Drosophila nonspecific lethal (NSL) complex is necessary to maintain positioning of nucleosomal organization at gene promoters. Their findings show that the NSL complex establishes a canonical nucleosomal organization that enables transcription and determines TSS fidelity.In this study, Lam et al. report that the  Drosophila nonspecific lethal (NSL) complex is necessary to maintain this stereotypical nucleosomal organization at promoters. Upon NSL1 depletion, nucleosomes invade the NDRs at TSSs of NSL-bound genes. NSL complex member NSL3 binds to TATA-less promoters in a sequence-dependent manner. The NSL complex interacts with the NURF chromatin remodeling complex and is necessary and sufficient to recruit NURF to target promoters. Not only is the NSL complex essential for transcription, but it is required for accurate TSS selection for genes with multiple TSSs. Furthermore, loss of the NSL complex leads to an increase in transcriptional noise. Thus, the NSL complex establishes a canonical nucleosomal organization that enables transcription and determines TSS fidelity.Nucleosomal organization at gene promoters is critical for transcription, with a nucleosome-depleted region (NDR) at transcription start sites (TSSs) being required for transcription initiation. How NDRs and the precise positioning of the +1 nucleosomes are maintained on active genes remains unclear. Here, we report that the  Chromatin structure and organization are fundamental to the regulation of gene transcription. Chromatin at active gene promoters is characterized by a distinct nucleosomal organization . TranscrThe standard model of transcription initiation is based on genes that are activated in a tissue-specific manner . The curDrosophila generally lack a TATA box or Inr elements but rather contain motif 1, motif 6, motif 7, and DRE  complex is a chromatin-modifying complex. It contains the histone H4 Lys16 acetyltransferase MOF, as well as NSL1, NSL2, NSL3, MCSR2, MBDR2, Z4, Chromator, and WDS  . Many genes showed a decrease in the nucleosome signal at +1 nucleosome position upon NSL depletion, mostly pronounced at promoter regions . Thus, the NSL complex affects the nucleosomal organization at promoters independent of the changes in Pol II recruitment.The NSL complex is required for the recruitment of RNA Pol II ; thus, cSupplemental Fig. S2A). We used a Gal4-NSL3 reporter system . Knockdown of NURF301 caused a strong reduction in luciferase activity, comparable with the reduction observed upon MOF knockdown . In contrast, INO80 and CHD3 knockdowns did not attenuate NSL3-mediated activation. Thus, the NURF complex is required for NSL3-mediated transcription activation.As NSL complex members have no chromatin remodeling activity reported to date, we asked whether they function with chromatin remodelers to position nucleosomes at promoters . We could specifically immunoprecipitate endogenous NSL proteins using antibodies raised against NURF301 but not against INO80 and CHD3 . Thus, the NSL complex biochemically interacts with the NURF complex.To determine whether the NSL complex physically interacts with the NURF complex, we immunoprecipitated endogenous NSL complex members  from nuclear S2 cell extracts using polyclonal antibodies. Immunoprecipitation experiments successfully enriched for the respective NSL proteins, the NSL complex members  B, and aland CHD3 C. Althouand CHD3 C. Homoloand CHD3 , which iand CHD3 . Consistnsl1 and the Nurf301, Iswi, and Nurf38 members of the NURF complex in flies. Loss-of-function mutants of nsl1, Nsl3, Nurf301, Iswi, Nurf38, and Mi-2 are lethal. However, heterozygous nsl1 mutants, although shown to possess reduced NSL1 activity 1 . We observed a strong negative genetic interaction between nsl1 and Nsl3, as expected for members of the same complex. An equally strong negative interaction was scored between nsl1 and all tested NURF complex members, whereas no genetic interaction was seen between nsl1 and Mi-2 . In further support of our genetic analysis, when a previously characterized dominant-negative IswiK159R allele 1 , to modiand Mi-2 D. The neR allele  was usedobserved D. These Drosophila genome, whereas only 605 (4%) and 2182 (15%) were bound by either NSL1 or NURF301, respectively . Next, we used a CAGE-based approach (MAPCap)  to map dominant TSSs—i.e., the TSS with the most reads, at single-base-pair resolution for each gene—and sorted the genes by the distance to their closest upstream antisense TSS, within 2000 bp . The nucleosomes aligned closely with both the sense and antisense TSS, and both the sense and antisense TSS showed extensive cobinding of NSL1 and NURF301. NSL1 and NURF301 colocalized at the NDR upstream of the +1 nucleosome, which is most affected upon NSL1 knockdown. When we overlaid the signal with TSS positions, the two proteins bind in close proximity to the TSS .We sought to determine whether this interaction is required only for a specific subset of genes or is a general mechanism. Chromatin immunoprecipitation combined with high-throughput sequencing (MNase-ChIP-seq) experiments against NSL1 and NURF301 revealed that 21,138 (91%) of the 23,194 NSL1-binding sites were also bound by NURF301. Conversely, 66% of the 32,232 NURF301-binding sites were co-occupied by NSL1, and 9004 (64%) of these cobound sites overlapped with 14,081 annotated TSSs in the  the TSS E. Simila the TSS . The NSLSupplemental Fig. S2B,H,I). Knockdown of NSL1 compromised NURF301 binding to these promoters, whereas knockdown of NURF301 left NSL1 binding unchanged . MNase-seq experiments revealed that nucleosomes displayed a similar shift toward the TSS upon NURF301 knockdown . Interestingly, only NSL1 knockdown led to a change in nucleosome occupancies, indicating that the NSL complex plays an additional role in maintaining the nucleosome pattern, consistent with the NSL complex being upstream of NURF complex recruitment. Thus, first, the NSL complex is important for maintaining nucleosome occupancy at +1 nucleosomes, and second, it recruits the NURF complex to position the nucleosomes at active TSSs in the Drosophila genome.To determine whether recruitment of the NURF complex could explain the defects in nucleosomal organization observed upon NSL1 knockdown, we depleted NSL1, and the remodelers NURF301, and INO80 in S2 cells . The bound DNA was subsequently purified and sequenced. Peak calling revealed that the GFP control enriched 3726, MCRS2 enriched only six, NSL1 enriched 1910, and NSL3 enriched 4614 regions . The achieved enrichment for MCRS2 and NSL1 remain very close to the enrichment obtained by the GFP control. Only the enrichments achieved with NSL3 are higher. Furthermore, 733 NSL1 regions overlapped with GFP regions, whereas only 29 NSL3 regions did so. Our data do not provide any positive evidence that MCRS2 binds specifically to DNA, but they do not rule out that MCRS2 could bind to DNA under a different condition as tested here. This inconclusive result promoted us to remove MCRS2 from further consideration. NSL1 regions have similar enrichments as the GFP control and overlap extensively with the GFP control, suggesting that the uncovered binding regions for these two proteins are unspecific. Again, this finding does not rule out that NSL1 binds specifically to DNA. NSL3 binds to specific regions in the genome, characterized by high AT content . We observed that the AT-rich sequences overlap extensively with NSL3 binding in vivo . This result suggests that NSL3 recognizes AT-rich sequences in the genome. However, a comparison of the in vitro and in vivo patterns of NSL3 binding at MAPCap TSSs showed that in vitro binding of NSL3 is correlated with the AT content, whereas in vivo NSL3 binds downstream from the AT content peak , suggesting that in vivo NSL3 binding is further refined by local chromatin context and other NSL complex members. Collectively, these results suggest that the AT content contributes to the targeting of the NSL complex to housekeeping promoters that lack canonical motifs such as TATA box or Inr.Because the NSL complex appeared upstream of NURF recruitment, we next addressed how the NSL complex recognizes target promoters. Previous genome-wide correlations suggested association of DRE sequence with NSL target sites . However in vivo B. Intere in vivo B. We cal in vivo C. FurtheSupplemental Table S2; Supplemental Fig. S6C,D). When considering the log2 fold change, 7643 genes were down-regulated : 4020 (64%) of the 6281 dominant MAPCap TSSs were significantly down-regulated at an FDR of 10%, whereas only 113 (2%) were significantly up-regulated and 2148 (34%) were not significantly changed. Clustering analysis revealed that the TSS expression and nucleosome changes correlate with NSL1 and NURF301 binding , indicating that changes in the nucleosomal organization are correlated with the down-regulation of TSSs in a NSL1- and NURF301-dependent manner. Promoters targeted by NSL1 and NURF301 show a broader TSS pattern than do nonbound ones .Consistent with our data, knockdown of NSL1 should have a strong effect on gene expression, and this is indeed what we observed in RNA sequencing (RNA-seq) experiments. The analysis revealed that 5225 (53%) of the 9850 genes for which we could detect expression in either control or NSL1 knockdown were significantly down-regulated at a false-discovery rate (FDR) of 10%, whereas only 774 (8%) were significantly up-regulated and 3851 (39%) were not significantly different  are selected by the position of the +1 nucleosome, a delocalized +1 nucleosome may influence TSS firing and selection. We noticed cases in which TSS preference changes upon NSL1 knockdown C. To ideSupplemental Fig. S6E). Thus, the NSL complex can influence TSS choice through canonical nucleosomal organization.The usage of an alternate promoter could have important consequences for the resulting mRNA. For example, a shift in TSS usage within a promoter can result in changes in 5′ untranslated region (UTR) length that may affect posttranscriptional regulation of the resulting mRNA . We compDrosophila S2 cells and cells depleted of NSL1 using the 10× genomics emulsion-based sequencing technology; 1753 cells and 4046 cells were sequenced for NSL-depleted and control samples, respectively. We selected cells with more than 2000 expressed genes and focused on a subset of genes that were expressed in >50% of the remaining cells. To detect changes in transcription noise. we used the BASiCS approach . Furthermore, the majority of genes with increased transcription noise were NSL1 targets and displayed disrupted nucleosome patterning upon NSL1 knockdown  binds to promoter-associated H3K4me2/3 and H4K16ac via its PHD and bromodomain, respectively, and the interaction is important for the recruitment of the NURF complex , 2011. Iof genes , which tTranscription stochasticity is known to play an instrumental role in processes such as cell differentiation, as well as cellular homeostasis in response to external stimuli . TATA-coDrosophila lines  besides the genes of interest, which were determined by E-RNAi . Double-For knockdown experiments, 2 mL of S2 cells at 1 million cells per milliliter was plated in six-well dishes and left to attach for 1 h. Ten micrograms of purified double-stranded RNA was diluted with Schneider's medium. The RNA transfection was performed using Lipofectamine RNAiMAX reagent (Life Technologies), according to the manufacturer's instructions. The double-stranded RNAs were mixed with the transfection reagent and kept for 15 min at room temperature before the mixture was added to the cells. The cells were harvested after 4 d, and cell numbers were counted.Supplemental Table S4.The primers used for generating dsRNA are indicated in Supplemental Material.Briefly, 100 µL of cells at 1 million cells per milliliter was plated on 96-well plates. Cells in each well were transfected with a plasmid mixture of (1) 200 ng of pG5luc, which contains the firefly luciferase gene whose expression is controlled by UAS sequences; (2) 2 ng of pRL-hsp70, which contains a constitutively expressed Renilla luciferase gene; and (3) 50 ng of pAc5.1 vector containing either Gal4DBD-tagged NSL3 protein or Gal4DBD alone. Transfections were performed with X-tremeGENE DNA transfection reagents (Roche). After 2 d of incubation, cells were lysed , and luminescence was measured by using a Mithras plate reader (Berthold). For further details, see the Supplemental Material.Briefly, S2 cells were cross-linked using 1.8% formaldehyde in crosslinking solution. After quenching with 125 mM glycine, the cells were washed with Paro 1, Paro 2, and RIPA buffers. The cells were then resuspended in RIPA buffer and were sheared using a Branson sonicator and Covaris sonicator. After verifying appropriate shearing, the chromatin was precleared using Protein A/Protein G beads. Appropriate antibodies were added to the chromatin and incubated overnight. Immunoprecipitation was performed using either Protein A or Protein G. The beads were washed thoroughly and reverse-crosslinked overnight at 65°C and treated with Proteinase K and RNase A. The DNA was purified using Minelute columns (Qiagen). For a detailed protocol, please refer to the Supplemental Table S5.The primers used for quantitative PCR are listed in Supplemental Material.Briefly, the cells were crosslinked with 1% formaldehyde in crosslinking solution. The samples were quenched using 125 mM glycine. NP-40 was used for cell permeabilization. The chromatin was then digested with MNase for 10 min at 25°C. The reaction was stopped by the addition of EDTA, NaCl, and SDS, and the samples were placed on ice. For ChIP, the following steps were the same as those mentioned in the ChIP protocol. For a detailed protocol, please refer to the Supplemental Material.Protein G magnetic Dynabeads (Life Technologies) were prepared with IPP buffer . We incubated 2.5 µg of anti-m7G antibody (SYSY 201 011) with the beads for at least 1 h in 4°C. The beads were finally washed twice with IPP buffer. RNA extractions were performed with a Direct-zol miniprep kit (Zymo Research). Abundant small RNAs (<200 nt) were removed using a RNA clean and concentrator kit (Zymo Research) and eluted in 100 µL TE buffer . RNA fragmentation was performed using a Covaris E220 focused-ultrasonicator . After sonication, the capped RNA was captured with the antibody-coupled Protein G magnetic beads for 1–2 h in 4°C. Then, the beads were washed three times with IPP buffer. RNA 3′ ends were repaired using PNK. Custom-made barcoded adapters were ligated to the RNA using T4 RNA ligase 1 for 1 h at 25°C. Excess adapters were washed away with IPP buffer, and RNA was purified by column purification. Isolated RNA was reverse-transcribed and treated with RNase H. cDNA was column-purified and circularized with CircLigase for 2–16 h. Circularized cDNA was directly PCR amplified; the amplified library was finally cleaned up using AMPure beads. For further information regarding MAPCap analysis, please refer to the Supplemental Material.For a thorough description of the ChIP-seq, MAPCap, MNase-seq, scRNA-seq, gDNA-IP-seq, and RNA-seq analysis, please refer to the All of the genome-wide data sets in this manuscript have been deposited in Gene Expression Omnibus under accession number GSE118726


GABRB3 gene linked to possible associations with schizophrenia.This study investigated the effects of haplotypes T‐G and C‐A derived from NG_012836.1:g.4160T>C and NG_012836.1:g.4326G>A on protein expression levels in vitro and identified the functional sequence in the regulatory region of the GABRB3 gene 5′ regulatory region were transfected into HEK‐293, SK‐N‐SH, and SH‐SY5Y cells. The relative fluorescence intensity of the two haplotypes and different sequences was compared using a dual luciferase reporter assay system.Recombinant plasmids with haplotypes T‐G and C‐A and 10 recombinant vectors containing deletion fragments from the GABRB3 gene 5′ regulation region from −177 bp to −18 bp (ATG+1). We also found an expression suppression region from −1,735 bp to −1,638 bp and an enhanced regulatory region from −1,638 bp to −1,335 bp. Multiple inhibitory functional elements were identified in the region from −680 bp to −177 bp.The relative fluorescence intensity of haplotype C‐A was significantly lower than that of T‐G. We shortened the core promoter sequence of the GABRB3 receptor expression.We demonstrated that haplotype C‐A might increase the risk of schizophrenia and found multiple regulatory regions that had an effect on  By binding with its receptor, GABAi can lead to an imbalance between brain excitation and inhibitory signals and have potentially far‐reaching implications for the maintenance of normal cognition, emotion, and perception  is one of the candidate genes for studying mental illnesses, and has been a hot spot for recent research . It has also been confirmed that decreased GABRB3 gene expression may be a potential pathogenic factor in rCAE (NG_012836.1). This segment was used as an amplification template to construct 10 truncated fragments with a 5′ deletion. The purified target gene was also cloned into the pGM‐T vector. Through transformation, the recombinant vector of the target fragment was accurately screened by sequencing. The pGM‐T recombinant vector was then cloned into a pGL‐3 vector. Recombined pGL3 vectors were selected in the same manner to perform eukaryotic cell transfection.Primers Table  and clea2.4Human embryonic kidney cell line HEK‐293 and human neuroblastoma cell lines SK‐N‐SH and SH‐SY5Y were transfected with recombinant vectors. HEK‐293 cells were cultured in HyClone® DMEM high glucose medium containing 10% fetal bovine serum , while SK‐N‐SH cells were cultured in KeyGEN BioTECH® DMEM high glucose medium with 0.011 g/L sodium pyruvate containing 15% fetal bovine serum. SH‐SY5Y cells were cultured in HyClone® DMEM/F‐12 mixed medium. Cell transfection was performed when cell density reached 90%.2.55 cells per well. After 36–48 hr in culture, recombinant vectors and the pRL‐TK vector  were cotransfected with Lipofectamine® 3000 transfection reagent . After 24 hr, the cell lysate was collected to detect luciferase (LUC) and sea kidney luciferase (TK). In each experiment, recombinant vectors were represented in triplicate and the experiment was repeated three times.Cells were inoculated in 24‐well plates at a density of 2 × 102.6SD (standard deviation). The mean comparison of multiple samples was performed by one‐way analysis of variance (ANOVA), while the mean comparison of two samples was performed using the LSD‐T test. p < 0.05 represents a statistically significant difference. SPSS 20.0 software was used for statistical analysis.LUC/TK was calculated as the relative fluorescence intensity. Each sample is expressed as the mean ± 2.7http://jaspar.genereg.net/cgi-bin/jaspar_db.pl)  were used to report luciferase levels. By comparing the relative fluorescence intensity with pGL‐Control, both haplotypes were shown to express luciferase. The relative fluorescence intensity of the C‐A plasmid in the HEK‐293 cell line was significantly lower than in the T‐G plasmid (3.3GABRB3 gene regulatory region on GABRB3 protein expression. The target fragment of 290 bp located in (−177 to +113 bp) (ATG+1) had the highest relative fluorescence intensity in HEK‐293, SK‐N‐SH, and SH‐SY5Y cell lines.In this experiment, 10 recombinant vectors with different fragment lengths were transfected into the three cell lines to verify the effect of different positions in the p values were 0.000, 0.000, 0.002, 0.001, 0.000, and 0.000, respectively  versus ,  versus , (−680 to +113 bp) versus (−587 to +113 bp), (−587 to +113 bp) versus (−485 to +113 bp), (−485 to +113 bp) versus (−365 to +113 bp), and (−365 to +113 bp) versus (−177 to +113 bp)(ATG+1). p values were 0.019, 0.001, 0.007, 0.014, 0.000, and 0.000, respectively  versus ,  versus , (−680 to +113 bp) versus(−587 to +113 bp), (−587 to +113 bp) versus (−485 to +113 bp), (−485 to +113 bp) versus (−365 to +113 bp), and (−365 to +113 bp) versus (−177 to +113 bp)(ATG+1). p = 0.000). In addition, only (−485 to +113 bp) versus (−365 to +113 bp) and (−365 to +113 bp) versus (−177 to +113 bp)(ATG+1) were found to have significant statistical differences in relative fluorescence intensity  versus  and  versus (ATG+1) were similar to the previous two cell lines, and the relative fluorescence intensity was significantly different  had the highest relative fluorescence intensity, which might indicate a central transcriptionally active region of the GABRB3 gene. The −230 bp to −18 bp (ATG+1) sequence in the GABRB3 gene 5′ regulatory region was previously confirmed as the core promoter region . Rs20317 is a SNP‐site located in this region. Its C allele regulates transcriptional activity by binding to transcription factors CMYB and EGR‐3, thus promoting protein expression , (−587 to +113 bp), (−485 to +113 bp), (−365 to +113 bp), and (−177 to +113 bp)(ATG+1) increased step by step and that stronger relative fluorescence intensity was observed closer to the core promoter region. There may be many regulatory factors in the −680 bp to −177 bp (ATG+1) sequence that can inhibit protein expression activity. In SH‐SY5Y cells, only (−485 to +113 bp), (−365 to +113 bp), and (−177 to +113 bp) (ATG+1) showed significant differences in increased luciferase expression activity. This may be due to differences between species and cell lines or possibly due to region specificity of certain regulatory factors, thereby affecting different regulatory factors in different cell types. Several studies identified the transcription factor binding site RE‐1 in the region from −680 bp to −365 bp (ATG+1) that can bind to the inhibitory transcription factor NRSF (neuron‐restrictive silencer factor) or rest (the repressor element 1‐silencing transcription factor) to inhibit transcriptional activity and likely plays an important role in neuronal epigenetics . We found an expression suppression region from −1,735 bp to −1,638 bp and an enhanced regulatory region from −1,638 bp to −1,335 bp (ATG+1). Multiple inhibitory functional elements in the region from −680 bp to −177 bp (ATG+1) were also identified. However, transcription factor binding sites, transcription factors, and other regulatory factors in the GABRB3 gene still need to be identified in future research.In conclusion, using luciferase reporter gene experiments, we further demonstrated that the haplotype C‐A derived from NG_012836.1:g.4160T>C and NG_012836.1:g.4326G>A may increase the risk of schizophrenia at the level of protein expression in vitro. By constructing pGL‐3 recombinant vectors with different length fragments from the 5The authors declare no conflict of interests.


TFF1 persistent site disrupts the formation of its LDEC resulting in the loss of E2-dependent expression of TFF1 and its neighboring genes within the same TAD. The LDEC overlap with nuclear ERα condensates that coalesce in a ligand and persistent site dependent manner. Furthermore, formation of clustered enhancers, as well as condensates, coincide with the active phase of signaling and their later disappearance results in the loss of gene expression even though persistent sites remain bound by ERα. Our results establish, at TFF1 and NRIP1 locus, a direct link between ERα condensates, ERα enhancer clusters, and transient, but robust, gene expression in a ligand-dependent fashion.Unliganded Estrogen receptor alpha (ERα) has been implicated in ligand-dependent gene regulation. Upon ligand exposure, ERα binds to several EREs relatively proximal to the pre-marked, unliganded ERα-bound sites and affects transient but robust gene expression. However, the underlying mechanisms are not fully understood. Here we demonstrate that upon ligand stimulation, persistent sites interact extensively, via chromatin looping, with the proximal transiently ERα-bound sites, forming Ligand Dependent ERα Enhancer Cluster in 3D (LDEC). The E2-target genes are regulated by these clustered enhancers but not by the H3K27Ac super-enhancers. Further, CRISPR-based deletion of  ERα occupies its cognate binding sites on the genome, upon estrogen stimulation, to bring about the signaling mediated response. However, ERα has been implicated in gene regulation even in the absence of hormone and its underlying mechanisms are not known. Here we show that, unliganded ERα-bound genomic sites are necessary for the emergence of active enhancers upon estrogen stimulation. Several ERα sites emerge in proximity to these unliganded ERα sites which loop with each other in three-dimensional nuclear space forming the enhancer:ERα clusters that we call Ligand dependent enhancer clusters (LDEC). These clusters are essential for expression of genes that are associated with the LDEC’s as deletion of unliganded ERα-bound site completely perturbs the formation of clusters and expression of genes within the same TAD while clusters in neighboring TAD remain unaffected. At the mesoscale, these LDEC’s appear as ERα condensates that emerge dynamically only upon addition of hormone. Further, the disappearance of clusters coincide with the loss of gene expression and condensate disappearance, marking the end of signaling response. Upon ligand stimulation ERα regulates gene expression by binding to distal regulatory elements ,2,3. SimAlthough it is known that ERα bound sites exhibit proximity, whether the proximity is ligand-dependent has been debated with evidences both for ,9 and agbona fide ERα clustered enhancers are tethered in such ERα condensates is not known. Importantly, whether enhancer condensates continue to exist during the entire interval when the ligand is present, or whether they exist only during the active signaling phase marked by robust transcription is not known.Super-enhancers exhibit high density of coactivators caused by presence of multimeric enhancer units. Intrinsically disordered regions (IDR) in these coactivators mediate their molecular condensation on super enhancers . ERα, wipersistent, ligand-independent ERα-bound sites. However, we find that the ligand-dependent enhancer clusters (LDEC) are distinct from previously reported super-enhancers [Here we show that upon E2 treatment, as expected, ERα binds to numerous locations across the genome. As recently observed , the newnhancers . LDECs eOverall, our results establish and clarify the role of unliganded ERα binding in priming enhancer clusters in 3D that drive transient, but robust, gene expression in a ligand-dependent fashion. Our work suggests a model of E2-induced gene regulation where during active phase, liganded ERα decorates the EREs closer to pre-marked unliganded ERα site. These pre-marked and new ERα-bound sites exhibit E2-dependent extensive chromatin looping, forming functional LDEC that drives gene expression. These LDECs correspond to ERα puncta in the nucleus that emerge upon E2 stimulation. Finally, upon ERα degradation at 3h or upon deletion of persistent site, these clusters disappear leaving persistent site behind still bound by ERα as bookmark, but that alone is unable to drive gene expression.persistent sites, the 2880 lost sites as -E2 unique sites and the newly acquired 18,055 E2-dependent sites as transient sites.ERα binds predominantly in the intergenic regions of the genome upon E2 stimulation . Furtherrd quantile persistent sites in particular, exhibited stronger ERα binding as well as significantly greater DHS in both post and pre-E2 treated cells   . Furtheruantile) . These duantile) . Interro kb away . Interes kb away  indicatiy . ERα occupancy at transient sites around TFF1 persistent site was completely lost in ΔPS−TFF1 MCF7 cells as compared to the wild-type cells as seen by ChIP-seq of ERα in these cells  or more (TFF1 LDEC) genes present within LDECs.Given that the transient sites cluster around persistent sites, we assessed whether persistent sites play a direct role in the emergence of clustered enhancers. We either deleted or blocked persistent site (PS) from LDECs of the most E2 inducible genes se cells . Importaring TAD . The ERαring TAD . Similarring TAD  but the ring TAD , supportring TAD  also perring TAD . We furtas noted . InteresF1 cells ; these gF1 cells . These dTFF1 LDEC. BAC clone overlapping LDEC was used. ERα intensity on TFF1 LDEC increased significantly upon ligand stimulation as compared to random regions  persistent sites, the binding strength recovers almost to the maximum levels ~24 hours post-E2. These results suggest that, LDEC emerge transiently around persistent sites to drive active phase of signaling in terms of target gene expression but they disappear during later phase of signaling which is recapitulated by loss of E2-target gene expression. However, the persistent sites remain ERα-bound as bookmarks , even when the levels of ERα are raised by ERα overexpression , 0 min  at 10nM concentration for various periods as mentioned in the respective figures. For untreated control, cells were either treated with equal microliters of ethanol on the third day or with ERα inhibitor ICI182780  at 10nM concentration for 24 h after two days of stripping.MCF7 cells were obtained from ATCC. They were cultured in High glucose DMEM media (Invitrogen) at 37°C and 5% COCells pretreated with either E2 or vehicle for 30 min were then treated with 5% 1,6-HD for 30 min in the same E2/vehicle containing media. Cells were then cross-linked to be processed for ChIP or were directly subjected to RNA isolation using Trizol method (Invitrogen) and subsequently cDNA synthesis was performed.-ΔΔct method or percent over input was calculated as described in https://www.thermofisher.com/in/en/home/life-science/epigenetics-noncoding-rna-research/chromatin-remodeling/chromatin-immunoprecipitation-chip/chip-analysis.html.ChIP protocol was followed as described in . BrieflyLibrary preparation was performed as per manufacturer instruction for NEB Next ChIP-seq library preparation Kit (New England Biolabs). Briefly, at least 10 ng chromatin was subjected to end repair, T tailing and adapters compatible with Illumina sequencing platform were ligated. The amplified library was size-selected using AMPure XP beads to enrich fragments from 300 to 500 bp in size. 12 pico moles of library was used for cluster generation and sequencing was performed in 1X50 bp format on Hiseq 2500 (Illumina Inc.).The sequenced reads were aligned to hg18 assembly using default Bowtie2  options.ERα ChIP-seq peaks for vehicle and E2 condition were obtained from  was usedFor the temporal data analysis we used time course ChIP-seq data from . We haveChIP-seq peaks called (described above) were selected if they were within 20kb distance of each other. Clustered enhancers were considered as clusters of three or more such peaks.https://bitbucket.org/young_computation/) using the aligned ChIP-seq reads as input with parameters -s 15000.Super-enhancers were identified using the ROSE (Rank Ordering of Super-enhancers) algorithm  across the human genome as a bigwig file was obtained from UCSC genome browser. This was then used to plot Phast-Cons scores across different regions.The reads were aligned to hg18 assembly using default Bowtie2 options. The reads were counted from the region between +1kb of gene promoter proximal end to 13kb of the gene body using HOMER for each gene. The read densities were calculated similar to that of the ChIP-seq analysis in a strand specific format.Cells were transfected with FOXA1 specific siRNAs (L-010319-00-0005). Cells were harvested 24h post transfections to measure the RNA and protein levels of ERα and FOXA1.http://www.bioconductor.org/) in 5C oligos were designed in alternate orientation  on ERα pERα ChIA-PET in MCF-7 interaction files were obtained from ENCODE. All three replicates were merged to obtain common ends in all replicates using pairtopair tool from BED tools. Interaction ends were annotated as the different classes of ERα sites using pairtobed BEDtool. The number of interacting partners from each annotated interaction end was defined as the degree of the ERα site and the degree was then plotted for the different classes.Entropy values for every 100 kb bins before and after E2 treatmnets were directly taken from  and wereImmunostaining was performed as mentioned in . BrieflyCells were imaged using PLAPON 60x/1.42 oil objective of Olympus FV3000 microscope. Images were taken with intervals of 1.1 seconds.IF/DNA-FISH was performed following the protocol as described in . BrieflyFollowing BAC probe (BACPAC Resources) was used:NRIP1: RP11-213G23 TFF1: RP11-619I15 The Immunofluorescence coupled DNA-FISH slides were imaged using PLAPON 60x/1.42 oil objective of Olympus FV3000 microscope with the same settings. IMARIS was used for the colocalisation analysis. ERα immunofluorescence and FISH signal was used to identify spherical spots of 0.8um and 1um diameter respectively. Spots were identified using the same intensity thresholds. Spots colocalising with each other were identified with distance threshold of 1 pixel and classified as colocalised and non-colocalised spots. The intensity of ERα signal at the ERα and NRIP1 spots was calculated and plotted.For the TFF1 FISH analysis we have used normalised intensity values to correct for any differences in the total ERα intensities between wildtype and TFF1 delete cells. These values were calculated by first identifying TFF1 FISH spots as described above. The nucleus is then identified based on DAPI. The mean intensity of ERα was calculated on the FISH spot. This value is then divided by the mean intensity of ERα in the corresponding nucleus to obtain the normalised intensity of ERα on the FISH spot. This value is a measure of relative enrichment of ERα signal on the FISH spot compared to the corresponding nucleus.We used an arbitrary normalised intensity (enrichment ratio) of 1.4 to define co-localisation of ERα with the the FISH spot.To test the specificity of ERa signal on TFF1 FISH spot, we generated random ROIs (region of interest) within the nuclei of the different fields using matlab. We then identified spots and quantified the normalised intensity within random nuclear regions as described earlier.μm using 100% laser power and images were collected every two seconds. The intensity of the photo-bleached ROI was calculated across all the frames in Fiji [GFP-ERα (Addgene #Plasmid 28230) was overexpressed in MCF7 cells in phenol free media with charcoal stripped serum. Cells were treated with Ethanol (Vehicle) or with E2 (10 nM) for one hour post 24h of transfections. For 1,6-HD treatments, 5% 1,6-HD was given to transfected cells that were pretreated with E2 for 30 min to continue other 30 min but along with 1,6-HD. FRAP was performed on Olympus FV3000 microscope with 488nm laser. Bleaching was performed over an area of 1 in Fiji . Backgro in Fiji .-ΔΔct formula. GAPDH or b-actin expression was taken as control for fold changes in gene expression.RNA was isolated using Trizol (Invitrogen) as per the manufacturer protocol. 1 μg of RNA was taken for cDNA synthesis using random hexamer as per manufacturer guideline . cDNA was subjected to real time PCR in triplicates in CFX96 touch real time PCR (Bio-rad laboratories) using oligos mentioned in The raw reads were mapped to hg18 assembly using bowtie 2. The HOMER program makeTagDirectory was first used to create tag directories with tbp 1. Data was further processed by HOMER in order to remove small fragment and self-ligations using makeTagDirectory with the following options: -removePEbg -removeSpikes 10000 5. Next, findTADsAndLoops.pl was used to obtain overlapping TADs, produced at a 20kb resolution with 40kb windows. Smallest overlapping TADs were selected and intersected with ERα clusters. Strength of these TADs were obtained as Inclusion Ratios (the ratio of intra-TAD interactions relative to interactions from the TAD to the surrounding region (both upstream and downstream of the TAD to regions the same size as the TAD) across the different conditions using findTADsAndLoops.pl score option.Cells were washed twice in phosphate buffered saline (1XPBS) and cell pellet was carefully suspended in 200ul of SFI Buffer  with PIC. Cells were incubated for 30 mins at 4°C and then pelleted down at 2900 rpm for 5 min at 4°C, and the supernatant was collected (Soluble fraction). The pellet was carefully washed twice in SFI buffer and chromatin bound fraction was extracted by adding 2X protein loading dye to the pellet. Fractions were loaded on 15% SDS PAGE and western was done for ERα (sc-8002 Santa Cruz Biotechnology), GAPDH (sc-32233 Santa Cruz Biotechnology), Histone H3 . Cellular lysates prepared were separated on 15% SDS-PAGE gels. The protein transfer was carried out in Tris-glycine buffer at 30V for 1 hour on ice using 0.45μ PVDF membrane (Millipore). Membrane was blocked in 5% non-fat dry milk made in TBST and further incubated for three hours with primary antibodies followed by stringent washings. Membranes were probed with HRP-conjugated secondary antibody (Bio-rad Laboratories). Signal amplification was performed using ECL substrate . Images were captured on Image Quant LAS4000 with CCD camera. Anti-FOXA1  and MED1  were used for total protein levels.crispr.mit.edu to design the gRNAs for ERα peak in the GREB1 and TFF1 persistent and transient sites  ERα binding strength is affected upon siRNA mediated knockdown of ERα in untreated cells  (G) ERα binding strength on 2880 -E2 unique sites is affected upon siRNA mediated knockdown of ERα in untreated cells .(A) Venn diagram shows distribution of ERα on various genomic regions. (B) Strength of ERα occupancy, DHS, and H3K27ac levels at ERα peaks before and after E2 treatments. (C) Heatmaps of ERα, DHS, and H3K27ac 2.5 kb upstream and downstream regions from the center of the ERα peaks in untreated and E2 treated condition. (D) Heatmaps depicting the relative binding of ERα on -E2 specific unique sites, persistent sites and transient sites across different datasets. (E) H3K27ac enrichment plot in minus and plus E2 on 2880 -E2 unique, persistent, transient closer to 3(TIF)Click here for additional data file.S2 Fig−1136) and transient sites (p = 10−5402) whereas FOXA1 is enriched uniquely in persistent with p = 10−226 and in -E2 unique sites p = 10−174. (D) Heatmaps representing the strength of FOXA1 binding in different categories of ERα peaks in treated and untreated cells. Strength was measured at 1.5 kb upstream and downstream of center of ERα peak.(A) Heatmap showing the relative ERα binding strength on various classes of ERα binding sites after 7 days of stripping and frequent change of media. (B) Immunoblot for ERα, GAPDH and Histone H3 in nucleoplasmic (soluble) and chromatin bound biochemical fractions in cells stripped for 7 days followed by 60 and 180 min E2 treatments. (C) (top panel) Known Motif enrichment analysis identifies full ERE in both persistent (p = 10(TIF)Click here for additional data file.S3 Figrd quantile persistent, transient, and transient near persistent sites.(A) ERα binding strength in clusters with and without persistent sites in E2 untreated and treated conditions. (B) Number of ERα peaks are greater in clusters with persistent site as compared to clusters without persistent sites. (C) Phast-cons score of persistent, 3(TIF)Click here for additional data file.S4 Fig(A) Heatmaps exhibit the loss of ERα binding strength at every 2 consecutive EREs from persistent site (B) Heatmap shows DHS signal on sites in panel A.(TIF)Click here for additional data file.S5 FigGREB1 LDEC as shown in TFF1 and UMODL1 genes in MCF-7 cells.(A) GRO-seq tag count shows the relative higher expression of genes near clusters with a persistent site. (B) GRO-seq tag count shows higher expression of genes closer to persistent vs. transient, random and -E2 unique sites. Note: relative higher expression of these genes (first bar) even in untreated cells. (C) ChIA-PET data plotted from one ERα ChIA-PET replicates on (TIF)Click here for additional data file.S6 FigTFF1 region showing blue highlighted persistent sites. Dashed line box marks the deleted regions. (B) Surveyor assay using the oligos specific for the region outside the deleted PS. Wt genomic DNA exhibits the larger molecular weight amplicon compared to the amplicon from ΔPS-Tff1 genomic DNA. (C) Sanger sequencing chromatogram shows the fusion of yellow and blue highlighted regions in TFF1 delete line, whereas these regions are 1611 nucleotide apart in wild type cells. (D) UCSC genome browser snapshot on TFF1 PS region shows the loss of ERα ChIP-seq peaks in delete cells as compared to wild-type cells (Upper track). Browser snap shot on the wider region around deleted TFF1 site, highlighted region depicts the deleted region (Lower track). (E) UCSC genome browser snap shot of GREB1 region showing blue highlighted persistent site which was blocked by specific gRNAs. (F) gRNA’s cut the specific region within the enhancer as shown by surveyor assay using oligos outside of blocked region, PCR was performed on population of cells after transfection so larger and smaller both amplicons are seen.(A) UCSC genome browser snap shot of (TIF)Click here for additional data file.S7 Fig(A) Conformation of TFF1 persistent site deletion in second CRISPR clone (Left panel) and conformation of deletion by sequencing of genomic DNA (Right Panel) (B) UCSC genome bowser track on TFF1 cluster showing ERα ChIP-seq peaks in untreated and treated cells. Highlighted regions depict the deleted region. (C) ChIP-qPCRs shows the loss of ERα binding at different regions of TFF1 cluster upon persistent site deletion in second CRIPSR clone. (D) ChIP-qPCRs confirm the loss of ERα binding at P3 site where gRNAs were targeted in the GREB1 cluster (Upper panel); a modest loss of ERα binding was seen on distal ERα binding site in neighboring TAD (Lower panel). (E) ChIP-qPCRs shows effect in ERα binding strength at GREB1 enhancer region when persistent site near promoter was blocked with gRNAs.(TIF)Click here for additional data file.S8 FigUCSC Genome Browser shots show the occupancy of ERα in WT and ΔTFF1-PS line at various E2 target genes.(TIF)Click here for additional data file.S9 FigNRIP1 loci by immunoFISH upon ICI and E2 treatment for 1h. (C) Time lapse microscopy images of GFP-ERα upon 1,6-HD treatment in cells treated with E2 for one hour. 0 vs. 440 sec shows significant loss of ERα punctate pattern (D) UCSC genome browser snapshot of TFF1 region showing the ERα ChIP-seq peaks in untreated (Top track) and E2 treated (Bottom track) conditions. Dashed boxes mark the regions on which ERα occupancy was measured upon 1,6-HD treatments. (E) ChIP-qPCRs for ERα on R3 and R4 peaks upon 1,6-HD treatments in E2 treated and untreated conditions.(A) Representative immunoFISH image for NRIP1 (Left panel) and mean ERα intensity on NRIP1 FISH spots (Right panel). (B) Comparison of ERα intensity on all puncta vs. the puncta that overlap with (TIF)Click here for additional data file.S10 Fig(A) GRO-seq tag counts from plus and minus strands at different categories of ERα peaks during E2 time course. (B) Relative mRNA expression of genes closer to LDEC at different hours of E2 signaling. (C) Immunoblots probed for MED1, FOXA1 and GAPDH in ICI and 60, 180min and 24h post E2 exposure. (D) Levels of ESR1 and FOXA1 by qRT-PCR (upper Panel) and immunoblot for ERα and GAPDH (lower Panel).(JPG)Click here for additional data file.S1 MovieRest period 4.8sec, exposure 3.2 sec. total interval 8 sec. Three movies taken for different periods were stitched together.(ZIP)Click here for additional data file.S2 MovieRest period 4.8sec, exposure 3.2 sec. total interval 8 sec.(ZIP)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.


Transcription factors play a crucial role in the regulation of gene expression and adaptation to different environments. To better understand the underlying roles of these adaptations, we performed experiments that give us high-resolution binding of transcription factors to their targets. We investigated five transcription factors involved in lipid metabolism in yeast, and we discovered multiple novel targets and condition-specific responses that allow us to draw a better regulatory map of the lipid metabolism. Saccharomyces cerevisiae transcription factors involved in lipid metabolism, Ino2, Ino4, Hap1, Oaf1, and Pip2, in response to four different environmental conditions in chemostat cultures, which allowed us to keep the specific growth rate constant. Chromatin immunoprecipitation with lambda exonuclease digestion (ChIP-exo) enabled the detection of binding events at a high resolution. We discovered a large number of unidentified targets and thus expanded functions for each transcription factor . Moreover, condition-dependent binding of transcription factors in response to cell metabolic state  was clearly suggested. Combining the new binding data with previously published data from transcription factor deletion studies revealed the high complexity of the transcriptional regulatory network for lipid metabolism in yeast, which involves the combinatorial and complementary regulation by multiple transcription factors. We anticipate that our work will provide insights into transcription factor binding dynamics that will prove useful for the understanding of transcription regulatory networks.To build transcription regulatory networks, transcription factor binding must be analyzed in cells grown under different conditions because their responses and targets differ depending on environmental conditions. We performed whole-genome analysis of the DNA binding of five IMPORTANCE Transcription factors play a crucial role in the regulation of gene expression and adaptation to different environments. To better understand the underlying roles of these adaptations, we performed experiments that give us high-resolution binding of transcription factors to their targets. We investigated five transcription factors involved in lipid metabolism in yeast, and we discovered multiple novel targets and condition-specific responses that allow us to draw a better regulatory map of the lipid metabolism. Saccharomyces cerevisiae  play a key role in this regulation. The complexity of transcriptional regulation in eukaryal cells and the lack of knowledge regarding the structure of regulatory networks, however, limit understanding of how this type of reprogramming occurs. Approximately 200 sequence-specific TFs have been characterized or predicted in the model organism revisiae . In trania coli 24) or in ch other  and wherch other . To bettTo generate a genome-wide lipid metabolic transcriptional regulatory network, we used chromatin immunoprecipitation with lambda exonuclease digestion (ChIP-exo) , followeS. cerevisiae. Ino2 and Ino4 are paralogues that belong to the basic helix-loop-helix (bHLH) family and form a heterodimer that is involved in the phospholipid biosynthetic pathway   , but thes (OREs) . The pres (OREs) .S. cerevisiae.In our study we used chemostat cultures to generate chromatin binding data because this allows the analysis to be performed under well-controlled growth conditions and operation under different environmental conditions at the same specific growth rate. Four different limited conditions regarding different nutrition and oxygen availabilities were used to cover a wide range of different environmental growth conditions for in situ C-terminal tandem affinity purification (TAP) tag to enable immunoprecipitation of the TF. Each strain was cultured in chemostats with different limiting conditions in biological duplicates: nitrogen (N-lim), glucose (Glu-lim), glucose and oxygen , and ethanol (Et-lim). Samples from each chemostat experiment were used to cross-link and purify DNA-protein complexes and digest nucleotides not covered by the cross-linked protein by the ChIP-exo method. Following release from the DNA-protein complex, the DNA was sequenced, and the reads were aligned to the reference genome assembly R64-2-1 of S. cerevisiae S288C. The identified TF-binding events were assigned to the closest gene and further analyzed for their biological impacts.Strains expressing each of the five transcription factors (TFs) were constructed to have an S/N), where the duplication showed a good correlation  of >1 are plotted. Download FIG S1, TIF file, 3.1 MB.Correlation between duplicates of ChIP-exo. The duplicates with logCopyright © 2018 Bergenholm et al.2018Bergenholm et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00215-17.2FIG S2 FIG S2, TIF file, 1.4 MB.Distribution of peaks identified on the promoter regions. The promoter regions have been divided into 300-bp fragments where the number of peak summits have been summed. Download Copyright © 2018 Bergenholm et al.2018Bergenholm et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the We further compared all the identified targets for each TF  and 3′-trimmed reads (treated data) mapped to this region are shown. The binding of Hap1 can be manually identified from raw data (consensus motifs marked by yellow triangles) but was not detected using the treated data. This can lead to missing targets, which is a weakness in current high-throughput genome-scale analysis. Download FIG S3, TIF file, 0.5 MB.Effect of data treatment on the identification of Hap1-binding events. The binding on Copyright © 2018 Bergenholm et al.2018Bergenholm et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00215-17.10DATA SET S1 2(S/N)], motif, and direction and distance to ATG for each gene bound under each condition. Table S2 shows Ino4 data, including the peak loci, highest peaks [log]2(S/N)], motif, and direction and distance to ATG for each gene bound under each condition. Table S3 shows Hap1 data, including the peak loci, highest peaks [log]2(S/N)], motif, and direction and distance to ATG for each gene bound under each condition. Table S4 shows Oaf1 data, including the peak loci, highest peaks [log]2(S/N)], motif, and direction and distance to ATG for each gene bound under each condition. Table S5 shows Pip2 data, including the peak loci, highest peaks [log]2(S/N)], motif, and direction and distance to ATG for each gene bound under each condition. Table S6 shows primers used for strain construction and ChIP-qPCR. Table S7 shows adapters and primers used for ChIP-exo. Table S8 shows the overlap of binding targets of Oaf1 and Pip2 between this study and previous reports. Table S9 shows binding of TFs on lipid metabolism-related genes. Download DATA SET S1, XLSX file, 1 MB.Table S1 shows Ino2 data, including the peak loci, highest peaks [log]Copyright © 2018 Bergenholm et al.2018Bergenholm et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the OLE1 encoding delta-9 fatty acid desaturase. The binding is highly concentrated in the same region. Looking closer at this region reveals that two binding motifs of Hap1 surrounded the binding sites of Oaf1 and Ino2, constituting a highly optimized region for responses to different signals, including oxygen levels, fatty acids, and membrane composition. This region overlaps with a previously reported region that has been called the fatty acid-responsive element (FAR)  . With ou10.1128/mSystems.00215-17.4FIG S4 FIG S4, TIF file, 1.3 MB.Venn diagram showing the overlap of gene targets for TFs under each condition. Download Copyright © 2018 Bergenholm et al.2018Bergenholm et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the We studied the motifs of binding sequences for each TF under each condition using the GEM software and then the MEME suite. The consensus motifs are displayed in S/N) and the number of binding events identified for each k-mer group in motif discovery. As seen in We calculated the distribution of binding ratio (CHO2 for Ino2 and EEB1 for Pip2), Ino2 and Pip2 bind to additional targets  only under respiratory metabolic conditions . We overlaid nucleosome distribution data adapted from reference HXT3 site, whereas for HXT6 and HXT7, the chromatin at the corresponding sites is closed. The opposite behavior can be observed during growth at 0.05% glucose (glucose respiration). This difference in chromatin structure is consistent with the different binding of Ino2/4 to this region between fermentative and respiratory metabolic conditions.One explanation for the differential binding could be different expression levels of the TFs under different conditions. However, the transcription level of each TF does not correlate with the number of targets identified for each condition . This in10.1128/mSystems.00215-17.5FIG S5 FIG S5, TIF file, 1.1 MB.Plotting of the transcription levels of the five TFs and the number of their binding genes under four growth conditions. No correlation could be observed between the two data sets. Download Copyright © 2018 Bergenholm et al.2018Bergenholm et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2(S/N) values and allowed GO terms that had P values of <0.01 to be selected as significant reporter terms  sets. For each TF, we ran a gene set analysis using the Piano R package . For inper terms . While lanalysis . Malate analysis , 27, wer10.1128/mSystems.00215-17.6FIG S6 2 binding ratio (S/N) of >1 were used for the analysis. A cutoff P value of <0.01 was applied to the terms. Download FIG S6, TIF file, 2.8 MB.Reporter Gene Ontology process terms enriched by gene set analysis of TF-binding genes under different conditions. Target genes with a logCopyright © 2018 Bergenholm et al.2018Bergenholm et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Condition-dependent functions of the TFs were clearly revealed by the reporter GO term analysis of target genes. In line with the differential binding described above, Ino2 showed expanded binding under the respiratory metabolic conditions relative to the two fermentative metabolic conditions . In addi2(S/N) of >1. The differential functions of TFs in regulating specific metabolic processes were further studied by investigating their targets with binding ratios higher than logINO2 or INO4 relative to the wild type for similar growth conditions as applied here  of Ino2 and the fold change in expression level in ino2Δ relative to the reference strain in N-lim and Glu-lim chemostat cultures are shown. The major lipid components are shown in blue. Abbreviations: GA3P, glyceraldehyde 3-phosphate; DHAP, dihydroxyacetone phosphate; Ins, inositol; Etn, ethanolamine; Cho, choline; G3P, glycerol 3-phosphate; PA, phosphatidate; DAG, diacylglycerol; PI, phosphatidylinositol; PS, phosphatidylserine; PE, phosphatidylethanolamine; PC, phosphatidylcholine; PL, phospholipids; AcCoA, acetyl-CoA; HMG-CoA, 3-hydroxy-3-methylglutaryl-CoA; SE, sterol esters; TAG, triacylglycerol; FA, fatty acids. Download FIG S7, TIF file, 2.9 MB.Regulation of lipid metabolic genes by Ino2. The binding ratio | of >1 (FC stands for fold change) to define differentially expressed genes, we performed a t test to examine whether the bound genes for each TF had an altered expression or not. We used a two-sided t test (is there a difference between the bound genes and the nonbound genes in expression profiles), one-sided up t test (are the bound genes upregulated compared to the nonbound genes), and one-sided down t test (are the bound genes downregulated compared to the nonbound genes). We integrated TF deletion data for Ino2 and Ino4 from reference t test was used, some TFs showed significance. However, when splitting the TF deletion data into upregulated or downregulated genes in one-sided t tests, we found that for all TFs, the general trend was significant for downregulated genes and some showed significance for upregulated genes as well. The only exception was Hap1 under anaerobic conditions which was significant only for upregulated genes. This is consistent with the previous finding that Hap1 acts as a transcriptional repressor under anaerobic conditions (t test table can be seen in 2(S/N) can be seen in As we could see few TF-bound genes that have been strongly affected by the deletion of a TF when using a strict cutoff of |lognditions . The res10.1128/mSystems.00215-17.8FIG S8 2(FC)| of >1. Download FIG S8, TIF file, 1.3 MB.Integration of TF deletion data and binding data. For each TF, the fold changes in the levels of gene expression in TF deletion mutants relative to wild type from published sources were incorporated with the binding data presented in this study. We highlight genes with a cutoff of |logCopyright © 2018 Bergenholm et al.2018Bergenholm et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the ENO1 , we belpromoter . This isThe emerging computer-assisted tools for pattern recognition using neural networks and deep learning algorithms may allow for better motif discovery –48 and h–Saccharomyces cerevisiae .The TF-tagging strains were constructed by transforming the uracil auxotrophic strain CEN.PK 113-5D of revisiae  with a tne locus . This in−1. The pH was maintained at pH 5.0 using 2 M KOH. Minimal medium containing vitamin  and trace metal  (4)2SO4, 5.3 g K2SO4, 3 g KH2PO4, 0.5 g MgSO4·7H2O, 60 g glucose; (ii) for glucose-limited media, 5 g (NH4)2SO4, 3 g KH2PO4, 0.5 g MgSO4·7H2O, and 7.5 g glucose; (iii) for oxygen- and glucose-limited media, 5 g (NH4)2SO4, 3 g KH2PO4, 0.5 g MgSO4·7H2O, 7.5 g glucose, 420 mg Tween 80, and 10 mg ergosterol; (iv) for ethanol-limited media, 5 g (NH4)2SO4, 3 g KH2PO4, 0.5 g MgSO4·7H2O, and 5 g ethanol.Single colonies from fresh agar plates were inoculated into 50 ml yeast extract-peptone-dextrose (YPD) medium in shaking flasks and grown for 12 to 24 h. Cells were harvested by centrifugation and resuspended in sterile water to obtain inoculum. Chemostat cultivation in liquid medium with a working volume of 500 ml was carried out using 1.2-liter DASGIP fermentors operated at 30°C with a dilution rate of 0.1 h.1 g KI)  as well −1 (aerobic cultures) or 0.2 ml liter−1  was added to the feeding media. For aerobic cultures, airflow of 30 liters h−1 and stirring speed of 600 rpm (Glu- and N-lim cultures) or 800 rpm (Et-lim culture) were used to keep the dissolved oxygen above 30% of air saturation. For Ox,Glu-lim culture, the fermentor was sparged with 30 liters h−1 of nitrogen gas with a stirring speed of 300 rpm. The oxygen and carbon dioxide in the off-gas were measured using the DASGIP GA4 exhaust analyzer after being cooled by a condenser operated at 4°C.Antifoam 204 at 0.05 ml liter600], dissolved oxygen, and off-gas profiles became constant) for 48 to 60 h. Formaldehyde at a final concentration of 1% (wt/vol) and distilled water were added to the cultures to cross-link protein-DNA complexes at an OD600 of 1.0 and a total volume of 100 ml. Cross-linking was performed for 12 min at room temperature with shaking followed by quenching, washing, and freezing as previously described  to increase the resolution. The Integrative Genomics Viewer (IGV) browser (2(S/N) ratios of >1 are considered to be reliable, where earlier studies on Chip-exo data have used a log2(S/N) of >1.5  and decreased contents of long-chain and very-long-chain fatty acids (C18 to C26) indicate the downregulation of ELO1 in the OAF1 deletion strain. The oaf1Δ strain was constructed from strain CEN.PK113-11C using URA3 as a selection marker. The strains were cultured in minimal medium containing 20 g liter−1 glucose and 100 mg liter−1 histidine for 72 h. Then, total fatty acids were extracted and analyzed as previously described (FIG S9, TIF file, 0.5 MB.Effect of escribed . Data reCopyright © 2018 Bergenholm et al.2018Bergenholm et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


FAT10, which is also known as diubiquitin, has been implicated to play important roles in immune regulation and tumorigenesis. Its expression is up-regulated in the tumors of Hepatocellular Carcinoma (HCC) and other cancer patients. High levels of FAT10 in cells have been shown to result in increased mitotic non-disjunction and chromosome instability, leading to tumorigenesis. To evaluate whether the aberrant up-regulation of the FAT10 gene in the tumors of HCC patients is due to mutations or the aberrant methylation of CG dinucleotides at the FAT10 promoter, sequencing and methylation-specific sequencing of the promoter of FAT10 was performed. No mutations were found that could explain the differential expression of FAT10 between the tumor and non-tumorous tissues of HCC patients. However, six single nucleotide polymorphisms (SNPs), including one that has not been previously reported, were identified at the promoter of the FAT10 gene. Different haplotypes of these SNPs were found to significantly mediate different FAT10 promoter activities. Consistent with the experimental observation, differential FAT10 expression in the tumors of HCC patients carrying haplotype 1 was generally higher than those carrying haplotype II. Notably, the methylation status of this promoter was found to correlate with FAT10 expression levels. Hence, the aberrant overexpression of the FAT10 gene in the tumors of HCC patients is likely due to aberrant methylation, rather than mutations at the FAT10 promoter. FAT10, which is sometimes referred to as diubiquitin, was initially identified as one of the genes at the major histocompatibility complex locus in human chromosome 6 , in thatAn in vivo study found that FAT10 ‘knock-out’ mice displayed minimal phenotypic changes. However, these mice are more sensitive to endotoxin challenge, and their lymphocytes are more susceptible to spontaneous apoptotic death .FAT10 has been implicated to play important roles in immune regulation and tumorigenesis. The cytokines IFN-γ and TNF-α were reported to up-regulate the expression of the FAT10 gene ,13,14. TGenetic variation at the 5’UTR and coding region of the FAT10 gene was reported  to be asguardian-of-the-genome” [The expression of the FAT10 gene was reported to be regulated by p53  during t-genome” .As the overexpression of FAT10 results in increased chromosome instability and plays a role in oncogenesis, elucidating the mechanism behind its aberrant expression in the tumors of HCC patients will facilitate the design of strategies to prevent its aberrant overexpression. We hypothesize that aberrant up-regulation of the FAT10 gene in HCC tumor tissues may be a result of mutations or aberrant methylation at the FAT10 promoter.hMSH2 gene in suspected cases of hereditary nonpolyposis colorectal cancer (HNPCC) and sporadic early onset colorectal cancer patients were found to affect its promoter activity as well as the transcription start site and the transcriptional factor binding site, resulting in a novel DNA-protein complex [Mutations within the promoters of genes have been shown to result in the overexpression of several genes in cancer cells. For example, a mutation of the CDE/CHR (cell cycle-dependent element/cell cycle genes homology region) repressor elements at the survivin promoter was found to result in the overexpression of survivin in some cancer cells . Similar complex . Hotspot complex .Aberrant methylation at the promoters of genes may also account for the aberrant expression of the genes involved in the tumorigenesis process. The hypermethylation of CG (CpG) islands in the promoters of cancer-related genes is often associated with transcriptional inactivation ,29,30. NSince FAT10 is aberrantly overexpressed in the tumors of HCC and other cancer patients , this stAll of the human samples were obtained in accordance with the guidelines and the approval from the Institutional Review Board (IRB) of the National Cancer Centre of Singapore (NCCS) or the Singapore General Hospital .A region of 1379 bp  of the FAT10 promoter was sequenced to screen for mutations or polymorphisms in the tumor and adjacent non-tumorous liver tissues from 37 HCC patients and 39 DNA samples from healthy people in the Singapore Chinese population with informed consent from the patients/volunteers and prior approval from the NCCS Institutional Review Board . This region of the promoter was selected, as it conferred the maximum promoter activity . TM Tissue Kit . Primers for sequencing the 1379 bp fragment of the FAT10 promoter were designed according to the reference sequence AL031983 from Genbank using Primer Premier version 5.00 . The primers used were F-5'ACTAATAGAGGTGGTTCCTTA (forward primer) and R-5′ CTCTCCCCAACTCTTGAAAGT (reverse primer). PCR reaction was carried out as follows: 95 °C for 15 min, then 30 cycles at 94 °C for 30 s, followed by 55 °C for 45 s and an extension at 72 °C for 1 min, followed by a final extension at 72 °C for 5 min. Sequencing was performed using the ABI PRI 3100 Genetic Analyzer . In addition, we sequenced the same region in the lymphoblastoid cell lines from three global populations, including 37 Chinese (CH), 31 European-American (EA), and 32 African-American (AA) from American Type Culture Collection (ATCC). Genomic DNA was isolated using the DNeasyTo identify somatic mutations, genetic variants of the tumor and adjacent non-tumorous liver tissues were compared. Polymorphisms were identified by comparing genetic variants in HCC patients versus healthy, Singaporean Chinese population controls. Haplotype frequencies were estimated using the program Arlequin , which iTo evaluate the functionality of mutations/SNPs at the FAT10 promoter region, PCR site-directed mutagenesis as previously described  was emplTotal cellular RNA was isolated from 13 HCC patients using an RNeasy mini-kit (Qiagen). The quantitation of FAT10 and β-actin transcript levels was performed as previously reported . QuantitTo determine the methylation status of the FAT10 promoter in HCC patients, genomic DNA from a tumor and an adjacent non-tumorous liver were treated with sodium bisulfite, as described . All of ® Multiplex PCR kit (Qiagen), according to the manufacturer’s instruction. The cycling condition was as follows: 95 °C for an initial 15 min followed by 25 cycles at 95 °C for 45 s, 55 °C for 45 s, and at 72 °C for 90 s, followed by a final extension at 72 °C for 10 min. The amplified product was purified using a QIAquick® PCR Purification Kit (Qiagen). Half of the amplified product was treated with the CpG Methylase  and S-adenosylmethionine (SAM) to methylate the seven CpG sites at the FAT10 promoter, while the other half of the PCR product remained unmethylated or mock-methylated.The previously described pFAT10-EGFP construct , in whic® PCR Purification Kit.The EGFP reporter gene from the pEGFP-1 construct (Clontech) was also amplified using a forward primer (5’-CGCCTTCTCCAGGGATCCA-3’) that contains a BamHI site and anneals at the multiple cloning site upstream of the EGFP gene, and the reverse primer (5’-GAGTTTGGACAAACCACAACT-3’) that anneals to the 3’ end of the EGFP gene downstream of the poly A region of the EGFP gene. The amplification condition for the EGFP gene is similar to the amplification of the FAT10 promoter. The EGFP PCR product was also purified using the QIAquick® PCR Purification Kit. Ligation of the FAT10 promoter (methylated/unmethylated) with the EGFP reporter was then performed, and the ligated product was separated from the unligated product through gel electrophoresis. Only the ligated product was isolated and purified using the QIAquick® Gel Extraction Kit (Qiagen) according to the manufacturer’s protocol.Both the methylated and unmethylated FAT10 promoter, as well as the EGFP amplified products, were digested with the BamHI restriction enzyme and purified using the QIAquickTo evaluate the activity of the methylated and unmethylated FAT10 promoters, the methylated/unmethylated FAT10 promoter-EGFP reporter fusion fragments were transfected into Hep3B cells using the siPORT™ Amine Transfection Agent  and the EGFP reporter activities evaluated.t-test was used to test the difference in the β-galactosidase activity between the constructs carrying the wild-type haplotype and other haplotypes in the FAT10 promoter. Similarly, Student’s t-test was also applied to identify the difference in promoter activity between the methylated and unmethylated groups.Fisher’s exact test was employed to evaluate the differences in allele or haplotype frequencies among the populations, as well as between the age-matched HCC and non-HCC individuals. The Student’s To evaluate whether mutations/polymorphisms at the FAT10 promoter could account for the aberrant overexpression of FAT10 in the tumors of HCC patients, we sequenced approximately 1.3 kb of the FAT10 promoter in the tumor, and paired non-tumorous tissues from 37 Chinese HCC patients and 39 normal healthy Chinese individuals of a similar age. No difference in the sequence of the 1.3 kb of the FAT10 promoter examined was observed between the tumor tissues and adjacent non-tumorous tissues, suggesting that within the 1.3 kb region, there are no mutations that could account for the differences in the expression between the tumor and adjacent non-tumorous tissues.Nonetheless, we identified six single nucleotide polymorphisms (SNPs) in this region . All of TM software program (p > 0.05)  algorithm in the Arlequin program . Haploty program . Haploty > 0.05) . Four ofp > 0.05)  A, there  > 0.05) A. More sp < 0.01) between the various mutated haplotypes and the wild-type haplotype GGTCAA. Interestingly, haplotypes III, V, and VII  result in significantly higher FAT10 promoter activity, while haplotypes II, IV, XI, and XII  mediate significantly lower FAT10 promoter activity. From these data, changing SNPs six, five, or two generally results in significantly higher FAT10 promoter activity. The only exception is when one or two of these are simultaneously changed with either one or four, as observed in haplotypes IV and XI. These haplotypes resulted in significantly lower FAT10 promoter activity. Changing SNPs three and four was observed to result in significantly lower FAT10 promoter activity. These results suggest that polymorphisms within the FAT10 promoter may alter FAT10 promoter activity and expression.We proceeded to experimentally evaluate whether the SNPs at the FAT10 promoter may alter FAT10 promoter activity. The inferred haplotype of SNPs A at the We proceeded to determine whether there is any association between the haplotype of SNPs and the differential expression of FAT10 in the tumors of HCC patients. Of the 37 patients that we genotyped, 20 displayed haplotype I, and were homozygous for the major allele of all six SNPs; meanwhile, five displayed haplotype II, in which SNP3 was the alternative C-allele. We did not examine the other patients, because there were heterozygous SNPs and the phase of the haplotype could not be determined with certainty. As evident in A total of seven CG dinucleotides (CG-1 to CG-7) reside in the region from −975 to +209 bp of the FAT10 promoter, which showed the highest promoter activity . All of Tumor and adjacent non-tumorous liver tissue samples from 13 HCC patients were used in this study. As evident in To experimentally demonstrate whether the methylation of the CG dinucleotides will result in lower FAT10 promoter activity, the entire FAT10 promoter region was methylated in vitro using SssI methylase  and ligated upstream of the EGFP gene. The FAT10 promoter (methylated/unmethylated)-EGFP fusion was then transfected into the Hep3B cells, and the FAT10 promoter activity was evaluated by quantitating the EGFP protein levels. As evident in Hence, methylation may play a role in the regulation of the FAT10 expression level.The overexpression of FAT10 has been observed in the tumors versus the adjacent non-tumorous tissues of HCC and gastrointestinal and gynecological cancers . The higNonetheless, although no mutations were found in the tumor of HCC patients that may account for differential FAT10 expression between the tumor and the adjacent non-tumorous tissues, we identified polymorphisms in both the tumor as well as the adjacent non-tumorous tissues of the HCC patients. As these polymorphisms were found in both the tumor and the adjacent non-tumorous tissues, they are unlikely to account for the differential FAT10 expression that was observed. Nevertheless, these SNPs may affect the basal FAT10 expression and account for the differences in the basal FAT10 expression among different individuals. Polymorphisms in the promoters of genes have previously been demonstrated to affect promoter activity and hence gene expression ,47,48,49A total of six polymorphisms were identified. Two of the six SNPs were found to be monomorphic in normal individuals, and occur at a low frequency in the HCC patients. Polymorphisms within the FAT10 gene locus were reported  to be asp > 0.05). As those SNPs and haplotypes that are different between the HCC patients and normal individuals occur at very low frequency, more samples need to be examined before a conclusion can be made. The haplotypes of the SNPs in this study cannot be compared with those from the study by Yuan et al. [While seven haplotypes can be inferred from the six SNPs identified in the HCC patients, only five haplotypes can be inferred from the five SNPs that were found in the normal individuals A. Three n et al. , since tp > 0.05).From in silico analyses, all of the polymorphisms observed at the FAT10 promoter either remove an existing putative transcription factor binding site, introduce a new putative transcription factor binding site, or change a putative binding site to another . ExperimCG dinucleotides are present in the regulatory regions of many genes . In normSeven CG sites were observed to reside in the FAT10 promoter region. In order to study the correlation between the methylation status of these CG dinucleotides and the aberrant expression of FAT10 in HCC patients , we perfCuriously, although the promoters of most genes are generally hypomethylated in adult tissues, the adjacent non-tumorous tissues of HCC patients seem to display hypermethylation at the FAT10 promoter, while the tumorous tissues seem to be hypomethylated. One possible explanation for this observation is that the adjacent non-tumorous tissues of HCC are generally cirrhotic, and cirrhosis may have resulted in the hypermethylation of the FAT10 promoter. The progression from a cirrhotic liver to a tumorous liver may then change the methylation status of the FAT10 promoter to activate the FAT10 gene. This hypothesis remains to be examined. Future studies could validate these observations in additional cell lines, as well as additional healthy volunteers/HCC patient samples, and determine the contribution of non-genetic factors. It would also be pertinent to address whether the polymorphisms or differential methylation at the FAT10 promoter would modulate the response of the FAT10 promoter to inflammatory cytokines, including TNFα or IFNγ. As the expression of the FAT10 gene was reported to be regulated by p53 , it woulIn summary, although no mutations were identified at the FAT10 promoter in the tumor of HCC patients, polymorphisms at this promoter was identified, which mediated differential FAT10 promoter activities. Notably, the methylation status at this promoter correlated significantly with FAT10 expression levels as well as differential promoter activity. Thus, epigenetics (methylation) play an important in regulating the expression of FAT10.


Transposase-Accessible Chromatin followed by sequencing (ATAC-seq) is a simple protocol for detection of open chromatin. Computational footprinting, the search for regions with depletion of cleavage events due to transcription factor binding, is poorly understood for ATAC-seq. We propose the first footprinting method considering ATAC-seq protocol artifacts. HINT-ATAC uses a position dependency model to learn the cleavage preferences of the transposase. We observe strand-specific cleavage patterns around transcription factor binding sites, which are determined by local nucleosome architecture. By incorporating all these biases, HINT-ATAC is able to significantly outperform competing methods in the prediction of transcription factor binding sites with footprints.The online version of this article (10.1186/s13059-019-1642-2) contains supplementary material, which is available to authorized users. DNase-I hypersensitive sites sequencing  and AComputational methods scanning open chromatin profiles to find footprints have been shown to predict transcription factor binding sites (TFBS) with high accuracy in DNase-seq data , 8. AmonATAC-seq has several experimental advantages over DNase-seq: it requires fewer cells (50.000 to single cells) and is less laborious . Not surk-mer sequences around the start of aligned reads, by estimating the probability of finding a k-mer at read start sites against occurrences in the genome  with size k around genomic position i. For a given genome sequence G, this is defined as w[i]=G.Next, we correct the cleavage event profiles by sequence-specific cleavage bias considering the word w, the bias is defined as For an arbitrary word p(w|obs) is the probability of k-mer w around the position of a cleavage event and p(w|exp) is the probability of finding word w in the genome. For a position i in the genome, the bias-corrected signal is obtained as [where ained as : 3\docui and w[ i] normalized by the bias of surrounding genomic regions.where b(w): (i) the k-mer-based approach, which is widely used in the DNase-seq literature, (ii) a PWM-based approach, which is standard for modeling DNA-protein interactions, and (iii) our novel approach using PDMs.We describe below three distinct approaches to calculate cleavage bias estimates k-mers to estimate the probability p(w)  represents one or more nucleosome reads (+1N) and the interval . We then evaluate distinct strategies with combinations of these signals: all reads (All), signal from nucleosome-free reads (Nfr), signals from nucleosome-free reads and signals from reads with one or more nucleosomes (Nfr & +1N), and signals from nucleosome free, signals from one nucleosome, and signals from one or more nucleosomes (Nfr & 1N & +2N).For paired-end ATAC-seq libraries, we also filter signals by only considering cleavage events from paired-ends with a particular size range mentclass2pt{minimFor each decomposition strategy, we apply the cleavage bias correction, a within-signal normalization by averaging non-zero counts inside bins and a between-signal normalization by applying a logistic function. To estimate the slope of the signals, we apply a Savitzky–Golay smoothing filter by fitting the data into a second order polynomial and performing a convolution (based on a specific window length) with a vector containing Savitzky–Golay coefficients. Normalization steps are described in details in .For example, in the case of All cleavage events with strand specific signals, we have a signal and a slope value for either positive and negative strands, that is: X as input for the HMM model. The HMM contains a multivariate normal density function with full covariance matrix as emission probability for each state. The dimension depends on the decomposition strategy and varies from 2 (non-strand specific) to 12 dimensions . For a given TF, we obtain regions with ChIP-seq peaks and a motif predicted binding site (MPBS) as described in the “S states and select one state to represent the FOOTPRINT. We use a semi-supervised algorithm to train the HMM [S−1 states  to measure the strength of TF binding in a particular biological condition. First, we identify all binding sites of a particular TF overlapping with footprints e is the length of the binding site and xj is the cleavage event signal (after bias correction) at genomic position j.where The activity score can be seen as a combination of the protection score , which mHere, we are interested in identifying TFs with change in activity between two conditions. This is given by the difference in ACT scores between two biological conditions, this is: ω1 and ω2 are normalization factors based on median-of-ratios [F corresponding to binding sites supported by footprints in at least one of the conditions. We use this score to rank TFs with a known motif, where highest ΔACT(TF) indicate TFs with specific binding in condition 2.where f-ratios . These fWe used ATAC-seq data of cell lines GM12878 and K562 from  and singFirst, adapter sequences were trimmed from FastQ files using Trim Galore  with theμL Tagment DNA Enzyme 1 (TDE1) for 60 ‘min at 37 °C. Pre-amplification was with NEBNext Ultra II Q5 Master Mix and Nextera PCR Primers (5 cycles). Quantitative PCR amplification was with NEBNext Ultra II Q5 Master Mix, Nextera PCR Primer and SYBR Gold to determine the number of additional cycles. PCR amplification of additional cycles was as for pre-amplification. PCR fragments were purified with Qiagen MinElute PCR Purification Kit and library concentration and quality were determined by Agilent High Sensitive DNA Kit and Bioanalyzer, respectively. ATAC-seq libraries were sequenced with a Illumina NextSeq 500 Platform with 75 bps paired-end reads in duplicates. Trimming, alignment (to mouse genome mm9), and peak calling were performed as for other ATAC-seq libraries. Replicate libraries were merged previously to footprinting.Dendritic cells (DC) are professional antigen presenting cells that comprise different subsets: classical DC type1 and type2  and plasmacytoid DC (pDC). In this study cDC1 or pDC were obtained in a two-step in vitro culture system according to . Briefly−4. Then, we created labels by combining MBPSs with ChIP-seq data for every TF. Specifically, MPBSs with ChIP-seq evidence (MPBS located within 100 bp from the ChIP-seq peak summit) are considered true binding sites and MPBSs without ChIP-seq evidence are considered false binding sites. Footprint/MPBS pairs supported by ChIP-seq peaks are considered true positives (TP), while footprints with no ChIP-seq support are considered false positives (FP). TN and FN are defined accordingly. We rank the predictions for all methods by tag count (TC) as this has been shown to be the best method for ranking predictions [ChIP-seq of TFs and motif-predicted binding sites (MPBSs) were used as ground truth to evaluate the footprinting prediction in this work following , 27. We dictions .https://www.synapse.org/#!Synapse:syn6131484/wiki/405275). This score is equal to the sum over all six normalized ranking measures of −log(r/(N+1)) where r is the rank of an algorithm for a specific performance measure  and N is the total number of methods. Therefore, best measures should have a high ranking in several of the evaluated measures.To assess the accuracy, we created receiver operating characteristic (ROC) curves and evaluated area under ROC at 100%, 10%, and 1% FPR by using the contingency table . We also measured the area under Precision-Recall (auPRC) at 100%, 10%, and 1% recall as these measures are more suitable for data with skew on negative classes , 67. As We divided our evaluation data in three distinct sets. The first set (training dataset) is composed of 32 TFs ChIP-seq from the GM12878 and different ATAC/DNase-seq data. To evaluate bias correction methods, we used ATAC-seq data from standard protocol based on 50.000 cells , Fast prFinally, for the comparative evaluation of ATAC-seq and DNase-seq protocols, we defined a comprehensive data set by combining all TF-ChIP data 124) from previous data sets. This dataset is further enhanced by using single cell ATAC-seq libraries . We also annotated transcription factors regarding their structural family in accordance to JASPAR . For sta24 from pp values based on multi-comparison tests were corrected using the Benjamini-Hochberg method.Statistical comparison of computational methods regarding TF binding sites was performed with the non-parametric Friedman-Nemenyi hypothesis test. Such a test provides a rank of the methods as well as the statistical significance of the out-performance of a particular method. Comparisons based on non-paired distributions were performed with the Wilcoxon rank sum test. All reported Additional file 1Supplementary methods, figures and tables. Additional file 2Description of ATAC-seq and DNase-seq data. (XLSX 19 kb)


Both are rich on clusters of cis-acting sites that interact with sequence-specific DNA-binding transcription factors (TFs). At the level of chromatin, these regions display increased nuclease sensitivity, reduced nucleosome density, including nucleosome-free regions, and specific combinations of posttranslational modifications of core histone proteins. Together, “open” and “closed” chromatins represent transcriptionally active and repressed states of individual genes, respectively. Cellular differentiation is marked by changes in local chromatin structure. Lens morphogenesis, regulated by TF Pax6, includes differentiation of epithelial precursor cells into lens fibers in parallel with differentiation of epithelial precursors into the mature lens epithelium.Promoters and enhancers are cis-regulatory logic of lens differentiation through known  and novel  motifs. Twenty-six DNA-binding TFs, recognizing these cis-motifs, are markedly up-regulated in differentiating lens fibers. As specific examples, our ATAC-seq data uncovered both the regulatory regions and TF binding motifs in Foxe3, Prox1, and Mip loci that are consistent with previous, though incomplete, experimental data. A cross-examination of Pax6 binding with ATAC-seq data demonstrated that Pax6 bound to both open (H3K27ac and P300-enriched) and closed chromatin domains in lens and forebrain.Using ATAC-seq, we investigated dynamics of chromatin changes during mouse lens fibers and epithelium differentiation. Tissue-specific features of these processes are demonstrated via comparative studies of embryonic stem cells, forebrain, and liver chromatins. Unbiased analysis reveals cis-DNA motifs that establish the molecular foundation for temporally and spatially regulated gene expression in lens. Together, our data and models open new avenues for the field to conduct mechanistic studies of transcriptional control regions, reconstruction of gene regulatory networks that govern lens morphogenesis, and identification of cataract-causing mutations in noncoding sequences.Our study has generated the first lens chromatin accessibility maps that support a general model of stage-specific chromatin changes associated with transcriptional activities of batteries of genes required for lens fiber cell formation. Analysis of active (or open) promoters and enhancers reveals important The online version of this article (10.1186/s13072-019-0272-y) contains supplementary material, which is available to authorized users. Cellular differentiation is driven by the coordinated expression of batteries of genes that encode proteins required for cellular specialization and function. Differentiation is mostly regulated at the level of transcription. Tissue specificity of transcription is primarily regulated by a combinatorial action of sequence-specific DNA-binding transcription factors and their interactions with promoters and distal enhancers , 2. Bothcis-regulatory grammar [The ocular lens is a highly specialized tissue that is formed from a single lens progenitor cell type. The progenitors give rise to the lens vesicle, comprised of lens precursor cells that ultimately differentiate into lens epithelial and lens fiber cells , 9. Thus grammar  of the lThe entire process of lens differentiation, from the birth of individual lens progenitor cells to the terminal differentiation of lens fibers and massive up-regulation of crystallin gene expression, is directly or indirectly regulated by the transcription factor Pax6 , 14, 15.Recent advances in genome technologies greatly simplify the identification of open chromatin domains using a single assay, the ATAC-seq . Using tp values (q) < 0.05 as cutoff  > 0.95, Additional file p = 0.21 for percentages of promoter peaks in E14.5 vs. P0.5 epithelium and p = 0.08 for E14.5 vs. P0.5 fibers; Chi-squared test). Principal component analysis of the top 500 peaks with biggest variances separated the eight samples into four groups and organized them into two differentiation pathways that matched the known biology of lens differentiation . We detected thousands of differentially accessible regions (DARs) using EdgeR  by the fp = 0.13 for percentages of promoter peaks in E14.5 vs. P0.5 epithelium, p = 0.28 for E14.5 vs. P0.5 fibers, Chi-squared test)  for each cluster of peaks using the GREAT software (v 3.0.0) . We founcis-regulatory grammar of transcription, we next searched for the transcription factor binding motifs that were significantly enriched inside each cluster in relation to all ATAC-seq peaks using the HOMER (v 4.7) software [cis-element in the group of peaks with highest signals in E14.5 epithelium as well as within the peaks shared between E14.5 and P0.5 epithelium. The Maf and Sox motifs were the top motifs enriched in the peaks unique in E14.5 fibers and the shared peaks in E14.5 and P0.5 fibers  [To further examine DARs in the context of lens differentiation, we divided all the DARs at DEGs into “lens fiber cell differentiation path”  and “lens epithelium maturation path” . In total, we collected 14,768 ATAC-seq peaks (mapped to 5249 DEGs detected in RNA-seq) and 1810 peaks (mapped to 1318 DEGs detected in RNA-seq) in the Path1 and Path2, respectively. Using hierarchical clustering of the ATAC-seq signals, we could divide the peaks in the lens fibers and epithelium differentiation paths into 6 and 3 clusters, respectively , including c-Maf –36, Proxf [Prox1 , 37, Hsfx1 [Hsf4 , 39, Soxf4 [Sox1 , compounafG/MafK , and N-MK [N-Myc . In contK [N-Myc , Foxe3 [K [N-Myc , 45, andK [N-Myc  are knowntiation  and specFoxe3 and Myc loci had open chromatin regions upstream of their transcription start sites. Etv1, Pitx3, Rxra, Hsf4, Nfat5, Etv5, Tead1, Meis2, Prox1, and Sox1 loci also display open chromatin regions inside of their gene bodies. In all instances, the open chromatin regions overlap with evolutionarily conserved noncoding sequences, making them strong candidates for distal enhancers. Likewise, we found potential distal enhancers for Lim2, Mip, and Sptbn1, all of which encode important lens structural proteins. Collectively, these results underscore the values of present ATAC-seq data in providing novel comprehensive information for predicting both lens promoters and enhancers.At the individual gene and locus level, we highlighted specific examples of open chromatin regions at potential enhancers of genes encoding key lens transcription factors and structural genes   Fig. b. Moreovst) Fig. c. For exU test, p < 0.05; Fig. Pax6 in the lens [Pax6 depletion compared to Pax6 sites located at open regions , compared to the group with open chromatin regions only in lens epithelium  and early lens epithelium → maturing lens epithelium (Path2). Based on the genome-wide correlations of open chromatin and steady-state mRNA levels, the present study unravels three principles of lens-specific gene regulation that are generally applicable to other tissues. First, cis-regulatory logic of promoters and enhancers emerges from commonly enriched cis-motifs as well as expression dynamics and abundance of individual DNA-binding transcription factors. Second, identification of active promoter and enhancer regions in lens, coupled with evolutionarily conserved noncoding sequences, enables the identification of noncoding mutations and their roles in lens diseases, including cataract and presbyopia. Third, distinct patterns of Pax6 binding in open/H3K27ac+/P300+ marked and closed chromatin regions provide novel insights into molecular mechanisms of this key regulatory factor of embryonic lens development.A major question in the area of developmental biology, gene expression, genomics, and epigenomics is to understand how cis-motif analysis. The whole analysis is highly dependent on the current knowledge of TFs and will be affected by the lack of knowledge for some TFs, such as Prox1 (see below for details) and Pknox1 [Despite the progress, there are several limitations in the study. First, we found that many differentially accessible regions were not assigned to differentially expressed genes. This could be caused by a plethora of transcriptional regulatory mechanisms. For example, multiple enhancers can exist for genes and changes of chromatin accessibility for a single enhancer may not be sufficient to elicit any significant mRNA level changes. Additionally, some enhancers might locate at distal regions even beyond multiple proximal genes and therefore could not be assigned to the correct target genes based on the current annotation strategy. It is also possible that TFs residing in DARs require activation through extracellular signaling and/or presence of additional activators. Another limitation is the d Pknox1 . Additiod Pknox1  and Six3d Pknox1 , were deLctl, Fgfr3, Fgfrl1, and Crybb1 [The lens fiber cell differentiation cascade (Path1) is regulated by a well-established group of transcription factors, including c-Maf, Hsf4, Pax6, Prox1, and Sox1, together with signal-regulated transcription factors, nuclear effectors of BMP, FGF, Notch, and Wnt signaling . Analysid Crybb1 , 33 (Add+ cells which are thought to drive lens growth [The lens epithelium differentiation–maturation pathway (Path2) remains poorly understood compared to Path1. Unlike the terminally differentiated fiber cells, the lens epithelium contains the stem cell-like Sox2s growth . Notablys growth , 12. Apas growth , FoxE3 [s growth , 71, 72,s growth , 74. Thes growth –78. In cs growth , 79, c-Ms growth , 80–82 is growth , 84.Foxe3, Prox1, and Mip loci as examples  and novel candidate factors . The present studies also uncover novel molecular functions of Pax6, namely its presence in both open and closed chromatin domains, marked by differential levels of histone modifications, consistent with its dual roles as transcriptional activator and repressor. Use of other vertebrate model organisms, such as chicken, provides comparative insights into the chromatin changes during lens cellular differentiation [Our study provides the first in-depth analysis of open and closed chromatin dynamics coupled with mRNA changes during mammalian lens differentiation. The results reveal groups of co-regulated genes required for lens fiber cells and lens epithelium differentiation and maturation. The molecular base of the co-regulation is illuminated by an unbiased discovery of highly enriched motifs in putative promoters and enhancers, which are comprised of arrays of ntiation . CollectMouse lenses from E14.5 to P0.5 CD1 mice (Charles River) were micro-dissected under the microscope into epithelium and fibers as described earlier , 113. Sihttps://www.bioinformaticsbabrahamacuk/projects/trim_galore/) to remove adaptors and then mapped to the mouse genome (mm10) using Bowtie2 (version 2.3.3.1) [https://www.broadinstitutegithubio/picard/), we used MACS2 (v 2.1.0) [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124497). The fastq files for E12.5 and E14.5 forebrain , E14.5 liver , and ESCs ATAC-seq (GSE66390) [The 75-bp paired-end ATAC-seq reads were first trimmed by trim-galore   with “-X2.3.3.1)  and picav 2.1.0)  for peakv 2.1.0)  (parametv 2.1.0) , we idenSE66390)  data werSE66390)  and GEO SE66390)  and procSE66390)  and convSE66390) .The peaks were associated with genes in the Refseq annotation downloaded from the UCSC genome browser  in June The read pair numbers inside each ATAC-seq peak were calculated using the HTseq (v 0.8.0)  with parThe differentially expressed genes were obtained from the E14.5 and P0.5 lens RNA-seq data at FDR < 0.05 . Here, wMip, Foxe3, and Prox1, and used the FIMO software [The enriched motifs inside each cluster of peaks were identified using the HOMER software v 4.7) . For ATA.7 . For software  to scan software   , 28. TheAdditional file 1: Table S1. Summary for the ATAC-seq data.Additional file 2: Fig. S1. Quality controls of ATAC-seq data. a. Distributions of insert sizes from 8 ATAC-seq libraries. b. Heatmap shows Pearson correlation coefficients among samples, computed from reads mapped to peaks. c. Scatterplots of mean normalized counts  between pairwise spatial and temporal comparisons.Additional file 3: Table S2. Numbers of DARs and DARs at DEGs from each comparison group.Additional file 4: Fig. S2. Overlaps between DARs from 8 comparison groups.Additional file 5: Table S3. Number of peaks inside unique and shared clusters.Additional file 6: Fig. S3. Analysis of unique or shared clusters of open chromatin regions and their corresponding gene expression levels. a. b. Heatmaps show ATAC-seq signals and corresponding gene expression for unique and shared peak groups.Additional file 7: Fig. S4. Correlation of DARs and DEGs. a. Pie charts show the percentages of differentially expressed genes , reversely differentially expressed genes  and not differentially expressed genes  associated with each group of differentially accessible regions (DARs). b. Bar plots show the percentages of DEGs and DEGs reverse in each group of DARs mapped genes divided by the number of peaks annotated to the genes.Additional file 8: Fig. S5. Hierarchical clustering of transcription factors predicted for lens fiber cell differentiation and lens epithelium maturation paths. a. 27 TFs showed higher expression in Path1. b. 51 TFs showed higher expression in Path2.Additional file 9: Fig. S6. ATAC-seq peaks at previously described Prox1 binding regions at Lctl, Fgfr3, Fgfrl1, and Crybb1.Additional file 10: Fig. S7. Potential enhancers at key transcription factors and structural proteins in lens. a. b. Examples of putative enhancers for lens transcription factors (a) and structural proteins (b) based on ATAC-seq enrichment. The evolutionary conservation (cons) tracks are shown in green. ATAC-seq data are from mouse E14.5 lens epithelium, lens fibers, forebrain, liver, and ESCs.Additional file 11: Fig. S8. Pax6 binding sites at open and closed chromatin play distinct roles for gene expression. a. Examples of P1 lens Pax6 peaks at open chromatin regions in Prox1 (left) and closed chromatin regions in Hivep2 (right). b. Table shows the number of Pax6 peaks detected at open and closed chromatin regions in mouse ATAC-seq atlas. c. Venn diagram shows the overlaps of genes with lens Pax6 peaks at open and closed chromatin regions and the differentially expressed genes in Pax6 KO vs WT. d. Venn diagram shows the overlap of Pax6 peaks at E12.5 forebrain and P300 peaks (E11.5 forebrain).