Escherichia coli K-12 MG1655, which were computationally predicted to be transcription factors (TFs). First, we used a multiplexed chromatin immunoprecipitation method combined with lambda exonuclease digestion (multiplexed ChIP-exo) assay to characterize binding sites for these candidate TFs; 34 of them were found to be DNA-binding proteins. We then compared the relative location between binding sites and RNA polymerase (RNAP). We found 48% (283/588) overlap between the TFs and RNAP. Finally, we used these data to infer potential functions for 10 of the 34 TFs with validated DNA binding sites and consensus binding motifs. Taken together, this study: (i) significantly expands the number of confirmed TFs to 276, close to the estimated total of about 280 TFs; (ii) provides putative functions for the newly discovered TFs and (iii) confirms the functions of four representative TFs through mutant phenotypes.Bacteria regulate gene expression to adapt to changing environments through transcriptional regulatory networks (TRNs). Although extensively studied, no TRN is fully characterized since the identity and activity of all the transcriptional regulators comprising a TRN are not known. Here, we experimentally evaluate 40 uncharacterized proteins in  Bacteria employ a broad range of mechanisms to regulate gene expression to achieve and maintain phenotypic states . The priEscherichia coli K-12 transcriptional regulatory network (TRN) is of particular importance to the scientific community because it provides fundamental information not only for unravelling regulatory network architectures that are host to individual regulators and their target genes, but also for studying the interactions among multiple regulators. Although E. coli K-12 MG1655 is one of the best understood model organisms, our current knowledge of its TRN is still incomplete  to generate massive protein-DNA interactions datasets for these candidate TFs, RNAP, and the sigma factor RpoD. Combining these data, we successfully uncover 588 binding sites of 34 TFs from 40 initial candidates, in which 283 binding sites are located upstream. Based on the number of target genes, we classify these TFs into three groups: (i) one global regulator (>100 target genes), (ii) twenty-nine local regulators (<100 target genes) and (iii) four single-target regulators , and support the estimated total of 280∼300 TFs comprising the TRN in E. coli K-12 MG1655 . For ChIP-exo experiments, the E. coli strains harboring 8-myc were generated by a λ red-mediated site-specific recombination system targeting the C-terminal region as described previously  with 0.2% (w/v) glucose. The M9 minimal medium was also supplemented with 1 ml trace element solution (100X) containing 1 g EDTA, 29 mg ZnSO4.7H2O, 198 mg MnCl2.4H2O, 254 mg CoCl2.6H2O, 13.4 mg CuCl2 and 147 mg CaCl2. The culture was incubated at 37°C overnight with agitation and was then used to inoculate fresh media (1/200 dilution). The volume of the fresh media was 150 mL per biological replicate. The fresh culture was incubated at 37°C with agitation to the mid-log phase where optimal density at 600 nm (OD600) was around 0.5. To create oxidative stress, the overnight cultures were inoculated at an OD600 of 0.01 into the fresh 70 mL of glucose M9 minimal medium in a 500 ml flask supplemented with 250 μM paraquat (PQ) at an OD600 of 0.3 and incubated for 20 min with stirring. The strains in the ChIP-exo experiments were grown under the conditions listed in Dataset S2.The strains used in this study are 2O2, mid-log phase cells (OD600 ≈ 0.5) were harvested, washed with phosphate-buffered saline (PBS), and resuspended in M9 minimal medium. The culture was then treated with 60 mM H2O2  for 15 min. Samples were taken before and after the treatment, diluted, and plated in triplicate on LB plates. Viable counts were determined following incubation at 37°C for up to 24 h. The sensitivity of cells to the lethal effect of the stimulus was expressed as percent survival of treated cells relative to that of untreated cells determined at time zero.To evaluate the susceptibility of bacterial cells to HE. coli K-12 MG1655 and yciT deletion strains were incubated on M9 minimal medium with a sole carbon source  at 37°C overnight with agitation. The concentration of the carbon sources was 0.2% (w/v). These cultures were then used to inoculate the same fresh media (1/200 dilution) and were incubated again at 37°C with agitation. Growth curves were monitored by measuring OD600 every 30 min using a Bioscreen C , and repeated twice with three biological replicates.To examine the effects of carbon sources on cell growth, E. coli K-12 MG1655 and yciT deletion strains were grown on M9 minimal sorbitol (0.2% w/v) media and the same media supplemented with 0.5 M NaCl at the beginning of the culture, respectively. The culture was incubated at 37°C with agitation and monitored by measuring OD600 every 30 min using a Bioscreen C, and repeated twice with three biological replicates.To determine the effects of osmotic stress on the growth, A multiplexed ChIP-exo experiment was performed through simple modification of our standard ChIP-exo method described previously . Here, ain vivo, the DNA bound to each candidate TF from formaldehyde cross-linked E. coli cells were isolated by chromatin immunoprecipitation (ChIP) with the antibody that specifically recognizes the myc tag  and Dynabeads Pan Mouse IgG magnetic beads (Invitrogen). This step was followed by stringent washings  for 25 min at room temperature. The formaldehyde was quenched by 2.5 M glycine (Thermo Fisher Scientific) for an additional 5 min and the cells were washed with ice-cold TBS (Thermo Fisher Scientific) three times. The resulting pellets were lysed with Ready-lyse lysozyme solution (Epicentre). Lysates were sonicated using a sonicator (QSonic) to generate 300–500 bp randomly sheared chromosomal DNA fragments. The extent of shearing was monitored with a 1% agarose gel and confirmed by separation on a 2100 High sensitivity Bioanalyzer chip (Agilent Technologies) upon completion of the immunoprecipitation. Immunoprecipitation was carried out at 4°C with overnight incubation and 15 μl anti-c-myc mouse antibody . The protein of interest, together with its cross-linked DNA and covalently bound mouse antibody, was captured with 50 μl Dynabeads Pan mouse IgG (Invitrogen) and washed with buffer I (50 mM Tris–HCl (pH 7.5), 140 mM NaCl, 1 mM EDTA, 1% Triton X-100).To identify the binding map of each candidate TF washings . Cells wf exonuclease-treated chromatin was eluted from the beads and incubated overnight at 65°C to reverse the protein–DNA cross-link. RNAs- and proteins-removed DNA samples were used to perform primer extension and second adaptor ligation with following modifications. The DNA samples incubated for primer extension as described previously  that specifically recognizes σ70 was used to detect the binding sites of σ70 in E. coli K-12 MG1655. Each step was also performed following the manufacturer's instructions. ChIP-exo experiments were performed in biological duplicates (Dataset S3 and S4).ChIP materials (chromatin-beads) were used to perform on-bead enzymatic reactions of the ChIP-exo method . The sheeviously  were trehttps://sites.google.com/view/systemskimlab/software?authuser=0) and NimbleGen's SignalMap software.Peak calling was performed as previously described . SequencE-value < 1e-3)  . For YciP-value <0.01 was considered significant.Regulon genes were categorized according to their annotated clusters of orthologous groups (COG) category . Functiog for 10 min. The supernatant was decanted, and any residual supernatant was removed by inverting the tube once onto a paper towel. Total RNA samples were then isolated using a RNeasy Plus Mini kit (Qiagen) following the manufacturer's instruction. Samples were then quantified using a NanoDrop 1000 spectrophotometer (Thermo Scientific) and quality of the isolated RNA was checked by running RNA 6000 Pico Kit using an Agilent 2100 Bioanalyzer (Agilent). Paired-end, strand-specific RNA-seq libraries were prepared using KAPA RNA Hyper Prep kit (KAPA Biosystems), following the instructions . The strains were grown under the same conditions as those used in the ChIP-exo experiments. Transcripts were stabilized by mixing 3 ml of cell cultures at the mid-log phase with 6 ml of RNAprotect Bacteria Reagent (Qiagen). Samples were immediately vortexed for 5 s, incubated for 5 min at room temperature, and then centrifuged at 5000 × ructions ,21. ResuP-value. The Wald test calculates P-values from the subset of genes that pass an independent filtering step, and they are adjusted for multiple testing using the procedure of Benjamini and Hochberg (2(fold-change) ≥ log2(2.0) and adjusted P-value <0.05 or log2(fold-change) ≤–log2(2.0) and adjusted P-value <0.05 was considered as differentially expressed (Dataset S6).Expression profiling was performed as previously described . Raw seqHochberg . ExpressHomology models of the candidate transcription factors YidZ, YfeC, YciT, YcjW, YdcN and YgbI were constructed using the SWISS-MODEL pipeline . MultiplE. coli K-12 MG1655 following our previously reported and validated pipeline   using a homology-based algorithm . We expeR (YcfQ) , YcjW (2R (YcfQ) , SutR (YR (YcfQ) , RcdB (YR (YcfQ) , NimR (YR (YcfQ) , CsqR (YR (YcfQ) ,33, YqhCR (YcfQ) ,35. HoweTo predict the family types of candidate TFs, we employed Hidden Markov Models to annotate them based on the homology to the collection of known protein structures in the SUPERFAMILY 2 database  Table 1 37). Th. Th37). Next, to characterize binding sites of these candidate TFs on the genome, we constructed 40 myc-tagged strains corresponding to each candidate TF of interest and employed a multiplexed ChIP-exo method to increase the throughput of the assay .We obtained the binding profiles for all candidate TFs using the peak-calling algorithm MACE , and conFor the 34 validated candidate TFs, we analyzed the conserved binding motifs using the MEME algorithm  and obtaE. coli are recognized by the sigma factor RpoD (σ70), also known as the housekeeping sigma factor , three local regulators , and one single-target regulator (YgbI)—were selected as representative TFs. To infer their regulatory roles, we combined the binding sites with gene expression profiling to analyze the most significant enrichment of pathways in which validated TFs are involved. The remaining five validated TFs can be found in the Supplementary Material.E-value = 1.2e–140, Figure We identified 118 binding sites of YidZ Figure  and thenin vivo and RNA polymerase, we integrated YidZ binding with the previous ChIP-exo data of RpoB and σ70. Among 27 YidZ intergenic bindings, we identified 12 binding sites at the promoters in the presence of core RNAP and σ70, 9 binding sites at the promoters in the presence of core RNAP, and 6 binding sites at the promoters in the absence of core RNAP and σ70  were down-regulated, while genes involved in carbohydrate transport and metabolism  were up-regulated , indicating that genes directly or indirectly regulated by YidZ are not, as a group, strongly associated with any specific function(s).Overall, we observed two notable features of the YidZ binding profile. First, YidZ has a large number of binding sites, with 77% (91/118) located within the coding regions and 23% (27/118) located within the intergenic regions. Second, YidZ is associated with diverse gene functions, based on Clusters of Orthologous Groups (COGs) annotations of differentially expressed genes (DEGs)  Figure . HoweverE. coli K-12 MG1655 , translation , post-translational modification , and cell envelope (lpp).Functional classification showed that 50 YfeC binding sites are involved in various functional groups, from DNA replication, transcription, translation, to cell envelope biogenesis Figure . To iden5 Figure . Combini5 Figure . Of thes5 Figure . These drna, hns, nlpI, rfaD and yfeC altered eDNA production in E. coli. These mutations were related to general cellular processes, such as transcription , lipid transport (nlpI), cell envelope (rfaD), and unknown function (yfeC)  . These rin vivo DNA binding affinity had not been reported. Here, we identified 49 binding sites of YciT in E. coli K-12 MG1655 , a putative pyruvate formate-lyase activating enzyme (ybiY), and fructose-6-phosphate aldolase1 (fsaA)  , and outer membrane protein X (ompX). The remaining genes  are of unknown function.YciT was annotated as a DeoR-type putative transcription factor via the Hidden Markov Model. However, its 5 Figure , and the) Figure . Some of) Figure , copper/yciT deletion strain. It was found that target genes involved in metabolic pathways  and membrane components (cusF) were indeed differentially expressed upon the deletion of the yciT gene , and found that the deletion of the yciT gene did not reveal significant growth deficiencies compared to the wild type strain. However, the final OD600 of the yciT deletion strain at the stationary phase was slightly lower than the wild type strain , which modulates signal transduction pathways  stress response.To confirm YbcM’s physiological role, the survival rate of the wild type and s Figure . The surygbI and ygbJ genes, indicating the autoregulation of ygbI .In this study, we identified a single divergent binding site between the I Figure . We alsoygbI mutant  are upregulated after the deletion of ygbI  as a repressor, which is consistent with the prediction of a regulatory effect.To examine this assumption about the regulation of YgbI, we compared gene expression profiling between the wild type and the t Figure . The resI Figure . This suygbJKLM had putative functions in catabolic pathways for acid sugars , we suggest that YgbI is a repressor involved in the catabolic pathway for l-tartrate in E. coli K-12 MG1655.Previous studies reported that the downstream gene cluster d sugars , and hype medium . To verie Figure . Taking N Figure . Based oE. coli genome, around 35% of its genes are still poorly characterized, including some uncharacterized transcription factors  that are likely to regulate a nearby promoter (Dataset S4), and provide a coarse-grained functional prediction. Finally, we inferred the putative functions for ten of these candidate TFs , and verified the biological roles of the representative TFs with detailed analysis. The implications of our results are below.Despite extensive research over many decades focused on the  factors ,45. Our E. coli K-12 MG1655, close to the estimated total number of 280 . There may be two reasons for this. The first is the false-positive predictions of candidate TFs due to the limitations of the sequence homology search. Specifically, YgeR has been recently re-annotated as putative lipoprotein involved in septation . YggD ha a toxin . YfjR is a toxin , but a r a toxin . A secon a toxin . Thus thfsaA, ybiY, ybiV). This result hinted at an uncharacterized pathway composed of genes encoding DUF1479 domain-containing protein (ybiU), a sugar phosphatase (ybiV), a putative pyruvate formate lyase (PFL) (ybiW), a putative pyruvate formate-lyase activating enzyme (PFL-AE) (ybiY), and a fructose-6-phosphate aldolase1 (FSA) (fsaA)  . However2 MG1655 . These bE. coli K-12 MG1655. Previous studies had not delved into a possible relationship between eDNA release and YfeC. Therefore, we employed a yfeC mutant to better understand any possible connections. The common mechanism of eDNA release in bacteria is through membrane vesicles (MVs) secretion . As. AsE. coThe whole dataset of ChIP-exo and RNA-seq has been deposited to GEO with the accession number of GSE159777 and GSE159658, respectively.gkab735_Supplemental_FilesClick here for additional data file.


Aspergillus fumigatus conidia. We determined the location within the promoter region of SFTPD that exhibits a response to conidia infection. AliBaba 2.1 software was used to predict the transcription factor involved as well as the binding sites in the SFTPD promoter region. The results of a decoy assay show that the HSF1 transcription factor is sufficient to decrease the SFTPD expression. Using chromatin immunoprecipitation, we confirmed that HSF1 directly binds to the selected sequence, which is located in the response region (−142 to −134 bp). These findings suggest that inhibiting the binding of HSF1 to the promoter region of SFTPD is an important step to prevent conidia infection.Aspergillosis is a life-threatening disease in patients with compromised immune systems. The process of fungal invasion is an important step during host cell infection. We investigated the transcription factor and promoter region of SFTPD, which is activated during the infection process in conidia-treated cells. To investigate the promoter activity of SFTPD in fungal-infected cells, we cloned various lengths of the promoter region (−1000 to +1) of SFTPD and examined its activity in A549 cells treated with  Aspergillus can result in diseases ranging from allergic reactions in the topical area to invasive infections or even pneumonia. In recent publications, several other risk factors were identified, such as systemic steroid treatment, intensive care unit admission, and influenza [Aspergillus fumigatus [A. fumigatus is classified as an opportunistic pathogen that is ubiquitous in the natural environment [Invasive fungal infections are uncommon in immunocompetent individuals, whereas immunocompromised patients run the risk of morbidity and mortality from these diseases. Medical advances have increased the survival of chronically immunosuppressed patients, but the downside of this progress is an increasing number of patients at risk for fungal infections ,2. Invasnfluenza ,4,5,6. Tumigatus ,8. A. fuironment .Caenorhabditis elegans, Drosophila melanogaster, and yeast, mammals have four distinct HSFs (HSF1–HSF4).Pulmonary surfactant proteins exist on lung alveolar surfaces, consist of phospholipids and proteins, and are produced by alveolar type II cells and Clara cells, which are the major types of peripheral airway cells. Surfactant proteins play a key role as a defense mechanism in humans. They reduce tension to prevent an alveolar collapse, inhibit pathogens from spreading, and regulate immune reactions . PulmonaHSF1 is a major regulator of heat shock protein transcription in eukaryotes and has been implicated in several fundamental biological processes, independent of HSR, including metabolism, gametogenesis, development, and aging. It has also been linked to various pathological conditions ,16,17. HA. fumigatus conidia was used to evaluate the cellular immune reaction at the transcription level in A549 human lung epithelial cells. The transcription factors that function during the transcription process were identified. The results indicate that transcription factor HSF1 is an effective therapeutic target for the treatment of a variety of fungal infections.In this study, p < 0.05). However, there was no suppression of SP-D_−100/+1 with conidia. Next, we measured the promoter activity devoid of the -200 to −100 bp region (p < 0.05) was not affected by conidia treatment. This suggests the presence of repressive regulatory elements in the region between positions −200 and −100 bp.To investigate the mechanisms that regulate SFTPD expression, the 5′-flanking region of SFTPD was dissected into a series of deletion fragments termed SP-D_1k/+1 (−1000 to +1 bp), SP-D_−500/+1 (−500 to +1 bp), SP-D_−200/+1 (−200 to +1 bp), and SP-D_−100/+1 (−100 to +1 bp). A luciferase reporter assay was used to measure the transcriptional activity of the fragments . Comparip region . TranscrTo define a more exact location of transcription factor binding in the promoter region associated with the suppression of SFTPD transcription, we used each decoy oligonucleotide approach to find the specific transcription factor binding site(s) in conidia-treated cells. The decoy oligonucleotides bind to transcription factors and inhibit binding to the corresponding cellular target sequences.We designed the decoy oligonucleotides (25 bp) against four different regions . The lochttp://www.gene-regulation.com/pub/programs/aliba-ba2/index.html. (accessed on 1 October 2019)), was used for in silico analysis. Known transcription factors with binding sites within this sequence were identified by this software.The Decoy 3 oligonucleotide inhibited conidia-induced suppression during SFTPD expression in A549 cells. We identified transcription factor–DNA interaction in this region. The online prediction bioinformatics software, AliBaba2  predicted a clustered site containing sites for the transcription factor HSF1 . Other fThe in silico analysis predicted that HSF1 is a transcriptional repressor located in the −150/−126 region of the SFTPD promoter. To confirm that this region is essential for HSF1-mediated suppression of SFTPD expression, we replaced the nucleotide sequence GGTTTCCAG with CCAAAGGTC in the Decoy 3 oligonucleotide to inhibit HSF1 binding A. We cotChIP assays were performed to test whether conidia exposure induces binding of HSF1 to the SFTPD promoter region in A549 cells. ChIP lysates were prepared from serum-starved and conidia-treated cells and immunoprecipitated with an anti-HSF1 antibody or a control antibody. As shown in A. fumigatus conidia-infected cells. HSF1 mRNA was highly expressed following a 6-h exposure  forms oligomers in the nucleus and increases affinity for binding to what are known as heat shock elements under stress conditions . HSF1 isA. fumigatus conidia-infected cells. More importantly, HSF1 as a transcription factor serves as a negative regulator of SFTPD expression, and an increased SFTPD level helps cells protect from an infectious conidia invasion by A. fumigatus. These findings clearly indicate that the HSF1 transcription factor is a good indicator of A. fumigatus conidia infection via SFTPD and that it represents a candidate for use in aspergillosis treatment.Our results indicated that HSF1 is also increased in 2. The medium was changed every 3 days. A549 cells were grown in six-well tissue culture plates for 18 h, and the cells were incubated with conidia (5 × 107 spores) for the experiments.All chemicals were obtained from Sigma-Aldrich . All cells were incubated in RPMI 1640 medium supplemented with 10% FBS and antibiotics and maintained in a humidified incubator at 37 °C and 5% COThe PCR reactions consisted of an initial denaturation at 94 °C for 5 min and 35 cycles at 94 °C for 1 min, 55 °C for 1 min, 72 °C for 1 min, and 72 °C for 10 min. The sequence-confirmed PCR products were subsequently cloned into pGL4.14-lac2  at the KpnI and XhoI sites for the luciferase assays. A549 cells were transfected with pGL4.14-lac2 containing various SFTPD promoter regions using Lipofectamine 2000 reagent  according to manufacturer’s instruction. After 24 h of transfection, we analyzed the promoter activity with a luciferase assay.Decoy oligonucleotides corresponding to the conidia response element inhibited the suppression of SFTPD promoter activity. A549 cells were cotransfected with SFTPD constructs in the presence of wild-type or mutant decoy oligonucleotides. The wild-type decoy oligonucleotide attenuated the conidia-induced suppression (reported as %) of the reporter gene construct containing a conidia response site in A549 cells. These results were from at least two independent transfection experiments performed in triplicate.Firefly and Renilla luciferase activities were measured by dual luciferase assays (Promega) and then normalized to Renilla luciferase activity. The luciferase activity of the SFTPD promoter constructs in A549 cells was measured. The promoter activity of each construct was expressed as the percent activity of vector-only treated cells. Histograms represent the mean values of three independent transfection experiments with two different plasmid preparations.http://www.gene-regulation.com/pub/databases.html (accessed on 1 October 2019) and the Transfac 7.0 database. Binding sites identified using TRANSFAC are used for this purpose, whereas AliBaba2 is currently the most specific tool for predicting such sites.A portion of the 50-bp SFTPD promoter region was subjected to computer-aided analyses using the Alibaba2.1 net-based transcription factor binding site (TFBS) search tool (Chromatin immunoprecipitation assays (ChIP) assays were performed with the ChIP-IT enzymatic kit . Briefly, cells were cross-linked with 1% formaldehyde, and the chromatin was sheared with an enzymatic shearing cocktail. Chromatin-containing DNA fragments with an average size of 500 bp were immunoprecipitated with an anti-HSF1 monoclonal antibody  with rabbit IgG as a negative and histone H3 rabbit mAb as a positive control. DNA fragments were amplified using primers designed around the HSF1 binding sites in the SFTPD promoter region.The protein–DNA complexes were incubated with protein A beads, and cross-links were reversed by incubating at 65 °C. DNA was recovered by phenol-chloroform extraction, precipitated with ethanol, and then subjected to semiquantitative PCR analysis. The primer sets were as follows: 5′-CTGGACTCCTGATTTCTAGAC-3′ and 5′-AGCCTTTGCTTGGCAGGCA-3′ . PCR products were resolved on a 2% agarose gel and visualized by ethidium bromide staining.β-actin [Ct−ΔΔ method was used to calculate the Ct value for each gene, and target gene expression levels were normalized to that of the β-actin housekeeping gene [Total RNA  was used for reverse transcription. Quantitative RT-PCR was performed according to the manufacturer’s instructions  using a Rotor-Gene Q device (Qiagen). Each run was assayed three times with 2× qPCR SYBR Green Mix  and 100 nM of each primer. β-actin . The oneing gene .


Schizophyllum commune, comparative genomics and transcriptomics have previously resulted in a regulatory model of mushroom development. However, little is known about the role of epigenetic regulation. We used chromatin immunoprecipitation sequencing (ChIP-Seq) to determine the distribution of dimethylation of lysine 4 on histone H3 (H3K4me2), a mark for transcriptionally active genes, during monokaryotic and dikaryotic development. We identified a total of 6032 and 5889 sites during monokaryotic and dikaryotic development, respectively. The sites were strongly enriched near translation initiation sites of genes. Although the overall epigenetic landscape was similar between both conditions, we identified 837 sites of differential enrichment during monokaryotic or dikaryotic development, associated with 965 genes. Six transcription factor genes were enriched in H3K4me2 during dikaryotic development, indicating that these are epigenetically regulated during development. Deletion of two of these genes (fst1 and zfc7) resulted in arrested development of fruiting bodies, resulting in immature mushrooms. Together these results indicate that H3K4me2 ChIP-Seq is a powerful new tool to map the restructuring of the epigenetic landscape during mushroom development. Moreover, it can be used to identify novel developmental regulators.Mushroom formation represents the most complex multicellular development in fungi. In the model mushroom  Mushrooms are a nutritious food source, produce pharmacological compounds and express a vast array of lignocellulolytic enzymes2. Their development is regulated by a complex, but largely unknown, developmental program7. Comparative genomics and transcriptomics have recently revealed many genetic elements related to mushroom development, creating a regulatory model of mushroom development12. However, these methods do not provide a complete overview of the restructuring of chromatin during mushroom development.Mushroom fruiting bodies are the sexual structures of basidiomycete fungi and produce sexual spores for reproduction13. This process is partially facilitated by modifications on the protruding tails of the histones that form the nucleosomes. These modifications can change the chemical attraction between the tail and DNA and recruit regulatory proteins to the site of modification15. There is a wide variety of histone modifications, including methylation, acetylation, ubiquitination and phosphorylation16. Among the best studied are lysine methylations of the N-terminal tail of histone H3. Methylation of the lysine 4 of histone H3 can occur as mono- (H3K4me1), di- (H3K4me2) or trimethylation (H3K4me3) and is strongly associated with transcription start sites (TSS). Therefore, H3K4 methylation is considered a reliable marker of transcription start sites17, although the exact distribution of mono-, di- and trimethylation around active genes can vary19.During development, the chromatin in eukaryotic genomes is generally heavily restructured to modulate gene expression by making regions of the genome either more or less accessible and by the recruitment of regulatory proteins20. Indeed, antibodies against histone modifications are often shown or predicted to recognize the same modification across many species and even kingdoms, including fungi. However, data on histone modification recognition by antibodies is limited to ascomycotes, including Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, Fusarium graminearum and Zymoseptoria tritici26. Furthermore, not all histone modifications are present in all fungal clades27. For example, H3K27 methylation is lost in multiple ascomycete species, including species within the Taphrinomycotina, Saccharomycotina, Eurotiomycetes and the basidiomycete Ustilago maydis29. In Agaricales, the presence of all H3K methyltransferases of these marks is predicted in Coprinopsis cinerea27.Histones and their epigenetic modifications are highly conserved proteins due to their essential role in all eukaryotes30. With this technique, antibodies against a specific histone modification are used to selectively pull-down fragmented chromatin enriched with the histone modification of interest. Next, the DNA is extracted from the chromatin, sequenced, and aligned to the reference genome. This reveals the differential enrichment of the histone modification across the genome and, by extension, the genetic accessibility of genomic regions during development.Histone ChIP-Seq (chromatin immunoprecipitation followed by sequencing) is a powerful technique to map the locations of histone modifications across the genomeSchizophyllum commune is a basidiomycete model organism for mushroom development. It has a short, well-defined lifecycle of ten days in the lab and is genetically accessible32. Recently, the efficiency of gene deletion was greatly increased with the use of CRISPR-Cas9, enabling more in-depth study of gene function33.S. commune and mapped the distribution of H3K4me2 in the genome during monokaryotic and dikaryotic development. We chose H3K4me2, which is enriched downstream of the TSS of active genes19. By assessing the differential enrichment of H3K4me2 between monokaryotic and dikaryotic colonies, we were able to identify potential regulators of mushroom development. We confirmed the role of two transcription factors, fst1 and zfc7, by gene deletion. This study shows that histone ChIP-Seq is a powerful technique that can further elucidate the complex developmental process of mushroom formation. To our knowledge this is the first report of the use of ChIP-Seq in a mushroom-forming basidiomycete.Here, we developed a method for histone ChIP-Seq in the model mushroom 27, it is currently not known if these are sufficiently conserved for recognition with commercially available mammal-derived antibodies. To assess the conservation of the amino acid sequence surrounding the methylation sites, we aligned the first 50 amino acid residues of histone H3 of several commonly studied species. The N-terminal tail is highly conserved across all the studied species, but immediately downstream of H3K27 there are the substitutions S28T and P30N and the insertion A29_P30insA  that are unique to S. commune  and dikaryotic colonies grown at 30 °C in a 16/8 h day/night cycle. The developmental stage of these cultures was (symmetric and homogenous) vegetative growth and the formation of secondary hyphal knots, respectively Fig. a,b. The The ChIP pull-down was performed with an anti-H3K4me2 antibody, and the resulting DNA was sequenced and aligned to the genome assembly  displayed similar read coverage along the genome, whereas the immunoprecipitated samples were concentrated in peaks that represent the binding sites of histone 3 with dimethylation on lysine 4  of that locus. A differential peak analysis with DiffBindons Fig. d. This ions Fig. .matA mating type locus. However, due to the variation in mating type loci between compatible strains, this enrichment is likely an artefact of alignment to the reference genome, which only contains the mating type loci of matA4336. Therefore, the mating type loci were excluded from the enrichment analysis. No PFAM domains were significantly enriched in the dikaryotic samples.These relatively enriched peaks in the monokaryon and dikaryon were associated with the gene(s) they presumably regulate. Based on the general pattern in Fig. To verify that H3K4me2 enrichment correlated with gene expression, we performed RT-qPCR on the 10 genes in a 20 kb region on scaffold 2 from 1,742,000 to 1,762,000  and zfc7 (ProteinID: 2601101), named after the fungal-specific transcription factor (PF04082) and zinc finger C2H2 (PF00096) domains, respectively. Both genes had nearby peaks that were enriched during dikaryotic growth  when compared to monokaryotic growth , with a relative expression increase of 2.0 and 14.8-fold, respectively. To study their role, the genes were deleted in a Δku80 strain by homologous recombination with a plasmid containing a nourseothricin resistance cassette flanked by 1 kb upstream and downstream flanks of the target gene. To increase efficiency, Cas9 preassembled with two sgRNAs against the gene of interest were co-transformed33. Candidate gene deletions were selected on nourseothricin and counter-selected on phleomycin. Gene deletions were confirmed by colony PCR . However, both strains showed stunted primordia and mushroom formation . We used the resulting data to identify two genes predicted to play a role in the early stages of primordia formation. Indeed, homozygous deletion strains of either fst1 or zfc7 were unable to form mature mushrooms, but instead stopped their development in the primordia stage. This indicates that H3K4me2 enrichment may serve as a marker for the identification of candidate genes involved in processes downstream of the time of sampling.Comparative genomics and transcriptomics have become commonplace to study the development of mushroom-forming fungiSaccharomyces cerevisiae and Z. tritici38. In some plant-pathogenic species histone modifications can be chromosome-specific due to the presence of accessory chromosomes with low gene content39. We did not observe such a distribution in the genome of S. commune, due to the absence of accessory chromosomes. While in general H3K4me2 is associated with active genes39, we did identify genes previously associated with dikaryotic development that were enriched with H3K4me2 during monokaryotic development. This includes pri2, which is strongly upregulated during mushroom-formation and is implicated in dikaryotic development in Lentinula edodes40. Recently, H3K4 methylation has also been associated with repression of specific gene clusters in S. cerevisiae, which can perhaps explain the methylation profile of pri2, although a better understanding of histone modifications in S. commune is required to confirm this41.There are no previous studies on the distribution of H3K4me2 in mushroom-forming fungi, or any other basidiomycete. However, the distribution we observed downstream of the TIS is similar to those observed in ascomycetes, including 20, we found that significant changes in the protein sequence of histones may limit our ability to probe certain histone marks in S. commune with commercially available antibodies. Therefore, we were unable to confirm the presence of inhibitory H3K27me3 histone mark in S. commune. While this is likely due to amino acid substitutions and insertions surrounding lysine 27 on histone H3, it is also possible that H3K27 methylation is not conserved in S. commune. It has previously been shown that H3K27 methylation does not occur in multiple ascomycetes, including several Aspergillus species and yeasts, due to the absence of KMT6 homologs29. In the basidiomycete yeast Cryptococcus neoformans H3K27 methylation was previously detected, and in the mushroom-forming species C. cinerea H3K27 methylation is predicted based on presence of a KMT6 ortholog42. Other basidiomycete species, like Ustilago maydis, do not have a KMT6 ortholog, which suggests a loss off function in some species.While epigenetic histone marks are generally strongly conserved across eukaryotes44. Data on more histone modifications during additional stages of development should increase our understanding of the underlying chromatin modifications regulating gene expression in mushroom-forming fungi. Moreover, deletion of histone methyltransferase genes followed by ChIP-Seq may result in more insight into their impact on the epigenetic landscape during development.With H3K4me2 ChIP-Seq we observed a correlation of 0.95–0.97 within replicates and 0.79–0.81 between monokaryotic and dikaryotic samples. This indicates that the majority of the H3K4me2 landscape is very similar between these samples. This is in line with the relatively low number of differential peaks we identified. Additionally, the distribution around the TIS is strongly correlated between both developmental stages. In human cell lines, it has previously been described that the distribution and location of H3K4me2 can change drastically during development7. However, in our approach we sampled the entire colony of a monokaryotic and dikaryotic culture. Likely, the chromatin is constantly remodeled during mushroom development, leading to different patterns of chromatin regulation in various parts of the colony. Therefore, sampling a full dikaryotic colony likely hides some of this complexity by averaging out these differences. Having developed this technique, we can now sample individual developmental stages to accurately determine their epigenetic profile.Despite the large degree of similarity in the H3K4me2 landscape between monokaryotic and dikaryotic life stages, there were also differences. Surprisingly, more genes had enrichment of H3K4me2 during monokaryotic development than during dikaryotic development, with 771 and 194 genes identified, respectively. Canonically, mushroom development is considered the most complex developmental program of mushroom-forming fungi, but our data suggests that this is not directly reflected in this type of epigenetic regulationS. commune. Furthermore, we have shown that this approach can be used to identify novel developmental regulators. To our knowledge this is the first report of the use of ChIP-Seq in a mushroom-forming basidiomycete, and it should be straightforward to adapt it to other mushroom-forming fungi. Moreover, it opens the door to performing ChIP-Seq on specific transcription factors, allowing us to determine their binding dynamics in the genome. Combined, these techniques will further reveal the important layer of epigenetic regulation during mushroom development and other processes. Therefore, the important addition of the study of epigenetic regulation to the molecular toolkit of S. commune will facilitate the functional characterization of the genome, leading to important new insights into the biology of this important group of fungi.We have developed an efficient ChIP-Seq protocol for Schizophyllum commune was grown from a small agar inoculum on solid Schizophyllum commune minimal medium (SCMM) supplemented with 1.5% agar at 30 °C45. All strains are derived from S. commune H4-8  and the compatible isogenic strain H4-8b (matA41matB43)36. For deletions, a previously published Δku80 strain was used31. For selection on nourseothricin  or phleomycin , SCMM was supplemented with 15 μg/mL and 25 μg/mL antibiotic, respectively46. For phenotypical characterization, strains were grown for 7 days at 25 °C in a 16/8 h day/night cycle.hht1 from S. cerevisiae. The sequences were aligned with MAFFT 7.307 and coloured according to the Clustal X colour scheme48. A species tree was constructed based on 181 highly conserved single copy genes determined by BUSCO v2 (dataset ‘fungi_odb9’)49. The sequences were concatenated and aligned with MAFFT 7.30747 and well-aligned positions were selected with Gblocks 0.91b50. This resulted in 61723 positions per species and from this a tree was calculated with FastTreeMP51. Animal and plant species were added manually based on the NCBI taxonomy. The final tree and alignments were visualized with the interactive Tree Of Life v452. The branch lengths are not drawn to scale.Histone H3 was identified in each organism based on the best blast-hit with Schizophyllum commune was grown as a monokaryon or dikaryon for 4 days in a 16/8 h day/night cycle on porous polycarbonate (PC) membranes . After 90 h, half colonies were harvested and homogenized in a TissueLyser II  with 2 grinding beads in 2 mL Eppendorf Safe-Lock tubes  at 25 Hz for 2 min. The homogenate was resuspended in 500 µL cell lysis buffer , 1 × cOmplete protease inhibitor cocktail  and incubated for 10 min at 4 °C. The samples were centrifuged for 5 min at 2500 g at 4 °C and the pellet was resuspended in 250 µL nuclei lysis buffer . The samples were incubated at 4 °C for 10 min and cellular debris was removed by centrifugation for 10 min at 15,000 g at 4 °C. As a positive control, 45 µg of HeLa whole cell lysate was used . All samples were mixed with 250 µL 2 × Laemmli sample buffer  and incubated at 100 °C for 10 min. The protein extracts were size-separated by SDS-PAGE on Mini-PROTEAN TGX Stain-Free Precast Gels  according to the manufacturer’s specifications and transferred to polyvinylidene difluoride membrane  according to the manufacturer’s specification. The membrane was blocked with 5% bovine serum albumin  in phosphate buffered saline supplemented with Tween (PBS-T)  for 60 min, followed by incubation with either Recombinant Rabbit Anti-Histone H3 (di-methyl K4)  diluted 1:2000 or, rabbit anti-Tri-Methyl-Histone H3 (Lys27)  diluted 1:1000 in PBS-T with blocking buffer for 60 min. The membrane was washed 3 times with PBS-T for 5 min before incubation with an HRP-linked anti-rabbit IgG  diluted 1:10,000 in PBS-T for 60 min. The membrane was washed 3 times in PBS-T for five minutes and imaged with Clarity ECL Substrate  according to manufacturer’s specification.Zymoseptoria tritici30. See also Supplementary Text S. commune was grown as a monokaryon and dikaryon at 25 °C on porous PC membranes in a 16/8 h day/night cycle. After 90 h 5 full colonies were collected per replicate and briefly washed in Tris-buffered saline (TBS) . The samples were crosslinked by vacuum infiltration in 1% formaldehyde in TBS for 15 min and crosslinking was quenched by vacuum infiltration in 0.125 M glycine for 5 min. The crosslinked mycelium was briefly dried and frozen in liquid nitrogen. Next, the mycelium was homogenized in stainless steel grinding jars in a TissueLyser II  at 30 Hz for 2 min. The homogenate was resuspended in 10 mL cell lysis buffer , 1 × cOmplete protease inhibitor cocktail  and incubated for 10 min at 4 °C. The samples were centrifuged for 5 min at 2500 g at 4 °C and the pellet was resuspended in 3 mL nuclei lysis buffer . The soluble chromatin was fragmented by sonication for 8 min on ice, using a branson sonifier 450 with microtip at setting 4 with 35% output. The samples and the tip were cooled every 2 min during sonication. After fragmentation, the samples were incubated for 10 min at 4 °C and then spun down for 10 min at 15,000 g at 4 °C. The supernatant contained the isolated chromatin. 300 μL was stored at − 80 °C for input control, while the remaining chromatin was used for ChIP.The chromatin immunoprecipitation method was adapted from previous studies in human cell lines and 3). The RNA was degraded by incubating with 50 mg RNAse A  for 1 h at 50 °C and then the samples were de-crosslinked overnight with 75 μL reverse crosslinking buffer ) at 65 °C. The input controls were adjusted to 500 μL with water and de-crosslinked under the same conditions.The entire chromatin immunoprecipitation (ChIP) was performed at 4 °C unless stated otherwise. ChIP dilution buffer  was added to 3 mL to the chromatin suspension. For pre-clearing, 20 μL Pierce Protein A Magnetic Beads  were incubated with the samples for 1 h and the supernatant was transferred to a new tube. Next, 5 μL rabbit-anti-histone H3K4me2  was incubated with each sample for 18 h. To each sample 20 μL of Pierce Protein A Magnetic Beads was added and the samples were incubated for an additional hour. The magnetic beads were collected and washed 1 time with low salt washing buffer , 2 times with high salt washing buffer , 2 times with lithium chloride washing buffer  and 2 times TE buffer. During each washing step the samples were incubated for 5 min. After the first wash with lithium chloride washing buffer the samples were transferred to room temperature. Chromatin was eluted twice for 10 min in 250 μL in fresh elution buffer  . The sample was mixed thoroughly and then stored at − 80 °C for 2 h. The DNA was collected by centrifugation at 15,000 g for 45 min at 4 °C and the DNA pellet was washed with 1 mL 70% ethanol. The pellet was collected by centrifugation at 15,000 g for 15 min at 4 °C and resuspended in 30 µL TE buffer. The DNA was further purified with the ChargeSwitch gDNA Plant Kit  according to the manufacturer's specifications and eluted in 50 μL ChargeSwitch elution buffer. The samples were concentrated to 25 μL using a speedvac and sent to the Utrecht Sequencing Facility  for library preparation and sequencing.The decrosslinked DNA was purified by phenol–chloroform extraction. Briefly, 1 volume of phenol–chloroform (1:1) was added, mixed thoroughly and the aqueous phase was extracted after centrifugation at 15,000For each sample, 80% was used for library prep with the NEXTflex Rapid DNA-Seq Kit Bundle  according to manufacturer's specifications. The resulting libraries were sequenced on the Illumina NextSeq500 2 × 75 mid output platform.S. commune H4-8 reference genome (version Schco336) with bowtie2 (version 2.3.4.1)53. The aligned reads were filtered on unique, paired-end alignments with a quality score > 1 with samtools (version 1.7)54. Duplicated reads were marked with picard (version 2.21.6)55 and removed with samtools. Peak calling was performed by macs2 (version 2.2.3)34 for individual replicates and across all replicates together. The differential peaks were determined with Bioconductor Diffbind (version 2.14.0)35. The initial peak distribution was determined with ChIPseeker56 and genes were correlated to peaks based on this distribution. Final distribution was determined by identifying the closest gene to a peak within 3000 bp upstream and downstream of the TIS (translation initiation site) and calculating the relative read depth of each position around the TIS of these genes  with 2 grinding beads in 2 mL Eppendorf Safe-Lock tubes  at 25 Hz for 1 min. The homogenized mycelium was resuspended in 1 mL TRIzol reagent  and the RNA was isolated according to the manufacturer’s specifications and resuspended in 100 µL nuclease-free water. The RNA was cleaned up with the GeneJET RNA purification kit  according to manufacturer’s specification and eluted in 100 µL nuclease free water. The RNA was then quantified and 1 µg was used for input in RT-PCR with the Quantitect Reverse Transcription Kit  according to manufacturer’s specification. Primers for qPCR were designed across introns with a product size of 100–150 bp  and zfc7 (ProteinID: 2601101) were constructed by amplifying the upstream and downstream flanks of each gene and cloned into plasmid pRO402 with a nourseothricin resistance cassette between both flanks. Plasmid pRO402 contains a pUC19 backbone with a phleomycin resistance cassette. For fst1, the upstream and downstream flanks were amplified with fst1_up_fw + fst1_up_rv and fst1_down_fw + fst1_down_rv, respectively, to create 1087 bp and 1025 bp fragments, respectively , resulting in deletion constructs pPV021 and pPV023 for fst1 and zfc7, respectively. The transformation with CRISPR-Cas9 was performed as previously described with two sgRNAs per gene close to the upstream and downstream flank respectively33. The sgRNAs were produced with the GeneArt Precision sgRNA Synthesis Kit  according to manufacturer's specifications with the p1_sgRNA and p2_sgRNA primers listed in Table Deletion vectors of fst1 (primers fst1_comp_fw and fst1_comp_rv; Table zfc7 (primers zfc7_comp_fw and zfc7_comp_rv; Table sc3 terminator. The fragments were assembled with pPV008 digested with HindIII and BamHI with gibson assembly. This resulted in complementation plasmids pPV024 and pPV026 for fst1 and zfc7, respectively. Strains were complemented as described previously and candidate complement strains were selected based on their ability to recover the wild type fruiting phenotype5.To complement the deletion strains, a 1000 bp promoter region and the coding sequence were amplified by PCR for Supplementary Information.


DNA can determine where and when genes are expressed, but the full set of sequence determinants that control gene expression is unknown. Here, we measured the transcriptional activity of DNA sequences that represent an ~100 times larger sequence space than the human genome using massively parallel reporter assays (MPRAs). Machine learning models revealed that transcription factors (TFs) generally act in an additive manner with weak grammar and that most enhancers increase expression from a promoter by a mechanism that does not appear to involve specific TF–TF interactions. The enhancers themselves can be classified into three types: classical, closed chromatin and chromatin dependent. We also show that few TFs are strongly active in a cell, with most activities being similar between cell types. Individual TFs can have multiple gene regulatory activities, including chromatin opening and enhancing, promoting and determining transcription start site (TSS) activity, consistent with the view that the TF binding motif is the key atomic unit of gene expression. Analysis of massively parallel reporter assays measuring the transcriptional activity of DNA sequences indicates that most transcription factor (TF) activity is additive and does not rely on specific TF–TF interactions. Individual TFs can have different gene regulatory activities. Major efforts have been undertaken to determine the DNA-binding specificities of TFs in vitro5 and map their binding positions in vivo7. TFs regulate gene expression by binding to distal enhancer elements and to promoters located close to the TSS9. Both enhancers and promoters are characterized by RNA transcription10, the presence of open chromatin11 and histone H3 lysine 27 acetylation (H3K27ac)12. In addition, promoters and enhancers are preferentially marked by histone H3 lysine 4 trimethylation and monomethylation13, respectively. Although these features can be mapped genome-wide in a high-throughput manner, they are correlative in nature and do not establish that an element can act as an enhancer, increasing expression from a promoter irrespective of position and orientation8. To more directly measure enhancer activity, MPRAs have been developed to study the activity of yeast14, Drosophila15 and human16 gene regulatory elements on a genome-wide scale. However, unbiased discovery of sequence determinants of human gene expression using only genomic sequences is made difficult by the fact that the genome is repetitive and has evolved to perform multiple functions. Furthermore, the human genome is too short to even encode all combinations, orientations and spacings of approximately 1,639 human TFs in multiple independent sequence contexts1. Thus, despite the vast amount of information generated by genome-scale experiments, most sequence determinants that drive the activity of human enhancers and promoters, and the interactions between them, remain unknown.The temporal and spatial pattern of gene expression is encoded in the DNA sequence; this information is read and interpreted by TFs, which recognize and bind specific short DNA sequence motifs15, in which putative enhancers are cloned downstream of a minimal promoter to the 3′ untranslated region (UTR) of a reporter gene. The constructs are transfected to cells, and the enhancer activity of the UTRs are then determined by RNA sequencing (RNA-seq) .To systematically characterize the sequence determinants of human gene regulatory element activity, we developed a set of four MPRA libraries that cover more than 100 times the sequence space of the human genome Fig. . The libeq) Fig. . Three leq) Fig. .Fig. 1Fe+ RNA from the transfected cells. The synthetic motif sequences that were transcribed to RNA were recovered using reverse-transcription PCR, and the abundance of each sequence was then quantified by massively parallel sequencing  assay17  revealed20. Consistent with this, we observed that the average activity of all consensus sequences was very low, and for the majority of the TFs, the enhancer activity increased as a function of the number of consensus sequences ; Extended Data Fig. Next, to identify possible novel sequence features that would allow more optimal classification, we trained a convolutional neural network (CNN)–based classifier similar to DeepBindR = 0.78; Fig. To determine whether enhancers are cell-type specific, we used the random enhancer library Fig.  to identTP53−/− GP5d cells. The signal was highly specific, as indicated by the fact that loss of TP53 resulted in loss of most enhancer peaks containing its motif . Consistent with the known association between accessible chromatin, TF binding and enhancer activity, the STARR-seq peaks overlapped significantly with chromatin accessibility; specifically, 30% of the STARR-seq peaks in GP5d and 27% in HepG2 cells overlap with assay for transposase-accessible chromatin using sequencing (ATAC-seq) peaks in the same cell types  and classical enhancer activity (STARR+ or STARR−) for defining different classes of gene regulatory elements along with ChIP-seq data for individual TFs; the histone marks H3K27ac, H3K9me3 and H3K27me3; and the structural chromatin protein CTCF , (2) cryptic enhancers (silenced STARR+ and ATAC− regions), (3) promoters (ATAC+ and STARR+/−), (4) chromatin-dependent enhancers (STARR−/low and ATAC+ with active histone mark H3K27ac), (5) structural chromatin elements  and (6) classical enhancers (STARR+ and ATAC+).We next used the differential signals for chromatin accessibility  or H3K9me3/HP1 repressive chromatin marks. The three other types of enhancers  appeared active based on the fact that inclusion of the corresponding features improved prediction of differential gene expression between GP5d and HepG2 cells  chromatin-dependent enhancers that cannot be effectively detected using STARR-seq  and have strong signal for open chromatin and the activating histone mark H3K27ac; and (3) closed-chromatin enhancers whose detection requires STARR-seq, as these elements are not strongly enriched for chromatin marks associated with enhancer activity.Analysis of the methylated genomic elements revealed that the cryptic enhancers were not silenced by methylation Fig. . Instead4 and consecutive affinity-purification systematic evolution of ligands by exponential enrichment (CAP-SELEX)24  were preferentially, and NRF1 (8.4 versus 1.3) as well as HNF1 motifs (4.3 versus 0.9) almost exclusively enriched at promoters . The transcript side was characterized by a modest increase in G across a relatively wide region (+10 to +35); this feature is also observed in genomic promoters . In addition, many TF motifs preferentially enriched close to the TSS . On the 3ʹ side, the enrichment declines more sharply with very little motif enrichment observed beyond the +20 position from the TSS . Specificity is also required to silence strongly active elements in cell types in which their target protein is not needed due to the substantial fitness cost of protein expression41. Further analysis using main cell types representing all three germ layers is needed to determine whether and to what extent differentiated human cell types have retained the regulatory mechanisms that existed in their common unicellular ancestor. Moreover, the contribution of specific TFs to the transcriptional activity in the cell could be further dissected, for example, by testing mutated genomic fragments in MPRAs.Our results also show that different cell types have very similar TF activities and that the topology of the gene regulatory network is hierarchical, with few TFs displaying very strong transactivation activity. This is consistent with our previous work showing that relatively few TFs show strong DNA-binding activity in a cell and that many of the strong binders are common to various cell types8. We find here that in addition to these elements, two other types of enhancing gene regulatory elements exist: chromatin-dependent enhancers and closed-chromatin enhancers  or a set of closely bound TFs that fit between or associate directly with well-ordered nucleosomes43. The prevalence of both the closed-chromatin enhancers and chromatin-dependent enhancers suggests that they may contribute substantially to regional control of gene expression35.The original functional definition described enhancers as genetic elements that can activate a promoter from a distance, irrespective of their orientation relative to the TSSers Fig. . Chromaters Fig. , indicat36 and Agarwal and Shendure44). Interestingly, we found that the promoters enriched from completely random synthetic sequences in a single experimental step are even more predictive of transcriptional activity than the genomic sequences themselves. By analysis of de novo promoters enriched from random sequences, we discovered a G-rich element that interacts with the TSS, potentially positioning RNA polymerase II to the TSS independently of the TATA box. Overall, TF activities could be classified into three groups: TSS position–determining activity , short-range promoting activity  and enhancing activity (many TFs). We did not detect a separate class of distal enhancing activity, suggesting that activities that would allow an enhancer to selectively act at a very long range are likely to be associated with chromatin-dependent enhancers and not classical enhancers46. The three classes of activities detected are not mutually exclusive, suggesting that TFs act at multiple levels and/or scales to regulate transcription . However, in the highly evolved genomic context, more specific interactions can exist between chromatin-dependent enhancers and particular promoters, as reported in a few cases, such as multichromosome structures that control the expression of the repertoire of olfactory receptor genes or the complex regulatory landscape of HOX genes48.Our random promoter–enhancer design allowed unbiased discovery of features that facilitate interactions between classical enhancers and promoters at a relatively short range. No specific pair of motifs controlling such interactions was found. This, together with the fact that no specific TF that only acts from an enhancer was found, is consistent with a generic and indirect mechanism of action, where the activities of individual TFs bound to an enhancer are aggregated and their total activity then activates the promoter. Molecularly, these results are consistent with mediation of the effect by the least specific type of biochemical interaction, steric hindrance. The simplest mechanism for enhancer action would involve opening of higher-order and local chromatin in such a way that the steric hindrance that prevents large macromolecular complexes such as Mediator or RNA polymerase II from loading to DNA is decreased . The sequence between SacI and AfeI was replaced with a sequence containing CG-depleted chicken lens δ1-crystallin gene (Sasaki) promoter51, a synthetic intron , an ORF (fusion of Nanoluc-EmGFP), homology arms for library cloning with AgeI and SalI restriction enzyme (RE) sites flanking the ccdB gene, a small 52-bp DNA stuffer (a part of the neomycin resistance cassette) and a 20-bp sequence from the 3ʹ-Illumina adapter for optimally sized final library for Illumina sequencing and the SV40 late poly(A) signal from the pGL3 backbone .We designed a modified STARR-seq reporter construct pGL4.10-Sasaki-SS (a) based on an earlier published designAgeI and SalI RE sites, the 52-bp DNA stuffer and the 20-bp sequence from the 3ʹ-Illumina adapter as above was introduced to the CpG-free vectors using the NheI site.To enable the analysis of CpG methylation on enhancer activity, we designed modified STARR-seq vectors in a CpG-free backbone with Lucia reporter gene  driven either by the EF1α promoter (b. pCpG-free-EF1α-SS) or the Sasaki promoter (c. pCpG-free-Sasaki-SS-v1) as above. To facilitate the cloning of the synthetic DNA library to the 3ʹ UTR of the reporter gene, the cloning cassette from the pGL4.10-Sasaki-SS vector (a) containing the homology arms with AgeI and SalI RE sites with the AflII and PvuII sites devoid of CG dinucleotides and introducing a DNA stuffer of 1.2 kb between the RE sites to the resulting pCpG-free-Sasaki-SS-v2 vector (d) to unambiguously detect and purify the linearized reporter backbone for downstream cloning.Standard Illumina adapters harbor CG dinucleotides, and to make our modified STARR-seq design completely CpG-free, we designed custom adapters for Illumina sequencing  in the pCpG-free-promoter–enhancer-SS vector (e). The cloned random promoter–random enhancer input library is paired-end sequenced to map the promoter–enhancer pairs, and thus, the random enhancer sequences obtained after sequencing the reporter-specific RNA library can be used to identify the corresponding promoter sequence from the input library. In total, the constant sequence between promoter and enhancer elements is 872 bp in the pCpG-free-Sasaki-SS-v2 construct and 215 bp in the pCpG-free-promoter–enhancer-SS construct.For the binary STARR-seq approach in which random sequences were cloned as both promoters and enhancers, the pCpG-free-Sasaki-SS-v2 vector (d) was modified by replacing the Sasaki promoter with a custom CpG-free 5ʹ-adapter sequence and an The new reporter vectors (a–e) were used in different experiments as follows  for 4 h at 37 °C with the reaction volumes scaled for 62.5 µg plasmid DNA per reaction and inactivated for 20 min at 65 °C, followed by purification and ethanol precipitation of the methylated library.The genomic DNA library was methylated using l-glutamine and 1% penicillin–streptomycin.The cell lines used in this study were the colon cancer cell line GP5d , the liver cancer cell line HepG2  and the retinal pigment epithelial cell line hTERT-RPE1 . The cells were maintained in their respective media  supplemented with 10% fetal bovine serum, 2 nM TP53-null GP5d cell line was generated by CRISPR-Cas9 targeting of exon 4 of the TP53 gene using Alt-R CRISPR-Cas9 from Integrated DNA Technologies. Briefly, annealed sgRNA duplex from crRNA . For random promoter–enhancer libraries in HepG2 and RPE1 cells, a total of 400 and 480 million cells were transfected, respectively. Briefly, a day before transfection, 6.7–10 million cells were plated per 15-cm dish in their respective media without antibiotics. The next morning, plasmid DNA was mixed with transfection reagent optimized for each cell line (Transfex (ATCC) for GP5d, Transfectin (Bio-Rad) for HepG2 and FuGENE HD (Promega) for RPE1) at a 1:3 ratio in Opti-MEM medium (Gibco), incubated for 15 min at room temperature and added dropwise to the cells. The cells were incubated for 24 h in a 37 °C incubator with 5% CO2.In STARR-seq experiments, 1 µg of each input library DNA was transfected per million cells. For TF motif DNA libraries, a total of 50 and 35 million GP5d cells were transfected for the libraries in the pGL4.10-Sasaki-SS (a) and pCpG-free-EF1α-SS (b) vectors, respectively. Experiments were performed in two replicates with random enhancer libraries in GP5d and HepG2 cells and random promoter–enhancer libraries in GP5d cells (250 million cells per each replicate). Genomic STARR-seq experiments were performed in two replicates in HepG2 cells (170 million cells per replicate) and four different conditions in GP5d cells (wild-type and + RNA fraction was purified using the Dynabeads mRNA DIRECT Purification kit (Invitrogen) followed by DNase treatment using TurboDNase (Ambion) and purification using RNeasy Minelute kit (Qiagen) as previously described15.Cells were harvested and total RNA isolated 24 h after transfection using the RNeasy Maxi kit (Qiagen) with on-column DNase I digestion. The poly(A)15 essentially in all steps but with primers matching our modified STARR-seq vectors, and the exact protocol is described in The library preparation protocol was adapted from Arnold et al.To generate a sequencing library using a template-switch strategy, a 40-µg aliquot of total RNA from the random promoter–random enhancer STARR-seq experiment in the GP5d cell line was used. See 52 using the following antibodies (2 μg per reaction): H3K27ac, H3K9me3 and H3K27me3 ; FOXA1 ; p53, HNF4a, IRF3 and CTCF ; SMC1 ; and normal rabbit, mouse and goat IgG . To analyze the genomic occupancy of TP53, GP5d cells were treated with 350 µM 5-fluorouracil (Sigma) 24 h before harvesting the cells. To analyze the effect of STARR-seq plasmid transfection on cellular alarm signals by ChIP-seq, RNA-seq and ATAC-seq, HepG2 cells were collected 24 h after the following treatments: mock (DMSO), 350 µM 5-fluorouracil treatment and transfection of the genomic STARR-seq library using similar conditions as in the STARR-seq experiments described above. The details of conditions, protocols and analysis parameters are described in ChIP-seq was performed as previously described53 from 1 µg total RNA. The bisulfite sequencing data for DNA methylation in GP5d cells were obtained from Yin et al.5. The ATAC-seq libraries were prepared from 50,000 cells as previously described54 for GP5d and HepG2 cells. All samples were paired-end sequenced using Illumina platforms.For RNA-seq, total RNA was isolated using RNeasy Mini kit (Qiagen) and RNA-seq libraries were generated using KAPA stranded mRNA-seq kit for Illumina (Roche). CAGE library was prepared from total RNA isolated from GP5d cells as previously described17. Transcribed enhancer regions defined using TT-seq data are based on Lidschreiber et al.55. The experiments were performed from GP5d cells in two biological replicates as previously described56. The details of the protocols and analyses are described in The ATI assay in GP5d cells and processing of the data were done as previously described57 and unpublished draft motifs). The motif collection and library design, measurement of enhancer activity of TF motif consensus sequences, and generation of activity PWM are described in detail in the 58; BRE, MTE and DPE59; and BANP25.For testing activities of known TF motifs, a set of 3,226 HT-SELEX motifs were collected , and only combined sequences of length 170 were chosen. To avoid including PCR duplicates of the same sequence with few mismatches due to sequencing errors, the sequences were sorted four times based on 40-bp-long nonoverlapping subsequences from base 6 to 165, and only one sequence per identical subsequence at each sorting step was taken. This ensured that from sequences that had Hamming distance less than 4, only one was taken. Only one representative sequence from the similar sequences was used for downstream analysis, so each sequence is either present or absent in the sample. The sequences are sampled from a huge input DNA library, which prohibits precise determination of initial input frequencies of individual sequences. Thus, our analysis relies on finding common features of different selected sequences instead of their counts. The numbers of preprocessed sequences used in downstream analysis are shown in Supplementary Table First, 150-base-long STARR-seq RNA and input DNA paired-end reads were combined using the FLASH program61). The preprocessing of genomic STARR-seq data, peak calling, overlap with genomic features, de novo motif mining and conservation of genomic STARR-seq elements are described in detail in The active enhancers were identified by calling the peaks from the STARR-seq–enriched RNA fragments against the plasmid input sample using MACS2 (ref. 62), (2) sequences that mapped to plasmid backbone sequence using bowtie2 version 2.2.4 (ref. 63) and (3) outlier sequences in terms of nucleotide composition (count of any nucleotide more than three median absolute deviations higher than the median count) that removes, for example, those high-G-content sequences that are an Illumina artifact. After preprocessing, the correlation between observed dinucleotide frequencies and those expected based on mononucleotide frequencies was over 0.99 (GP5d random replicate 1 enhancers). Input DNA sequences were processed the same way. For promoter–enhancer pair analyses, the remaining promoter–enhancer pairs were collected, and pairs containing highly similar sequence as a promoter and an enhancer were removed. The numbers of sequences used in downstream analysis are shown in Supplementary Table The STARR-seq enhancer sequences derived from RNA were mapped to corresponding promoter–enhancer pairs in the input DNA by exact matches of the first 20 bases of the 150-base-long enhancer sequences. Duplicate sequences were removed as described for random enhancers, except that three 40-bp-long subsequences from 16 to 135 were used, thus ensuring that only one of the sequences with Hamming distance less than 3 was chosen (Preprocessing of random enhancer library sequences). Then, promoter and enhancer sequences were filtered separately by removing (1) all adapter sequences that included some  adapter sequence according to cutadapt version 1.9.1 (ref. 30 (hsEPDnew 006).First, the synthetic random sequences derived from spliced transcripts were identified using the constant sequence spanning the splice site after intron removal (cutadapt program); other sequences were not processed further. Next, the UMI sequence was removed from the 5ʹ end of each sequence, and the last 20 bp of its random part was used to recognize the corresponding promoter from the input DNA. To accurately recognize the first base of the transcript and thus the position of the TSS, it was assumed that the template-switch process had added at least three and at most four guanines to the 5ʹ end of the transcript. On this basis, only the RNA sequences starting with at least three Gs were used in the analysis. Each such sequence was aligned to the corresponding input DNA promoter sequence using an exact 20-bp match starting from the sixth base to allow for the extra Gs. Finally, the Gs added by the template switch were trimmed and discordant sequences removed according to the alignment. The frequency of the four Gs instead of three was estimated from the sequences that do not have G at the fourth position in the alignment to the input. For those that did have a G also in the input sequence, removing three or four Gs was decided randomly but so that the frequency of the fourth G matched the estimate. The two GP5d template-switch libraries were processed separately and then merged so that only one transcript was kept for each input DNA promoter sequence to prevent duplicate promoter sequences. The exact positions of the TSSs at the promoters were recorded, and the flanking sequences were used for further analysis of the positioning of different sequence features relative to TSSs. The numbers of sequences obtained are listed in Supplementary Table 64), and fold changes were estimated using the function PsiLFC in R package lfc. The details of motif matching and motif spacing analysis in STARR-seq random enhancers are described in The motif matching was done using MOODS version 1.9.3  and motif-match strand combination . The counts over the strand combinations were summed to get the total number of pairs, and the fold change of the number of pairs between input DNA and RNA was estimated using the function PsiLFC in R package lfc. If the promoter and enhancer occurrences are independent of each other, then the expected frequency of the pair of sites is the product of the individual frequencies. The expected logP value  for 253 pairs.To estimate the significance of the number of observed motif pairs, our null hypothesis was that the probability of observing a motif-match pair in a promoter–enhancer sequence pair was the same as estimated from the individual motif-match frequencies. The two-sided binomial tests done for 528,529 motif pairs resulted in a significant −4). The log2 fold changes of the motif match counts between TSS flanking set and control set (estimated with the lfc package) were then used as a positional activity score for each position and strand.For analyzing motif positioning within active promoters derived from synthetic random sequences, motifs were matched to sequences flanking the TSSs identified from the template-switch data, and for each motif, only the highest-affinity match per sequence was considered. The number of matches for each motif was then counted separately at each position and strand. To get positional activity scores for position-specific regression analysis, motif matching was done for TSS flanking sequences from position −100 to +20 in relation to TSS and for a control set generated by sampling for each TSS sequence a subsequence of same length from the same position from an input DNA promoter (background probability of a match 5 × 10−5) to highly selected sequences chosen by taking only sequences observed at least twice in both GP5d enhancer replicate experiments. A histogram of match start positions was generated by counting only the highest-affinity match in each sequence. A smoothed density estimate was generated using a Gaussian kernel (R ggplot geom_density with adjust=0.5).To study p53 motif-match positioning relative to the STARR-seq vector, the motif was matched ; details are described in The mutual information analysis was done as previously describedThe datasets used in each machine learning analysis and their division into training, test and validation sets are detailed in Supplementary Table 66; the CNN models were built on Keras  using TensorFlow 1.14.0 backend67; DeepLIFT version 0.6.12.0 (ref. 68) was used to visualize the TERT promoter mutations and study the sequence features learned by the random enhancer STARR-seq CNN model along with TF-MoDISco version 0.5.14.1 (ref. 69).The details of machine learning analyses using logistic regression and CNNs, discussion about the optimal classification of random enhancer STARR-seq data, details of differential expression prediction, interpretation of CNN classifiers and validation of the predicted promoter mutation effects with external data are described in The binary STARR-seq design allows looking for relatively short-range interactions between promoters and enhancers, and the details of machine learning analysis used for testing such interactions are detailed in the All the promoter models were trained on data where the TSS is 100 bp from the start of the training sequence. Thus, scoring any 120 bp sequence with these models gives a probability that the position 100 in these sequences is a TSS of a functional promoter sequence. Each possible TSS position within ±500 bp from the TSSs of the active GP5d promoters was analyzed by taking 100 bp upstream and 20 bp downstream from the candidate TSS and scoring these sequences with the promoter models. Active GP5d promoters were defined as those EPD promoters that overlapped with a GP5d CAGE peak. For each test set, active GP5d TSS and promoter model, the position obtaining the highest promoter probability from the corresponding model was taken as the predicted TSS position.30), and their preprocessing, together with analysis of the CAGE data, is described in Human promoter coordinates were obtained from the EPD version 006, hg19 (ref. Further information on research design is available in the Any methods, additional references, Nature Research 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/s41588-021-01009-4.Supplementary InformationSupplementary Figures 1–4, Note, Methods and References.Reporting SummaryPeer Review InformationSupplementary TableSupplementary Tables 1–12


Using an inducible version of this system, we show that silencing establishment is a stepwise process occurring over several cell cycles, with the progressive recruitment of the SIR complex. In contrast, we observed a rapid, SIR-independent perinuclear anchoring, induced by the high amount of LacI binding at the lacO array leading to nucleosome eviction at this array and to the phosphorylation of H2A in the neighboring nucleosomes by Mec1 kinase. While the initial phosphorylation of H2A (H2A-P) and perinuclear anchoring are independent of the SIR complex, its latter recruitment stabilizes H2A-P and reinforces the perinuclear anchoring. Finally, we showed that Sir3 spreading stabilizes nucleosomes and limits the access of specific DNA-binding protein to DNA.In eukaryotic cells, silent chromatin is mainly found at the nuclear periphery forming subnuclear compartments that favor silencing establishment. Here, we set up an inducible system to monitor silencing establishment at an ectopic locus in relation with its subnuclear localization in budding yeast. We previously showed that introducing LacI bound  In eukaryotic cells, discrete regions of the genome assume heritable chromatin structures that silence genes located in these regions. In most organisms, and most cell types, silent chromatin or heterochromatin is enriched at the nuclear periphery forming subnuclear compartments where general repressors of transcription concentrate, thus favoring silencing establishment at the nuclear periphery ,2.HML and HMR) and at the ribosomal DNA locus. In cycling cells, telomeres and HM loci are preferentially located at the nuclear envelope, forming repressive domains that sequester the yeast silencing factors (SIRs) [In budding yeast, silent chromatin is mainly found in subtelomeric regions, at the two cryptic mating type loci (s (SIRs)  in a mans (SIRs) ,4.Similarly to HP1, the yeast silencing complex SIR2/3/4 is able to spread along the chromatin and to repress gene transcription owing to the histone deacetylase activity of Sir2 that creates a binding surface for Sir3 .At telomeres, silencing is nucleated at the subtelomeric TG1-3 repeats by the binding of the transcription factor Rap1 that contains binding sites for the silencing factors Sir3 and Sir4 at its C-terminal end. At the cryptic mating type loci, recruitment of the SIR complex requires the presence of cis acting elements called silencers that are formed by combinations of binding sites for DNA binding proteins including Rap1, Orc1, and Abf1, which like Rap1 have other functions in the cell. A common feature of these factors is their strong ability to displace nucleosomes .HM loci and subtelomeres, Sir2 only is required for silencing at the rDNA, although Sir3 is found associated with the rDNA by ChIP and live microscopy [At the rDNA, Sir2 is recruited by Net1 that associates with the RNA-polymerase I and Fob1 that binds the rDNA replication fork barrier as part of the RENT complex . While Scroscopy ,9.HM loci and subtelomeres, Sir3 does not spread from these recruitment sites owing to the presence of H3K79me3 nucleosomes on the flanking regions [In addition to these canonical sites, Sir3 is also found by ChIP at few sites located on chromosome arms ,10,11,12 regions . What trHM silencers is sensitive to the distance to telomeres, which are located in compartments of the nucleoplasm enriched in silencing factors [An important feature of heterochromatin in budding yeast is the limiting amount of the silencing factors Sir2 and Sir3, leading to an intranuclear competition between their target sites ,14,15. A factors . Accordi factors ,19,20,21lacO arrays in proximity to a reporter gene favor the recruitment of the SIR complex in this context [We previously showed that introducing tight protein–DNA complexes such as LacI bound  context , acting As mentioned above, silent chromatin is preferentially associated with the nuclear periphery due to different anchoring pathways, one of them involving Sir4 association with a network of docking sites on the inner nuclear membrane . ReciproSIR3 alleles [Heterochromatin establishment has been mainly studied using different strategies, most of them using conditional or inducible  alleles ,30,31,32w/v) (referred as CSM-TRP plates or medium). All the strains were grown at 30 °C with shaking at 250 rpm. For galactose induction, YP was supplemented with 0,008% adenine-HCl . Typically,  was added to one half of the culture for galactose induction, the other half was kept in the same medium YPA 3% raffinose as a control, and cells were put back to grow. At the end of day 3, both cultures were diluted at 0.005 OD600 nm and grew overnight to obtain the 20 h (or 1200 min) time point.Yeast cells were grown either in rich medium (YP) supplemented with 2% glucose; 3% raffinose or 2% galactose (wt/vol) or in synthetic medium  plates or liquid enriched for 2600 nm overnight in 2 600 nm in 2 600 nm.For Figure 6F and The strains used in this study are listed in ADE2 ORF (nucleotide144-950) was amplified by PCR from pAT147  was obtained by directed mutagenesis on pAT378 using primers oAT1221 and oAT1222, yielding pAT391. pAT390, another version of the GFP-LacIR plasmid that is integrating at the LEU2 locus, was obtained by directed mutagenesis on pAT123 using primers oAT1168 and oAT1169. See sequences of the primers in The plasmid encoding the GFP-LacI construct to be integrated at the ADE2 locus (pAT378) was constructed as described below: a part of the DEI from ) using pDEI from  digested600 nm equivalent of cells was fixed in 20 mL with 0.9% formaldehyde for 15 min at 30 °C, quenched with 0.125 M glycine for 5 min, and washed once in cold 1A total of 20 ODOGG1 control locus in immunoprecipitated and input DNA samples. Plots represent the mean value obtained for at least three independent experiments; data are shown as mean ± s.e.m.; see ChIP quantification was performed by quantitative PCR (qPCR) either on the 7900HT Fast Real-Time PCR  or on the QuantStudio 5 Real-Time PCR System . Sequences of interest were amplified using the SYBR Green PCR Master Mix  and the primers listed in ADE2 and a couple of primers designed in ACT1  and a strain without LacI expression (wt No LacI yAT2001).Total RNA was isolated from yeast using the RNeasy kit  followed by a DNase treatment  to remove contaminating DNA. First strand cDNA was prepared from 0.5 µg of RNA and SuperScript™ III First-Strand Synthesis SuperMix for qRT-PCR . Quantitative PCR was performed on a 1/50 dilution of the reverse transcript mix with a couple of primers designed in The set of images from any given figure panel was acquired using identical acquisition parameters. For all fluorescent images, the axial (z) step is 200 nm, and images shown are a maximum intensity projection. Images were acquired using a wide-field microscopy system driven by MetaMorph software version 7.8  based on an inverted microscope  equipped with a 100Deconvolution of images acquired with the wide-field microscopy system was made using the Meinel algorithm in MetaMorph .All the microscopy image quantifications were done using either a single Fiji macro or MATLAB scripts with an additional expert supervision step to ensure that only relevant data are included in the statistical analysis.The spot localizations are extracted using a Fiji macro applying Laplacian of Gaussian filters on pre-processed images followed by an h-maxima transform. Due to the presence of highly variable signals present inside the yeast vacuoles, no reliable segmentation of the nuclear periphery has been achieved. Hence, a manual intervention from an expert was implemented in order to define two locations on the nuclear membrane: the closest point to the detected spot and on the opposite side of the nucleus in order to measure, respectively, the distance to the edge of the nucleus and its radius. The expert can also filter out any detection error and define the cell cycle.Two different schemes are used to extract the spot intensities. The first, encapsulated inside the same Fiji macro for the spot localization, sums the intensities—minus the image background measured as its median intensity—of all the pixels comprised inside a three-by-three-by-three cube centered around the detection.The other approach was preferred for data without the need to quantify the spot locations inside nuclei; thus, the signals inside vacuoles were not deemed problematic. It relies on qFOCI, a MATLAB application currently being developed to extract diverse information from yeast nuclei and the spots they may contain. With this method, the spots are fully segmented in 3D through multiple Hessian of Gaussian filters followed by an MSER detector  and theip-values are corrected using Tukey’s range test [In order to assess the presence of statistically significant differences between two given yeast strains or incubation time, be it among the localizations or intensities, Kruskal–Wallis tests by ranks were performed . This nonge test . All staHML-mediated gene repression is dependent on the proximity to telomeres [ADE2 gene flanked by the HML silencers is introduced at the LYS2 locus located 339 kb away from telomeres (hereafter named lys2::E-ADE2-I locus), yeast colonies are white, indicative of ADE2 expression [lacO array in proximity to this construct leads to the formation of pink colonies, indicative of ADE2 repression [GALS promoter to be able to tightly control its expression  our inducible strain formed white colonies, similarly to a strain bearing no LacI gene , suggesting that LacI binding does not require a passage through S phase. This percentage raised to 84% after 45 min, and above 95% after 90 min of induction (HIS3 promoter (here after the constitutive strain) is shown for comparison and is similar to the intensity observed after 45 min of induction . We noticed that in the constitutive strain, intensities of the GFP spots were lower in the G1 phase than in the S-G2 phase (p = 1 × 10−10), as expected, since the lacO array replicates during the S phase [p = 0.38 and 0.04 respectively), but it was statically significant after 90 min and even more obvious at later time points .As mentioned above, LacI induced silencing of the eriphery . We usednduction A. Foci induction . Intensi S phase . This dilacO array to the nuclear periphery using Nup49-mCherry to label the nuclear envelope. At early time points of induction, the spatial distribution of these foci in the nuclear space was slightly biased toward the nuclear periphery . In contrast, 90 min after LacI induction, we observed a strong shift toward the nuclear envelope (p = 4 × 10−6). This distribution was similar after 180 min of induction (>0.99) and even more peripheral after a long-term induction . At this stage the locus was more peripheral than in the strain expressing constitutively the LacI protein under the HIS3 promoter (p = 3 × 10−3). We observed a similar behavior when scoring the position of the locus in cells in the S-G2 phase, indicating that this perinuclear anchoring is mostly independent of the cell cycle . We thus conclude that perinuclear anchoring of the LYS2 locus was not induced by growth in galactose medium but requires both the presence of silencer elements at this locus and LacI expression.We monitored the relative distance of the eriphery  without ll cycle . In contADE2 reporter gene by measuring the ADE2 mRNA levels at different time points after LacI induction  that mRNA levels reached the levels of the constitutive strain. Such a delay between LacI binding that reached the levels sufficient to induce silencing in the constitutive strain after 45 min of induction and the decrease in mRNA levels cannot be explained by the half-life of the ADE2 mRNA  analyzed by quantitative PCR, using primers located along the locus at different time points after LacI induction . Consistently we observed a low level of Sir3 and Sir4 at these sites before LacI induction . After 3 h of induction the locus was shifted toward the nuclear periphery to a similar extent than in the wt strain (p = 0.9), but no further changes in localization were observed after a long-term induction in both sir3∆ and sir4∆ strains. Similar results were obtained in G1 or S-G2 cells . The only difference between these two strains is the amount of LacI expression and binding to the lacO array, the median intensity of the lacO spot being 3.7-fold higher in the induced strain than in the constitutive strain (p = 5 × 10−4). We thus conclude that the perinuclear anchoring of the lys2::lacO E-ADE2-I locus, following LacI binding to the lacO array, is partially independent of heterochromatin establishment.In the absence of silencers, the e strain C,D. TherlacO array. We thus set out to monitor the presence of histone H2A phosphorylation, a mark associated both with silent chromatin and DNA damage [lys2::lacO E-ADE2-I locus following LacI induction. To account for potential variation in H2A occupancy, we normalized the H2A phosphorylation (H2A-P) ChIP signal against total H2A ChIP performed on the same samples.We hypothesized that the Sir3-independent change in localization induced upon LacI binding could result from chromatin stress induced by the LacI binding to the A damage ,46, at tE silencer, suggesting that H2A was first phosphorylated there before the mark spread on the flanking regions  mutant that could not be phosphorylated by the PI3 kinases Mec1 and Tel1 , for Sir3 recruitment. Although we observed a slight defect of Sir3 recruitment at the E silencer after a long-term LacI induction, this was not the case at the ADE2 gene . In the absence of Sir3, the localization of the locus was not significantly different in the absence or presence of this mutation, although we noticed a higher variability of the data in the double mutant  allows the stabilization of this mark, which then accumulates over the long-term induction.In summary, the H2A phosphorylation event observed 45 min after LacI binding on the lacO array, from 45 min to 3 h after induction. At this time, H2A enrichment  was 3-fold lower than before LacI induction. However, we observed a slight but reproducible increase in H2A occupancy after long-term induction , and it is even lower in the absence of Sir3 ; (ii) the strength of the gene promoter [SIR3, thus affecting all silent loci simultaneously. These factors are not mutually exclusive and could even reinforce each other. Because Sir3 is available in a limited amount for silencing establishment, it might be more difficult to recruit Sir3 molecules when they are sequestered at other loci, and the distance to these loci might decrease further the probability to encounter Sir3 molecules [In contrast to H2A-P, SIR recruitment and silencing establishment is a slow process, with Sir3 and Sir4 binding increasing significantly after 90 min and accumulating over several cell cycles, leading to gene silencing only after long-term induction 20 h). This slow establishment of silencing is in contrast with previous reports monitoring silencing establishment at HM loci ,31,32,50promoter ; (iii) tolecules ,20,21,22 h. This LYS2 locus, when not silent, shows an almost random nuclear localization [Silent loci are enriched at the nuclear periphery, whereas the lization . Here welization ,22.lacO array. Binding and unbinding of the LacI protein could increase the turnover of nucleosomes independently of DNA replication, thus leading to the incorporation of unmodified histones that are more permissive to Sir3 binding. In particular, H3K79 tri-methylation, which is widespread in euchromatin, counteracts Sir3 spreading and prevents silencing establishment [lacO array.What is the mechanism leading to SIR recruitment upon LacI binding? We previously proposed that the replication stress arising from tight DNA–protein interactions could favor heterochromatin formation . Here welishment ,32,52. GE silencer and the lacO array is 1960 bp. Yet, it would be interesting to test whether shortening this distance to a length that could not be evenly divided into nucleosomes would prevent silencing establishment.Nucleosome-depleted regions (NDR) were recently shown to improve silencing establishment and by favoring nucleosome positioning when the distance between two NDRs allows a regular positioning of nucleosomes . HoweverAs mentioned above, the perinuclear anchoring of the locus that occurs independently of SIR recruitment could also contribute to SIR recruitment by bringing the locus in proximity to the pool of SIR proteins associated with silent loci enriched at the nuclear periphery. In turn, SIR recruitment reinforces the perinuclear anchoring of the locus, feeding a positive loop that increases both SIR recruitment and anchoring at the nuclear periphery.S. pombe [lacO array reduces LacI binding indicates that SIR bound to nucleosomes occlude the access of DNA binding protein to DNA. This is in good agreement with in vitro studies suggesting that association of the SIR complex to chromatin results in the formation of a fiber characterized by Sir3-dependent stabilization of nucleosomes and occlusion of DNA linkers [The use of our inducible system to study the establishment of silencing at an ectopic locus revealed a positive role for the SIR complex in the stabilization of nucleosomes in vivo. This was also observed at the natural subtelomere 6R where the sequence immediately adjacent to the telomeric repeats shows a low occupancy in wild-type cells, which is further reduced in the absence of Sir3. A similar effect of heterochromatin in stabilizing nucleosomes has also been reported in S. pombe ,55. This linkers ,57.In conclusion, using our inducible system, we showed that a massive binding of LacI protein leads to nucleosome eviction, inducing a chromatin stress as indicated by the phosphorylation of H2A at this locus and its anchoring at the nuclear periphery. This is followed by the recruitment of the SIR complex, which in turn stabilizes nucleosomes, H2A-P, and the perinuclear anchoring of the locus and prevents the access of DNA binding protein to DNA.HM silencers or telomeric repeats are made of combinations of binding sites for protein that are strong nucleosome-displacing factors (NDFs) [Although artificial, this system may reveal a mechanism at play at natural genomic loci. Indeed, natural sites of SIR recruitment such as s (NDFs) . Moreoves (NDFs) ,17,53. F