Plasmodium falciparum, remain controversial. Here, we revisited the 5mC in the parasite genome and the function of PfDNMT2. Low levels of genomic 5mC (0.1–0.2%) during asexual development were identified using a sensitive mass spectrometry procedure. Native PfDNMT2 displayed substantial DNA methylation activities, and disruption or overexpression of PfDNMT2 resulted in reduced or elevated genomic 5mC levels, respectively. PfDNMT2 disruption led to an increased proliferation phenotype, with the parasites having an extended schizont stage and producing a higher number of progenies. Consistent with PfDNMT2’s interaction with an AP2 domain-containing transcription factor, transcriptomic analyses revealed that PfDNMT2 disruption led to a drastic alteration in the expression of many genes, some of which provided the molecular basis of enhanced proliferation after PfDNMT2 disruption. Furthermore, levels of tRNAAsp and its methylation rate at position C38, and the translation of a reporter containing an aspartate repeat were significantly reduced after PfDNMT2 disruption, while the levels of tRNAAsp and its C38 methylation were restored after complementation of PfDNMT2. Our study sheds new light on the dual function of PfDNMT2 during P. falciparum asexual development.DNA modifications are critical in fine-tuning the biological processes in model organisms. However, the presence of cytosine methylation (5mC) and the function of the putative DNA methyltransferase, PfDNMT2, in the human malaria pathogen,  Saccharomyces cerevisiae and the nematode worm Caenorhabditis elegans , and it is a widely accepted paradigm that methylation is introduced th DNMTs ,10. DNMTth DNMTs . Howeverth DNMTs . It locatolytica , distriba embryo , and shu mitosis , suggest tRNAASP ,17–19. Htivities . Loss ofscoideum ,21,22. Iio rerio ,24. In trganisms ,25–30.Plasmodium falciparum, responsible for about half a million human deaths annually  through morphologically different stages   coding region was detected  but the other an extremely low level (0.01–0.05%) -rich proteins  . This inchizont) . DNA metdetected ,44. Two 1–0.05%) ,45. Rece1–0.05%) ,46, wherP. falciparum. Here, using an accurate mass spectrometry method, we determined the presence of low levels of 5mC in the parasite genomic DNA during the IDC. We conclusively demonstrated the substantial DNA methylation activity of PfDNMT2 using an in vitro enzyme assay. Interestingly, the PfDNMT2-disrupted parasite had a more rapid growth phenotype partially due to enhanced merozoite production and invasion efficiency, which were further verified by genetic complementation and overexpression of PfDNMT2. Transcriptomic analysis revealed considerable alterations in gene expression that were consistent with the growth phenotype. In addition, the disruptant line showed substantially reduced methylation of the tRNAASP C38 and the level of tRNAASP, correlating with reduced synthesis of a poly-D-containing reporter. This study provides solid evidence of the dual role of PfDNMT2 in DNA and tRNA methylation in P. falciparum, which influences gene expression and protein translation.These controversial studies have prompted us to perform a detailed functional analysis of PfDNMT2 in P. falciparum 3D7 strain and transgenic lines were cultured according to a standard method with 0.5% AlbuMAX II added to the buffered medium  and O+ human RBCs. Cultivation was performed at 37°C in a gas mixture of 5% CO2, 3% O2 and 92% N2  was added to 1 μg DNA/sample, which had been digested completely to nucleosides for 14–16 h following our simplified one-step protocol  ,54. BriePfDNMT2 fragment [nucleotides (nt) 948–2118] was amplified using primers Int-F and Int-R    and clonomitted)  and subcPfDNMT2 domain deletion was further confirmed by Southern blot with a specific probe located in the homologous region, which was generated by DIG-labeling PCR  as the control. RNA harvested at an 8 h interval was reverse transcribed using the SuperScript III RNase H reverse transcriptase (Invitrogen). cDNA was PCR amplified with the following primer pairs: PfDNMT2 Fw/Rv and stRNA Fw/Rv (http://www.appliedbiosystems.com). Results were visualized as percentages of PfDNMT2 mRNA relative to the stRNA mRNA.Real-time quantitative PCR was performed to monitor the relative transcription of NA Fw/Rv . PCR wasTo assess the PfDNMT2 protein expression during the IDC, synchronized parasite cultures were lysed with 0.06% saponin, and the parasite pellet was washed thrice with PBS. Proteins were extracted by incubating parasite pellets with 2% SDS. For subcellular fractionation, parasites were under a freeze-thaw process three times. After centrifugation at 10 000g for 5 min, the supernatants were obtained as the cytoplasmic fractions, the pellets were suspended in 2% SDS, and the supernatants were used as the nuclear fractions after the same centrifugation procedure. Western blot analysis was performed using transgenic parasites with PTP-tagged PfDNMT2 and the anti-Protein C antibodies . Cytoplasmic and nuclear fractions of rings, trophozoites, and schizonts were equally loaded in SDS-PAGE for detecting PTP-tagged PfDNMT2. To monitor the GFP signal in live PfDNMT2::GFP parasites, images were captured using an epifluorescence microscope  after the nuclei were stained by DAPI at 0.5 μg/ml (Invitrogen).9 parasites were lysed in 5 volumes of the hypotonic buffer  at 4°C for 10 min followed by centrifugation for 20 min at 500 × g. The supernatant was harvested as the cytoplasmic fraction. Five volumes of the PA150 buffer  containing a protease inhibitor cocktail (Roche) were added to the pellets and mixed thoroughly. The lysate was centrifuged for 10 min at 16 000 × g, and the supernatant was harvested as the nuclear fraction. The supernatants were incubated with 100 μl (settled volume) of IgG agarose beads  at 4°C for 2 h. The beads were washed twice with PA150 and equilibrated twice with the TEV buffer . The beads were incubated with 2 ml of TEV buffer containing 150 U of TEV protease and rotated overnight at 4°C to release the PfDNMT2 and its associated proteins from IgG beads. The supernatant was collected, and the beads were rinsed with another 4 ml of the PC-150 buffer . These eluted proteins either proceeded directly to protein identification by LC–MS/MS or were subjected to further purification. For the second step of TAP, 7.5 μl of 1 M CaCl2 was added to chelate the EDTA from the TEV buffer, and the combined supernatant was incubated with anti-protein C beads for 2 h at 4°C. The beads were washed four times with PC150 and eluted with the buffer containing 10 nM EGTA/5 mM EDTA.TAP was performed using the PTP-tagged PfDNMT2 parasite line according to the published method ,57,59. BP. falciparum protein database (v01/2014). Data were filtered at 1% protein and 0.2% peptide false discovery rates (FDR) and at least two unique peptides per protein. Mascot DAT files were parsed into the Scaffold software for validation and filtering to create a non-redundant list per sample. Protein data were analyzed by significance Analysis of INTeractome (SAINT) using a probability threshold above 93% and an FDR below 1%  and separated briefly in a 10% Bis–Tris SDS-PAGE gel for 10 min. Proteins in the gel were excised, and in-gel digestion was done as described . The digbelow 1% . The probelow 1%  partner 7 infected RBCs harvested from ∼5% parasitemia culture (∼4 × 108 RBCs from WBC-depleted blood) or ∼200 ng of TAP eluate purified from ∼2.5 × 108 PfDAMT2::PTP parasite-infected RBCs with 100 ng of purified bacterial C5-methyltransferase as the positive control. Nuclear extract from ∼4 × 108 of uninfected RBCs from WBC-depleted blood was used as uninfected RBC control. SG1-1027, a DNMT inhibitor (The DNMT activity of nuclear extracts and the TAP eluate were measured using the EpiQuik™ DNA Methyltransferase Activity Ultra Kit (Fluorometric) (EpigenTek) following the manufacturer's instructions. Assays were performed in triplicate on 10 μg of nuclear protein extracts from ∼2 × 10nhibitor , at 10 ain vitro drug susceptibility assay was performed as previously described  was added and incubated at room temperature for 30 min in the dark before measuring fluorescence on a TECAN spark multimode plate reader using a 485/535 nm filter. Analysis of the obtained counts plotted against the logarithm of SG1-1027 was performed with GraphPad Prism software 8.0 by fitting a curve with nonlinear regression  to generate IC50 values.The escribed ,69. BriePfDNMT2 disruptant (ΔPfDNMT2), complementation , and overexpression  lines during the IDC were compared with the wild-type (WT) as described / (merozoites per μl)].Merozoite invasion assay was performed as described ,71. To iΔPfDNMT2, complementation, and overexpression lines by the established methods  using HISAT2. The expression levels and differential expression were calculated by FeatureCounts and DESeq2, respectively. The differential expression analysis was performed using a P-adjustment value of < 0.01 as the cut-off. RNA-seq data were also normalized by transcripts per million (TPM) to validate the DEseq2 results. Differentially expressed genes between transgenic parasites and WT were selected according to the following criteria: 1) the absolute fold change of TPM higher than 1.5 in biological replicates, and 2) P-adjustment from the DEseq lower than 0.1. RNA-Seq data were submitted to NCBI GEO  according to the manufacturer's instructions with minor modifications . Th. ThΔPfDNications . The ampications .ASP level in the ΔPfDNMT2, complementation, and overexpression parasite lines compared to the wildtype 3D7 parasite line, total RNA harvested from 30 h trophozoites were reverse transcribed using the SuperScript III RNase H reverse transcriptase (Invitrogen). cDNA were PCR amplified with the following primer pairs: tRNAasp F/R and stRNA Fw/Rv . Totally 100000 cells per sample from the two transgenic lines with 6D-GFP expressed episomally were assayed using the Beckman Coulter MoFlo Astrios flow cytometer. The relative GFP fluorescence intensities in 36 h trophozoites were measured with a Violet laser (395 nm), and bandpass filter of 450/50 nm for detecting Hoechst labeled nucleus, and a blue laser (488 nm) and bandpass filter of 530/30 nm for measuring GFP fluorescence in the cells. The flow cytometry data were analyzed with the FlowJo software. Real-time quantitative PCR with a pair of primers (6 × D-GFP-F and 6 × D-GFP-R) (6 × D-GFP transcripts levels in the WT and disruptant line at 30 h post-invasion (hpi) relative to mRNA of the endogenous control stRNA.To determine the translational efficiency of Asp-rich proteins, we designed a GFP reporter, where a 6 × Asp tag was introduced by infusion cloning into the  and D6R . The 6 × and D6R ,75 at thD-GFP-R)  was perfAsp levels were analyzed by paired t-test. Methylation levels at Cytosine 38 of tRNA were compared by paired Wilcoxon test. Spearman correlation was used to compare the transcriptomes among the parasite lines with PfDNMT2 manipulation and the published transcriptome data of WT parasites.Parasite proliferation rates were compared by two-way ANOVA. DNA methylation levels, DNA methyltransferase activities, cycle lengths, merozoite number in mature schizonts, merozoite invasion rates, and mRNA levels of the reporter and tRNAP. falciparum genome, we cultured the parasites in WBC-free human blood to eliminate contamination from human DNA. Flow-cytometry only detected ∼5–10 WBCs in one million RBCs after WBCs depletion , the SPT16 subunit of the putative FACT complex, a protein kinase, and two RNA-binding proteins Table . In the ΔPfDNMT2 parasite displayed an increased proliferation rate . To understand this growth phenotype, we examined the parasite development progression through different IDC stages. We found that the schizont stage of the ΔPfDNMT2 parasite was ∼3 h longer than the WT . We then determined if the increased proliferation rate of the ΔPfDNMT2 parasite resulted from enhanced efficiency of parasite multiplication or merozoite invasion. We found that the ΔPfDNMT2 mature schizonts contained significantly more merozoites than the WT parasite . Merozoites isolated from the ΔPfDNMT2 parasite also showed a higher RBC invasion rate . These data collectively indicate that the higher proliferation rate of the ΔPfDNMT2 parasite was mainly due to the higher number of merozoites produced during the longer schizont development and partially due to higher invasion capacity.To investigate the function of PfDNMT2, we sought to disrupt this gene by single cross-over recombination . PfDNMT2e Figure . StartinT Figure , signifie Figure . HoweverΔPfDNMT2 was caused by PfDNMT2 gene disruption, we performed genetic complementation and overexpression studies by episomally expressing PfDNMT2-GFP in the ΔPfDNMT2 and 3D7 parasites, respectively , as well as a slightly lower proliferation rate, shorter IDC length, and lower merozoite invasion efficiency . Taken together, these PfDNMT2 genetic manipulation studies demonstrated that PfDNMT2 is involved in parasite development, proliferation, and invasion.To corroborate that the phenotypic change of the ectively . The epiectively . Episomay Figure . Moreove7 of ΔPfDNMT2 parasites, which exhibited a ∼50% reduction compared to the WT parasite , 16% (0.19% versus 0.15%) and 26% (0.18% versus 0.13%) in the ring, trophozoite and schizont stages, respectively , in contrast, PfDNMT2 complementation and overexpression parasite lines became more resistant to SGI-1027 (IC50: 54.10 ± 0.32 and 55.81 ± 0.86 nM) compared to the WT (IC50: 49.9 ± 0.93 nM)  . CollectPfDNMT2 manipulation parasite lines  by RNA-seq. RNA was prepared from tightly synchronized parasites at 10, 30, and 40 hpi in three biological replicates except for ΔPfDNMT2 at the schizont stage, which was prepared at 43 hpi to compensate for the delay in development compared to the WT , 2.3% (121) and 20.3% (1054) of genes differentially expressed in the ring, trophozoite, and schizont stages, respectively, indicating that PfDNMT2 overexpression caused more profound disturbance of gene expression throughout the IDC, especially in the schizont stage . Us. UsPfDNMy Figure . Gene ont Figure , E. Upon only 1% , 0.4%  of 5mC in the m genome , whereasm genome , leading.16–1.31%m genome ,82. The m genome . In contm genome . Previoum genome ,89. TherPfDNMT2 disruption led to a significant decrease in the DNA methylase activity of the nuclear extract and a reduction in the global DNA methylation level in parasite genomic DNA compared to the WT. Third, PfDNMT2 complementation restored the 5mC levels, while PfDNMT2 overexpression further elevated parasite genomic DNA 5mC level up to 221% of that in the WT parasite  purified from the parasite using a TAP procedure displayed high-level DNA methylase activity, rivaling a bacterial DNA methylase included as the positive control. This result is in stark contrast to the insignificant DNA methylase activity of recombinant truncated PfDNMT2 expressed in bacteria, which might have improperly folded, missed essential DNA methylase motifs , or lacke Figure . The shirganisms ,82.P. falciparum genome have any functional significance? Our transcriptome analysis detected extensive transcription perturbance from PfDNMT2 disruption, with the most significant effect on the trophozoite stage coincidental with peak PfDNMT2 expression  indicates that PfDNMT2 is involved in transcriptional regulation. These data, together with the predominant nuclear localization of PfDNMT2 in ring and schizont stages, demonstrate PfDNMT2’s involvement in the transcription regulation of specific cellular pathways. Recent studies showed that DNMT2 has a novel function in RNA-mediated epigenetic heredity in mice .gkad248_Supplemental_FilesClick here for additional data file.


Fusarium oxysporum species complex (FOSC) includes both plant and human pathogens that cause devastating plant vascular wilt diseases and threaten public health. Each F. oxysporum genome comprises core chromosomes (CCs) for housekeeping functions and accessory chromosomes (ACs) that contribute to host-specific adaptation. This study inspects global transcription factor profiles (TFomes) and their potential roles in coordinating CC and AC functions to accomplish host-specific interactions. Remarkably, we found a clear positive correlation between the sizes of TFomes and the proteomes of an organism. With the acquisition of ACs, the FOSC TFomes were larger than the other fungal genomes included in this study. Among a total of 48 classified TF families, 14 families involved in transcription/translation regulations and cell cycle controls were highly conserved. Among the 30 FOSC expanded families, Zn2-C6 and Znf_C2H2 were most significantly expanded to 671 and 167 genes per family including well-characterized homologs of Ftf1 (Zn2-C6) and PacC (Znf_C2H2) that are involved in host-specific interactions. Manual curation of characterized TFs increased the TFome repertoires by 3% including a disordered protein Ren1. RNA-Seq revealed a steady pattern of expression for conserved TF families and specific activation for AC TFs. Functional characterization of these TFs could enhance our understanding of transcriptional regulation involved in FOSC cross-kingdom interactions, disentangle species-specific adaptation, and identify targets to combat diverse diseases caused by this group of fungal pathogens.The  Fusarium oxysporum (FOSC) has been used as a model to study cross-kingdom fungal pathogenesis. Members within FOSC can cause devastating fusarium wilt diseases among economically important crops , Fo5176raport11 , Fo47 [2raport11 , and Folraport11  using HIraport11 . Mapped raport11 , at whicF. oxysporum genomes. MUMmer/3.22 was applied to align scaffolds of genome assemblies against 11 core chromosomes of the reference genome Fo47 using default parameters. The scaffolds aligned to the core chromosomes of Fo47 with a coverage larger than 5% were annotated as core scaffolds. The rest of the scaffolds were partitioned as accessory scaffolds. Genes residing on the core and accessory scaffolds were annotated as the core and accessory genes, respectively.The genome partition results for chromosome-level assemblies were retrieved from previous reports for Fol4287 , FoII5 , Fo5176 Protein sequences were aligned via MAFFT/7.313 . The iqtyEI):To understand genome regulation among FOSC, we developed two expansion index scores. The first uses two yeast lineages as the baseline (fEI), we directly compared F. oxysporum with its Fusarium relatives to calculate the expansion index as follows:In the second index score , and four other filamentous fungal species . To maintain consistency, the protein sequences for all of these genomes were retrieved from the MycoCosm portal [We compared 30 ascomycete fungal genomes  includinm portal .For a comprehensive TFome annotation, we used InterProScan (IPR) terms associated with fungal transcriptional regulation ,22 and cx) and the total number of TFs (y) within the same genome, we observed a strong positive correlation  (y = 17.239x + 3.553)  A. FOSC T 0.9361) . After p+ 3.553) . This suyEI):To understand the genome regulation among the FOSC, we developed an expansion index score using two yeast lineages as the baseline , TBP-associated factors (TAFs), RNA polymerase II elongation regulator Vps25, and CCAAT-binding factors (CBFs) related to ribosomal biogenesis.TBP mutations in humans [II28 are parts of the transcription factor TFIID complex. Interacting with TBP, TAFs form the TFIID complex and positively participate in the assembly of the transcription preinitiation complex [IIH works synergistically with TFIID to promote the transcription [S. cerevisiae had three members in each genome, and a clear single-copy orthologous relationship could be observed for each member , reflecting some level of gene family contraction among members of FOSC compared to the two yeast genomes .S. cerevisiae mating type protein alpha 1 has been reported to be a transcription activator that activates mating-type alpha-specific genes [F. oxysporum Mat1-1 type strains contained this TF, but not the Mat1-2 strains, even though sexual reproduction has not been observed in FOSC [TF family MATalpha_HMGbox including ic genes . Reflect in FOSC .A. nidulans, F. redolens, F. oxysporum strains NRRL26365, MRL8666, and PHW726 lost it. The functional implication of this loss remains to be discovered.TF family NOT4 was reported to be a component of the multifunctional CCR4-NOT complex, a global transcriptional repressor of the RNA polymerase II transcription . Most geThe contractions of the other two TF families, MADS SRF-like and HSF, were primarily caused by the whole genome duplication in yeast. In contrast to the contraction at the global scale, both TF families expanded among some FOSC strains when compared to other filamentous fungi .M. oryzae, MADS SRF-like TF, essential for the transcriptional regulation of growth-factor-inducible genes [S. cerevisiae genome contained two copies of this TF family, while all filamentous genomes encoded a single copy. However, we detected an average of 2.73 copies among the phytopathogenic FOSC strains. There were six copies in the genome of Fo5176, a pathogen of Brassicaceae plants including A. thaliana , with 1–2 copies encoded in ACs. Phylogenetically, all HSF TFs were clustered into three major clades, named as Skn7, Sfl1, and Hsf1 , HTH_AraC (yEI = 2), and HTH_Psq (yEI = 3.53), were absent in both yeast genomes, suggesting a gain of function among filamentous ascomycete fungi , bZIP (yEI = 5.80), Znf_C2H2 (yEI = 4.15), Homeobox (yEI = 2.28), PAI2 (yEI = 3.42), NDT80 (yEI = 3.47), and bHLH (yEI = 3.48). Based on the number of increments, the most significantly expanded TF families were Zn2-C6 (44 in yeasts versus 671 in FOSC) and Znf_C2H2 (40 in yeasts versus 167 in FOSC)  . Becausem < FOSC . AnnotatF. oxysporum including Ctf1 [Zn2-C6, a fungal TF family , was deting Ctf1 , Ctf2 [1ing Ctf1 , Fow2 [1ing Ctf1 , XlnR [1ing Ctf1  and Ebr1ing Ctf1 . Their fF. oxysprum TFs are Czf1 [Znf_C2H2 was reported to be the most common DNA-binding motif found in the eukaryotic transcription factors . Five reare Czf1 , Con7-1 are Czf1 , PacC [1are Czf1 ,107, Zafare Czf1 , and St1are Czf1 ,110. Claare Czf1 ,107.F. oxysporum bZIP TFs were reported including Atf1 [S. pombe, a homeobox-domain containing protein Phx1 was reported to be a transcriptional coactivator involved in yeast fission. In M. oryzae, the homeobox-domain containing protein Hox played roles in conidiation and appressorium development [Bacillus subtilis [S. cerevisiae [Ustilago maydis [F. graminearum, a bHLH-domain containing protein Gra2 was reported to regulate the biosynthesis of phytotoxin gramillin [Penicillium digitatum is required for anti-fungal resistance and full virulence in citrus fruits [The other five families were bZIP, Homeobox, PAI2, Ndt80, and bHLH. The bZIP domain contains a region for sequence-specific DNA binding followed by a leucine zipper region required for dimerization . Three Fing Atf1 , Hapx [1ing Atf1 , and Meaing Atf1 , all of elopment . The TF subtilis . The TF revisiae ,118 and o maydis  by promoo maydis . The TF o maydis . In F. gramillin , while as fruits .yEI ≤ 2) accounted for 20% of the TFome, with an average 9.6 copies in each genome examined (The other 20 TF families (expanded but with examined .yEI = 1.52), CBFA_NFYB (yEI = 1.35), Znf_RING-CH (yEI = 1.11), and ARID (yEI = 1.25). The TF family Bromodomain contained Spt7. As a crucial part of the SAGA complex in yeast, Spt7 has been reported to recognize the acetylated lysines of histones and eventually lead to chromatin unwinding [S. cerevisiae Dls1) that regulate RNA polymerase II transcription by controlling the chromatin accessibility  [S. cerevisiae Rkr1) [S. cerevisiae, playing an important role in chromatin remodeling. This domain is also required to transcribe a diverse set of genes including some retrotransposons [Four TF families were functionally linked to chromatin remodeling including Bromodomain (nwinding . The CBFlencing) . The TF ae Rkr1) . The domae Rkr1)  such as nsposons ,129.S. pombe, every genome encodes one copy. Binding to a DNA motif that mediates pheromone response, Ste12 TFs were reported to regulate fungal development and pathogenicity [F. oxysporum, Ste12, downstream of the Fmk1-mediated MAPK cascade, is involved in the control of invasive growth and fungal virulence [TFs belonging to the Ste12 family are only found in the fungal kingdom. Except for genicity  and are genicity . In F. oirulence .S. cerevisiae is seen in three proteins: Spp41, which is involved in the negative expression regulation of spliceosome components [S. cerevisiae Mbf1, belonging to Cro/C1-type HTH, is a transcriptional coactivator [Among the others, the Znf_NFX1 domain has been found in NK-X1, a repressor of the human disease-associated gene HLA-DRA . The HMGmponents ; Nhp6a, mponents ; and Ixrmponents . Fep1, amponents . S. cerectivator .F. oxysporum, F. graminearum, and other phytopathogenic fungi (Fusarium genomes (Fusarium TFome size). These newly annotated TFs include homologs of those without a domain annotation  to the lowest score of 0.5  (fEI = 1), with one gene per genome. Ten of these conserved groups were functionally validated in F. oxysporum (fEI = 3.54), Cos1 , Ftf1/Ftf2 , Ebr1/Ebr2 , and Ren1 . We also identified PacC (fEI = 1.57) as the second most expanded group within the highly expanded Znf_C2H2 family. We will further discuss these six groups (The rk_head) . Among txysporum . Twenty xysporum . Expandex groups .F. oxysporum f. sp. phaseoli [Fol, deleting either Ftf1 (AC encoding) or Ftf2 (CC encoding) reduced the fungal virulence [Fusarium species, and the AC copy Ftf1 was only found in F. oxysporum and F. redolens , indicating a potential role in host adaptation [Both Cos1 and PacC are part of the Znf_C2H2 family. In elopment  and funcelopment . PacC haxysporum ,107. Thiaptation . Interesaptation .F. verticillioides [fEI score of 2 among the FOSC. However, the only reported study on its function is in F. oxysporum f. sp. melonis, regulating the development of conidiation [Fug1 has a role in pathogenicity (maize kernel colonization) and fumonisin biosynthesis in llioides . The delllioides . Neitheridiation .To understand the functional importance of FOSC TFs, we took advantage of two recently reported transcriptomics datasets ,25 incluBy examining patterns of expression , we founWe further reviewed the expression patterns of the reported TFs in Fol4287 . Out of F. oxysporum strains including Fow2 and Sfl1. Fow2, a Zn2C6 TF, is required for full virulence but not hyphal growth and conidiation in F. oxysporum f. sp. melonis [M. oryzae [Using a higher stringency filter, selecting TFs upregulated under all in planta conditions, we searched for: (1) conserved core TFs that may be related to plant colonization and (2) expanded AC TFs that could be related to host-specific pathogenicity. In the Fol4287, Fo5176, and Fo47 genomes, 95, 62, and 44 core TFs were upregulated during plant colonization, respectively. Among them, ten copies were highly conserved  as they  melonis . Sfl1 wa. oryzae .Ftf1 and Ren1  and made up most of the TFs (56.7%) found within the FOSC TFome. For example, Ftf1, a TF belonging to Zn2-C6 and is involved in tomato pathogenicity, was most significantly expanded to 10 copies of accessory Ftfs in the tomato pathogen Fol4287 genome. Eight out of 10 were induced during plant colonization.The unique expansion of some TFs, driven by ACs, may provide clues as to host-specific interactions. Acquiring additional TFs will modify existing regulatory pathways, and this will require fine-tuning existing networks for this group of organisms to successfully adapt to different hosts under diverse environments. A previous survey of kinome (the complete set of protein kinases encoded in an organism’s genome) among the FOSC and other Ascomycetes also revealed a positive correlation between the size of the kinome and the size of the genome , identicRens) in the Arabidopsis pathogen Fo5176 genome and is involved in plant colonization. High throughput approaches such as chromatin immunoprecipitation sequencing (CHIP-Seq) and DNA affinity purification sequencing (DAP-Seq) [F. oxysporum. Gene regulatory networks [This realization further emphasizes the importance of additional functional studies. Reverse genetics is a powerful tool in defining the functional importance of a TF. For example, TF Ren1, a disordered protein, was identified by genetic and molecular characterization . This TFDAP-Seq)  can be unetworks ,157 can Fusarium biology [Our study offers a comprehensive look at the regulation from the evolutionary perspective while also providing an easily implemented computational pipeline to compare TFs and other functional groups in fungi. A better understanding of functions of TFs will not only inform  biology , but can


Enhancer RNAs (eRNAs) are non-coding RNAs produced from transcriptional enhancers that are highly correlated with their activities. Using capped nascent RNA sequencing (PRO-cap) dataset in human lymphoblastoid cell lines across individuals, we identified inter-individual variation of expression in over 80 thousand transcribed transcriptional regulatory elements (tTREs), in both enhancers and promoters. Co-expression analysis of eRNAs from tTREs across individuals revealed how enhancers interact with each other and with promoters. Mid-to-long range interactions showed distance-dependent decay, which was modified by TF occupancy. In particular, we found a class of ‘bivalent’ TFs, including Cohesin, which both facilitates and insulates the interaction between enhancers and/or promoters depending on the topology. In short ranges, we observed strand specific interactions between nearby eRNAs in both convergent or divergent orientations. Our finding supports a cooperative convergent eRNA model, which is compatible with eRNA remodeling neighboring enhancers rather than interfering with each other. Therefore, our approach to infer functional interactions from co-expression analyses provided novel insights into the principles of enhancer interactions depending on the distance, orientation, and the binding landscapes of TFs. Genes are expressed from promoters, where regulatory signals are integrated from proximal and distal enhancers to determine the amount of RNA product. Enhancers act as distant regulatory elements for promoters through specific TF binding, while at the same time produce bidirectional enhancer RNAs (eRNAs). Regulatory networks by enhancers are key to most cellular processes, including development, cell-type differentiation, and stress response, whereas their misregulation could cause disease. Numerous studies revealed that a large majority of disease associated genetic variations affect TREs14. Understanding TRE networks requires knowledge of how TREs interact with one another, and the mechanisms through which this regulation is achieved.Regulation of transcription is achieved mainly through the binding of transcription factors (TFs) at transcription regulatory elements (TREs), such as promoters and enhancers4. Consequently, relying on RNA products to identify TREs emerged as an efficient approach. For instance, the FANTOM5 consortium used Cap Analysis of Gene Expression (CAGE) to create an atlas of enhancer activity across numerous cell types and tissues6. While CAGE is a simple and powerful method to quantify transcription initiation at genes, it is less well suited for the quantification of transcriptional activity at enhancers, which produce particularly unstable eRNAs. Sequencing methods that capture nascent RNA, such as global Precision nuclear Run-On sequencing with 5´-capped (m7G) RNA enrichment (GRO-cap or PRO-cap), measure transcriptional activity directly and are, therefore, better suited to the quantification of TRE activity15.The bidirectional production of RNA from TREs, including both enhancers and promoters, is one of the most important hallmarks of regulatory activity in vertebrates, as transcribed enhancers are most active16. By coupling co-expression analysis to TF binding profiles, the mechanisms of the regulatory circuits can be characterized. While physical interaction between TREs, charted through chromatin conformation assays21, remains the gold standard for mapping chromatin interaction networks, such interactions are not always readily detectable. Functional interactions identified based on TRE co-expression can, therefore, complement physical interaction maps to fill gaps in the regulatory network.After identifying and quantifying active TREs, which TREs control gene expression and how they do so becomes an eminent question. Systematic analysis of expression variation can reveal these targets and mechanisms. Co-expression network uses variation in expression across different samples to elucidate regulatory circuits19. We investigate the interactions between tTREs measured by PRO-cap in lymphoblastoid cell lines (LCLs) from ~ 70 individuals. We use co-variation between tTREs as an indicator of functional interactions between sites and explore how those interactions are globally affected by various transcription factors. We identified thousands of putative interactions and found global signatures of either facilitation or inhibition of interactions for multiple TFs. We also explored interactions between the different strands of neighboring tTREs and found evidence of cooperativity at sites with converging polymerases, discovering new rules of eRNA interactions and their potential roles.This study leverages the variation in transcription initiation at transcribed TREs (tTREs), majority of which are eRNAs21, we leverage our previously published PRO-cap datasets from ~70 YRI LCLs to study functional interactions between tTREs using a tTRE co-expression analysis17. This dataset includes 87,826 tTREs, of which about 1/5 correspond to promoters and the remainder are defined as putative enhancers. These were identified based on bidirectional transcription of a pair of nascent RNAs within 300 bps of each other. Of these, about 40% are variably expressed across the individuals. As a measure of co-expression, we used Pearson’s correlation coefficients for linear regressions across individuals of the PRO-cap signals between pairs of variably transcribed tTREs in short range near SLFN5 gene promoter and in long range at BCL2 super-enhancer locus . The distribution of the coefficients reaches background levels  by 1 Mb , and the majority of the coefficients above background are for tTRE pairs within 200 kb . This distance effect is unlikely to be bias from linkage disequilibrium (LD) of the genotypes at tTRE pairs, since the YRI population is known to have LDs smaller than 5 kb22 and a similar degree of distance decay is observed using only the tTRE pairs with independent genotypes .We explored whether the co-expression reveals the principles of functional interactions between tTREs, such as the rule of distance. When we bin the correlation coefficients based on the distance between tTREs, covariation of the PRO-cap levels decreases as the distance increases . The tTREs located upstream of the TSSs have a covariation pattern that gradually decreases upon distance. Interestingly, coefficient distribution to the downstream tTREs seem to drop faster with increasing distance. This is consistent with a recent report that intragenic enhancers are less activating for their host gene, and can even attenuate expression23.We also explored how covariation between variably transcribed tTREs and mRNA levels varied based on the distance between the tTRE and gene promoters or mRNA TSSs  datasets from ENCODE’s Factorbook repository26 in a representative LCL (GM12878) to determine the effect of TF binding on the global tTRE interaction trends.Binding of transcription factors can modify interactions between TREs. Insulator proteins such as CTCF can disconnect the communication between two regions27 between them and plotted the distribution of correlation coefficients as a function of distance . The presence of CTCF sites between tTREs reduced co-expression overall and within each distance bin. We also looked at the activating effect of P300 on co-expression, comparing tTRE pairs that are occupied by P30026 to ones where P300 is not bound. As expected, P300-bound tTRE pairs tend to have higher levels of co-expression than the unoccupied pairs . These examples show that our co-expression analysis is able to detect the expected effects of TF binding, both when the TF is intersecting and when it is occupying the tTREs.To examine the insulating effect of CTCF on functional interactions, we separated tTRE pairs into “no intersection” and “intersection” categories based on the number of CTCF ChIP-seq peaks26. To generate a metric of how TF binding modifies the distance-dependent decay of co-expression, we focused on tTRE interactions in the top 5th percentile of the correlation coefficient distribution at each distance bin, and plotted the top 5th percentile as a function of the distance. We then calculated the difference in the area under the curve (ΔAUC) between “occupancy”/”no occupancy” and “intersection”/”no intersection” tTRE pairs. We compared the ΔAUC to permuted background ΔAUC distributions to evaluate the significance of the difference .We expanded our analysis to all 60 TFs for which ChIP-seq data were available from the ENCODE repositorySupplementary Fig. 2d). Insulating TFs, such as CTCF, reduce tTRE co-expression both when intersecting and occupying tTREs. The opposite is true for activating TFs, such as P300. Interestingly, the activating category contains many TFs that are immune or B-cell specific . This pattern is no longer evident for tTREs that are far enough apart to make direct read-through unlikely (> 5kb) . Also, if the enhancer pairs are interlaced by another enhancer in between, the convergent correlation pattern disappears  would show lower overall correlation than the diverging strands (upstream minus and downstream plus) . We perfsappears . These fsappears .We explored the activity, architecture, and functional interactions of eRNA transcribing tTREs using the variation in transcription initiation across human LCLs. The results reveal the principles of functional interactions between tTREs dependent on distance, TF binding, and direction of transcription.Identification of transcriptional regulatory elements (TRE) based on capped nascent RNA sequencing provides a direct measure of transcriptional activity and, therefore, shows higher sensitivity. A direct measure of transcriptional activity is important, as non-productive transcripts such as eRNAs and uaRNAs are rapidly degraded in the nucleus. Other transcription-based approaches, such as CAGE and nuclear short RNA analysis, are impeded by this instability. As we showed, CAGE performs well in identifying promoters but less efficiently detects enhancers than PRO-cap. Additionally, by focusing on bidirectional eRNA transcription start sites (TSS) from PRO-cap data enabled us to filter out spurious transcription from only one strand. Taken together, we propose that PRO-cap provides a high quality set of tTREs.17. Our model suggests a single TF binding site between the eRNA TSSs as a universal architecture, whereas other models have proposed two TF binding sites for promoter TREs, one for each strand34. Our interpretation is that two adjacent highly-directional tTREs form a larger nucleosome free region, where convergent strands are below the detection limit, and identified as a single TRE.Bidirectional transcription has been widely accepted as a general architecture of TREs. Our previous analysis of transcription initiation (tiQTL) and directional initiation quantitative trait loci (diQTL) implies that both the central TF binding and the core RNA polymerase initiating regions must be taken into consideration when assessing the potential impact of non-coding genetic variantsDrosophila that are formed by paused RNA polymerases35.Beyond understanding individual TREs, we explored how TREs interact with one another to form cis-regulatory networks. Physical interaction maps have been the standard for identifying direct interactions. However, co-expression network approaches to identify functional interactions, though indirect, provide independent evidence for interactions and a good complement to physical maps. We were able to capture mid-range interactions (within 200 kb), which were dependent on distance and insulator/co-activator binding in a manner consistent with current knowledge. Interestingly, factors associated with strong promoters were insulating, indicating that strong promoters may dominate functional interactions and repress looping across them. This may be analogous to chromosomal boundaries in 23. To compare the tTRE correlations independent of the genotype correlations in the population structure (linkage disequilibrium), we used genotypes at the SNPs nearest to the TSSs with the minor allele frequency greater than 0.05, and selected tTRE pairs within 1 Mb distance whose associated genotypes are not correlated .We used the variably expressed tTREs , and calculated correlation coefficients of the 75 individual normalized readcounts  between two tTREs within 5 Mb distance . For the distance analysis, we binned the correlation coefficients by the distance between 2 tTREs. The bins are generated based on fixed distance intervals up to 1,024 kb, or a fixed number of tTRE pairs  with variable distance intervals. With the variable interval bins, we generated plots for the 5th percentile, median and the 95th percentiles of the correlation coefficient distributions within each bin of 1000 pairs along the distance , and we obtained the z-score = +8.34 and p = 7.26 × 10−17 for CTCF intersections affecting correlation decay plots. The intersection and the occupancy scores of all other 76 factors available for GM12878 in FACTORBOOK were calculated in this way. Only factors with sufficient number of binding sites in each category were reported. These p-values and z-scores were used in hierarchical clustering and t-stochastic neighbor embedding (tSNE) analysis to cluster TFs.The top 5 percentile of the correlation coefficients in the variable interval bins was used as an indicator of tTRE co-expression. This correlation coefficient percentile serves as a function of the distance, and we refer to it as the correlation decay plot. To test whether the abundance of ENCODE FACTORBOOK binding sites - located either between the tTREs or at the tTREs - affect the correlation decay plots, we calculated the difference between the area under the correlation decay curves (ΔAUC) in the 0 – 200 kb distance range. The significance of the ΔAUC was estimated by comparing to the background distribution of ΔAUC which was generated by random shuffling of the factor binding sites. We calculated the background ΔAUC distributions by 100 permutations of 5,000 – 70,000 randomly shuffled ENCODE factor binding sites in GM12878. The background ΔAUC distributions followed a normal distribution and were dependent on the number of binding sites. We approximated the expected mean and standard deviation (sd) of the ΔAUC dependent on the actual number of the specific factor’s binding sites, and used it to generate a z-score and p-value of the ΔAUC between correlation decay curves enriched or depleted with the factor binding sites. For example, CTCF contains 41,465 ChIP-seq binding sitesFor each tTRE, the nearest neighboring tTRE downstream was identified and a linear regression between their strands was computed across the samples according to the following categories: Sense , antisense convergent (plus-minus), and antisense divergent (minus-plus). Pairs of tTREs were binned based on the distance between them and the distributions of Pearson correlation coefficients were compared between the categories. Pairs within 250 bp were not included to avoid any possibility of the same read being counted in both tTREs.


We examine mechanisms by which trans-acting factors in Suv39, PRC2, SETDB1 and SIR writer systems regulate spreading of the respective heterochromatic marks across chromatin. While these systems are in some cases evolutionarily and mechanistically quite distant, common mechanisms emerge which these trans-acting factors exploit to tune the spreading reaction.Heterochromatin is a gene-repressive protein–nucleic acid ultrastructure that is initially nucleated by DNA sequences. However, following nucleation, heterochromatin can then propagate along the chromatin template in a sequence-independent manner in a reaction termed spreading. At the heart of this process are enzymes that deposit chemical information on chromatin, which attracts the factors that execute chromatin compaction and transcriptional or co/post-transcriptional gene silencing. Given that these enzymes deposit guiding chemical information on chromatin they are commonly termed ‘writers’. While the processes of nucleation and central actions of writers have been extensively studied and reviewed, less is understood about how the spreading process is regulated. We discuss how the chromatin substrate is prepared for heterochromatic spreading, and how  The name was coined by Emil Heitz and describes chromosomal domains that remain condensed throughout the cell cycle . We haverevisiae –7, and pnogaster . At the . 2While the heterochromatin systems we will discuss below do have some differences in their requirements for spreading away from DNA-encoded nucleation sites, there are some universal features of the substrate, chromatin, that either encourage spreading or hinder it. On a first level, it is useful to think of heterochromatin spreading as a reaction that can occur to re-establish the initial stage (domain maintenance), or, to establish a repressed domain for the first time  phenomenon. PEV occurs when a normally expressed gene becomes silenced in some cells. Silencing results from a change in the gene position, for example due to recombination (i.e. when the gene becomes juxtaposed to heterochromatin), hence the name 'position effect' [Drosophila as a tool to study heterochromatin formation [su(var)3–9 mutant has a dominant effect over the majority of PEV modifier mutations. Later, mammalian SU(VAR)3–9 homologous (human SUV39H1 and murine Suv39h1) were shown to be histone methyltransferases (HMTs) that selectively methylate lysine 9 of the histone 3 (H3K9me) tail through their SET domains [A great deal of our understanding of heterochromatin formation in the last few decades derives from studies with  effect' ,33–35. S effect' ,37; but ormation ,39. A flormation ,38, with domains . The fac. 3.2Drosophila for instance, the N-terminus of SU(VAR)3–9 was found to interact with the HP1 CSD both in vitro and in vivo [S. pombe: The cryptic loci regulator 4 3–9 homologue), initiates H3K9 methylation independently of Swi6 (the main S. pombe HP1 homologue), but then the subsequent spreading of H3K9 methylation across the domain is Swi6-dependent [Another important modifier identified from the screens is SU(VAR)2–5, which encodes a heterochromatin-associated protein  ,42. HP1a in vivo . HP1 thu in vivo . HP1s roependent . Whether. 3.3S. pombe, but also for feedback by Clr4 itself, as the CD of Clr4 is quite specific for H3K9me3 [. The HDAC function of Clr3 is also important for preventing histone modifications associated with active transcription and limiting RNA polymerase II accessibility at the repressed site  [Beyond this central positive feedback and signal amplification role of HP1 it further contributes to spreading in two ways: first, HP1 recruits to H3K9me marked chromatin a diverse set of factors (more than 100 putative interacting proteins were identified by mass spectrometry) including chromatin remodellers and modifiers, such as histone deacetylases (HDACs) –57. Thes H3K9me3 ,61. The lencing) ,63. As alencing) –66.. 3.4in vivo [in vitro upon phosphorylation [in vivo. Whether these condensates represent the cytologically observed dense domains of heterochromatin, where gene expression is typically repressed, is not fully clear. It is possible that in this context, this formed liquid droplet encloses heterochromatic sequences and helps to exclude the transcription machinery, triggering gene silencing by forming a ‘boundary’ that separates heterochromatin from the surrounding chromatin [Second, HP1 may be required to promote an environment inside the nucleus conducive to spreading. Swi6 connects heterochromatin to the nuclear periphery via the nuclear rim protein Amo1, which associates with Swi6-interacting FACT (see above) and RIXC complexes . Localizin vivo . These cin vivo . HP1 hasrylation ,28 and irylation . It has hromatin ,74. Whethromatin ,28. By c. 3.5in situ on the chromatin substrate and enable spreading. In S. pombe, the Clr4 complex contains an E3 ligase [et al. identified a complex of the Clr6 HDAC with the Fkh2 transcription factor as specifically required for heterochromatin spreading at constitutive sites. Fkh2 recruits Clr6 to nucleation-distal chromatin sites in such contexts [Additional chromatin modifications regulate H3K9me spreading, for example by regulating chromatin structure. One such mark is trimethylation at lysine 20 of H4 (H4K20me3). This mark is produced from H4K20me1 by the SUV4-20H1 and H2 enzymes  in the nucleus [Drosophila Ph [Drosophila PSC oligomerizes [Drosophila protein has been shown to mediate nucleosome compaction [As alluded to above, H3K27me3 after this initial local spreading is capable of long-range spreading apparently both  nucleus . There,  nucleus ,101. Nuc nucleus ,102–106.phila Ph  as well phila Ph . Similaromerizes  and the mpaction –111. It mpaction ,111. Thempaction , a modelmpaction ,30. Such. 4.5in vivo remains to be determined. Finally, whether a linked oligomerization/product recognition cycle is operational for this system is not clear.While PRC1 oligomerization appears to allow the connection of distal sites, this type of oligomer formation is different from the coupling of oligomerization to writer product reading we saw for the Suv39 system. There is no apparent evidence that PRC1 oligomerization is connected to H3K27me reading. However, recent evidence implies that different PRC2 types can form dimeric complexes. For example, EZH1 containing PRC2 can dimerize on the nucleosome . The str. 4.6Interestingly, in plants, H3K27me3 spreading shares some features with Suv39 H3K9me3 spreading, as uniquely in plants, an HP1 protein LIKE HETEROCHROMATIN PROTEIN 1 (LHP1) appears to act downstream or in parallel to PRC2 . LHP1 bi. 5. 5.1SET domain bifurcated 1 (SETDB1) is a specific H3K9 methyltransferase that primarily acts in euchromatin sequences to silence retroelements or developmental regulators. An extensive amount of research has been conducted on the recruitment of SETDB1 to retroelements. Nucleation requires recognition of target sites by KRAB zinc finger proteins (KRAB-zfps), which then recruit SETDB1 via the critical co-repressor KAP1  ,121. KAP. 5.2 1.α, HP1β and HP1γ to heterologous loci is sufficient to induce the recruitment of SETDB1 and deposition of H3K9me3 [α, HP1β and HP1γ and this occurs in part due to SETDB1-deposited H3K9me3 [β KO mESCs [HP1 may perform ‘double duty’ as a key spreading regulator for both Suv39 and SETDB1. Targeting of HP1 H3K9me3 ,133. In  H3K9me3 . There iKO mESCs . The HP1 2.Beyond HP1, a specialized complex has been identified that may be required for SETDB1 to mark target domains with H3K9me3. Using a similar strategy to find the PEV elements in drosophila cells, a non-lethal forward genetic screen conducted in haploid human KBM7 cells identified the human silencing hub (HUSH), which consists of the proteins TASOR, MPP8 and periphilin . TASOR aA first indication of H3K9me3 spreading by SETDB1 is that KRAB/KAP1 binding sites may be found only in one region of the retroelement, while H3K9me3 enrichment occurs throughout the entire retroelement ,127. SepIt is also noteworthy that HUSH targets are enriched within transcriptionally active chromatin, as opposed to classical heterochromatin regulators ,143, whi. 6. 6.1Saccharomyces cerevisiae SIR genes encode a family of nuclear proteins that are targeted to specific genomic sequences and targeted for silencing. SIR proteins are associated with three classes of genomic sequences: subtelomeres , silenced mating-type loci (i.e. HMR and HML) and rDNA sequences. To efficiently silence the HM loci, Sir1, Sir2, Sir3 and Sir4 are required, while Sir2, Sir3 and Sir4 are required to silence subtelomeres effectively. The central writer of the SIR complement is Sir2 which is a NAD-dependent deacetylase that targets histone H3 and H4.Yeast Silent chromatin assembles in two stages at the molecular level: nucleation and spreading. Nucleation occurs when the Sir2/3/4 complex is recruited to silencers for the first time. The spreading step occurs following the assembling of the extended domain of silent chromatin by the complex. As a result of the intrinsic properties of the SIR proteins, the nucleation and spreading steps are closely linked. Nucleation without spreading and spreading without nucleation can be studied through mutations and other experimental manipulations (e.g. ).The process of nucleation relatively well understood. The Sir2/3/4 complex is recruited to proteins bound at silencers by a network of interactions. Both Sir3 and Sir4 associate with the transcription factor Repressor-activator protein 1 (Rap1), while Sir4 also associates with Origin Recognition Complex (ORC)-bound Sir1 ,146. The. 6.2According to the original sequential model of spreading, Sir2 first deacetylates the nucleosomes adjacent to silencers, creating additional recruitment sites for Sir2/3/4 complexes . Sir3 prA more detailed mechanism has been proposed recently, involving Sir3 and Sir4 propagation along the fibre. The domain architecture of Sir3 has some conceptual similarities to HP1 in that it contains a nucleosome binding and dimerization domain. The N-terminus of Sir3 contains a bromo-adjacent homology (BAH) domain (amino acids 11–196). Many chromatin-associated proteins, including Orc1, Dnmt1, Rsc1, Rsc2 and Mta1 ,155, con. 6.3in vitro association of SIR2-3-4 complex and Sir3 alone with recombinant trinucleosomes is enhanced by AAR [in vivo [in vivo using Hos3 (where Hos3 is targeted by a SIR3-Hos3 fusion), a deacetylase of the Rpd3 family that does not consume NAD nor produce AAR [But unlike the case of HP1, there is another interesting wrinkle to the SIR system, and that is regulation by one of the reaction products: Sir2 and other NAD-dependent protein Sir2/Class III HDAC uses NAD as a cofactor for deacetylation. Two products derive from NAD: nicotinamide and O-acetyl-ADP-ribose (AAR) ,161. AARd by AAR . Similard by AAR ,164. The[in vivo . Moreove[in vivo . For exa[in vivo ,166,167.[in vivo , leavingduce AAR . Despite. 6.4Spreading via Sir3 is also downregulated or limited in several ways. A prominent example is the acetylation of H4K16, which has been shown to impact Sir3 chromatin association by mutational analyses, ChIP and co-immunoprecipitation studies, as well as biochemical studies ,169–171.. 7trans-acting factors regulate of heterochromatin spreading by writers in four systems. Some common principles emerge about how trans-acting factors promote the spreading of heterochromatin by writer enzymes. These are also summarized in In the above, we have attempted to summarize what is known about how trans-acting factors directly promote the positive feedback inherent in most heterochromatic writers. They do so by acting as a second, redundant feedback layer. The redundant layer, obvious in Suv39, SIR, and likely SETDB1 systems, consists of ‘reader’ proteins binding the writer product on chromatin and recruiting more writers via direct physical interactions.First, Second, through processes of oligomerization on the chromatin substrate, writers like HP1 and Sir3 stabilize the structure and ensure that opportunities for redundant feedback are present across the forming heterochromatin domain. How the writers however do not end up getting ‘trapped’ in the core of the domain remains unresolved. A hint at a possible mechanism is differential preferences for methylation states of the chromatin mark in systems where methylation is the instructive chemical change . Swi6 and Clr4 have differential preferences for H3K9me2 and me3, with Clr4 strongly preferring H3K9me3 . Since Htrans-acting factors likely promote spreading by shaping a microenvironment conducive to spreading, in part via nucleosome stabilization and possibly by producing an altered biophysical environment. Spatial segregation and self-attraction of heterochromatin has been predicted to be required for efficient spreading and inheritance by modelling approaches [in vivo that may be required to promote the stability of heterochromatin and its spreading. In principle, condensate formation may promote writer enzyme activity directly, for example by increasing local concentration and altering the chromatin structure, or by rejecting antagonistic factors, such as excluding acetylases, transcription factors, or RNA polymerase (Third, proaches ,30. HP1 proaches . Nucleoslymerase . In eithHowever, there are also unique aspects not shared across the different heterochromatin systems. Firstly, the polycomb system's ability to perform long-distance spreading via PRC1 clusters in PBs does not have an exact parallel for other systems, and may be required for the reliable silencing of large developmental loci via redundant of spreading from dispersed nucleation sites . SIR proMore work remains on unravelling how some of these unique mechanisms shape the spreading reaction and how they interface with the common operating principles discussed above.


The nuclear folding of chromosomes relative to nuclear bodies is an integral part of gene function. Here, we demonstrate that population-based modeling—from ensemble Hi-C data—provides a detailed description of the nuclear microenvironment of genes and its role in gene function. We define the microenvironment by the subnuclear positions of genomic regions with respect to nuclear bodies, local chromatin compaction, and preferences in chromatin compartmentalization. These structural descriptors are determined in single-cell models, thereby revealing the structural variability between cells. We demonstrate that the microenvironment of a genomic region is linked to its functional potential in gene transcription, replication, and chromatin compartmentalization. Some chromatin regions feature a strong preference for a single microenvironment, due to association with specific nuclear bodies in most cells. Other chromatin shows high structural variability, which is a strong indicator of functional heterogeneity. Moreover, we identify specialized nuclear microenvironments, which distinguish chromatin in different functional states and reveal a key role of nuclear speckles in chromosome organization. We demonstrate that our method produces highly predictive three-dimensional genome structures, which accurately reproduce data from a variety of orthogonal experiments, thus considerably expanding the range of Hi-C data analysis. Here, using population-based modeling on ensemble Hi-C data, the authors provide an expansive overview of how the genic chromatin microenvironment influences its potential involvement in different functions, such as transcription, DNA replication, and chromatin compartmentalization. Their results unveil a key role of nuclear speckles in genome organization. Recent advances have led to a prolific development of improved technologies in live-cell and super-resolution microscopy10, as well as mapping technologies based on high-throughput sequencing26, for probing chromosome interactions and three-dimensional (3D) organization30. However, mapping the 3D nuclear locations of all genes simultaneously in single cells remains a major challenge. Several experimental technologies probe the mean distances 13) or association frequencies (nucleolus-associated domain sequencing (NAD-seq)31; DNA adenine methyltransferase identification (DamID)16) of genes with nuclear speckles, lamina-associated domains (LADs), and nucleoli. However, these methods do not have the technical capacity to collect all this information simultaneously within the same cell, and the considerable cell-to-cell variability of chromosomal structures adds additional layers of complexity. Several multiplex fluorescence in situ hybridization (FISH) and super-resolution microscopy techniques have recently provided such information7. For instance, DNA- and RNA-multiplexed error-robust FISH (MERFISH) imaging has detected, within the same cells, the nuclear locations of 1,137 genes, together with the positions of nuclear speckles and nucleoli, as well as the amount of mRNA transcripts6. However, at this point, the amount of probed genomic DNA regions is still sparse, representing ~1% of entire genomes.The spatial organization of eukaryotic genomes is linked to regulation of gene transcription, DNA replication, cell differentiation, and, upon malfunction, to cancer and other diseasesHere, we introduce an approach for modeling a population of single-cell 3D genome structures to describe the nuclear microenvironment of all genomic regions in single-cell models, defined by their nuclear locations relative to nuclear landmarks and nuclear compartments. Our aim is to evaluate the roles of the nuclear microenvironment and its cell-to-cell variability in chromatin function and identify characteristic nuclear microenvironments that distinguish chromatin in different functional states.34. We demonstrate that our method produces—from Hi-C data alone—highly predictive genome structures, which predict with high correlation the cytological distances of genomic regions to nuclear speckles and lamina from SON TSA-seq13 and lamin-B1 TSA-seq13 experiments, contact probabilities to the nuclear lamina from lamin-B1 protein A-DamID (pA-DamID)35 experiments, mean radial positions from genomic loci positioning by sequencing (GPSeq)36 experiments, and distance distributions and single-cell chromosome tracing data from 3D FISH18 and DNA-MERFISH6 experiments, respectively. We define the nuclear microenvironment of a genomic region by an array of structural descriptors, including its nuclear radial position; association frequencies with and mean distances to nuclear speckles, the lamina, and nucleoli; the local chromatin fiber compaction; and local compartmentalization in form of the trans A/B ratio, defined as the fraction of its inter-chromosomal interactions with chromatin in the A (active) or B (inactive) compartment6  are linked to associations with nuclear speckles.Our genome structure analysis provides several key findings. First, genomic regions with a strong preference for the same specific microenvironment across cells, thus having low structural cell-to-cell variability, are also most homogeneous in their functional properties. These chromatins are associated in most cells with either nuclear speckles or constitutive LADs and act as structural anchor points to genome organization. Second, our analysis shows that the subnuclear microenvironment of a genomic region reflects its transcriptional potential upon activation. Genes with high expression heterogeneity56, none so far has documented the predictive accuracy in reproducing multimodal experimental data, as presented here. Our findings demonstrate that our approach, from Hi-C data alone, produces predictive models that provide a detailed description of the subnuclear locations, folding, and compartmentalization of chromatin in diploid genomes. Therefore, our approach considerably expands the scope of Hi-C data analysis and is widely applicable to any cell type for which Hi-C data are available.Although other computational approaches have modeled entire chromosomes, or even diploid genomes, from Hi-C dataGM12878) from in situ Hi-C data39 at 200-kb (kilobase) resolution. Our method generates a population of 10,000 genome structures, in which all accumulated chromatin contacts are statistically consistent with contact probabilities from Hi-C experiments32. Structure optimization is achieved by solving a maximum likelihood estimation problem in an iterative fashion32  and 0.69 (trans) of missing data, P = ~0; Extended Data Fig. 35, lamin-B1 TSA-seq13, SON TSA-seq13, and genomic loci positioning by sequencing (GPSeq)36 experiments  and predict a gradual increase in average radial positions for chromatin replicated at later times58  accurately predict the missing Hi-C contact frequencies  shows a preferential location at the periphery and A2 chromatin shows a wide range of average locations without a marked radial preference , to quantify stochastic variations of radial positions between cells  alternate, in sharp transition, with regions of low variability (δRAD < 0)—transitions between high and low variability occur over relatively small sequence distances  (A-LV)  and is mostly replicated at the earliest G1b phase58. A-LV chromatin also shows significantly higher transcriptional activity than chromatin in the A compartment with high structural variability (δRAD > 0) (A-HV)  Fig.  is enricery Fig. . MoreoveBy contrast, A-HV chromatin lacks SON TSA-seq signal enrichment and thus has larger mean distances to nuclear speckles, and is enriched for facultative inter-LADs Fig. . NotablyδRAD > 0) and lowly variable (B-LV) (δRAD < 0)  ( 0) Fig. , right g 0) Fig. . Subsequ 0) Fig. , right,  0) Fig. .62 , capturing well both peak sizes and signal distributions  across the entire chromosome profile, despite containing few A1 regions (6.4%)  Fig. . Chromatr = 0.86, P = ~0)  in Fig. δSpD, S-TSA in Fig. 6 with high correlation  between our models and the experiment . Interestingly, we find a strong anticorrelation between the inter-chromosomal contact probability (ICP) of a genomic region and its mean speckle distance (SpD) , N-TSA in Fig. Moreover, our models also predict nucleolus-associated structural features  in single-cell models with the heterogeneity of single-cell gene expression from single-cell RNA sequencing (scRNA-seq) experiments37. Cumulatively ranked single-cell distances of a genomic region to its nearest predicted speckle . Thus, genes with transcripts in a large fraction of cells are also located close to speckles in a large fraction of models. We also validated these findings with transcription frequencies measured from RNA-MERFISH microscopy for 1,137 genes6. Here as well, we observe the identical highly significant correlation between TRF and SAF   and r = 0.45, P = 4.1 × 10−50 (RNA-MERFISH))  Fig.  shows suH)) Fig. . These otrans A/B), while being depleted in δRAD, δSpD, and δNuD. Thus, T10 genes show a strong preference for the same specific microenvironment in different cells, while B10 genes do not—their microenvironment is highly variable between cells without clear association preferences to nuclear bodies.Moreover, we found that genes in the top 10% of genes with the highest numbers of transcripts (T10) are distinguished in their nuclear microenvironment from genes in the bottom 10% (B10). T10 genes show strong enrichment for several structural features  are quite different between the T10 and B10 genes , in distinguishing T10 from B10 genes, as shown by the receiver operating characteristic (ROC) curves  show overall higher fold enrichments in structural features than genes controlled by regular enhancers (EN) . Thus, Sues Fig. .13 show distinct structural enrichment patterns, which gradually change with increasing SON TSA-seq values  and show no preferred associations with nuclear bodies studied here   is significantly larger than for chromatin regions in short trajectories (median length 4.8 Mb)  positively correlate with both the ICP     Fig. . The vasAD) Fig.  than typAD) Fig. .58 of chromatin are mirrored by distinct differences in their nuclear microenvironment  than features related to radial positions  in distinguishing early-replicating (G1b) from late-replicating chromatin (G2)  Fig. .K-means clustering based on structural feature vectors for compartment A chromatin predicts A1 and A2 subcompartment annotations with 94% accuracy. Chromatins in inactive subcompartments were predicted with an accuracy of 84% . In TSA-seq experiments, most of these regions were identified as type II peaks, with intermediate TSA-seq values. We also noticed that these regions compartmentalize into relatively small spatial partitions, dominated by intra-chromosomal interactions. Chromatin in the A2 subcompartment is enriched in this category. It is possible that the high structural variability of these regions could be linked to functional heterogeneity between cells. For instance, although they are transcriptionally active, these regions have higher levels of the silencing H3K9me3 mark and reduced levels of the activating H3K9ac mark than active regions with low structural variability. Moreover, gene transcripts for these regions are found in a smaller fraction of cells and show lower transcriptional activity.By contrast, active chromatin with high structural variability is characterized by a lack of preferences in nuclear locations. In a fraction of cells, these regions can be located in a silencing environment at the nuclear periphery; in others, it can be located towards the transcriptionally favorable interior. These genes show relatively low transcript frequencies, low inter-chromosomal contact probabilities with low Interestingly, structural heterogeneity is also an indicator that can distinguish nucleoli- and lamina-associated chromatin in the B compartment. Genomic regions with low structural variability are dominantly associated with the lamina compartment, contain constitutive LADs and are enriched in the B3 subcompartment. Genomic regions with high structural variability are associated with nucleoli and pericentromeric heterochromatin and are enriched in the B2 subcompartment.64. Therefore, LADs and nuclear speckles provide structural anchor points at the periphery and nuclear interior. We hypothesize that A-LV and B-LV regions associated to these anchors act similarly to recently reported fixed points in the nuclear organization of mouse embryonic stem cells7.Our results suggest that nuclear speckles, together with the lamina compartment, are a major organizing factor in genome structure. Chromatin with low structural variability between cells is dominantly associated with either nuclear speckles or constitutive LADs. LADs are mostly located at the nuclear periphery while speckles are mostly excluded from the periphery23. The high fraction of inter-chromosomal interactions for speckle-associated chromatin could explain the preferential locations of speckles toward the nuclear interior. The probability of inter-chromosomal interactions increases towards the nuclear interior  linked to associations with nuclear bodies.Chromatins with highest and lowest transcriptional activity are distinguished by their nuclear microenvironment. The SAF shows the highest correlation with the gene transcription frequencyIn summary, our method defines the nuclear microenvironment of a genomic region by calculating a large number of structural features from 3D genome structures. The nuclear microenvironment of a gene can be linked to its functional potential in transcription and replication and thus is relevant for a better understanding of genome structure function relationships. These features can be calculated from Hi-C data, and thus are applicable to many different cell types.A = (aIJ)N × N)N × N), and , i = 1,…,2N as coordinates of all diploid genomic regions in model m (we use lowercase letters i and i' to indicate a given copy of the genomic region I). We introduce a latent indicator variable W is a binary-valued third-order tensor specifying the contacts of homologous genomic regions in each individual structure of the population, such that Wm = (wm)N × 2N2 such that X) and the contact tensor (W) by maximizing the log-likelihood of the probability:i.Rnuc; i from the nuclear center in structure m.Nuclear volume constraint: all chromatin spheres are constrained to the nuclear volume with radius ii.Rex); Excluded volume constraint: this constraint prevents overlap between two regions represented by spheres, defined by their excluded volume radii  modeling step (M-step)—given the estimated Wk, generate model population Xk+1 at step k + 1 that maximizes likelihood to observe W. Structures in the M-step are calculated using a combination of optimization approaches, including simulated annealing molecular dynamics simulations.Our modeling pipeline uses a step wise iterative process in which the optimization hardness is gradually increased by adding contacts with decreasing contact probabilities in the input matrix. The iterative optimization procedure involves two steps, each optimizing local approximations of the likelihood function: (1) assignment step (A-step)—given the estimated structures 34 34. Chromosomes are represented by a chromatin chain model at 200-kb base-pair resolution. Each 200-kb chromatin region, in the diploid genome, is modeled as a sphere, defined by an excluded volume radius (Rex = 118 nm). Rex is estimated from the sequence length, the nuclear volume and the genome occupancy (40%), as described in ref. 34. The full diploid genome is represented with a total of 30,332 spheres.The nucleus is modeled as a sphere with 5-μm radius k and l is calculated as: dkl is the distance between the contact loci in the model, and D is the target contact distance (2 × Rsoft).Restraint residual. On average about 175,304 contact restraints are imposed in each of the 10,000 structures. The restraint residual of each contact restraint between loci r is defined as:k and l from experiment and models, respectively. Residual ratios are very small, and centered at a median of 0.03 (mean = −0.05) for intra-chromosomal and 0.001 (mean = −0.002) for inter-chromosomal contacts .A chromatin interaction network (CIN) is calculated for each model and for chromatin in each subcompartment separately as follows  of the subcompartment s in the structure m is calculated as:cs,m is number of maximal cliques for subcompartment s in structure m; and cr,m is the number of maximal cliques of a CIN constructed from randomly shuffled subcompartment regions in the same structure m. High MCE values show formation of a structural subcompartment with high connectivity between 200-kb regions of the same state.Maximal clique enrichment: A clique is a subset of nodes in a network where all nodes are adjacent to each other and fully connected. The maximal clique refers to the clique that cannot be further enlarged. The number of maximal cliques, 71. The overall neighborhood connectivity of the subcompartment s in the structure m is then calculated as:Ns,m is the number of nodes in the CIN of the subcompartment s in the structure m, and degj is the average neighbor degree of node j.Neighborhood connectivity: To calculate the neighborhood connectivity (NC) of a subcompartment CIN, we first calculate the average neighbor degree for each node using the average_neighbor_degree function in the NetworkX python package72, a graph clustering algorithm, which identifies highly connected subgraphs within a network. MCL clustering is performed for each subcompartment CIN in each structure by using the mcl tool in the MCL-edge software72. Unless otherwise noted, the 25% smallest subgraphs  are discarded from further analysis, to focus on highly connected subgraphs. The highly connected subgraphs are referred to as ‘spatial partitions’ throughout the text.Spatial partitions of subcompartments are identified by applying the Markov Clustering Algorithm (MCL)In addition to subcompartment partitions, we also predict speckle and nucleoli partitions as follows:Case 1: Predictions of speckle locations with knowledge of A1 subcompartment annotations.Speckle locations are identified as the geometric center of A1 spatial partitions identified by Markov clustering of A1 CINs. In each structure, only A1 spatial partitions with sizes larger than three nodes (chromatin regions) are considered for downstream analysis.Case 2: Predictions of speckle locations without knowledge of subcompartments.We first identify chromatin expected to have high speckle association. These regions are identified as those with unusually low and stable interior radial positions. We select 10% chromatin regions with the lowest average radial positions (78.4% of these regions are part of the A1 subcompartment). We then generate CINs for the selected group of chromatin regions in each structure of the population. Approximate speckle locations are then identified as the geometric center of the resulting spatial partitions identified by Markov clustering of the CINs. Only spatial partitions with sizes larger than three nodes (chromatin regions) are considered for downstream analysis.Case 3: Predictions using locations of A2 partition centers.For comparison, we also identify speckle locations as the geometric center of A2 spatial partitions identified by Markov clustering of A2 CINs similar to case 1. In each structure, only A2 spatial partitions with sizes larger than three nodes (chromatin regions) are considered for downstream analysis.73 regions and nucleolus organizing regions . Using these regions, we generate CINs in each structure of the population. Approximate nucleoli locations are then identified as the center of mass of the resulting spatial partitions identified by Markov clustering of the CINs. Only the top 25% largest spatial partitions are used as predicted nucleoli. For NOR regions, we use the first 25 restrained 200-kb regions that are closest in sequence to NOR regions in these five chromosomes, as NOR regions do not have Hi-C data and they are not restrained during the modeling protocol.Following the same protocol as in case 2 for speckle partitions, we first identify chromatin expected to have high nucleoli association. These regions are identified as those previously reported nucleoli-associated domain (NAD)N Mb, where N is the number of nodes in the partition that represents a 0.2-Mb region.Size of partitions: The size of a spatial partition is calculated as 0.2 × Eintra and Einter are the number of intra- and inter- edges in the partition, respectively.Fraction of inter-chromosomal edges (contacts): For each spatial partition, the inter-chromosomal edge fraction (ICEF) is calculated as:I are calculated from 2 copies (i and i') and 10,000 structures  in the following structural feature calculations.Unless otherwise noted, mean values of structural features for each genomic region i in structure m is calculated as:di,m is the distance of i to the nuclear center, and Rnuc is the nucleus radius which is 5 μm. ri,m = 0 means the region i is at the nuclear center, while ri,s = 1 means it is located at the nuclear surface.Radial position of a chromatin region The local compaction of the chromatin fiber at the location of a given locus is estimated by the radius of gyration (RG) for a 1 Mb region centered at the locus . To estimate the RG values along an entire chromosome we use a sliding-window approach over all chromatin regions in a chromosome.i in structure m is calculated as:N is the number of chromatin regions in the 1-Mb window, and dj,m is the distance between the chromatin region j to the center of mass of the 1-Mb region, in structure m.The RG for a 1 Mb region centered at locus For each 200-kb region, the closest speckle partition (or nucleolus partition) in each single structure is identified and the center-to-center distance is calculated (from the center of the region to the geometric center of the partition). The distances across the population are then averaged for each region to calculate mean speckle (or nucleolus) distances.F  is calculated as:M is the number of structures in the population (two homologous copies of each chromosome are present per structure); i and its homologous copy i′ have a distance to the nuclear body of interest (NB) smaller than the association threshold, dt, respectively. The dts are set to 500 nm, 0.35 × Rnuc, and 1,000 nm for SAF, LAF, and NAF, respectively. We tried different distance thresholds, and the selected thresholds resulted in the best correlations with experimental data. For SAF and NAF calculations, we use the predicted speckle and nucleolus partitions to calculate distances . For LAF, we use the direct distances of regions to the nuclear envelope. For all association frequency calculations, we calculate distances from the surface of the region to the center-of-mass of the partition or to the surface of the nuclear envelope.For a given 200-kb region, the association frequency to nuclear bodies  is calculated as:M is the number of models, L is the number of predicted speckle locations in structure m, dil is the distance between the region i and the predicted nuclear body location l, and R0 is the estimated decay constant in the TSA-seq experiment13 which is set to 4 in our calculations. The normalized TSA-seq signal for region i then becomes:ri,m) × Rnuc, where ri,m is the radial position of the 200-kb region i in structure m and Rnuc is the nucleus radius, which is set to 5 μm.To predict TSA-seq signals for speckle, nucleoli, and lamina from our models, we use the following equation:i, we define the neighborhood {j} if the center-to-center distances of other regions {j} to the target region are smaller than 500 nm, which can be expressed as a set; Nei = {j: j ≠ i,< 500 nm}. Inter-chromosomal neighborhood probability (ICP) is then calculated as:M is the number of structures, nintra  and ninter  are the number of intra- and inter-chromosomal regions in the set Nei in structure m for region i.For each target chromatin region i, we define the trans neighborhood {j} if the center-to-center distances of other regions from other chromosomes to itself are smaller than 500 nm, which can be expressed as a set; trans A/B ratio is then calculated as:i. The median of the trans A/B ratios for a region is then calculated from all the trans A/B ratios of the homologous copies of the region observed in all the structures of the population. The values are then rescaled to have values between 0 and 1.For each chromatin region 74), Scikit-learnv1.0 (ref. 75), scipyv1.5 (ref. 76), and networkxv2.3 (ref. 71)) together with the publicly available alabtools platform . The remaining panels and the final figures were assembled using Adobe Illustrator. Correlations between input and output contact matrices were calculated using HiCRep57 (https://github.com/TaoYang-dev/hicrep). Spatial partitions were identified using the MCL algorithm72 (https://micans.org/mcl/). Chromatin interaction networks were visualized with Cytoscape77. Images of 3D genome structures were generated using UCSF Chimera1.13 (ref. 78). For all other analysis, please refer to the The analyses and most of the figure panels were performed using custom Python scripts (matplotlibv3.4 (ref. Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41594-023-01036-1.Supplementary InformationSupplementary Figs. 1–26, Tables 1 and 2, Discussion.Reporting Summary