer only a weak protection against MNase and are quickly destroyed by MNase. Figure https://chip-atlas.org). As expected, we found a strong enrichment of TF binding sites at the loci occupied by fragile complexes   Fig. a. Surpri 1) Fig. . Using tncy Fig.  into ninncy Fig.  fitted wncy Fig. , confirmk1, is higher near DHSs . The middle and right panels in Fig. k2 and nucleosome occupancy, for the nucleosomes belonging to the indicated group.To test whether different regions of the genome have very different chromatin accessibilities, as suggested by DNase-seq and ATAC-seq experiments, we next analyzed the distribution of nucleosome release rates that we obtained along the genome. We first analyzed the DNaseI hypersensitive regions (DHSs) (obtained from ) and comTo test whether transcription affects the nucleosome accessibility, we split the genes into three tertiles according to the transcription levels obtained from RNA-seq data , and we k1 values for the nucleosomes corresponding to the five chromatin states are shown in Fig. Next, we tested whether the regions of the genome that could be away from promoters contain either highly accessible or inaccessible nucleosomes. For this, we compared the accessibility of the five previously reported chromatin states, obtained by principal component analysis and clustering of genome-wide distributions of 53 chromatin proteins, mapped using the DamID method . The disS. cerevisiae can be predicted using a simple biophysical model of “barrier complexes” occupying promoters and steric hindrance between nucleosomes and the barrier complexes [S. cerevisiae promoters are all relatively active and presumably occupied by general regulatory factors  and other transcription factors. Therefore, all yeast promoters are of the first type, characterized by NDRs and phased nucleosomes. On the other hand, Drosophila has multiple tissues, and each cell type has distinct sets of active genes and silent genes, which makes S2 cells an ideal setting for testing the prediction of our nucleosome positioning model  and aliata from . To confata from  and ATACata from   confirmed that the most active genes have nucleosome-depleted promoters and the inactive genes have nucleosome-filled promoters . All the results were confirmed using the new sets of experiments. Moreover, the typical nucleosome positions that we identified in the two cell types showed a good agreement. In all experiments, we identified > 677,000 nucleosomes, and about 80% of the nucleosomes have similar positions in both cell types (shifted by 20 bp or less). When nucleosome positions identified in the same cell type  were compared, about 93% of the nucleosome positions found in S2 cells were shifted by 20 bp or less, and about 87% of the positions were shifted by at most 10 bp. Interestingly, when we compared the nucleosome occupancies at the common positions that were identified in the two cell types, we obtained a Pearson correlation coefficient of only 0.36 (replicate 1) and 0.30 (replicate 2). These are substantially lower than the Pearson correlation between the occupancies estimated from different replicate experiments using the same cell type (> 0.78). This suggests that although the nucleosomes occupy about the same positions in S2 and Kc167 cells, the fractions of cells that contain a nucleosome at these positions (nucleosome occupancy) is very different in the two cell types.It has been traditionally assumed that in MNase-seq experiments, the nucleosome fragment counts along the genome are proportional to the fraction of cells containing a nucleosome at the corresponding locus. More recently, it was observed that the nucleosome fragment counts obtained in different experiments depend on the specific digestion conditions and on the DNA sequence of each nucleosome , 21, butMACC protocol [MACC score) and inaccessible (characterized by a negative MACC score)—partially addresses the issue of distinguishing nucleosome occupancy from DNA accessibility. However, this simple classification was limited to mononucleosomes recovered in a digestion series and could not account for nucleosome losses that occur with over-digestion. Indeed, we observed considerable heterogeneity between loci with respect to MNase sensitivity. Whether or not nucleosome losses seriously affected their conclusions could not be ascertained in the absence of a spike-in calibration standard, which is required for quantitative comparison of samples in genome-wide profiling experiments [MACC score during the initial stages of digestion and by a positive MACC score during the final stages of digestion.The protocol , which seriments , 44. Speeriments . In otheHere, we have provided a theoretical framework that explains the observed behavior of the nucleosome counts during the entire MNase digestion . We have developed a quantitative way of measuring the fraction of nucleosomes that are released from chromatin (q-MNase-seq), which allowed us to test and validate the predictions of the theoretical model.k1 rates). Comparing the nucleosome release rates for nucleosomes originating from different regions of the genome, we found that DHSs are also more accessible to MNase, and nucleosomes from these loci are released almost twice as fast as the nucleosomes away from DHSs. Surprisingly, when we compared the euchromatin and heterochromatin regions, we could not detect major differences between the chromatin accessibility of these regions, as previously reported in human cells [Using the newly developed protocol, we have found that some promoters are weakly protected by MNase-sensitive complexes, which are digested at a much higher rate compared to normal nucleosomes (one to two orders of magnitude difference between the corresponding an cells . The nucan cells  are compan cells . By deveDrosophila melanogaster S2 cells were obtained from the Drosophila Genome Resource Center (Stock #181) and grown in HyClone SFX-Insect media supplemented with 18 mM l-glutamine. D. melanogaster Kc167 cells were obtained as a gift from Lucy Cherbas and grown in HyClone SFX-Insect media supplemented with 18 mM l-glutamine and 10% fetal bovine serum.2 (to 2 mM) to activate the MNase. Reactions were stopped by the addition of 170 μL of 2XSTOP . After phenol-chloroform-isoamyl alcohol extraction and ethanol precipitation, the DNA pellet was dissolved in 50 μL 1 mM Tris-HCl pH 8, 0.1 mM EDTA and used directly for 1.5% agarose gel analysis and for making sequencing libraries as described [MNase-seq was performed on unfixed cells as described  with theescribed . For yeaescribed  were digescribed .D. melanogaster reference genome dm6 and S. cerevisiae reference genome sacCer3, using bowtie2 [https://github.com/rchereji/plot2DO). Nucleosome counts (defined as the number of Drosophila DNA fragments with the length between 100 and 200 bp) and spike-in counts (number of S. cerevisiae DNA fragments with the length between 100 and 200 bp) were obtained using MATLAB (Bioinformatics toolbox). Raw genome-wide nucleosome count profiles were normalized by the corresponding spike-in counts, such that the resulting number of spike-ins was 10,000 for every sample. The typical locations of all well-positioned nucleosomes were detected using a custom MATLAB script, using the map consisting of all detected nucleosomes . For every detected nucleosome , we used Eq.  for viewing in the IGV browser [http://www.mathworks.com/matlabcentral/fileexchange/24253-customizable-heat-maps). Additional details of the biophysical model of chromatin digestion by MNase and data analysis are available in the Additional file Paired-end sequencing PE25x25) was performed on barcoded libraries using an Illumina Hi-seq 2500. Reads were aligned to the  bowtie2  with par5x25 was Additional file 1:Figure S1. Dependence of the three species of nucleosomes on the digestion level. Figure S2. The predicted apparent nucleosome occupancy depends on the real occupancy O, and the two rates k1 and k2. Figure S3. The logarithm of the apparent nucleosome occupancy has the asymptotic behavior of −k2d for d ≫ 1/k2. Figure S4. Overview of a quantitative MNase-seq (q-MNase-seq) experiment. Table S1. The number of paired-end sequencing reads with the length between 100 bp and 200 bp. Figure S5. Simulation results. Figure S6. IGV snapshot of chromosome 2L: 13,789,750 - 13,794,300. Figure S7. Correlation between sets of parameters obtained after different normalizations of the nucleosome counts. (PDF 4086 kb)A quantitative investigation of MNase titrations. Additional file 2:Review history. (DOCX 30 kb)