Myrf. Despite being a master regulator of oligodendrocytes, oligodendrocyte enhancers governing Myrf expression remain elusive. Since chromatin conformation capture studies have shown that a gene and its enhancer tend to be found in the same topologically associating domain (TAD), we started with the delineation of the Myrf TAD. A genome-wide map of putative oligodendrocyte enhancers uncovered 6 putative oligodendrocyte enhancers in the Myrf TAD, narrowing down the search space for Myrf enhancers from the entire genome to 6 loci in a principled manner. Epigenome editing experiments revealed that two of them govern Myrf expression for oligodendrocyte development. Our new method is simple, principled, and powerful, providing a systematic way to find enhancers that regulate the expression of a gene of interest. Since it can be applied to most cell types, it would greatly facilitate our effort to unravel transcriptional regulatory networks of diverse cell types.Mapping enhancers to genes is a fundamental goal of modern biology. We have developed an innovative strategy that maps enhancers to genes in a principled manner. We illustrate its power by applying it to  A fascinating yet perplexing feature of enhancers is that they are often far away from target genes. For example, the ZRS enhancer is almost 1 Mb away from its target gene Shh3. This has made it difficult to annotate enhancers to genes. For this reason, the traditional approach to finding enhancers for a gene is to find conserved sequence segments in its vicinity and to test whether they work as enhancers in cell culture and/or transgenic animals9. If they do, it is assumed that they would regulate the endogenous gene in the genomic context. Although it has been extensively used to successfully characterize putative enhancers for genes of interest, this traditional approach has a couple of shortcomings. First, one has to make an arbitrary decision about where and how far to look in the genome for conserved sequence segments. Is upstream 100 Kb enough? Or do we have to look both upstream and downstream for as far as 1 Mb? Second, the traditional approach just assumes a regulatory relationship between a gene and an enhancer based on a distance criterion. If they are close to each other, which is again an arbitrary decision, it assumes that the enhancer would regulate the gene.Enhancers are short segments of DNA that orchestrate cell type-specific gene expression by serving as transcription factor binding platformsMyrf encoding a master regulator of oligodendrocytes (OLs)12. A unique aspect of Myrf, compared to other crucial OL genes, is that it is highly expressed in differentiating OLs, but not in OL precursor cells (OPCs), indicating that Myrf expression marks the onset of OL differentiation. Consistently, gene expression analysis of multiple sclerosis lesions indicated that OLs stalled in their differentiation are those that fail to upregulate Myrf expression. Hence, elucidating how Myrf expression is activated in OLs holds a great promise for revealing the molecular events underlying OL differentiation and developing novel remyelination therapies. The identity of enhancers governing Myrf expression in OLs remains elusive, and this is why Myrf was chosen as the first target for our new method. The only information available about Myrf expression regulation is that there is an enhancer called ECR9 in the first intron of Myrf5, which was active in OLs and some other cell types when tested in transgenic mice. ECR9 has been assumed, but not proved, to regulate Myrf expression in OLs. Our new method shows that ECR9 and a novel OL enhancer jointly control Myrf expression for OL development, demonstrating its effectiveness. Importantly, our new strategy can be applied to other genes and cell types and is expected to greatly accelerate our effort to unravel transcriptional regulatory networks of diverse cell types.To tackle this fundamental issue, we have developed a novel strategy that maps enhancers to genes in a principled manner. This paper illustrates its power by applying it to the gene Myrf as an exemplary case. First, a paradigm-shift discovery from chromatin conformation capture studies is that a gene and its enhancer tend to be found in the same topologically associating domain (TAD), a fundamental unit of genome organization and function17. With the TAD knowledge, one does not have to make an arbitrary decision about where and how far to look in the genome for enhancer candidates. The TAD information allows one to narrow down the enhancer search space in a principled manner. There are two features of a TAD that need to be distinguished – internal detail and boundaries18. The internal detail of a TAD reflects cell type-specific interactions among genes and enhancers, and it differs between cell types. In contrast, the boundaries of a TAD tend to be conserved between cell types and species13. Thus, even if there is no chromatin interaction data for OLs, we can still delineate the Myrf TAD by analyzing public chromatin interaction data for other cell types, as shown below. Second, we identify putative OL enhancers in the Myrf TAD, which are Myrf enhancer candidates because they are in the same TAD as Myrf. By comparing the Myrf TAD with a genome-wide map of putative OL enhancers (see below), we were able to identify 6 Myrf enhancer candidates. Third, we interrogate the Myrf enhancer candidates with CRISPRi22, a cutting-edge epigenome editing technique, to determine whether they govern Myrf expression in OLs. A definitive way of proving an enhancer-target gene relationship is to demonstrate that the inactivation of the enhancer downregulates the target gene. Unfortunately, this seemingly simple experiment used to be almost impossible due to the lack of tools that manipulate enhancer activity in the genomic context. This is why the traditional approach just assumes a regulatory relationship between a gene and an enhancer based on a distance criterion. With the advent of CRISPRi, we can inactivate enhancers in the genomic context, linking enhancers to target genes on the basis of experimental proof of causality in gene expression. Below, we describe each step in detail and demonstrate how principled and powerful this new strategy is for finding enhancers that govern the expression of a given gene.Our strategy consists of three steps Fig. , and we Myrf TAD, we analyzed publicly available kilobase-resolution Hi-C data for 7 diverse cell types from human and mouse23. The location of the MYRF/Myrf promoter is indicated by thin crossing lines in Fig. MYRF is well defined and conserved between different cell types , suggesting that critical Myrf enhancers would be found in the region spanning downstream 30 Kb and upstream 270 Kb of Myrf.In order to delineate the Myrf enhancers, the Myrf TAD is still quite large. It is impractical to apply CRISPRi (CRISPR interference) to such a large genomic region for enhancer discovery (see below). To remedy this situation, we have derived a genome-wide map of putative OL enhancers by analyzing public OL ChIP-seq (chromatin immunoprecipitation coupled with high-throughput sequencing) data, with the idea being that the genome-wide map of putative OL enhancers would reveal putative OL enhancers in the Myrf TAD, which are Myrf enhancer candidates because they are in the same TAD as Myrf.Although the TAD analysis helps to narrow down the search space for 27. Of note, all these ChIP-seq data are from cultured rat OL lineage cells. Second, they were filtered by the H3K27ac broad peaks of rat OL lineage cells24 because H3K27ac marks active enhancers28, leaving 28985 genomic regions. Third, since ChIP-seq data are not available for all OL transcription factors, we have developed a statistical method that predicts enhancers independent of transcription factor ChIP-seq data. We took advantage of a unique feature of enhancers in H3K27ac ChIP-seq profiles. Since enhancers are bound by transcription factors, they are usually depleted of nucleosomes. Hence, although H3K27ac is known to mark active enhancers, its enrichment is found in shoulders flanking an enhancer rather than in the enhancer itself. This is why a peak-valley-peak pattern is observed for enhancers in H3K27ac ChIP-seq data . Under the null model, the peak-valley-peak pattern of H3K27ac ChIP-seq reads for an enhancer would be unlikely, and this deviation from the null model can be quantified by the binomial cumulative distribution function (see Methods). Analysis of the public H3K27ac ChIP-seq data for OL lineage cells24 with the binomial cumulative distribution function revealed that 5804 of the 45212 H3K27ac broad peaks have at least one incidence of the peak-valley-peak pattern, leading to the prediction of 6939 OL enhancers. Interestingly, the 5804 broad peaks display a significantly higher level of H3K27ac signals than the rest , supporting the in vivo relevance of the putative OL enhancers. Third, gene ontology (GO) analysis by GREAT33 indicates that the putative OL enhancers are significantly associated with GO terms related to OL development and central nervous system (CNS) myelination , one in the first intron (EC1), and one in the downstream region (EC3). The one in the first intron  and a KRAB (Krüppel associated box) domain, is targeted to a specific locus by single guide RNAs (sgRNAs)22. When targeted to a promoter, dCas9-KRAB silences it, decreasing gene expression. When targeted to an enhancer, dCas9-KRAB inactivates it, which in turn downregulates target genes. To avoid the danger of false positive and false negative, each Myrf enhancer candidate was tested with at least 5 independent sgRNAs. In our CRISPRi experiment, dCas9-KRAB and sgRNA plasmids were transfected into primary mouse OPCs purified by immunopanning36, and transfected OPCs were cultured in a differentiation condition for 3 days to induce their differentiation into OLs and Myrf expression35. To easily monitor the expression level of Myrf, Rffl  was co-transfected. Since co-transfection efficiency is high and luciferase assay extraordinarily sensitive, co-transfection of Rffl allowed us to detect changes in Myrf expression in the few transfected cells by a simple luciferase assay without selecting them. Of note, it remains unknown whether Rffl governs the expression of Rffl, which does not concern the current study.When deriving the genome-wide map of putative OL enhancers, we used lenient criteria to minimize false negatives. Consequently, the six Myrf enhancer candidates with CRISPRi, we validated CRISPRi for the Myrf locus. We designed six sgRNAs for the Myrf promoter and tested them in Oli-neu cells, a widely used OL cell line39  was quantified for individual OLs by CellProfiler40. This revealed that Hoechst and tdTomato signals were comparable across the samples. In contrast, Myrf signals were much lower when dCas9-KRAB was targeted to the Myrf promoter, EC1, EC2, or EC1&2 . The Myrf antibody used for this experiment has been previously validated for immunofluorescencells Fig. . Co-exprion Fig. . However1&2 Fig. . These oMyrf. EC1 is in the first intron of Myrf, and EC2 is located 2 Kb upstream of Myrf. Creating repressive chromatin in an intron may hinder transcriptional elongation. Similarly, repressive epigenetic modifications induced by dCas9-KRAB for EC2 may spread to the Myrf promoter. Hence, it is possible that EC1 and EC2 came out positive in our epigenome editing analysis due to the non-specific effect of dCas9-KRAB. An exhaustive analysis for the GATA1 and MYC loci by dCas9-KRAB has shown that dCas9-KRAB is highly specific21, not displaying non-specific effects in promoter upstream regions and gene bodies  and the public single-cell ATAC-seq data (see below). These features make NC1 and NC2 suitable negative controls. Of the 8 sgRNAs tested for NC1, only one came out positive . In agreement with our epigenome editing analysis, they were not accessible in OLs  was cloned into pGL3-promoter 5. The minimal Myrf promoter had a very low basal activity in OLs . EC1 and EC2 are not marked by H3K9me3 and H3K27me3 in the human brain . The signal from each fluorescence channel was quantified for individual cells by CellProfiler. The quantitative image analysis revealed that targeting dCas9-KRAB to the Myrf promoter, EC1, EC2, or EC1&2 significantly decreases Mbp expression in primary mouse OLs . Three plasmids were co-electroporated into subventricular zone (SVZ) neural stem cells (NSCs) of CnpCre/+ mice at P1 44 . When dCas9-KRAB was targeted by Scr1, more than 87% of mCherry-positive cells were positive for Gst-π 44 Fig. . The fire44 Fig. . Since tt-π Fig. . It demoMyrf, a key OL gene that is indispensable for CNS myelination12. Three innovative features of our method enable a streamlined enhancer mapping for a gene of interest. First, it takes advantage of the transformative discovery from chromatin conformation capture studies that a gene and its enhancer tend to be found in the same TAD15. This provides strong spatial constraints on the possible locations of enhancers. Without TAD information, it is not clear where and how far to look in the genome for enhancers. TAD boundaries tend to be conserved between cell types and species13. Therefore, even if there is no Hi-C data for a particular cell type under study, one can still delineate the TAD by analyzing public Hi-C data23, as we did for Myrf. Second, we have derived a genome-wide map of 21324 putative OL enhancers. Without such map, the reduction of the enhancer search space by TAD analysis would not be really helpful because TADs are usually several hundred kilobases long. By comparing the genome-wide map of putative OL enhancers with the Myrf TAD, we uncovered 6 putative OL enhancers in the Myrf TAD, which are Myrf enhancer candidates because they are in the same TAD as Myrf. It is remarkable that the TAD information, together with our genome-wide map of putative OL enhancers, drastically reduces the search space for Myrf enhancers from the entire genome to just 6 loci, greatly facilitating the downstream CRISPRi analysis. Third, we have successfully used CRISPRi22, a cutting-edge epigenome editing technique, to interrogate enhancer candidates, finding two Myrf enhancers that are essential for OL development (EC1 and EC2). By inactivating enhancers in the genomic context, CRISPRi allows one to determine whether an enhancer candidate governs the expression of a given gene in the genomic context. As shown by this study and others, CRISPRi is exquisitely specific, even in promoter upstream regions and gene bodies, enabling the discovery of promoter-distal enhancers as well as those in promoter upstream regions and gene bodies. In sum, the combination of TAD, a cell type-specific genome-wide map of putative enhancers, and CRISPRi would significantly accelerate mapping enhancers to target genes. For cell type-specific genome-wide maps of putative enhancers, the single-cell ATAC-seq data and the NIH Roadmap Epigenomics Project data that we analyzed are good enough to cover most cell types. Of course, one can add publicly available cell type-specific genomic data to further strengthen such maps, as we did for OL lineage cells.Mapping enhancers to genes is a fundamental goal of modern biology. By combining recent advances in diverse fields into a coherent analysis pipeline, we have developed a simple yet powerful strategy that links enhancers to genes. This study illustrates its power by applying it to Myrf, we are currently applying our new method to other OL genes and other cell types. For example, our ongoing study on Olig2, a gene essential for oligodendrogenesis, found that the Olig2 TAD is well defined and conserved between cell types and species, leading to a small number of Olig2 enhancer candidates for CRISPRi analysis. The same was also true for key myelin components , critical OL transcriptional regulators , and OL differentiation inhibitors that are potential therapeutic targets for remyelination therapies (Gpr17 and Gpr37).Encouraged by the positive results for 47. In contrast, epigenome editing analysis of HS2 in human cells revealed that its inactivation by dCas9-KRAB significantly downregulates the expression of several β-globin genes20. Even though it is not possible to directly compare the two studies because of several differences , the human cell line study suggests that CRISPRi may be able to overcome the redundancy issue. On the other hand, CRISPRi can easily be multiplexed, and one potential way to overcome enhancer redundancy is to silence multiple enhancer candidates simultaneously by co-expressing different sgRNAs, as we did for EC1 and EC2. Alternatively, one may supplement CRISPRi with CRISPRa, an epigenome editing technique that activates enhancers and upregulates target genes in the genomic context48, which may suffer less from enhancer redundancy than CRISPRi.If an enhancer is fully redundant with another, it may elude discovery because its inactivation would not show any effect. It is not clear how such redundancy would affect CRISPRi. In this regard, a previous analysis of HS2, an enhancer in the β-globin locus control region, by dCas9-KRAB is informative. Genetic deletion of HS2 in mice had only a mild effect on the expression of β-globin genes despite it being a strong enhancerMyrf cDNA that encodes the 1139 amino acid-long isoform was kindly provided by Dr. Ben Emery. The Myrf cDNA was cloned into pcDNA3. To generate epigenome editing constructs, we amplified dCas9-KRAB from pHAGE EF1α dCas9-KRAB (Addgene #50919) by PCR and replaced the Cre portion of pCAG-Cre (Addgene #13775) with it and an IRES -tdTomato cassette. For SVZ electroporation, the piggyBac ITRs  were inserted, and tdTomato replaced with nuclear-targeted mCherry (Addgene #20972). To generate sgRNA expression vectors, the EF-1α promoter of pSBbi-RN (Addgene #60519) was replaced by the sgRNA scaffold taken from lentiCRISPR v2 (Addgene #52961). For the SVZ electroporation of sgRNAs, the content of PB-CA (Addgene #20960) was replaced by the sgRNA scaffold. Rffl, a Myrf luciferase reporter, was generated by cloning a rat genomic fragment (rn4 chr10:71034166–71034749) into pGL3-promoter (Promega). The sequence information of all constructs was verified by Sanger sequencing, and protein expression was confirmed by Western blot. The Myrf antibody was generously provided by Michael Wegner5. The sources of the commercial antibodies used for the study are as follows: FLAG (Sigma F1804) RFP (Rockland 600-901-379), MBP (Millipore MAB386), CC1 (Millipore OP80), Gst-π (MBL 311), donkey anti-rat IgG, Alexa Fluor 594 (Invitrogen A21209), donkey anti-rabbit IgG, Alexa Fluor 488 (Invitrogen A21206), donkey anti-mouse IgG, Alexa Fluor 488 (Invitrogen A21202), and donkey anti-chicken IgY, Alexa Fluor 594 (Jackson 703-585-155).A mouse 36. The original immunopanning protocol for mouse OPCs35 did not work well in our hands. Instead, we found that the immunopanning protocol for rat OPCs works well for mouse OPCs, and this is why we used it to purify mouse OPCs. Primary mouse OPCs and Oli-neu cells39 were kept in a proliferative condition by supplementing the Sato media36 with PDGF (10 μg/mL), NT3 (1 μg/mL), CNTF (10 μg/mL), and NeuroCult™ SM1 Neuronal Supplement. They were maintained in a humidified 8% CO2 incubator at 37 °C. Transient transfection was performed using Lipofectamine 2000 as per the manufacturer’s instructions.The current study was conducted in strict accordance with the protocol (approved protocol number #NA-Park2) approved by the Institutional Animal Care and Use Committee of SUNY Buffalo, which is licensed by the National Institutes of Health Office of Laboratory Animal Welfare . OPCs were purified from mouse pups of P7 ~ P9 by immunopanning49. All the OL ChIP-seq data are from cultured rat OL lineage cells, and their accession numbers are as follows: Brg1 , Chd7 (GSM1869162), H3K27ac , H3K4me3 , Olig2 , Sox10 , Tcf7l2 . The Myrf ChIP-seq data were downloaded from the journal website . ChIP-seq reads were mapped to rn4 by Bowtie50, and peaks called by MACS251. The genome-wide map of 21324 putative OL enhancers were derived in rn4 and mapped to mm9 by liftOver using default options. For comparison with the Roadmap Epigenomics Project data, the six Myrf enhancer candidates were mapped to hg19 by liftOver using default options.The OL ChIP-seq data were downloaded from the Sequence Read Archivei.e., uniformly). We quantify the deviation of the observed read distribution around the valley from the null model by the cumulative binomial distribution function as follows. For the left shoulder, compute the cumulative binomial probability of observing VR or less reads for V given the total number of reads being VR + LSR under the probability of VS/(VS + LSS). Since the sizes of valleys and shoulders are not pre-defined, we allow the valley to take any value from 250, 300, 350, and 400 bps. Shoulders are allowed to take any value from 300, 400, and 500 bps. From these 12 combinations, we take the lowest p value as a score for the valley. We repeat the same calculation for the right shoulder. Then we take the greater of the two p values as the final score for the valley. Once this computation was finished for the entire genome, the valleys were ranked by their scores. We leniently picked the top 6939 valleys as putative OL enhancers by cross-examining the valleys’ scores with their distributions of H3K27ac ChIP-seq reads.For the statistical analysis of OL H3K27ac profiles, the three OL H3K27ac ChIP-seq data  were merged, and the merged H3K27ac profile analyzed by the cumulative binomial distribution function. For a hypothetical valley (V) flanked by the left and right shoulders (LS and RS), we count the number of reads mapped to each region. VR: # of reads for V, LSR: # of reads for LS, and RSR: # of reads for RS. Also we define their sizes in base pairs (bps). VS: size of V, LSS: size of LS, and RSS: size of RS. Under the null model, H3K27ac ChIP-seq reads would be distributed randomly (http://atlas.gs.washington.edu/mouse-atac). The NIH Roadmap Epigenomics Project data were visualized by the WASHU Epigenome Browswer.The ATAC-seq data were downloaded from the Shendure laboratory website (Single guide RNAs (sgRNAs) were designed by using a web service from the Zhang laboratory (zlab.bio/guide-design-resources). For epigenome editing, dCas9-KRAB, gRNAs, Rffl, and pRL-TK  were transfected into primary mouse OPCs, which were then cultured for 3 days in the differentiation condition. The reporter activity of Rffl relative to that of pRL-TK was determined by the Promega dual luciferase reporter assay kit, as per the manufacturer’s instructions.Gapdh. Each PCR reaction contained 2 µL of cDNA, 5 µL of the iTaq Universal SYBR Green Supermix (Bio-rad #1725124), and 500 nM of forward and reverse primers. The primer sequences are shown in Table Total RNA was purified using Trizol (Thermo Fisher Scientific #15596026), and cDNA synthesized by the SuperScript First-Strand kit (Invitrogen #11904-018). Quantitative PCR was performed by C1000 Touch thermal cycler with the CFX384 optical reaction module (Bio-rad). The expression level of a gene was normalized to that of DNA plasmids that express dCas9-KRAB and sgRNAs were transfected into primary mouse OPCs using Lipofectamine 2000. Transfected OPCs were kept in the differentiation condition for 3 days. Cells were fixed with 4% formaldehyde and permeabilized with 0.1% Triton X-100. Upon blocking with 1% BSA, they were incubated with primary antibodies diluted in the blocking buffer at 4 °C overnight, followed by incubation with fluorochrome-conjugated secondary antibodies. Nuclei were stained with Hoechst 33342 (Invitrogen). Fluorescence was visualized with a Leica DMi8 microscope with an ORCA-Flash4.0 sCMOS camera. Images were taken in a blind manner. The signal from each fluorescence channel  was quantified by CellProfiler for an objective quantitative analysis.CnpCre/+ pups were anesthetized by hypothermia. Five µl of DNA solution, including dCas9-KRAB, gRNA, the hyperactive piggyBac transposase, and 0.05% of Fast Green Dye for visualization, was injected into brain ventricles with a fine-tipped micropipette. After injection, electroporation paddles  were placed on the sides of the pup head, and five 40 V, 50 ms square pulses at a frequency of 60 Hz, were delivered. Our electroporation protocol mainly targets SVZ NSCs on the striatum side. The pup was allowed to recover on a heating pad. Electroporated pups were euthanized at P28. The brain tissue was fixed in 4% paraformaldehyde in PBS for 2 days, which was followed by an incubation in 30% sucrose for 1~2 days. The brain tissue was cut in 10 µm thickness. Brain sections were washed in PBS, permeabilized in 0.3% Trition-X100, and blocked in 5% FBS, 0.3~0.6% Triton-X100 in PBS. The sections were incubated with primary antibodies, washed in PBS, incubated with secondary antibodies, and washed in PBS. They were mounted in a mounting medium (VectaShield H-1000). Fluorescence was visualized with the Leica DMi8 microscope in a blind manner. Cells were counted in a blind manner by two independent researchers for robust statistical analysis.P1 Supplementary Information


During an adaptive immune response, B cells can change their surface immunoglobulins from IgM to IgG, IgE or IgA through a process called class switch recombination (CSR). Switching is preceded by inducible non-coding germline transcription (GLT) of the selected constant gene(s), which is largely controlled by a super-enhancer called the 3′ regulatory region (3′RR). Despite intense efforts, the precise mechanisms that regulate GLT are still elusive. In order to gain additional insights into these mechanisms, we analyzed GLT and CSR in mutant B cells carrying a duplication of the promoter of the α constant gene (Iα) downstream of 3′RR. Duplication of the Iα promoter affected differently GLT and CSR. While for most isotypes a drop in GLT was accompanied by a decrease in CSR, that was not the case for switching to IgA, which diminished despite unchanged GLT. Unexpectedly, there was no obvious effect on GLT and CSR to IgG3. Remarkably, specific stimuli that normally induce switching to IgG2b had contrasting effects in mutant B cells; Iγ2b was now preferentially responsive to the stimulus that induced Iα promoter. We propose that one mechanism underlying the induced 3′RR-mediated activation of GL promoters involves, at least in part, specific transcription factories. IgH locus and leads to a shift in immunoglobulin (Ig) isotype expression from IgM to IgG, IgE or IgA. Recombination involves highly repetitive DNA sequences called switch (S) sequences, located upstream of the constant exons. The donor S region is invariably Sμ and the downstream acceptor S region is chosen depending on the nature of the extracellular stimulus 1. The type of signal received by the B cell mobilizes different signaling pathways, ultimately resulting in the recruitment of a specific set of transcription factors that can suppress or induce transcription from constant genes promoters (e.g.3). These so-called germline (GL) or I promoters are localized upstream of all constant genes except Cδ . CSR occurs exclusively at the see Fig. . Non-codIgH locus4. The 3′RR has been shown to effect a long-range enhancing activity on the multiple I promoters as well as on ectopic promoters when inserted upstream of the 3′RR, probably involving a competition between target promoters for 3′RR activity (e.g.8). In this context, replacement of Iγ3 by Iγ1 promoter, resulting in a duplication of Iγ1 promoter upstream of the 3′RR, led to a decrease in GLT from the upstream, ectopic Iγ1, but not from the downstream, endogenous, Iγ1 promoter7. This raises the question as to how duplication of a GL promoter downstream of the 3′RR would affect activation of the upstream promoters.Various mutational studies have shown that GLT is regulated by the inducible 3′ regulatory region (3′RR), which contains four enhancers  located downstream of the 9  in which a chimeric transcription unit driven by the Iα promoter has been inserted downstream of the 3′RR so that the 3′RR is now flanked by identical Iα promoters9 Fig. . In thisIn order to determine if insertion of the Iα promoter downstream of the 3′RR Fig.  had any 9. Although already active in 2Iα resting B cells, the ectopic promoter was further induced by TGFβ stimulation, as measured by β-globin transcript levels. This change was not significant with the other stimulation conditions , downstream of the 3′RR (hs4-NI mice) had no effect on CSR16. In this regard, the reduction seen in this study for Sγ2b (upon LPS stimulation), Sγ1, Sγ2a and Sε transcript levels was unexpected. It remains possible that altered local chromatin structure at the different insertion sites of PGK-NeoR (hs4-NI) and Iα promoter (2Iα) prevents access and interaction of the 3′RR with the upstream I promoters, although this fails to explain why only some promoters are affected. It is also possible that the ectopic insertion had perturbed the 3D structure of the 3′RR per se. However, the normal CSR to all isotypes seen in hs4-NI mice and to IgG3 in 2Iα mice suggests otherwise. One plausible explanation is that the induced PGK promoter and the ectopic Iα promoter affect the 3D architecture of the IgH locus through different mechanisms. Bioinformatics analysis using JASPAR software revealed the presence of a putative CTCF binding site (TCCACCGGTAGGCGCCA) in the PGK promoter but not in Iα promoter. In this context, CTCF bound to the PGK promoter may interact with CTCF bound to cognate sites downstream of the IgH locus17 leaving intact the interactions established between the 3′RR and upstream GL promoters. The potential binding of CTCF to the PGK promoter could also explain why insertion of PGK-NeoR in the IgH locus affects transcription from upstream (relative to the insertion site), but not from downstream promoters (discussed in5).Interestingly, insertion of the neomycin resistance gene under the control of the phosphoglycerate kinase promoter , Sγ1, Sε and Sγ2a transcript levels may result from the continuous activity of the ectopic Iα promoter under LPS, IL4 and INFγ stimulations. While we cannot formally exclude this possibility, this does not fully explain the observed phenotype because the highest activity of the ectopic Iα promoter (as measured by the human β-globin transcript levels) was detected upon TGFβ stimulation Fig. , which d19. This raises the possibility that 3′RR-mediated activation of GL promoters occurs in distinct transcription factories, in a stimulus dependent manner. Such “specialized” transcription factories would ensure that GL promoters that share a specific set of transcription factors would preferentially co-localize in the same transcription factory, while GL promoters that do not would be excluded. Correlation between shared transcription factors and shared transcription factories has been established for some regulatory factors such as KLF-1, which plays an important role in the expression of globin genes as well as other genes20 and NF-κB for several TNFα-induced genes21. Significantly, promoter identity appears to play an important role in this process22. In the specific case of 2Iα mice, one speculative scenario is that continuous activity of the ectopic Iα promoter9 retains the 3′RR in a transcription factory enriched in TGFβ-induced transcription factors. Promoters that do not respond to TGFβ pathway would be down-regulated. Iγ3 promoter may somehow be an exception because of the 3D proximity with the 3′RR (discussed in9). Whether the transcription factors induced by TGF-β also mediate the long-range interactions between the 3′RR and target promoters is presently unknown.An alternative mechanism may be proposed based on the striking finding that Sγ2b transcript levels were reduced in LPS stimulation but were normal in TGFβ stimulation. The latter mobilizes members of the SMAD transcription factors family and other factors such as RUNX3 that bind to their cognate sites at Iγ2b and Iα promotersi.e. TGFβ stimulation. Thus, our study suggests that one mechanism underlying the 3′RR-mediated activation of GL promoters involves, at least in part, specific transcription factories. Whether it is the 3′RR that directs the responsive GL promoters to the appropriate transcription factories or the other way around is presently unknown, though our data point to an active role of the GL promoter.In conclusion, we provided evidence that duplication of the Iα promoter downstream of the 3′RR led to pleiotropic effects on GLT and CSR. For most isotypes, we found a good correlation between decreased GLT and impaired CSR. However, there were remarkable exceptions. GLT that initiates from Iγ3 promoter was normal as was CSR to IgG3. In contrast, CSR to IgA was reduced despite normal levels of pre-switch transcription. Remarkably, Iγ2b promoter which is induced by either LPS or TGFβ stimulation, was activated upon TGFβ stimulation only. Thus, with the exception of Iγ3, GLT derived from all other promoters not induced by TGFβ stimulation was decreased. This indicates that the 3′RR activity is not optimally available to these promoters even in stimulation conditions that normally induce them. Moreover, the case of Iγ2b promoter reveals that repression of these promoters is not irreversible provided they respond to TGFβ stimulation. It thus appears that the active Iα promoter has “sequestered” the 3′RR in a compartment that is permissive to only those GL promoters that respond to the same stimulus as Iα, The WT and mutant mice are of 129 Sv background. All analyses were performed on homozygous 2Iα mice. Three mice of each genotype were used per experiment, and each experiment was repeated at least 3 times. 5–7 weeks old mice were used. All experiments on mice have been carried out according to the CNRS ethical guidelines and were approved by the Regional Ethical Committee (Accreditation N° E31555005).5 cells/ml in the presence of 25 µg/ml of LPS (Sigma) and 3 ng/ml anti-IgD-dextran (Fina Biosolutions) (LPS stimulation); 25 µg/ml LPS and 3 ng/ml anti-IgD-dextran and 20 ng/ml IFNγ (R&D) (IFNγ stimulation); 25 µg/ml LPS and 3 ng/ml anti-IgD-dextran and 25 ng/ml IL4 (eBiosciences) (IL4 stimulation); and 25 µg/ml LPS and 3 ng/ml anti-IgD-dextran and 10 ng/ml IL4 and 5 ng/ml IL5 (R&D) and 5 ng/ml BLys (R&D) and 2 ng/ml TGFβ (R&D) (TGFβ stimulation).Splenic single cell suspensions were obtained by standard techniques and B cells were negatively sorted by using CD43-magnetic microbeads and LS columns (Miltenyi) and cultured for 2 days (for pre-switch GLT) and 4 days (for CSR and post-switch GLT), at a density of 5 × 104 viable cells by using a Coulter XL apparatus (Beckman Coulter).At day 4 post-stimulation, B cells were washed and stained with anti-B220 APC (BioLegend) and either anti-IgG3-FITC (BD-Pharmingen), anti-IgG2b-PE (BioLegend), anti-IgG2a-PE (BioLegend) or anti-IgG1-FITC (BioLegend). Activated B cells from AID-deficient mice were included as negative controls. Data were obtained on 3 × 10Actin transcripts were used for normalization and the results are shown as percentage of actin. (−RT) controls were included in all of the experiments. The primers used have been described9.Total RNAs were prepared from resting splenic B cells, and B cells at day 2 or 4 post-stimulation. Total RNAs were reverse-transcribed (Invitrogen) and subjected to qPCR using Sso fast Eva Green (BioRad). p < 0.05 (*), very significant if p < 0.01 (**), and extremely significant if p < 0.01 (***).Results are expressed as mean ± SD (GraphPad Prism), and overall differences between values from WT and mutant mice were evaluated by a two-tailed t test. The difference between means is significant if


Neisseria Type IV pilus (Tfp). In the pathogenic species, RpoD and the integration host factor (IHF) protein regulate expression of pilE, encoding the Tfp structural subunit. We previously reported that in commensal species, pilE transcription is regulated by RpoN, IHF, and activator Npa. Npa has many hallmarks of response regulators in two‐component regulatory systems, leading us to search for its response regulator partner. We report that Npa partners with sensor kinase Nps to control pilE transcription. Among the genes involved in Tfp biogenesis and function, only pilE is controlled by RpoN and Npa/Nps. We summarize our findings in a model, and discuss the implications of the differential regulation of pilE the context of Neisseria Tfp biogenesis.Over 20 genes are involved in the biogenesis and function of the  NEIELOOT_00058 is present in all other commensal Neisseria genomes published on the web.Npa has motifs typical of response regulators of two‐component regulatory systems (TCRS), namely, a receiver domain in the N terminus and an output domain in the C terminus  as the model commensal. We show that Nps is required for pilE transcription, and that Nps and Npa act in concert as a TCRS. Finally, we show that RpoN and Nps/Npa control transcription only of pilE and not the other Tfp biogenesis genes reported to date. The implication of these different control mechanisms for Type IV pilus biogenesis is discussed.In this work, we tested the hypothesis that NEIELOOT_00058 is the SK that activates Npa to regulate transcription of 22.1Neisseria strains were routinely grown at 37°C with 5% CO2 in GCB agar or liquid GCB containing Kellogg's supplements  was used. TMMOD server was used to predict transmembrane domains .Various online programs were used to analyze the sequences. To predict conserved domains, NCBI domain    and treated with DNAse free (Ambion) to remove DNA, as recommended by the manufacturer. The integrity of the RNA was determined by agarose gel electrophoresis. cDNA was generated with 1,000 μg of RNA using M‐MLV (Promega) per manufacturer's instructions. Reactions without reverse transcriptase were used as negative controls. To ensure that equal amounts of RNA were used, a reaction to amplify 16S rRNA was performed. All primers used, and gene products are listed in Table Total 2.7g for 30 min). Polyclonal antibodies to this pilus preparation were raised by immunization of a rabbit with 4 weekly doses of purified pili (Alpha Diagnostic Intl. Inc). Nonspecific antibodies were removed from the immune sera by absorbing with intact Nel ΔpilE cells. Finally, specific anti‐PilE antibodies were captured by affinity purification using purified Nel pilin. The antibodies were released by addition of 0.2 M glycine pH 2.5, following by incubation at RT for 5 min. The solution with antibodies was collected and neutralized with 1 M Tris–HCl pH 8. The specificity of the antibodies was tested by western blot  were suspended in sample buffer. Ten microlitre of sample was added to each lane of a 15% acrylamide gel. To ensure a good separation, the gel was run at 90 volts for 2.5 hr. Proteins were transferred onto a nitrocellulose membrane using a Trans‐Blot SD semidry transfer cell (Bio‐Rad). The membrane was dried and then blocked with 5% milk‐TBST for 1 hr. Anti‐PilENel antibodies were diluted 1:10,000 (in 5% milk‐TBST) and incubated with the membrane for 1 hr. The membrane was washed 3× and incubated with secondary goat anti‐rabbit antibodies (LICOR) for 1 hr. The western blot was visualized using an Odyssey Clx Infrared Imaging System (LICOR) instrument.Equal amounts of bacteria  per the manufacturer's instructions. A 5′ Rapid amplification of cDNA ends (RACE) reaction was used to determine the transcription initiation site (TIS) using the SMARTer® RACE 5′/3′ kit (Takara‐Clontech). Primers used to determine TIS and their targets are described in Table 33.1pilE transcription in commensal Neisseria requires RpoN and the activator Npa  . Upstream of nps and npa is the DNA topoisomerase IV subunit A, parC (accession number: AJE19475.1); all three ORFs are arranged in the same orientation. This organization of the three genes is identical in the human commensals N. mucosa (Nmu) and N. sicca (Nsi), and in the animal commensals N. musculi (Nmus) and AP206, a Neisseria sp isolated from rhesus macaque , Neisseria shayeganii (Nsh), Neisseria wadsworthii (Nwa), Neisseria subflava (Nsu), Neisseria flavescens (Nfl), Neisseria cinerea (Nci), Neisseria polysaccharea (Npo), and Neisseria lactamica (Nla), npa is followed by a fourth ORF, rsmJ, encoding a putative ribosomal RNA small subunit methyl transferase (locus tag NMA1806 in Nme). In pathogen Neisseria meningitidis (Nme), parC is followed by NMA1803, NMA1805, then rsmJ. NMA1803 is a homolog of Nps that lacks the last 76 bases of the ORF , parC is followed by rsp, a fusion of npa and nps , NMA1805 (npa), and NMA1806 (rsmJ) are transcribed as an operon  (47.5% identity and 75.5% similarity) and PilS of Pseudomonas aeruginosa (Pae) (28.8% identity and 64.3% similarity). In Kki and Pae, PilS is required for transcription of pilA, encoding the major subunit of their Tfp . NelΔnps, failed to produce detectable amounts of pilE mRNA ‐inducible promoter, restored pilE mRNA production. IPTG‐inducible promoters are known to be leaky, and Nel Δnps+inps produced low levels of mRNA even in the absence of the inducer  restored pilE transcription, PilE production and microcolony formation . We confirmed by RT‐PCR that levels of nps and npa transcripts were not affected by the point mutations in Nel npsH325A and Nel npaD58A  on the RR . Another minor pilin gene, pilV, is transcribed as a monocistronic unit  and the three genes form an operon , HAMP , and HPT (histidine containing phosphotransfer) motifs. Nps has six membrane‐spanning regions interspersed with very short loops. Pae PilS, the Nps homolog, also has six such transmembrane domains. Pae PilS is both a kinase and phosphatase; it senses the levels of membrane‐bound pilin (PilA) and regulates pilA expression through its phosphatase activity . Iron depletion affected pilE expression in commensal Neisseria is summarized in Figure pilE transcription (Rendón et al., pilE transcription also requires IHF binding to its cognate sequence, which is situated between the RpoN and Npa recognition sites (Rendón et al., pilE transcription is abolished.Our model for pilE are constitutively expressed. Our findings indicate they are transcribed from a RpoD‐dependent promoter (Figure Our model implies that Tfp biogenesis genes other than r Figure , but theNeisseria evolved different mechanisms to regulate pilE? This question might be explained by current information on Neisseria Tfp biogenesis. A large number of proteins situated in the inner membrane, periplasm and outer membrane are involved in the assembly and export of PilE and minor pilins (Brown et al., Why have commensal and pathogenic pilE is expressed constitutively (Fyfe et al., pilE transcription under certain conditions. pilE expression is decreased in a pilF mutant and increased in a pilT mutant (Dietrich, Mollenkopf, So, & Friedrich, pilE transcription (Carrick et al., pilE (De Reuse & Taha, pilE transcription in Ngo or Nme. Future studies will allow a better understanding of pilE transcriptional in the pathogens.In the pathogens, The authors declare not conflict of interest.The authors adhere to sharing data and materials policies described in the guidelines for authors. Click here for additional data file.


Placental dysfunction is implicated in many pregnancy complications, including preeclampsia and preterm birth (PTB). While both these syndromes are influenced by environmental risk factors, they also have a substantial genetic component that is not well understood. Precisely controlled gene expression during development is crucial to proper placental function and often mediated through gene regulatory enhancers. However, we lack accurate maps of placental enhancer activity due to the challenges of assaying the placenta and the difficulty of comprehensively identifying enhancers. To address the gap in our knowledge of gene regulatory elements in the placenta, we used a two-step machine learning pipeline to synthesize existing functional genomics studies, transcription factor (TF) binding patterns, and evolutionary information to predict placental enhancers. The trained classifiers accurately distinguish enhancers from the genomic background and placental enhancers from enhancers active in other tissues. Genomic features collected from tissues and cell lines involved in pregnancy are the most predictive of placental regulatory activity. Applying the classifiers genome-wide enabled us to create a map of 33,010 predicted placental enhancers, including 4,562 high-confidence enhancer predictions. The genome-wide placental enhancers are significantly enriched nearby genes associated with placental development and birth disorders and for SNPs associated with gestational age. These genome-wide predicted placental enhancers provide candidate regions for further testing in vitro, will assist in guiding future studies of genetic associations with pregnancy phenotypes, and aid interpretation of potential mechanisms of action for variants found through genetic studies. The placenta is a complex temporary organ, essential for successful pregnancy. The placenta performs many vital functions including transfer of nutrients to the developing fetus and protection against infectious agents . PlacentIn vivo studies in model organisms have lent insight to early placental development, but the rapid evolution of pregnancy across taxa often limits the translatability of this work [Precisely controlled gene expression during pregnancy is crucial to proper development, and these gene regulatory “programs” are mediated by gene regulatory elements, like enhancers, that play a large role in development and thus disease –12. Disrhis work .in vitro, and propose mechanisms of action for variants found through GWAS. To facilitate their use, all the enhancer predictions are integrated into GEneSTATION (v2.0) [To address the challenge of mapping gene regulatory elements active in the placenta, we used the EnhancerFinder  machine N (v2.0) .To predict placental enhancers, we used the EnhancerFinder algorithm, which integrates DNA sequence, evolutionary, and functional properties of known enhancers to build statistical models that enable the identification of new enhancers . This apWe trained our classifiers using enhancers defined by cap analysis of gene expression (CAGE) from the FANTOM5 Transcribed Enhancer Atlas . AnalyziThen, using these features, we trained a multi-kernel support vector machine (SVM) classifier—with one kernel for each of the three data types—to distinguish robust enhancers from random, length-matched non-enhancer genomic background regions ; Step 1.To assess the performance of our trained classifiers, we used 10-fold cross validation to compute average receiver operating characteristic (ROC) curve and precision-recall (PR) curves. In 10-fold cross validation, ten models are trained using a different 90% of the positive and negative training regions, and then each model is evaluated on remaining 10% of the regions. We quantified our method’s overall performance by the average area under the curve (AUC) over the 10 runs.The trained Step 1 classifier performs very well at identifying FANTOM enhancers from genomic background  within the kernel were from placental and related tissues , and the least informative features came from cellular contexts unrelated to pregnancy .To identify genomic regions with potential placental regulatory activity genome-wide, we applied our trained classifiers to the human genome by tiling all human chromosomes into regions the length of an average FANTOM5 placental enhancer (400 bp) overlapping by 200 bp. We filtered out tiles that overlapped gaps in the genome assembly, exons, and likely promoter regions (5 kb upstream of each transcription start site (TSS)). Tiles assigned to both the enhancer and placental enhancer classes by the SVM classifiers were considered putative placental enhancers. Those with strong predictions in both classifiers (SVM score > 1) were considered high confidence putative placental enhancers. Merging overlapping tiles yielded 4,562 high-confidence placental enhancers, covering 3,475,438 bp of the genome, and 33,010 putative enhancers, covering 38,893,990 bp of the genome , Table 1To evaluate the relevance of our high-confidence predicted placental enhancers to placental biology and pregnancy, we examined nearby genes in the context of known gene annotations. Using the functional enrichment analysis tool GREAT , we mappP < 0.0001) with a calculated fold enrichment of 2.69 (relative to the mean of the randomized sets).To assess the biological importance of our high-confidence placental enhancers, we tested for enrichment of regions associated with gestational age and preterm birth in a recent genome-wide association study (GWAS) . Forty-tP = 3.80e–147) and “embryonic placenta development” (P = 3.82e–99). The candidate enhancers were also enriched for GWAS regions associated with preterm birth and gestational age . Thus, there is evidence to suggest that additional regulatory regions relevant to placental biology are present in the candidate set.To compare the high-confidence placental enhancer set to the candidate placental enhancer set, we tested the enrichment for specific functions near the candidate regions using GREAT and for overlap with the pregnancy-related GWAS regions. We found similar placenta-related GO terms enriched near the larger candidate placental enhancer set, for example: with GO terms such as “placenta development”  and our high-confidence placental enhancers. We found 82 elements  overlapped between the two sets. Because the biological information used differed between the sets, it is not surprising that our predictions and the placental TFBS clusters identify largely distinct regions of the genome. Furthermore, nearly all of our high-confidence predicted enhancers  overlap TFBS clusters defined by the ENCODE project over 161 TFs . This suP = 2.95e–15 and P = 2.33e–17, respectively) as among our predicted enhancers. The results were similar for enrichment for pregnancy-related GWAS regions. While 43 of our placental enhancers fell within a GWAS region associated with preterm birth and gestational age with a calculated fold enrichment of 2.69 (P < 0.0001), the TFBS clusters overlapped 13 elements had a fold enrichment of 3.07 (P = 0.0006). Overall, comparing the significant functional annotations of the placental TFBS clusters with our predicted placental enhancers revealed similar levels of enrichment for relevant functional terms.To evaluate the functional relevance of the previously published placental TFBS clusters, we tested for enriched relevant functions using GREAT and for enrichment in overlap with preterm birth and gestational age GWAS regions. We examined the GO biological process terms “placenta development” and “embryonic placental development” and both were comparably enriched among genes near the placental TFBS clusters  compared to the genomic expectation. However, the age distribution of TEs present in the placental enhancers compared to TEs overlapped by permuted enhancer sets is significantly enriched for TEs originating in the common ancestor of theria or before . The enrichment for ancient TEs and depletion of more recent TEs is a common pattern across validated enhancers [Transposable elements (TEs) often create regulatory elements in pregnancy-related tissues –26. We cnhancers , and thuUsing an established machine learning framework, we identified 4,562 high-confidence placental enhancers, as well as an expanded set of 33,010 candidate placental enhancers. These putative regulatory regions are enriched near genes relevant to pregnancy, are enriched for overlap with variants associated with diseases of pregnancy, and have similar transposable element profiles as validated enhancers. In addition, the predicted enhancers significantly expand previously published sets of placental enhancers, and thus provide greater power to interpret genetic associations with diseases influenced by the placenta. For example, the fact that 12 out of 14 regions associated pregnancy complications in a recent GWAS are in high linkage disequilibrium with a predicted enhancer underscores the utility of these genome-wide enhancer maps. These candidates suggest targeted regions for testing when seeking the causal variants in these regions and dissecting how they influence pregnancy. More accurate interpretation of these and future GWAS hits is necessary for understanding the complex biology of pregnancy and eventually improving the identification and prevention of disorders such as preterm birth. To facilitate the use of our enhancer maps, they are now integrated into the GEneSTATION web platform for studying pregnancy and preterm birth .Our predicted enhancer maps can be improved in several dimensions. First, they are undoubtedly incomplete. Enhancer activity is highly context and stimulus dependent. Due to the paucity of training data from diverse contexts, we have focused on identifying a set of candidate regions that have hallmarks of potential regulatory activity in the placenta broadly without making specific contextual predictions. Furthermore, the patterns learned by our machine learning classifier generalize existing patterns in the evolution, sequence, and functional genomics of known placental enhancers, but are constrained by what is currently known. Finally, there is heterogeneity in the cellular makeup of the placenta and existing data do not enable cell-specific predictions. As more enhancer data become available from relevant cellular contexts, we will continue to refine our predictions and integrate them with other annotations.While the costs and technical difficulties of agnostically identifying enhancers are decreasing, many tissues and cell types remain difficult to assay due to biological constraints and ethical considerations. These challenges are compounded for tissues like the placenta that are rapidly evolving between species, limiting the utility of information garnered through the study of model organisms. Computational approaches, such as those presented here, paired with growing collections of experimentally validated regulatory regions provide a promising avenue for enabling researchers to interrogate the gene regulatory architecture of the placenta and other tissues that are difficult to assay.We based our approach on the EnhancerFinder two-step machine learning algorithm for predicting enhancers and their tissues of activity. We first trained an SVM classifier based on diverse sequence, evolutionary, and functional genomics features to distinguish known enhancers active in a range of tissues from the genomic background. Then in the second step, additional classifiers were trained to distinguish enhancers active in different tissues from one another. In this step, all enhancers active in a tissue of interest (placenta) are used as positive training examples and all enhancers not active in the tissue are treated as negatives.We downloaded the hg19 genomic locations of all 38,538 robust human enhancers identified by CAGE from the FANTOM5 Transcribed Enhancer Atlas. The data included 748 human placental enhancers, 24% (178) of which had activity only in the placenta among all tissues tested by FANTOM5. The average length of a FANTOM5 placental enhancer is 400 bp.To train the enhancer classifier (step 1), the positive set consisted of a random subset of 385 robust human enhancers (fixed to a length of 400 bp at the center of any enhancer). Our negative set consisted of 2,000 random genomic regions matched to the length and chromosome distribution of the positive set and excluding FANTOM5 enhancers and hg19 genome assembly gaps. The random genomic regions were generated using shuffleBed . To traiThree types of data were used as features in the MKL algorithm: functional genomics, evolutionary conservation, and DNA sequence motifs. Each type of data was assigned to its own kernel. Following the approach used in previous applications of EnhancerFinder , we usedet al [For the functional genomics kernel, we obtained 980 histone modification datasets  and 39 DNase datasets from 128 cellular contexts in the Human Epigenome Atlas , as wellet al . FeatureAll classifiers were trained using the Multiple Kernel Learning (MKL) functionalities of the SHOGUN Machine Learning Toolbox . The algWe evaluated the performance of our trained classifiers using 10-fold cross validation and computing ROC curves and precision-recall (PR) curves averaged over folds. In a 10-fold cross validation, the training data are partitioned into 10 equal subsets, and the classifier is trained 10 times. Each time, only 9 of the 10 subsets are used to train the classifier. The trained classifier is then applied to the held-out subset and evaluated based on the true status of these regions. The performance of the classifier is then quantified using ROC AUC and a PR AUC.Based on positive and negative training data, our algorithm reports the kernel and feature weights learned during training. The total kernel weight is computed along with the weight for each individual feature weight within that kernel. Positive values are assigned to features associated with the positive input set and features associated with the negative input set score more negatively. After training our placental enhancer classifier (Step 2), we examined the individual weights within its functional genomics kernel to determine whether placenta-related histone modifications were weighted higher than histone modifications found in other cellular contexts. In this case, positive weights are associated with placental enhancer activity and negative weights are associated with enhancer activity in other cellular contexts.To predict placental enhancers genome-wide, we tiled each autosome into 400 bp regions  in overlapping increments of 200 bp. We omitted the sex chromosomes from our analyses. These regions were filtered to remove any tiles that overlapped an exon or fell within 5 kb of a TSS to minimize association with promoter regions. Coordinates for exons and TSSs were downloaded from the Ensembl GRCh37 Feb 2014  using thTo identify the functional annotations, phenotypes, and pathways enriched among genes nearby the predicted placental enhancers, we used the web tool GREAT with the default settings. GREAT takes a set of genomic regions and associates them with their putative target genes and target gene annotations . We usedWe calculated the enrichment for GWAS SNPs in our candidate placental enhancer set and high-confidence placental enhancer set. We obtained 14 preterm birth and gestational age GWAS regions (omitting 3 regions on the X chromosome) from a recent GWAS . For eacWe downloaded a set of 2,216 placental enhancers defined using transcription factor binding site (TFBS) clusters related to placental function from supplementary material of Tuteja et. al . Of the http://hgdownload.soe.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeRegTfbsClustered/wgEncodeRegTfbsClusteredV3.bed.gz.We considered TFBS clusters defined by the ENCODE project based on ChIP-seq experiments for 161 different TFs from the UCSC Genome Browser. The data were downloaded on Nov. 9, 2018 from: TE genomic locations were retrieved from RepeatMasker v4.0.5 . The claFor both the FANTOM5 placental enhancers and the high-confidence predicted placental enhancers, we used shuffleBed  to shuffS1 Fig(A) Precision-recall (PR) curves for the classifiers trained to distinguish enhancers from non-enhancers (Step 1) and (B) placental enhancers from enhancers active in other tissues (Step 2). Both perform significantly better than expected by chance with areas under the PR curve (AUC) of 0.78 and 0.70 respectively. The shaded region represents the performance range observed over the 10 cross validation runs.(PDF)Click here for additional data file.S1 TableThis table contains the results of applying GREAT functional enrichment analysis with default parameters to the 4562 high-confidence (high-conf) and 33,010 candidate (candidate) enhancer predictions.(XLSX)Click here for additional data file.S1 FileThis tab-delimited BED file gives the locations of 4562 high-confidence placental enhancers in hg19 coordinates.(BED)Click here for additional data file.S2 FileThis tab-delimited BED file gives the locations of 33,010 candidate placental enhancers in hg19 coordinates.(BED)Click here for additional data file.S3 FileThis gzipped tab-delimited text file gives the locations of 118,345 TFBS from the ENCODE consortium that overlap the predicted high-confidence enhancers.(GZ)Click here for additional data file.


Gossypium hirsutum L.), and the flowering time is closely correlated with the timing of FLOWERING LOCUS T (FT) expression. However, the mechanism underlying the coordination of various cis-regulatory elements in the FT promoter of cotton has not been determined. In this study, a 5.9-kb promoter of FT was identified from cotton. A bioinformatics analysis showed that multiple insertion–deletion sites existed in the 5.9-kb promoter. Different expression levels of a reporter gene, and the induction by sequential deletions in GhFT promoter, demonstrated that 1.8-kb of the GhFT promoter was stronger than 4.2-, 4.8-, and 5.9-kb promoter fragments. The binding sites of the CONSTANS (CO) and NUCLEAR FACTOR Y transcription factors were located within the 1.0-kb sequence upstream of the FT transcription start site. A large number of repeat segments were identified in proximal promoter regions (−1.1 to −1.4 kb). A complementation analysis of deletion constructs between 1.0 and 1.8 kb of G. hirsutum, Gossypium arboretum, and Gossypium raimondiiFT promoters revealed that the 1.0-kb fragment significantly rescued the late-flowering phenotype of the ArabidopsisFT loss-of-function mutant ft-10, whereas the 1.8-kb promoter only slightly rescued the late-flowering phenotype. Furthermore, the conserved CORE motif in the cotton FT promoter is an atypical TGTG(N2-3)ATG, but the number of arbitrary bases between TGTG and ATG is uncertain. Thus, the proximal FT promoter region might play an important role affecting the activity levels of FT promoters in cotton flowering.Flowering transition is a crucial development process in cotton ( FLOWERING LOCUS T (FT) gene in Arabidopsis thaliana is understood to involve at least six major pathways controlling flowering: the photoperiod, vernalization, ambient temperature, age, autonomous, and gibberellin pathways. They converge at the integrator FT gene , 5 mM K4[Fe(CN)6]·3H2O, 10 mM EDTA-Na2, pH 7.0 and 1 mM 5-bromo-4-chloro-3-indolyl-b-D-glucuronic acid]. After incubation at 37 °C overnight, the plant materials were rinsed with 70% ethanol. The resulting stained tissues were observed under a microscope.To evaluate the activity of the –ΔΔCt method [Total RNA for each sample was isolated using the RNAprep pure Plant Kit  and treated with RNase-free DNase according to the manufacturer’s protocol. The quality, quantity, and integrity of the total RNA extracted were assessed as previously described . cDNA wat method .The results were based on three independent experiments with at least three replicates. The SPSS software package  was used for the statistical analysis, as previously described .FT from G. hirsutum and revealed that the activity level of the truncated GhFT promoter varied in different tissues. Further studies on the 1.0- and 1.8-kb fragments of the FT promoters in G. hirsutum, G. arboreum, and G. raimondii showed that the activity of the 1.0-kb FT promoter was higher than that of the 1.8-kb promoter. These results illustrate that the proximal FT promoter fragment might play an important role affecting the activity levels of FT promoters in cotton flowering.In this study, we identified a 5.9-kb promoter of


Bombyx mori bidensovirus (BmBDV) is a single-stranded DNA (ssDNA) virus from the genus Bidensovirus of the Bidnaviridae family, which, thus far, solely infects insects. It has a unique genome that contains bipartite DNA molecules (VD1 and VD2). In this study, we explored the detailed transcription mapping of the complete BmBDV genome (VD1 and VD2) by rapid amplification of cDNA ends (RACE), reverse transcription quantitative real-time PCR (RT-qPCR), and luciferase assays. For the first time, we report the transcription map of VD2. Our mapping of the transcriptional start sites reveals that the NS genes in VD1 have separate transcripts that are derived from overlapping promoters, P5 and P5.5. Thus, our study provides a strategy for alternative promoter usage in the expression of BmBDV genes. Bombyx mori bidensovirus (BmBDV) is a unique bipartite DNA virus that currently represents the only species in the genus Bidensovirus of the Bidnaviridae family . T. T23]. TFor VD2, NS3 started at nt 565, 19 nts downstream of the P10 promoter TATA box at the 5′ end of VD2. The P133 transcript started 24 nt downstream of the P89 promoter TATA box at the 3′ end of VD2 (nt 5367), only 10 nts upstream of the first ATG, and it terminated at nt 1771. In contrast to VD1, NS3 and P133 transcripts terminated at two polyadenylation sites, which were far apart from each other, unlike like VP and PolB mRNAs, which had 4 nts of overlap.Using 5′ RACE, we observed that two NS transcripts were initiated from different locations on VD1 . This isThe results of RACE and RT-qPCR revealed that the NS genes in VD1 expressed separate transcripts. Since there were two TATA box elements upstream of the transcription initiation of NS1, we speculated that the NS1 transcripts may be under the control of two overlapping promoters (P5/5.5). To test this hypothesis, various reporter plasmids were constructed A, and weΔ observed through the DPE-like sequence GGTCA in P6 was mutated to CCGA. As expected, the luciferase activity of P6Δ declined rapidly compared to that of P6.To investigate the function of P5/5.5 in NS1 transcription, we constructed plasmids (P5 to P8) A. We alr* and P6*- only generated NS2 transcripts of NS2 by knocking out TATA1 and Inr1. As indicated from the results, the activity of P2* was similar to that of P2. These results indicate that the two overlapping promoters (P5/5.5) had weak activity levels, which further proved our previous hypothesis. P6*- and P6- had high levels of activity, and the initiation of NS1 and NS2 transcription was closely positioned at either side of the NS2 initiation codon  in 2012 [Bidensovirus genus identified in insects. With the development of metagenomics, more and more viruses have been found in environmental and biological samples [Bidnaviridae family in sponges and corals has been reported [BmBDV was classified as a member of the family  samples . Recentlreported . Aedes albopictus densovirus  [Brevidensovirus genus is the ancestor of other parvoviruses [Brevidensovirus genus and perhaps employs the above transcription strategies to ensure the stable inheritance of AalDV.In this study, we analyzed the transcription strategies of BmBDV. Transcription mapping was used to show the transcription initiation and termination sites of each gene in BmBDV see . Interes   during t  . Therefo  . Obviousoviruses . AalDV bRemarkably, despite our finding that NS2 ATG negatively regulates the activity of P5/5.5, P1 has no activity see B when thBidensovirus studies is the lack of permissive insect cells that support virus replication in vitro [BmBDV exclusively infects the columnar cells of the larvae midgut epithelium. A major impediment to in vitro . BmBDV cin vitro . BmBDV hin vitro . To achi


The mechanism by which c-Myc exerts its oncogenic functions is not completely clear and different hypotheses are still under investigation. The knowledge of the capacity of c-Myc to bind exclusively E-box sequences determined the discrepancy between, on the one hand, genomic studies showing the binding of c-Myc to all active promoters and, on the other hand, the evidence that only 60% or less of the binding sites have E-box sequences.In this review, we provide support to the hypothesis that the cooperation of c-Myc with transcriptional cofactors mediates c-Myc-induced cellular functions. We produce evidence that recently identified cofactors are involved in c-Myc control of survival mechanisms of cancer cells.The identification of new c-Myc cofactors could favor the development of therapeutic strategies able to compensate the difficulty of targeting c-Myc. Myc is the most frequent amplified oncogene in human cancers and its alteration is observed in a wide range of tumors, including breast, lung and prostate cancer . DeregulRegardless of its specific mechanism of action, c-Myc remains one of the targets for effective antineoplastic therapy, due to its deregulation in numerous tumors. Unfortunately, c-Myc presents specific, significant obstacles to develop a strategy for its direct inhibition. Indeed, c-Myc lacks enzymatic activity, this limiting those approaches that require its direct inhibition. Rather, c-Myc activity is exerted by protein-protein interactions, which remains a technical barrier impeding organized efforts in drug discovery. The biological behavior of c-Myc in physiology and disease must still be fully elucidated, requiring comprehensive mapping of its target genes and the importance of c-Myc cofactors. These molecules function, at least in part, by affecting chromatin structure through their intrinsic enzymatic activities, including ATPase/helicases, histone acetyl-transferase (HATS) and histone deacetylase (HDACs). Therefore, a possible model of targeting c-Myc could involve the inhibition of these coactivator proteins, critical to c-Myc-specific initiation and elongation.Myc-interacting Zn finger protein-1). It belongs to the BTB/POZ family of zinc finger proteins and interacts with DNA in a sequence-specific manner. Both Max and Miz-1 interact with the HLH domain of Myc suggesting that Max and Miz-1 may form alternate complexes with Myc. In particular Miz-1 is involved in the c-Myc-dependent mechanism of repression of particular genes like Cyclin D1 [One of the first c-Myc cofactors was discovered by Peukert K et al in 1997. The authors identificated a protein that interacts with the carboxy-terminal HLH domain of Myc, Miz-1  is a transcriptional cofactor involved in the regulation of gene expression by connecting specific transcription factors to the general transcriptional machinery. It is an ubiquitarious RNA polymerase II-binding protein exerting many cellular functions in diverse solid tumors. Che-1 promotes cell cycle progression by inhibiting the growth suppression functions of the pRb protein , and by INI1 is a protein that belongs to SWI/SNF complex, an important chromatin remodeler. This complex is pivotal for transcriptional activity, because it allows transcriptional machinery to sit on DNA, freeing the chromatin structure from nucleosomes. SWI/SNF is considered a tumor suppressor and results either deregulated or mutated in many types of tumor . INI1 isin-vivo models showed that elimination of a single Btpf allele is sufficient to delay tumor initiation and progression. Thus, disruption of the BPTF-c-Myc interaction may represent a valuable strategy for the therapy of c-Myc-driven tumors [Bromodomain PHD transcription factor (BPTF) is a subunit of mammalian NURF (ATP-dependent nucleosome-remodeling factor) that uses ATP hydrolysis to catalyse nucleosome sliding . BTPF ren tumors  . Other members of the Hippo pathway are large tumor suppressor 1 (LATS1) and LATS2 , 48. Folese mice . Interesese mice . Turato ese mice  showed aese mice . An indiese mice . In oralese mice . In chroese mice . Li et aese mice  showed tels Fig. .Fig. 3Coin vivo indicating that, beyond the HSE, the local chromatin structure and interaction of different transcription factors may play an important role in transcriptional response to heat stress [de novo lipogenesis, mitochondrial biogenesis, polyamine metabolism and glycolysis. HIF1 depletion resulted also in reduced proliferation and increased apoptosis. Furthermore, the analysis of HIF1 and c-Myc correlation in HCC specimens showed that amplification of either molecules belonging to the HCC subgroup with poorer prognosis and a more aggressive phenotype. Altogether, these experimental evidences indicate that HSF1 is a pivotal c-Myc cofactor required for liver tumor tumorigenesis [Heat shock factor 1 (HSF1) is a stress inducible transcription factor that, in non-stressed status, is involved in a repressive complex comprising HSP-90 and HDAC6. When triggered, HSF1 becomes trimerized and phosphorylated, and then translocated into the nucleus, where it binds to conserved heat shock-responsive DNA elements (HSEs) to upregulate genes coding for heat shock proteins (HSPs) .Otherwist stress . HSF1 hat stress . In tumot stress . HSF1 cat stress . A growit stress . In esopt stress . In HCC,igenesis –70 (Fig.The revision of the latest discovered c-Myc cofactors strongly supports the hypothesis that c-Myc activates and represses selected target genes, with RNA amplification occurring only as a secondary phenomenon Table . Physiol


Here we find that, indeed, a similar type of regulation occurs at the locus for the human oncogene MDM2. We observe evidence of transcriptional interference between the two MDM2 promoters, which produce a poorly translated distal promoter-derived uORF-containing mRNA isoform and a well-translated proximal promoter-derived transcript. Down-regulation of distal promoter activity markedly up-regulates proximal promoter-driven expression and results in local reduction of histone H3K36 trimethylation. Moreover, we observe that this transcript toggling between the two MDM2 isoforms naturally occurs during human embryonic stem cell differentiation programs.We recently described an unconventional mode of gene regulation in budding yeast by which transcriptional and translational interference collaborate to down-regulate protein expression. Developmentally timed transcriptional interference inhibited production of a well translated mRNA isoform and resulted in the production of an mRNA isoform containing inhibitory upstream open reading frames (uORFs) that prevented translation of the main ORF. Transcriptional interference and uORF-based translational repression are established mechanisms outside of yeast, but whether this type of  These studies have provided evidence for translation of thousands of uORFs and widespread existence of alternate transcript isoforms .LUTIMDM2 were designed and cloned into the lentiviral pU6-sgRNA EF1Alpha-puro-T2A-BFP vector. Lentivirus was packaged by co-transfecting sgRNA-expression plasmids and the packaging vectors pCMV-dR8.91 and pMD2.G into 293T cells using the TransIT-LT1 Transfection Reagent (Mirus). Cells were treated with ViralBoost (Alstem) to allow for efficient lentivirus production and lentivirus was harvested 72 h post-transfection. CRISPRi-directed gene knockdown was achieved by transducing MCF-7-dCas9, -dCas9-KRAB and K562-dCas9 cell lines with sgRNA-containing lentivirus in the presence of 8μg/ml polybrene (Millipore Sigma). Successfully transduced cells were puromycin-selected  and harvested 7 days post-infection. The pU6-sgRNA EF1Alpha-puro-T2A-BFP vector was a gift from Jonathan Weissman (Addgene plasmid # 60955).sgRNAs targeting 2 plates per sample) were treated with 1% formaldehyde  for 10 min at room temperature to crosslink DNA and protein. The crosslinking reaction was stopped by adding 0.125M PBS-glycine and cells were harvested by centrifugation. Cells were subsequently resuspended in ice-cold PBS containing 0.25mM PMSF and 10ug/ml aprotinin (Millipore) and pelleted by centrifugation. Chromatin immunoprecipitation of these pellets was performed as previously described  using isoform-specific primers, based on the differential splicing of the two transcripts that results in unique 5′ sequences . To directly test if P1-dependent transcription interferes with P2-dependent transcription, we inhibited transcription from P1 by using CRISPRi  in MCF-7 cells using CRISPRi. Given that p53 is a well-characterized transcriptional activator for P2, it was not surprising that TP53 knockdown resulted in a significant, 43% reduction of the P2-derived PROXMDM2 transcript  in PROXMDM2 expression   . We alsoMDM2 transcript are known modes of gene regulatory control. However, our study shows that transcriptional interference can be combined with the production of alternate transcripts of differential translatability in mammals, in a manner that resembles the LUTI-based regulation seen in yeast. These findings suggest value in revisiting the sequential model of gene expression control that dominates the interpretation of past and current data in favor of a more holistic view of different levels of gene expression control.We report here that an integrated transcriptional and translational regulatory strategy that relies on obligate coupling of transcriptional interference and uORF-based translational repression occurs in human cells. Based on the ubiquitous use of alternative promoters and uORF translation in humans , this tyMDM2, we argue that its two promoters are fundamentally different in function. The P1 promoter produces a poorly translated LUTIMDM2 transcript and the production of LUTIMDM2 from this promoter interferes with P2 activity in cis, reducing the transcription of the well-translated PROXMDM2 isoform. Therefore, P1-driven LUTIMDM2 mRNA production serves to downregulate MDM2 expression. Further, this repression occurs in a tunable manner (Adh gene in fruit fly, which is expressed from two distinct promoters (Canonical models to explain the prevalence of mammalian alternative promoter usage suggest that one promoter might serve as a “back-up” or that the use of two promoters could simply allow activation by different transcription factors that are present in different cell types (e manner . A similMDM2 isoforms during differentiation shown here suggests that the broad use of this type of regulation for developmental modulation of gene expression may be conserved.Contrary to traditional gene regulatory models, mRNA and protein abundances generally show poor correlations over developmental programs in genome-scale yeast and vertebrate studies . The natMDM2 transcripts, based on increased transcription from the P2 (MDM2 levels are elevated in a variety of cancers (reviewed in (


Candida albicans is an opportunistic pathogen that normally lives on the human body without causing any harm. However, C. albicans is also a dangerous pathogen responsible for millions of infections annually. C. albicans is such a successful pathogen because it can adapt to and thrive in different environments. Chemical modifications of chromatin, the structure that packages DNA into cells, can allow environmental adaptation by regulating gene expression and genome organization. Surprisingly, the contribution of chromatin modification to C. albicans biology is still largely unknown. For the first time, we analyzed C. albicans chromatin modifications on a genome-wide basis. We demonstrate that specific chromatin states are associated with distinct regions of the C. albicans genome and identify the roles of the chromatin modifiers Sir2 and Set1 in shaping C. albicans chromatin and gene expression.The fungus  Candida albicans, a prevalent fungal pathogen in humans, can rapidly adapt and thrive in diverse host niches. The contribution of chromatin to C. albicans biology is largely unexplored. Here, we generated the first comprehensive chromatin profile of histone modifications  across the C. albicans genome and investigated its relationship to gene expression by harnessing genome-wide sequencing approaches. We demonstrated that gene-rich nonrepetitive regions are packaged into canonical euchromatin in association with histone modifications that mirror their transcriptional activity. In contrast, repetitive regions are assembled into distinct chromatin states; subtelomeric regions and the ribosomal DNA (rDNA) locus are assembled into heterochromatin, while major repeat sequences and transposons are packaged in chromatin that bears features of euchromatin and heterochromatin. Genome-wide mapping of γH2A, a marker of genome instability, identified potential recombination-prone genomic loci. Finally, we present the first quantitative chromatin profiling in C. albicans to delineate the role of the chromatin modifiers Sir2 and Set1 in controlling chromatin structure and gene expression. This report presents the first genome-wide chromatin profiling of histone modifications associated with the C. albicans genome. These epigenomic maps provide an invaluable resource to understand the contribution of chromatin to C. albicans biology and identify aspects of C. albicans chromatin organization that differ from that of other yeasts.Eukaryotic genomes are packaged into chromatin structures that play pivotal roles in regulating all DNA-associated processes. Histone posttranslational modifications modulate chromatin structure and function, leading to rapid regulation of gene expression and genome stability, key steps in environmental adaptation.  Packaging of genomes into chromatin is the key determinant of nuclear organization , 2. The 3) and acetylated on lysine 9 (H3K9Ac), while coding sequences are enriched in a different set of histone modifications, such as acetylated lysine 16 on histone H4 (H4K16Ac)  and is enriched in repressive histone marks such as methylation of lysine 9 on histone H3 (H3K9me) and methylation of lysine 27 on histone H3 (H3K27me)  –10. In c4K16Ac) –.These re4K16Ac) –, 11, 12.Saccharomyces cerevisiae, heterochromatin is devoid of H3K9me and H3K27me marks but contains hypomethylated H3K4 and hypoacetylated H3K9 and H4K16  depend on the chromatin state of the genomic region undergoing repair. Furthermore, extensive chromatin changes, including recruitment of γH2A, are linked to repair of DNA breaks . ConsequChromatin modifications are reversible, and specific histone modifiers maintain or erase the histone modification state associated with different chromatin regions. Among these, histone acetyltransferases (HATs) and histone deacetylases (HDACs) maintain and erase histone acetylation, respectively, while histone methyltransferases (HMTs) and demethylases (HDMs) are responsible for the methylation state of histones , 3. ThesCandida albicans. C. albicans is a benign commensal organism that colonizes the mouth, the skin, and the urointestinal and reproductive tracts of most individuals. However, C. albicans is also the most common causal agent of invasive fungal infections, and systemic infections are associated with high mortality rates (up to 50%)  . C. albiaptation .C. albicans genome has 8 diploid chromosomes that contain 6,408 annotated protein-coding genes, as well as a large number of noncoding RNAs (ncRNAs) (–TLO genes (The (ncRNAs) –28. The ncRNAs) –. TelomerLO genes , 30.The rDNA locus consists of a tandem array of ∼12-kb units repeated 50 to 200 times. Each unit contains the two highly conserved 35S and 5S rRNA genes that are separated by nontranscribed spacer region 1 (NTS1) and NTS2, whose sequences are not conserved with other eukaryotes , 31.C. albicans chromosomes  units flanked by nonrepetitive HOK and RBP-2 elements. Each RBP-2 element contains a protein-coding gene, FGR6, important for morphological switching (MRS loci are long (10-to-100-kb) tracts of nested DNA repeats found on 7 of the 8 omosomes , 32. Thewitching .C. albicans genome, including 16 classes of LTR retrotransposons (Tca1 to Tca16) and Zorro non-LTR retrotransposons that are present in 5 to 10 copies per cell, dispersed along the chromosomes. Among those, Tca2, Tca4, Tca5, Zorro-2, and Zorro-3 are capable of transposition  events, chromosome rearrangements, and aneuploidy , marking euchromatic and heterochromatic regions and potential recombination-prone unstable sites. Genome-wide mapping of RNA polymerase II (RNAPII) and transcriptome expression profiling unveiled the link between histone modification states and transcriptional activity. We demonstrate that specific chromatin states are associated with the repetitive and nonrepetitive C. albicans genomes. While gene-rich regions are associated with active chromatin marks mirroring their transcriptional state, different types of repetitive elements are assembled into distinct chromatin types. Finally, we present the first C. albicans quantitative ChIP-seq (q-ChIP-seq) methodology, which has permitted us to elucidate the roles of HDAC Sir2 and HMT Set1 in shaping the chromatin state of C. albicans genome and regulating gene expression.In this study, we used chromatin immunoprecipitation with massively parallel deep sequencing (ChIP-seq) technology to establish the first comprehensive genome-wide map of C. albicans genome contains two homologous pairs of divergently transcribed histone H2A and H2B genes as well as histone H3 and H4 genes in addition to a single histone H3 gene  Histone gene names and locations, according to assembly 22. (B) Alignment of C. albicans H2A, H3, and H4 protein sequences against S. cerevisiae and S. pombe homologues. Residues highlighted in red were investigated in this study and are conserved across all species. Download FIG S1, TIF file, 0.9 MB.(A) (Top) Diagram of genome organization of Copyright © 2019 Price et al.2019Price et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the C. albicans repetitive and nonrepetitive regions, we globally mapped the genomic locations of H3K4me3, H3K9Ac, H4K16Ac, and γH2A by performing chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq). Since nucleosomes are not equally distributed across genomes, we accounted for nucleosome occupancy by performing genome-wide profiling of unmodified histone H3 and histone H4. To correlate specific histone modification profiling with transcriptional activity, we performed genome-wide mapping of RNA polymerase II (RNAPII) occupancy. We also performed transcriptome analysis by strand-specific RNA sequencing (RNA-seq) to profile gene expression levels.To explore the chromatin signature of C. albicans wild-type (WT) cells grown under standard laboratory growth conditions  using antibodies specific for modified or unmodified histones. Input (I) samples and immunoprecipitated (Ip) samples were sequenced using an Illumina HiSeq2000 platform , with the exception of centromeric regions, where the histone H3 variant Cse4CENP-A replaced histone H3 (r = 0.72) (ChIP-seq was performed using s genome . Unmodifstone H3 . Further = 0.72) .10.1128/mBio.01376-19.2FIG S22) of histone H3 relative to unmodified H4 across the centromeric and pericentromeric regions of all 8 chromosomes in C. albicans. The CenpA enrichment profile is shown for comparison. The blue bars indicate statistically significantly depleted regions for histone H3. (B) Histogram of RNA-seq counts (log2) in WT cells across all protein-coding genes, according to assembly 22. Genes were grouped into four sets organized by expression level as follows: no expression , low expression , medium expression  and high expression . Download FIG S2, TIF file, 0.9 MB.(A) Fold enrichment  and 5′ regions of genes, and H4K16Ac was enriched across gene bodies  as revealed by RNA-seq analysis . Enrichmed genes . TherefoC. albicans coding genes, we analyzed the chromatin state of the C. albicans repetitive genome, focusing on the major classes of DNA repeats: subtelomeric regions, the rDNA locus, MRSs, and transposable elements (LTR and non-LTR retrotransposons). Sequence analysis of these elements can be problematic because of incomplete sequencing and because of their repetitive nature  of H3K4me3, H3K9Ac, H4K16Ac, γH2A, and H3 relative to unmodified H4 across the 20-kb left and right terminal regions of each of the 8 C. albicans chromosomes. (Middle) Diagrams of coding genes found at these regions (grey: TLO), according to assembly 22. (Bottom) Diagram depicting statistically significantly enriched (red) or depleted (blue) domains for each histone modification. Download FIG S3, TIF file, 1.1 MB.(Top) Fold enrichment  are associated with chromatin, which is largely hypomethylated on H3K4  and D. IC. albicans genome are marked by different chromatin modifications depending on their transcriptional state  than the 50-kb γH2A domains surrounding DSBs  Regions of enriched/depleted histone modifications compared to unmodified histone H4 in WT cells, as determined by DESeq2. (F – H) Regions of enriched/depleted histone modifications in mutant strains compared to the WT cells, as determined by two sample t-test.  RNA-seq analysis in mutant strains compared to the WT cells, as determined by DESeq2. Download Copyright © 2019 Price et al.2019Price et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Analysis of γ-sites indicates that they are present with three classes of genomic loci: (i) subtelomeric regions, (ii) longer genes, which are often convergent; and (iii) origins of replication  Fig. 4A.We detected statistically significant γH2A enrichment at 13/16 subtelomeres . We suspr = 0.062), γ-sites are more likely to mark genomic regions that are acetylated on H4K16 and H3K9 . We also detected a weak negative correlation between γH2A occupancy and H3K4me3 occupancy  for each histone modification normalized to unmodified histone H4 is displayed within a region spanning ±2 kb around the γH2A peak summits. The blue-to-red color gradient indicates high to low levels of enrichment in the corresponding region. MIN (minimum), −1.5 log2; MAX (maximum), +1.5 log2. Download FIG S4, TIF file, 1.5 MB.Chromatin signature of γ-sites throughout the Copyright © 2019 Price et al.2019Price et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the +URA3 marker gene integrated at different genomic locations (URA3 loss was more frequent when the gene is integrated in proximity to γ-sites (rDNA locus and subtelomeres and less than 15 kb away from a γ-site[s]) than when integrated far away (>30 Kb) from γ-sites  of H3K9Ac and H4K16Ac relative to unmodified H4 in WT cells, and relative to the WT in sir2 Δ/Δ cells, across the 20 kb left and right terminal regions of each of the 8 C. albicans chromosomes. (Bottom) Diagrams of coding genes found at these regions (grey: TLO), according to assembly 22. Download FIG S5, TIF file, 0.9 MB.(Top) Fold enrichment  or downregulated  in strain set1Δ/Δ compared to the WT  of H3K4me3 relative to unmodified H4 in WT cells, and relative to the WT in set1 Δ/Δ cells, across the 20 kb left and right terminal regions of each of the 8 C. albicans chromosomes. (Bottom) Diagrams of coding genes found at these regions (grey: TLO), according to assembly 22. Download FIG S6, TIF file, 0.8 MB.(Top) Fold enrichment  the use of antibodies that recognize modified histones with high specificity and (ii) the use of appropriate biological controls. The specificity of antibodies used in this study has been tested using S. pombe or S. cerevisiae histone mutants lacking the modifiable amino acid  (Our first objective was to obtain “proof-of-concept” epigenomic maps of chromatin modifications associated with gene-rich regions of the H2AS129) , 59. To C. albicans active chromatin conforms to the histone modification pattern reported in other organisms: active genes, associated with high levels of RNA Pol II, are assembled into canonical euchromatin, with H3K4me3 and H3K9Ac associated with promoters and H4K16Ac enriched at gene bodies. Thus, the specific histone modification pattern predictive of active transcription in other organisms is conserved in C. albicans.The results confirm that the experimental approach is valid and that C. albicans genome that are more likely to have properties of heterochromatin. The chromatin in NTS regions of the rDNA locus and in the subtelomeric regions resembles the heterochromatic structure of the budding yeast S. cerevisiae, which lacks H3K9me/H3K27me systems  Sir2 is specifically targeted to subtelomeres and the rDNA locus or (ii) other histone deacetylases act redundantly with Sir2 in regulating hypoacetylation and gene expression at other genomic locations. In support of the second hypothesis, note that the C. albicans genome encodes 9 histone deacetylases and that functional redundancy among these enzymes has been reported in C. albicans and other organisms , anti-H3 , anti-H4 , anti-H3K4me3 , anti-H3K9ac , anti-H4K16ac , and anti-RNA polymerase II .600] = 0.6 to 0.8). Cells (OD600 = 20) were fixed with 1% formaldehyde (Sigma) for 15 min at room temperature. Reactions were quenched by the addition of glycine to reach a final concentration of 125 mM. Cells were lysed using acid-washed glass beads (Sigma) and a DisruptorGenie (Scientific Industries) for four cycles of 30 min at 4°C with 5 min on ice between cycles. Chromatin was sheared to 200 to 500 bp using a BioRuptor sonicator (Diagenode) for a total of 20 min  at 4°C. Immunoprecipitation was performed overnight at 4°C using 2 μl of the appropriate antibody and 25 μl of protein G magnetic Dynabeads (Invitrogen). ChIP DNA was eluted, and cross-links were reversed at 65°C in the presence of 1% SDS. All samples were then treated with RNase A and proteinase K before being purified by phenol/chloroform extraction and ethanol precipitation. Libraries were prepared and sequenced as 50-bp single-end reads on an Illumina HiSeq2000 platform by the Genomics Core Facility at EMBL . All ChIP-seq experiments were carried out in biological duplicate.Chromatin immunoprecipitation with massively parallel deep sequencing (ChIP-seq) was performed as follows. A 5-ml volume of an overnight culture grown in YPAD was diluted into fresh YPAD and grown until the exponential phase was reached . C. albicans cells were combined with S. cerevisiae cells (OD600 = 10) and then fixed with 1% formaldehyde (Sigma) for 15 min at room temperature. After the cells had been fixed, the q-ChIP-seq sample was processed as a single ChIP-seq sample throughout the experiment until completion of DNA sequencing. All q-ChIP-seq experiments were carried out in biological duplicate.Quantitative ChIP-seq (q-ChIP-seq) was performed similarly to the ChIP-seq method, except 5 ml of an overnight culture of 600 = 0.6 to 0.8) using a yeast RNA extraction kit  following the manufacturer’s instructions. RNA quality was checked by electrophoresis under denaturing conditions in 1% agarose–1× HEPES–6% formaldehyde (Sigma). RNA concentrations were measured using a NanoDrop ND-1000 spectrophotometer. Strand-specific cDNA Illumina barcoded libraries were generated from 1 μg of total RNA and sequenced as 50-bp single-end reads using an Illumina HiSeq2000 sequencer by the Genomics Core Facility at EMBL . All RNA-seq experiments were carried out in biological duplicate.RNA was extracted from exponential cultures  were pregrown overnight from independent single colonies. Each culture was diluted to a concentration of 100 cells/μl and grown for nine generations (18 h). Cells were plated on synthetic complete (SC) plates containing 1 mg/ml 5-FOA  and on nonselective SC plates and grown at 30°C. Colonies were counted after 2 days of growth, and the frequency of 5‐FOA‐resistant (5‐FOAr) colony appearance  was calculated using the formula F = m/M, where m represents the median number of colonies obtained on 5‐FOA medium corrected by the dilution factor used and the fraction of culture plated and M the average number of colonies obtained on YPD corrected by the dilution factor used and the fraction of culture plated . For each coverage track, reads per million (RPM) were calculated. The histone modification coverage tracks were normalized to unmodified histone H4, and the RNAPII track was normalized to the corresponding input sample. All coverage tracks were visualized using IGV (Illumina reads were mapped using Bowtie2  to a cuss genome . Reads ts genome  and the s genome . For eac package  69). Ge. GeC. al package , and datsing IGV . Metaplo10.1128/mBio.01376-19.7FIG S73, H3 and H4 IPs in WT strains; (B) H3K9Ac, H4K16Ac IP in WT and sir2 Δ/Δ strains; (C) H3K4me3 IP in WT and set1 Δ/Δ strains; (D) RNA-seq WT and sir2 Δ/Δ strains; (E) RNA-seq in WT and set1 Δ/Δ strains. Download FIG S7, TIF file, 1.7 MB.Heat maps of pairwise correlations between different ChIP-seq and RNA-seq samples. The Pearson correlation coefficients are shown. The gradient red-to-blue color indicates the strength of correlation between the corresponding samples. (A) H3K9Ac, H4K16Ac, H2AS129P, H3K4meCopyright © 2019 Price et al.2019Price et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the (ii) q-ChIP-seq analysis. To isolate the reads that uniquely aligned to the C. albicans genome, the full data sets were first aligned to the S. cerevisiae genome (sacCer3). The unaligned reads were outputted as separate fastq files, and these reads were then aligned to a custom haploid version of assembly 22 of the C. albicans genome , according to the method described previously by Orlando et al. , and the mutant strain track coverage data were normalized to the WT coverage. All tracks were visualized using IGV . Ge. Ge(ii)  package , and RRPsing IGV . Metaplo(iii) RNA-seq analysis. Reads were aligned to a custom haploid version of assembly 22 of the C. albicans genome using HISAT2 , an, an(iii)g HISAT2 . Differekage see  69). Sc. Sc(iii)https://www.ncbi.nlm.nih.gov/bioproject) under BioProject identifier (ID) PRJNA503946. Coverage tracks and genome files used in our analyses are available at https://www.kentfungalgroup.com/price-2019-data.Raw data sets generated and analyzed during this study are available in the BioProject NCBI repository (