In yeast, NuA3 histone acetyltransferase (NuA3 HAT) promotes acetylation of histone H3 lysine 14 (H3K14) and transcription of a subset of genes through interaction between the Yng1 plant homeodomain (PHD) finger and H3K4me3. Although NuA3 HAT has multiple chromatin binding modules with distinct specificities, their interdependence and combinatorial actions in chromatin binding and transcription remain unknown. Modified peptide pulldown assays reveal that the Yng1 N-terminal region is important for the integrity of NuA3 HAT by mediating the interaction between core subunits and two methyl-binding proteins, Yng1 and Pdp3. We further uncover that NuA3 HAT contributes to the regulation of mRNA and lncRNA expression dynamics by antagonizing the histone deacetylases (HDACs) Rpd3S and Rpd3L. The Yng1 N-terminal region, the Nto1 PHD finger and Pdp3 are important for optimal induction of mRNA and lncRNA transcription repressed by the Set2-Rpd3S HDAC pathway, whereas the Yng1 PHD finger–H3K4me3 interaction affects transcriptional repression memory regulated by Rpd3L HDAC. These findings suggest that NuA3 HAT uses distinct chromatin readers to compete with two Rpd3-containing HDACs to optimize mRNA and lncRNA expression dynamics. Covalent modifications of histones, including acetylation, methylation, phosphorylation and ubiquitination play essential roles in eukaryotic transcription. Histone acetylation promotes RNA Polymerase II (RNA Pol II) transcription by inducing the open chromatin structure and disrupting the interaction between DNA and histones. In addition, this modification generates binding sites for factors that regulate chromatin structure and transcription. Histone acetylation is a highly dynamic modification that is regulated by the antagonistic function of histone acetyltransferases (HATs) and histone deacetylases (HDACs) ,2. HistoCo-transcriptional histone H3 methylations at K4 and K36 regulate histone acetylation at distinct regions of genes . Severalin vitro  and nto1  mutants, the delitto perfetto strategy was used (Yeast strains used in this study are listed in was used . The seqWhole cell extracts were prepared with binding buffer (50 mM Tris-HCl (pH 7.5), 0.1% NP-40) containing 150–300 mM NaCl and protease inhibitors. Two hundred and fifty micrograms of whole cell extracts was incubated with 1 μg of biotinylated histone peptides (Anaspec) and 25 μl of streptavidin coupled Dynabeads (Invitrogen) at 4°C for 4 h. Beads were washed five times with 1.5 ml of binding buffer and precipitates were resolved by SDS-PAGE followed by immunoblot analysis.Chromatin immunoprecipitations (ChIPs) were done as previously described  with oliRNA was extracted from cells with hot phenol. Total RNAs was treated with DNase I (Thermo Fisher Scientific) and first-strand cDNA was prepared with 1 μg of total RNA, ReverTra Ace® qPCR RT kit (TOYOBO), and gene-specific primers. cDNA was analyzed by real-time qPCR using THUNDERBIRD SYBR qPCR Mix (TOYOBO) and CFX96 cycler (Bio-Rad). The sequences of oligonucleotides used in this study are listed in Cells expressing TAP-tagged or myc-tagged proteins were grown in YPD to mid-log phase. Cells were lysed using lysis buffer  with protease inhibitors  and glass beads. Protein concentration was quantitated by Bradford assay. For SDS-PAGE and western blot analyses, 15–30 μg of whole cell extracts was used. Proteins were separated in SDS-PAGE and transferred onto a PVDF membrane (Millipore). The blots were visualized on film with SuperSignal West Pico Chemiluminescent Substrate (Thermo Fisher Scientific). Anti-TAP  or anti-Myc  was used for western blot analysis.32P] dATP with only one primer. Hybridization was done in a buffer containing 1% BSA, 7% SDS, 1 mM EDTA (pH 8.0) and 300 mM sodium phosphate buffer (pH 7.2). The membranes were washed with 2× SSC and 0.1% SDS for 20 min and exposed to PhosphoImager.Total RNA was isolated from cells with hot phenol. Ten micrograms of total RNA was separated on an agarose gel and then transferred to nylon membrane (Bio-Rad). Northern blot analysis was done as previously described . The seqWhole cell extracts were prepared with binding buffer (50 mM Tris-HCl (pH 7.5), 0.1% NP-40) containing 150 mM NaCl and protease inhibitors . Three hundred and fifty micrograms of total extracts were incubated with 25 μl of IgG Sepharose  at 4°C for 4 h. After binding, beads were washed five times with 1.5 ml of binding buffer and precipitates were resolved by SDS-PAGE followed by immunoblot analysis.8/ml and subjected to 3-fold serial dilutions in synthetic complete (SC) media lacking any carbon sources. Three microliters of each dilution was spotted on the indicated plates.For spotting analysis, cells were resuspended at 2.5 × 10https://github.com/najoshi/sickle) with reads below a quality score of Q20 and a length <50 nucleotides removed. The reads were aligned to the Saccharomyces cerevisiae reference genome  using TopHat v2.0.13  after ribosomal RNA was depleted using the Ribo-Zero yeast kit (Epicenter) according to the manufacturer’s instructions. Transcriptome sequencing was performed with Illumina NextSeq 500 sequencing platform for 101-mer paired-end reads. Raw sequencing data were filtered using Sickle v1.33 ( v2.0.13 . For strS. cerevisiae genome using Burrow-Wheeler Aligner (BWA) v0.7.5a . Average enrichment profiles were generated using the Cis-regulatory Element Annotation System (CEAS) v1.0.2  v1.0.2 .Although NuA3 HAT has multiple chromatin binding subunits, and the interaction between specific histone modifications and individual subunits is well known, their interdependence or combinatorial actions remains elusive ,17,21,27PDP3, no binding of Sas3-myc, Nto1-TAP and Yng1-TAP to H3K36me3 was seen  and third (#3) promoters were down- and upregulated during galactose incubation, respectively  in set2Δ or rco1Δ background  was differentially regulated by Set2 and Rco1. Although cryptic transcripts from this promoter were slightly increased in rco1Δ during galactose exposure, this pattern was not seen in SET2 deleting cells  in set2Δ also caused the reduction of AAD10 mRNA expression compared with the effect of a single deletion  had no detectable phenotypes in the presence of benomyl. However, yng1 ΔN, but not the W180A mutation, slightly suppressed the growth defect of set2Δ in benomyl-containing media . We identified 112 genes that were positively regulated by NuA3 HAT  for the first galactose exposure. After 120 min, the cells were transferred to glucose for 120 min and back to galactose (15 and 30 min) for the second galactose exposure (Figure RRN11 and TEA1, two TREM genes were downregulated rapidly upon the second galactose exposure in wild-type cells. However, this repression was delayed in the pho23 W305A mutant that fails to bind to H3K4me3 (yng1 W180A mutation was introduced in the pho23 mutant, the kinetics of repression in the double mutant was similar to that in wild-type cells, indicating that the Yng1 PHD finger contributes to TREM (Figure RRN11 and TEA1 promoters was higher in the pho23 W305A mutant than in wild-type cells (Figure pho23 W305A mutant (Figure Recently, we have shown that 544 genes exhibit stronger and faster repression during galactose incubation when the cells were previously exposed to the same carbon source. This phenomenon is termed TREM and is partly regulated by Rpd3L HDAC ,46. The e Figure . As showalactose 0 min forM Figure . Next, Cs Figure . Consistt Figure . Sas3–6Ht Figure . These rHistone acetylation and deacetylation at promoters and within coding regions play important roles in initiation and elongation of RNA Pol II transcription ,2. UnlikSet2-mediated H3K36 and its downstream effector, Rpd3S HDAC, inhibit RNA Pol II elongation, initiation from internal cryptic promoter and histone exchange within transcribed regions ,9,40,47.pho23 mutant were reversed by mutating the Yng1 PHD finger (Figure We recently showed that many yeast genes display memory of their previous transcriptionally inactive state to allow effective suppression if they are no longer required for cellular functions. This TREM is important to rapidly turn off unnecessary genes and utilize cellular energy and the transcriptional machinery for genes supporting cell survival and fitness ,46. Rpd3r Figure  and C. TChromatin modifying enzymes or complexes have multiple subunits that interact with distinct histone modifications. Although their exact functions remain elusive, NuA4 HAT includes Yng2 PHD finger and Eaf3 chromodomain recognizing H3K4me3 by Set1 and H3K36me3 by Set2, respectively ,51. Rpd3PDP3 or YNG1 was deleted, indicating that a portion of NuA3 HAT includes both Yng1 and Pdp3 (Figure It has been suggested that NuA3 HAT exists in two distinct complexes: NuA3a containing Yng1 acting at promoters and NuA3b with Pdp3 functioning during transcription elongation . Consist3 Figure  and E. U3 Figure . Based o3 Figure . DetermiThe RNA sequencing data sets that support the findings of this study have been deposited in the Gene Expression Omnibus with the accession code GSE148674.gkaa781_Supplemental_FileClick here for additional data file.


The NRF transcription factors NRF1, NRF2, and NRF3, are a subset of Cap‘n’collar transcriptional regulators which modulate the expression of genes harboring antioxidant-response element (ARE) sequences within their genomic loci. Despite the emerging physiological importance of NRF family members, the repertoire of their genetic targets remains incompletely defined. Here we use RNA-sequencing-based transcriptional profiling and quantitative proteomics to delineate the overlapping and differential genetic programs effected by the three NRF transcription factors. We then create consensus target gene sets regulated by NRF1, NRF2, and NRF3 and define the integrity of these gene sets for probing NRF activity in mammalian cell culture and human tissues. Together, our data provide a quantitative assessment of how NRF family members sculpt proteomes and transcriptomes, providing a framework to understand the critical physiological importance of NRF transcription factors and to establish pharmacologic approaches for therapeutically activating these transcriptional programs in disease. The nuclear factor (erythroid 2)-like (NRF) transcription factors are a family of basic leucine zipper (bZIP)-containing transcriptional regulators related to nuclear factor erythroid 2 , that are characterized by a conserved 43 amino acid Cap‘n’collar (CNC) motif . VertebrUnlike NFE2, which plays a developmental role in promoting megakaryopoiesis, the NFE2-like NRF transcription factors inducibly respond to the presence of cellular stressors . A relatUnlike NRF2, the functional importance of other NRF transcription factors is less well defined. An initial focus of NRF1 research relied on the early observation that NRF1 deficient animals die late during embryonic development due to insufficient hepatic erythropoiesis . In contThe least explored of the NRF transcription factor family is NRF3, likely stemming from the observation that knockout animals display no obvious phenotypes and do not additively contribute to the deleterious effects of animals lacking NRF2 or p45 . RecentlC. elegans possess only the CNC-containing gene SKN-1 (Skinhead family member 1) whereas flies contain only the CNC-containing factor CncC  1) by RT-qPCR, confirming that the designed transgenes are capable of upregulating specific target transcripts. This selectivity was further demonstrated by showing overexpression of NRF1 and NRF2 in HEK293T cells promoted robust activation of co-transfected luciferase reporters PSMA4-ARE-LUC and NQO1-ARE-LUC, respectively (PSMA4) and NQO1, which we previously demonstrated to specifically capture pharmacological activation of NRF1 and NRF2 activation in cells, respectively [We envisioned a cellular platform in which transient overexpression of constitutively active versions of NRF transgenes might allow for interrogation of their impact on the cellular transcriptome and proteome. As such, we first designed overexpression constructs wherein the encoded NRF transgenes would be minimally influenced by cellular regulatory machinery. As NRF1 and NRF3 are anchored N-terminally within the ER lumen, we constructed expression plasmids lacking the N-terminal ER-targeting sequence, a modification that allows for cytoplasmic translation and thus bypass of the deglycosylation and cleavage steps required for exit from the ER ,23. Uniqy active C. Furtheectively D. These ectively . NRF3 ovTo identify the transcripts, protein levels, and genetic programs regulated by NRF overexpression, we coupled our overexpression platform to a data acquisition and analysis pipeline consisting of RNA-sequencing in conjunction with mass spectrometry-based quantitative proteomics . This app < 0.05). Specifically, we found that expression of NRF1  and NRF2  induced robust transcriptional programs in cells, comprising 7.2% and 9.3% of the active transcriptome, respectively. Interestingly, we observed considerably fewer transcripts were regulated by NRF3 overexpression , suggesting that NRF3 may enact a more muted transcriptional profile than NRF1 or NRF2 in HEK293T cells. From GO term-based analyses using DAVID, transcripts upregulated by all NRFs were found to be involved in stress responses, proteostasis-associated biological pathways, and nucleotide-binding, whereas downregulated transcripts were associated with RNA metabolism and development  demonstrated the robustness of our overexpression systems at manipulating the transcriptome of target cells , and DESelopment , top. Aselopment . NRF1-up2 fold change of the RPKM values in cells overexpressing NRF2 relative to a vector control displayed a moderate correlation (r2 = 0.53) between differentially expressed transcripts identified by the Liu RNA-seq study and this work (2 fold changes at the protein and transcript level (r2 = 0.86)  . This ul = 0.86) .p-value < 0.002; p < 0.01; We next analyzed our RNA-seq data using GSEA to determine which transcriptional programs were most significantly modulated in response to NRF2 overexpression. For this analysis, we used a curated list of 6960 gene sets from the Molecular Signatures Database (MSigDB), a collection that spans many cellular pathways, treatment conditions, disease states, and cell types . Of thes2 fold change of the RPKM values in cells overexpressing NRF1 relative to vector controls demonstrated a moderate correlation (r2 = 0.49) between the Liu study and ours (2 fold changes at the protein level (r2 = 0.49)  C. These PSMB5, PSMC3, PSMD6), NRF1 activation additionally promotes the upregulation of gene products involved in protein folding, including multiple classes of chaperones , IL12 Signaling  and MYC targets . GTEx catalogs the RNA-sequencing-based quantification of transcript levels from 54 postmortem tissues derived from over 1000 individuals, providing a unique opportunity to probe the coordinated regulation of specific genes across multiple tissues. Given the comprehensiveness of this dataset, we leveraged the GTEx database to determine if the high confidence, consensus gene sets generated for NRF transcription factors were co-regulated across tissues using correlative analyses for co-expression. Correlation coefficients were calculated between the expression levels of each pair of genes in the NRF1, NRF2, and NRF3 gene sets in the frontal cortex of the brain, the liver, and the left ventricle of the heart. The gene sets for each transcription factor were then clustered in each tissue into three groups using k-means clustering based on the Euclidean distance between each gene’s correlation coefficients. Surprisingly, among the thirty genes categorized as upregulated by NRF2 overexpression, we observed significant differences in gene co-expression across tissues ; Table 1ene sets . These rHere, we have used a transient overexpression system to define the functional targets of the NRF transcription factors NRF1, NRF2, and NRF3. Our work serves as a complement to the study of Liu et al., which aimed to profile the transcriptional targets of the NRF family. Here we have used both transcriptomics and quantitative proteomics to define how NRF1, NRF2, and NRF3 sculpt the proteomes and transcriptomes of mammalian cells. Collectively, we defined genetic targets corresponding to overlapping and distinct transcriptional targets among NRF transcription factors and used an analysis pipeline which incorporates transcriptional profiling, proteomics, and the data of Liu et al. to define high confidence target lists for NRF1, NRF2, and to a lesser extent NRF3. The genetic programs described herein will likely be of key utility in further defining the role of the NRF transcription factors.One of the most salient observations from our profiling work involved the composition of the high confidence NRF1 target gene set. Included in this repertoire of NRF1 targets were a number of chaperones including heat shock proteins  and the chaperonin TCP complex , a gene set which corresponded with a heat shock response by GO analysis. Previously, NRF1 has been described as mediating the proteasomal ‘bounce back’ response to proteasome inhibitor treatment by augmenting the transcription of components of the 20S proteasome and 19S regulatory complex, but a role in mediating the transcription of chaperones has not been described . Our worSurprisingly, our transcriptional profiling results suggested that NRF3 enacted a considerably smaller transcriptional program relative to NRF1 or NRF2. This observation as well as the moderate overlap in NRF3 targets between this work and the work of Liu et al. may indicate that NRF3 activates a tissue-specific transcriptional program or requires additional yet undescribed co-regulatory machinery for robust transcriptional activation. Interestingly NRF3 is not broadly expressed across tissues like NRF1 and NRF2. Instead, NRF3 displays uneven tissue distribution, with the highest expression in the placenta and other female-specific tissues. Ultimately, further work understanding the upstream stimuli which promote NRF3 activation and delineating in which cell types and in what physiological contexts NRF3 is active will help uncover the role of this cryptic transcription factor.As this work represents the first time that consensus genetic targets derived from multiple studies could be defined for the NRF family members, we sought to understand how the consensus sets generated in this work might be co-regulated across human tissues. In the simplest circumstance, one might expect the expression levels of genes within each set to be a function of the active levels of a given NRF member in that tissue, which would result in high correlation coefficients among all or nearly all genes within each gene set. Unexpectedly, we found that most NRF1, NRF2, and NRF3 core consensus gene sets were not co-regulated basally in tissues but instead displayed tissue specific preferences in their expression, as co-regulated genes within a set within one organ were often not correlated within another organ. This observation likely spells out the existence of other transcription factors or epigenetic states playing a predominant role in regulating these genes basally in tissue. Often stress-responsive transcriptional programs are treated collectively, as investigators frequently use one or two representative transcripts to report on the entirety of a transcriptional program. This data suggests caution in pursuing such an approach, as our data suggest that even genes perceived to be core transcriptional targets within a given set are not basally co-regulated. Thus, we believe this result to be of critical importance in studying the regulation of stress-responsive signaling in tissue. Instead, our data provide a preliminary roadmap for evaluating the expression and the relationships of NRF-driven transcripts in tissue.Here, we have used a transient overexpression system in conjunction with RNA-seq and proteomic profiling to annotate the functional genetic programs enacted by the Cap‘n’collar transcription factors NRF1, NRF2, and NRF3. Integrating the work of a complementary study performed by Liu et al., we have defined the consensus genetic targets of the NRF family members from experiments performed with human cell lines. Analysis of the comprehensive tissue expression GTEx indicated these gene sets are likely not basally co-regulated in human tissues but instead display organ-specific co-expression patterns. Together, our data provide a useful resource for future lines of inquiry aimed at understanding the roles of NRF transcription factors in normal physiology and disease.


Palladin is a ubiquitous phosphoprotein expressed in vertebrate cells that works as a scaffolding protein. Several isoforms deriving from alternative splicing are originated from the palladin gene and involved in mesenchymal and muscle cells formation, maturation, migration, and contraction. Recent studies have linked palladin to the invasive spread of cancer and myogenesis. However, since its discovery, the promoter region of the palladin gene has never been studied. The objective of this study was to predict, identify, and measure the activity of the promoter regions of palladin gene.Palld isoforms and revealed the presence of a variety of transcriptional regulatory elements including TATA box, GATA, MyoD, myogenin, MEF, Nkx2-5, and Tcf3 upstream promoter regions. The transcriptome profiling approach confirmed the active role of predicted transcription factors in the mouse genome. This study complements the missing piece in the characterization of palladin gene and certainly contributes to understanding the complexity and enrollment of palladin regulatory factors in gene transcription.By using promoter prediction programs, we successfully identified the transcription start sites for the  Palld) was described as a complex structure that spans approximately 400 kb and is composed of 25 exons coding for several isoforms  vector  with a downstream tagged luciferase gene. DNA fragments of palladin isoform 90-kDa, 140-kDa, and 200-kDa were respectively inserted into SacI and BglII, KpnI and EcoRV, and KpnI and HindIII sites of the multiple cloning region of the vector.Multiple DNA fragments of predicted C2C12 cells were transfected with various palladin promoter constructs tagged with a luciferase gene or pGL4.74  empty vector  as a control plasmid by using Lipofectamine 2000 reagent (Invitrogen) and further incubated for 4 days following differentiation induction.Renilla luciferase activity for each construct.The Dual-Glo Luciferase Reporter Assay System Kit (Promega) was used according to the manufacturer’s protocol. Luminescence of each sample was quantified on a Tecan Infinite F200 Pro ELISA reader (USA). Values were normalized to the control vector, and the results were expressed as the ratio of firefly luciferase activity to Total RNA from C2C12 cells was extracted by using High Pure RNA Isolation Kit (Roche) according to the manufacturer’s instructions. The RNA concentration was determined using NanoDrop 2000 spectrophotometer (Thermo fisher) and RNA purity and integrity was evaluated using Experion™ Automated Electrophoresis System. The samples were submitted to Yourgene Bioscience (Taiwan) for sequencing. The strand specific Poly-A mRNA libraries were generated and sequenced by using Illumina HiSeq™ 4000  platform and procedure. Quality trimming was performed and paired-end reads were mapped to the mouse reference genome GRCm38.p6 using TopHat software. Assembled transcripts were acquired by using Cufflinks and transcriptome was assembled via Cuffmerge. Gene expression levels were normalized by computing the Fragments Per Kilobase of transcript per Million mapped reads (FPKM). Because of the downstream usage of sequencing results not discussed in the present work, the data have not yet been made available in a public repository. For sequencing data partially used to support our analysis, raw data can be accessed on request.−∆Ct, a derivative of Livak method [The same RNA pools used for RNA seq were employed for qPCR. Complementary DNA (cDNA) was synthesized using iScript cDNA Synthesis Kit (Bio-Rad). The primers used were listed in Additional file k method .p ≤ 0.05 were considered statistically significant.Two-way repeated-measures analysis of variance (ANOVA) with Bonferroni corrections for multiple group comparisons at various time point were calculated. Values of To predict and identify the nature of regulatory elements controlling the activation or repression of palladin isoforms, gene fragments of 3000 bp upstream the first exon were generated and the palladin gene promoter region analyzed. The promoter regions were predicted to be within the − 2917 bp/+ 83 bp, − 2721 bp/+ 279 bp, and − 2871 bp/+ 129 bp, respectively for palladin isoform 90-kDa, 140-kDa, and 200-kDa. Promoter 2.0 Prediction Server data indicate the presence of at least one putative eukaryotic polymerase II binding site within each isoform  and opossum (Monodelphis domestica) for palladin 90-kDa isoform however the mouse shared three conserved regions with the human (Homo sapiens) and six intronic regions with the chimpanzee (Pan troglodytes)  Fig. b. Six inRenilla luciferase activity was measured for the palladin 90-kDa isoform -Mef2a, Mef2b, Mef2c, homeobox protein Nkx2-5, and Tcf3 in an inclusive way , GATA, MyoD, myogenin, MEF, Nkx2-5 and E47 sites. Most of the predicted transcription factors are involved in the maturation process of myoblast cells into myofibers. As reported previously, C2C12 cells myoblast remodeling during differentiation requires activation of myogenic regulatory factors (MRF) for example myogenic termination gene (MYOD), myogenin, MRF4, and myogenic factor 5 (MYF5) –18. In selopment . Additioelopment . In a caelopment . These fPalld gene. Thus, these results might be considered as a starting point for a more explorative study.Transcriptome analysis of C2C12 cells’ ongoing differentiation revealed the functional role of elements predicted to regulate palladin isoform gene. However, at this point in the investigation, we are unable to experimentally attest the presence of the predictive regulatory elements within the promoter regions of the Additional file 1: Table S1. Oligonucleotides used for qPCR.Additional file 2: Table S2. Transcription start sites (TSS) prediction by Promoter 2.0 Prediction Server.Additional file 3: Figure S1. Transcription factors general expression.Additional file 4: Figure S2. Expression of palladin isoforms and myogenic genes.


Enhancers are distal regulators of gene expression that shape cell identity and control cell fate transitions. In mouse embryonic stem cells (mESCs), the pluripotency network is maintained by the function of a complex network of enhancers, that are drastically altered upon differentiation. Genome-wide chromatin accessibility and histone modification assays are commonly used as a proxy for identifying putative enhancers and for describing their activity levels and dynamics.Here, we applied STARR-seq, a genome-wide plasmid-based assay, as a read-out for the enhancer landscape in “ground-state”  and “metastable”  mESCs. This analysis reveals that active STARR-seq loci show modest overlap with enhancer locations derived from peak calling of ChIP-seq libraries for common enhancer marks. We unveil ZIC3-bound loci with significant STARR-seq activity in SL-ESCs. Knock-out of Zic3 removes STARR-seq activity only in SL-ESCs and increases their propensity to differentiate towards the endodermal fate. STARR-seq also reveals enhancers that are not accessible, masked by a repressive chromatin signature. We describe a class of dormant, p53 bound enhancers that gain H3K27ac under specific conditions, such as after treatment with Nocodazol, or transiently during reprogramming from fibroblasts to pluripotency.In conclusion, loci identified as active by STARR-seq often overlap with those identified by chromatin accessibility and active epigenetic marking, yet a significant fraction is epigenetically repressed or display condition-specific enhancer activity. Mouse embryonic stem cells (mESCs) derived from the inner cell mass of the early developing embryo can propagate indefinitely in vitro and are pluripotent –3. PluriIt is well appreciated that gene expression is regulated by a complex network of regulatory elements that promote (promoters) and enhance (enhancers) RNA transcription through proximal and distal binding of key transcription factors (TFs), respectively . Given tDespite significant progress, it remains difficult to estimate enhancer potency from epigenetic marking. For example, “poised enhancers,” that are occupied by P300 and establish significant interactions with promoters of lineage specifying genes in ESCs, are repressed by H3K27me3 . Seed enHere, we applied whole genome STARR-seq in mESC cultured in 2iL or SL. Comparing genome-wide quantitative STARR-seq active loci and chromatin marking reveals a class of SL-specific enhancers located near naïve and primed pluripotency genes that are activated in naked DNA by ZIC3, but remain repressed within the chromatin context of SL-ESCs. Furthermore, we detected P53-driven enhancers with strong STARR-seq signal, but very low chromatin accessibility and histone marking. These enhancers gain H3K27ac upon treatment with Nocodazole or transiently during reprogramming. Taken together, our STARR-seq assay reveals active, dormant, and chromatin masked enhancers.p value < 0.05 in both biological replicates, which included a total of 25,616 peaks. These STARR-seq peaks were reproducible between biological replicates and mostly located in intronic and intergenic loci  in fact overlap genomic loci tentatively labeled as “active” enhancers, henceforth referred to as class C1-loci. Still, 7072 STARR-seq peaks do not display an active chromatin signature, but possess enhancer activity in the reporter assay suggesting that these are inactive/masked in a chromatin context (termed C2-loci). Finally, 1950 APK-elements displayed no STARR-seq signal in any of the four replicates  with STARR-seq enrichment at C1-loci, indicating that enhancer strength cannot be easily predicted from these chromatin features , occupied by P300 and flanked by H3K27ac (henceforth APK-elements), a chromatin signature associated with active regulatory elements . This anNext, we set out to determine which TFs were associated with C1-, C2-, and C3-loci. To this end, we computed enrichment for TF motifs in each class relative to motifs found in a matched background set  and the fraction of C3-loci that overlap a CpG island is higher (8%) compared to C1- (5%) or C2-loci . Differential STARR-seq analysis using a model that accounts for differences in sequencing depth and input coverage  is the highest expressed TCF/LEF member in ESCs, with similar expression in 2iL- and SL-ESCs , but not a single ZIC3 binding site had higher STARR-seq signal in 2iL  showed that genetic ablation of Zic3 shifts the expression profile of cells cultured in SL, but barely affects that of cells cultured in 2iL . In contrast, we detected 945 DEGs in Zic3−/− cells cultured in SL compared to WT, of which 70% were upregulated in the Zic3 KO cells , in line with results reported for Zic3 RNAi ECSs cultured in SL [Nanog , Klf2 , Tcfcp2l1 , Esrrb , and most prominently, Prdm14  are significantly downregulated in Zic3−/− SL-ESCs, although most of them are still highly expressed  and primed  pluripotency factors, combined with upregulation of DNA methyltransferases (DNMTs) discriminates cluster 2 from cluster 3. The fourth cluster comprising Zic3−/− ESCs consists of cells cultured in SL that express high levels of the endodermal transcription factors and lost expression of pluripotency markers  endodermal fate, which explains the dramatic upregulation of these genes in our bulk RNA-seq.In summary, our STARR-seq data corroborates that TCF3 represses enhancers in SL, and to a much lower degree in 2iL. More striking is the significantly elevated STARR-seq signal at enhancers occupied by ZIC3 when ESCs are cultured in SL as compared to 2iL. Luciferase experiments show that enhancer activity is lost for STARR-seq signal at C2-loci Fig. d shows tDNMT genes causes global loss of CpG methylation  motif are predictive of elevated DNA methylation . This result agrees with the finding that in particular IAPs gain expression upon TRIM28 deletion [To investigate whether H3K9me3 is associated with specific TFs, we analyzed DNA motifs enriched at C2 STARR-seq peaks coinciding with H3K9me3 peaks as compared to C2-loci without H3K9me3. Motifs enriched and co-occurring at H3K9me3 repressed enhancers are TBX20, FOXP1, CRE, and also NRF1. In contrast, AP-1, YY1, and in particular P53 are found in loci without active marks that are also without H3K9me3  revealed a noticeable enrichment for the P53 motif in the absence of H3K9me3  are void of active marks. Finally, APK-peaks well covered by the STARR-seq input libraries, but lacking STARR-seq signal  were examined. C3-loci overlapping promoter regions of transcripts annotated by Refseq and Gencode were removed, but of the remaining C3-loci ~ 60% overlapped a CAGE peak classified as a TSS by the Phantom consortium. The properties of C1–C3 loci and the TFs associated with each class are summarized in Fig. In this study, we used STARR-seq to generate a compendium of genomic loci with enhancer potential in murine ESCs. We found that 25%  of our STARR-seq peaks overlap an ATAC-seq accessible region, bound by P300 and covered by H3K27ac (APK-peak). To examine the chromatin state of the remaining 75% of our STARR-seq peaks, we segmented the mouse genome using chromatin accessibility assays, histone modifications, and TF ChIP-seq collected for 2iL- and SL-ESCs. Using this segmentation, we separated STARR-seq peaks as “active”  or inactive (C2-loci) in each of the culture conditions. Although we now found that 72%  of the STARR-seq peaks are embedded in “active” chromatin, the remaining 28%  endodermal lineage [Zic3 has also been shown to interact with primed pluripotency genes like Otx2 and Oct4 [Zic3 may therefore block the transition towards epiblast-like cells, resulting in cells that adopt an early endodermal cell fate when their pluripotency network is compromised.To validate the results of the STARR-seq approach and to derive additional insights, we examined C1 STARR-seq peaks with the differential signal between 2iL- and SL-ESCs. Motif enrichment analysis pointed to an overrepresentation of TCF/LEF motifs in STARR-seq active loci in 2iL. This result corroborates our STARR-seq approach as TCF/LEF depression is a well-established feature of 2iL-ESCs , 53 asso lineage . Our sined in SL  and coul lineage , 79. Zicand Oct4  and was and Oct4 . Loss ofA second class of STARR-seq peaks (class C2) have a chromatin signature associated with gene repression. These loci have higher DNA methylation and/or H3K9me3 occupancy compared to actively marked loci. Specifically, we found a significant STARR-seq signal at the CpG-rich binding sites of NRF1, a TF whose occupancy is restricted by DNA methylation . GeneticZic3 increases the fraction of SL-cultured cells that differentiate and remarkably, these cells adopt an endodermal cell fate. Finally, STARR-seq identifies chromatin-masked enhancers at which TF occupancy is impeded by repressive marks as well as dormant enhancers that are occupied by P53, but only gain H3K27ac in specific conditions. Our compendium of STARR-seq enhancers will be beneficial for future stem cell research, and we expect that a similar comparison between STARR-seq and chromatin-based assays will yield important regulatory insights in other cell types.In conclusion, our STARR-seq assay revealed enhancers that recapitulate known differences between 2iL- and SL-ESCs. Active enhancers are accessible, marked with active histone modifications, and bound by pluripotency- and other known transcription factors. STARR-seq shows that TCF3 represses enhancers and that ZIC3 act as an activator in SL, but not in 2iL. Genetic loss of Mouse embryonic stem cells E14Tg2a (E14) were purchased from ATCC and cultured without feeders in the presence of LIF. SL-ESCs were maintained in DMEM with 15% ES grade fetal calf/bovine serum. 2iL-ESCs were maintained in Ndiff227  medium supplemented with 1-μM MEK inhibitor PD0325901 and 3-μM GSK inhibitor CHIR99021. Zic3 KO and Trp53 KO ESCs were tested by western blot. E14 cells were purchased from ATCC and were not authenticated.The mouse genome was isolated and sonicated. Sheared genomic DNA fragments of 700 ~ 1200 bp were size selected on 1% agarose gel to construct the STARR-seq plasmid pool following the STARR-seq protocol . IlluminFour hundred micrograms of plasmid pool was transfected into 400 million cells with Lipo3000 . The medium was refreshed after 12 h and cells harvested after 24 h. We isolated total RNA with the RNeasy maxi prep kit  from half of the harvested cells (~ 0.2 billion cells) and incubated the total RNA with Dynabeads Oligo-dT25  to isolate polyA+ RNA. The polyA+ RNA was concentrated following the clean-up procedure of RNeasy Mini kit  and was transcribed in 24 reactions with reporter-RNA specific primer and cDNA amplified with reporter-specific primers for 15 cycles . The ampThe reporter constructs were isolated from the second half of the transfected cells with Plasmid Maxi Kit  as input. Every 100 ng input was amplified using input-specific primers  and then size-selected (1600 bp ~ 2200 bp) on 1% agarose gel in 10 reactions. The indexes were added to purified input DNA following the same indexing procedure with STARR cDNA in 10 reactions and purified on 1% agarose gel.Cells were fixed with 1% formaldehyde for 10 min followed by 0.125 M glycine to stop the reaction. The fixed cells were washed with PBS twice and then scraped and collected into 50 ml falcon tubes. Every 10 million cells were pelleted and snap-frozen in liquid nitrogen and then stored in − 80 freezer for ChIP. The cells were lysed in 300 μl 1% SDS buffer  and sonicated to 200 bp ~ 600 bp. Three hundred microliters of fragmented chromatin was diluted with 3 ml IP buffer  and pre-cleared with Protein A/G beads for 1 h on a rotating wheel at 4 °C. The used beads were trashed and the pre-cleared chromatin was incubated with antibody and unused protein A/G beads overnight at a 4 °C rotator. Beads were washed successively in 4 °C with low salt washing buffer , high salt washing buffer , LiCl washing buffer , and TE buffer . Beads were transferred to new Eppendorf tubes and washed one more time with TE buffer. Washed beads were resuspended in 200 μl elution buffer , and then, the tubes were placed in the thermo mixer at 65 °C and 1000 rpm for 30 min. Supernatants were transferred to new tubes that contained 8 ul 5 M NaCl and were de-crosslinked overnight in a thermo mixer at 65 °C. DNA was purified with MinElute PCR Purification Kit  following treatment with RNase and Proteinase K. Libraries for sequencing were constructed following the KAPA Hyper Prep Kit . The antibodies used in this study were P53 (Novocastra laboratories #NCL-p53-CM5p) and ZIC3 (Abcam #222124).RNA was extracted using the RNeasy Mini Kit (Qiagen #74106). One microgram of RNA was used to construct the library using the KAPA RNA HyperPrep Kit with RiboErase (KAPABiosystems #KK8560).For scRNA-seq, cells were processed using SORT-seq . Cells crispr.mit.edu and Benchling) were used to design guide RNAs that were cloned into the vector (Table SNickase Cas9n was used to delete the target genome regions. The vector was purchased from Addgene (pSpCas9n(BB)-2A-Puro (PX462) V2.0, #62987). Online tools  with fresh added EDTA-free protease inhibitor cocktail (Roche #4693132001). Protein concentration was measured with the Bio-rad protein assay (Bio-Rad #500-0006). Cell extracts were loaded and separated by 10% SDS-PAGE gel, electrotransferred to nitrocellulose membranes and incubated with primary antibody (1:1000 diluted in blocking buffer) overnight at 4 °C, and then washed with 1x TBST for 5 min 5 times. Next, the membrane was incubated with a second antibody at room temperature for 1 h and then washed. ECL substrate (Thermo #32106) was added and images were acquired. The primary antibodies used in the study are ZIC3 (Abcam #222124) and GAPDH (Abcam #8245). The secondary antibodies used are Swine anti-Rabbit HRP (Dako #P0217) and Rabbit anti-Mouse HRP (Dako #P0161).BWA MEM [EstimateLibraryComplexity), GC% bias (CollectGcBiasMetrics), insert size metrics (CollectInsertSizeMetrics), and PCR duplication (MarkDuplicates) were estimated with PICARD Tools.bedtools (v2.27.1) [STARR-seq libraries were sequenced on an Illumina Nextseq 500. Paired-end FASTQ files were mapped with BWA MEM  using stv2.27.1)  using thMACS2 [p = 1e−8.” The number of input reads, unique (non-duplicated) STARR-seq reads, and total STARR-seq reads within each library were counted with featureCounts [# successes = total number of STARR-seq reads per peak;# trials = max , using the rationale that each unique STARR-seq fragment must have been in the input library;Probability of success = 0.5 .Initial STARR-seq peaks were called for each library with MACS2  using threCounts . Input rp values were corrected for multiple testing using the Benjamini-Hochberg correction [VGAM R-package [p value < 0.05 in both individual libraries.To determine a suitable enrichment- and FDR threshold, 1 million GC% matched random sequences with median length matching the STARR-seq peaks were sampled per library. Binomial rrection . Enrichm-package . A 3-folTo estimate the relation between the number of significantly enriched enhancers and library sequencing depth we first merged the two 2iL and SL libraries. Next, we downsampled the BAM files to a fraction  of the original size and repeated the enhancer calling procedure above.p value < 0.05) was generated by concatenating the 2iL and SL peak BED files, followed by the “bedtools merge” command on the concatenated peak list. This “merged” list of 25,616 loci was used as follows:Identify genomic loci with STARR-seq activity in 2iL or SL;Use these genomic loci to compute differential STARR-seq activity between 2iL- and SL.The union of the STARR-seq peaks called in 2iL- and SL /sizeFactor(input_j) * input_ij,DEseq2 paper [DESeq2 R-paclage. Input_ij is the maximum of the input reads and the unique (non-duplicated) STARR-seq reads, using the rationale that a fragment cannot give STARR-seq signal if it was not in the input library. Finally, NF_j is scaled over the four libraries to have geometric mean = 1 following DESeq2 recommendation. STARR-seq peaks with an absolute fold change ≥ 2.5 and p < 0.05 between SL and 2iL were considered differential.where the sizeFactor function is defined in the q2 paper  and impl2 transformed the FvR values and linearly scaled the n = 39 values to the STARR-seq log2 enrichment values.Luciferase signal was computed as the Firefly vs Renilla (FvR) signal averaged over 3 technical replicates, normalized by the FvR at a control region. Two batches of experiments were performed and batch-corrected using linear regression. To compare luciferase signal with STARR-seq, we logBowtie2 [MACS2 [p value threshold of 0.1, followed by IDR [MACS2 using a p value threshold of 1e−8. For H3K27me3 and H3K9me3, we used MACS2 in broad peak calling mode with parameters p = 1e−12 and broad-cutoff = 1e−5, based on a manual inspection in the IGV browser. Similarly, H3K27ac peaks in iPSCs were called using MACS2 in broad peak calling mode with parameters p = 1e−15 and broad-cutoff = 1e−7. In all cases, MACS2 was used with input DNA as control. Similar to the STARR-seq loci, bedtools merge [featureCounts [DESeq2 [deeptools bamCoverage [ATAC-seq, ChIP-seq libraries, and public data were mapped using Bowtie2 . In the 2 [MACS2  in narrod by IDR  with a tls merge  was usedreCounts , followe [DESeq2 . Bigwig Coverage  and normNucleoATAC [Danpos2 [Danpos2 nucleosome summit value within 74 bp from a peak summit was used.Nucleosome occupancy profiles of P53-bound and control loci were derived from ATAC-seq and MNAse-seq. In brief, our ATAC-seq libraries were merged with public ATAC-seq data of 2iL- and SL-ESCs to reach a better coverage  were bulk-downloaded on April 30, 2019. Finally, peak sets were intersected using the R/Bioconductor package icRanges .STAR [DESeq2 with standard settings.RNA-seq libraries were mapped to the Gencode M1-NCBIM37 (mm9) reference genome using STAR . Read noSTAR. UMI counts for a whitelist of 384- cell barcodes were determined using a custom R-script. Read normalization and cluster analysis were performed with the Seurat v3.0 R-package [Single-cell RNA-seq reads were first mapped to the Gencode M1-NCBIM37 (mm9) reference genome using -package .Bismark [Whole genome bisulfite data for 2iL and SL-ESCs previously generated in our lab  were proBismark . The fraAdditional file 1: Figure S1. Quality assessment and chromatin-based classification of STARR-seq (lacking) loci. Figure S2. TCF3 and ZIC3 are associated with STARR-seq peaks induced in 2iL- and SLESCs respectively. Figure S3. C2-loci are active STARR-seq regions due to the absence of DNA methylation and repressive histone modifications. Figure S4. P53-bound STARR-seq loci are characterized by low chromatin accessibility and are categorized into active (class C1) and inactive enhancers (class C2) based on the presence/absence of active histone modifications.Additional file 2: Table S1. Public data.Additional file 3: Table S2. Primers and loci.Additional file 4: Table S3. STARRseq 2iL SL ESCs.Additional file 5: Table S4. Zic3 WT KO RNAseq.Additional file 6: Table S5 Trp53 WT KO RNAseq.Additional file 7: Table S6. Cistrome ChIPseq enrichment.Additional file 8. Review history.


Plasmodium falciparum. This parasite possesses the ability to convert in multiple stages in various hosts, cell types, and environments. Recent findings indicate that P. falciparum is talented at using efficient and complementary molecular mechanisms to ensure a tight control of gene expression at each stage of its life cycle. Here, we review the current understanding on the contribution of the epigenome, atypical transcription factors, and chromatin organization to regulate stage conversion in P. falciparum. The adjustment of these regulatory mechanisms occurring during the progression of the life cycle will be extensively discussed.Over the past decade, we have witnessed significant progresses in understanding gene regulation in Apicomplexa including the human malaria parasite,  Plasmodium, belonging to the phylum Apicomplexa. Although, five plasmodial species can infect human, P. falciparum is associated with the greatest morbidity and mortality.Malaria affected 228 million people and 405,000 deaths in 2018  and remaP. falciparum is complex and can be divided in two parts: the sexual phase in mosquito vector and asexual phase in human liver and red blood cells. After a bite by an infected female Anopheles, sporozoites are injected into the human and invade hepatocytes. This asymptomatic phase leads to the multiplication of parasites, which are released into the bloodstream and initiate the intraerythrocytic developmental cycle (IDC) inside red blood cells. During this cycle, merozoite develop to immature ring and progress to mature schizont, before dividing into 16 to 32 new merozoites, which immediately invade new erythrocytes. A portion of these parasites differentiate and mature into male and female gametocytes, and are ingested by another female mosquito. Thousands of sporozoites will be produce and migrate to the salivary glands to complete the cycle.The life cycle of Plasmodium species  upstream of the transcription start site (TSS), the binding site of the preinitiation complex (PIC), while silenced genes exhibit a higher nucleosome occupancy in their promoter regions hampering the interaction of the PIC  on the protruding N-terminal tails of histones supplement this regulatory mechanism. Histone modifications are relatively well conserved in eukaryotes including trometry . A more  species . Quantit species , and are species . These h species . Their a species . These p species . Inhibitlciparum , making asmodium . As geneP. falciparum using histone peptide pull-down coupled to quantitative mass-spectrometry  as epigenetic regulators has emerged in most eukaryotes including protozoan parasites. By definition, ncRNAs are split into two groups, small (sncRNA) and long ncRNA (lncRNA) based on their respective length. In higher eukaryotes, lncRNAs influence various essential cellular processes such as chromosome maintenance, epigenetic remodeling, transcription, translation and control of protein activity . In P. fcterized . Among tcterized . We descPlasmodium. Initially, fluorescence microscopy and fluorescence in situ hybridization (FISH) techniques have been used to investigate the chromatin organization in the parasite nucleus. Chromosome conformation capture (3C) methodology, was developed several years ago to reveal the spatial chromatin structure and nearby genomic loci , TBP-associated factors (TAFs), the general transcription factors TFII, and the RNA polymerase II (RNA pol II) . In Plascription . Moreovecription . Similarcription . It has cription . These iP. falciparum, is one of highest in eukaryotes indicating a relative paucity of transcription factors  has been quite challenging  as well as HP1 , morphologically distinguishable  seems to play a specific role at this stage. Compared to other virulence genes, pf3d7_1255200 showed low level of H3K9me3 and an AP2-exp binding motif, known to control expression of a subset of clonally variant gene families  and the University of California, Riverside (NIFA-Hatch-225935 to KLR).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Transcription factor networks have evolved in order to control, coordinate, and separate, the functions of distinct network modules spatially and temporally. In this review we focus on the MYC network , a highly conserved super-family of related basic-helix-loop-helix-zipper (bHLHZ) proteins that functions to integrate extracellular and intracellular signals and modulate global gene expression. Importantly the MYC network has been shown to be deeply involved in a broad spectrum of human and other animal cancers. Here we summarize molecular and biological properties of the network modules with emphasis on functional interactions among network members. We suggest that these network interactions serve to modulate growth and metabolism at the transcriptional level in order to balance nutrient demand with supply, to maintain growth homeostasis, and to influence cell fate. Moreover, oncogenic activation of MYC and/or loss of a MYC antagonist, results in an imbalance in the activity of the network as a whole, leading to tumor initiation, progression and maintenance. Networks, in diverse fields including computer sciences, telecommunications, sociology, and biology, are generally defined as clusters of distinct nodes connected by what are termed “edges.” In biology, complex systems are frequently represented in terms of networks which can be subdivided into smaller groups or modules thereby revealing key relationships that underlie network activity ,4. The uBoth MAX and MLX were discovered through independent protein interaction screens that sought to identify dimerization partners for MYC  and for in vivo due to phosphorylation by casein kinase II [Unlike MYC, MAX is capable of forming homodimers but these are inhibited from binding DNA inase II . Furtherinase II . MLX andinase II . Therefoin vivo with half-lives on the order of 6–8 h, while MYC, MXD, MNT, and MGA have considerably shorter half-lives, generally less than 1 h (dependent on cell type) suggesting that they are rate-limiting for heterodimer formation [Both MAX and MLX are stable proteins ormation –20 present in the genomes of a small group of avian retroviruses responsible for transformation of fibroblast cells in tissue culture and for different types of hematopoietic neoplasms in animals. It was subsequently determined that the v-myc gene was derived by retroviral acquisition of the cellular MYC gene [MYC gene , while having critical functions in normal animal growth and development, are subject to frequent alterations in a significant fraction ( > 30%) of human cancers comprising a wide range of tumor subtypes [MYC family genes include gene amplifications, chromosomal translocations, viral integration, and regulatory mutations in MYC promoter or enhancer regions. Much evidence has accumulated indicating that the alterations in MYC are associated with key stages of tumorigenesis including initiation, progression, and maintenance. Despite the plethora of genetic rearrangements occurring at the MYC locus, the vast majority of these do not directly affect the MYC protein coding region. This is consistent with the notion that it is the deregulation of MYC expression, rather than altered or neomorphic changes in its protein function, that is at the root of MYC driven cancers [The MYC protein family  can be considered the founding members of the MYC network. The MYC gene ,24. Furtsubtypes . The alt cancers ,27.MYC enhancers and promoters. Other factors regulate MYC mRNA transport, half-life and translation. Moreover, MYC protein levels are maintained by a balance between synthesis and regulated degradation. Importantly, in many cancers in which MYC family genes are rearranged, the tightly regulated control of MYC expression is frequently lost, resulting in constitutive expression of MYC at high levels compared to most normal cells  to mitogenic signals. For example, treatment of quiescent fibroblast cells with mitogens results in the rapid induction of MYC mRNA and protein associated with cell cycle entry ,29. Manytivation ). Thus itivation ,31). Indtivation ) enforcetivation ,34.As mentioned above, MYC family proteins function as transcriptional regulators. Heterodimerization with MAX through the HLHZ regions of both proteins permits the dimeric basic regions to form induced-fit helices that recognize the symmetric DNA sequence CACGTG . This seWhile MYC-MAX heterodimers directly bind to DNA, they also recruit other proteins to genomic E-boxes. In general, these are factors that mediate transcription . SeveralIn taking an overview of the MYC family branch of the network it is reasonable, as a first approximation, to view it as a protein module that integrates mitogenic signals from diverse sources and enables initiation and/or reinforcement of gene expression programs compatible with the cellular environment and the maintenance of cell fate during growth and division.In addition to forming heterodimers with MYC family proteins, MAX heterodimerizes with all members of the MXD family , MXD3, and MXD4) and the related MNT protein, each of which possesses a bHLHZ region ,48–51 F. MoreoveIt is not entirely straightforward to ascribe a broad functionality to the MXD/MNT/MGA module as a whole. While their heterodimerization with MAX and their ability to bind E-box DNA might suggest that the MXD/MNT/MGA module functions similarly to the MYC module, a great deal of evidence argues against this idea. Indeed, MXD/MNT/MGA appear to act as antagonists of MYC function. First, whereas MYC-MAX binding predominantly promotes or reinforces active transcription (see above), the MXD/MNT/MGA module proteins act as transcriptional repressors. The MXD family members and MNT contain a short conserved amino acid sequence, which directly interacts with mSIN3A or mSIN3B co-repressor complexes ,49,57,58MGA (pronounced mega) stands somewhat apart from the MXD/MNT family due to its large size (with > 3000 residues MGA is ~14 fold larger than the MXD family and 5 fold larger than MNT) and the presence of both bHLHZ and T-box domains , as wellMLX was first identified through its ability to dimerize with MXD1, MXD4 and MNT  ,22. MoreDrosophila melanogaster where MondoA-Mlx (orthologs of vertebrate MLXIP-MLX) acts as a master regulator of the physiologic response to sugar by both directly and indirectly modulating expression of effector genes mediating lipid, carbohydrate, and amino acid metabolism [Drosophila MondoA-MLX target genes have human orthologs that contain polymorphisms associated with high levels of circulating triglycerides and coronary artery disease [A key function of MLXIP- and MLXIPL-MLX heterodimers is to mediate the cellular transcriptional response to changes in glucose and glutamine levels. MLXIP-MLX dimers are localized to the outer mitochondrial membrane and MLXIPL-MLX dimers are present in the cytoplasm where they directly or indirectly sense G-6-P (glucose-6-phosphate) and other metabolites derived from glucose ,75–78, rtabolism ,82. Seve disease . In addi disease ).We earlier mentioned that among the advantages of having an integrated network organization is the capacity to separate, control, and coordinate the specific activities of the network modules in time and space. This concept is likely to apply to the MYC network. Moreover, the notion of a “tipping point” — in which an imbalance in the regulation, abundance or activity of an individual network member may dysregulate the integrated function of the network as a whole — can be considered to apply to the oncogenic alterations leading to deregulation and increased expression associated with the MYC module (for review see ) as wellThe notion that MYC-MAX and the MXD-MAX/MLX arms of the network may act antagonistically was initially prompted by the findings that MXD/MNT proteins contain transcriptional repression domains that recruit histone deacetylases to their DNA binding sites in chromatin while, in contrast, MYC proteins recruit complexes that promote transcription by facilitating chromatin accessibility and release of paused RNA polymerase . The recOne setting where this antagonism serves to regulate normal cellular events is during cell cycle entry. In the G0 to G1 transition MNT-MAX levels are constant while MYC is strongly induced, resulting in an increased ratio of MYC-MAX:MNT-MAX complexes, consistent with the idea that MYC and MNT are in competition for available MAX. Either MNT overexpression or MYC loss inhibits cell cycle entry, suggesting that antagonism between these proteins and their balanced expression may set the threshold for the transition between quiescence and proliferation . HoweverDrosophila melanogaster where deletion of dm1  produces growth arrest at an early stage of larval development and diminished expression of growth related genes. By contrast, flies lacking both dMyc and dMnt  display significantly augmented growth and development accompanied by a partial restoration of the expression of dMyc activated growth genes [The above examples of functional interactions within the network occur in contexts where MXD/MNT/MGA and MYC proteins are present at the same time and in the same sub-cellular compartment. However significant interactions between MYC and MXD/MNT may also occur if the proteins in question are not simultaneously present. This is relevant considering that endogenous expression of many of these proteins are correlated with different cell cycle and differentiation states. MYC expression is predominantly, but not solely, confined to proliferating cells while MXD1, MXI1, and MXD4 are detected in resting or differentiated cells and are downregulated when cells enter the cell cycle. This inverse correlation with MYC expression does not hold for MNT, which is co-expressed with MYC but is maintained upon differentiation, when MYC is downregulated (for review see ). Howeveth genes . Interesth genes ,101.Taken together, the evidence suggests that a regulated balance between MYC and MXD/MNT factors is important in maintaining growth homeostasis in response to environmental signals . In the Drosophila as described above [Drosophila [A loss of network balance may also come into play in the case of the surprising findings that inactivating mutations or deletions of MAX are associated with tumorigenesis, particularly involving cells of neuroendocrine origin ,85. MAX ed above . Anotherosophila .Given that deregulated MYC has been shown to stimulate aerobic glycolysis and to reprogram multiple aspects of metabolism  it is noC. elegans stimulates expression of autophagy genes subsequent to golgi disruption [If MLXIP-MLX functions to suppress stress in MYC-driven cancers we might anticipate its involvement in other forms of stress response. In this regard it has been reported that MLX in sruption  and actssruption . This issruption . In addisruption . Furthersruption  and T cesruption , it is usruption . Moreovesruption ,114. Forsruption . These dsruption .www.encodeproject.org). However, as mentioned above, MYC-MAX binds to thousands of genes whose identity is dependent both on the nature of the cellular transcriptome and the abundance of MYC protein [The integrated functions of the network are dependent on the binding of heterodimeric complexes of network members to DNA . Indeed, protein –42. It iin vitro selection for co-bound factors found that cooperative transcription factor binding to DNA is surprisingly common, estimating that ~25 000 distinct pairs of transcription factors may be associated with mammalian genomic DNA [A recent study employing omic DNA (for reviomic DNA ). Cooperomic DNA . At the omic DNA  and MLXIomic DNA . Detaileomic DNA . CooperaA detailed description of the network will involve mapping the genomic binding sites of all members of the three network modules as well as the effects of genetic and chemical perturbation of the network on genomic binding and expression of bound genes. In addition it is worth noting that the MYC network itself belongs to the larger super-family of bHLH transcription factors that recognize E-box DNA . These i


Human genomes contain about 100,000 LINE-1 (L1) retroelements, of which more than 100 are intact. L1s are normally tightly controlled by epigenetic mechanisms, which often fail in cancer. In bladder urothelial carcinoma (UC), particularly, L1s become DNA-hypomethylated, expressed and contribute to genomic instability and tumor growth. It is, however, unknown which individual L1s are activated. Following RNA-immunoprecipitation with a L1-specific antibody, third generation nanopore sequencing detected transcripts of 90 individual elements in the VM-Cub-1 UC line with high overall L1 expression. In total, 10 L1s accounted for >60% of the reads. Analysis of five specific L1s by RT-qPCR revealed generally increased expression in UC tissues and cell lines over normal controls, but variable expression among tumor cell lines from bladder, prostate and testicular cancer. Chromatin immunoprecipitation demonstrated active histone marks at L1 sequences with increased expression in VM-Cub-1, but not in a different UC cell line with low L1 expression. We conclude that many L1 elements are epigenetically activated in bladder cancer in a varied pattern. Our findings indicate that expression of individual L1s is highly heterogeneous between and among cancer types. Roughly one-sixth of the human genomes are derived from long interspersed element 1  retroelements. Most L1s are truncated or heavily mutated, but more than one hundred are intact and could in principle be capable of retrotransposition. Intact elements contain an internal promoter at their 5′-end and two open reading frames encoding the RNA-binding protein ORF1p and the reverse transcriptase/endonuclease ORF2p ,4,5. TogTTC28 locus at chromosome 22q in colorectal cancers. This conclusion is further supported by studies based on comprehensive RNA analyses, with or without enrichment of L1 transcripts by various techniques, e.g., refs.  downloaded from L1Base (rite.de)  using miTo identify transcripts from protein-coding genes co-precipitated during RIP, htseq-count  was used to identify the highest expressed genes in the ORF1p precipitated RNA samples. Differential expression analysis was carried out via the R-package DESeq2 , using a total RNA-Seq analysis of VM-Cub-1 cells as a reference.Total RNA was extracted from powdered tissues or cell cultures using acid phenol extraction following column purification to minimize DNA contamination as described . SynthesqRT PCR was performed as described previously  on a Roc® Express Magnetic Chromatin Immunoprecipitation Kit, 53008, Active Motif, Waterloo, Belgium). In brief, 1.5 × 107 cells were treated with 1% paraformaldehyde to cross-link protein and DNA. Chromatin was extracted and sheared into 300–500 bp fragments (about 1–2 nucleosomes) by sonication. The sheared chromatin (25 µg) was incubated with primary antibody (3 µg) and magnetic Protein G-coupled beads at 4oC overnight. The primary antibodies employed in this study comprised Anti-Histone H3K4me3 , Anti-Histone H3K9me3 , Anti-Histone H3K18Ac , Anti-Histone H3 , Anti-Histone H3K27me3 , normal mouse IgG  and normal rabbit IgG . Antibody-bound chromatin complexes were precipitated by magnetic separation. Cross-linking reversal of chromatin was performed, and DNA was eluted from the magnetic beads.ChIP was performed according to the kit instruction , glutamate metabotropic receptor 6 (GRM6) and GAPDH by SYBRGreen-based qPCR using immunoprecipitated (IP) and input DNA samples as templates as previously described [® 96 System  using QuantiTect SYBR Green PCR kit  and specific primers (Histone modifications were determined on the L1 promoter (5′-LINE-1), the indicated specific L1s, and a set of control genes, escribed . All sam primers . The enrt-test or Mann–Whitney test was used to determine the difference between two independent groups. p values < 0.05 were considered statistically significant.Two-sample


Hormone-dependent activation of enhancers includes histone hyperacetylation and mediator recruitment. Histone hyperacetylation is mostly explained by a bimodal switch model, where histone deacetylases (HDACs) disassociate from chromatin, and histone acetyl transferases (HATs) are recruited. This model builds on decades of research on steroid receptor regulation of transcription. Yet, the general concept of the bimodal switch model has not been rigorously tested genome wide. We have used a genomics approach to study enhancer hyperacetylation by the thyroid hormone receptor (TR), described to operate as a bimodal switch. H3 acetylation, HAT and HDAC ChIP-seq analyses of livers from hypo- and hyperthyroid wildtype, TR deficient and NCOR1 disrupted mice reveal three types of thyroid hormone (T3)-regulated enhancers. One subset of enhancers is bound by HDAC3-NCOR1 in the absence of hormone and constitutively occupy TR and HATs irrespective of T3 levels, suggesting a poised enhancer state in absence of hormone. In presence of T3, HDAC3-NCOR1 dissociates from these enhancers leading to histone hyperacetylation, suggesting a histone acetylation rheostat function of HDACs at poised enhancers. Another subset of enhancers, not occupied by HDACs, is hyperacetylated in a T3-dependent manner, where TR is recruited to chromatin together with HATs. Lastly, a subset of enhancers, is not occupied directly by TR yet requires TR for histone hyperacetylation. This indirect enhancer activation involves co-association with TR bound enhancers within super-enhancers or topological associated domains. Collectively, this demonstrates various mechanisms controlling hormone-dependent transcription and adds significant details to the otherwise simple bimodal switch model. Thyroid hormone (T3) is a central regulator of growth, thermogenesis, heart rate and metabolism. In the liver T3 binds thyroid hormone receptor beta (TRβ) controlling expression of genes involved in processes such as lipid and cholesterol metabolism. The molecular mechanisms controlling TR-dependent gene regulation are centred on a bimodal switch model. In the absence of T3 co-repressors bind TR reducing gene expression. When hormone binds TR, co-repressors dissociate, and co-activators are recruited inducing gene expression. This model predominates the current understanding of T3-regulated gene expression. However, only a few studies have tested this model by genome-wide approaches. We have quantified histone3 acetylation genome-wide in the liver of hypo- and hyperthyroid mice and identified gene regulatory regions regulated by T3. Probing TR and co-regulators at these regulatory regions, and analysing histone3 acetylation in mouse models for disrupted co-repressor and TR activity, reveal additional insights to the mechanisms regulating T3-dependent gene expression. We suggest a revision of the prevailing bimodal switch model which helps understanding T3-regulated gene expression in tissues such as liver. We hope that this study, together with future studies, will add new perspectives on nuclear receptor-mediated transcriptional regulation to reveal general principles. Active enhancers are defined by DNase accessible chromatin, histone 3 (H3) hyperacetylation (e.g. H3K9Ac and H3K27Ac), H3K4 mono- and dimethylation (H3K4me1 and H3K4me2) and occupancy of the mediator complex . EstabliImportantly, in addition to direct TR binding to the genome , thyroid hormone regulates multiple processes by alternative mechanisms, including T3 actions by TR tethering to other transcription factors (TFs) (type 2 actions) and non-genomic actions in a TR-dependent (type 3 actions) and independent manner (type 4 actions) . Thus, iHere we used a genomics approach to test if the prevailing bimodal switch model sufficiently explains thyroid hormone regulation of enhancers. As a model system we used livers from hypo- and hyperthyroid animals and identified regulatory regions in the genome, where histones (H3K27 and H3K9) are hyperacetylated in response to T3 (i.e. regulatory regions activated by T3). First, we defined hyperacetylated regions bound by TR and discovered considerable amount of hyperacetylated regions not occupied by TR yet required TR for activation. Next, we carefully characterized hyperacetylated regions with and without occupancy of TR using a combination of already published HiC data, extensive DNA motif analysis and ChIP-seq against co-regulators. Finally, we functionally tested the role of the NCoR complex using a mouse strain expressing a NCOR1 mutant unable to interact with TR (L-NCOR1ΔID). Collectively, this suggests that the canonical bimodal switch model does not fully explain T3-dependent activation of enhancers. We present a revised model that adds significant details to the mechanistic action of thyroid hormone signalling.Thrsp  was the most significantly enriched motif, indicating direct TR binding to response elements within hyperacetylated regions  mutant mouse model , where TTo address secondary genomic effects of T3 we quantified genome wide H3K27Ac in liver from hypothyroid mice treated acutely with T3 for 2 or 6 hours (h). The vast majority (~85%) of hyperacetylated regions with a TRBS had increased H3K27Ac after 2h of T3 treatment , indicatde novo motif analysis of DHSs associated with hyperacetylated regions with and without TRBSs. In agreement with presence of TRBSs we identified the DR4 motif exclusively in the DHSs associated with regions occupied by TR  we initially extracted all TRBSs within hyperacetylated regions (n = 720). We subsequently quantified occupancy of different co-regulators known to interact with unliganded TR (HDAC3 and NCOR1) and ligand bound TR . Note that p300 and SRC1 were only quantified at selected genomic regions due to low ChIP-seq signal to noise ratio for these factors. Based on HDAC3 ChIP-seq tag density in hypothyroid condition we binned the TRBSs in high and low HDAC3 bound regions, referred to as type 1A and type 1B TRBS, respectively . This clIn hypothyroid condition TR primarily occupied type 1A TRBSs , suggestFocusing on the type 1B TRBSs showed that occupancy of CBP, p300 and SRC-1, as well as HDAC3 and NCOR1, was increased in response to T3 . TogethePdp2 and Dio1, represent examples of poised (type 1A) and established (type 1B) TRBSs, respectively (Taken together these data indicate that type 1 actions of TR should be subdivided into at least two categories. The high NCOR1-HDAC3 occupied TRBSs (type 1A) are poised enhancers, where the NCoR complex may function as a histone acetylation rheostat regulating the overall level of histone acetylation deposited by HATs. In contrast, activation of low NCOR1-HDAC3 occupied TRBSs (type 1B) is established in a T3-dependent manner by recruitment of TR and HATs leading to increased histone acetylation. Regions near the well-known TR target genes, ectively .The poised enhancer model described above infers that disruption of NCoR recruitment to chromatin will result in increased histone acetylation at unliganded type 1A TRBSs and not type 1B TRBSs. To test this, we used the L-NCOR1ΔID mouse model. These mice express a liver-specific NCOR1 mutant lacking two TR interaction domains resulting in disruption of NCoR interaction with TR . In agreTo further characterize the function of NCoR we isolated hyperacetylated regions occupied by type 1A and type 1B TRBSs . In agreTo link histone hyperacetylation to gene expression we initially performed RNA-seq in hypo- and hyperthyroid WT mice and identified 1015 genes induced by T3 treatment . Of thesTo specifically test if NCoR recruited to TRBS in hypothyroid condition is involved in nearby gene expression, we performed RNA-seq from liver of hypothyroid L-NCOR1ΔID mice and analysed differential gene expression compared to hypothyroid WT mice. Strikingly, only a minority (10%) of the T3-induced genes were affected in mice expressing NCOR1ΔID . This suHere we used a genomics approach to study enhancer activation by T3 in liver tissue. We focused exclusively on enhancers hyperacetylated in response to hormone treatment and found that enhancers are activated by several possible mechanisms depending on the chromatin context. These include a) hyperacetylation of poised enhancers as a result of HDAC3 loss and not increased HAT recruitment to the enhancer, b) hyperacetylation of dormant enhancers as a result of TR and HAT recruitment to the enhancer and c) hyperacetylation of enhancers not bound by TR in part as a result of higher-order chromatin structures. Importantly, hyperacetylation at all T3-regulated enhancers require functional TR expression. Moreover, we observed that MED1 is generally ligand dependently recruited to TR-bound hyperacetylated enhancers, suggesting that mediator recruitment is a hallmark of hyperacetylated TR occupied enhancers in response to T3.Hypoacetylated enhancers, associated with H3K4me1, hypersensitive to DNase and co-occupied by HATs and HDACs, are inactive yet poised for activation when the right molecular signal arrives ,11 but ide novo chromatin accessibility Please let us know if you have any questions while making these revisions.Yours sincerely,Gregory P. CopenhaverEditor-in-ChiefPLOS GeneticsWendy BickmoreSection Editor: EpigeneticsPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The article of Praestholm et al is a genome wide analysis of mouse liver chromatin, which aims is to understand the mechanism of action of thyroid hormone (T3) and its nuclear receptors (TRs).Overall this is a very interesting and very novel study, bringing a lot of information on a basic mechanism of gene regulation, which interest goes beyond the narrow field of T3 endocrinology. The data are quite convincing, well analyzed, and the authors reach interesting interpretations. Although the recurrent use of arbitrary threshold is risky (100 kb between TRBS and transcription start site for example), the conclusions seem reliable.The main weakness of the study is the text presentation, which I found very difficult to follow. This is due in part to the complexity of the experiments, but the writing is also sometimes obscure. For example, the sentence line 295-299 is very long and I had to read it several times to understand it. Part of the problem is that there no abbreviation for entities like “chromatin regions in which histone acetylation is induced after T3 treatment”, and the authors might consider creating some.I have a number of questions, and suggestions for additional experiments:1) The hypothyroid animals were treated for 5 days with T3. This is a very long time, which leaves time for a cascade of indirect effects to take place. I would recommend using a much shorter treatment, perhaps 3 hours only, and ideally to perform a time course experiment. This would help to separate causes from consequences. In particular:- Fig3D: does TR come first, decompacting chromatin? Is histone methylation first giving access to chromatin? Or is a T3 responsive gene encoding some unknown decompacting factor?- is the acetylation of regions which are not occupied by TR delayed, compared to the one with a TRBS? This would address the possibility that T3 induces the expression of genes encoding transcription factors able to induce chromatin acetylation at distant sites. This seems to be a good way to challenge the model showed Fig 6C.2) The authors mention the classical SRC1 histone acetyl transferase coactivator, but did not perform a ChipSeq analysis for this obvious candidate. Is this for technical reason? This gap should at least be acknowledge as a limitation of the study.3) The authors use two sets of ChipSeq for TRs, coming from different publications. The two datasets contain very different number of TRBS . This must affect the analysis, and the authors should explain to what extend it does.4) ChipSeq evaluate chromatin association, not DNA binding, therefore a TRBS can reflect tethering of TR by multiprotein complexes. Is this visible in super-enhancers or TADs as the models suggest? One would expect that TRBS appear simultaneously (only one with a consensus TRE) after T3 treatment at several locations. I also believe that if tethering happens, one would expect a better contrast on figure 2C, if only TRBS which contain a DR4 element had been considered.5) Figure S3 motif search: I suppose that if the DR4 is actually enriched, one should also find enrichment for the half-site (6mer), and thus, accidentally, for other half-sites organization. Therefore it seems that there is no reason to believe that other nuclear receptors are actually present. This should be clarified.6) I suspect that there is a continuum, ( ie no clear threshold) between poised and dormant enhancers . Perhaps the use of other available TR Chipseq datasets  could help to ask whether TR requires cell-specific factors to access the DR4 elements, and whether this explains why different cell types activate different genes in response to T3.7) After reading, one has no response to simple questions: how much of the transcriptional response to T3 is explained by the canonical model, and the alternative models presented by the authors? And how much remains unexplained? Previous studies reported different modes of T3 response for different gene clusters . Can this be related to the present models? Fig5A indicates that only half of the T3 induced genes are associated with hyperacetylation, while Fig5b 30% of these have no proximal TRBS. This leaves only 35% of the T3 induction explained by the presence of a proximal TRBS. In general, the relationship between chromatin status and gene expression should be better discussed.8) I suspect that if a 100 kb threshold distance is used, as the authors did, most if not all genes might have a proximal TRBS. In that case it is difficult to understand why only 1015 genes are upregulated by T3. Should the TRBS and the gene be in the same TAD? It would be interesting to consider numbers in the other direction: among all the DR4 consensus found in the genome, which fraction is occupied by TR before and after T3 treatment? Among those, which ones are occupied by NcoR in hypothyroid mice, and Med1 is T3 treated mice? Is the T3 response of a gene always accompanied by hyperacetylation of the nearby chromatin? how many genes are close to a gene which is T3 responsive?I believe that it would be in the authors interest to perform at least some of the suggested experiments. In conclusion, although I found the reading difficult, I am convinced that the authors made a substantial progress in the understanding of gene regulation by T3, in a relevant physiological system, which is a very significant achievement.Other points:Line 179 or 767: How is exactly made the random selection?Line 194: is it possible to make this statement statistically significant?Fig 2F: subTAD is not defined and the interpretation obscure. This might require further explanation.FigS3F1: I believe that this is DR4 not DR1Fig 2H: I do not really followed the explanation in the legend and the text. Perhaps this could be clarified.Fig 3C: Unless I missed it, the legend or the method section do not explain how this heatmap was generated.Fig 3E: the genomic coordinates of the amplified fragments should be given somewhere.Fig 4H: the genomic coordinates of these regions should be indicated in the legend (and the possible presence of genes).**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: Yes**********what does this mean?). 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