The pig is an economically important livestock species and is a widely applied large animal model in medical research. Enhancers are critical regulatory elements that have fundamental functions in evolution, development and disease. Genome-wide quantification of functional enhancers in the pig is needed.PPP3CA gene is associated with the loin muscle area trait and the QKI gene is associated with alkaline phosphatase activity that may be regulated by distal functional enhancers.We performed self-transcribing active regulatory region sequencing (STARR-seq) in the porcine kidney epithelial PK15 and testicular ST cell lines, and reliably identified 2576 functional enhancers. Most of these enhancers were located in repetitive sequences and were enriched within silent and lowly expressed genes. Enhancers poorly overlapped with chromatin accessibility regions and were highly enriched in chromatin with the repressive histone modification H3K9me3, which is different from predicted pig enhancers detected using ChIP-seq for H3K27ac or/and H3K4me1 modified histones. This suggests that most pig enhancers identified with STARR-seq are endogenously repressed at the chromatin level and may function during cell type-specific development or at specific developmental stages. Additionally, the In summary, we generated the first functional enhancer map in PK15 and ST cells for the pig genome and highlight its potential roles in pig breeding.The online version contains supplementary material available at 10.1186/s40104-022-00726-y. Shh) expression. Kvon et al. [A fundamental goal in modern biology is to identify and characterize non-coding regulatory elements that control gene expression in development and disease , 2. Enhan et al.  employedSus scrofa) is an economically important livestock species and is widely used as a large animal model for preclinical studies due to its human-like size and physiology [Drosophila [The domestic pig  PK15 cells were maintained in DMEM media  and ST cells were maintained in MEM media  supplied with 10% FBS  and 1% penicillin/streptomycin . All cells were tested for mycoplasma using Myco-Blue Mycoplasma Detector .Pig  according to the manufacturer’s protocol. The inhibitors BX-795  and C16  were used to suppress the expression of immune-reated genes in transfected PK15 and ST cells. For PK15 cells, 1 μmol/L of both inibitors was added to the culture medium, while for ST cells, 0.5 μmol/L of both inhibitors was used. We generated two biological replicates of the PK15 and ST cells.+ RNA fraction was enriched using VAHTS mRNA Capture Beads . Genomic DNA was digested with 5 U DNase I  at 37 °C for 20 min. First strand cDNA was synthesized using TransScript One-Step gDNA Removal and cDNA synthesis SuperMix kit  at 50 °C for 30 min and 85 °C for 5 s with the library specific primer (5'-GAC TGG AGT TCA GAC GTG TGC-3'). We used 10–30 ng cDNA as template in a 50-μL PCR reaction with TransStart FastPfu Fly DNA Polymerase with the reporter gene specific primers (Forward primer: 5'-AAC AAG AAT TGG GAC AAC TCC AGT GAA-3' and Reverse primer: 5'-GAC TGG AGT TCA GAC GTG TGC-3'). PCR products were purified by GeneJET PCR Purification Kit . Purified PCR products were used as templates for the second round PCR  with TransStart FastPfu Fly DNA Polymerase and the index primers in the VAHTS Multiplex Oligos set1/set2 for Illumina Kit . Second round PCR products were then purified using a GeneJET PCR Purification Kit.Total RNA was extracted from the transfected PK15 and ST cells using the TransZol™ Up Plus RNA Kit . The poly(A)+ RNA, the remaining aqueous solution was treated with 10 μL RNase A  at 37 °C for 60 min to remove any RNA in the solution. Input plasmid templates were purified with a GeneJET PCR Purification Kit. Purified plasmid DNA was amplified with the TransStart FastPfu Fly DNA Polymerase and the index primers from the VAHTS Multiplex Oligos set1/set2 for Illumina.After capture of poly(A)Both the cDNA and input plasmid libraries were sequenced on the Illumina HiSeq X Ten platform at BerryGenomics .To verify the enhancers identified in this study, we used the input plasmid library backbone and inserted candidate regions. Plasmids were co-transfected with luciferase control vector ) into PK15 cells with TurboFect™ Transfection Reagent following the manufacturer’s protocol. Primers of candidate regions are reported in Additional file RNAs were extracted using TransZol™ Up Plus RNA Kit. Contaminating genomic DNA was digested with 5 U DNase I and cDNAs was then synthesized using the HiScript III 1st Strand cDNA Synthesis Kit . RT-qPCR used ChamQ Universal SYBR qPCR Master Mix  with 95 °C for 5 min, followed by 40 cycles of 95 °C for 15 s, 60 °C for 30 s with the melting curve program. Primers used for RT-qPCR are described in Additional file P value < 0.001 and FDR < 0.1.To remove low-quality reads and adaptor sequences, the STARR-seq data was processed using Trimmomatic v0.39 with following parameters: TruSeq2-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 . Reads wRNA was extracted from PK15 cells using the TransZol™ Up Plus RNA Kit. Libraries construction and sequencing were performed at Novogene.Trimmomatic v0.39  was usedTo obtain potential biological functions of the enhancers identified in this study, each enhancer was assigned to a putative gene based on the closest genomic distance. Gene Ontology (GO) enrichment analysis of the putative genes was performed using DAVID . In addi2, 0.1% IGEPAL CA-630) and incubated with Tn5 transposase . Next, the transposed DNA was amplified through 14 PCR cycles with indexed primers according to the manufacturer’s protocol for the TruePrep Index Kit V4 for Illumina . We sequenced two independent libraries using the Hiseq X ten platform .ATAC-seq library preparation was performed as previously described . PK15 ceTrimmomatic 0.39  with defP values less than 0.001.ChIP-Seq libraries were prepared following the ENCODE guidelines . ChIP-seWe identified TADs and Hi-C contacts from the Hi-C data, which was download from the SRA database  of the enhancers were located in repetitive sequences (Sscrofa11.1), and secondly in intergenic regions (222/2576 = 8.61%)  Fig. B, emphasosophila . We alsoosophila , 17, 21,To investigate whether enhancers are preferentially enriched in active or silent genes, we separated genes into four groups based on gene expression levels  detected by RNA-seq  and H3K27ac   and H3K27me3   that were enriched in active modifications and not in repressive modifications  of the enhancers were enriched with the repressive histone signal H3K9me3  and genome wide association study (GWAS) mapping for important economical traits have been widely applied in pigs –49. Due Sus scrofa chromosome 8 that may impact the loin muscle area trait and contained the candidate gene PPP3CA. The QTL region overlaps with a functional pig enhancer   is actively involved in regulating the muscle fiber phenotype in pig [PPP3CA gene were encompassed by a topologically associating domain (TAD) and linked by significant Hi-C contact, which indicates that the PPP3CA gene is associated with the loin muscle area trait in the pig and is potentially regulated by a distal functional enhancer   levels in the pig. The QTL region contains a functional enhancer , which is 182 Kb upstream of the KH domain containing RNA binding (QKI) gene  a second enhancer may regulate the QKI gene associated with ALP activity levels. PPP3CA is regarded as a candidate gene that plays an important role in muscle fiber differentiation and affects meat quality of livestock [QKI gene has been identified as the culprit gene for a patient with intellectual disabilities and has important roles in broader biological systems, such as cardiovascular development, bone metabolism and cancer progression [PPP3CA and QKI genes in muscle development and physiological processes in the pigs, respectively. The comprehensively identified pig functional enhancers found in this study provide insights into the functional complexity of enhancers in the pig.Previous studies have indicated that strong selection signals and regions associated with complex traits in the pig genome are located in non-coding regions , 61–63. ivestock . The QKIgression , 66. AddPPP3CA gene is associated with the loin muscle area trait and the QKI gene is associated with ALP activity levels, with both genes regulated by distal functional enhancers. Our first map of functional enhancers in the pig genome provide an important resource for enhancer studies and supply the new regulatory elements for pig breeding and construction of human disease models.In all, we performed STARR-seq in pig PK15 and ST cells and identified 2576 functional enhancers in the pig genome. These enhancers poorly overlap with chromatin accessible regions and are highly enriched with the repressive histone modification H3K9me3. By integrating functional enhancers with pig complex traits, we found that the Additional file 1: Table S1. Enhancers verified using the RT-qPCR method. Primers used to construct the reporter vectors.Additional file 2: Table S2. Primers used for RT-qPCR.Additional file 3: Fig. S1. DNA transfection induced immunoreaction and its suppression with treatment with immune inhibitors in ST cells. Fig. S2. Assessment of the immunoreaction (DMSO) and treatment effect of immune inhibitors after DNA is transfected into cells. Fig. S3. Distribution of fragment sizes in the STARR-seq libraries. Fig. S4. Genome coverage of STARR-seq libraries in pig non-repetitive regions. Fig. S5. GC-content analysis for the STARR-seq libraries. Fig. S6. Reproducibility of STARR-seq. Fig. S7. Correlation analysis of enhancers strength in two biological replicates of ST cells. Fig. S8. Venn diagram showing the overlap of enhancers between our STARR-seq approach and other published studies. Fig. S9. GO analysis of genes in proximity to enhancers. Fig. S10. ATAC-seq and STARR-seq enhancers integrated analysis. Fig. S11. Snapshots of signals at three types of chromatin state for the STARR-seq enhancer regions. Fig. S12. The enhancer  and PPP3CA gene interacted by a TAD region in pig muscle tissues. Fig. S13. The enhancer  and QKI gene interacted by a TAD region in pig liver tissues.Additional file 4: Table S3. Sequencing statistics of STARR-seq, RNA-seq, ATAC-seq and ChIP-seq libraries.Additional file 5: Table S4. Characterization of a total of 2576 functional enhancers in the pig genome.Additional file 6: Table S5. Potential transcription factor binding sites in the enhancers.Additional file 7: Table S6. Epigenetic mark enrichment analysis of the pig enhancers.Additional file 8: Table S7. QTL regions overlapping functional enhancers.


Genomic organization and gene expression regulation in trypanosomes are remarkable because protein-coding genes are organized into codirectional gene clusters with unrelated functions. Moreover, there is no dedicated promoter for each gene, resulting in polycistronic gene transcription, with posttranscriptional control playing a major role. Nonetheless, these parasites harbor epigenetic modifications at critical regulatory genome features that dynamically change among parasite stages, which are not fully understood.Trypanosoma cruzi and its differentiation program using FAIRE-seq approach supported by transmission electron microscopy. We identified differences in T. cruzi genome compartments, putative transcriptional start regions, and virulence factors. In addition, we also detected a developmental chromatin regulation at tRNA loci (tDNA), which could be linked to the intense chromatin remodeling and/or the translation regulatory mechanism required for parasite differentiation. We further integrated the open chromatin profile with public transcriptomic and MNase-seq datasets. Strikingly, a positive correlation was observed between active chromatin and steady-state transcription levels.Here, we investigated the impact of chromatin changes in a scenario commanded by posttranscriptional control exploring the parasite Taken together, our results indicate that chromatin changes reflect the unusual gene expression regulation of trypanosomes and the differences among parasite developmental stages, even in the context of a lack of canonical transcriptional control of protein-coding genes.The online version contains supplementary material available at 10.1186/s13072-022-00450-x. Chromatin is the stage where gene expression and epigenetic regulation occur. The epigenome of a cell consists of a series of heritable chromatin modifications that orchestrate and mirror essential aspects of gene regulation. For example, in general, the promoter regions of highly expressed genes have an open chromatin state and are enriched in active histone marks such as H3K4me. In contrast, genomic regions with lower transcription rates are present in a more compact chromatin state and enriched in histone marks such H3K9me3 and H3K27me3 , 2. EpigTrypanosomatids are early-divergent eukaryotes that present some unique molecular characteristics, such as a hypermodified thymine base, known as base J, intronless genes, polycistronic transcription of functionally unrelated genes, mRNA maturation by trans-splicing, and posttranscriptional regulation of gene expression . RNA PolTrypanosoma cruzi, the etiologic agent of Chagas disease, alternates between a vertebrate host and a triatomine vector [T. cruzi life forms are also distinguished by the capacity of cell replication (epimastigotes and amastigotes) and cell infection  [T. cruzi metacyclogenesis [e vector . In addiacyclic) –16. Profacyclic) . Evaluatacyclic) . Differeacyclic) . The phoogenesis , 21.Nucleus and chromatin also change throughout the parasite life cycle. In epimastigotes and amastigotes, the nucleus is spherical, containing a large nucleolus and high amounts of euchromatin, which is considered transcriptionally active. In contrast, the trypomastigote forms (TCTs and metacyclics) present an elongated nucleus, absence of nucleolus, and abundant heterochromatin, which is dispersed in the nucleoplasm , 17, 18.The genome of this parasite was proposed to be compartmentalized in two regions that differ in gene and GC content: the core compartment  and the disruptive compartment . GP63, dispersed gene family 1 (DGF-1), and retrotransposon hot spot (RHS) multigene families can occur in both compartments . MultigeT. cruzi life forms and follow parasite differentiation [T. cruzi differentiation is followed by a global decrease in transcription, translation, and alterations in nuclear morphology in a background of transcriptional regulation absence of protein-coding genes, we comprehensively investigated the impact of chromatin changes in a scenario commanded by posttranscriptional control. By FAIRE-seq, we identified important differences in T. cruzi genome compartments and a developmental change at tRNA loci (tDNA). Of note, the latter may be associated with the regulatory mechanism associated with translation control required for parasite differentiation and/or the intense chromatin remodeling detected as differentiation advances. The integrative analysis of FAIRE-seq with public transcriptomic and nucleosome mapping datasets allowed us to shed light on the mechanisms related to the differentiation of infective forms and help disentangle the impact of chromatin remodeling in a scenario commanded mainly by posttranscriptional control.In recent years, our group has demonstrated that changes in epigenetic marks, such as histone posttranslational modifications (PTMs), histone variant deposition, and nucleosome occupancy, change among ntiation –26. As TT. cruzi, we performed a FAIRE-seq genome-wide approach. We rescued these regions from the genome of epimastigotes under exponential proliferation. Biological duplicates were processed together with input control samples (reversed cross-link before DNA extraction) to account for any bias in genome sequencing and assembly. FAIRE-seq reads presented high duplication levels  and their corresponding control samples (C). Figure Regions covered by the core compartment  had higher RPGC-normalized counts than the disruptive compartment regions (red tracks) Fig.  B. RemarBecause the distribution of open chromatin was revealed to be distinct between genome compartments, we asked whether their landscapes, at a gene level, were also different. Strikingly, the landscape of open chromatin greatly differs among virulence factors (present in the disruptive compartment), among them, and among the remaining protein-coding genes. Figure , transcription of protein-coding genes initiates mainly at dSSRs and terminates at cSSRs; however, transcription might also start at some non-SSRs, mainly close to tRNA genes [T. cruzi, we inspected dSSRs as a proxy of transcription start sites, which resulted in enrichment of open chromatin compared to PTUs, as evidenced in Fig. In trypanosomatidsNA genes , 27, 28.PTU regions were hierarchically clustered into three groups based on their log2 (E/C) RPGC level  compared to metacyclics Fig. C. The hi.9 times Given the different open chromatin profiles observed among PTUs Fig. D, we invThen, we explored whether regions enriched in open chromatin would have transcripts expressed at higher levels. CDSs were first classified as high, medium or low expressed in each life form based on their TPM counts obtained from a transcriptomic study published elsewhere  Additio. SurprisFAIRE-seq analysis highlighted a global decrease in the levels of open chromatin in metacyclics compared to epimastigotes. However, we wondered whether the open chromatin profile would give us more clues about the differences in gene expression and phenotype found between life forms, especially those related to their differentiation program. Comparing all genomic features, we detected striking differences in open chromatin enrichment at the regions coding for the small nuclear RNAs (snRNAs) and tRNA genes. Of note, this enrichment was significantly higher  in epimastigotes than in metacyclics Figs. A, B.Fig.Hierarchical cluster analysis of the E/metacyclic ratio revealed that the majority of tDNAs had at least six times more open chromatin in epimastigotes than in metacyclics Fig. D. This dFinally, we addressed whether the increase in open chromatin within tDNAs would be associated with tRNA expression levels. Previously, Garcia-Silva  showed bT. cruzi into nonreplicative life forms is followed by a global decrease in RNA transcription, which is mainly observed in fully differentiated forms, such as trypomastigotes and metacyclics [The differentiation of acyclics , 18. Chracyclics . Here, wacyclics , 31. OurT. cruzi genome has PTUs whose dSSRs have different levels of open chromatin. Whether it is associated with differential deposition of the RNA Pol II machinery, histone variants, and histone PTMs and, more importantly, whether it has any consequences on the PTU transcription levels needs to be further investigated. Global nascent transcriptomic analysis comparing transcription along and within PTUs is absent in trypanosomes but can clarify this issue. In addition, hierarchical cluster analysis of PTUs without SSRs indicates different levels of open chromatin between PTUs, but with a clear flat pattern along the entire PTU. The latter indicates a uniform open chromatin status along PTUs, possibly reflecting the polycistronic transcription nature of trypanosomes.To further explore the genomic context of open chromatin, we used the FAIRE approach, a methodology previously known to enrich for active genomic regions , 5, 32. T. cruzi, proposed by Berná et al. [T. cruzi exhibited great differences in the enrichment of open chromatin, with the former highly enriched in open chromatin compared to the latter. The disruptive compartment is composed of virulence factors that have a very repetitive nature. Therefore, we cannot rule out that mapping and genome assembly bias influence these data, although removing multimapping reads did not affect the results. Regardless, because virulence factors are mainly expressed in infective forms, it was expected that these chromatin regions would be more open in metacyclics if a gene-specific transcriptional regulation scenario existed. However, it was determined that both the disruptive and core compartments were lower in metacyclics. These findings confirm that posttranscriptional regulation of virulence factors indeed plays a key role in their expression, as previously indicated [We envisaged that the presence of two genomic compartments in á et al. , may indndicated , 34.T. cruzi, base J was also found flanking PTUs and within subtelomeric regions, which are rich in virulent factors (located at the disrupted compartment). Interestingly, the presence of base J at these genes was shown to be developmentally regulated [Moreover, the multigenic families MASP, GP63, mucin, and TS exhibited distinct open chromatin landscapes compared to the other nonmultifamily members. The reason for this may be related to the greater GC content of the disruptive compartment than the core compartment , becauseegulated , 37. ThuT. cruzi. These findings are remarkable once trypanosome gene expression relies mainly on posttranscriptional events. However, this is not unprecedented. Previously, it was determined that the nucleosome landscape reflects steady-state gene expression mainly regarding the depth of the nucleosome-depleted region around 5’UTR [T. cruzi chromatin proteomics reveals the presence of at least 13 mRNA binding proteins [Strikingly, the open chromatin state based on FAIRE-seq read enrichment is associated with steady-state transcription levels in nd 5’UTR , 25, 38.proteins , but furOf note, the open chromatin landscape seems to be conserved between life forms, except for a global decrease at their levels on metacyclics. This agrees with a global decline in transcription levels along with parasite differentiation . One of Of note, the absence of transcription machinery at tDNA loci can be the cause or consequence of the closed chromatin status. However, the implications of the developmental regulation of this status can be hypothesized. The mentioned changes may influence the expression of the nearby PTUs, which, in turn, would be preferentially affected along with differentiation. In this sense, it is interesting that tDNA hierarchical cluster analysis pointed out a distinct distribution of tDNA openness based on their location related to adjacent PTUs . We hypothesize that the loss of open chromatin status at tDNAs located within a codirectional PTU may affect transcription mainly at the downstream PTU. This is supported by the findings that indicate that tDNA loci and their associated transcription factors, mainly TFIIIC, may function as insulators. The latter has a critical role in chromatin domain organization, working on separating active and silenced domains .T. cruzi differentiation [Developmentally regulated chromatin changes at tDNA loci are especially intriguing considering that translation control has a key role in trypanosomatid differentiation. During the differentiation of epimastigotes to metacyclics, a global downregulation of a large proportion of the translatome occurs , 21, 44.ntiation , blockinntiation , indicatntiation . In yeasntiation . We enviOverall, the open chromatin genome-wide approach recapitulates unique aspects of trypanosomatid gene regulation such as polycistronic transcription and lack of RNA pol II transcription regulation. However, it also indicated that different dSSRs may have different levels of open chromatin, suggesting PTU-specific regulation. In addition, the status of open chromatin correlates with the presence of conserved and disruptive genome compartments, which further correlates with the expression of virulence factors. Finally, the obtained data indicated developmental chromatin regulation at tDNA loci, which may orchestrate an essential step in posttranscriptional regulation control necessary for parasite differentiation and survival.T. cruzi (Dm28c strain) epimastigotes were cultured at 28 °C in LIT medium supplemented with 10% fetal bovine serum (FBS—Vitrocell), glucose 0.4%, hemin 0.1 µM, and penicillin-G 59 mg/L as previously described [6 parasites/mL and cultivated for 5 days to reach the stationary phase. Then, parasites were resuspended at 5 × 108 parasites/mL in triatomine artificial urine medium  for 2 h at 28 °C. Next, parasites were diluted to 5 × 106 per mL in TAU medium supplemented with 10 mM L-proline, 50 mM L-glutamate, 2 mM L-aspartate, and 10 mM glucose and maintained for 96 h at 28 °C. metacyclics were obtained from the supernatant for 72 h and purified by DEAE columns.escribed . Metacycescribed . Brieflyhttp://fiji.sc/wiki/index.php/Fiji). Aligned images were saved as image sequence files and then imported into the 3D reconstruction program Free-D 1.15 [Image stacks previously obtained by focused ion beam scanning electron microscopy (FIB-SEM)  were aute-D 1.15 . Heteroc7 parasites  were fixed for 5 min with formaldehyde and directly added to the medium to a final concentration of 1%. Formaldehyde was quenched with 125 mM glycine, followed by extensive washing with PBS. Pellets were resuspended in 1 mL of cold lysis buffer TELT  and sonicated with six cycles of 30 s with an interval of 1 min each (Output 4 and Duty 30 parameters) in a Tommy Ultrasonic Disruptor UD-201 apparatus. To prepare the input DNA control, 100 μL of each sample was centrifuged at 15,000 g for 5 min at 4 °C. The supernatants were incubated with 1 μL of RNaseA DNase-free for 30 min at 37 °C followed by treatment with 1 μL of proteinase K for 1 h at 55 °C. The cross-links were reversed by overnight incubation at 65 °C. DNA was extracted with phenol:chloroform:isoamyl alcohol solution and precipitated with 3 M sodium acetate (pH 5.2) and 95% ethanol. To prepare FAIRE DNA, the remaining cell lysate was centrifuged at 15,000 for 5 min at 4 °C, and DNA was extracted as described above; however, RNaseA treatment, protein removal by proteinase K and cross-linking reversal were performed after DNA purification. The obtained DNA was quantified using a NanoDrop spectrophotometer (Thermo Fisher). A total of 500 ng of input DNA control was run on a 1% agarose gel to verify the sonication efficiency. DNA fragments were deep sequenced using an Illumina NextSeq 500 (2 × 75 bp) at the Polyomics Facility (Glasgow University).The FAIRE samples were prepared as described in  with somTrypanosoma cruzi Dm28c genome  using the GATK pipeline [The methods applied to analyze the FAIRE-seq data are shown in Additional file pipeline , which epipeline  as the apipeline . MAPQ fipipeline  to generT. cruzi Dm28c  and a custom Python script . Briefly, the script parses the GFF file, and a polycistron is considered for annotation if two or more CDSs of the same strand direction are found consecutively. The ID of each feature is incorporated in the polycistron annotation content, along with the number of features found. Polycistrons with only one feature were also annotated. In a second analysis, the script processes the SSRs, and dSSR is considered when a region between the start of a negative-strand polycistron followed by the start of a positive-strand polycistron is found. cSSR is considered when a region between the end of a positive-strand polycistron followed by the end of a negative-strand polycistron is found. If none of those conditions are met, i.e., a positive-strand CDS polycistron is followed by another type of feature. The genomic region between them is annotated as an Intergenic Polycistron Region.Polycistronic regions (same strand CDS cluster region), dSSR and cSSR were annotated using the GFF file from 5’ and 3’ UTR annotations were obtained with UTRme  using thT. cruzi Dm28c (V46) genome using STAR [RNA-seq reads were preprocessed according to Smircich et al., 2015, with an additional step of sequencing adapter removal using cutadapt . Then, ting STAR . For eacing STAR . RPGC-noing STAR , passingTo avoid plotting regions from small sequences, only features from the largest contigs were selected. Thus, the first 188 sequences of the genome were analyzed, which comprised 85% of the total number of bases. Genomic coordinates of each feature analyzed were obtained from the final GFF annotation (after using the annotatePolycistron Python script and UTRme) as bed files. Landscape profiles and heatmaps were plotted using the computeMatrix  and plotHeatmap  functions from deepTools. The final GFF annotation, bed files, and RPGC-normalized data (as wig files) were used as input in IGV (Integrative Genomics Viewer)  for addihttp://bioinformatica.fcien.edu.uy/cruzi/browser/downloads.php.Gene Ontology (GO) analysis was performed by passing the feature IDs to TriTrypDB and then using the GO Enrichment tool. The obtained results were exported to REVIGO  for visuMNase-seq data were retrieved and processed according to our previous study . The ali7) in the exponential growth phase were fixed with 4% paraformaldehyde in PBS for 20 min at room temperature. Pellets were resuspended in NH4Cl under agitation for 10 min, washed with PBS, and adhered to slides with an adhesive frame previously treated with polylysine. Then, parasites were permeabilized with 0.2% Triton X-100 in PBS for 5 min, blocked and prehydrated with 2% BSA, 5X Denhardt, 4X SSC and 35% formamide for 2 h. A total of 1 ng per mL of fluorescent oligonucleotide was added to the hybridization solution: 1 mM EDTA, 1% BSA, 0.5 M NaHPO4, 7% SDS, and heated at 75 °C for 5 min. For hybridization, a wet chamber was prepared with 50% formamide in 2X SSC, and the slides inside were incubated at 37 °C overnight. The slides were washed twice with 4X SSC, four times with 50% formamide and 2X SSC, three times with 2X SSC, three times with 1X SSC, and three times with 0.5X SSC. The slides were mounted with Vectashield plus DAPI solution (Vectors). Images were captured by an Olympus BX51 microscope, and the intensity of fluorescence was quantified by ImageJ with mean gray values.Parasites (107 parasites (epimastigotes and metacyclics forms) were extracted with TRIzol (Invitrogen) according to manufactures instructions. RNA was quantified with Qubit (Thermo). Three-hundred nanograms of RNAs or the equivalent of 7.6 × 106 cells  were run in 15% denaturing polyacrylamide gel  and transferred to a nylon membrane. The membrane was cross-linked in UV-light for 3 min and pre-hybridized with 5 mL of hybridization buffer  for 1 h at 30 °C. Next, the membrane was incubated with 20 pmol of biotin labeled probes  overnight. The membrane was processed and the stained detected according to the instructions in the Chemiluminescent Nucleic Acid Detection Module Kit (Thermo). The chemiluminescence signal intensity was measured by ImageJ with mean gray values.Briefly, total RNA from 5 × 10Additional file 1: Figure S1. A. Data summary for sequenced samples  and control for each replicate and read pairs. B. FastQC mean quality scores of sequenced samples. C. Scheme of the pipeline used to explore the FAIRE-seq data. D. Number of mapped reads against the Trypanosoma cruzi Dm28c genome for each dataset . Blue bars represent total dataset reads; orange bars indicate the mapped reads; gray bars show the remaining mapped reads with MAPQ scores above 10. The percentage is relative to the total reads of each dataset (blue bars). Spearman correlation (E) and PCA (F) of read counts for each dataset  from epimastigote (EPI), metacyclic trypomastigote (metacyclic) life forms and control samples generated with deepTools. Figure S2. A. Representative IGV snapshot of FAIRE-seq data mapped against the T. cruzi Dm28c genome (contig PRFA01000010). The data were normalized using RPGC . Epimastigote biological replicates (EPI-1 and EPI-2) are shown in red, and metacyclic trypomastigote biological replicates (META-1 and META-2) are shown in blue. Datasets not filtered with MAPQ score 10 are labeled as “WithoutQ10”. At the genomic features, repeat regions are represented in yellow. The dashed rectangle highlights a long repeat region and its low coverage upon MAPQ10 removal. B. IGV snapshots of two contigs comparing the raw mapped FAIRE read profile to the removal of multimapped reads (Q10 - arrows) in epimastigotes and metacyclic trypomastigotes. Core and disruptive genomic compartments are shown as red and green tracks, respectively. Figure S3. A. Superposition of FAIRE-seq (red) and MNase-seq (blue) in epimastigotes. The y-axis represents RPGC normalized tag counts for FAIRE-seq on the right side and MNase-seq on the left side. The profile CDSs transcribed by RNA Pol II is showed. B. Comparison of core and disruptive compartments of MNase-seq data using RPGC counts in epimastigotes (red) and trypomastigotes (blue). The median values for epimastigores were 2.04 and 1.09 for the core and disruptive compartments, respectively; for trypomastigote, the median values were 2.13 and 1.14. *** Wilcoxon-Mann-Whitney test with p-value = 0.001. C. RPGC tag counts at multigenic family genes and other CDSs transcribed by RNA Pol II for epimastigotes (red) and metacyclic trypomastigotes (blue). D. Summary plots for virulence factors in epimastigotes and metacyclics. *** Wilcoxon-Mann-Whitney test with p-value = 0.001. Figure S4. A. Landscape profile of cSSR (blue) and dSSR (green) showing median values in RPGC log2 ratio of epimastigotes/metacyclics (left). Start and End represent the first and last bases of each feature, respectively. Boxplots showing RPCG counts for dSSR (upper) and cSSR (lower) for epimastigotes (red) and metacyclics (blue). *** Wilcoxon-Mann–Whitney test with p value = 0.001. B. The percentage of bases of multifamily genes for each polycistron according to clustering is shown in Figure 4D. In B, clusterization was performed considering 1 kb upstream and downstream from the polycistron, while in C, clusterization considered only the PTU. Statistical analysis was performed using the Wilcoxon-Mann-Whitney test . D. Landscape profiles of all CDSs (upper) and clustering based on the RPGC log2 ratio of epimastigotes/metacyclics (lower). Figure S5. Genes classified as high, medium and low expressed based on TPM values for epimastigote (red) and metacyclic trypomastigote (blue) life forms. *** Wilcoxon-Mann-Whitney test with p-value = 0.001. Figure S6. A. IGV snapshots of FAIRE-seq enrichment in epimastigotes (duplicates – red) compared to metacyclics (duplicates – blue) in three different loci. tDNAs are highlighted in yellow rectangles. B. Number of tRNA genes in each cluster. The tRNA genes grouped in each cluster after hierarchical clustering, as shown in Figure 6C, were counted and plotted in column graphics according to R group classification of the amino acids. C. Representative scheme of tRNA gene localization at divergent, convergent and codirectional PTUs. The arrows indicate the sense of transcription in each PTU. D. Quantification of tRNA-FISH UUC (Glu),GUC (Asp) in epimastigote (blue) and metacyclic (green) forms. Mean gray values represent intensity per area. Unpaired T-test: ** , and *** . E. Quantification of tRNA-Northern blot UUC and GUC in Epi (blue) and MT (green) forms. The expression levels are represented by intensity per area . Unpaired T-test: * , and *** . ns, non-significant.Additional file 2: Table S1. Repetitive regions at T. cruzi genome. Table S2. Log2 RPGC at core and disruptive compartment. Table S3. RPGC at the indicated features in epimastigotes and metacyclics . Table S4. RPGC at the indicated features in epimastigotes and metacyclics . Table S4. Hierarchical clustering output from PTUs (with or without SSRs) and tDNA.Additional file 3: Videos S1–S4. 3D reconstruction of euchromatin (yellow), heterochromatin (purple) and nucleolus (red) in epimastigotes, intermediate 1 and 2, and metacyclics.https://repositorio.butantan.gov.br/handle/butantan/3965.


Arabidopsis H3K4 demethylases suggest roles for H3K4me1 in transcription. However, the control of H3K4me1 remains unexplored in Arabidopsis, in which no methyltransferase for H3K4me1 has been identified. Here, we identify three Arabidopsis methyltransferases that direct H3K4me1. Analyses of their genome-wide localization using ChIP-seq and machine learning reveal that one of the enzymes cooperates with the transcription machinery, while the other two are associated with specific histone modifications and DNA sequences. Importantly, these two types of localization patterns are also found for the other H3K4 methyltransferases in Arabidopsis and mice. These results suggest that H3K4me1/2/3 are established and maintained via interplay with transcription as well as inputs from other chromatin features, presumably enabling elaborate gene control.Mono-, di-, and trimethylation of histone H3 lysine 4 (H3K4me1/2/3) are associated with transcription, yet it remains controversial whether H3K4me1/2/3 promote or result from transcription. Our previous characterizations of  Methylation of histone H3 lysine 4 is associated with transcription. Here the authors describe three Arabidopsis methyltransferases that direct H3K4me1 and propose that one methyltransferase works co-transcriptionally while the others are recruited to genomic and epigenomic features. Histone H3 lysine 4 methylation (H3K4me) is a conserved histone mark found in actively transcribed genes; however, the molecular basis and functional significance of the interrelationship between H3K4me and transcription remain elusive3. It has been proposed that H3K4me can be viewed as a memory of transcription because transcription directs H3K4 methylation7. On the other hand, in some experimental systems, H3K4me is deposited independent of transcription9. The underlying mechanisms that coordinate transcription-coupled and transcription-independent H3K4me are largely unexplored.Posttranslational modifications of histone residues shape the epigenome and affect gene expression12. Our previous genetic and genomic studies in Arabidopsis thaliana (hereafter Arabidopsis) have demonstrated key roles of the gene body H3K4me1 in various epigenetic phenomena. A putative H3K4me1 demethylase, LYSINE-SPECIFIC DEMETHYLASE1-LIKE2 (LDL2), mediates silencing of genes with the repressive histone mark H3K9me2 by removal of H3K4me113. Another related demethylase, FLOWERING LOCUS D (FLD), downregulates transcriptional elongation and initiation by removing H3K4me1 in genes that have high levels of convergent overlapping transcription 14. Although these findings regarding the H3K4 demethylases of Arabidopsis suggest a link of H3K4me1 with other chromatin features and transcription, the mechanisms of H3K4me1 control remain elusive, mainly because methyltransferase(s) for H3K4me1 has not been identified in plants.Another layer of H3K4me complexity is its occurrence in three states . Generally, H3K4me3 and H3K4me2 are found on the first several nucleosomes of genes, while H3K4me1 is found in downstream regions within the gene bodies15. However, yeasts have lost Trx/Trr-type H3K4 methyltransferases during evolution16, and yeast Set1 is the sole H3K4 methyltransferase responsible for all H3K4me1/2/3. Arabidopsis has five Trx/Trr-type enzymes (Arabidopsis Trithorax (ATX) 1 to 5) and at least one Set1-type enzyme (Arabidopsis Trithorax Related (ATXR) 7)  by increasing H3K4me1/2/3 levels within the FLC locus24. In the control of FLC, ATXR7 was shown to counteract FLD24, which demethylates H3K4me114. However, the genome-wide impacts of these putative H3K4 methyltransferases have not been elucidated.In both animals and plants, two types of H3K4 methyltransferases are known: Set1-type and Trithorax/Trithorax-related (Trx/Trr)-type H3K4 methyltransferases 7) Fig. . AdditioATX1, ATX2, and ATXR7 genes. Our results clarify the division of labor among H3K4 methyltransferase genes and provide powerful genetic materials for elucidating the functions of each H3K4 methylation state. In addition, we analyzed the genomic localization of these enzymes. The subsequent application of machine learning algorithms revealed that the ATXR7 protein colocalizes with the transcription machinery, while ATX1 and ATX2 are associated with other chromatin modifications (epigenome) and specific DNA sequences (genome). These two types of localization patterns were also found by the reanalysis of other Set1- and Trx/Trr-type H3K4 methyltransferases, including those of animals. These findings lead us to a new perspective: some H3K4me marks may function as records of transcription, while other H3K4me marks may function as mediators of information encoded in the epigenome and/or genome.Here, we explored the involvement of seven H3K4 methyltransferase genes in H3K4me1 as well as H3K4me2/3 by analyzing single and multiple mutants using chromatin immunoprecipitation sequencing (ChIP-seq). H3K4me1 levels are substantially decreased by the simultaneous loss of the atx2 and atxr7 showed relatively large decreases in H3K4me1 levels  involved in H3K4me1, we examined mutants of six predicted H3K4 methyltransferases: ATX1 to 5 and ATXR7 Fig. . Among tion Fig. . Clusteratx1/atx2 double mutants and found stronger effects on H3K4me1 than were observed in either of the single mutants   resulted in the loss of H3K4me1  genes. Based on the similarities of the protein domain structures and the effects on H3K4me1 patterns, we examined nts Fig. . Additiome1 Fig. , while Hme1 Fig. . Consist26, our Western blot and ChIP-seq analyses showed that H3K4me3 and H3K4me2 modifications are mainly mediated by ATXR3 and ATX3/4/5, respectively  group were mutually exclusive  protein that regulates H3K4me1, we determined the genome-wide localization of the ATX1, ATX2 and ATXR7 proteins using transgenic plants expressing FLAG-tagged proteins Fig. . ChIP-seTo elucidate the chromatin-targeting mechanism(s) of these ATX1/2/R7 proteins, we screened for the determinants of ATX1/2/R7 localization by using a machine learning algorithm (random forest). Using chromatin and genomic features listed in Fig. The most ‘important’ feature for distinguishing ATXR7-bound genes was the level of RNA Polymerase II (RNAP2) around the TTS Fig. . ATXR7-batx1/2/r7 induced the concomitant loss of H3K4me1 and H3K36me3  mutation, which causes a drastic reduction in H3K36me3 levels  mutant resulted in decreased localization of ATX2 protein in genes that are predicted to lose ATX2 by in silico simulation of H2Bub loss   such as H2Bub  and trained the linear support vector machine (lSVM) algorithm to distinguish ATX1 (or ATX2)-bound TSS regions from unbound TSS regions, and ATXR7-bound TTS regions from unbound TTS regions. This process achieved good accuracy for ATX1 and ATX2 Fig.  but not To better understand the underlying mechanism, we sought to annotate the sets of DNA motifs that specify ATX1/2 binding. We selected the 120 predictive 6-mers, including the sixty 6-mers with the highest positive weights  and the sixty 6-mers with the greatest negative weights (opposing binding). The prediction accuracy for these 120 features was not much lower than that of the full model Fig. . Many of38. While the ATX protein itself is not known to show DNA binding specificity, these additional DNA binders, such as the GAGA binding factor BPC640 and TCP and trihelix TFs, may physically link ATX to DNA motifs. The combination of GAGA and RGCCCAW is characteristic of TSSs that harbor short noncoding RNAs  with unknown functions41. Indeed, ATX1/2-bound genes were found to significantly overlap with sppRNA genes . The hypothesis predicts that ATXR7 is responsible for this correlation through H3K4me1 deposition in transcribed genes, while ATX1 and ATX2 disrupt this correlation by depositing H3K4me1 regardless of transcription. In agreement with this prediction, the correlation became significantly stronger in 1/2 Fig.  compared1/2 Fig. , and weaxr7 Fig. , as quanxr7 Fig. .Fig. 5PoArabidopsis has at least four other putative H3K4 methyltransferases. Hence, we wondered how the localization patterns of the other methyltransferases were determined. A random forest analysis using the reported genome-wide distribution of the ATXR3 protein42 revealed that the levels of total and phosphorylated RNAP2 and transcription presented the highest ‘importance’ and that they were significantly higher in ATXR3-bound genes than in ATXR3-unbound genes    Fig. . The gennt) Fig. , suggest43,). Fungi have lost Trx/Trr-type genes during evolution and exhibit only one Set1-type gene16. We analyzed mammalian SET1A, MLL2, and MLL3/4, for which genome-wide localization data in mouse ESCs are available. The reanalysis of the ChIP-seq datasets revealed that SET1A44 and MLL245 localize around TSSs  and, to a lesser extent, around enhancers  , while MLL3/4 target chromatin irrespective of transcription, similarly to ATX1/2. MLL2 appears to be an intermediate. Therefore, it is plausible that Set1-type H3K4 methyltransferases tend to function in a cotranscriptional manner across eukaryotes, while Trx/Trr-type H3K4 methyltransferases are informed by other chromatin and DNA sequence features . This observation implies that other H3K4 methyltransferases can catalyze H3K4me2/3 modifications on unmethylated H3K4 (H3K4me0) in their target regions. Accordingly, ATXR3, for example, was shown to catalyze the H3K4me3 from H3K4me0 in vitro19. In addition, the observation that H3K4me2/3 of ATX1/2/R7-marked genes being not largely affected by atxr3 or atx3/4/5 mutation implies that multiple methyltransferases from the ATX1/2/R7, ATX3/4/5 and ATXR3 groups (and/or other unidentified H3K4 methyltransferase(s)) are redundantly involved in their deposition. Our study clarified the functional division of labor among ATX(R)s genome wide. In contrast to Arabidopsis, yeasts have a single H3K4 methyltransferase, Set1, which is responsible for all H3K4me modifications. The Arabidopsis mutants showing the specific reduction of each H3K4me state identified here will serve as powerful materials for understanding the specific functions of H3K4me1/2/3.Here, we revealed in atx1 shows H3K4me3 loss49 and that atx2 shows H3K4me2 loss22, on the basis of ChIP-qPCR analyses of selected loci, while our data show that the redundant roles of these enzymes have the largest effect on the global H3K4me1 level. The catalytic domains of ATX1/2/R7 contain a bulky tyrosine at the tyrosine/phenylalanine switch  also have PHD domains  attach more firmly to ubiquitinated nucleosomes53.Random forest analyses of the genome-wide localization of ATX1 and ATX2 suggested that H3K36me3, H4K16ac and H2Bub are candidate chromatin features for the recruitment of ATX1/2 Fig. , while Hins Fig. . H2Bub hArabidopsis, promoters without TATA boxes (a major core promoter motif) tend to contain GAGA stretch, which are suggested to be equivalent to animal CpG islands54. MLL2 also binds to non-TATA(= CpG)-type promoters45. Second, ATX1/2-bound TSS regions potentially recruit polycomb-group (PcG) proteins, considering that the combination of telobox and GAGA sequences is necessary and sufficient for the recruitment of Arabidopsis PcGs55, which are antagonists of trithorax group (TrxG) proteins. In both plants and animals, TrxG and PcG often target the same genomic regions. Third, both ATX1/2-bound and MLL3/4-bound regions produce noncoding RNAs: sppRNAs and enhancer RNAs46. These results suggest that Trx/Trr methyltransferases bind to a common set of features across eukaryotes.Motif mining with the lSVM algorithm revealed three notable characteristics of the ATX1/2-bound TSS sequences that coincided with known features of animal Trx/Trr-bound sequences. First, the ATX1/2-bound regions are GAGA-type promoters rather than TATA-box promoters. In 7. An interactome analysis of RNAP2 recovered Set1-type methyltransferases but not Trx/Trr-type methyltransferases in human cells56, and the colocalization of RNAP2 with Set1-type, but not Trr/Trx-type methyltransferases, is prominent at the microscopic level in the Drosophila polytene chromosome57. In contrast, various recruitment factors in addition to RNAP2 have been proposed for Trx/Trr-type methyltransferases, including chromatin modifications as described above, TREs, and long noncoding RNAs58. These genome- and/or epigenome-informed methyltransferases may have provided the foundation for multicellularity via the elaborate control of H3K4me, considering that Trr/Trx-type methyltransferases are present in animals and plants but are absent in yeasts. Regulation by both transcription-coupled and (epi)genome-encoded mechanisms may be further generalized to other chromatin modifications, such as H3K36me59.Our analyses suggested that Set1-type methyltransferases typically function in a cotranscriptional manner and that Trx/Trr-type methyltransferases are less informed by transcription. Although this bifurcation has not been explicitly noted previously, it may underlie the observations of a number of previous works. For instance, the concept that H3K4 methylation occurs cotranscriptionally was founded on studies of Set1 in budding yeastArabidopsis, the histone demethylase LDL2 removes H3K4me1 marks from the gene bodies that accumulate H3K9me2 and silences gene expression13. Another histone demethylase, FLD, removes H3K4me1 from sites where convergent overlapping transcription takes place14. Although the biological function of H3K4me remains unclarified, these diverse mechanisms converging on H3K4me1 suggest that the roles of H3K4me require the coordination and integration of a wide range of information. H3K4me is proposed to coordinate splicing60, transcriptional stability61, cryptic transcription62, sense-antisense transcriptions63 and to confer transcriptional memory65. These apparent multifaceted functions may reflect the multiple regulation mechanisms of this modification. In future studies, the dissection of H3K4me based on its controlling mechanisms may help elucidate the functions of H3K4me.In addition to transcription-coupled and (epi)genome-encoded pathways which regulate H3K4 methyltransferases, demethylases also shape the pattern of H3K4me. In 67 used in this study were previously described. Names of mutant alleles such as “atx1-1” or “atx1-2” collide between papers, and the names provided below are not consensus, but examples; atx1-2 , atx1-3 (SALK_119016C), atx2-1 , atx2-2 (SALK_117262), atx3-2 , atx3-1 , atx4 , atx5 , atxr7-1 , atxr7-2 (SAIL_446_F12), atxr3 , ashh1 , ashh2 , ashh3 , ashh4 , ashr3 , hub1-4 (SALK 122512) and hub2-2 (SALK 071289 C). For atx1, atx2, and atxr7 mutants, atx1-2, atx2-1, and atxr7-1 alleles were mainly used. atx1-3, atx2-2, atxr7-2 were used as a replicate to confirm bulk H3K4 methylation levels of atx1/2/r7 mutant in Fig. atx3 mutants, we used SAIL_582_H12 (UTR insertion) if not stated otherwise. atx3-1 allele (exon insertion20) was used in Fig. 20. For tagged ATX1, ATX2, and ATXR7 lines, the constructs were made with the uniform design; native promoter, which is 3 kb region upstream from initiation codon, followed by 3 x FLAG sequence, coding regions (genomic DNA sequence including introns until just before stop codon) and HA sequence, cloned into pPLV01 vector68. The plasmids were transferred into respective single mutants  via Agrobacterium tumefaciens GV3101::pMP90. Plant lines having homozygous transgene were selected in later generations. All the mutants and ‘wild type (WT)’ are in the Columbia-0 background. For all the experiments, seeds were sown on Murashige and Skoog (MS) plates and kept in dark at 4 °C for a few days, then grown for 15 days under long-day conditions (8 h dark and 16 h light) at 22 °C. Whole seedlings were used for experiments.T-DNA insertion mutantsBulk histones for western blotting were prepared using Histone extraction kit (Active Motif) or essentially as described in ref. 14. Briefly, approximately 1.5 g frozen seedlings were ground into fine powder and suspended in 25 ml of nuclei isolation buffer ). The buffer for epitope-tagged proteins additionally contains 0.1 mM spermine, 0.5 mM spermidine and 1.5 mM ethylene glycol bis(succinimidyl succcinate) . Also, 0.6% Triton X-100 was replaced with 0.3% IGEPAL CA-630. The suspension was incubated 10 min in RT to crosslink, quenched with 130 mM glycine, filtered through 40 µm nylon cell strainer and pelleted by centrifugation at 3000 rpm at 4 °C for 10 min. The pellet was resuspended in 300 µl of nuclei isolation buffer and layered on top of 500 µl of nuclei separation buffer  and pelleted by centrifugation at 3000 × g for 5 min at 4 °C. For histone modifications, the nuclear pellet was lysed with 150 μl of lysis buffer  then diluted with 800 μl of dilution buffer . For epitope-tagged proteins, the pellet was directly resuspended in 950 µl RIPA buffer . The suspension was sonicated using a Covaris S220 Focused-ultrasonicator (Covaris) and milliTUBE 1 ml AFA Fiber (Covaris) with the following settings: time, 20 min; duty factor, 5%; Cycles per Burst, 200; temperature (water bath), 4–6 °C; and Peak Incident Power, 140 for histones and repeat alternately 105 and 140 for epitope tagged proteins. The sonicated chromatin was then centrifuged at 13,000 g for 3 min, and the supernatant was diluted with RIPA buffer and aliquoted. The chromatin solution was incubated with 1 μg of antibodies overnight at 4 °C. Antibodies are H3, H3K4me1/2/3 (see the section of Western blot), H3K4me0 . Chromatin of S. pombe was prepared as follows; cells were fixed with 1% formaldehyde for 10 min in 37 °C, quenched with glycine, resuspended in Buffer 1  and disrupted with bead shocker. The suspension was sonicated by Covaris S220 with a setting similar to that of Arabidopsis histone, except the sonication time being 15 min. The sonicated suspension was centrifuged for 5 min at 13,000 rpm at 4 °C. ChIP-seq libraries were made with KAPA Hyper Prep Kit (Kapa Biosystems), and dual size-selected using AMPureXP (Beckman Coulter) to enrich 200–500 bp fragments. The libraries were 50-bp single-end sequenced by HiSeq4000 sequencer (Illumina) in Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley, or 150 bp paired-end sequenced by the HiSeqX Ten sequencer (illumina). Biological replicates were conducted on independently grown plants.ChIP targeting histone modifications, RNAP2, and epitope-tagged proteins were essentially carried out as described previously71 and NEB Next High Fidelity PCR Mix for 7 cycles, and purified with Agencourt AMPure XP. The libraries were 50-bp single-end sequenced.For ATAC-seq, nuclei preparation, tagmentation and library prep procedure followed sucrose sedimentation protocol of ref. 72. DNA was extracted with Nucleon PhytoPure (cytiva) and subjected to bisulphite conversion using MethylCode Bisulfite Conversion Kit (Life Technologies). Bisulphite-treated DNA molecules were PCR amplified with 10 cycles using KAPA HiFi HotStart Uracil+ ReadyMix (Kapa Biosystems) and purified with Agencourt AMPure XP (Beckman Coulter). The libraries were 150 bp paired-end sequenced by the HiSeqX Ten sequencer (llumina).Whole-genome bisulfite sequencing (BS-seq) was conducted as described beforeTotal RNA was extracted from seedling with RNeasy Plant Mini Kit (Qiagen), then polyA selected, fragmented, and made into a library with KAPA Stranded mRNA-seq Kit (Kapa Biosystems) following the manufacturer’s protocol. The libraries were 50-bp single-end sequenced as described above. One sample corresponds to one individual seedling, and three samples were sequenced for one genotype.Arabidopsis, TAIR10 reference genome and Araport 11 gene annotations were used. In this paper, ‘gene’ otherwise stated refers to all the transcribed features in Araport11 annotation, including TE genes or non-coding RNAs. ‘protein_coding’ genes refer to the nuclear genes that are annotated as such in Araport11 annotation, excluding the ones that lack primary transcript annotation. For Mus musculus, we used reference genome mm9, which is not the latest, for compatibility reasons. Gene annotations are from NCBI RefSeq. Enhancer annotations are from Enhancer Atlas V273. Corresponding to the cell types of the data origin, MLL3/4 data was analyzed with R1 enhancer annotation, while MLL2 and SET1A data were analyzed with V6.5. For S. pombe, the Ensembl_EF2 genome was used.For Arabidopsis, the genomic data were reanalyzed from the following works; H3K9me2, DRR02325113; H3K9ac, GSM701925 and H3K18ac, GSM70192774; H3.1, GSM856055 and H3.3, GSM85605475; H1, GSM2544793 and H2A.Z, GSM254479176; H3K14ac, GSM205128577; Like-Heterochromatin Protein 1, GSM202810878; H3K56ac, GSM202781879; H3K23ac, GSM1701017 and H4K16ac, GSM170101880; H3K9me1, GSM124241181; and H2B ubiquitination, GSM309201682. Growth conditions and the tissue-of-origin of these data are summarized as Supplementary Data For M. musculus, the genomic data were reanalyzed from the following works; SET1A44, MLL2 and its control45, MLL3/4, its control, RNAP2, ATAC and H3K27ac46, H4K16ac83, H3K36me284, H3K9ac, H3K56ac, H3K36me3 and H3K79me385, H4K12ac, H3K9me3 and H4K20me286, H3K18ac, H3K122ac, H4K5ac, H4K8ac and H3K27me387, and DNAme88. The exact names of the analyzed files are provided in Supplementary Data For Arabidopsis, ChIP-seq and ATAC-seq data were processed essentially as described in14. Briefly, our sequenced data were mapped to the TAIR10 reference genome using bowtie89 -v 2 -m 1 option. In cases of spike-in ChIP-seq, the sequenced data were also mapped to the S. pombe genome. Mapped reads for single-end sequence reads were extended to 250 bp to represent sequenced fragments, then counted for coverage over the region of interest specified as bed files, using SAMTools90 and BEDTools91. The coverage was RPM/RPKM normalized with custom scripts. Metaplot profiles and heat maps were generated with ngs.plot92. mRNA-seq data were also processed as described in14 using STAR93. BS-seq data were processed as described in13 using Bismark94. For the reanalyses of the published data, SRA data were adapter trimmed following the original papers’ procedure, mapped, and counted essentially the same as above.For M. musculus, SRR data of ChIP-seq targeting H3K4 methyltransferases were adapter-trimmed and mapped to mm9 reference genome with -v 2 -m 1 option, then counted and normalized as described above. The other genomics data of M. musculus, namely the data of chromatin modifications used for the random forest, were curated and parsed from bigWig format provided as supplementary files at Gene Expression Omnibus (GEO)  compared to WT for each mutant. The regions from TSS to TTS of the 3000 genes were split into 6 bins. For the WT and the six mutants, the number of reads mapped to each of the 6 sub-gene regions were counted, RPM normalized, and the difference between WT and the mutant was calculated. To capture the trend of which ones of the 6 bins lose H3K4me1, 6 bins in each one gene were standardized. Thus, for each mutant, a matrix of 6 regions x 3000 genes was obtained. These matrices were vectorized, and Euclid distances were calculated, which were used for hierarchical clustering Fig. . To explArabidopsis, differentially modified regions were identified by comparing mutant to WT using MACS296 with option -g 1.3e8. Peaks detected in centromeres are removed. Peaks were assigned to overlapping genes by bedtools ‘intersect’ with default options. If a peak is assigned to multiple genes, all genes are kept. If multiple peaks are assigned to one gene, one gene is added to the list.For 96 with two settings; the default (-g 1.3e8) and more relaxed option (-g 1.3e8 -q 0.3 –nomodel). Both counts are provided as Supplementary Data. M. musculus, enrichment peaks of H3K4 methyltransferases were called with default options of MACS2. The distances of the summits of the called peaks from the nearest TSS or the midpoint of enhancer region  were measured and visualized as a histogram. Based on the histogram, we identified the region that SET1A and MLL2 typically occupy to be 150 bp upstream and 300 bp downstream of TSS, coinciding with the ‘TSS region’ of Arabidopsis. Similarly, we defined ‘enhancer region’, where MLL3/4 localizes, to be 900 bp on both sides from the center of Enhancer Atlas’s annotation.Enrichment peaks of H3K4 methyltransferases were identified by comparing against negative control (ChIP-seq with FLAG antibody in non-transgenic WT) using MACS2Arabidopsis and to S/S.p.). RWT_H3 : Rmutant_H3 = RWT_H3K4me1 : Expected Rmutant_H3K4me. /(Expected Rmutant_H3K4me1) for each mutant was plotted in the bar graph. The raw counts of reads are summarized in Supplementary Data The summary statistic R, plotted in Supplementary Fig. For ATX1, ATX2, and ATXR7 models, genes shorter than 500 bp were filtered out. Gene was divided into three regions; TSS region, TTS region (definitions described above), and “gene body” region. The “gene body” region is the transcribed region minus TSS and TTS regions. Similarly, for ATXR3 models, the transcribed regions of genes longer than 800 bp were divided into TSS, TSS2, TTS, and “gene body2” regions. “Gene body2” region is the coding region minus TSS, TSS2, and TTS regions.Explanatory features are levels of chromatin modifications/chromatin states calculated over these three regions per gene. We generated data from samples under the same conditions as ATX(R) ChIP for some modifications that seemed to be particularly variable or well-studied  to minimize tissue/stage derived variation. For the other modifications, raw read files reported by preceding works . We uniformly set the number of bound genes to 3000, at the potential expense of predictive accuracy, in order to reduce parameter variance between models.Objective variables were set as two classes; ATX(R)-bound and -unbound genes. ATX(R)-bound genes are defined as the top 3000 genes that have the highest ChIP-seq signal -Tag expressing lines - negative control) in the typical occupying region defined from the above-mentioned peak calling; TSS region for ATX1 and ATX2, TTS region for ATXR7, TSS2 region for ATXR3. ATX(R)-unbound genes are the rest of protein coding genes -unbound genes were randomly selected, and the data on chromosome 1 to 4 were used for training random forest (R package ‘RandomForest’), while data on chromosome 5 were held out as test data.For SET1A, MLL2, and MLL3/4 models, explanatory features are levels of chromatin states calculated over TSS regions or enhancer regions, using previously reported data in mESC . Objective variables were set in the same manner as ATX(R)s models; that is, Set1/MLL2/MLL3/4-bound regions were defined as the top 3000 regions with the highest TSS region signals regions or enhancer regions, and unbound regions were down-sampled into 3000. 25% of the data were held out from the training and used as test data.Arabidopsis and M. musculus, the random sampling and training steps were repeated 5 times, and the average and the standard deviation of the importance derived from the 5 models were plotted. ROC was plotted based on the prediction of the 5 models, using test data.Both for In order to simulate ATX1/2 localization in the absence of H2Bub, a feature matrix, in which all H2Bub data was replaced with 0, was given to the trained random forest models to make predictions. ‘Genes that lose ATX1 in the in silico hub’ was defined as genes that are predicted to be ATX1-bound at least once among the 5 models that constitute the original random forest model, and predicted to not to be bound at least once among the 5 models that are given the H2Bub-0 feature matrix.97. Briefly, DNA sequences on the coding strand of TSS regions were converted to k-mer frequency vectors of 4^k features. The frequency vectors were standardized and given bound or unbound labels in the same way as random forest.Application of SVM to DNA sequence essentially followed previous work on human enhancer classificationThe data on chromosome 5 were held out as test data. Data on the chromosome 1 to 4 were used for 5 cross-fold validation for training to select the optimal parameters. For each trial of cross-fold validation, data of the unbound class were randomly sampled independently to balance the two classes. The averaged weights of features (k-mer sequences), which were calculated based on the 5 independently trained models using the best parameters, were used for further motif annotations. ROC was plotted based on the prediction of the 5 models with test data. We trained 5,6,7-mer lSVM or 6-mer kernel-SVM to find that the accuracy of 6-mer lSVM was about the same or better than the rest , which allows us to interpret the highly weighted motifs to be predictively important ones. Using the weights averaged from the 5 best models, sixty 6-mers that had the largest positive weights, largest negative weights, and smallest absolute weights, and sixty random 6-mers were selected.98 package in R.To make clusters of related motifs within each group of sixty 6-mers, similarity scores were calculated between all pairs of the 6-mers. Similarity scores are defined as; reverse complement (=1) > 1 nt offset > 1 nt offset on the opposite strand > the others (=0). The resulting similarity score matrices were visualized with the qgraphArabidopsis databases included in the MEME Suite’s motif_databases.12.1999. For each motif, TOMTOM hits were summarized in Supplementary Data Next, to annotate the 6-mers while considering the context the 6-mer is located, we calculated the ATGC frequency in the flanking three bases on both sides of each 6-mer. The flanking ATGC frequency of the positively weighted sixty 6-mers was calculated with the sequence of ATX-bound TSS region, while negatively weighted and random/near-zero weighted 6-mers were calculated with the sequence of ATX-unbound and random TSS region, respectively. The resulting probability matrices representing 3 nt + 6 nt + 3 nt were converted into meme format and searched for matching motifs using TOMTOM against all the 100 score calculated by ref. Arabidopsis genes as an index of sequence conservation.The PhastConSpearman correlation between mRNA-seq and ChIP-seq data of H3K4me was calculated using all protein-coding genes. mRNA-seq data was FPKM normalized, ChIP-seq data targeting H3K4me1 was RPKM, H3K4me2/H3K4me3 was RPM normalized. For color-filled contour plots, genes for which no transcription was detected were excluded.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


Associations of chromatin with the nuclear lamina, at the nuclear periphery, help shape the genome in 3 dimensions. The genomic landscape of lamina-associated domains (LADs) is well characterized, but much remains unknown on the physical and mechanistic properties of chromatin conformation at the nuclear lamina. Computational models of chromatin folding at, and interactions with, a surface representing the nuclear lamina are emerging in attempts to characterize these properties and predict chromatin behavior at the lamina in health and disease. Here, we highlight the heterogeneous nature of the nuclear lamina and LADs, outline the main 3-dimensional chromatin structural modeling methods, review applications of modeling chromatin-lamina interactions and discuss biological insights inferred from these models in normal and disease states. Lastly, we address perspectives on future developments in modeling chromatin interactions with the nuclear lamina. Genome organization relies on chromosomal interactions  and at ts (LADs) . Some LAs (LADs) , underscComputational modeling of chromatin structure  creates Here, we highlight the heterogeneous nature of the NL and LADs, outline the main 3D chromatin structural modeling methods currently used, review computational models of chromatin-NL interactions, and discuss biological insights deducted from these models in normal and pathological conditions. Lastly, we address perspectives on applications of modeling interactions of chromatin with the NL with the aim of better appreciating the multiple facets of functional genome organization.via LADs, and thereby radially organize the genome  and B-type lamins   . The NL 2 genes) . Studies2 genes) . Additio2 genes) . Interes2 genes) . Whether2 genes)  remains 2 genes)  and provIn mammalian cells, hundreds of LADs have been mapped throughout the genome  using vaLADs are a general feature of genome organization, but not all LADs have similar properties . Some arLMNA gene  polymer physics models chromatin as a semi-flexible polymer chain that can adopt many configurations within physical constraints applied to the chain; 2) restraint-based modeling represents chromatin by beads in a Euclidian space with restrained interactions between them commonly determined from chromosome interaction data. We next provide an account of these modeling approaches in light of their relevance for modeling chromatin configuration at the NL. For details on 3D genome modeling methods, we refer to an excellent exhaustive review .P of the polymer—that is, the length under which the polymer behaves as a rigid rod and above which it behaves as a flexible chain. The repeating units of chromatin, modeled as monomers in the polymer chain, further limit the number of conformations the polymer can adopt during simulations; this limitation is typically achieved by introducing a self-avoidance effect to prevent clashes between monomers  of similar epigenomic signatures, or “colors”  Figure . Self-avhromatin . Despitehromatin .a priori knowledge of the modeled chromatin region, such as chromatin states or transcription factor binding profiles. Block copolymer simulations can also model the behavior of a chromatin chain that can assume various thicknesses and compositions, providing a more realistic view of chromatin  that mediate these interactions  Figure . Again, hromatin . Polymerhromatin .Other chromatin 3D modeling approaches use restraint-based methods to infer spatial information directly from experimental data and reconstruct structures without assumptions on folding mechanisms. Contact matrices derived from Hi-C other 3C-sequencing data are used to identify pairs of interacting domains as primary constraints. One restraint-based approach is to reconstruct a single consensus structure representing the average of 3D conformations in the cell population under study . ConsensTo enable this, other methods simulate many structures. Resampling methods carry out a large number of independent optimizations from the same input data. Optimizations start from, most commonly, a random chromosome configuration and use the same scoring function aiming to reach a state where no constraint violations remain, producing a quasi-stable structure  Figure . Of noteWe have also reported ensembles of chromatin structures  relying Chromatin folding at, and interactions with, the NL have been modeled in attempts to identify physical processes driving these events, infer mechanisms of chromatin association with, and dissociation from, the NL, and provide more accurate spatial genome structures at the nucleus level.C 360 nm representing a ∼50 kb region to enable modeling interactions of small vLADs or euchromatic sub-LAD regions  or heterochromatin (higher persistence length) at the NL can be approximated  surface representing a NL, fitted with an attraction potential towards the polymer . Chromat regions . The poled chain . Furtheroximated .Data from simulations indicate that in the absence of attraction potential from the NL surface, the polymer must be pinned by at least one bead to enable any interactions with the surface. Second, a flexible chain explores a greater area than a semi-flexible or rigid chain, so one may infer that a euchromatic region can explore a greater space near the NL than a more rigid heterochromatic domain . Third, Without attraction potential, polymer interactions with the surface are only transient and barely detectable (except at the anchors). This is consistent with other models where turning off an attraction potential between the NL surface and a chromatin polymer promotes polymer detachment . In contThe absence of attraction potential towards a NL may still enable expected polymer positioning relative to a physical constraint. Indeed, when a self-avoiding polymer is confined to a nucleus sphere, non-specific entropic forces alone can remarkably shape and position chromatin polymers in the sphere and approximate high-order localization of loose (thin) and compact (thick) segments in the sphere center or periphery, respectively .These findings have been extended by modifying the polymer-surface interaction parameter as a fraction of the chromatin polymer bound to the NL, and introducing a chromatin volume fraction (modeling a hydration effect) and an intra-chromatin attraction potential . Tuning in vivo chromatin imaging observations and, with only a small number of parameters, account for changes in phase separation (chromatin vs. aqueous) that may drive mesoscale chromatin reconfiguration in developing Drosophila larvae  and degradation of the NL, which releases heterochromatin from the nuclear envelope and elicits SAHFs . To disein silico by introducing LBR as a “binder” mediating these interactions in block copolymer models , which are only detectable in later passage . NonetheData from simulations are not uncommonly interpreted as “explaining” a biological observation, as exemplified in some recent studies . WhereasIntegration of genomic datasets from wet-lab experiments into restraint-based models of 3D genome structure have provided new spatial insights into genomic consequences of pathological states, which may open to new therapeutic avenues. For instance, statistical analyses of Chrom3D models of human fibroblast genomes indicate that UV-induced DNA lesions are predominantly detected in LADs, suggesting greater UV-susceptibility of chromosomes at the nuclear periphery . Even moCorroborating these findings, 3D genome models generated from Hi-C and radial positional information of loci reveal a decrease in the frequency of single nucleotide polymorphisms from the nuclear periphery towards the nucleus center, especially of those associated with melanomas or lung cancer . This isThese studies provide seminal examples of how restraint-based genome models may generate predictions on consequences of pathological insults or mutations on genome integrity with a 3D perspective. Currently, these models cannot explain a pathology, but hypotheses generated from statistical analyses of modeled structures open the door to better designed studies aiming to target specific regions in a 3D nucleus space which would not be predicted from one-dimensional data.A powerful outcome of computational models of chromatin behavior is the opportunity to generate testable hypotheses on the physical properties of chromatin. A recent elegant example is the use of block copolymer models of chromatin to make predictions on the nature and dynamics of homotypic chromatin interactions occurring in microphase-separated compartment throughout the nucleus . TestingDespite their increasing complexity and power, chromatin models still ignore some information inherent to chromosome structure important for nuclear function, such as additional physical properties of chromatin. For example, chromatin domains can intermingle or partly invade the NL. So chromatin cannot simply be considered as a hard entity (non-penetrable beads in models). Instead, beads may be fitted with a soft permeable outer zone and an adjustable penetrability factor to enhance flexibility of the chromatin chain and better approximate chromosome compaction . SimilarRecent analyses of NL structure and organization  suggest Polymer models of chromatin folding at the NL in progeroid cells  predict Further developments of 3D genome modeling to yield mechanistic insights into changes in nuclear architecture linked to disease would also be beneficial. Evidence implicates lamin mutations in alterations in LADs and chromatin conformation, and nucleus size and shape are also affected in cancer cells . IntroduDNA repeats constitute more than half of the human genome and play a role in re-wiring epigenomes and gene expression programs in a variety of developmental and pathological conditions . Repeat in situ hybridization techniques allow their visualization in nuclei  due to their repeat nature, repeat sequence reads are often discarded from analyses, 2) they are not relevant for the questions asked, or 3) they can simply be modeled as monomers (beads) alike any other chromatin domain. The latter is illustrated by the hidden inclusion of L1 repeats as LAD-associated topological domains (beads) in restraint-based whole-genome 3D models . Today hn nuclei . Altogetn nuclei .Recent evidence indicates that the NL constrains enhancers at the nuclear periphery, within LADs  and betwin situ hybridization (Combinations of more sophisticated computational approaches with rapidly evolving wet-lab technologies such as high-throughput genome editing , live chdization  and biopdization , will ex


Human poly(ADP)-ribose polymerase-1 (PARP1) is a global regulator of various cellular processes, from DNA repair to gene expression. The underlying mechanism of PARP1 action during transcription remains unclear. Herein, we have studied the role of human PARP1 during transcription through nucleosomes by RNA polymerase II (Pol II) in vitro. PARP1 strongly facilitates transcription through mononucleosomes by Pol II and displacement of core histones in the presence of NAD+ during transcription, and its NAD+-dependent catalytic activity is essential for this process. Kinetic analysis suggests that PARP1 facilitates formation of “open” complexes containing nucleosomal DNA partially uncoiled from the octamer and allowing Pol II progression along nucleosomal DNA. Anti-cancer drug and PARP1 catalytic inhibitor olaparib strongly represses PARP1-dependent transcription. The data suggest that the negative charge on protein(s) poly(ADP)-ribosylated by PARP1 interact with positively charged DNA-binding surfaces of histones transiently exposed during transcription, facilitating transcription through chromatin and transcription-dependent histone displacement/exchange. Poly(ADP-ribosyl)ation (PARylation) is an essential covalent protein modification introduced by various poly-ADP-ribose polymerases (PARPs), a family of enzymes that transfer adenosine diphosphate ribose (ADP-ribose) from NAD+ onto a variety of proteins ,2. PARP1The members of the PARP family of enzymes participate in regulation of gene expression ,19,20,21Herein, we utilized in vitro approaches for analysis of the effect of human PARP1 and PARylation on the efficiency of transcription through chromatin and the accompanying histone displacement/exchange. Our data suggest that both PARP1 protein and its catalytic activity are required to facilitate transcription through chromatin and transcription-dependent histone displacement during transcription. Accordingly, anti-cancer drug and catalytic inhibitor of PARP1 olaparib strongly represses PARP1-dependent transcription in vitro.32P] GTP (-UTP reaction) to advance yPol II to the position -80 (EC-80) along the template  and to pulse-label RNA. Then, the templates were incubated in the absence or presence of PARP1 and NAD+, and transcribed in the presence of an excess of all unlabeled NTPs lation (PARylation) of the target proteins; PARP1 itself is one of the primary targets for auto PARylation. Consistently, in our transcription reaction PARP1 is heavily self-PARylated in NAD+-dependent manner . The othAs expected, during transcription of the nucleosomal templates in the absence of PARP1, characteristic pauses were observed at the positions +(11–15), +(26–27), +(35–37) and +(45–48) bp from the promoter-proximal nucleosome boundary b 28]. T. T28]. TIn contrast, in the presence of PARP1/NAD+, when PARP1 was able to PARylate itself , core hiThe experiments described above were conducted using yeast yPol II; yPol II is an enzyme that shares many important properties relevant for transcription through chromatin with human Pol II (hPol II) . HoweverPreviously, transcription through a nucleosome was conducted using promoter-initiated hPol II . In compTo further evaluate the possibility that PARP1-induced PARylation strongly facilitates yPol II transcription through the nucleosome, we used a mutant PARP1 enzyme with the catalytic activity compromised by the mutation E988K . TranscrTo further evaluate the role of PARP1-induced PARylation in yPol II transcription through the nucleosome, transcription was conducted in the presence of PARG—an enzyme that strongly reverses PARylation by PARP1 . The outTaken together, the data suggest that in the absence of NAD+, PARP1 moderately inhibits transcription through the nucleosome, likely forming PARP1-nucleosome complexes ,40,42. IThe pattern of nucleosome-specific pausing is dictated by formation of various intermediates containing nucleosome with perturbed structure during transcription ,43. TherTo study the effect of PARP1 on the pausing, time-courses of transcription through the 603 nucleosome in the presence or absence of PARP1 were analyzed a. yPol Ip value < 0.01) of the entire set to the sequential model using KinTek software  that facilitate transcription through the nucleosome likely interact with the positively charged octamer surface exposed during transcription, it is expected to compete with the DNA-histone interactions in the nucleosome, and thereby destabilize transcribed chromatin, and possibly induce histone eviction. To analyze the efficiency of histone eviction during transcription, 603 nucleosomes were DNA end-labeled, transcribed in the absence or presence of PARP1/NAD+, and the products were analyzed by native PAGE a. TranscSince the catalytic activity of PARP1 is essential for its effect on transcription through the nucleosome, it is expected that clinical PARP1 inhibitors affecting the active center of the enzyme  woulIn summary, our data show that human PARP1 strongly facilitates transcription through the nucleosome in the presence of NAD+ , and itsPreviously, we and others have shown that non-PARylated PARP1 can interact with nucleosomes ,39,40 anIn contrast, when NAD+ and PARP1 are present in the reaction, transcription through the nucleosome by Pol II is strongly facilitated. Since catalytic activity of PARP1 is essential for this activity, PARP1-dependent PARylation of one or several proteins present in the reaction  region of nucleosomal DNA , where tAs we have shown previously ,51, in tOlaparib is a competitive inhibitor of the catalytic center of PARP proteins , and is Our in vitro data suggest the following scenario for PARP1 action on transcribed genes. PARP1 is associated with nucleosomes at the promoters, replacing linker histone H1 ,27,54 anOur data suggest that the enzymatic activity of PARP1 is essential for more efficient overcoming of the nucleosomal barrier to transcript elongation by Pol II and for nucleosome survival during this process. During transcription, one or some of the proteins participating in transcript elongation through a nucleosome are poly(ADP-ribosyl)ated by PARP1 (PARP1 itself and/or Pol II), thus acquiring a negative charge. The negatively charged surfaces of the protein (s) likely interact with positively charged surfaces of other proteins (most likely core histones) that are normally involved in formation of the nucleosomal barrier to transcription through their interaction with nucleosomal DNA. As a result, the histone-DNA interactions are weakened, the height of the nucleosomal barrier is decreased and Pol II can more efficiently traverse the nucleosome. This function of PARP1 is probably essential for transcription through the +1 nucleosome and possibly other nucleosomes on a gene in vivo.TspR1 and purified by a gel extraction kit . Then, DNA templates were mixed with purified chicken erythrocyte H2A/H2B histones dimers and H3/H4 histones tetramers at a 1:1.8:1.2 molar ratio in the presence of shredded salmon sperm DNA (in three-fold weight excess over DNA templates) in the following buffer: 2 M NaCl, 10 mM Tris-HCl (pH 7.4), 0.1% NP-40, and 0.2 mM EDTA (pH 8). The mixture of DNA and histones was then dialyzed at 4 °C against set of buffers containing 10 mM Tris-HCl (pH 7.4), 0.1% NP-40, and 0.2 mM EDTA (pH 8.0), and stepwise decreasing concentrations of NaCl , as described  NTPs to pulse-label the RNA. Reactions were then incubated at 22 °C in the absence or presence of 100 nM PARP1 and 100 μM NAD+ for 15 min. Transcription was then resumed by the addition of a large excess of unlabeled NTPs in the presence of 150 mM KCl for 10 min. Samples were then analyzed on 8% denaturing poly-acrylamide gel (19:1), and the bands were scanned on Pharos FX Plus Molecular Imager .Yeast Pol II elongation complexes were assembled on a 50-bp DNA fragment by incubation of nine-nucleotide RNA fragment, yPol II and 59-bp non-template strand as described [32P] ATP prior to the assembly of elongation complexes, and transcription was carried out in the presence of unlabeled NTPs at 150 mM KCl, as described above for transcription experiments. Samples were analyzed on 4% native poly-acrylamide gel (39:1) in 0.5× TBE. Amount of histone-free DNA was then calculated as a fraction of transcribed templates using OptiQuant Software, Version 04.00   GTP to advance the polymerase to the -5 position and were then incubated in the presence of all NTPs in TB150 for 10 min at RT. p value  was calculated by the software. Average rate constants and standard deviations were calculated from three independent experiments. Time-courses of the transcription through nucleosome were measured for indicated time intervals at 22 °C as described in ,44. The Transcription reactions were performed as described above. The proteins were then separated on 4–12% Novex PAAG  and transferred to PVDF membrane. The membrane was blocked with PBS-T +5% milk and then incubated with primary anti-PAR monoclonal antibodies  and anti-PARP1 polyclonal antibodies . After incubation with secondary HRP-conjugated antibodies signals were developed by incubation of the membrane with SurerSignal West Femto Maximum Sensitivity Substrate . Nucleosomes were incubated in the transcription buffer in the presence or absence of PARP-1 and/or bio-NAD+ at room temperature for 25 min. The proteins were then separated on 4–12% Novex PAAG , stained with Sypro Orange and transferred to PVDF membrane. The membrane was blocked with PBS-T +5% milk and then incubated with horse radish peroxidase (HRP)-conjugated streptavidin. Signals were developed after incubation of the membrane with SurerSignal West Femto Maximum Sensitivity Substrate .