The eyes and skin are obvious retinoid target organs. Vitamin A deficiency causes night blindness and retinoids are widely used to treat acne and psoriasis. However, more than 90% of total body retinol is stored in liver stellate cells. In addition, hepatocytes produce the largest amount of retinol binding protein and cellular retinoic acid binding protein to mobilize retinol from the hepatic storage pool and deliver retinol to its receptors, respectively. Furthermore, hepatocytes express the highest amount of retinoid x receptor alpha (RXRα) among all the cell types. Surprisingly, the function of endogenous retinoids in the liver has received very little attention.Based on the data generated from chromatin immunoprecipitation followed by sequencing, the global DNA binding of transcription factors including retinoid x receptor α (RXRα) along with its partners i.e. retinoic acid receptor α (RARα), pregnane x receptor (PXR), liver x receptor (LXR), farnesoid x receptor (FXR), and peroxisome proliferator-activated receptor α (PPARα) has been established. Based on the binding, functional annotation illustrated the role of those receptors in regulating hepatic lipid homeostasis. To correlate the DNA binding data with gene expression data, the expression patterns of 576 genes that regulate lipid homeostasis were studied in wild type and liver RXRα-null mice treated with and without RA. The data showed that RA treatment and RXRα-deficiency had opposite effects in regulating lipid homeostasis. A subset of genes (114), which could clearly differentiate the effect of ligand treatment and receptor deficiency, were selected for further functional analysis. The expression data suggested that RA treatment could produce unsaturated fatty acids and induce triglyceride breakdown, bile acid secretion, lipolysis, and retinoids elimination. In contrast, RXRα deficiency might induce the synthesis of saturated fatty acids, triglyceride, cholesterol, bile acids, and retinoids. In addition, DNA binding data indicated extensive cross-talk among RARα, PXR, LXR, FXR, and PPARα in regulating those RA/RXRα-dependent gene expression levels. Moreover, RA reduced serum cholesterol, triglyceride, and bile acid levels in mice.We have characterized the role of hepatic RA for the first time. Hepatic RA mediated through RXRα and its partners regulates lipid homeostasis. The eyes and skin are obvious retinoid target organs. Vitamin A deficiency causes night blindness and retinoids are widely used to treat acne and psoriasis. However, more than 90% of total body retinol  is stored in liver stellate cells . In addiThe broad and complicated roles of retinoids can be explained by the presence of multiple receptors for retinoic acid (RA), the biological active form of retinol. The receptors for RA are retinoic acid receptor (RAR) as well as retinoid x receptor (RXR) . In addiRXRα is highly expressed in the liver . Liver sTo understand the global roles of RXRα and RARα at the genomic level, chromatin immunoprecipitation using anti-RXRα and -RARα antibodies followed by sequencing (ChIP-seq) was performed. Since RXRα is an essential partner for other nuclear receptors, we compared ChIP-seq data to RXRα binding locations with locations from previous studies for PXR , LXR 1919, FXR , and PPATo understand the global roles of RXRα and RARα at the hepatic genome level, ChIP-seq was performed using anti-RXRα and -RARα antibodies. Single read sequencing yielded 18 and 32 million uniquely mapped reads for RXRα and RARα, respectively. After filtering by including peak scores that were greater than 20 and distance within 10 kb from the transcriptional start site, 17,973 peaks were detected for RXRα and 18,697 peaks for RARα. Since RXRα is an essential partner for other nuclear receptors, we compared ChIP-seq data of RXRα with those of PXR , FXR 20. Our datCluster and principal component analysis (PCA) were performed using the binding data. Overlapping bindings between RXRα and RARα, PPARα, FXR, LXR, and PXR were analyzed. According to the length of the vertical branches Figure A, LXR anTo understand the potential biological role of genes likely to be targeted by RXRα-heterodimers, DAVID functional annotation was performed. The top ten processes for each of the heterodimers formed by RXRα and other nuclear receptors were investigated in this study Table . AlthougHepatic RXRα KO mice have elevated serum cholesterol and triglyceride levels , and theTaken together, ligand (RA) treatment and hepatic RXRα deficiency resulted in opposite effects. Figure Additional analysis was done to understand which other nuclear receptors may be involved in regulating the expression of these 114 RA/RXRα target genes, which have a role in lipid homeostasis. The binding data generated in the current study (RXRα and RARα) were compared with the binding data of PXR, LXR, FXR, and PPARα. Figure ChIP-Seq and RNA expression profiling indicate the role of RA in controlling lipid homeostasis in the liver. Serum cholesterol, triglyceride, and bile acid levels were quantified to test the genetic findings. The data showed that RA reduced serum cholesterol, triglyceride as well as bile acid levels in wild type mice Figure . HoweverThis study establishes the role of nuclear receptors and RA in regulating lipid homeostasis in the liver. In addition, the mechanisms by which nuclear receptors and RA regulate lipid homeostasis were illustrated at the gene, pathway, and systemic levels. Furthermore, relationships between RXRα and PXR, LXR, FXR, as well as PPARα in regulating lipid homeostasis were analyzed. These nuclear receptors depend on RXRα to execute their functions and more than 85% of their bound genes overlap with those bound by RXRα. The nuclear receptor binding data were strengthened by profiling the expression patterns of 576 lipid genes, which showed that RA treatment and RXRα-deficiency had an opposite effect in regulating lipid homeostasis. Nuclear receptor binding data also illustrated extensive cross talk among the studied nuclear receptors. Thus, our novel in vivo data provided extensive evidence showing the role of RA in dictating lipid homeostasis in the liver.It is intriguing that more than 85% of the PXR, LXR, FXR, and PPARα binding sites overlapped with those of RXRα. In contrast, only 43% of RARα binding sites overlapped with those of RXRα. It has been shown that RARα can form homodimers . It is aClustering and PCA showed that the genome-wide binding pattern of RARα is not similar to that of PPARα, LXR, PXR, and FXR. Biological function annotation also showed that RARα has some unique features including protein processing, protein localization, and RNA processing. However, the five studied nuclear receptors also demonstrated functional redundancy. For example, there are four pathways, including oxidation reduction, carboxylic acid catabolic process, organic acid catabolic process, and cofactor metabolic process, that can be regulated by more than four nuclear receptors. This finding suggests the importance of these four pathways in the liver, and the role of RARα in them.Although PPARα, LXR, FXR, and PXR have extensive roles in regulating lipids, they also have specific roles in regulating different types of lipids. RXRα/PPARα prefers to bind to genes that participate in neutral lipids, glycerol ether, and organic ether as well as fatty acid metabolism processes. All of which are either fatty acid-derived products or precursors for the biosynthesis of fatty acids. Another pathway bound by RXRα/PPARα is the acylglycerol metabolic process, which is involved in triglyceride homeostasis. RXRα/LXR tends to regulate genes involved in sterol metabolism, which is consistent with its known role . RXRα/FXAll of the 114 genes that showed differential effects of RA treatment and RXRα deficiency are bounded by RXRα and RARα, PXR, LXR, FXR, and PPARα heterodimers. These findings indicate that those studied nuclear receptors retain RA response in vivo and the effect of RA is dependent upon those nuclear receptors. RA has a broad spectrum of effects including biosynthesis of retinoids, phospholipids, and unsaturated fatty acids. It also has a role in eliminating retinoids, oxidizing saturated fatty acids, and breaking down triglycerides. It seems that RA has extensive beneficial effects in maintaining the health of the liver. Specifically, RA induced the expression of Cyp2c37/38/50/54/70 and Cyp2j5. These genes encode enzymes involved in the generation of epoxyeicosatrienoic acids , which hTaken together, the differentiation and morphogenetic effect of RA is well known. However, the current study provides a comprehensive analysis of the role of RA in lipid homeostasis. All-trans RA is the most abundant retinoid that can be easily detected in the liver. Thus, RA is likely to act as a regulator to control hepatic lipid metabolism. Since the effect of RA is broad, it is important to develop specific retinoids in order to target specific pathways. Such efforts may allow us to identify compounds that can be used to treat or prevent metabolic syndromes and other lipid-related health issues.Male wild type mice (12 weeks old) and hepatocyte RXRα-deficient mice (KO) ,11, whicAll ChIP-grade antibodies except anti-RNA Pol II  were purchased from Santa Cruz Biotechnology, Inc. . DNA purification kit was purchased from Qiagen Co. . All other ChIP-related reagents were obtained from Invitrogen Co. .Retinoic acid  was given to wild type and hepatic RXRα-deficient mice at a dosage of 150 mg/kg diet for 7 days. As controls, mice were fed with normal diet.ChIP was performed according to our previously published study . After fBy using the End-It DNA End Repair Kit , DNA fragments prepared from ChIP were ligated with specified adaptors and amplified, then size-selected (175-225 bp) on an agarose gel followed by sequencing .http://hgdownload.cse.ucsc.edu/goldenPath/mm10/bigZips/) by Bowtie 0.12.7 [The target sequences were aligned to the mouse genome  was used to determine the genome-wide mRNA expression levels. Microarray data were annotated using Affymetrix Expression Console (MAS5). The probe signal with http://david.abcc.ncifcrf.gov/) [All data were treated with the same cut off criteria. The generated RXRα binding data were compared with the data for RARα, PXR, LXR, FXR, and PPARα. The principle component analysis (PCA) and cluster analysis package in SPSS program was used to analyze the global binding data. For both PCA and cluster analysis, called peaks were assigned the value of 1. Not called peaks were assigned the value of 0. Genes with overlapping binding sites of RXRα and each of RARα, PXR, LXR, FXR, and PPARα at the same location were functionally analyzed by the DAVID (rf.gov/) .http://www.genome.jp/kegg/). The expression of those 579 genes were determined in wild type and liver RXRα KO mice treated with and without RA (n = 3) for 7 days. After multiple comparisons, only 30 and 36 out of all 579 lipid homeostasis genes showed significant change at the corrected p-value of 0.05 after RA treatment and RXRα knockout, respectively. Therefore, IBM SPSS PCA package was used to differentiate groups based on the global expression pattern of all 579 lipid homeostasis genes.Genes (579) involved in regulating lipid homeostasis were extracted from the KEGG database  that was modified to a 96-well format. Spectrophotometric analysis was conducted with a Bio-Tek microtiter plate reader .http://www.ncbi.nlm.nih.gov/geo/).The microarray and ChIP-Seq data supporting the results of this article are available with accession numbers of GSE50028 and GSE46762, respectively, in the GEO repository (RA: Retinoic acid; ChIP: Chromatin immunoprecipitation; seq: Sequencing; RXRα: Retinoid x receptor alpha; RARα: Retinoic acid receptor alpha; PXR: Pregnane x receptor; LXR: Liver x receptor; FXR: Farnesoid x receptor; PPARα: Peroxisome proliferator-activated receptor alpha; DAVID: Database for annotation, visualization and integrated discovery; PCA: Principal component analysis; WT: Wild type; KO: Knockout; NR: Nuclear receptors; KEGG: Kyoto encyclopedia of Genes and genomes; PE: Phosphatidylethanolamine; PC: Phosphatidylcholine.The authors declare that they have no competing interests.YH: Performed experiments, analyzed data, generated figures and tables as well as prepared manuscript. LG: Microarray experiments and analysis. YF: Sequence alignment and call peaks. QZ: Functional annotation. H-XL: Animal experiments. YL: Statistics analysis. GLG: FXR ChIP Seq data generation. LL-M: Microarray data generation. JF: Sequence alignment and call peaks. Y-JYW: Generated idea and supervised the all overall performance of the project. All authors read and approved the final manuscript.


Caenorhabditis elegans was the first multicellular organism with a completely sequenced genome, how this genome is arranged within the nucleus is not known.Although C. elegans embryos via chromatin immunoprecipitation. Large regions of several megabases on the arms of each autosome were associated with LEM-2. The center of each autosome was mostly free of such interactions, suggesting that they are largely looped out from the nuclear membrane. Only the left end of the X chromosome was associated with the nuclear membrane. At a finer scale, the large membrane-associated domains consisted of smaller subdomains of LEM-2 associations. These subdomains were characterized by high repeat density, low gene density, high levels of H3K27 trimethylation, and silent genes. The subdomains were punctuated by gaps harboring highly active genes. A chromosome arm translocated to a chromosome center retained its association with LEM-2, although there was a slight decrease in association near the fusion point.We determined the genomic regions associated with the nuclear transmembrane protein LEM-2 in mixed-stage C. elegans chromosomes within the nucleus.Local DNA or chromatin properties are the main determinant of interaction with the nuclear membrane, with position along the chromosome making a minor contribution. Genes in small gaps between LEM-2 associated regions tend to be highly expressed, suggesting that these small gaps are especially amenable to highly efficient transcription. Although our data are derived from an amalgamation of cell types in mixed-stage embryos, the results suggest a model for the spatial arrangement of  The nuclear envelope, which consists of nuclear membranes, nuclear pore complexes and the nuclear lamina, primarily functions to separate the nuclear contents from the cytoplasm, and to maintain the structural integrity of the nucleus. However, this barrier is also physically associated with chromatin, which has led to the hypothesis that the nuclear envelope helps to control the spatial arrangement of the genome within the nucleus -4. This Interactions between the nuclear envelope and chromosomes have been mapped in fly, mouse, and human cells by recording associations between the genome and B-type lamins and emerin -8. B-typCaenorhabditis elegans utilizing a different approach, chromatin immunoprecipitation (ChIP) of the LEM-2 protein coupled with detection by tiling microarray (ChIP-chip) and next-generation sequencing (ChIP-seq). LEM-2 is a transmembrane protein localized to the inner nuclear membrane, with homologs in a wide variety of organisms, including yeast, mouse, human, and C. elegans [C. elegans, LEM-2 interacts with lamins in vitro and requires lamins for its localization to the nuclear membrane [C. elegans cell [C. elegans causes 15% embryonic lethality [C. elegans reaches 100% if the level of emerin is simultaneously reduced [C. elegans, illustrating one way that LEM-2 may interact with chromatin in vivo [In this study, we identify genomic regions associated with an inner nuclear membrane protein in  elegans -16. In hmembrane ,13. Thusans cell ,13. Its  reduced  by block reduced . LEM-2 i in vivo .C. elegans chromosomes, are associated with LEM-2 at the inner nuclear membrane, while the central regions are not. The large LEM-2 domains at the arms consist of smaller subdomains, which are characterized by a high density of repetitive sequences and a low density of genes. These subdomains are transcriptionally inactive, whereas the gaps between the subdomains are transcribed. Finally, we show that chromosome ends relocated to the center of a chromosome through an end-to-end chromosomal fusion remain associated with LEM-2, albeit at somewhat reduced levels. This shows that association with the nuclear membrane is characteristic of each chromosomal region, and only partly dependent on relative chromosome position. We provide a model of the spatial and functional arrangement of the C. elegans genome, which is physically supported by domain-scale and subdomain-scale association with the nuclear membrane.Our data show that the distal regions of the autosomes, which are called 'arms' despite the holocentric nature of C. elegans LEM-2 protein. The specificity of the antibodies was confirmed by western blotting, which detects a strong band at the expected size of 55 kDa in wild-type C. elegans embryos. The band was not present in extract prepared from lem-2(ok1807) null mutant animals  or high-throughput sequencing (ChIP-seq) to identify regions associated with LEM-2 genome-wide. For ChIP, we used chromatin extracts from ing MA2C , and ChIn Figure . We confn Figure . We obsen Figure . The LEMThe X chromosome exhibits a pattern of LEM-2 interaction distinct from that of the autosomes. On X, only the left arm has a characteristic large LEM-2 domain, whereas the right arm has very weak LEM-2 associations Figure . FurtherThe meiotic recombination rate and the density of repetitive sequences are known to differ between the chromosomal arms and central regions ,22. The C. elegans [Repetitive sequences are over-represented on chromosomal arms in  elegans ,22. AnalThe unique LEM-2 pattern on the X chromosome let us examine whether the high recombination rate and the high density of repeats are general characteristics of the LEM-2 domains. Repeats are concentrated on the left end of X, in the regions of high LEM-2 association, whereas the right end of X harbors fewer repetitive sequences and is only weakly associated with LEM-2 Figure . In contr = 0.80, P = 0.11; Pearson's product-moment correlation).The data presented above demonstrate the binding of LEM-2 to broad domains of chromosome arms. We next examined the pattern of LEM-2 binding within these domains more closely. We found that, within LEM-2 domains, there are many interruptions that result in generating smaller LEM-2-associated regions Figure ,b. TheseLEM-2 subdomains exhibit characteristic distribution patterns across the chromosomes. First, larger subdomains are typically located closer to the chromosome ends and become smaller as a function of proximity to the centers . To confirm this association, we assessed the distribution of gene translation start sites across the LEM-2 subdomain-gap boundaries of all chromosomes. The profile confirmed that gene density decreases as one moves from gaps to subdomains and further revealed that translation start sites of genes preferentially occur just outside the LEM-2 subdomains , but the overall profiles are similar. Together, the data indicate that coding genes are over-represented in LEM-2 gaps, and that genes' translation start sites are preferentially located just outside of the LEM-2 subdomains regardless of their orientation.We tested whether gene density, which is highly variable across the s Figure . A similC. elegans mixed-stage embryos in quadruplicate by microarrays and calculated the average level of expression among replicates for each transcript . Next, we categorized transcripts as falling into LEM-2 subdomains  or gaps  based on the location of the corresponding gene's transcript start site . These data demonstrate that genomic regions associated with LEM-2 are more likely to be inactive, whereas gaps are more likely to possess active genes.We next asked if genes in LEM-2 subdomains and gaps are expressed. We measured transcript levels of C. elegans development. We used publicly available RNA-seq data [We examined whether the inactive state of genes at the nuclear membrane is stable during seq data  to deterC. elegans are described in our companion papers [H3K27 trimethylation (H3K27me3) is generally linked to transcriptionally inactive regions . We thern papers ). We fouOther histone modifications linked to transcriptionally inactive regions are H3K9me2 and H3K9me3 . In contWe then analyzed H3K27 methylation and H3K9 methylation distributions relative to the location and expression level of genes. While inactive genes in LEM-2 subdomains harbor high levels of H3K27me3, active genes within LEM-2 subdomains possess, like those in gaps, low levels of H3K27me3 Figure . In contC. elegans genome [To determine if the high RNA levels of genes in gaps Figure  reflect s genome , also has genome . H3K4me3We further tested the relationship between markers of active transcription and gaps by plotting average levels of RNAPII, HTZ-1, and H3K4me3 across boundaries of nuclear membrane association Figure . The occThe variation in the sizes of LEM-2 gaps Figure  implies C. elegans embryos was determined by fluorescence in situ hybridization (FISH) [baf-1 gene, which was found mostly in the nuclear interior by FISH, is indeed located in chromosome III's central region, which lacks LEM-2 association   . Therefore, some other property of small loops, perhaps a property inherent to the small loops themselves, supports higher levels of transcription.A possible explanation for high expression in small gaps is that proximity to a boundary facilitates higher expression. We ruled this out, since higher transcription was not observed nearer to the boundaries of medium or small gaps Figure . Even inC. elegans [We next explored if there is any bias for genes with critical developmental roles to reside at the nuclear membrane or in the gaps. We examined phenotypic annotations from previous RNA interference (RNAi) experiments . We found that a set of RNAi phenotypes that characterize essential genes, such as 'embryonic lethal' and 'maternal sterile', are under-represented in LEM-2 subdomains Figure . In cont elegans , consistThe genes linked to these RNAi phenotypes that occurred in LEM-2 gaps tend to be highly expressed. Among the top quartile of genes expressed in embryos were 81% of 'protruding vulva' genes in small gaps, 71% of 'embryonic lethal' genes in medium gaps, and 69% of 'slow growth' genes in large gaps. Thus, LEM-2 subdomain-gap structure is tightly linked to the expression of genes critical for animal development and function.mnT12), in which the right end of chromosome X is fused with the left end of chromosome IV [Our studies have shown that nuclear-membrane association selectively occurs at chromosome arms but also correlates with local characteristics of the genome. We therefore performed a test to determine if nuclear-membrane association is more dependent on a region's position along the chromosome or local signals. This test employed a strain possessing a fusion chromosome  to 50% (autosomes) of each chromosome. This arm-specific pattern of nuclear membrane association was not observed in lamin B1-genome associations in Drosophila melanogaster, mouse, or human cells [C. elegans may be a consequence of the unique organization of its chromosomes. This organization, however, appears to have been conserved over at least 100 million years, since Caenorhabditis briggsae shares characteristics of chromosomal arms, including the high density of repetitive sequences and high rate of recombination [In romosome . In contan cells -8. Therebination . It willDrosophila, the precise coverage of lamina-associated regions is difficult to determine because low-resolution microarrays were used for mapping [C. elegans. Although these proportions reflect membrane associations occurring in different cells, a general property of metazoan chromatin organization may be association of approximately half of the genome with the nuclear membrane.In  mapping . However mapping ,7. This C. elegans hermaphrodites, each of the two X chromosomes undergoes chromosome-wide transcriptional repression of approximately two-fold to achieve dosage compensation [C. elegans X chromosome lacks the degree of LEM-2 association typical of autosomes, suggesting a largely nucleoplasmic localization  were produced against amino acids 1 to 100 of LEM-2 by genetic immunization at SDIX . Mouse monoclonal antibodies for H3K9me2 (clone 6D11), H3K9me3 (clone 2F3), and H3K27me3 (clone 1E7) are kindly provided by Dr. Hiroshi Kimura . Anti-H3K4me3 antibody , anti-nuclear pore complex antibody , anti-histone H3 antibody  and a rabbit polyclonal negative control antibody  were commercially available.lem-2 mutant , and IV;X fusion  worms were obtained from the Caenorhabditis Genetics Center . Standard worm growth techniques were used to obtain embryos from worms grown in S liquid media [N2 (wild type), id media .C. elegans embryos collected from gravid worms by hypochlorite treatment were frozen in liquid nitrogen and stored in methanol at -20°C until use. Embryos were rehydrated in PBST  for 15 minutes followed by fixation with 1% formaldehyde. Fixed embryos were blocked by goat sera and then incubated with PBST containing rabbit anti-LEM-2  and mouse mAb414 antibodies (1:400 dilution). Immunocomplexes were fluorescently labeled using secondary antibodies conjugated with Alexa 488 (anti-rabbit) or Texas Red (anti-mouse), and DNA was stained with 4',6-diamidino-2-phenylindole (DAPI). Fluorescent signals were captured using a TCS SP2 laser scanning confocal microscope .For immunofluorescence, To visualize chromosomes in oocytes, adult worms were fixed by 4% formaldehyde and then incubated with DAPI for 10 minutes. Signals were detected as described above.Proteins from embryos were separated by an SDS-polyacrylamide gel and transferred to a polyvinylidene fluoride (PVDF) membrane. Blocked membranes were incubated with anti-LEM-2 antibodies  and then with secondary antibody conjugated with horseradish peroxidase . Signals were detected by ECL Plus Western Blotting Detection system  through autoradiography. Total proteins on membranes were stained by Coomassie Brilliant Blue dye for loading controls.The ChIP-chip experiments performed in this study are summarized in Table S4 in Additional file Amplification of DNA from histone H3 or methyl mark ChIP was performed using ligation-mediated PCR (LM-PCR) as described previously . LM-PCR C. elegans tiling array (see below) at 42°C for 16 to 20 hours. Microarrays were scanned by using a GenePix 4000B Scanner with associated software . Raw signal intensities of the images were extracted by using NimbleScan software v2.5 according to the NimbleScan User's Guide . Complete procedures employed for the microarray hybridization and signal detection are described in the NimbleGen Arrays User's Guide  Klenow fragment. Dye orientation of experiments is described in Table S4 in Additional file May 2008 ). For hyThe ChIP-seq experiments performed in this study are summarized in Table S4 in Additional file For sequencing library preparation, the LEM-2 ChIP DNA or input DNA was blunt-ended as described in the 'ChIP-chip' section. For H3K4me3 ChIP DNA, fragments were blunt-ended by a custom mixture of polynucleotide kinase, Klenow fragment, and T4 DNA polymerase. All ChIP or input DNA fragments were then modified to provide an A-overhang as described in the 'ChIP-chip' section, ligated with the single-end adaptor , and then amplified by PCR with single-end PCR primers  [The us (GEO)  accessio2 ratio ) of each probe based on the probe behavior estimated by its GC content, and then smoothed the value by assigning the median across sliding windows of 300 bp. The resultant values are MA2C scores. For data analysis, we combined four replicates of LEM-2 ChIP-chip datasets or two replicates of control IgG datasets , disallowing mismatches. According to the size of DNA excised from the gels for the sequencing, we extended the aligned reads computationally to 200 bp (H3Kme3) or 300 bp . The numbers of the aligned reads and the coverage of the extended reads on the genome were: LEM-2, 6.0 million (18% coverage); control IgG, 2.5 million (coverage 7.6%); H3K4me3 replicate 1, 11.6 million (23% coverage), H3K4me3 replicate 2, 4.6 million (9.2% coverage); and input DNA, 10.0 million (30% coverage). Since the two replicates of the H3K4me3 datasets were highly concordant with each other, we used only replicate 1 for subsequent analyses.Using the MAQ program , we aligTo obtain the 'base-count' profile for LEM-2 ChIP, control IgG ChIP and H3K4me3 ChIP, the number of the extended reads overlapping each base of the genome was counted. For input DNA, we randomly selected a number of aligned reads corresponding to the number of those in LEM-2 or control IgG ChIP-seq datasets, and then generated base-count profiles for normalization as described below.To normalize the LEM-2 and control IgG datasets using the input DNA data, we subtracted base-count of the input DNA from that of LEM-2 or control IgG ChIP at each base. The subtracted base-counts were transformed to z-score using the mean and the standard deviation /SD). Finally, z-scores were smoothed by averaging values in a 300-bp sliding window with a 50-bp offset. The normalized z-score at every 50 bp was used for visualization in figures and for subsequent LEM-2 subdomain calling.To normalize H3K4me3 ChIP-seq data, each base-count was divided by the average base count across the genome, and then plotted for visualization.To define LEM-2 subdomains, we binarized the replicate-combined LEM-2 ChIP-chip data by transforming positive or negative MA2C scores to +1 or -1, respectively. We averaged the binarized values in 200-probe (approximately 10 kb) windows, sliding one probe (50-bp offset) across the genome to subsequently identify windows with high average binary values. In this analysis, we used any genomic probe including those overlapping with repetitive sequences to generate windows across the genome. We then performed the same procedure for the replicate-combined control IgG ChIP-chip data to estimate the number of false-positive windows. We defined LEM-2 positive windows when a window binary value is over 0.8, at which the ratio of the number of positive control IgG windows to that of positive LEM-2 windows  is <2.5%  and generated 360 regions, which we defined as 'LEM-2 subdomains'. Chromosome coordinates for these LEM-2 subdomains are listed in the Table S1 in Additional file The 354 boundaries we used for the sliding window analyses are listed in Table S6 in Additional file For sliding window analyses around transcript start and end sites Figure , genes tC. elegans embryos were suspended in Trizol reagent (Invitrogen) with chloroform. RNA was isolated from the aqueous phase and purified by isopropanol precipitation. We obtained RNA from four biological replicates . Figure S6: validation of a strain with a fusion chromosome. Figure S7: coverage of helitrons and satellite repeats in LEM-2 subdomains along chromosomes. Figure S8: LEM-2 subdomain calling.Click here for fileTables S1 to S7. Tables are in individual tabs in Additional file Click here for file


Since the discovery of MeCP2, its functions have attracted the interest of generations of molecular biologists. Its function as a transducer of DNA methylation, the major post-biosynthetic modification found throughout genomes, and its association with the neurodevelopmental disease Rett syndrome highlight its central role as a transcriptional regulator, and, at the same time, poses puzzling questions concerning its roles in physiology and pathology. The classical model of the MeCP2 function predicts its role in gene-specific repression through the binding of methylated DNA, via its interaction with the histone deacetylases and co-repressor complexes. This view has been questioned and, intriguingly, new roles for MeCP2 as a splicing modulator and as a transcriptional activator have been proposed. Recent data have demonstrated that MeCP2 is extremely abundant in the neurons, where it reaches the level of histone H1; it is widely distributed, tracking the methylated CpGs, and regulates repetitive elements expression. The role of MeCP2 in maintaining the global chromatin structure is further sustained by its involvement in other biologically relevant phenomena, such as the Line-1 repetitive sequences retrotransposition and the pericentromeric heterochromatin clustering during cellular differentiation. These new concepts renew the old view suggesting a role for DNA methylation in transcriptional noise reduction, pointing to a key role for MeCP2 in the modulation of the genome architecture. In 2012, the twentieth anniversary of MeCP2 protein identification will be celebrated . The impin vitro,” if at least 12 symmetrically methylated CpGs are available. Like many important findings, MeCP2 was discovered “by accident” by MeCP2 was the second methyl-CpG-binding protein to be identified, although it was the first to be cloned. In fact, MeCP2 is able to bind at a genome-wide level, with the need of a single, methylated CpG. This weak discrimination is in agreement with its diffuse nuclear signal in rat cells. In mouse cells, given their peculiar heterochromatin organization, the staining is extremely evident in the pericentromeric heterochromatin, closely resembling the distribution of major satellite DNA . Mouse sD. melanogaster . These proteins, called MBD1, MBD2, MBD3, and MBD4  capable of repressing transcription through the binding of methylated DNA.Rett syndrome (RTT) is a sporadic post-natal progressive neurodevelopmental disorder occurring with a frequency of 1/10000–15000 live females births and is considered the second most common cause of mental retardation in females . The larBesides the large number of studies on patients, the modeling of RTT in mice has been instrumental in order to elucidate the molecular basis of the disease. Mouse models have also been pivotal in the study of expression profiling alterations, necessary to identify putative MeCP2 target genes. They have helped in the elucidation of many questions of biomedical importance: is RTT a pure neuronal disease? Is MECP2 dosage important for the establishment of a pathogenic status? Is RTT reversible?Two Mecp2 null mice obtained with Cre-LoxP technology and carrying an ubiquitous deletion, were viable but affected by severe neurological symptoms characteristic of RTT . The comin vivo and even a mild over-expression of this gene can have a dramatic effect.MeCP2 dosage matters: a mouse over-expressing a transgene containing the human MECP2 locus that shows a near twofold MeCP2 expression, showed severe progressive neurological phenotypes . The effThe concept of RTT as a pure neuronal disease has recently been challenged with results implicating the involvement of the glial cells in the pathogenesis of RTT . More reThe apparent dichotomy of MeCP2 functions (genome-wide vs gene-specific regulator) has been widely debated. Transcriptional profiling studies comparing the total brains of RTT patients or mouse models with controls have revealed only subtle differences in gene expression dampening a role for MeCP2 as a global regulator of transcription . A numbeFigures B). Furthermore, it has been confirmed that MeCP2 directly binds the promoter region of the genes down-regulated in the Mecp2 null mice and up-regulated in the MECP2-Tg mice, while sequential ChIP assays have revealed that the promoter of the activated genes is simultaneously associated with both MeCP2 and the known transcriptional activator CREB1 . These data suggest that MeCP2 regulates the expression of a wide range of genes in different brain sub-regions and point to a role for MeCP2 as a modulator of transcription that can both activate or repress target genes  transcription and retrotransposition: these are, in fact, increased in the mouse brains from null mice and in the neural precursor cells obtained from iPSC and postmortem brains from RTT patients .The L1 elements are retrotransposons representing 20% of mammalian genomes that may induce genomic alterations, such as insertions and deletions . MoreoveFigures B).As already described regarding the interaction between MeCP2 and CREB, proteins with which it interacts may modify the roles of MeCP2  has also been reported. Brm and MeCP2 assemble on the methylated genes involved in cancer and on the FMR1 gene in fragile X syndrome . TherefoA physical interaction between the heterochromatin protein 1 (HP1) and MeCP2 has been demonstrated during the myogenic differentiation. In particular, this interaction leads to the re-localization of HP1γ to the heterochromatin, which correlates with the presence of MeCP2 . There iFigure 2). These events are accompanied by an increase in the methylation of major satellite DNA and the accumulation of MeCP2 and MBD2 proteins in the nuclei of terminally differentiated muscle cells. Interestingly, the over-expression of MeCP2 and MBD2 in C2C12 myoblasts in the absence of differentiation also induces an aggregation of the chromocenters, indicating that these proteins may be directly involved in the reorganization of heterochromatin architecture. Moreover, studies in Mecp2 null mouse neurons have revealed significant differences in the number and size of the nucleoli and chromocenters compared to WT animals (DNA methylation affects the nuclear architecture, as measured by the gene position alterations in the chromosome territories . A direc animals . Already animals . The devIn 2007, LaSalle and colleagues reported, by ChIP-chip analysis on a neuroblastoma cell line, that more than half of the MeCP2 binding sites are intergenic and that only a small number of them reside in the CpG islands. Moreover, among binding sites located in the CpG islands, many of them are associated with actively transcribed genes, supporting the view of a more complex function of MeCP2 .Different approaches, reagents, and technologies led, some years later, to the re-establishment of MECP2 as a protein with a global regulatory role . The utiin vivo tracks the density of methyl-CpG in the genome (Furthermore, an analysis of binding sites around known regulated genes, such as BDNF and Dlx5/6, transcriptionally active in this cellular system, has revealed a MeCP2 binding across the entire locus, except for the CpG island regions, suggesting that these active promoters are unable to bind MeCP2 due to its hypomethylation state. Moreover, high-throughput data suggest that the MeCP2 binding e genome . These lThe described data suggest that, in addition to the role of MeCP2 as a gene-specific transcriptional regulator, mediated by the association with specific cofactors, the global chromatin-binding function of MeCP2 is crucial for global chromatin dynamics especially during brain maturation. MeCP2 may thus be seen as a multifunctional and structural organizing factor. Furthermore, the interaction of MeCP2 with most regions of the genome, such as the intergenic DNA and repetitive elements, should contribute to keep the rate of somatic mutation and transcriptional noise in the brain low and allows to hypothesize further pathogenic roles for MeCP2 in RTT. This evidence recalls the concept we previously described, focusing on the role of MeCP2 as a key player in genome architecture and regulation.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.


IL6 co-stimulation reduces the promoter sensitivity towards the BMP signal, because the SMAD and STAT transcription factors compete for recruiting RNA polymerase to the transcription start site. This may explain why inflammatory signals disturb iron homeostasis in anemia of inflammation. Taken together, our results reveal why the iron homeostasis circuit is sensitive to perturbations implicated in disease.Systemic iron homeostasis involves a negative feedback circuit in which the expression level of the peptide hormone hepcidin depends on and controls the iron blood levels. Hepcidin expression is regulated by the BMP6/SMAD and IL6/STAT signaling cascades. Deregulation of either pathway causes iron-related diseases such as hemochromatosis or anemia of inflammation. We quantitatively analyzed how BMP6 and IL6 control hepcidin expression. Transcription factor (TF) phosphorylation and reporter gene expression were measured under co-stimulation conditions, and the promoter was perturbed by mutagenesis. Using mathematical modeling, we systematically analyzed potential mechanisms of cooperative and competitive promoter regulation by the transcription factors, and experimentally validated the model predictions. Our results reveal that hepcidin cross-regulation primarily occurs by combinatorial transcription factor binding to the promoter, whereas signaling crosstalk is insignificant. We find that the presence of two BMP-responsive elements enhances the steepness of the promoter response towards the iron-sensing BMP signaling axis, which promotes iron homeostasis  in vivo. The analysis showed that the presence of multiple redundant regulatory elements in the hepcidin gene promoter facilitates homeostasis, because changes in iron blood levels are sensed with high sensitivity. We further suggest that inflammatory signals establish molecular competition at the hepcidin promoter, thereby reducing its iron sensitivity and leading to a loss of homeostasis in anemia of inflammation. We conclude that quantitative insights into hepcidin expression regulation explain features of systemic iron homeostasis.The nutritional iron uptake is tightly regulated because the body has limited capacity of iron excretion. Mammals maintain iron homeostasis by a negative feedback loop, in which the peptide hepcidin senses the iron blood level and controls iron resorption. Molecular perturbations in the homeostasis loop lead to iron-related diseases such as hemochromatosis or anemia of inflammation. Quantitative studies are required to understand the dynamics of the iron homeostasis circuitry in health and disease. We investigated how the biological activity of hepcidin is regulated by combining experiments with mathematical modeling. We present a multi-scale model that describes the signaling network and the gene promoter controlling hepcidin expression. Possible scenarios of hepcidin regulation were systematically tested against experimental data, and interpreted using a network model of iron metabolism  Hepcidin is a humoral polypeptide that plays a central role in systemic iron homeostasis  signalling pathway Hepcidin expression is also regulated by inflammatory cytokines and hypoxia Combinatorial gene regulation by binding of multiple different transcription factors to the same promoter is a recurrent aspect of transcription networks. Thermodynamic modeling employs methods from statistical thermodynamics to describe combinatorial binding of transcription factors and RNA polymerase (RNAP) to the promoter, depending on the protein concentrations and binding energies. In this work, we combined experimental measurements and thermodynamic modeling to quantitatively analyze how the iron-sensing BMP and inflammatory IL6 pathways coordinately control hepcidin expression.Systemic iron homeostasis is maintained by an auto-regulatory negative feedback loop that involves the transcriptional induction of hepcidin in the liver: elevated iron levels in the blood induce hepcidin expression by activating the BMP signaling pathway in the liver. Released hepcidin in turn induces the degradation of intestinal iron transporters, thereby lowering the blood iron level. We analyzed a conceptual mathematical model of this circuitry to understand how systemic iron homeostasis can be maintained despite imbalances in iron availability and consumption  and the merged data is shown in Co-stimulation with IL6 and BMP further enhanced the luciferase activity of the WT promoter compared to mono-stimulation. We confirmed by qPCR measurements that this co-stimulation response quantitatively reflects the expression of endogenous hepcidin mRNA : Mono-stimulation with either BMP or IL6 raises luciferase activity to intermediate levels only, and co-stimulation with both ligands is required for maximal expression . This suThe coordinated regulation of hepcidin expression by BMP and IL6 may involve crosstalk at the level of signal transduction. We analyzed signaling pathway interactions by measuring the phosphorylation of STAT3 and SMAD1/5/8 after stimulation with BMP and/or IL6 using quantitative immunoblotting. Cells were stimulated for 12 h after starvation, and phosphorylated SMAD1/5/8 and STAT3 were detected in the cell lysate using phospho-specific antibodies  may bind to the transcription start site (TSS) at a basal level . Model variants with different numbers of parameters were compared with respect to goodness-of-fit to the training data using the Akaike information criterion (see Methods). Based on these measures, the data were best explained by a promoter module containing a single cooperative interaction among SMAD and STAT TFs bound to BRE1 and STATBS, respectively . Nine model parameters enter the sigmoidal Hill functions which describe the stimulus-induced TF phosphorylation, and signaling crosstalk between transcription factors. The remaining 10 parameters enter the promoter module, and describe the TF affinity for DNA binding sites, the TF interaction strength with RNAP, and the cooperativity of TF binding to DNA. The nature of the model parameters is described in detail in Supplemental 2<135), not only for the best-fit solution (see Methods). The model predictions in The best-fit model  compriseIn our model, hepcidin expression was mostly determined by the dynamics of TF binding to the promoter, while inhibitory signaling crosstalk played only a minor role: Elimination of signaling crosstalk did not significantly change the simulated luciferase activities, and this conclusion held true for all measurement-compliant parameter sets  stimulation , where pPromoter saturation also explains why the BRE1 single mutation strongly reduces expression at intermediate BMP concentrations, while having lesser impact at basal and strong stimulation conditions  which reflects the dietary iron content. Iron blood levels control the activity of the BMP signaling pathway, and thus hepcidin expression. Negative feedback regulation was considered in the model by assuming that the iron influx is negatively influenced by hepcidin  responds to a change dietary iron content (Fei), efficient homeostasis implying that a given fold-change in Fei elicits a much lesser fold-change in Feb. The key assumption we made was that hepcidin-mediated feedback regulation has a very strong impact on the iron influx. For the limit of strong feedback, it can be shown analytically that the degree of iron homeostasis is solely determined by the steepness of the hepcidin promoter response, and independent of the remaining model parameters  and primary hepatocytes in vivo and in vitro, there is abundant evidence that the in vivo data that qualitatively mirrors the results obtained in vitro. For instance, Wang et al. reported that SMAD4 is essential for hepcidin activation both in mice and in primary hepatocytes In this study, we used in vivo.The hepcidin promoter contains two BMP-responsive elements as opposed to a single STAT binding site, raising the question of why such a promoter design may be advantageous for the regulation of systemic iron homeostasis. We find that the presence of two BREs ensures that hepcidin expression is very sensitive towards changes in the iron-sensing BMP pathway . This maThe mathematical model describing hepcidin expression 2/σi2. Mi, Di and σi are the simulated value, the measured value and the experimental error, respectively. The fitted scaling factor si accommodates that the model is formulated in absolute concentration units, while signaling and luciferase activities could only be measured in arbitrary units. The simulated phospho-SMAD/STAT concentrations were fitted to the immunoblotting data  and the likelihood ratio test , and were analysed with respect to parameter ranges and parameter correlations  supplemented with 10% heat inactivated low-endotoxin fetal bovine serum , 1% penicillin, 1% streptomycin and 1 mM Sodium Pyruvate. Cell cultures were maintained in a 5% CO5 per well) were seeded onto six-well plates. The next day, 500 ng of pGL3 reporter vectors containing the hepcidin promoter constructs were transfected, together with 10 ng of a control plasmid containing the Renilla gene under the control of the CMV promoter. Plasmid transfections were performed using Lipofectamine 2000 (Invitrogen) according to manufacturer's instructions, and medium was replaced by FBS-free medium. Twenty-four hours after transfection, cells were treated with human BMP-6  and/or IL-6 . Cells were harvested in Passive Lysis Buffer (Promega) for measurement of luciferase activity and cellular extracts were analyzed using the Dual-Luciferase-Reporter assay system (Promega) and a Centro LB 960 luminometer (Berthold Technologies).Generation of the luciferase reporter construct containing a 2762-bp fragment of the human hepcidin promoter (WT) and derivate constructs with mutations in BMP-responsive element 1, BMP-RE2 , STAT binding site , and in BMP-RE1 and BMP-RE2 (BRE1mBRE2m), have been previously described 5 per well) were seeded onto 6-well plates and the day after the culture medium was exchanged to FBS-free medium. After 12 hours the cells were treated with increasing doses of BMP-6  and/or IL-6  for 12 hours and then harvested for protein analysis. Cells were washed twice in ice cold Dulbecco's phosphate-buffered saline (PBS). Cell pellets were lysed in ice-cold NET buffer (1% Triton X-100 (v/v), 50 mM Tris-HCl pH 7.4, 150 mM NaCl, 5 mM EDTA, 20 mM NaF, 1 mM Na3VO4) supplemented with 1× Complete Mini Protease Inhibitor Mixture . The protein concentration was measured using the BCA (bicinchoninic acid) Protein Assay (Pierce). Protein lysates (15 µg) were subjected to 10% SDS-PAGE and transferred to a nitrocellulose membrane (Whatman) for protein immunodetection using rabbit anti-phospho SMAD 1/5/8 , mouse anti-phospho STAT3  and mouse anti-actin . Blots were then incubated with horseradish peroxidase conjugated anti-mouse or anti-rabbit secondary antibodies (Sigma Aldrich) and then subjected to chemiluminescence . For the densitometric analysis the resulting bands were digitalized and quantified using the NIH Image J software (rsb.info.nih.gov/nih-image/)HUH7 cells  are strongly correlated with expression fold-changes in qPCR measurements of endogenous hepcidin mRNA (y axis). The green and blue data points indicate qPCR measurements for two independent biological replicates, each with technical replicates (n = 2). The data points correspond to the following stimulus concentrations: 6 ng/ml IL6, 25 ng/ml IL6, 200 ng/ml BMP6, 800 ng/ml BMP6, 200 ng/ml BMP6 + 25 ng/ml IL6, 800 ng/ml BMP6 + 25 ng/ml IL6, and 800 ng/ml BMP6 + 6 ng/ml IL6. The blue and green solid lines show linear fits to the data, and R indicates the Pearson correlation coefficient of each qPCR replicate series. See Supplemental (JPG)Click here for additional data file.Figure S2A)–(D) HuH7 cells were stimulated with increasing doses of IL6 in the presence or absence of BMP  or vice versa . Signaling crosstalk was analyzed by immunoblotting against phosphorylated SMAD and STAT. Actin levels serve as loading controls. Two biological replicates were performed .Immunoblotting of SMAD/STAT phosphorylation upon co-stimulation indicates moderate inhibitory signaling crosstalk. ((JPG)Click here for additional data file.Figure S3Fitting and analysis of a model with non-cooperative STAT and SMAD binding to STATBS and BRE1 sites. (A) and (B) Best-fit of the non-cooperative model (variant 1 in (JPG)Click here for additional data file.Figure S42<135) were collected (see Methods). The box plot indicate the distribution of each parameter . (B) and (C) Relationship of model parameters describing the activities of BRE1 and BRE2. (B) Comparison pSMAD binding affinities of BRE1 and BRE2 . (C) Comparison of RNAP interaction strength of BRE1-bound and BRE2-bound pSMAD . Each circle corresponds to one measurement-compliant parameter (defined as in panel A), the solid line indicates the bisectrix.Analysis of parameter identifiability. (A) Box plots of the measurement-compliant parameter ranges. The model with inhibitory signaling crosstalk and BRE1-STATBS cooperativity (variant 4 in (JPG)Click here for additional data file.Figure S5bound) was fitted to the data in 2<135) were analyzed (see Methods): The simulated luciferase levels with signaling crosstalk were plotted against the luciferase activities of model variants where crosstalk was eliminated . The luciferase activities are essentially unaffected by the deletion of crosstalk.The luciferase measurements can be quantitatively modeled without assuming crosstalk between signaling pathways. (A) Best-fit of a hecipidin expression model without crosstalk at the level of BMP and IL6 signaling pathways. Luciferase expression was simulated using Eqs. S3.1 and S2.13 Click here for additional data file.Figure S6Buffering of BRE2 and STATBS single deletions. Systematic analysis of transcription factor binding site deletion effects confirms buffering of BRE2 and STATBS single deletions. The impact of binding site deletions was calculated by taking the luciferase activity ratios of different promoters (indicated at the bottom) and expressed as a log10-fold change (y axis). The fold-change upon a combined deletion of BRE2 and STATBS (red) is higher than the product of the single deletion fold-changes (green and blue) at high BMP stimulation. This indicates promoter saturation (see main text). Data points are mean and standard deviation, and model predictions represent the range of measurement-compliant parameter sets, as derived from a parameter identifiability analysis (see Methods). Co-stimulation conditions were considered to ensure that BRE2 and STATBS both contribute to expression.(JPG)Click here for additional data file.Figure S7Integration of BMP and IL6 signals at the level of hepcidin expression. (A) The WT hepcidin promoter integrates BMP and IL6 signals in a sub-multiplicative manner, while mutants show multiplicative behavior. The x dimension shows the experimentally observed fold-expression-change over basal upon co-stimulation with BMP and IL6. The y dimension shows the product mono-stimulation responses over basal with the same doses of BMP and IL6, respectively. Each data point represents one co-stimulation condition (different concentrations of BMP and IL6 and/or different promoter constructs). The colors of the data points correspond to different promoter constructs (legend). The bisectrix (solid line) marks the expectation for a multiplicative system . (B) Basal BMP signaling pathway activity is required for optimal IL6 responsiveness of the hepcidin promoter. The fold expression change in response to very strong IL6 mono-stimulation is shown as a function of the basal phospho-SMAD level (using the parameters of the best-fit WT model). Basal BMP signaling is required for optimal IL6 responsiveness of the promoter, indicating that both stimuli synergistically regulate hepcidin expression  in this regime of weak BMP signaling. This model prediction is supported by experiments in HuH7 cells showing that SMAD4 siRNA lowers the IL6 inducibility of the hepcidin promoter ((JPG)Click here for additional data file.File S1Matlab code of the hepcidin expression model. The file comprises the signaling model describing SMAD and STAT phosphorylation, and the thermodynamic model describing hepcidin promoter regulation. The simulation results cf.  are plot(TXT)Click here for additional data file.Protocol S1Comparison of luciferase signals with endogenous hepcidin mRNA expression.(PDF)Click here for additional data file.Protocol S2Model implementation, model fitting and model selection.(PDF)Click here for additional data file.Table S1max, EC50) represent a combination of multiple signaling reaction constants, and were thus constrained such that they match the experimental measurements of transcription factor phosphorylation  allows for promoter saturation to occur upon stimulation. Likewise, the KD values of transcription factor binding to cognate promoter sites (parameters 11–13) were allowed to vary over a broad range to accommodate weak and strong binding. The parameter ranges for the constants describing protein-protein interactions on the promoter (parameters 14–19) represent the typical interaction energies of 1–5 kcal/mol reported in the literature SM and nST reflect transcription factor dimerization and trimerization, implying that values of up to 3 can be expected.Best-fit model parameters. Best-fit parameters for the models with and without signaling crosstalk (rylation . Most pa(PDF)Click here for additional data file.Text S1Conceptual model of systemic iron homeostasis.(PDF)Click here for additional data file.Text S2Derivation of a thermodynamic model for the hepcidin promoter.(PDF)Click here for additional data file.Text S3Modeling the dose-response behavior of signaling pathways.(PDF)Click here for additional data file.Text S4Detailed model of systemic iron homeostasis.(PDF)Click here for additional data file.


Transcription in eukaryotic cells is efficiently spatially and temporally regulated, but how this genome-wide regulation is achieved at the physical level remains unclear, given the limited transcriptional resources within the nucleus and the sporadic linear arrangements of genes within chromosomes. In this article, we provide a physical model for chromatin cluster formation, based on oscillation synchronization and clustering of different chromatin regions, enabling efficient systemic genome-wide regulation of transcription. We also propose that the electromagnetic field generated by oscillation of chromatin is the driving force for chromosome packing during M phase. We further explore the physical mechanisms for chromatin oscillation cluster (COC) formation, and long-distance chromatin kissing. The COC model, which connects the dots between chromatin epigenetic modification and higher-order nuclear organization, answers many important questions, such as how the CCCTC-binding factor CTCF contributes to higher-order chromatin organization, and the mechanism of sequential transcriptional activation of HOX clusters. In the COC model, long non-coding RNAs function as oscillation clustering adaptors to recruit chromatin modification factors to specific sub-nuclear regions, fine-tuning transcriptional events in the chromatin oscillation clusters. Introns of eukaryotic genes have evolved to promote the clustering of transcriptionally co-regulated genes in these sub-nuclear regions. The regulation of eukaryotic gene transcription can be viewed as forming order out of chaos. To understand it, it is necessary to explore the physical nature of oscillating heterogeneous chromatin regions with nonlinear contacts and interactions. Recent advances in the fields of applied mathematics and physics together with technologies evolved from the application of high-throughput DNA sequencing allow us to envision nuclear chromatin organization as clustered. This knowledge provides us with new insights into the regulation of gene transcription, the functions of long non-coding RNAs and the evolution of introns in eukaryotic cells.et al. reported that genome-wide Hi-C maps indicate a fractal globule model for chromatin conformation that enables maximally dense packing while preserving the ability to fold and unfold any genomic locus easily. They suggested spatial segregation of open and closed chromatin to form two genome-wide compartments and the presence of chromosome territories [Lieberman-Aiden ritories . HoweverSynchronization and clustering of pulse-coupled oscillators are determined by two major factors: the natural frequencies of the oscillators and the coupling strength within a clustering compartment. At a given coupling strength, unsynchronized oscillators form multiple clusters, each of which has a synchronized average frequency. These clusters are preferentially formed among oscillators with similar natural frequencies at a given coupling strength among them. As the coupling strength increases the oscillation clusters merge with each other forming fewer and bigger clusters. Each of these has an average frequency that is shared among the synchronized oscillators within the cluster. At certain threshold coupling strength, all oscillation clusters merge into one synchronized oscillation system -5.Using the pulse-coupled oscillation clustering model, eukaryotic nuclear chromatin organization can be viewed as the result of the differential clustering of oscillating chromatin regions. The coupling strengths of chromatin regions are determined by physical interactions among chromatin-associated proteins, the electromagnetic fields around the oscillating chromosomal regions, and the hydrogen and other bonds linking different chromatin regions within the same chromosome. The natural frequency of an oscillating chromatin region is determined by the physical properties of DNA-protein complexes in that region, which can be changed by its epigenetic state and the protein factors associated with it. The pulse-coupled oscillation clustering model explains the proximal enrichment effect of intra- and inter-chromatin contacts observed by Lieberman-Aiden et al. , as the Andrews et al. have studied the effects of high frequency electric fields on mammalian chromosomes in vitro. This research confirmed that high frequency electric fields (range 2 to 50 MHz) on human and Chinese hamster chromosomes can be oriented, aligned and translated by an oscillating electrical force. They also observed that above certain threshold field strengths, the chromosomes orient themselves with their long axes along the field direction. The dependence of this threshold on frequency was measured and was found to be much greater at low than at high frequencies . Using eHowever, the physical mechanisms that regulate the higher-order chromosome packing of metaphase chromosomes have not been fully characterized. Here we present a hypothetical mechanism of chromosome compaction. The 30 nm chromatin fiber is initially formed through electrostatic forces between neighboring nucleosomes. Under intracellular stochastic energy excitation, electric dipolar oscillation would be generated between such nucleosomes. After synchronization and coupling of the oscillations, regulated oscillations are generated along the 30 nm chromatin fiber, and the oscillation coupling process further compacts the 30 nm fiber -14. The Theoretically, the oscillation clustering model explains the closely-juxtaposed configuration of duplicated chromosomes during M phase, which is counter-intuitive from the perspective of electrostatic repulsions between the duplicated chromosome arms. As the homologous chromosomal regions comprise synchronized chromosomal oscillation clusters with identical natural frequencies, they tend to cluster together. The same scenario can apply to synapsis during meiosis, as the oscillations of the electric fields of homologous chromosomes match each other, preventing synapsis between non-homologous chromosomes.Non-M phase chromatin organization is of great research interest, and attempts have been made to capture 3D chromatin organization at high resolution -19. ThesCertain chromatin regions function as synchronized oscillators, either by coupling of the electromagnetic field generated by longitudinal oscillation of nucleosomes, or by the physical interactions of protein-DNA complexes. The density and positioning of nucleosomes in a chromosome region, the physical properties of histone octamers, as well as the protein complexes binding to the chromatin region, together determine the coupling strength of that region. Through the mechanism described above, these chromatin regions will cluster with each other if they share similar oscillation frequencies, and function as chromatin organizers to shape the higher-order chromatin structures. Figure  from thiThe chromatin oscillation cluster (COC) has a common role in transcriptional regulation of eukaryotic genes. For example, sequential transcriptional activation of the HOX gene clusters can be viewed as a sequential uncoupling event of different HOX transcriptional loops within COCs -27. The Multiple lines of evidence indicate that COC formation increases the epigenetic stability of the clustered chromatin regions. CTCF binds to CBSs, which function as a chromatin barrier, creating chromosome boundaries that segregate transcriptionally active from heterochromatin regions. Through shaping higher-order chromatin structures, CTCF organizes chromosomal loop formation across the vertebrate genome. Yoon Jung Kim et al. reported that OCT4 antagonizes heterochromatin loop formation by preventing cohesin loading at CBSs, and knocking-down CTCF or over-expression of OCT4 abolishes HOXA cluster formation and up-regulates several HOXA genes . HandokoComponents of the nuclear envelop(NE) such at the nuclear pore complex(NPC) and nuclear lamina also play a role in the establishment of COC. These components specifically target certain genes to cluster at sub-nuclear territories that function as the transcription factories.,30 BrickDrosophila tissues where they are co-repressed, and mutations in any of the clustered chromatin regions weaken the silencing of genes in the others. This finding indicates that HOX gene clustering and co-regulation are not restricted to the same HOX gene cluster but can span across chromatin territories more than 10 Mbp in extent [Long non-coding RNAs are known to function as scaffolds that recruit certain chromosome remodeling complexes to the promoter regions of specific genes to regulate their transcription . Cavallin extent .Chambeyron et al. suggest that higher-order chromatin structures regulate the expression of the HOX cluster at several levels, including locus-wide changes in chromatin structure and the temporal programming of expression of different genes . Many laCertain long non-coding RNAs mediate the sequential transcriptional activations of the HOX gene clusters through epigenetic modifications of their proximal chromatin domains within the COC, uncoupling the transcriptional loop from the repressive COC and thus activating transcription within the loop. After a period of transcription, the epigenetic state of the transcriptional loop is reverse-modified to a silent state because of inability to cluster in close proximity to the long non-coding RNAs recruited with transcription activators. It is possible that the uncoupling of the first transcriptional loop from the COC results in the exposure of the second transcriptional loop to the long non-coding RNAs within the region. Alternatively, long non-coding RNAs transcribed from the first transcriptional loop could cluster in close proximity to the second transcriptional loop of the COC.So the oscillation clustering events in chromatin permit exact spatial and temporal regulation of transcriptional activators and repressors in specific sub-nuclear loci, contributing to efficient and precise sub-nuclear level transcriptional regulation. Huarte et al. reported that a P53-regulated long non-coding RNA, lincRNA-p21, is able to repress a number of pro-survival genes to mediate P53 apoptosis programs, partly through binding to the hnRNPK protein and recruiting this chromatin modifier to the promoter regions of those genes . With thOne of the characteristics of eukaryotic genomes is the evolution of a large number of introns, which are usually much longer than exons. If introns exist only for the purpose of mRNA transcription and splicing, they are not economical for the cell. However, from the oscillation clustering point of view, introns evolved to establish specific clustering events in chromatin regions. The insertion of introns into a gene would adjust the average frequency and coupling strength of the relevant chromatin region to promote formation of a COC and allow the transcription of eukaryotic genes to be systematically regulated. Moreover, introns and intergenic sequences would facilitate the transcription of genes at the uncoupled transcriptional loops to extend out of the repressive cluster regions. Thus, transcription can take place in the sub-nuclear regions where transcriptional resources are most available, and transcriptional effects on the rest of the genes in the COC, where a repressive state is maintained, are minimized.Escherichia coli cells. They found that H-NS, a global transcriptional silencer, formed two compact clusters per chromosome, driven by oligomerization of DNA-bound H-NS through interactions mediated by the amino-terminal domain of the protein. H-NS sequestered the regulated operons into these clusters and juxtaposed numerous DNA segments broadly distributed throughout the chromosome. Deleting H-NS led to substantial chromosome reorganization [Interesting questions arise as how to verify the COC hypothesis experimentally. From the reductionist point of view, verification could be achieved by transfection of artificial mini-chromosomes into live cell nuclei. Artificial mini-chromosomes can be specifically designed to contain key chromosome organizer motifs such as the CTCF binding site, and artificially designed introns and exons. Fragments of these mini-chromosomes can be labeled with different fluorescent labels, so their locational changes within the living cell nucleus can be observed by microscopy. In combination with chromosome conformation capture (3 C) and Hi-C techniques, we can study the genomic landscape of chromosomal regions that cluster with the artificial mini-chromosomes. Such technologies have already been developed. For instance, using the 3D two-color FISH and immunostaining technique (FISH-I), Grimaud et al. reported that an artificial mini-chromosome containing Fab-7, a well-characterized PRE-containing element, recurrently clusters with PcG bodies, which are endogenous chromosomal regions containing homologous Fab-7 copies . Wenqin nization . With soOn the other hand, we can utilize the currently popular Hi-C techniques and bioinformatic approaches to study chromosome clusters. With such technologies, Changying Guo et al. reported two types of chromatin loops in the immunoglobulin heavy-chain gene locus; one type links chromatin regions forming the first order chromatin cluster, while the other links different chromatin clusters into a juxtaposed position forming a higher-order chromatin cluster . Deep miIntracellular electric fields remain an obscure subject to the majority of biologists, as there are still many technological barriers to overcome for definitive characterization of such fields within a living cell . Some ofThe authors declare they have no competing interests.YZ conceived the general concepts and writes the article, QZ shared insights and gives advices to the article. Both authors read and approved the final manuscript.


Considering the importance of AmrZ for the ecological fitness of pseudomonads and taking advantage of the full sequencing and annotation of the Pseudomonas fluorescens F113 genome, we have carried out a ChIP-seq analysis from a pool of eight independent ChIP assays in order to determine the AmrZ binding sites and its implication in the regulation of genes involved in environmental adaption.AmrZ, a RHH transcriptional regulator, regulates motility and alginate production in pseudomonads. Expression of amrZ and algD promoters in P. aeruginosa. We have analyzed the role of AmrZ in the regulation of iron uptake genes, to find that AmrZ represses their expression under iron limiting conditions.154 enriched regions (AmrZ binding sites) were detected in this analysis, being 76% of them located in putative promoter regions. 18 of these peaks were validated in an independent ChIP assay by qPCR. The 154 peaks were assigned to genes involved in several functional classes such as motility and chemotaxis, iron homeostasis, and signal transduction and transcriptional regulators, including genes encoding proteins implicated in the turn-over of c-diGMP. A putative AmrZ binding site was also observed by aligning the 154 regions with the MEME software. This motif was present in 75% of the peaks and was similar to that described in the The results presented here show that AmrZ is an important global transcriptional regulator involved in environmental sensing and adaption. It is also a new partner in the complex iron homeostasis regulation. Pseudomonas sequenced so far. AmrZ appears to be an important regulator in Pseudomonas aeruginosa, since a null mutant is severely impaired in a murine in vivo pathosystem, being displaced by the wild-type strain with Bowtie v2[P. fluorescens F113 gene annotation. Enriched regions with a score, −10 · Log10 , equal or bigger than 1000 and a fold enrichment equal or bigger than 5.5 were selected for further study. A perl script was designed to extract nucleotide sequences 800 bps long, centered on selected peaks from reference genome and submitted to MEME[Illumina reads were clipped and trimmed to remove low quality nucleotides as well as putative Illumina adapters trails by using Trimmomatic, specifyowtie v2. Alignmeowtie v2. Reads h to MEME to detecInterPro and Gene Ontology semantic values were used to assign functional categories to genes close to enriched regions.SigmaStat software package (Systat software) was used for all statistical analyses. The data were compared using Student’s t-test for independent samples (p < 0.05 or p < 0.1).http://www.ncbi.nlm.nih.gov/sra/?term=SRP039494.The raw sequence data set supporting the results of this article is available in the NCBI SRA repository, Bioproject accession number SRP039494,The following additional data are available with the online version of this paper. Additional data file 1 is a table listing the genes that correspond to the peaks from the ChIP-seq. For each peak, the score, overrepresentation, presence of the conserved motif and the p value of the motif, is given. Additional data file 2 is a table listing the oligonucleotides used in this study.RHH: Ribbon-Helix-Helix; ChIP: Chromatin immunoprecipitation; qPCR: quantitative or real time PCR; c-diGMP: cyclic diguanylate monophosphate; MEME: Multiple Em for Motif Elicitation; HA: Hemagglutinin peptide; MACS: Model-based Analysis of ChIP-SeqThe authors declare that they have no competing interests.FM-G participated in study design, performed immunoprecipitation and expression experiments, participated in writing the Ms. MR-N carried out bioformatic analysis and participated in writing the Ms. PV performed gene expression analysis. MM and RR conceived and supervised the study, and participated in writing the Ms. All authors read and approved the final Ms.ChIP-Seq peaks. Nearest gene, score, overrepresentation, presence of the conserved motif and its p value are given for every peak.Click here for fileOligonucleotides used. List and sequence of primers used for ChIP, q-PCR and AmrZ fusions.Click here for file


Recent advances in high-throughput technologies have transformed methodologies employed to study cell-specific epigenomes and the approaches to investigate complex cellular phenotypes. Application of next-generation sequencing technology in the skeletal muscle differentiation field is rapidly extending our knowledge on how chromatin modifications, transcription factors and chromatin regulators orchestrate gene expression pathways guiding myogenesis. Here, we review recent biological insights gained by the application of next-generation sequencing techniques to decode the epigenetic profile and gene regulatory networks underlying skeletal muscle differentiation. During developmental processes, in response to external cues, changes in chromatin involving activation and repression of transcription factors and chromatin regulators  underlie commitment of specific cells to defined lineages. Importantly, transcription factors and chromatin modifiers are also able to alter the cellular expression program to maintain cell identity even upon removal of the initiating differentiation stimuli . During  Myogenic lineage commitment and execution of the terminal differentiation program relies on the activity of the paired-box transcription factors Pax3 and Pax7 and of the muscle regulatory factors (MRFs) MyoD, Myf5, MRF4, and myogenin [ In response to extrinsic signals, Pax3+ cells are committed to myoblasts and undergo terminal myogenic differentiation through the transcriptional network activated by MRFs. During postnatal life, muscle growth relies on satellite cells, which are a subpopulation of somite-derived cells that reside between myofibers and the basal lamina . Satelli MRFs achieve the task of transcriptional activation through the participation of nonmuscle restricted transcriptional activators, such as the Mef2, Six, and Runx family members . The pro Moreover, other sets of epigenetic regulators exert their function by repressing gene transcription: HDACs   and hist Several lines of evidence showed that enhancer of Zeste homolog 2 (Ezh2), the catalytic subunit of the PRC2 complex, is recruited to a subset of muscle-specific promoters in myoblasts , 20, 21 In summary, PcG role in satellite cells is to prevent ectopic differentiation, thus maintaining quiescence and proliferation states and safeguarding the mis-expression of non-myogenic genes in skeletal muscle cells. Another layer of control in myogenesis and muscle homeostasis is exerted by microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) , 24. miR In this paper we will focus on recent findings depicting the epigenetic landscape of myoblasts cells, obtained through the application of Next Generation Sequencing (NGS) technology. The most recent results presented by three independent research groups shed new light on the genomewide epigenome of myoblast cells, focusing on histone modifications signature, the genomewide location of the MyoD transcription factor and of chromatin modifiers, for example, PcG proteins. In recent years, high-throughput technologies have been developed and rapidly improved to interrogate several aspects of cellular processes. Likewise, traditional approaches employed to interrogate the epigenome have undergone profound transformation since their coupling to massive parallel sequencing. As a result, approaches that investigated restricted groups of genomic loci have been adapted to genomewise analysis, with unprecedented resolution and specificity, and with dramatically decreasing sequencing costs.Methodologies currently employed to investigate global epigenetic signature are summarized in (i) 4C-Seq (circular chromosome conformation capture) is used to map long-range chromatin interactions and relies on proximity-based DNA ligation and sequencing of chromosomal regions contacting a bait DNA region .Hi-C-Seq is also employed to define chromosome architecture but it is free of bias for a bait locus .(ii) ChIA-PET-Seq (chromatin interaction through DNA-binding protein) combines chromatin immunoprecipitation (ChIP), proximity ligation, pair-end tag, and deep sequencing to detect chromatin interactions mediated by a protein, at the genomewide scale , 35.(iii) MNase-Seq exploits the property of micrococcal nuclease (MNase) to preferentially cleave linker region DNA and the MNase-digested DNA can therefore be used to annotate nucleosomes distribution . DNase s(iv) Chip sequencing (ChIP-Seq) couples the conventional chromatin immunoprecipitation technique with massive parallel sequencing, and it is used to profile histone modifications, and map transcription factors, core transcriptional machinery and chromatin modifiers recruitment in a genomewide scale. The genomewide map of generated reads provides information on the location and the intensity of the protein-DNA interaction , 39.(v) Bisulfite-Seq relies on the ability of bisulfite to convert unmethylated cytosines to uracils so that massive parallel sequencing can identify cytosine methylation status at the single base pair resolution . Alterna(vi) Furthermore, global mRNA expression profiling can now be achieved through a deep-sequencing approach (RNA-Seq) that sequences steady-state RNA (known and novel ones) in a sample, offering larger than microarrays dynamic range and sensitivity. However, a gene list is not the final step of this approach, deeper insight is gained by bionformatics resources such as gene enrichment analysis, pathways, and regulatory network analysis . One key Exploiting Chip assays coupled to massive parallel sequencing, Cao et al. identified the genomewide binding of MyoD, in C2C12 myoblast cells, satellite cells, and in mouse embryonic fibroblasts (MEFs) converted to myotubes by the stable expression of MyoD . As pred Most of MyoD bound regions regulate genes expressed both in undifferentiated C2C12 and differentiated myotubes, while, in approximately 10% of MyoD targets, the binding is associated with distinct peaks in myotubes versus proliferating cells. Therefore, MyoD constitutively binds the majority of identified sites in both proliferating and differentiated C2C12 cells. Most of MyoD binding sites do not represent classical enhancer elements, since out of 25 MyoD bound regions only 10 showed enhancer activity in a transient transfection assay. Interestingly, MyoD broad binding throughout the genome correlates with chromatin regions with overall increase in their histone H4 acetylation levels in MyoD-transduced MEF cells versus control cells. Conversely, MyoD expression in MEF cells marginally impacts the distribution of the H3K4me3 marks. Thus, MyoD may play a crucial role in broadly reprogramming the epigenetic architecture of the lineage-specified cells. The latter findings suggest that MyoD association throughout the genome leads to the recruitment of histone acetyltransferases and most likely of chromatin remodeling factors , possibl Asp et al. performed an initial comprehensive description of myoblasts and myotubes histone modifications profile . The aut Furthermore, genes involved in nonmuscle lineage commitment can be grouped in a distinct cluster characterized by the H3K27me3 mark and a transcriptionally silent state, both in myoblasts and myotubes. Notably, mesoderm-related genes, such as genes involved in myogenesis, adipogenesis, and osteogenesis are not present in this cluster. Interestingly, as shown for other lineage committed cells , only a  Because every cell lineage displays a specific genomewide profile of histone modifications, a comprehensive description of the chromatin signature of skeletal muscle cells will represent a precious resource for scientists investigating mechanisms underlying myogenic transcriptional regulation. Genomewide mapping of chromatin factors has provided insights into the gene networks regulated by histone acetyltransferases, HDACs, and chromatin modifiers in ESCs and hematopoietic lineages , 48, whi NGS technologies open novel avenues in the study of myopathies: the new methodological approach will shed new light on the biological pathways involved in skeletal muscle diseases and become an essential tool to study genetic causes of myopathologies. Furthermore, it will likely become a valuable technological approach in molecular diagnosis . A recent report by Geng et al. highlights the role of ChIP-Seq technology in revealing the molecular pathways modulated by the homeobox protein DUX4, a candidate gene misexpressed in facioscapulohumeral dystrophy (FSHD) . The aut NGS approaches may also represent a suitable diagnostic tool for Duchenne and Becker muscular dystrophy, because of the heterogeneity in the mutational spectrum of the disease and the large size of the dystrophin gene . Therefo Comprehensive understanding of the myoblast/myotube epigenome will extend our fundamental knowledge of molecular mechanisms orchestrating biological processes, such as myogenic differentiation, muscle development, maintenance, and regeneration, in physiological and myopathological conditions. Deep sequencing methodologies will assist stem cell research toward the comprehension of mechanisms underlying muscle cells identity. This knowledge has important implication for regenerative medicine, underscoring new paradigms for satellite cells biology and offering novel targets of pharmacological intervention.Recent advances in NGS technology allow addressing several epigenetic questions at the genomewide scale . To dateCharacterization of the histone modifications landscape in skeletal muscle cells can be exploited further to annotate lineage-specific functional genomic elements, such as enhancers and insulators –57. Chro Potential drawbacks of NGS techniques are the amount of starting material that frequently is a limiting factor. Isolation of quiescent satellite cells by cell sorting greatly reduces the number of cells one can use for RNA-Seq or ChIP-Seq experiments. Nevertheless, new protocols for chromatin immunoprecipitation assays coupled with massively parallel sequencing have been developed and optimized for limiting sample material . Further Another challenge introduced by NGS technology is the growing need for bioinformatics tools to analyze the vast amount of data generated by each experiment. Moreover, large-scale data obtained from NGS platforms need to be made available to the broad scientific community in a standardized and simple annotation form, as these data represent a precious framework to infer biological information. Bioinformatics pipelines require further refinements to enable expansion of mapping capabilities to the genome , improve Besides implementing computational methods in biological studies, successful application of NGS technologies imposes a more profound revolution related to experimental design and forces a transition from single-gene hypothesis-driven experiments to a genomewide view, in which understanding of the integration and the cross-talk of different circuitries is at the forefront. The achievements of these remarkable research capabilities lie in the combination of conventional molecular and cellular biology approaches with a systemwide genetic view, which enables us to explore so far not addressable questions and will help us to gain a more comprehensive insight into mechanisms guiding skeletal muscle cells during differentiation, its maintenance and response to pathological insult.