Arabidopsis thaliana. Recent advances in genomic, transcriptomic, imaging, and modeling technologies have allowed us to explore the interplay between transcription factors, secreted peptides, and plant hormones. Two different classes of plant hormones, cytokinins and auxins, and their interaction are particularly important for controlling SAM and FM development. This review focuses on the current issues surrounding the crosstalk between the hormonal and genetic regulatory network during meristem self-renewal and organogenesis.Plants, unlike animals, have developed a unique system in which they continue to form organs throughout their entire life cycle, even after embryonic development. This is possible because plants possess a small group of pluripotent stem cells in their meristems. The shoot apical meristem (SAM) plays a key role in forming all of the aerial structures of plants, including floral meristems (FMs). The FMs subsequently give rise to the floral organs containing reproductive structures. Studies in the past few decades have revealed the importance of transcription factors and secreted peptides in meristem activity using the model plant  Arabidopsis thaliana, the SAM produces rosette leaves during the vegetative phase  family of small peptides. The CLV3 peptide binds to the CLV receptor proteins . F. F36]. FFM sizes ,40,41. Fntenance ,40. In tntenance . In Arabntenance ,44,45. PPlant hormones  are signaling molecules that influence a variety of physiological processes. Phytohormones are classified into several different groups depending on their chemical structures ,47. PlanRecent studies of the regulators of the SAM and FM have revealed that plant hormones contribute to the fine-tuning of meristem maintenance and organ formation. Cytokinin and auxin are two core plant hormones that function in the SAM and FM regulatory network. Cytokinins are required for cell division in meristematic tissues, whereas auxins promote organ formation, growth, and differentiation. Cytokinins and auxins often work together in multiple organs, tissues, and cells . AlthougWUS expression and lead to stem cell fate in the surrounding cells   . I. IIPT7 e in rice . Arabidoto LOG9) . Among tectively . The cytlability a . W. WWUS exnd ARR15 a 101]. . WUS expHEC1 in the OC interferes with the maintenance of the SAM. HEC1 represses CLV3 expression and activates type-A ARR7 and ARR15 expression . Shared target genes of HEC1 and WUS, such as ARR7 and ARR15, are regulated in an opposite manner. How this competitive regulation by HEC1 and WUS is regulated remains to be elucidated. Multiple feedback systems mediated by hormonal components and transcription factors act in parallel to control meristematic fate and WUS expression in the SAM. A feedback circuit-driven regulatory mechanism is a common strategy for reliable, irreversible cell fate determination . . PIN-FORransport b. Hence,STM through histone deacetylation and negatively regulates the STM target, IPT7  in an auxin-dependent manner ) and ARF4 are noncanonical class B ARFs that lack a domain for interaction with Aux/IAA proteins ,142,143.et, IPT7 a,c 145]STM throumeristem a [145]. t manner a [144,14AG, ectopic WUS expression beyond stage 6 is sufficient to trigger indeterminacy of the FM. AG is expressed throughout FM formation beginning at stage 3 of flower development and terminates FM activity at stage 6 of flower development by regulating gene expression. Recent studies have shown that AG influences cytokinin and auxin homeostasis during both the earlier and later stages of FM development. After FM termination, cytokinin and auxin homeostasis play key roles in gynoecium formation.Unlike the SAM, the FM is determinate, and its meristematic fate must be terminated. WUS ensures the maintenance of the stem cell pool in the SAM and FM. The MADS-box transcription factor AGAMOUS (AG) plays central roles in early stages of FM development and the termination of the meristematic fate of the FM during later stages of development ,150,151.ETT expression partially though GIANT KILLER (GIK), which encodes an AT-HOOK MOTIF CONTAINING NUCLEAR LOCALIZED (AHL) protein [IPT3, IPT5, and IPT7 and mediates cell-cycle-related gene expression [SUPERMAN (SUP), which encodes a transcriptional repressor with a C2H2 zinc-finger DNA-binding domain and an EAR repression motif [SUP downregulates the expression of YUC1 and YUC4 to decrease local auxin biosynthesis. SUP interacts with histone modification enzymes to deposit negative histone marks, thereby silencing gene expression [During the early stages of FM formation, AG controls FM activity by maintaining proper hormone levels. AG modulates  protein ,153. AHL protein . The avapression . AG alsoon motif ,158,159.pression ,161,162.ag mutants [ag ett double mutants, suggesting that ETT genetically interacts with cytokinin to regulate FM determinacy. ETT integrates AG and auxin inputs to repress cytokinin activity. ETT negatively controls cytokinin biosynthesis. Similar to its activity during FM formation, ETT directly represses the expression of the cytokinin biosynthetic genes IPT3, IPT5, and IPT7 during FM termination , to enable FM determinacy via auxin  , which encodes a transmembrane protein of the tetraspanin family [TRN2 by CRC partially contributes to the creation of auxin maxima by interfering with auxin transport [YUC4 and TRN2, act in parallel to repress WUS expression for FM determinacy and subsequent organ formation  and the auxin efflux carrier gene PIN3 [Although it is well known that auxin and cytokinin act antagonistically during plant growth and development, several studies on the molecular mechanism between these phytohormones have focused on their synergistic activity, which maximizes their effects during gynoecium formation ,55,56. Uene PIN3 ,180,181.ene PIN3 , threshoene PIN3 ,176,183.The robust establishment of the SAM and FM is primarily controlled by conserved secreted peptides and master transcriptional regulators. In the past five years, plant stem cell researchers have identified various interactions between these key factors and phytohormones using a candidate gene approach or omics data. Phytohormones help determine when and where these factors function. The spatiotemporal fine-tuning of these factors by phytohormones appears to be regulated by complex multilayered networks of hormonal components, since hormones affect not only transcription, but also the epigenetic state, protein stability/protein–protein interactions, and metabolic rates of meristematic cells. However, in practice, most omics data are available only for bulk samples rather than single cells. Furthermore, technologies to integrate multilayer omics data are not yet well established. To elucidate these networks, multi-omics data at the single-cell level are needed. The integration of multilayered omics data via high-resolution readouts across hierarchies will provide new insights into phytohormone-mediated meristem activities.


Aspergillus oryzae. Genomic SELEX-Seq (gSELEX-Seq) can reveal the in vitro binding sites of a TF in a genome. To date, the gene expression network controlled by AoXlnR in A. oryzae is not fully explored. In this study, the data from gSELEX-Seq analysis and data mining were applied toward a comprehensive investigation of the AoXlnR-regulated transcriptional network in A. oryzae.Transcription factors (TFs) specifically bind to DNA sequences and control the expression of target genes. AoXlnR is a key TF involved in the expression of xylanolytic and cellulolytic enzymes in the filamentous fungi, Around 2000 promoters were selected as AoXlnR-binding DNAs using gSELEX-Seq, consequently identifying the genes downstream of them. On the other hand, 72 differentially expressed genes (DEGs) related to AoXlnR had been determined by microarray analysis. The intersecting set of genes, that were found using the gSELEX-Seq and the microarray analysis, had 51 genes. Further, the canonical AoXlnR-binding motifs, 5′-GGCT(A/G) A-3′, were successfully identified in gSELEX-Seq. The motif numbers in each promoter of the DEGs and differential expression levels were correlated by in silico analysis. The analysis showed that the presence of both 5′-GGCTAA-3′ and 5′-GGCTGA-3′ motif has significantly high correlation with the differential expression levels of the genes.A. oryzae. The pipeline, which is based on integrated mining of data consisting of both in vitro characterization of the DNA-binding sites and TF phenotype, can be a robust platform for comprehensive analysis of the gene expression network via the TFs.Genes regulated directly by AoXlnR were identified by integrated mining of data obtained from gSELEX-Seq and microarray. The data mining of the promoters of differentially expressed genes revealed the close relation between the presence of the AoXlnR-binding motifs and the expression levels of the downstream genes. The knowledge obtained in this study can contribute greatly to the elucidation of AoXlnR-mediated cellulose and xylan metabolic network in The online version of this article (10.1186/s12864-018-5375-5) contains supplementary material, which is available to authorized users. Transcription factors (TFs) interact with the enhancers and promoters on a genome and play a central role in transcription regulation in cells . IdentifDNA microarray (DNA-chip) analysis, which is a comprehensive DNA hybridization technique, was proposed by Brawn et al. . This teA. nidulans , an AoXlnR-binding fragment retaining two AoXlnR-binding motifs reported in the previous study [A. oryzae genomic DNA with 0.25 μM of each of the primers XRE-S-bio and XRE-AS  was amplified from A. oryzae genomic DNA with 0.25 μM each of xynF1-F-bio and xynF1-R , was amxynF1), AO090023000787, or AO090701000885 promoter region were determined by using the BLI on a BLItz system equipped with streptavidin sensors . A baseline was initially established in Kinetics Buffer 10× . Then, 4 μL of each biotinylated ligand (500 nM in Kinetics Buffer 10×) was captured (2 min) and a baseline was re-established in Kinetics Buffer 10× (30 s). To analyze the affinity between MBP-AoXlnR and the immobilized ligand, 4 μL of the given MBP-XlnR1–183 was captured (2 min), and the bound analyte was dissociated in 200 μL of Kinetics Buffer 10× (3 min). AoXlnR was diluted with Kinetics Buffer 10× to the desired concentration . All measurements were performed at room temperature with agitation. Data analysis and fitting were performed by using global fitting mode in a 1:1 binding model.The BLI is an optical analytical technique for monitoring biomolecular interactions utilizing the interference pattern of white light as it is reflected from a layer of immobilized biomolecules versus an internal reference layer . The affA. oryzae strains [Correlations between HXlnR/ΔXlnR ratio for each expression level  and sevA. oryzae genomic DNA was fragmented to approximately less than 100 bp, ligated with linkers at both ends, and amplified using PCR , saturation of the enrichment of the bound fragments was observed in selection round 3 of gSELEX analysis [1–183 and not crude protein solution.First, for performing gSELEX-Seq, the analysis . This inA. oryzae genome and the detection of peaks with high numbers of the mapped tags, 50-bp DNA fragment around the top of the peaks were cut out.All the DNA pools selected from the genomic library were sequenced using an Illumina MiSeq system, followed by bioinformatics analysis for genome-wide identification of AoXlnR-binding sites. After the mapping of the sequenced tags onto the A. oryzae upstream1000 dataset. Based on the annotation, we successfully identified 1948 promoters, including those of eight genes under the control of AoXlnR  [Each 50-bp tag from round 3 was annotated by local BLAST with the nd celD)  . Generally, the genes identified as DEGs in microarray analysis should include ones regulated primarily or secondarily by the TFs. Together, the expressions of the 51 DEGs downstream of the promoters are considered to be directly controlled by AoXlnR. On the other hand, the other 21 DEGs, which were not included among the genes downstream of the candidate promoters, are considered to be indirectly regulated by AoXlnR.In order to identify AoXlnR targets, Noguchi et al. carried out DNA microarray analysis in which the data based on spots of the scanned microarray images were analyzed for measure the expression level in each gene according to the procedure reported by Tamano et al. , 27. By de novo motif analysis of AoXlnR-binding sites was performed using the extracted 50-bp tags from gSELEX-Seq analysis Fig. . The resp < 0.01; Fig. p < 0.01; Fig. p < 0.01) as well as that of the major canonical binding motif, 5′-GGCTAA-3′ sites  . These results suggest that the 5′-GGCTGA-3′ site present upstream of the gene can greatly contribute to the activation by AoXlnR in A. oryzae cells.To further investigate the transcriptional mechanisms regulated by AoXlnR, correlations between various parameters related to AoXlnR binding and the differential expression levels were analyzed  than XRE-WT (apparent KD = 656 nM) GGcT(A/g)(A/t)(a/t)-3′ was clearly detected as an AoXlnR-binding motif Fig. . Recent  protein .The results of integrated data mining with differential expression levels obtained from microarray analysis suggested that the 5′-GGCTGA-3′ sites in the promoters are closely related to the regulation by AoXlnR . By contrast, there was no significant correlation in the case of 5′-GGCTAA-3′ and 5′-GGCTAG-3′ (Spearman’s rank correlation coefficient = 0.0632) or 5′-GGCTGA-3′ and 5′-GGCTAG-3′ (Spearman’s rank correlation coefficient = 0.138). The data mining strongly suggests that the coexistence of 5′-GGCTGA-3′ and 5′-GGCTAA-3′ sites in the promoter enhances the expression of genes located downstream of the sites. In fact, the xylan-dependent expression level of a reporter gene decreased moderately by introducing of a point mutation into the 5′-GGCTGA-3′ site [cis-element that further enhances the transcriptional activation by AoXlnR bound preferentially onto the 5′-GGCTAA-3′ site.Of the 29 promoters possessing one or more 5′-GGCTGA-3′ sites among the 72 DEGs identified by microarray analysis , most of-3′ site . TogetheA. oryzae metabolic engineering approaches utilizing cellulose as a starting material. Several synthetic biological approaches with a genetically engineered A. oryzae for cellulosic fermentation have been reported [A. oryzae transformant to enhance the cellulase activity. Considering the hypothesis described above, AoXlnR can further activate the expression of a target intrinsic cellulose gene by newly introducing the 5′-GGCTGA-3′ motif into the promoter of the gene. The motif can be inserted at a desired site onto the A. oryzae genome by using the CRISPR/Cas9 system of filamentous fungi [A. oryzae as a cell factory. Hence, if our pipeline is combined with the CRISPR/Cas9 system, it may be possible to rationally design a metabolic process in A. oryzae.The approach adopted in this study can be used for the analysis of AoXlnR-mediated cellulose and xylan metabolic network. In addition, the information of the correlation between the presence of the AoXlnR motifs and the expression of genes located downstream of the sites can be applied to reported , 32. In us fungi –35 to inA. oryzae by using DNase I digestion coupled with high-throughput sequencing (DNase-Seq), consequently identifying the binding site of 19 known TFs based on the digestion profile [cis-elements related closely to the expression of the target genes. A further robust transcriptome analysis system may be established by incorporating the DNA shape features described above as parameters into our pipeline.Recently, Wang et al. performed a genome-wide analysis of TF-binding sites in  profile . In addiA. oryzae. In addition, the pipeline, which is based on integrated mining of data consisting of both in vitro characterization of the DNA-binding sites and TF phenotype, can be a robust platform for comprehensive analysis of gene expression network via TFs.In this study, the genes regulated directly by AoXlnR were successfully identified by integrated mining of data obtained from gSELEX-Seq and microarray. The data mining of the promoters of the DEGs showed that the presence of both the canonical AoXlnR-binding motifs, 5′-GGCTAA-3′ and 5′-GGCTGA-3′, are related significantly to the expression of genes located downstream of the sites. These findings will contribute greatly to the elucidation of AoXlnR-mediated cellulose and xylan metabolic network in Additional file 1:Table S1. Oligonucleotide primers list. (DOCX 94 kb)Additional file 2:Figure S1. Promoter region used for affinity analysis. (A) The regions of XRE-WT and xynF1_upsteram_1 in xynF1Blue and red sequences indicate the regions of xynF1_upstream_1 and XRE-WT, respectively. Lower cases indicate mutation sites to derive from the designed primer. (B) The regions of egl-242, egl-363 and egl-617 are derived from promoter of AO090023000787. Blue, red and green sequences indicate the regions of egl-242, egl-363 and egl-617, respectively. (C) The regions of abf-680 and abf-837 are derived from promoter of AO090701000885. Blue and red sequences indicate the regions of of abf-680 and abf-837, respectively. AoXlnR binding motifs are shaded. Asterisks indicate the summit position of the detected peaks from the selection round 3 in gSELEX. Italic characters indicate possible AoXlnR binding sites. (DOCX 213 kb)Additional file 3:Table S2. Parameter list in data mining of AoXlnR binding sequence. (N), the number of the site; (P), the posion of the site; (FE), fold enrichment. The Gene ID in gray column indicates that the peak was detected in the promoter in gSELEX-Seq analysis. (DOCX 609 kb)Additional file 4:Figure S2. Construction of A. oryzae genomic library. (A) Genomic DNA shearing by using DNA-shearing system M220 (Life Technologies). (B) Gel extraction of size-fractionated genomic library after linker ligation. Bands of approximately 100–250 bp were excised using a spatula. (C) Genomic library after PCR amplification using the gel extraction product as a template. Red arrow indicates the amplicon. (TIFF 33973 kb)Additional file 5:Figure S3. Flow cytometric analysis of selected DNA pools from gSELEX by using bead display. (A) Dot plot of fluorescence analysis. X-axis is quantified fluorescence intensity in the FL1 channel and Y-axis is quantified fluorescence intensity in FL5. (B) Relative binding affinities to XlnR was calculated by the dividing the intensity of FL1 by that of FL5 and the binding affinity of Round 0 against AoXlnR was set as 1. (TIFF 33973 kb)Additional file 6:Table S3.A. oryzae promoter regions selected by gSELEX-Seq. (DOCX 239 kb)Additional file 7:Table S4. Correlation analyses between various factors related to AoXlnR binding and differential expression levels. (DOC 49 kb)Additional file 8:Figure S4. Summits of peaks detected using gSELEX-Seq in the promoter regions of xynF1, xynG1, xynG2, xylA, celA, celB, celC and celD. gSELEX-Seq peaks were detected using MACS (v1.4.2). Blue squares indicate the summits of the peaks. Red square frames indicate canonical XlnR binding motifs. (DOCX 278 kb)Additional file 9:Figure S5. Mapping of selected DNAs in xynF1 promoter region. Sequence tags from each round were mapped on A. oryzae mapped with Bowtie (v2). The mapping results were visualized by Integrative Genomics Viewer (IGV). Red arrow indicates the translation initiation site. (TIFF 33973 kb)


The few papers addressing this topic refer mostly to lipid metabolism-related pathologies, whereas in cancer research, data are even more scarce. The aim of this review was to summarize major epigenetic and post-transcriptional mechanisms that impact CD36 expression in relation to various pathologies while highlighting the areas in need of further exploration.CD36 is a transmembrane protein involved in fatty acid translocation, scavenging for oxidized fatty acids acting as a receptor for adhesion molecules. It is expressed on macrophages, as well as other types of cells, such as endothelial and adipose cells. CD36 participates in muscle lipid uptake, adipose energy storage, and gut fat absorption. Recently, several preclinical and clinical studies demonstrated that upregulation of CD36 is a prerequisite for tumor metastasis. Cancer metastasis-related research emerged much later and has been less investigated, though it is equally or even more important. CD36 protein expression can be modified by epigenetic changes and post-transcriptional interference from non-coding RNAs. Some data indicate modulation of CD36 expression in specific cell types by epigenetic changes  Plasmodium falciparum binding  , and Pla binding . CD36 wa binding , a fatty binding , anionic binding , and lon binding . These d binding , and the binding .via thrombospondin binding  and consists of 17 exons and 18 introns and can generate four protein isoforms via alternative splicing (https://www.ncbi.nlm.nih.gov/gene/948). Out of the four isoforms, three have at least one spliced exon  (UniProtkB ID: P16671). At least 23 alternative transcripts are known for CD36, and six alternative first exons have been described, yielding different transcripts in different tissue types (The CD36 gene is located on the long arm of chromosome 7 (7q21.11) , which binds to enhancer regions of CD36 . This is due in part to the large blocks of linkage disequilibrium across the gene in several of these population studies. CD36 polymorphisms are more frequent in Asian and African American populations than Caucasians (Gene promoter translation is under the control of core binding factor (CBF) family members  and CCAA of CD36 , but oth of CD36 . Geneticutations . More thucasians . Severalucasians  and genoucasians . A gene-ucasians .CD36-related modifications occur mostly with loss of genetic sequences (Regarding copy number variations (CNVs), equences , leadingequences .In addition to genetic alterations, epigenetic and post-transcriptional interventions can also modify the final protein output, impacting protein function. Gene expression can be further altered in the cytoplasmic compartment by non-coding RNA species that can interfere with mRNA translation and modulate protein output . Some ofEpigenetics is defined as “the study of changes in gene function that are mitotically and/or meiotically heritable and that do not entail change in DNA sequence” . AlthougDNA methylation involves the transfer of methyl groups from S-adenosylmethionine to cytosines in CpG dinucleotide sequences. The methylation patterns of CpG sites are created and maintained by a family of DNA methyltransferases  and can be reversed by ten-eleven translocation (TET) enzymes . HypermeHistone (H) proteins organize chromatin into structural units called nucleosomes, which consist of DNA wrapped around an octamer of H2A, H2B, H3, and H4 core histones connected by linker DNA and stabilized by H1 . EpigeneNon-coding RNAs (ncRNAs) are transcribed from DNA but not translated into proteins. Although not all categories of ncRNAs can be classified as epigenetic factors, several types have been shown to control epigenetic mechanisms and, in turn, to be regulated by the epigenetic machinery . The claCD36 promoters have been studied more extensively in relation to lipid metabolism and associated disorders. One study investigated the impact of CD36 promoter SNPs and DNA methylation sites on postprandial lipid uptake and clearance in adipose and heart tissue  from non-obese individuals than obese individuals   supplementation study in young overweight women, y weight . CD36 waividuals . Fat depividuals . CD36 hyndition) . Anothern)  exposure, a HDAC inhibitor, which can also modulate the expression and DNA methylation level of genes involved in liver steatosis  or PPAR-γ are subjected to both histone acetylation and methylation . Althougipocytes , monocytipocytes , and hepipocytes , but alsipocytes , fibroblipocytes , endotheipocytes , and micipocytes . A summaCD36 in human adipocytes has been shown to be activated by both H3K4me3 at P3, the major CD36 promoter, and H3K27Ac in PPAR-γ enhancer , a specific HDAC inhibitor, that stimulates acetylation of H4 at the CD36 promoter, increases CD36 mRNA, followed by higher uptake of oxLDL in macrophages  promoters and increasing macrophage infiltration and hepatic inflammation. In a study investigating the efficiency of VPA treatment in patients with spinal muscular atrophy, non-responsiveness to the drug was linked to CD36 overexpression, which suppressed the inhibitory effect of VPA on HDACs   that stalncRNAs are non-protein-coding transcripts longer than 200 nucleotides with higher tissue specificity than protein-coding genes . ThroughCD36 mRNA is targeted by different species of miRNA that modulate its expression at a post-transcriptional level in a tissue-specific manner. in vitro models using these cells. For example, in an in vitro atherosclerosis model based on oxLDL-stimulated dendritic cells derived from circulating monocytes, Given the association of CD36 with lipid metabolism and the robust expression on circulating monocytes and tissue macrophages, a lot of data have been collected from CD36 mRNA, downregulating both the mRNA and protein in macrophages -CD36 pathway in macrophages and may serve as a potential biomarker and potent therapeutic agent for atherosclerosis  and also stimulates NEAT1_2-mediated paraspeckle formation, which then suppresses lipid uptake by stabilizing CD36 mRNA in paraspeckles   and nucland PRC2 , it is nspeckles .CD36 mRNA expression had a high correlation coefficient with two co-expressed lncRNAs (NONCODE IDs n382000 and n341587)  , demonstCD36, uc.372 binds to the terminal loop region of pri-miR-4668, blocking its maturation and relieving its gene silencing effect on CD36. Because uc.372 promotes hepatic steatosis through these mechanisms, uc.372 inhibitors could be potential therapeutic agents for NAFLD.Finally, CD36 expression and that of other genes involved in lipid synthesis and uptake can be controlled by the interaction between an ultraconserved (uc) lncRNA and two species of miRs . The uprCD36, and changes in protein expression have been repeatedly reported in relation to cardiac artery disease, metabolic syndrome and obesity, malaria, and tumor spreading. Beyond genetic variants of CD36, epigenetic and post-transcriptional controls of CD36 gene expression play a significant role in protein function and may serve as regulatory circuits, which are yet insufficiently explored. DNA methylation of CD36 has been studied mostly in relation to lipid metabolism and obesity, whereas changes in CD36 histone marks have been linked to inflammation. Both epigenetic mechanisms can serve as targets for drug development in their associated pathologies. Thus far, a clear relationship has been demonstrated between certain miRNAs, CD36, and altered lipid profile that can be further exploited therapeutically. However, miRNAs do not stand alone in this post-transcriptional regulation, as long-noncoding RNAs are also responsible for the stability of CD36 mRNA. No data are yet available on the involvement of other small non-coding RNA species in CD36 expression, such as endogenous siRNAs or piRNAs. Thus, in terms of epigenetic control of CD36, there are still many gaps to be filled, especially in cancer research.CD36 is a widespread protein with various functions depending on tissue localization, from adhesion protein to scavenger for oxidized phospholipids and lipoproteins, or fatty acid translocator. CD36 is involved in cardiovascular and metabolic diseases, as well as immune responses and cancer metastasis. However, CD36 has been studied in detail in some cells (monocytes-macrophages) and diseases , whereas other areas (tumor metastasis) are less covered. Genetic mutations in C-MN, A-ME, and MH gathered the data and wrote the manuscript; MH corrected the final proof. All authors have read and approved the submitted form.This manuscript was funded by the Ministry of Research and Innovation in Romania, under Program 1—The Improvement of the National System of Research and Development, Subprogram 1.2—Institutional Excellence—Projects of Excellence Funding in RDI, Contract No. 7PFE/16.10.2018 and grant COP A 1.2.3., ID: P_40_197/2016.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.


Drosophila melanogaster models, we investigate TE activation in association with Tau pathology in Alzheimer’s disease (AD). Leveraging RNA sequencing from 636 human brains, we discover differential expression for several retrotransposons in association with neurofibrillary tangle burden and highlight evidence for global TE transcriptional activation among the long interspersed nuclear element 1 and endogenous retrovirus clades. In addition, we detect Tau-associated, active chromatin signatures at multiple HERV-Fc1 genomic loci. To determine whether Tau is sufficient to induce TE activation, we profile retrotransposons in Drosophila expressing human wild-type or mutant Tau throughout the brain. We discover heterogeneous response profiles, including both age- and genotype-dependent activation of TE expression by Tau. Our results implicate TE activation and associated genomic instability in Tau-mediated AD mechanisms.Aging and neurodegenerative disease are characterized by genomic instability in neurons, including aberrant activation and mobilization of transposable elements (TEs). Integrating studies of human postmortem brain tissue and  Drosophila melanogaster models, Guo et al. show that Alzheimer’s disease Tau neurofibrillary tangle pathology activates transcription of transposable element loci. An altered retrotransposon transcriptional landscape and associated genomic instability are implicated in Tau-mediated neurodegenerative mechanisms.Integrating studies of human postmortem brain tissue and   Drosophila, Tau induces global nuclear chromatin relaxation (Alzheimer’s disease (AD) is the most common neurodegenerative disorder and the leading cause of dementia, with more than 13 million individuals projected to be affected in the United States by 2050 . At autolaxation , abnormalaxation . ImportaHERV-K) has been demonstrated in human cortical and spinal neurons in ALS, and the encoded Envelope (Env) protein is neurotoxic (Transposable elements (TEs) are mobile genetic sequences present in all eukaryotic genomes examined to date . Althougurotoxic .Drosophila transgenic models, highlighting global TE activation in AD and implicating Tau-mediated mechanisms.Despite the evidence for genomic instability, retrotransposon expression has not been systematically evaluated in AD. In one small study, no differences in L1 genomic copy number were detected based on targeted PCR . Here, wTo examine whether AD neurofibrillary tangle pathology is associated with TE activation, we first leveraged data from 2 prospective human clinical-pathologic studies, the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). Our analyses included 636 deceased subjects with completed brain autopsies along with transcriptomic profiling of the dorsolateral prefrontal cortex based on RNA sequencing (RNA-seq). Clinical and demographic characteristics of our study cohort are detailed in HERV-Fc1, which is unique for being present at low genomic copy number . Indeed, positive associations , indicating enhanced H3K9Ac, were detected at 4 out of 13 regions with available data; 3 regions remained significant after adjustment for multiple testing  or a mutant form associated with familial FTD (TauR406W) causes age-dependent neuronal loss in association with hyperphosphorylated, misfolded Tau protein in the adult fly brain. We initially profiled 12 Drosophila TEs, including 8 LTR retrotransposons and 4 non-LTR retrotransposons, which have been previously demonstrated to be active in the fly nervous system, either following aging and/or manipulation of TE surveillance mechanisms  and L1-like, non-LTR retrotransposons (het-a). Notably, the gypsy TE is an endogenous insect retrovirus , aging, and the specific element examined. These findings were confirmed using an available RNA-seq dataset, permitting comprehensive assessments of TE expression signatures in 20-day-old TauWT versus control flies  demonstrated positive changes consistent with Tau-dependent activation . As a further control, the TauWT and TauR406W strains were each independently backcrossed to w1118 controls for 5 generations, ensuring a homogeneous genetic background. qPCR confirmed Tau-dependent increases in het-a, copia, and gypsy expression in aged animals  with reverse transcriptase and endonuclease activity. Notably, most L1 retrotransposition in the human genome is believed to be due to the evolutionary young L1H subfamily, and such elements were absent from our top hits , atm, p53, and Ago3, are potent modulators of Tau-induced neurodegenerative phenotypes in Drosophila ; (2) ELAV-GAL4/+; UAS-TauWT/+; and (3) ELAV-GAL4/+; UASTauR406W/+. qPCR was performed from total RNA prepared from fly heads in triplicate samples, using RpL32 as an internal control. Expression  was normalized to that of 1-day-old control animals. One-way ANOVA was performed to detect differences between group mean expression values, considering each TE and time point separately. Subsetted t tests (two-tailed) were subsequently performed for post hoc comparisons of each Tau genotype with control animals. For copia, two-way ANOVA was secondarily performed to differentiate genotype and age effects. Error bars in all analyses represent the SEM. RNA-seq data from ELAV-GAL4/+; UAS-TauWT/+ or control (ELAV-GAL4/+) fly heads were analyzed using SalmonTE. Statistical significance was set to FDR < 0.1. For analyses of TE copy number, genomic DNA was extracted from heads of 10-day-old flies prior to qPCR.The escribed . The fol1234


Objective: The development of obesity is inseparable from genetic and epigenetic factors, and transcription factors (TFs) play an essential role in these two mechanisms. This review analyzes the interaction of TFs with epigenetic modifications and the epigenetic mechanisms underlying peroxisome proliferator-activated receptor (PPAR)γ, an important transcription factor, in the development of obesity.Methods: We describe the relationship between TFs and different epigenetic modifications and illustrate the several mechanisms described. Next, we summarize the epigenetic mechanisms of PPARs, an important class of transcription factors involved in obesity, that induce obesity with different triggering factors. Finally, we discuss the mechanisms of epigenetic modification of PPAR-related ligands in lipid metabolism and propose future avenues of research.Results: TFs participate in epigenetic modifications in different forms, causing changes in gene expression. The interactions between the different epigenetic modifications and PPARs are important biological developments that affect fat tissue differentiation, lipogenesis, and lipid metabolism, thereby inducing or inhibiting the development of obesity. We then highlight the need for more research to understand the role of epigenetic modifications and PPARs.Conclusions: Epigenetic mechanisms involved in the regulation of PPARs may be excellent therapeutic targets for obesity treatment. However, there is a need for a deeper understanding of how PPARs and other obesity-related transcription factors interact with epigenetic modifications. Obesity is currently the world's most conspicuous nutritional disease. It is currently considered a serious threat to human health because of its high prevalence . The expHowever, the complex mechanisms of adipocyte differentiation remain to be further elucidated . EpigeneEpigenetics can be described as reversible and heritable changes to the DNA that regulates chromatin structure and gene expression without altering the DNA sequence . The genDNA methylation is a process in which cytosine is converted to 5-methylcytosine (5-mC) by DNA methyltransferases (DNMTs) with S-adenosylmethionine (SAM) as a methyl donor , 33. In in vitro and in vivo  proteins, which comprise eleven known proteins containing the MBD domain , 42. TheThe expression of DNMT and demethylase genes are affected by TFs. TFs modulate promoter methylation levels by recruiting and forming complexes with DNMTs . KnockinDNA methylation can also modify TFs. Methylation of TFs belongs to non-histone methylation. Previous studies have indicated that non-histones can be methylated at lysine residues . For exaHistone modifications are another essential component of epigenetics. Histones comprise core histones  and linker histones, H1/H5. Two copies of the four core histones are wrapped around ~165 bp of DNA to constitute nucleosomes and form eukaryotic chromatin with the linker histone H1 , 51. In TFs act as a “porter” to recruit histone modification enzymes to regulate gene expression. For example, during histone acetylation, specific TFs repress or activate gene expression through HDAC or HAT recruitment, respectively . The actModification of the histone code widely occurs in PTM of histone tails. A modification may either enhance or inhibit another –69. For Chromatin remodeling is a dynamic modification of chromatin architecture, which opens the access for regulatory transcription machinery proteins and condenses DNA to control gene expression. Remodeling is accomplished in three ways: (1) covalent histone modifications by enzymes such as HATs, HDACs, and HMTs; (2) ATP-dependent chromatin-remodeling complexes , such asGenes in eukaryotic cells are packaged in chromatin. TFs must reorganize local nucleosome structure and create an open site for successful interaction with genomic regulatory elements during transcription. TFs with this characteristic are termed “pioneer factors” . ExampleTFs cannot induce chromatin remodeling independently during transcriptional regulation, but need to recruit chromatin remodeling complexes. The PRotein Interactions by Structural Matching (PRISM) algorithm shows that GR binds to several subunits of SWI/SNF such as BAF60a, BAF250, and BAF57 . C/EBPα in vitro  . ncRNA aTFs and miRNA play an important role in gene transcription and post-transcriptional regulation. Studies found that genes regulated by the same TF and miRNA present a significant co-expressed pattern , 97. DurLncRNAs can alter the transcription of genes by interacting with TFs, chromatin-modifying complexes, or mRNA , 101. ThLncRNAs act as co-factors or inhibitors to regulate the activity of TFs and control NF-κB signaling . LncRNA-TFs and lncRNAs form a binary network and combine with genes to form a ternary complex , 106. InLncRNA can form complexes with TFs to regulate transcription of target genes , 111. FoPPARγ is a critical transcriptional regulator of adipogenesis in mammals, is closely related to regulation of lipids and glucose metabolism, and is associated with the control of obesity and related diseases , 115. SiIn addition, PPARs also play an important role in lipid metabolism. After feeding, carbohydrates and fats are converted into glucose and chylomicrons, respectively, and enter circulation. Most of the glucose is absorbed by the liver. If hepatic glucose storage capacity is reached, the remaining glucose is used for lipogenesis. Studies have shown that rats in the fed state increase the amount of SREBP1, which promotes the conversion of glucose to acetyl-CoA, followed by synthesis of fatty acids. Fatty acids are further converted into triglycerides and very low-density lipoproteins and are stored in the body , 121. ThStudies have shown that DNA methylation regulates the expression of PPARγ. Two studies examined the CpG methylation of the PPARγ gene in 9–year–old overweight children , 124, anStudies on PPARγ in animals have revealed that the PPARγ2 gene promoter is highly methylated in mouse 3T3-L1 pre-adipocytes . HoweverHistone methylation regulates PPARγ gene expression. Several HMTs and HDMs are known to be involved in the regulation of PPARγ expression and lipogenesis . Myeloidin vitro in a lipogenic cell model , bind reactive elements in target genes, and induce transcription. PPARγ can modulate fatty acid metabolism, promote differentiation of adipocytes, and regulate lipid storage through transcription of a series of lipid-related proteins in the posterior segment . In animin vivo, thereby reducing the occurrence of obesity and obesity-related diseases (Figure PPAR is a pivotal regulator of fat formation and metabolism. Knocking out PPARγ in adipose tissue of mice can prevent high-fat diet-induced obesity and insulin resistance . Becauses Figure .A variety of physiological, biochemical, genetic, and behavioral factors can cause obesity. To control the occurrence of this disease, it is necessary to reasonably control the diet and perform a moderate amount of exercise so as to reduce the storage of residual energy within the body. In addition, it is also necessary to control the excessive proliferation and differentiation of adipocytes. Knowledge of epigenetic modifications provides novel insights into the pathogenesis of obesity. Owing to the importance of PPARγ in lipogenesis and lipid metabolism in humans and other animals, the epigenetic mechanisms underlying transcriptional regulation of PPARγ will remain a research focus in the field of lipid biology and medicine and may result in improved treatments for obesity. Although much evidence for epigenetic regulation has been reported in recent years, few studies have shed light on the role of epigenetic modifications, transcription factors, and upstream and downstream mechanisms involved in obesity. In the future, a better understanding of the interrelationship between regulation of obesity-related transcription factors and epigenetics will be needed to explore effective therapeutic methods for the treatment of obesity using epigenetic modifications.QH and CM prepared the first draft and wrote the final version of the manuscript. LC and DL were involved in literature searches. FL and RC critically revised the manuscript and gave constructive opinions on articles.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.


Drosophila embryos. The results show that H1 binding is positively correlated with the stability of beads-on-a-string nucleosome organization likely through stabilizing nucleosome positioning and maintaining nucleosome spacing. Strikingly, nucleosomes with H1 placement deviating to the left or the right relative to the dyad shift to the left or the right, respectively, during early Drosophila embryonic development. H1 occupancy on genic nucleosomes is inversely correlated with nucleosome distance to the transcription start sites. This inverse correlation reduces as gene transcription levels decrease. Additionally, H1 occupancy is lower at the 5′ border of genic nucleosomes than that at the 3′ border. This asymmetrical pattern of H1 occupancy on genic nucleosomes diminishes as gene transcription levels decrease. These findings shed new lights into how H1 placement dynamics correlates with nucleosome positioning and gene transcription during early Drosophila embryonic development.The linker histone H1 is critical to maintenance of higher-order chromatin structures and to gene expression regulation. However, H1 dynamics and its functions in embryonic development remain unresolved. Here, we profiled gene expression, nucleosome positions, and H1 locations in early  The linker histone H1 binds to nucleosome to form the chromatosome to further condense the chromatin structure2. Previous studies showed that the abundance of linker histones in the nucleus was about equal to that of the nucleosome in higher eukaryotes3. This suggests that H1 plays an important structural and functional role in chromatin. For example, the histone H1 is essential for the chromosome architecture and segregation in mitosis4. The binding of histone H1 to nucleosomes facilitates nucleosomal stabilization through the formation of linker histone/DNA stem structure6. Linker histones now appear to also exert functions in regulating fundamental biological processes, including gene expression7, stem cell differentiation8, and mouse embryonic development9. Thus, the precise in vivo roles of linker histones remain elusive.Eukaryotic genomic DNA is packaged into chromatin through the formation of nucleosome. The nucleosome is the basic repeating unit of eukaryotic chromatin, consisting of an octamer of histones with two copies of each of histone proteins H2A, H2B, H3, and H4, around which ~146 bp of DNA is wrapped in a left-handed toroid10. The N-terminal region is not important for H1 binding to nucleosome6, whereas the C-terminal tail is required for H1 binding to chromatin in vivo11. However, the globular domain alone is sufficient for chromatosome formation and protects the same linker DNA in the native chromatin against micrococcal nuclease digestion as the full-length linker histone12. The studies of nucleosome recognition by linker histones have led to many conflicting models for how the globular domain binds to the nucleosome. Previous studies suggested that linker histones bound to the nucleosome on13 or off15 the dyad. Whether the globular domain interacts with both or only one linker DNA(s) is also unresolved15. It was also reported that the globular domain was located inside the DNA gyres and interacted with H2A16. Thus, the position of linker histones in the nucleosome particle has remained controversial.Linker H1 histones typically comprises three parts: a central globular domain, a short N-terminal region, and a long unstructured C-terminal tailThe in vivo positioning of nucleosomes and linker histones is dynamic. The aforementioned binding models of linker histones to nucleosome core particle were mainly inferred from the structural studies using nuclear magnetic resonance (NMR) spectroscopy and cryo-electron microscopy (cryo-EM), which reveal the static contact between linker histones and nucleosomes and often fail to represent the bona fide in vivo context. However, knowledge of the precise placement of linker histones on nucleosomes is essential to understand the structural role in chromatin and regulatory role in cellular functions of linker histones.Xenopus laevis has five, both human and mouse have eleven17. This has largely limited progress in molecular, biochemical, and functional study on H1. In contrast, only a single linker histone H1 was detected in somatic cells through Drosophila embryonic development upon zygotic genome activation20. Therefore, Drosophila provides an ideal model to study H1.However, it is a challenge for histone H1 study because H1 has many variants. For example, chicken have eight distinct variants, Drosophila embryos at two stages. We applied epigenomics analyses to dissect the placement patterns of H1 on nucleosomes, how H1 binding affects nucleosome positioning, and the role of H1 in regulation of gene expression in vivo. Our study provides new insight into the binding patterns of linker histone H1 to nucleosome core particle, its roles in affecting chromatin structure and regulating cellular functions during early Drosophila embryonic develoment, from an epigenomics angle.We here generated genome-wide high-resolution maps of H1 and nucleosome positions in early Drosophila embryos at 3–4 h after egg laying (AEL). The distribution of H1 around nucleosome dyad shows that H1 frequently locates on both borders of nucleosomes as well as much less frequently on the nucleosome middle point , 35–40 (41.1%), 40–45 (19.2%) , 1.2–1.6 (19.1%), 1.6–2.0 (17.4%) , we examined the correlation between H1 occupancy level and its distance to TSS. To remove the impact of nucleosome occupancy, we calculated the occupancy ratio of H1 to nucleosome. The results show that the occupancy ratio is significantly lower in the regions closer to TSS than the farther downstream regions Fig. . Namely,We next investigated the role of H1 in the nucleosome array organization since nucleosomes are arranged as a linear array as “beads on a string” in the genome. We identified 71,053 nucleosome arrays (defined as three or more continuous nucleosomes containing no NFRs). Intriguingly, H1 occupancy is positively correlated with the nucleosome occupancy of arrays Fig. . Taken tNFRs are often found at transcription start and end sites and play a critical role in regulation of gene expression. To investigate how H1 placement influences NFR formation, we then collected 100-bp or larger regions lacking nucleosomes as NFRs and examined H1 occupancy flanking NFRs. The results show that H1 occupancy on the nucleosomes at both borders of NFRs significantly decreases as the lengths of NFRs increase, same as nucleosome occupancy at both borders of NFRs Fig. . This suDrosophila embryos at two stages (3–4 h and 14–15 h AEL), and identified fixed nucleosomes whose midpoint shifted ≤10 bp between the two stages, and lost nucleosomes whose midpoint shifted ≥127 bp from 3–4 h to 14–15 h AEL. H1 occupancy in 3–4 h AEL is significantly higher in the fixed nucleosomes than the lost ones . As expected, H1 occupancy is significantly higher in the remained orphan nucleosomes than the lost ones from 3–4 h to 14–15 h AEL Fig. . MoreoveDrosophila early embryonic development.Shift is one of major nucleosome positioning dynamics. It remains unexplored how H1 impacts nucleosome shift. To address this issue, we identified all nucleosomes whose midpoint shifted up to 74 bp, half of a nucleosome, from 3–4 h to 14–15 h AEL. We collected three groups of nucleosomes: shift 0–10, 30–40, and 50–74 bp  Fig. . H1 loca22. However, the organization of H1 around TSS and in the gene body and its relationship with gene activity remain enigmatic. To get insight into this question, we profiled H1 distribution around TSS grouped by transcription level. The result shows that there is a broad H1-depleted region closely upstream of TSS and predominant H1 occupancy in the gene body  were cultivated in two cages  in an incubator at 25 °C and 55–60% humidity. Flies were fed on fresh grape juice plates with yeast paste for at least two days. The plates were replaced with new plates at least once a day. Prior to embryo collection, we replaced the plates with new plates. Exactly after one hour, these new plates that contained 0–1 h AEL embryos were removed from the incubator. Let the embryos continue to develop at 25 °C for 3 and 14 additional hours to get 3–4 h and 14–15 h AEL embryos, respectively. Then the embryos were collected from the plates and transferred into the mesh by a soft brush with PBST . Wash embryos with tap water to remove the yeast. Then embryos were immediately transferred into 100 ml beaker and dechorionated with bleach (5% sodium hypochlorite) for 3 min. Next, embryos were rinsed with tap water for 2 min and dried on paper. The embryos were ready for use.Young (<5-day old) and healthy Total RNA was extracted from embryos by TRIzol (Invitrogen). Genomic DNAs were removed with Turbo DNA-free kit (Ambion). The RNA sequencing libraries were constructed using standard Illumina libraries prep protocols. Sequencing was conducted on Illumina HiSeq2500 platform using 49-bp single-end protocol.The embryos were cross-linked immediately after collection with 1.8% formaldehyde in 3 ml of ChIP-fixed buffer (50 mM HEPES (pH 7.6), 100 mM NaCl, 0.1 mM EDTA, 0.5 mM EGTA) and 9 mL heptane on a shaker at 300 rpm for 15 min. Discard the supernatant after short centrifuge. Add 0.25 mM glycine with PBST to cease the cross-linking reaction. The cross-linked embryos were rinsed by PBST for three times, then were store at −80 °C for future use.2, 15 mM HEPES (pH 7.6), 0.5% Trition X-100, 0.1% NP-40, 0.5 mM DTT, 1 × EDTA-free protease inhibitor cocktail ) on ice. The large tissue residuals were filtered by Miracloth  to get nuclei suspension. The mixture was centrifuged at 3500 rpm at 4 °C for 5 min. Then, the supernatant was discarded. The pellet was washed once with MNase digestion buffer (10 mM Tris-HCl (pH 7.5), 15 mM NaCl, 60 mM KCl, 1 mM CaCl2, 0.15 mM spermine, 0.5 mM spermidine, 1 × PI). The mixture was centrifuged at 3500 rpm at 4 °C for 5 min. Nuclei were suspended with 500 μl of 37 °C pre-heated MNase digestion buffer with 45 U MNase  and incubated at 37 °C for 20 min. Reaction was terminated on ice by adding EDTA to a final concentration of 10 mM for 10 min. Then the mixture was centrifuged at 3500 rpm at 4 °C for 5 min. The pellet was washed with A2 buffer , 1 mM EDTA, 0.5 mM EGTA, 1% Triton X-100, 0.1% sodium deoxycholate, 1 × PI). The pellet was resuspended in A2 buffer with 0.1% SDS. The pellet  was dissolved through sonication with three cycles of 20 s duration with at least 40 s pauses between cycles at the power setting of 6 (out of 20) on a Misonix sonicators XL-2000. The mixture was centrifuged at 13,000 rpm at 4 °C for 10 min. The pellet was discarded. Nucleosomal DNA was extracted by phenol–chloroform and dissolved in ddH2O.The cross-linked embryos were homogenized using Dounce All-Glass Tissue Grinders  with A1 buffer . The dose of anti-H1 antibody followed the specifications. ChIP reaction was done as described in the manual of ChIP kit . In brief, the mixture containing chromatin, antibody, and ChIP buffer  was incubated overnight on a rotator at 4 ˚C. Then 30 μl of ChIP-Grade Protein G Magnetic Beads  was added to IP reaction. The mixture was incubated for another 2 h with rotation. Then, beads were washed three times with low salt wash buffer  and once with high salt wash buffer . Five minutes for each wash. Removed wash buffer and added 150 μl of 1 × ChIP elution buffer  to suspend the beads.In all, 20–25 μg of cross-linked genomic DNA was sonicated using XL-2000 Misonix sonicator with power output of 7 Watts. It is critical that the average length of the sheared chromatin is about 250 bp, with length ranging from 150–500 bp. The fragmented DNA was immunoprecipitated with anti-H1 antibody  using TopHat (v 1.3.1) with default parameter setting. The uniquely mapped reads were assembled into transcripts guided by reference annotation with Cuffdiff (v 1.3.0)36 to calculate gene expression levels that were normalized as Fragment Per Kilobase per Million mapped fragments (FPKM).Sequencing reads were aligned to the Drosophila genome (dm3 assembly) using bowtie (v 0.12.7)37 allowing maximal two mismatches. The uniquely mapped reads were retained for nucleosome prediction. Each read was moved 73 bp interior to its end to represent nucleosome dyad. The nucleosome prediction tool GeneTrack38 was used to call nucleosomes with default parameters. Nucleosome read count were normalized by RPNM (reads per nucleosome per million mapped reads) as the nucleosome occupancy. Analysis was performed on only those 496,992 nucleosomes with RPNM value of 0.1 or higher, although virtually identical patterns (and conclusions) were achieved when all nucleosomes were included. Fuzziness were defined as the standard deviation of the coordinates of all reads defining the same nucleosome as described previously33. It measures how spread out a nucleosome position is. Each nucleosome was assigned to promoter, genic or intergenic regions depending on the location of the nucleosome midpoint.Sequencing reads were aligned to the Linkers with length >100 bp are defined as NFRs. Three or more continuous nucleosomes with no linkers with length >100 bp form a nucleosome array. Orphan nucleosomes are those that are flanked by an NDR on each side.The distance between the midpoints of the two closest nucleosomes from the two developmental stages measures nucleosome positioning dynamics. If the distance is greater than 127 bp, the nucleosome is dissembled or reassembled. If the distance is less than 10 bp, the nucleosome is fixed. The rest of nucleosomes are shifted.Nucleosome organization in a region was plotted as heatmap as follows: nucleosome occupancy was z-scored to 0–1 and used as signal density. The dyad represents nucleosome position. Fuzziness represents nucleosome width.Drosophila genome as nucleosomal reads as above. H1 reads that overlap a nucleosome are assigned to this nucleosome. These reads were used to calculate occupancy and fuzziness of H1 as nucleosome’s as above.H1 ChIP-seq reads were aligned to The composite distribution of H1 around nucleosome dyads was calculated by aggregating H1 read count at each distance relative to the dyad as follows: each read represents an H1. We summed total H1 read counts at each site within ±1 kb of dyads of interest. The H1 occupancy equals to the read count normalized as RPNM. We further binned the H1 occupancy by a 10-bp interval of H1 read distance to the dyad, and smoothed it with 5-bin moving average and 1-bin step size.The non-directed composite distribution of H1 around the TSS was calculated as H1 distribution around dyads as above.The directed composite distribution of H1 around TSS differed in that all genic H1 was put in 5′-to-3′ direction before aggregating according to the transcription direction of the genes where H1 locates.The RNA-seq, MNase-seq and ChIP-seq data sets have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE101330.SupplementalFile


Super-enhancers (SEs) are clusters of transcriptional enhancers which control the expression of cell identity and disease-associated genes. Current studies demonstrated the role of multiple factors in SE formation; however, a systematic analysis to assess the relative predictive importance of chromatin and sequence features of SEs and their constituents is lacking. In addition, a predictive model that integrates various types of data to predict SEs has not been established. Here, we integrated diverse types of genomic and epigenomic datasets to identify key signatures of SEs and investigated their predictive importance. Through integrative modeling, we found Cdk8, Cdk9, and Smad3 as new features of SEs, which can define known and new SEs in mouse embryonic stem cells and pro-B cells. We compared six state-of-the-art machine learning models to predict SEs and showed that non-parametric ensemble models performed better as compared to parametric. We validated these models using cross-validation and also independent datasets in four human cell-types. Taken together, our systematic analysis and ranking of features can be used as a platform to define and understand the biology of SEs in other cell-types. A myriad of transcription factors (TFs) bind to enhancers and regulate gene expression by recruiting coactivators and RNA polymerase II (RNA Pol II) to target genes7. A typical mammalian cell is estimated to have thousands of active enhancers, a number which rises to roughly one million in the human genome8. It has been more than three decades since the first enhancer was discovered9, but our understanding of the mechanisms by which enhancers regulate gene expression is still limited. However, development of methods such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) and DNase I hypersensitivity followed by sequencing (DNase-seq), have helped to discover and characterize enhancers at a genome-wide scale. Many factors have been associated with enhancer activity, including mono methylation of histone H3 at lysine 4 (H3K4me1), acetylation of histone H3 at lysine 27 (H3K27ac), binding of the coactivator proteins p300 and CBP, and DNase I hypersensitivity sites (DHSs)11. By exploiting these factors and other genomic features many supervised and unsupervised machine learning approaches have been developed to predict enhancers genome-wide13.Enhancers are 15. The genomic regions co-bound by the embryonic stem cell (ESC) pluripotency TFs Oct4, Sox2, and Nanog (OSN) have shown robust enhancer activity16. By using ChIP-seq data for Oct4, Sox2, and Nanog in ESC, 10,227 co-bound regions have been identified and classified into super-enhancers (SEs) and typical enhancers (TEs), ranked by Mediator subunit Med1 signal17. SEs are clusters of enhancers, which are cell-type specific, associated with key cell identity genes, and linked to many biological processes which define the cell identity17. These SEs are densely loaded with the Mediator complex, master TFs, and chromatin regulators20. Many disease- and trait-associated single nucleotide polymorphisms (SNPs) have been found in these regions18. SEs differ from TEs in terms of size, ChIP-seq density of various cofactors, DNA motif content, DNA methylation level, enhancer RNA (eRNA) abundance, ability to activate transcription, and sensitivity to perturbation23. Furthermore, studies have found SEs involved in multiple cancers and demonstrated their importance in cellular-identity and disease and emphasizing their use as potential biomarkers26. Other parallel studies have shown similar patterns by using different approaches and termed them ‘stretch enhancers’27 and ‘enhancer clusters’28.The Mediator is a transcriptional coactivator, forms a complex together with Cohesin to create cell-type specific DNA loops, facilitating enhancer-bound TFs to recruit RNA Pol II at the promoters of target genes17. BRD4, a member of the BET protein family, was also used to distinguish SEs from TEs as it is highly correlated with MED119. H3K27ac was extensively used to create a catalogue of SEs across 86 different human cell-types and tissues due to the availability of ChIP-seq data for H3K27ac18. Other studies used the coactivator protein P300 to define SEs30. However, the knowledge about the ability of these and several other factors to define a set of SEs in a particular cell-type and their relative and combinatorial importance remains limited. The master TFs which form the SE domains are largely unknown for most of the cell-types. Current studies demonstrated the possibility of multiple cofactors with important roles in SE formation. Nevertheless, a computational model that integrates various types of data to predict SEs and their constituents , and to identify key signatures has not been developed. In addition, the degree to which the sequence-specific features of the constituents themselves explains the differences between SEs and TEs remains unknown.Since the discovery of SEs, the research community used ChIP-seq data for different factors to differentiate SEs from TEs in different cell-types. ChIP-seq data for Med1 optimally separated SEs and TEs by comparing it with H3K27ac, H3K4me1 and DHSsk-Nearest Neighbor (k-NN), Adaptive Boosting (AdaBoost), Naive Bayes (NB), and Decision Tree. We validated these models using 10-fold stratified cross-validation and also by using independent datasets in four human cell-types. We also used publicly available data to define SEs by using Cdk8, Cdk9, and Smad3 in mouse ESC (mESC) and by using Smad3 in pro-B cells. Our comprehensive machine learning based analysis discovered new signatures of SEs and ranked them based on their predictive importance. Our feature ranking and analysis can serve as a resource to further characterize and understand SEs in other cell-types.Herein, to identify key features of SEs and to investigate their relative contribution in the prediction of SEs, we integrated diverse types of publicly available datasets, including ChIP-seq data for histone modifications, chromatin regulators and TFs, DHSs, and genomic data. Using predictive modeling, we found that H3K27ac, Brd4, Cdk8, Cdk9, Med12, p300, and Smad3 with higher predictive importance and significantly correlated with Med1. We implemented and compared six different parametric and non-parametric machine learning models including Random Forest, Support Vector Machine (SVM), 18. A thorough understanding of the occupancy of these factors at the constituents of SEs and TEs is not well characterized yet. Therefore, we extensively analyzed 32 publicly available ChIP-seq and DNase-seq datasets to unveil their association with the constituents of SEs and TEs in mESC. We found that most of these factors, which are enriched in SEs, were also highly enriched in the constituents of SEs relative to TEs  scores across the constituents of SEs and TEs in mESC and pro-B cells.SEs differ from TEs in terms of size, ChIP-seq density of various cofactors, TF content, ability to activate transcription, and sensitivity to perturbation34, active conformation35, and nucleosome formation36. These results suggest that GC richness may have important roles in the formation of SEs and it can be a defining feature to distinguish them from TEs.By looking at the GC-content, we found that constituents of SEs are significantly more GC-rich than the constituents of TEs  Fig. . Previou27. Hence, we checked the size (bp) of constituents and found that constituents of SEs are significantly larger in size than the constituents of TEs   Fig. . This su38. We did not find any significant difference in conservation at constituents of SEs and TEs in mESC . In contrast, in pro-B cells, the conservation score was significantly higher   Fig. , which mst) Fig. . Taken t39, to assess the relevance of each feature by ranking them based on their predictive importance  were enriched in SEss17 Fig. . It will41. Taken together, these results suggest that Smad3 is the most informative feature to distinguish SEs in differentiated cells, but this needs further validation in other differentiated cell-types. Moreover, this analysis provides a ranked list of previously characterized and new features that could be used to define SEs.We also ranked the chromatin regulators and TFs together and found H3K27ac, Brd4, Cdk8, and Cdk9 as the most informative features, while Smad3 was ranked higher than p300 and ESC specific master TFs Fig. . To testNext, to assess the individual and combinatorial predictive power of the top-ranked features, we developed a supervised machine learning workflow by integrating six state-of-the-art machine learning models and several types of features. The detailed workflow of our computational prediction pipeline is illustrated in Figure . To summ42, to increase the minority class and achieved an AUC of 0.98. Third, we used a hybrid approach by first applying over-sampling on the minority class and then under-sampling on the majority class. In general, the predictive performance of the model increased after performing data sampling. Using imbalanced data, the model achieved AUC = 0.87 but very poor precision-recall curve (PRC) of 0.57. We achieved an equal AUC = 0.98 after using over-sampling and hybrid-sampling approach but a stable PRC = 0.98, only using hybrid sampling. Therefore, we selected the hybrid-sampling approach for further analysis.Since our training data was highly imbalanced, containing fewer SEs and more TEs, and to avoid overfitting, we applied three sampling strategies Figure . First, We then compared the six supervised machine learning models, including Random Forest, linear SVM, k-NN, AdaBoost, Naive Bayes, and Decision Tree by using all the features. We used chromatin regulators, TFs, and sequence-specific features to train these models individually, and evaluated their performance using 10-fold cross-validation. Using all features, the Random Forest performed the best with an AUC = 0.98, while Naive Bayes performed poorly with an AUC = 0.85 Figs , S5D. LiWe applied our integrative workflow to test the predictive ability of the top-ranked chromatin and genomic features. As expected, the features which were ranked as the most important predictors Fig. , achievek-mer based approach43. We achieved an AUC = 0.74 for stitched sequences of SEs and TEs, and an AUC = 0.75 for the constituents of SEs and TEs , GC content and repeat fraction, and investigated their individual and combinatorial predictive power. The model achieved an AUC = 0.58 for phastCons, 0.63 for GC content, and 0.64 for repeat fraction Fig. . By combs Figure . Taken t45. Above, we noticed significant correlation among many chromatin regulators and TFs at the constituents of SEs. This suggests the existence of a combinatorial relationship betwe’en these factors, which might dictate an accurate explanation for their role in SEs formation. This combinatorial information could also be more predictive than the individual information of each factor. We, therefore, investigated the combinatorial predictive power of chromatin, TFs, and sequence-specific features. We tested 22 different combinations of these features and reported precision, recall, F1-score, AUC, and PRC , multiple myeloma (MM1.S), small cell lung carcinoma (H2171) and glioblastoma (U87). We chose these cell-types because ChIP-seq data for MED1 and the two top-ranked chromatin features, BRD4 and H3K27ac, were publicly available Table . InitialWe then trained the model on mESC data and applied it on each of the four human cell-type data and achieved an AUC = 0.92 for P493-6, 0.90 for MM1.S, and 0.86 for U87 cells Fig. . We also29. However, little is known about the binding of Cdk8, Cdk9, and Smad3 at SEs. To validate the importance of these features further, we first characterized the genome-wide ChIP-seq binding of Cdk8, Cdk9, and Smad3 at SEs identified by using Med1 in mESC and observed that Cdk8, Cdk9, and Smad3 are highly correlated with Med1 and co-occupy SEs genome-wide   Figure . When we) Figure . The fre19. To test this, we associated genes with the SEs and TEs as described in18. We found that 65% of the Med1 SEs associated genes were also associated with SEs identified by Cdk8, Cdk9, and Smad3  and are associated with cell-type specific genes, including Nanog and Dppa3 , a member of the BET protein family which functions as an epigenetic reader and transcriptional regulator that binds acetylated lysines in histones50, was ranked as the second highest important feature. Brd4 has been associated with anti-pause enhancers (A-PEs) which regulate the RNA Pol II promoter-proximal pause release52. Brd4 regulates the positive transcription elongation factor b (P-TEFb) to allow RNA Pol II phosphorylation and the subsequent elongation of target genes53. In ESC it specifically governs the transcriptional elongation by occupying SEs and by recruiting Mediator and Cyclin dependent kinase 9 (Cdk9) to these SEs54. Cdk9, a sub-unit of P-TEFb, has been found at enhancers and promoters of active genes along with the Mediator coactivator19. Cyclin-dependent kinase 8 (Cdk8), a subunit of Mediator complex, positively regulates precise steps in the assembly of transcriptional elongation, including the recruitment of P-TEFb and BRD440. Another recent study has demonstrated that Cdk8 regulates the key genes associated with SEs in acute myeloid leukaemia (AML) cells55. We also showed that Cdk8 and Cdk9 can define known and new SEs in mESC, which further validates their importance in the SE formation and cell identity.Through integrative modeling of chromatin features, we found that Brd4, H3K27ac, Cdk8, Cdk9, Med12, and p300 were the top six features and by assessing their individual predictive powers we achieved higher AUC for higher ranked features. Previous studies showed the importance of these highly ranked features and their role in transcriptional regulation. Through our ranking of features, H3K27ac achieved higher ranking as compared to other histone modifications. H3K27ac is known as a mark to differentiate active enhancers from poised enhancers41. We found a significant correlation between Smad3 and coactivators p300/CBP at SEs as previous studies have shown that p300/CBP interacts with Smad333. The evidence for the enrichment of Smad3 at SEs shows how the transforming growth factor beta (TGF-β) signaling pathway can converge on key genes that control ES cell identity. A recent study validates these results by showing that SEs provide a platform for signaling pathways, including TGF-β, to regulate genes that control cell identity during development and tumorigenesis56. To further validate, we identified and characterized SEs using Smad3 in mESC and pro-B cells. Our analysis through the integration of ChIP-seq and RNA-seq data suggested the importance of Smad3 in the SE formation and cell identity.Through integrative modeling of TFs, we found that Smad3, Esrrb, Klf4, Tcfcp2l1, Nr5f2a, and Stat3 were the top-ranked features and by assessing their individual predictive powers we achieved higher AUC for higher ranked features. It was particularly interesting to see that Smad3 was ranked as the best feature among the TFs including Esrrb and Klf4. It is known that Smad3 is a target of the TGF-β signaling pathway, and studies have shown that Smad3 is recruited to enhancers formed by master TFscis-regulatory potential of a sequence35. GC-rich and AT-rich chromatin domains are marked by distinct patterns of histone modifications. GC-rich chromatin domains tend to occur in a more active conformation and histone deacetylase activity represses this propensity throughout the genome35. Also, the GC content and nucleosome occupancy are positively correlated34, and GC-rich sequences promote nucleosome formation36. TFs tend to bind GC-rich regions in the genome, regardless of the distance and orientation34. This suggests that there is a role for the GC content in the formation of SEs, which control the cell-type specific gene expression.By investigating sequence features, we found that the constituents of SEs were significantly GC-rich. The GC-richness of a genomic regions is associated with several distinctive features that can affect the 57. A recent study showed that multiple enhancer variants cooperatively contribute to alter the expression of their gene targets58. Nevertheless, it is not well understood whether constituents of SEs work synergistically or additively. The constituents of SEs make frequent physical contacts with each other59 and extensive cooperative binding of TFs have been found at SEs60. A study in ESC showed the functional importance of SE constituents56. Furthermore, two recent studies have suggested additive and functional hierarchy among the constituents of α-globin and Wap SE locus, respectively62. In contrast, a different study argues that it is still needs to be determined whether the constituents of a SE function synergistically or additively63. Through computational modeling and correlation analysis, we noticed a combinatorial relationship between chromatin regulators and TFs at the constituents of SEs. This advances our current understanding on the determinants of SEs and leads us to hypothesize that these combinatorial patterns may be involved in mediating SEs. Moreover, the significant correlation of many cofactors at the constituents of SEs may suggest cooperative and synergistic interactions, but it requires additional experiments to validate. In summary, our systematic analysis of the SEs features and their rankings can be further used as a platform to define and understand the biology of SEs in other cell-types.The enhancer activity is due to a cooperative and synergistic interplay of different coactivators and TFsWe downloaded 32 publicly available ChIP-seq and DNase-seq datasets in mESC from Gene Expression Ominibus. These include four histone modifications , DNaseI, RNA Pol II, transcriptional co-activating proteins , P-TFEb subunit (Cdk9), sub-units of Mediator complex , other chromatin regulators , Cohesin , subunits of Lsd1-NuRD complex , and 11 TFs  Table .To validate the model using independent data, we downloaded ChIP-seq datasets for MED1, BRD4, and H3K27ac in four human tumor cell-types, including B-cell lymphoma (P493-6), Multiple myeloma (MM1.S), Small cell lung carcinoma (H2171) and Glioblastoma (U87), which were not used in the training of the model. The ChIP-seq datasets used to validate the model are listed in Table . To perf64 and17 for mESC and pro-B cells, respectively. We downloaded SE regions in mESC and pro-B cells identified using Med1 ChIP-seq occupancy from dbSUPER, which were identified using similar approach65. We also used other genomic features including GC content, conservation score (phastCons) and repeat fraction downloaded from the UCSC table browser66.We also obtained RNA-seq based gene expression data (RPKM) from67 (v0.12.9) with the parameters . We calculated read densities for the ChIP-seq datasets at the constituents of SEs (646) and TEs (9981) and normalized them as described in68. Briefly, for each constituent region, reads were extended by 200 bp and the density of reads per base pair was calculated using bamToGFF (https://github.com/BradnerLab/pipeline). Next, these densities were normalized in units of reads per million mapped reads per base pair (rpm/bp) with background subtraction. We used a similar approach for DNase-seq data but without background subtraction.Initially, ChIP-seq reads were aligned to mouse genome version mm9 using bowtie69 (v1.4.2) to perform the peak calling and to find ChIP-seq-enriched regions over the background. We used a p-value (10−9) as the enrichment threshold. To generate wiggle files, we used MACS with parameter -w -S–space = 50.The data for model validation was aligned to the human genome version hg19 as described above. We used MACS 70 for all the 11 TFs  from the transfac professional database version 2014TFs Fig. . We comp42 to perform oversampling with the following parameter settings: nearest neighbours = 5, random seed = 1 and oversampling percentage = 500. Under-sampling was achieved by randomly selecting a subset of the same size as the size of the minority class.There are two commonly used data sampling approaches, oversampling and under-sampling. The data used in this analyses was highly imbalanced. To overcome this, we used a hybrid approach by oversampling to increase the size of the minority class and under-sampling to reduce the size of the majority class. We used the Weka implementation of SMOTE39 to rank features. Briefly, it finds important features by measuring the relevance of each original feature with respect to a reference attribute using Random Forest. Second, we used Random Forest out-of-bag approach to calculate the relative importance of each feature. Briefly, this approach takes one feature out and measures its relative importance and contribution to the model. The reason behind to use two approaches to rank features was to see the consistency of feature ranking. To test the interpretability of the feature selection approaches, we computed the Pearson correlation between the features importance scores and observed that the feature scores are significantly correlated  with 95% confidence interval  are constituents of SEs, and 50%  are constituents of TEs. We considered constituents of SEs as positive class and constituents of TEs as a negative class. In total, we have 45 features, including 20 chromatin features, 11 TFs, 11 DNA motifs, and three sequence-specific features. We excluded Med1 from our training data because SEs were defined based on Med1 ChIP-seq signal17. Not surprisingly, we achieved best classification results by using Med1 as a feature.We downloaded 10627 loci of constituents of SEs and TEs in mESC defined based on Med1 ChIP-seq signal72, SVM73, k-NN74, AdaBoost75, Decision Tree, and Naïve Bayes.We investigated six state-of-art supervised machine learning models including Random ForestRandom Forests are ensemble and non-parametric models that fit a number of decision tree classifiers on various sub-samples of the dataset and use averaging to run efficiently on large datasets without overfitting. In computational biology, Random Forests are popular due to their performance and flexibility. The major drawback is their lack of reproducibility, as the initialization of the forest is random.76 with a linear kernel and regularization parameter C = 1.0, and also compared non-linear variants of SVM  and Polynomial kernels). A drawback of the SVMs is that they are computationally intensive and sensitive to noisy data.The SVM model transforms the features into a higher dimension by using kernel functions to find an optimal separating hyperplane between two classes. This is achieved by maximizing the margin between the classes. In this study, we used LibSVMK-Nearest Neighbor (k-NN) is a non-parametric method, where k is the number of nearest training examples in the feature space. A class label is assigned based on a majority vote of its k nearest neighbors. The value of k is always a positive integer and if k = 1 then the class of the single nearest neighbor is assigned.The Adaptive Boosting (AdaBoost) is an ensemble algorithm with meta-estimators, which initially fits on a sequence of weak learners with repeatedly modified versions of the data. To make a final prediction, all the predictions from the weak learners are combined through a weighted majority vote.Decision Trees are a non-parametric model that predicts the value of a target variable by learning simple decision rules inferred from the data features.Naïve Bayes (NB) is a simple probabilistic classifier based on the Bayes theorem, which considers all features to be independent of the probability of a class label. In this study, we used the Gaussian Naive Bayes, which assumes the likelihood of the features to follow a Gaussian distribution.77. Model parameters were optimized using scikit-learn’s GridSearch function with cross-validation. The AUC scores reported in this manuscript are based on the Random Forest model, unless stated otherwise. We calculated an out-of-bag error to find the optimal number of trees (n = 20) to use for Random Forest , a Python library for machine learningest Fig. . For oth43 to classify constituents of TEs and SEs. We used the default settings with k-mer size = 5, spectrum kernel, regularization parameter (C) = 1.0. We used 5-fold cross-validation for model validation.We used a sequence-specific enhancer prediction method (Kmer-SVM)78. To evaluate the performance of the models, we reported precision, recall, F1-score, area under the ROC curve (AUC) and the precision-recall curve (PRC). The receiver-operating characteristic (ROC) is a graphical representation of the true positive rate (sensitivity) v/s false positive rate (1-sensitivity). The true positive rate is also known as sensitivity or recall. The false positive rate is also known as (1-sensitivity). The F1 score is an accuracy measure, which considers both the precision and the recall of the test to compute the score. We also tested if the increase in model AUC is statistically significant by using permutation test (1000 runs)  to validate models, which makes the folds by preserving the percentage of samples for each class. Stratified CV is generally considered a better scheme than standard CV in terms of bias and variance) Figure .17. These constituent enhancers were then stitched together within 12.5 kb and +/−2 kb away from TSS. These stitched regions were ranked based on the ChIP-seq signal by using the ROSE software to define SEs17. The SEs data downloaded from dbSUPER65 was also generated using the same pipeline.We used 10,227 co-bound regions of Oct4, Sox2, and Nanog in ESC and 13,814 regions of master TF PU.1 in murine progenitor B (pro-B) cells as constituent enhancers, which were obtained from18. It is known that enhancers tend to loop and communicate with target genes7, and most of these enhancer-promoter interactions occur within a distance of ~50 kb79. This approach identified a large proportion of true enhancer/promoter interactions in ESC80. Hence, we assigned all transcriptionally active genes to SEs and TEs within a 50kb window.We assigned genes to SEs and TEs using a proximity rule as described in81, to generate heat maps and normalized binding profiles at the constituents of SEs and TEs and their flanking 3 kb regions.We generated box plots using R programming language by extended the whiskers to 1.5x the interquartile range. The P-values were calculated based on the Wilcoxon signed-rank test for box plots, by using wilcox.test function in R. We used ngs.plothttps://github.com/asntech/improse. A detailed documentation can be found at http://improsedoc.readthedocs.io/.To foster the reproducible research, we developed our analysis pipeline as an open-source Python package with a command line interface and made it freely available for academic use at Supplementary MaterialsSupplementary Table 1Supplementary Table 2


Here we use electroporation of plasmid reporters to define critical parameters of a specific cis-regulatory element, ThrbCRM1, during retinal development. ThrbCRM1 is associated with cone photoreceptor genesis and activated in a subset of developing retinal cells that co-express the Otx2 and Onecut1 (OC1) transcription factors. Variation of reporter plasmid concentration was used to generate dose response curves and revealed an effect of binding site availability on the number and strength of cells with reporter activity. Critical sequence elements of the ThrbCRM1 element were defined using both mutagenesis and misexpression of the Otx2 and OC1 transcription factors in the developing retina. Additionally, these experiments suggest that the ThrbCRM1 element is co-regulated by Otx2 and OC1 even under conditions of sub-optimal binding of OC1.Enhancer activity is determined by both the activity and occupancy of transcription factors as well as the specific sequences they bind. Experimental investigation of this dynamic requires the ability to manipulate components of the system, ideally in as close to an  Summary: Systematic variation of the levels of a transcriptional reporter plasmid, its trans-acting factors and transcription factor binding sites reveals properties of a retinal enhancer during development. Elucidation of this process during development is further complicated by temporal dynamics as cells have rapid shifts in active gene regulatory networks (GRNs). However, identification of these networks provides insights into how transcription factor expression and activation are coordinated to direct cell fate choices during development.The rules and logic of cis-regulatory activity that underlie dynamic gene regulation during development are an area of great interest . At presElectroporation is one method to identify and characterize cis-regulatory elements as components of GRNs. Several studies have used this method to identify cis-regulatory elements, providing critical insights into retinal development and other developmental contexts . In addiThe vertebrate retina is an excellent model tissue to investigate the development of nervous system complexity. Recent analysis has suggested that the retina may be composed of as many as 100 cell types, each of which are generated from multipotent retinal progenitor cells during development . RecentlHere we describe a quantitative analysis of the ThrbCRM1 element in developing retinas. Using electroporation as a method to introduce fluorescent reporter plasmids and flow cytometry to quantitate reporter activity, the activity of ThrbCRM1 elements were measured in terms of total reporter-positive cells as well as fluorescence level of cells within the population. This analysis revealed distinct differences in concentration-dependent reporter activity that depended on the copy number of cis-regulatory elements. Misexpression of the Otx2 and OC1 activating transcription factors also led to concentration-dependent changes in reporter activity that suggested saturation of reporter activation also occurred. Flow cytometry was used to determine the likely functional Otx2 binding site. Lastly, using these mutated ThrbCRM1 plasmids in combination with Otx2 and OC1 misexpression plasmids suggested that the co-requirement for these two transcription factors is likely not at the step of complex formation on DNA.ex vivo for 2 days, dissociated into single cells, and analyzed by flow cytometry. In all experiments, the activity of a cell-type specific EGFP reporter construct was assessed relative to a broadly active red fluorescent protein reporter construct. For both model animal systems, the ThrbCRM1 element was the main reporter plasmid used. ThrbCRM1 is a 40 base pair element containing an OC1 binding site and two potential Otx2 binding sites  or 200 ng/µl CAG::Nucβ-gal  show a large number of mCherry single-positive cells and very few cells that express EGFP. A plot of this data reveals that the basal vector used at the standard concentration of our ex vivo chicken experiments (160 ng/µl) had extremely low levels of EGFP expression in all samples. There was no statistically significant ectopic activation of EGFP even at the highest concentration of CAG-containing plasmids . This suggests that the Stagia3 reporter vector used in these experiments (1) possesses low basal activity and (2) this basal activity is not altered by the presence of high concentrations of additional plasmids that possess strong regulatory elements.The Stagia3 plasmid was used for all experiments and has been reported to have low basal activity in qualitative assessments . A flow The Stagia3 reporter plasmid has been used in a number of studies to test the activity of potential cis-regulatory elements. In previous studies, a specific concentration  has been repeatedly used in chicken and mouse retinas, though it has not been empirically determined whether this is the ideal concentration for assessment . When coIn addition to calculating the total number of cells, the fluorescence intensity of the cells in that population was assessed by determining the distribution of the EGFP-positive cells across five Bins, with Bin 1 being the weakest EGFP-expressing cells and Bin 5 the strongest expressing cells A,B. The The concentration curves for ThrbCRM1(4X) and ThrbCRM1(2X) shown in A previous study has identified the ThrbCRM1 element as containing predicted Onecut and Otx2 binding sites and a chromatin immunoprecipitation experiment confirmed their occupancy of this element in the developing chicken retina . The curThe distribution of EGFP-positive cells in these transcription factor misexpression experiments was calculated using the same Bin system as described above in To determine the concentration effect of misexpressing the Otx2 and OC1 factors on ThrbCRM1(4X) activity, plasmids encoding misexpression constructs for both transcription factors were introduced at similar relative proportions to each other. A dose-dependent increase in activity was observed with a distribution that suggested that even one-tenth the amounts of misexpressed Otx2 and OC1 that were used previously were sufficient to increase the output of reporter activity A. While The ThrbCRM1 element is not active when electroporated into the mouse postnatal retina . This isA previous study identified OC1 and Otx2 binding sites in the ThrbCRM1 element through chromatin immunoprecipitation and functionally tested the requirement of the OC1 binding site by mutating five of the core base pairs that compose this site . HoweverWe first qualitatively tested these constructs in the context of the intact chicken retina B–G. ThrbTo more quantitatively examine the effect of these mutations, each ThrbCRM1(2X)::EGFP construct was co-electroporated with UbiqC::TdT and analyzed by flow cytometry. The percentage of cells that activated the EGFP reporter was calculated and plotted H. The wiTo further test the requirements of the binding sites identified in the mutagenesis experiments, we determined whether misexpression of either OC1 or Otx2 could activate mutated ThrbCRM1 reporters that lacked OC1 or Otx2 binding sites. Mutated ThrbCRM1 reporters were electroporated into chicken E5 retinas in combination with Otx2 or OC1 misexpression plasmids and analyzed by flow cytometry. We first tested the ThrbCRM1[Otx2Mut1/2]::EGFP plasmid, which lacks any consensus Otx2 binding sites A. As expThese experiments suggest that the necessity for one of the transcription factor binding sites can be overcome by misexpression of the other transcription factor that has an intact binding site. Two major possibilities exist to explain this phenomenon. One is that under normal conditions, co-expression of both transcription factors is needed to lead to stable occupancy of ThrbCRM1 and detectable reporter expression. High misexpression of one of the transcription factors could lead to stable occupancy and reporter activation by this transcription factor, independent of the other transcription factor. However, this explanation is not supported by previous data in which misexpression of either Otx2 or OC1 in tissues that lacked the other transcription factor were unable to induce the ThrbCRM1 reporter . A seconin vitro assays where differences in binding partners and cellular context are lacking. Thus, the generation and use of quantitative assays in the context of developing tissue, as shown here, is of critical importance.The analysis of GRNs has yielded insights into fundamental developmental processes . HoweverThis study shows that the concentration of reporter plasmid is an important variable in experimental design, though this aspect has been ignored by the vast majority of previously published electroporation studies. In cases of sequence element mutagenesis, the ideal concentration of reporter plasmid is below the saturation point for reporter output. This concentration allows for the detection of either partial loss of activation or an increase in reporter activity due to loss of a repressor site. We speculate that the use of reporter plasmid concentrations above the saturation point could obscure meaningful reporter output changes, depending on the mechanism affected by the mutation. For example, mutations that lower the affinity of activating transcription factors for binding sites in the element may lead to a rightward shift in the plasmid concentration curve, but not necessarily the maximal reporter output. Thus, comparisons of such a mutant with the wild-type form at concentration points where they are both saturated would not be informative. This study also demonstrates that saturation points are likely to be unique for a given cis-regulatory element in a particular biological context. As an example, the two different multimeric forms of ThrbCRM1 had significantly different saturation points in the chicken retina, and the ThrbCRM1 saturation point was different in the context of the mouse retina where Onecut factors were not expressed. In addition to these important technical considerations, we also suggest that the quantitative flow cytometry assay used here reveals saturation kinetics that are a direct result of transcription factor occupancy of cis-regulatory elements. A confirmation of the direct binding kinetics of the transcription factors in these cells is not possible with current techniques, but the increased fluorescence levels at the per cell level induced by increased transcription factor expression supports this interpretation.Similar to reporter plasmids, a systematic evaluation of misexpression plasmid concentration in electroporation experiments has not previously been examined. Given the demonstrated effects of transcription factor expression levels inducing specific cell fates  , the conThe small size of the ThrbCRM1 element and the known identity of two key regulators of its activity make this cis-regulatory element an ideal candidate to explore at the functional level. The limited sequence constrains the regulatory information that can be encoded and thus the complexity of its regulation. Otx2 and OC1 are the only known regulators and the current model supports a necessary and sufficient model for their regulation of the ThrbCRM1 element . The mutin vitro Selex studies (TAATCC)  differs from the canonical monomeric site identified in (TAATCC) . In this(TAATCC) . We specdenticle . This coIn summary, this study quantitatively assesses the effects of multiple experimental parameters on the activity of the restricted RPC ThrbCRM1 element. This has provided insights not only into the specific sequence requirements and transcription factor/DNA binding dynamics for this particular element, but more generally into the use of electroporation to investigate cis-regulatory elements during development.All methods used in animal studies were approved by City College of New York, CUNY animal care protocols. Fertilized chicken eggs and CD-1 mice were obtained from Charles River. Eggs were stored in a 16° room for up to 10 days before incubation and incubated in a 38°C humidified incubator. Retinas were isolated from chicken embryos and mouse P0 pups without regard to sex.The Stagia3 EGFP reporter plasmid uses a minimal TATA box from Herpes Simplex Virus and is described in DNA electroporation mixes were made with a volume of either 50 or 55 ul, with 50 ul used in the electroporation chamber for all experiments. A 10X phosphate buffered solution (PBS) was used to generate a final concentration of 1X PBS. To aid in accurate pipetting of viscous DNA solutions, a positive displacement pipettor  was used. For all experiments that involved a comparison between the effects of a particular plasmid, a mastermix was generated that included PBS and other plasmids found in all samples. For experiments in which the amount of EGFP reporter varied, 55 µl DNA mixes were prepared by adding the determined amount of EGFP plasmid to a tube and preparing a mastermix of PBS and the other plasmids found in all samples before adding to the EGFP tubes. A NanoDrop blank sample was prepared by adding the appropriate volume of TE and mastermix and then prepared DNA mixes were measured at 260 nm. The average of three spectrophotometer readings were used to empirically determine the amount of EGFP plasmid present in mixes, to avoid data skewing by pipetting error and was used in the plotting of data.Methodology for DNA electroporations was as described in Retinal pigment epithelium and excess vitreal tissue was removed in HBSS media  using forceps and the retina was placed in microcentrifuge tube with 200 µl HBSS. A papain activating solution of 200 µl/retina containing 11.6 mM L-cysteine, 1.11 mM EDTA and 5 µl papain  was added and incubated at 37°C for 15–25 min for chicken retinas or 35–45 min for mouse retinas. During this period, each tube was individually flicked to help break down the tissue into smaller clumps. 600 µl of 10% FBS /DMEM  was added to stop the reaction. 10 µl/retina of DNase  was added, incubated in a 37°C water bath for 5 min, washed in DMEM and then fixed in 4% paraformaldehyde/1X PBS for 15 min. Cells were washed three times in 1 ml of 1X PBS upon being filtered through a 40 µm strainer . All centrifuge spins were at 1700rcf for 5 min and supernatant removed with a P1000 pipettor.The dissociated single-cell suspensions were analyzed via flow cytometry using a BD Biosciences LSR II or FACS Aria machine. Approximately 300,000 cells were analyzed for each sample.U-test was applied using JASP software  and sectioned to 20 µm thickness on a Leica CM1950 cryostat and placed on glass slides . Slides were processed for immunofluorescence as previously described . Primary