We demonstrate that sorafenib largely reverses the changes in histone modifications that occur during EMT in A549 alveolar epithelial cells. Sorafenib also significantly reduces the coordinated epigenetic switching of critical EMT-associated genes in accordance with their expression levels. Furthermore, we show that sorafenib potentiates histone acetylation by regulating the expression levels of histone-modifying enzymes. Collectively, these findings provide the first evidence that sorafenib inhibits the EMT process through an epigenetic mechanism, which holds enormous promise for identifying novel epigenetic candidate diagnostic markers and drug targets for the treatment of human malignancies.The epithelial to mesenchymal transition (EMT) has been well recognized for many decades as an essential early step in the progression of primary tumors towards metastases. Widespread epigenetic reprogramming of DNA and histone modifications tightly regulates gene expression and cellular activity during carcinogenesis, and epigenetic therapy has been developed to design efficient strategies for cancer treatment. As the first oral agent approved for the clinical treatment of cancer, sorafenib has significant inhibitory effects on tumor growth and EMT. However, a detailed understanding of the underlying epigenetic mechanism remains elusive. In  As one of the most common types of human diseases, cancer is now the third leading cause of mortality in China and worldwide for both men and women, and more than 90% of deaths from cancer are due to the metastatic spread of primary tumors Epigenetic modifications, especially DNA methylation and histone modifications, work in concert with genetic mechanisms to regulate transcriptional activity in normal tissues. These modifications emerge as a dynamic and efficacious mechanism for the establishment and regulation of gene expression patterns during development, differentiation and tumorigenesis Recently, the successful development of small chemicals that disrupt several fundamental signaling pathways has signified a paradigm shift in medical therapies in vitro model for assessing EMT, carcinogenesis and drug metabolism During metastasis, an increasing number of key growth factors have been found to be responsible for driving EMT, of which transforming growth factor-β (TGF-β) is a master inducer The findings above prompted us to further explore the underlying epigenetic mechanisms of EMT regulation by sorafenib. Histone modifications, but not DNA methylation, had been previously reported to undergo widespread reprogramming during EMT mediated by TGF-β1 We further performed pair-wise comparisons among the three treatment conditions to assess the changes in the histone modifications within specific genomic regions during EMT. Significant differential histone modification regions (DHMRs) were defined based on strict criteria see . By crosNext, we used the set of DHMGs identified above to examine whether sorafenib exerts functional effects on the progress of EMT. To address this question, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis was performed E-cadherin were decreased and both these two active modifications in the promoter of N-cadherin were increased during EMT, which is consistent with the changes in their expression levels  were dominated by H3K4me3 and H3K9ac following EMT and these switches were also significantly suppressed by sorafenib , suggesting a general anti-EMT action of sorafenib.We further employed another lung carcinoma cell line (PC9 cells) to corroborate and extend the epigenetic mechanism of anti-EMT actions of sorafenib. Similar to the findings in A549 cells, PC9 cells treated with TGF-β1 also underwent EMT and this process could be counteracted by sorafenib . We nextGCN5 was up-regulated in response to external TGF-β1 stimulation. Moreover, exposure of A549 epithelial cells to sorafenib eventually resulted in a significant increase in the expression of GCN5 but had only a subtle effect on the mRNA level of PCAF  were enhanced during EMT, whereas these changes were reciprocally blocked in TGF-β1+sorafenib-treated A549 cells, suggesting that sorafenib acts as a potential inhibitor of a large group of HDACs. Taken together, these results show that sorafenib potentiates the activating histone modification H3K9ac by regulating the expression levels of histone-modifying enzymes.Because histone deacetylases (HDACs) also contribute to chromatin remodeling by catalyzing the removal of acetyl groups on the NHin vitro model, we found that treatment with sorafenib in during TGF-β1-induced EMT resulted in a loss of active histone markers (H3K4me3 and/or H3K9ac) at the promoters of TGF-β1, Smad2/3 and downstream EMT mediators such as Snail and Slug . Sorafenib  was manufactured by Bayer Pharmaceuticals . Primary antibodies against H3K9ac, H3K4me3, H3K27me3 and H3K9me3 were purchased from Millipore . The rabbit monoclonal antibody against E-cadherin was purchased from Cell Signaling Technology . Other primary antibodies described in this paper, including the anti-fibronectin and anti-β-actin antibodies, were purchased from Sigma-Aldrich .Recombinant human TGF-β1 was purchased from 2.Human alveolar epithelial A549 cells were obtained from ATCC  and cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS)  at 37°C with 5% COTo detect the protein levels of epithelial and mesenchymal markers, cells treated as indicated were lysed with 200 µl of lysis buffer and subjected to western blot analysis TM200 to generate chromatin fragments between 100 and 500 bp. The solubilized chromatin fragments were then immunoprecipitated with antibodies against H3K9ac, H3K4me3, H3K27me3 and H3K9me3, respectively. The ChIPed DNA was used for cluster generation and standard single-end sequencing with 50 bp reads (SE50) using Illumina Hiseq 2000.Chromatin immunoprecipitation (ChIP) was performed as previously described After SE50 sequencing, the Illumina reads were post-processed and aligned to the human reference genome (UCSC hg19) using SOAP  2.01 with default parameters that excluded reads with more than five mismatched bases t-test. If the p-values from the double t-test were less than 0.01, then these 5 bins were used as a seed window to slide across the genome with a 1 bin step size. Double t-test was performed for each step until a p-value of >0.01 was encountered or the number of bins reached fifty. Second, only the regions that were identified as differentially enriched based on statistical analysis and that possessed a minimum of a 2-fold change in the ChIP signal between the two samples were kept. The differential regions that were separated by gaps of size <1kb were further merged. Finally, we applied an algorithm previously described by Adli et al to evaluate signal enrichment in these regions with small adjustments p-value by comparing the window value to the background. For H3K4me3 and H3K9ac, windows with Poisson p-value <10E−5 were identified as enriched, and for H3K9me3 and H3K27me3, windows with Poisson p-value <10E−3 were identified as enriched. The differential regions that did not contain enriched regions in both of the samples were filtered out.Differential histone modification regions (DHMRs) for sample-to-sample comparisons were identified based on three strict criteria: First, the whole genome was partitioned into 100-bp long bins, and the average ChIP signal for each bin was calculated. Initially, the ChIP signal values from five bins were subjected to double http://bioinfo.vanderbilt.edu/webgestalt/) and the DAVID  web server (http://david.abcc.ncifcrf.gov/).We collated a set of 25,489 RefSeq genes . Pathway enrichment analysis was performed using the KEGG (Kyoto Encyclopedia of Genes and Genomes) web server  and are accessible through GEO Series accession number GSE41479.The ChIP-seq data generated for this work have been deposited in the NCBI Gene Expression Omnibus (GEO)  in the absence or presence of sorafenib (5 µM) were subjected to in vitro scratch assay with images captured at 0 h and 24 h after incubation using a phase-contrast microscope. The edge of the scratch is marked by imaginary dashed lines.(TIF)Click here for additional data file.Figure S2Overview of histone modifications in the genic and promoter regions. (A) Distribution of histone modification signals in genic regions for H3K4me3, H3K9ac, H3K27me3 and H3K9me3. The mRNA region of each gene (from TSS to TTS) was divided into 100 portions, and the up-5k and down-5k region of each gene was divided into 25 portions, respectively. The X-axis indicates the relative distance to the TSS, the Y-axis indicates the intensity of each histone mark and the curve represents the mean signal of all genes in this region. (B) Box plots show the intensities of histone modification signals in the promoter regions of all genes. The promoter is defined as a region crossing upstream 2000 base pairs (bp) and downstream 2000 bp of the transcriptional start site (TSS). The black line in the box represents the mid-value of the signals for all of the promoters. Z-test was used to identify significant differences between control and TGF-β1-treated cells or between control and TGF-β1+sorafenib-treated cells. Significance is indicated by ** based on a threshold of p-value <1E-5.(TIF)Click here for additional data file.Figure S3Treatment with sorafenib counteracts TGF-β1-induced EMT in human lung adenocarcinoma PC9 cells. (A) PC9 cells were treated with TGF-β1 (5 ng/mL) and/or sorafenib (5 µM) for 48 h, lysed and immunoblotted using antibodies as indicated. (B) H3K4me3 modification and RNA expression changes in PC9 cells. The data are representative of three similar experiments and are displayed as the mean ± SE. **, p<0.01 as evaluated using the Student’s t-test.(TIF)Click here for additional data file.Figure S4Sorafenib regulates the expression levels of DNMTs. After treatment with TGF-β1 (5 ng/ml) in the absence or presence of sorafenib (5 µM) for 24 h and 48 h, A549 cells were subjected to real-time qRT-PCR analysis to determine the effect of sorafenib on the expression levels of DNMT1, DNMT3A and DNMT3B. Normal A549 cells without any treatment (at 0 h) were taken as the control. The quantitative ratios are normalized to the expression of GAPDH. The data are representative of six similar experiments and are displayed as the mean ± SE. **, p<0.01 as evaluated using the Student’s t-test.(TIF)Click here for additional data file.Table S1Summary of uniquely mapped reads per histone marker.(XLS)Click here for additional data file.Table S2Optimized primers used for real-time qPCR.(XLS)Click here for additional data file.Table S3Primers used for H3K4me3 ChIP-qPCR.(XLS)Click here for additional data file.Dataset S1DHMGs reciprocally regulated by sorafenib during EMT and KEGG Pathway Enrichment Analysis. The file contains the lists of DHMGs including H3K4me3, H3K9ac, H3K9me3 and H3K27me3 that are reciprocally regulated by sorafenib during EMT, and KEGG Pathway Enrichment Analysis of these DHMGs.(XLS)Click here for additional data file.Dataset S2DHMGs during EMT and KEGG Pathway Enrichment Analysis. The file contains the lists of DHMGs during EMT including H3K4me3, H3K9ac, H3K9me3 and H3K27me3, and KEGG Pathway Enrichment Analysis of these DHMGs.(XLS)Click here for additional data file.Dataset S3DAVID Pathway Enrichment Analysis of DHMGs reciprocally regulated by sorafenib during EMT. The file contains DAVID Pathway Enrichment Analysis of DHMGs reciprocally regulated by sorafenib during EMT.(XLS)Click here for additional data file.


Plasmodium falciparum. To elucidate the role of chromatin remodeling in transcriptional regulation associated with the progression of the P. falciparum intraerythrocytic development cycle (IDC), we mapped the temporal pattern of chromosomal association with histone H3 and H4 modifications using ChIP-on-chip. Here, we have generated a broad integrative epigenomic map of twelve histone modifications during the P. falciparum IDC including H4K5ac, H4K8ac, H4K12ac, H4K16ac, H3K9ac, H3K14ac, H3K56ac, H4K20me1, H4K20me3, H3K4me3, H3K79me3 and H4R3me2. While some modifications were found to be associated with the vast majority of the genome and their occupancy was constant, others showed more specific and highly dynamic distribution. Importantly, eight modifications displaying tight correlations with transcript levels showed differential affinity to distinct genomic regions with H4K8ac occupying predominantly promoter regions while others occurred at the 5′ ends of coding sequences. The promoter occupancy of H4K8ac remained unchanged when ectopically inserted at a different locus, indicating the presence of specific DNA elements that recruit histone modifying enzymes regardless of their broad chromatin environment. In addition, we showed the presence of multivalent domains on the genome carrying more than one histone mark, highlighting the importance of combinatorial effects on transcription. Overall, our work portrays a substantial association between chromosomal locations of various epigenetic markers, transcriptional activity and global stage-specific transitions in the epigenome.Epigenetic mechanisms are emerging as one of the major factors of the dynamics of gene expression in the human malaria parasite,  Plasmodium falciparum. The complex life cycle of P. falciparum comprises various morphological and functionally distinct forms and is completed in two different hosts. Various regulatory mechanisms are employed by these parasites to complete their life cycle and survive in human hosts. Epigenetic mechanisms, though not fully explored, have been implicated as one of the key players in gene regulation, morphological differentiation and antigenic variation. Here, we present a comprehensive epigenetic map of 12 histone post-translational modifications during the intraerythrocytic life cycle of P. falciparum. We have been able to identify at least eight histone modifications whose dynamic patterns correlate with the transcriptional regulation across the life cycle. In particular, we have shown that a set of euchromatic histone marks work in synergy, creating a dynamic unique histone code that is linked with gene expression during the progression of the Plasmodium intraerythrocytic developmental cycle. These findings enhance our knowledge of complex gene regulation and will help to identify novel targets for fighting malaria.Malaria is a devastating parasitic disease caused by the protozoan protist  Plasmodium falciparum, the causative agent of the most severe form of malaria, is also the model organism to study the parasite development due to its ability to be grown in vitro. The clinical manifestations of malaria are a result of the parasite development in the red blood cells where it completes its asexual intra-erythrocytic developmental cycle (IDC). Even though transcriptional regulation is important for all developmental stages, the IDC transcriptome revealed a particularly distinct temporal transcriptional regulatory system in P. falciparumP. falciparum also displays several diverse features of its epigenome such as the absence of linker histone H1 P. falciparum chromatin is predominantly in a euchromatic state with only a few heterochromatic islands marked by trimethylation of lysine 9 of histone 3 (H3K9me3) Saccharomyces cerevisiae where K16 acetylation is the dominant modification present at 80% of all H4 molecules P. falciparum H4 P. falciparum have been shown to affect transcription P. falciparum likely via switching between hetero- and euchromatic structures at several genetic loci that mainly encode factors involved in host-parasite interactions P. falciparum and stress the need to characterize the overall chromatin landscape as well as its effect on transcriptional regulation during the life cycle.In spite of worldwide efforts, malaria remains one of the most devastating illnesses with an estimated 216 million episodes leading to 655,000 deaths in 2010 P. falciparum histones including acetylations and methylations have been recently identified P. falciparum IDC were reflected in the genome wide epigenomic profiles of eight of the studied histone marks. The transcription linked patterns were associated with enrichment of most histone marks predominantly at the start of coding regions while only one modification, acetylation of H4 at lysine 8 (H4K8ac) was found predominantly at the putative promoter regions. Our data also demonstrate co-operative binding of acetylation marks in modulating gene expression across the IDC.A total of 44 different post-translational covalent modifications on P. falciparum IDC. The main rationale was to recapture the transcriptional cascade of the IDC P. falciparum DNA microarray that contains probes representing both the open reading frames (ORF) and the upstream intergenic regions (IGR) , and (2) in addition to statistical significance (P<0.05), a minimum 1.5 fold change in the occupancy profiles across the IDC  The statistical significance of the measured change in occupancy across the experimental time points with respect to experimental replicas  varied from 35 to 48%  between mean-centered profiles of the dynamic histone modification occupancy and the corresponding mRNA. For example, 49% (out of 7260) of the H4K8ac dynamic occupancy profiles showed positive correlations (r≥0.4) with transcription while 22.8 and 22.5% showed no or negative correlation, respectively . The ove5 to 48% . The gen5 to 48% . Interes5 to 48% . There w5 to 48% . One intNext, we were interested in the biological significance of the transcription-linked histone modifications. We analyzed the distribution of mRNA correlating histone-marked loci with respect to their position in the gene and subsequently investigated the functional involvement of these genes . InteresP<0.05) based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Malaria Parasite Metabolic Pathway (MPMP)  . The traPlasmodium parasites. Given the highly dynamic character of histone modifications observed by this as well as previous studies attBcg6 locus (cg6 gene (dashed line) that is normally characterized by high levels in rings and gradually declines through trophozoites and schizonts. The luciferase activity profiles were also similar to the acetylation patterns of all transfected promoters (data not shown). For one of the promoters (PF14_0705), there was an incomplete “carry-over” of the H4K8ac occupancy profile that was matched only in the ring stage. This may be due to unknown factors like insufficient promoter length. Overall our data suggest that the promoter regions of P. falciparum genes carry DNA regulatory elements that establish H4K8ac independently of their endogenous chromatin environment.Presently, very little is known about the mechanisms of chromatin remodeling in g6 locus . We foung6 locus . These pP value as described above  overlapped with probes common to the individual ChIPs. As an example, the distribution of ChIP signals on chromosome 4 defined common areas of enrichment between chromatin immunoprecipitated with either H3K56ac or H3K9ac independently, or H3K56ac followed by H3K9ac sequentially  associate with distinct functionalities that could be broadly divided into two main biological categories: (i) growth , and (ii) host parasite interaction   . This maP. falciparum, the HDAC inhibitor apicidin causes a massive hyperacetylation of H3K9 and H4K8 (and demethylation of H3K4) residues leading to global deregulation of the IDC transcriptional cascade Plasmodium HDACs P. berghei infection in mice Plasmodium represents a promising target area for malaria drug development, and more efforts in developing new compounds with higher selectivity as well as bioavailability are ongoing P. falciparum, will provide a solid reference for all epi-drug development in malaria as well as other parasitic diseases in the future.Given the essential role of epigenetic regulation in gene expression, unique factors associated with these processes are presently considered as drug targets for malaria as well as other human parasitic diseases P. falciparum strain T996 were cultured at 5% parasitemia and 2% hematocrit under standard conditions Highly synchronized cells of Equal amounts of total protein lysate obtained from parasite pellets from the 6 time points were separated by 12% SDS PAGE and transferred onto nitrocellulose membrane. Western hybridizations were carried out using antibodies  directed against the modified histones. Horseradish peroxidase conjugated secondary antibody was purchased from GE Healthcare. We also performed immuno-localization with these antibodies as described Cross-linked cells were homogenized with 200 strokes of a dounce homogenizer and lysed using 1% SDS. The resulting nuclear extract was sonicated with 8 bursts of 10 sec with 50 sec rest between bursts to shear DNA to a final length of 200 to 1000 bp. The sonicate was then centrifuged for 10 min at 13,000× g, and sheared DNA incubated with the immunoprecipitating antibody overnight at 4°C followed by incubation with salmon sperm DNA/Protein A agarose slurry (Millipore) for 1 h at 4°C. Protein A agarose was gently pelleted followed by extensive washes. The DNA bound to protein of interest was reverse cross-linked using 0.2 M NaCl and incubation overnight at 65°C. Recovered DNA was purified using the QIAEX II kit (QIAGEN). Amplification of immunoprecipitated DNA as well as sonicated genomic DNA (input) was carried out as described RNA was isolated to carry out transcriptional profiling at the appropriate time points. RNA extraction and cDNA synthesis were carried out as described P. falciparum microarrays containing 5,402 50-mer intergenic oligonucleotide probes and 10,416 70-mer ORF probes representing 5,343 coding genes P. falciparum genome providing an even coverage with at least one probe per 1.542 kb. P. falciparum strain T996 was chosen to carry out these experiments due to an exact IDC length of 48 h and the ease with which it can be synchronized. Since the probes on the array have been designed for 3D7, we excluded vars, rifins and stevors from our analysis. The microarray hybridization was carried out at 63.5°C or 65°C in the automated hybridization station  for ChIP DNA or cDNA, respectively, as described Equal amounts of Cy5 and Cy3 labeled samples were hybridized to 2 ChIP/input ratios where high and low ratios represent strong and weak enrichment respectively of modified histones. For expression analysis, each gene profile was represented by an average expression value calculated as an average of all probes representing a particular gene. For ChIP-on-chip, all microarrays were done in triplicates. For each time point, probes with data present in at least 2 out of 3 triplicates were included. Data was presented as an average of triplicates after Kth nearest neighbor (KNN) imputation. Data was further filtered to include only those probes where signal from ChIP DNA was obtained in at least 2 consecutive time points. To address dynamics across the life cycle, the average ChIP/input ratio of each time point was used to detect the summit and bottom time point of enrichment for every probe. P value was assigned based on a student's t-test between the replicates. Significantly oscillated occupancy profiles (relative occupancy defined by log2 ChIP/input ratio) between the summit and bottom time points were defined as P value<0.05 and fold change ≥1.5 across the IDC referring to all detectable dynamic probes (within the limit of the applied ChIP-on-chip technology) and probes showing the highest level of change in marked histone occupancy, respectively.Lowess normalized data was processed to filter out spots with signal intensity less than twice the background intensity for both Cy5 and Cy3 fluorescence. The relative occupancy of histone marks is represented by log2 ratios were plotted against probe position from −1000 bp to +3000 bp with respect to ATG at every time point.To assess overall ChIP enrichment at every time point, probes were divided into 3 groups based on expression of the respective genes at each time point: top, middle and bottom 10% of total probes with highest, intermediate and lowest levels of expression, respectively. For each group, ChIP/input log2 ratios across all the time points.Phaseograms for expression and ChIP data were generated by fast Fourier transform method where probes/genes were sorted according to phase from −π to π with the mean-centered logP value<0.0005). The same analysis was also performed for correlations between histone modifications. Dice's coefficient was calculated in order to assess the overlap between any two histone modification profiles across the IDC.Pearson's Correlation coefficient (PCC or r) was calculated between the histone mark profiles and corresponding mRNA profiles across the IDC and skewness of correlation distribution was calculated. Negative skew values indicate a long tail on the negative side (higher frequency on positive side). For ease of understanding, skew is represented as the negative of skew throughout the manuscript such that positive skew means a higher frequency on the positive side. To test how statistically significant the histone levels correlate with expression levels at each probe, we randomly generated marked histone and expression profile pairs by randomizing profiles between probes 100 times. The two-sample Kolmogorov-Smirnov (KS) test was used to test whether our observed correlation distributions are different from the random ones (P<0.05 and fold change ≥1.5 across the IDC). For each histone mark, the number of ChIP-enriched probes showing positive (r≥0.4) and negative (r≤−0.4) correlation with expression profiles of corresponding genes were normalized to total probes in the input. Data were arranged into bins ranging from −1500 bp upstream of ATG to +3000 bp into the gene and plotted against the percentage of probes falling in each bin.ChIP enrichment with respect to position in the gene was calculated using probes with oscillated profiles (RTQ-PCR was carried out on immunoprecipitated and input DNA using the SYBR Green PCR Master Mix (Roche) according to manufacturer's instructions. ChIP enrichment was calculated by using the ΔCt method (Ct of immunoprecipitated target gene - Ct of input target gene) where Ct is the threshold cycle. All PCR reactions were done in duplicates or triplicates.P. falciparum strain Dd2attB were provided by D. Fidock. The GFP cassette from the vector pLN-ENR-GFP hsp 86 3′ UTR from the plasmid pPF86 Bam HI/Apa I sites to create the plasmid pLN-Luc. All constructs were confirmed by sequencing. For transfection, promoter regions of various genes were cloned upstream of the luciferase gene at the Bam HI/Sph 1 sties of pLN-Luc. Transfections of P. falciparum strain Dd2attB and subsequent drug selection were carried out as described cg6 locus was confirmed by PCR.Vector pLN-ENR-GFP and All primer sequences used in the current study are listed in The microarray data have been submitted to NCBI GEO with accession number GSE39238.MAL13P1.122: SET domain protein, putative (SET2)Plasmodium protein, unknown functionPF14_705: conserved PFD0240c: 6-cysteine protein (P41)PFC0210c: Circumsporozoite proteincg6: gene coding for glutaredoxin-like protein (GLP3)hsp 86: gene coding for heat shock protein 90 (HSP90)GFP: Green fluorescent proteinLuciferase: Luciferase proteinFigure S1Validation of antibodies used for ChIP-on-chip. (A) Equal amounts of protein extracted at 8, 16, 24, 32, 40 and 48 hpi (from the same time course used for ChIP-on-chip) were used in 12% SDS PAGE. Western blots were carried out using antibodies against the studied histone epitopes. Actin was used as a loading control. (B) The specificity of the antibodies was further confirmed in peptide competition assays for 7 representative antibodies. The modified peptides (Millipore or Abcam) used correspond to respective modifications on the histone against which the antibodies were raised. Western blots were done with equal amounts (5 µg per lane) of ring stage protein. Antibody was incubated for 2 h with no peptide, 100-fold molar excess of specific peptide, 200-fold molar excess of specific peptide and 200-fold molar excess of a non-specific peptide before reacting with the blot. HDAC1 was used as a loading control in all 4 lanes. (C) Same antibodies were used for immuno-fluorescence analysis (IFA) of histone modifications in P. falciparum. Nuclear localization of histone marks was confirmed by IFAs done on formaldehyde fixed ring, trophozoite and schizont stage parasites. Nuclear DNA was stained with DAPI (blue) and all histone marks can be seen in pink.(TIF)Click here for additional data file.Figure S2Validation of ChIP results. (A) Using immunoprecipitated DNA, quantitative real time PCR was carried out on representative genes chosen for three histone modifications to validate the ChIP occupancy profiles across the IDC. Primers for real time PCR were designed to amplify 200–300 bp regions around the respective probe on the array. The x-axis represents hours post invasion and Y-axis the smoothed log2 ratio of ChIP enrichment over input for both real time PCR and microarray results. Solid red and purple lines refer to real time PCR profiles obtained for IGR and ORF, respectively, of the specified gene, whereas dotted lines refer to the microarray profiles of corresponding genes. Numbers at the bottom right corner of each graph refer to the Spearman's rank coefficient between ChIP-on-chip and RT PCR profile at IGR (red) and ORF (purple) regions. Note that except in two instances, the rest of the profiles from microarray and real time results show positive correlation. (B) Performance of microarray hybridization achieved from amplified DNA at AT rich IGRs was analyzed by comparing the relation between signal intensity and Cy5/Cy3 ratio. MA plots were derived for Cy5 labeled H4K5ac DNA hybridized against Cy3 labeled input DNA (sonicated genomic DNA) at 6 time points (TP1 to TP6) and also for Cy5 labeled input DNA hybridized against Cy3 labeled input DNA. ‘M’ stands for log2 ratio of Cy5/Cy3 after lowess normalization (y-axis) and ‘A’ stands for average log2 intensity of Cy5 and Cy3 (x-axis). Red line is the lowess smoothed M by A which is used to indicate data trend for each graph. “r” is Pearson's correlation coefficient of M and A (indicated at top of each plot). As shown, Cy5/Cy3 signal ratio is independent of signal intensity at both ORFs (left panel) and IGRs (right panel).(TIF)Click here for additional data file.Figure S3Gene models of ChIP-on-chip enrichment. (A) The graph represents the percentage of ChIP-enriched probes for each histone mark with P<0.05 and fold change ≥1.5 across the IDC in both intergenic regions (IGRs) and open reading frames (ORFs). Chi-square test was done to identify a positional bias towards IGR or ORF. Dark and light green boxes at top indicate preference at ORF and IGR, respectively. (B) The graph represents the percentage of ChIP-enriched genes (represented by probes for respective genes) for each histone mark with P<0.05 and fold change ≥1.5 across the IDC in both IGRs and ORFs. Overlap refers to the genes represented by probes associated with the histone marks in both IGR and ORF.(TIF)Click here for additional data file.Figure S4Transcription associated histone marks. (A) PCC was calculated between profiles of ChIP enriched probes (P<0.05 and fold change ≥1.5 across the IDC) and expression profiles of corresponding genes. The y-axis represents the percentage of probes showing positive (r≥0.4), no (r between 0.4 and −0.4) or negative (r≤−0.4) PCC for each histone mark. (B) Phaseograms representing histone mark profiles for probes showing maximum correlation with expression across the IDC. The yellow/blue color scale represents lowess smoothed profiles calculated from centered curves of relative occupancy ratios measured by ChIP/input (log2) for the 6 time points (TP1-6 representing 0 to 48 hpi). The gene/probe order in each phaseogram was determined independently using Fast Fourier transformation (see tion see . Vertica(TIF)Click here for additional data file.Figure S5Enrichment of selected gene families. Heat maps showing the marked histone occupancy of selected gene families. To maximize our dataset, we included all probes showing oscillating profiles (P<0.05 without consideration of fold change) across the IDC for every histone mark. Each row represents PCC (r) of ChIP profiles with expression profiles of the respective genes. For enrichment of multiple probes for the same gene, the probe with the maximum correlation with expression was included and hierarchical clustering was carried out. Scale bar indicates PCC between ChIP and expression profiles.(TIF)Click here for additional data file.Table S1Processed microarray hybridization data. The table contains log2 ratio of cDNA/pooled cDNA or ChIP/input DNA signal for all 6 time points across the IDC. Data was averaged for all probes of the same gene in case of cDNA and each probe of same gene has been assigned the same value in this table. For every histone mark, only those probes were included having ChIP data in at least two time points out of six. All experiments were done in triplicate and averaged across the triplicates after KNN imputing. First and second columns contain the probe id and corresponding gene, respectively, for each row. The next six rows contain RNA expression data. The following columns contain ChIP data, P value across the IDC Click here for additional data file.Table S2Overlap between histone marks. This table shows the data for overlap between any two pairs of histone modifications. Probes with fold change ≥1.5 were included in this analysis. Chart 1: Dice's coefficient calculated to assess the degree of overlap between any two marked histone pairs. Chart 2: P values based on KS test (as described in (XLSX)Click here for additional data file.Table S3Sequential ChIP. The table shows the average log2 ratios of ChIP/Input microarray signal from duplicate experiments. Sequential ChIP (ring stage parasites) was carried out using anti-H3K56ac antibody followed by either anti-H3K9ac antibody or no antibody as control. Data was compared to immunoprecipitated DNA using antibody against H3K56ac or H3K9ac alone.(XLSX)Click here for additional data file.Table S4Sequence of primers used for amplifications in the current study.(XLSX)Click here for additional data file.


Advances in genomic studies have led to significant progress in understanding the epigenetically controlled interplay between chromatin structure and nuclear functions. Epigenetic modifications were shown to play a key role in transcription regulation and genome activity during development and differentiation or in response to the environment. Paradoxically, the molecular mechanisms that regulate the initiation and the maintenance of the spatio-temporal replication program in higher eukaryotes, and in particular their links to epigenetic modifications, still remain elusive. By integrative analysis of the genome-wide distributions of thirteen epigenetic marks in the human cell line K562, at the 100 kb resolution of corresponding mean replication timing (MRT) data, we identify four major groups of chromatin marks with shared features. These states have different MRT, namely from early to late replicating, replication proceeds though a transcriptionally active euchromatin state (C1), a repressive type of chromatin (C2) associated with polycomb complexes, a silent state (C3) not enriched in any available marks, and a gene poor HP1-associated heterochromatin state (C4). When mapping these chromatin states inside the megabase-sized U-domains (U-shaped MRT profile) covering about 50% of the human genome, we reveal that the associated replication fork polarity gradient corresponds to a directional path across the four chromatin states, from C1 at U-domains borders followed by C2, C3 and C4 at centers. Analysis of the other genome half is consistent with early and late replication loci occurring in separate compartments, the former correspond to gene-rich, high-GC domains of intermingled chromatin states C1 and C2, whereas the latter correspond to gene-poor, low-GC domains of alternating chromatin states C3 and C4 or long C4 domains. This new segmentation sheds a new light on the epigenetic regulation of the spatio-temporal replication program in human and provides a framework for further studies in different cell types, in both health and disease. Previous studies revealed spatially coherent and biological-meaningful chromatin mark combinations in human cells. Here, we analyze thirteen epigenetic mark maps in the human cell line K562 at 100 kb resolution of MRT data. The complexity of epigenetic data is reduced to four chromatin states that display remarkable similarities with those reported in fly, worm and plants. These states have different MRT: (C1) is transcriptionally active, early replicating, enriched in CTCF; (C2) is Polycomb repressed, mid-S replicating; (C3) lacks of marks and replicates late and (C4) is a late-replicating gene-poor HP1 repressed heterochromatin state. When mapping these states inside the 876 replication U-domains of K562, the replication fork polarity gradient observed in these U-domains comes along with a remarkable epigenetic organization from C1 at U-domain borders to C2, C3 and ultimately C4 at centers. The remaining genome half displays early replicating, gene rich and high GC domains of intermingled C1 and C2 states segregating from late replicating, gene poor and low GC domains of concatenated C3 and/or C4 states. This constitutes the first evidence of epigenetic compartmentalization of the human genome into replication domains likely corresponding to autonomous units in the 3D chromatin architecture. Arabidopsis thalianaCaenorhabditis elegansDrosophila. To our knowledge, no such a drastic dimensional reduction has been reported in mammalian organisms so far. The application of a multivariate Hidden Markov Model (HMM) Understanding the role of chromatin structure and dynamics in the regulation of the nuclear functions including transcription and replication, is a major challenge of current research in genomics and epigenomics Arabidopsis thalianaDrosophila, mouse and human origins How epigenetic mechanisms and gene expression coordinate with DNA replication has been a long-standing question DrosophilaDrosophilaThe recent blooming of genome-wide mean-replication timing (MRT) data in yeast Here we perform principal component analysis (PCA) We investigated relationships between the genome-wide distributions of eight histone modifications, one histone variant and four DNA binding proteins in the immature myeloid human cell line K562  at the 100 kb resolution of corresponding MRT data In a second step, to objectively identify the prevalent combinatorial patterns of the thirteen chromatin marks, we performed a PCA To determine the number of clusters, we used two statistical criteria . Four is the optimal choice according to the within-cluster sum of squares that clearly displays an elbow (abrupt slowing down of the decay) at the cluster number equal to four . The gapDrosophilaDrosophilaArabidopsisThe four prevalent chromatin states so identified and further labeled C1, C2, C3 and C4, were respectively found in 6572 (23.8%), 5312 (19.2%), 6603 (23.9%) and 6758 (24.4%) among the 27656 100 kb loci with a defined MRT . Indeed, we removed from the analysis the 2411 (8.7%) loci that were not properly classified in any chromatin state. More than 90% of the loci in C1 are associated (positive enrichment) with the histone modifications H3K36me3, H3K4me3, H3K27ac and H3K79me2, the hallmarks of transcriptionally active chromatin  [2], [6]Methylation of H3K9 is well known to be implicated in heterochromatin formation and gene silencing DrosophilaThe insulator-binding protein CTCF is known to establish chromatin boundaries to prevent the spreading of heterochromatin into transcriptionally active regions Despite the original association of H4K20 methylation with repressive chromatin To assess the generality of the four prevalent chromatin states, we ran the same clustering procedure on the lymphoblastoid cell line GM12878 and on a third blood cell line . Some of these genes (3001) were not taken into account in our analysis because their promoter don't belong to any chromatin state. The mean density of the 20817 genes that belong to one of the four chromatin states is 8.24 promoters per Mb. The only chromatin state that is highly enriched in gene promoters is the early replicating euchromatin state C1 that harbours 62.0% of gene promoters even though it represents about 25% of the total genome coverage by the four chromatin states  and 2. TDrosophilaTo investigate gene expression in chromatin states, we used a data set of 17872 genes with a valid expression value in K562 . Of those genes, 15869 belong to one of the chromatin states. We found that a vast majority of expressed genes with a   are in t , which cWe assessed gene function on the basis of gene ontology Along the line of the isochore model d for C1  with . T GC-rich  but gene GC-rich  and mostCytosine DNA methylation is a mediator of gene silencing in repressed heterochromatin regions, while in potentially active open chromatin regions DNA is essentially unmethylated nd C4  . Thus re CpG o/e  and MRT  CpG o/e  observedNote that chromatin state compositional content in Monocd14ro1746 is quite the same as in K562  resolution 100 kb of available genome-wide MRT data, we have found that these chromatin states actually replicate at distinct periods of the S-phase, C1 replicates early, C2 is a mid-S phase phase state whereas C3 replicates later than C2 but before C4 that replicates very late, almost at the end of S-phase. In the Supplementary Data are reported, for comparison, the results of a similar integrative analysis of epigenomic data in the lymphoblastoid cell line GM12878  and high (1 kb) resolutions in parallel, we should be in position to investigate the global reorganization of replication domains during differentiation (or disease) in relation to coordinated changes in chromatin state and gene expression. For example, this multivariate approach should shed a new light on the so-called replication domain “consolidation” phenomenon Timing profiles for the immature myeloid cell line K562 and the lymphoblastoid cell line GM06990 were obtained from the authors http://genome.ucsc.edu/FAQ/FAQformat.html). Broadpeaks format is a table of significantly enriched genomic intervals. Most of the data correspond to the release 3 (August 2012) of the Broad histone track. We downloaded the tables from: http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeBroadHistone/. The CBX3 and Sin3A data corresponds to the release 3 (September 2012) of the HAIB TFBS track. Tables were downloaded from the UCSC from: http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeHaibTfbs/For all ChIP-seq data, we downloaded data in the Encode standard format “broadpeaks” .All statistical computations were performed using the R software . The first three components were retained which accounts for Principal component analysis was performed on the rank transformed dataset using the function clara function implemented in the R package cluster. The options were set to: stand = FALSE, sampsize = 500, samples = 20, metric = euclidean.We used Clara algorithm To assess the number of clusters, we used the pooled within-cluster sum of squares around the cluster mean. Suppose that the data set of size The pooled within-sum of squares for the k clusters is:The pooled within-cluster sum of squares necessarily decreases with the number of clusters. A good choice for the number of clusters is the critical point where some clear crossover is observed from a fast decrease of ters see .We also used the Gap statistic i.e. a high value of clusterSim  with homogeneous epigenetic features. 91% of all 100 kb non-overlapping windows of the human genome were assigned to one of the four chromatin states C1, C2, C3 or C4.The number of transitions from i to j, As human gene coordinates, we used the UCSC Known Genes table. When several genes presenting the same orientation overlapped, they were merged into one gene whose coordinates corresponded to the union of all the overlapping gene coordinates, resulting in 23818 distinct genes.http://hgdownload.cse.ucsc.edu/goldenPath/hg18/encodeDCC/wgEncodeCaltechRnaSeq/).Expression data were retrieved from the Genome Browser of the University of California Santa Cruz (UCSC). To construct our expression data set, we used RefSeq Genes track as human gene coordinates. Genes with alternative splicing were merged into one transcript by taking the union of exons. Hence the TSS was placed at the beginning of the first exon. We obtained a table of 23329 genes. We downloaded expression valuess from the release 2 of Caltech RNA-seq track (ENCODE project at UCSC: Expression for one transcript is given in reads per kilobase of exon model per million mapped reads (RPKM) l is the number of masked nucleotide gaps plus one, i.e.L-l is the number of dinucleotide sites. The CpG o/e was computed over the sequence after masking annotated CGIs. The GC content was computed on the native sequence.CpG observed/expected ratio (CpG o/e) was computed as We detected contiguous windows of the same chromatin state (C1 to C4). We then kept the coordinates of the blocks of contiguous windows. To form chromatin state blocks of states (1+2), we merely detected contiguous windows of state 1 or 2. The same procedure was applied to define chromatin blocks of states (3+4). For chromatin blocks (1+2) and (3+4), we authorized the inclusion of isolated windows which don't belong to any chromatin state so to not disrupt very long blocks.http://go.princeton.edu/cgi-bin/GOTermMapper. Then for each chromatin state (C1 to C4), the number of occurrences of each GO term was determined by the number of promoters belonging to that state and associated to this GO term. The enrichment for each GO term in each cluster was tested using Fisher's exact test. We applied a procedure to control the false discovery rate (FDR) as described in Each gene name of our annotation dataset was associated to several GO terms from GO SLIM (high level GO terms) using the online mapper: Figure S1PCA analysis. (A) Percentage of variance accounted by the first thirteen principal components ordered according to their corresponding variance . (B) Cumulative variance.(EPS)Click here for additional data file.Figure S2Dichotomic analysis with two chromatin states. (A) Results of our clustering procedure when using two clusters (the number of clusters is the only parameter of the procedure). We found a segmentation between transcriptionally active chromatin (red) and silent chromatin (blue). (B) Same representation for chromatin state blocks (1+2) (light red) and (3+4) (light blue) as defined in (EPS)Click here for additional data file.Figure S3GO term enrichment of the Biological Process ontology in the four chromatin states. Fisher's exact test odd ratios were computed for each GO term of the Biological Process ontology in the four chromatin states. If the test was unsignificant the corresponding cell was left blank (see lank see  otherwis(EPS)Click here for additional data file.Figure S4GO term enrichment of the Cellular Component ontology in the four chromatin states. Same as (EPS)Click here for additional data file.Figure S5GO term enrichment of the Molecular Function ontology in the four chromatin states. Same as (EPS)Click here for additional data file.Figure S6Defining the four chromatin states for the GM12878 cell line. Scatterplot of the data points onto the first three principal components for the GM12878 cell line. Color dots indicate the four chromatin states as found by our clustering procedure . Points in dark grey are not classified in any chromatin state (see tate see .(EPS)Click here for additional data file.Figure S7Repartition of epigenetic marks in the four chromatin states for the GM12878 cell line. Boxplots of the decimal logarithm of epigenetic mark ChIP-seq read density in 100 kb non-overlapping windows per chromatin state. Same color coding as in (EPS)Click here for additional data file.Figure S8MRT in the four chromatin states for the GM12878 cell line. (A) Boxplots of MRT computed in 100 kb non-overlapping windows per chromatin state. (B) Empirical cumulative distribution function (c.d.f.) of MRT in the four chromatin states. Same color coding as in (EPS)Click here for additional data file.Figure S9Sequence composition in the four chromatin states in the GM12878 cell line. (A) Boxplots of GC percent computed in 100 kb non-overlapping windows per chromatin state. (B) Boxplots of CpG o/e computed in 100 kb non-overlapping windows per chromatin states. Same color coding as in (EPS)Click here for additional data file.Figure S10Repartition of epigenetic marks in the four chromatin states for the Monocd14ro1746 cell line. Boxplots of the decimal logarithm of epigenetic mark ChIP-seq read density in 100 kb non-overlapping windows per chromatin state. Same color coding as in (EPS)Click here for additional data file.Figure S11Sequence composition in the four chromatin states in the Monocd14ro1746 cell line. (A) Boxplots of GC percent computed in 100 kb non-overlapping windows per chromatin state. (B) Boxplots of CpG o/e computed in 100 kb non-overlapping windows per chromatin states. Same color coding as in (EPS)Click here for additional data file.


Long non-coding (lnc) RNAs are defined as non-protein coding RNAs distinct from housekeeping RNAs such as tRNAs, rRNAs, and snRNAs, and independent from small RNAs with specific molecular processing machinery such as micro- or piwi-RNAs. Recent studies of lncRNAs across different species have revealed a diverse population of RNA molecules of differing size and function. RNA sequencing studies suggest transcription throughout the genome, so there is a need to understand how sequence relates to functional and structural relationships amongst RNA molecules. Our synthesis of recent studies suggests that neither size, presence of a poly-A tail, splicing, direction of transcription, nor strand specificity are of importance to lncRNA function. Rather, relative genomic position in relation to a target is fundamentally important. In this review, we describe issues of key importance in functional assessment of lncRNA and how this might apply to lncRNAs important in neurodevelopment. The co-occurrence of massively parallel sequencing technology applied to RNA and the recognition that non-coding, functional RNA species may not be restricted to X-chromosome inactivation  or to prNon-protein coding RNA has long been recognized in cells. Transfer RNAs and ribosomal RNAs were identified over 50 years ago; neither encodes a peptide chain, though they are integral components of the machinery for protein synthesis. Identification of these RNAs demonstrated that ncRNAs interact with proteins, perform specific cell functions, and operate autonomously from information transfer. Francis Crick’s central dogma (1955) described information transfer amongst DNA, mRNA, and protein, and even at that time was recognized as an oversimplification. Crick himself subsequently built substantial flexibility into the model in 1970 such as the idea that RNA may be prone to “special” and “unknown” transfers of information . While hSeveral recent papers have identified new ncRNA species of particular function, and mechanistic insight into some of these different varieties of RNA reveal overlapping features, both in plant and animal cells. This diversity of RNA has been extensively reviewed with respect to small RNA-induced silencing complex (RISC)-related RNAs e.g.,  and lncRSeveral recent reviews have delineated ncRNA species into sub-categories based on size (less or greater than 200 bases – often used as the definition of long versus short ncRNA), position , molecular interactions , and molecular function, a good example of which is competitive antisense (AS) RNA that binds to microRNA and acts as a sponge to inhibit competitively microRNA from binding to a sense mRNA transcript . It is uin situ hybridization (FISH). Because translation occurs in the cytoplasm this might be evidence for the lack of coding potential. This analysis is somewhat arbitrary though, because ncRNA might be identified in the nuclear, chromatin, or cytoplasmic fraction of cells. Compartmentalization of lncRNAs in one of these fractions may be a defining feature of different lncRNA and may help to guide future classification schemes. Functional studies of lncRNA have also led to a proliferation of potential future categories for lncRNA, some of which are listed in Table 1, but this categorization creates its own problems in that many lncRNAs have overlapping features. This is a major issue at the moment and one likely to increase in complexity given the number of RNAs that can be detected from so many regions of the genome.What makes a lncRNA a lncRNA rather than some other RNA species? Are they a functionally distinct RNA product or are they a small part of the transcriptome that has been suggested to occur from large portions of the genome, mostly from recent ENCODE data ? CertainThe current classification system will likely evolve as more RNA species are discovered, and classification of each ncRNA might follow a similar trajectory to that of protein coding gene classification. Genes that lead to an mRNA product are not divided up by length, genomic position, whether they are spliced or not for example, and numerous coding genes fit into different classification categories. Instead they are classified by function or conserved domains. Likely it is the novelty of the RNA field, facilitated by the detection of so many transcripts by massively parallel sequencing that is leading to the classification conundrum, but this may diminish as individual RNAs are functionally analyzed.Several recent reports have carefully documented lncRNAs over a very unique range of function. To understand how lncRNAs are similar or different in both structure and function, we synthesize this information from recent papers to determine if there are any patterns or consistencies across RNA species. We focus on currently defined long RNA (>200 bp) and omit discussion of small RNAs such as microRNA, piwiRNA, or imprinting-related RNA’s.HOTAIR ( cis and trans (it is transcribed on chromosome 12 from the HoxC cluster and can regulate the chromosome 2 HoxD gene cluster) opened a new chapter for RNA molecules. HOTAIR defined a class of molecules distinct from housekeeping RNAs, microRNAs, and others, and which were not involved in fundamental imprinting processes. It hinted at the existence of RNA in the genome with regulatory functions directly related to their particular sequence and provided an explanation for the targeting specificity required by ubiquitous binding molecules such as large chromatin modifying complexes. Since HOTAIR’s description, the function of many other lncRNAs has been revealed. Shown below is the large diversity of these molecules, from their genomic position in relation to the genes they regulate, their size, processing, and mechanism of action. While this diversity is large, there are also similarities, especially in reference to function. To demonstrate differences and similarities, we have selected all reports from the last 2 years (2011–2013) that have characterized positional, processing, and functional information of specific lncRNAs. Table 2 lists structural information from lncRNAs that have been characterized functionally and this information is synthesized with the functional characteristics in the concluding remarks.The recognition of HOTAIR  as a lncCOLDAIR presents a series of themes for lncRNAs with respect to function. COLDAIR recruits polycomb repressive complex 2 (PRC2), a complex of proteins that can alter histone chemical groups to decrease gene expression, through an intermediate protein  and the binding of COLDAIR occurs through a CXC domain of this intermediate protein (see Table 3 for a discussion of RNA:protein interaction domains). COLDAIR is expressed at equal ratios over time, despite an increasing repression of the target, suggesting increased affinity for the PRC2 interaction. COLDAIR reveals several potential areas of diversity/similarity amongst lncRNAs. What determines expression of the lncRNA itself? Is the lncRNA regulated in conjunction with the target or independently from it? Is the lncRNA action direct on the target or indirect? Is the lncRNA repressive or activating? Does it act on a single target or a cluster of targets at a locus?The lncRNA IRT1 differs significantly from the mechanistic action of COLDAIR, but also functions in a repressive manner to block expression of the target gene IME1. IRT1 can respond within hours to a cell stressor to aid in the inhibition of gametogenesis, which means the repressive mechanism used by IRT1 may be specific to fast-acting effects. IRT1 completely covers the 2 kb promoter of the target gene and functions to block transcription factors from binding and promoting transcription and acts in cis, similar to COLDAIR. Because IRT1 is transcribed over the promoter of the target gene in the sense direction, it has an identical specificity to the DNA of the IME1 promoter. The blocking of transcription factors in combination with aiding in the establishment of a repressive chromatin state through histone methyltransferases and deacetylases suggests that IRT1 can physically hinder TFs but also guide repressive chromatin complexes. Here the repressive effects are different than COLDAIR in that repression is due to the deposition of H3K4m2 and H3K36me by factors traveling with the RNA polymerase transcribing IRT1. Little is known about the regulation of IRT1, but it must be under tight control to hinder or allow expression of the target gene within such a timeframe of only hours.The lncRNA Ifng. Evidence for trans action comes from NeST being genetically unlinked to its target gene and from experimental injection of NeST into cells. NeST action on the target gene is similar to IRT1 and COLDAIR in that it acts through a histone complex, but in this case it physically interacts with WDR5, which has a WD repeat domain of ~40 amino acids (see Table 3). WDR5 is a core subunit of complexes that catalyze the methylation of histone H3 at lysine 4, a mark of active gene expression, so NeST interacts directly with the histone modifying complex, unlike COLDAIR. It is likely that NeST functions to physically bring the histone modifying complex in close proximity to the target gene; it is 59 kb downstream from its target in mouse and 166 kb in human.NeST is a lncRNA that functions to increase transcription of the target gene and appears to act in trans although it is physically proximal to its target gene, Bvht is a cis-acting lncRNA expressed only in mouse, meaning it may be a lncRNA that has recently gained a function. In a continuing theme for lncRNAs, it interacts directly with SUZ12 (a component of the PRC2 complex) and functions upstream of a key gene in lineage commitment. This differs from the action of COLDAIR that requires a binding partner for interaction with PRC2, whereas bvht directly interacts with one of the subunits. Notably, SUZ12 has a zinc finger motif, which may explain the protein/RNA binding (see Table 3).cis and affects genes in a large chromosomal region, in contrast to COLDAIR, IRT1, NeST, or bvht, which appear to regulate a single target, although these targets often trigger expression of many other genes. DBE-T is transcribed from the first repeat of the D4Z4 repeat domain that is important for recruitment of PRC2. Repression of the region, controlled by PRC2 binding and spreading, commences at the repeat region, thus the basal state in adult cells is the repression of genes at this chromosomal locus. Loss of PRC2 at the repeat region corresponds with the binding of ASHL1, a histone lysine N-methyltransferase that is part of the TrixG group, which recruits DBE-T to chromatin. Thus, this lncRNA is at the crossroads of crosstalk between conflicting histone modifying complexes. While little is known about the regulation of lncRNAs, DBE-T may be an example of a positive feedback loop which may be a common theme for other lncRNAs – in other words, lncRNA expression may be regulated by targets of the target that the lncRNA itself regulates.DBE-T is a human lncRNA expressed only in a diseased condition only that acts in Similar to the positive feedback observed between DBE-T and ASHL1, HOTTIP lncRNA and WDR5 operate analogously. Similar to NeST, HOTTIP physically interacts with WDR5, and WDR5 forms a complex with MLL1, which is a H3K4 methyltransferase, triggering gene expression. HOTTIP maintains an appropriate level of the WDR5/MLL1 at a gene cluster, and its influence over the gene cluster dissipates as a function of distance from its site of transcription. Thus, this lncRNA interacts indirectly with a histone modifying complex, is involved in a feedback loop with its interacting partner, and activates expression of a cluster of genes as a function of distance from its site of expression.INK4, through complementary base pairing and can act at the promoter to recruit both PRC1 and PRC2 to repress transcription. ANRIL, while seemingly with a wider diversity of function than other polycomb recruitment lncRNAs, may actually foreshadow the function of other PRC-recruiting lncRNAs. Specifically, that they may have a wide variety of functions at a particular locus, and the only reason this has not yet been identified is because of experimental design strategies. We suspect many PRC-interacting lncRNAs will have many other functions that complement their effects. The multi-mechanistic function of ANRIL also showcases the idea that not all lncRNAs operate by recruiting large histone modifying complexes. Instead, recently identified lncRNA often operate by binding to the primary target or acting as a decoy of repressive effectors of the target.ANRIL is a lncRNA transcribed immediately upstream of a cluster of genes important in human cell proliferation and is probably the most studied lncRNA to date because of its important role in cancer. ANRIL is transcribed on the AS strand of three intimately linked genes. It can bind to the transcript of the nearest gene at the locus, Drosophila. All lncRNA described to date provide locus specificity for activating or repressive complexes to neighboring target genes, or interact directly with a target through sequence complementarity. TINCR on the other hand, appears to target specific RNA transcripts actively through an RNA sequence motif. lincMD1 also diverges drastically in that it is a by-product of pri-microRNA processing and acts to sponge the microRNAs from which it was initially processed. There may be many other pri-microRNA by-products that function similarly.lincMD1 and TINCR are two examples of non-PRC-recruiting lncRNAs with novel function to refine expression of a target. In contrast to lncRNAs COLDAIR, IRT1, NeST, BVHT, DBE-T, HOTTIP, and ANRIL, lincMD1, is a lncRNA that appears to be a by-product or remnant of microRNA processing . SpecifiFigure 1A).Another lncRNA that reportedly does not use large histone modifying complexes to alter a target, but instead operates through binding of a primary target, is AS-UCHL1. AS-UCHL1 has been shown recently to be important for proper targeting of sense transcript to polysomes, suggesting a stabilizing function for this lncRNA, demonstrated by a strong increase in UCHL1 protein with no difference in UCHL1 transcript on over-expression of AS-UCHL1. This principle of RNA stabilization to affect protein levels of targets may be a continuing theme for lncRNAs e.g., . This lnSERPINE1 and FLJ21870 mRNAs between their 3′ UTR Alu element and the Alu element in a single lncRNA .The idea of repeat elements in the genome, acting through lncRNAs, has also been described with respect to Alu repeats, one of the most common repeats in the human genome. The description of overlapping Alu repeats, one in an AS strand and one in the 3′UTR of the sense strand, can lead to formation of a STAU1 binding site, which allows for STAU1 to stabilize base pairing and target the RNA duplex for degradation. Similar to all lncRNAs described here, these Alu-containing lncRNAs can regulate the levels of a transcript through an mRNA decay pathway. This was specifically demonstrated for These examples support an important role for lncRNAs in the genome, and highlight the diverse function of lncRNAs, but also some similarities. First, there appears to be no relationship between the particular function of a lncRNA, its size, or how it is processed. This suggests that lncRNAs will represent a diverse range of characteristics. Second, neither transcriptional direction nor strand specificity appears to have an effect on function. The key element is that lncRNAs are produced either within their target gene or in the vicinity of target genes. Those lncRNAs produced from overlapping regions of their target gene are more likely to bind to the target, however, due to direct complementarity with the target. Whether these lncRNAs come from the same or the AS strand as a target appears not to have functional impact. Future experiments should document this for all newly described lncRNAs to determine whether this remains the case. These ideas may help guide issues of categorization of lncRNAs, and we propose a system that anchors lncRNAs in the target molecule. This may not prove useful for those lncRNA, like the intergenic lncRNAs, that do not appear to have nearby targets. Their function may prove to be completely different and independent from those lncRNAs expressed in relationship to mRNAs.HOX gene cluster that it regulates. There is little evidence for lncRNAs that operate in isolation  and it seems the position of lncRNAs in relation to the target(s) are of fundamental importance to their function. While there are many remarkable functions attributed to lncRNAs, we strongly suspect that the function of even these lncRNA will prove more diverse as they undergo further investigation.Functional and mechanistic data generated by studying ncRNAs in different molecular systems and species suggests lncRNAs likely play an important role in all cellular systems. As evidenced in the previous sections, lncRNAs most likely act as modifiers of a complementary RNA, interact with large histone complexes, interact with complementary DNA sequences, or act completely independently in the nucleus with no obvious partners required. Given this diverse potential, the complexity of the nervous system in any species might be partially due to the additional level of control over the cellular machinery by lncRNAs. lncRNAs may provide a means to tweak a cellular system at many levels and to operate rapidly in response to external signals whether axon guidance cues or environmental exposure. In line with these ideas, we synthesize recent reports of lncRNAs in the developing nervous system.Some of the first experiments to underscore the importance of lncRNAs were done in mouse or human stem cells from. Stem cells used for research are either derived from the inner cell mass of a fertilized embryo  or inducWhile ES (embryonic stem cells)- and iPSC (induced pluripotent stem cell)-derived NSCs may not perfectly capture the developmental progression of the human brain, they provide an excellent model with which to screen for important factors as the cells develop from stem cells to electrically active neurons. A study monitoring iPSC-NSC differentiation accompanied by RNA sampling at different timepoints, contrasted with brain temporal lobe brain tissue RNA levels from the same donor has revealed a gradual increase in the expression of different lncRNAs as NSCs differentiate . This isSox4 and Sox11 loci produced during development of the mouse cerebral cortex (Sox4 and Sox11 transcripts are produced during proliferating and differentiating states, suggesting that the regulation of these important genes is by complementary lncRNAs. Recently, this same group documented a similar effect with respect to Nrgn and Camk2n1 gene product in mouse cerebral corticogenesis. A recent study in adult brain also suggests that electrical activity in neurons stimulates lncRNA (in vitro NSC models. Of intense interest as well are the loci of the genome where AS transcripts are transcribed from the same genomic locations as brain-relevant genes and how some of these may be specific to human. For example, BDNF is transcribed from chromosome 11 and an AS transcript is produced from the opposite strand in humans but not in mouse (In the mammalian brain, lncRNAs have long been recognized as important in neurodevelopment, although they were traditionally referred to as AS transcripts. An example of this is the AS transcripts near the l cortex . Sox pros lncRNA . Given tin mouse . The dis116HG, a lncRNA retained in the nucleus, as well as the small nucleolar RNA SNORD116 (UBE3a locus, a gene found immediately upstream of the 116HG locus and known to be important in neurodevelopment. Their data further suggest that 116HG interacts with RBBP5, a subunit of the MLL complex, which acts as a transcriptional activator by methylation of H3K4. This model conforms nicely to what is known of lncRNA functions in other species; 116HG might associate with MLL complex and interact with histones at the UBE3a locus. How 116HG itself is regulated is unknown, but this will be clearly important to understand better the neurobiology of PWS.Recent data from our group suggest that lncRNAs may be important in neurodevelopmental disease ; we showSNORD116 . Both nclncRNAs likely have a role in many aspects of the cell, and brain development might be an area where their structure and function is particularly suited. This may suggest that many more lncRNAs await discovery in novel systems as well as in added layers of control for well known processes of neurodevelopment.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.


Col2a1) expression, which mimics the process of chondrocyte dedifferentiation. Although the functional importance of Col2a1 expression has been extensively investigated, mechanism of transcriptional regulation during chondrocyte dedifferentiation is still unclear. In this study, we have investigated the crosstalk between cis-acting DNA element and transcription factor on Col2a1 expression in primary chondrocytes. Bioinformatic analysis revealed the potential regulatory regions in the Col2a1 genomic locus. Among them, promoter and 3′ untranslated region (UTR) showed highly accessible chromatin architecture with enriched recruitment of active chromatin markers in primary chondrocytes. 3′ UTR has a potent enhancer function which recruits Lef1 (Lymphoid enhancer binding factor 1) transcription factor, leading to juxtaposition of the 3′ UTR with the promoter through gene looping resulting in up-regulation of Col2a1 gene transcription. Knock-down of endogenous Lef1 level significantly reduced the gene looping and subsequently down-regulated Col2a1 expression. However, these regulatory loci become inaccessible due to condensed chromatin architecture as chondrocytes dedifferentiate which was accompanied by a reduction of gene looping and down-regulation of Col2a1 expression. Our results indicate that Lef1 mediated looping between promoter and 3′ UTR under the permissive chromatin architecture upregulates Col2a1 expression in primary chondrocytes.Type II collagen is the major component of articular cartilage and is mainly synthesized by chondrocytes. Repeated sub-culturing of primary chondrocytes leads to reduction of type II collagen gene ( Type II collagen is the major collagen synthesized by chondrocytes in articular cartilage and forms an integral component of extracellular matrix (ECM) Col2a1) expression . This indicates the existence of a balanced interaction between positive and negative factors that govern the expression of type II collagen. Col2a1 locus encompasses around 50 kb of chromosome 12 and 15 in case of human and mouse respectively. The gene spans a region of about 30 kb from the TSS (transcription start site) to the polyadenylation site. A conserved promoter with a TATA box, GC box but no CAAT box generates a major Col2a1 transcript of 5 kb that is translated into a polypeptide with approximately 1,400 amino acids. Coding sequences of mouse and human type II collagen are 89% identical at the nucleotide level and in the mature polypeptide 37 amino acids are altered. The gene locus contains numerous conserved non-coding sequences (CNSs) which are also highly conserved between human and mouse Col2a1 locus and their high rate of conservancy between mouse and human, only the first intron so far has been identified to function as an enhancer Type II collagen is transcribed at a high rate in the chondrocytes that are also responsible for producing many types of ECM proteins. These cells following isolation and regular sub culturing in monolayer gradually lose their phenotype and adapt a flattened fibroblast like phenotype with a significant decrease in type II collagen gene , defined as having 75% or greater identity over at least 100 bp stretches upstream and downstream of the Col2a1 locus  was cloned into pXPG luciferase vector cis-acting element for Col2a1 transcription. Into this basic construct, each CNS fragment was cloned into the upstream of the minimal promoter  and performed transient reporter assay. Luciferase assay with the 3′ UTR alone or in conjunction with the minimal promoter (−57/+166) was performed in HTB-94 human chondrosarcoma cell line. The 3′ UTR alone has basal promoter activity and showed a strong enhancer activity upon coupling with the promoter . To checpromoter .To further identify the key regulatory region within the 3′ UTR and transcription factors that bind to the locus, we performed luciferase assay with a series of 3′ UTR deletion constructs. High levels of transactivity of the 3′ UTR is maintained until the length of 189 bp (+29054/+29243). However, removal of another 56 bp to a size of 133 bp (+29110/+29243) results in a significant loss of its activity. Analysis of the genomic sequence of 3′ UTR by TRNASFAC database revealed a location of conserved Lef1 binding site within the 56 bp region . Thus, wCol2a1 expression in primary chondrocytes. Lef1 expression plasmid or Lef1 siRNA was transfected into primary chondrocytes and Col2a1 expression was measured by qRT-PCR. Lef1 over-expression  and confirmed its binding to the 3′ UTR (Col2a1 expression in chondrocytes.To test whether interaction of Lef1 to the 3′ UTR is occurring e 3′ UTR . Additioe 3′ UTR . Howevere 3′ UTR . These rcis-acting elements. We and other groups have previously demonstrated that Lef1 can regulate its target genes such as Cox2 and MMP13 through gene looping Col2a1 promoter by performing chromosome conformation capture (3C) assay Col2a1 locus and we chose three cleavage sites near the Col2a1 promoter (+607), the 3′ UTR (+27298) and between them (+6502)  . Primer  (+6502) . Primer  (+6502) . Primer  (+6502) . The lig (+6502) . We asse (+6502) . Primer  (+6502)  and conf (+6502) . Enricheenhancer  in primaCol2a1 expression as well as on gene looping in Col2a1 locus. Transfection of Lef1 siRNA into primary chondrocytes significantly reduced the amount of 3C product formation by primer pairs A+F or F′+A′  between the two stages  and P4 stages of dedifferentiated chondrocyte (low expression of Col2a1) by ChIP assay. As expected, much higher level of Lef1 binding was observed in P0 stage compared with P4 stage of chondrocyte  collagen (Col11a1), alpha 2 (XI) collagen (Col11a2) and alpha 1 (II) collagen, is mainly expressed in the articular cartilage and vitreous fluid of the eye, and is responsible for the proper type II collagen fibril formation Col2a1 expression.Lef1 introduces a sharp bending of about 130 degrees in the DNA and loop the DNA bringing distantly parted e effect . These f looping . Hence rin vitro by sub-culturing monolayer cultures of chondrocytes from both mouse and human Col2a1 regulation is also associated with dedifferentiation of chondrocytes by adapting the in vitro chondrocyte differentiation model. Compared with primary chondrocyte (P0 stage), dedifferentiated chondrocyte (P4 stage) expressed significantly lower Col2a1 transcript  mice were housed in specific pathogen-free barrier facilities and used in accordance with protocols approved by the Animal Care and Ethics Committees of the Gwangju Institute of Science and Technology (GIST). The human chondrosarcoma cell line, HTB-94  4 cells/cm2 and cultured until confluence (passage P0). The cells were then dissociated with trypsin-EDTA and subcultured three more times at 2–3 days intervals to get the terminally differentiated stage (passage P4).Primary chondrocytes were isolated from ribs of 4-day-old ICR mice and cultured as previously described G CAC TTG CCA AGA CCT GAA A -3′ and R-5′- TTT CCT TGC TCT TGC TGC TCC A-3′). Mouse hypoxanthine-guanine phosphoribosyl transferase (HPRT) primer HPRT (F-5′-TTA TGG ACA GGA CTG AAA GAC-3′ and R-5′-GCT TTA ATG TAA TCC AGC AGG T-3′), was used for quantitative RT-PCR to normalize the amount of cDNA used for each condition.Total RNA was extracted from the P0 and P4 stage of chondrocytes using TRIZOL Reagent  according to the manufacturer’s protocol. For reverse transcription, 1 µg of total RNA was used and cDNA was generated using oligo (dT) primer  and Improm-II Reverse Transcriptase  in a total volume of 20 µl. The mRNA level was determined using 1 µl of cDNA by real-time PCR with SYBR green using the protocol provided by the manufacturer (MJ Research Chromo4). The primer sequences used are as follows: Col2a1  as potential regulatory elements, mouse and human genomic sequence spanning the 30 kb of the 2, 0.5% NP-40, 0.15 mM spermine, 0.5 mM spermidine) 2) with or without 5 U MNase/ml  and were further incubated at 37°C for 10 min. Reactions were terminated by adding 20 µl stop solution  and 10 ml SDS 10% (w/v). DNA was isolated using the DNA blood genomic prep kit  and eluted into 100 µl TE. DNA recovered from MNase samples was checked for fragmentation in 1% agarose gel. Isolated genomic DNA from each samples were quantified spectrophotometrically and 50 nanograms of each sample were subjected to SYBR green based qRT-PCR to measure the relative abundance of target regions using the primer sets described in Chromatin accessibility by Real Time PCR (CHART-PCR) was performed as described previously The Col2a1 minimal promoter (−57/+166) and the 3′ UTR constructs  were cloned in the luciferase reporter vector pXPG and were confirmed by sequencing. HTB-94 human chondrosarcoma cells and P0 stage of mouse chondrocytes were transfected with the reporter constructs and or with plasmid encoding Lef1 using METAFECTENE  according to the manufacturer’s protocol. After 18–24 hrs, luciferase activity was measured by the dual luciferase assay system . pRLTK was cotransfected with each sample to normalize the data by the activity of Renilla luciferase.ChIP analysis was carried out essentially as described Chondrocytes  were lysed in cell lysis buffer , protease inhibitor cocktail ) and protein concentrations were estimated. Thirty micrograms of whole cell lysate was used for SDS-PAGE and Western blot was carried out with α-Lef1 antibody. The same membrane was stripped with mild-stripping buffer  and reprobed with α-β-actin  antibodies.2, 2 mM MgCl2) containing protease inhibitor cocktail  for 10 min on ice and centrifuged at 3000 rpm for 10 min at 4°C. The pellet obtained were incubated in 1 ml of NP-40 buffer  for 5 min at 4°C and centrifuged at 3000 rpm for 10 min at 4°C. Nuclei were washed with 1 ml of Buffer A . Nuclei were lysed with 100 µl of Buffer C  5′- CTT GTG TTT TGT TCT TTG TTT TG-3′ and Lef1 consensus: 5′AAT TCC GGC CTT TGA TCT TTG CTA-3′ 32P] ATP  and T4 polynucleotide kinase  were used to end label the double-stranded oligonucleotides which were subsequently purified using G-50 column . Labeled probes were incubated with prepared nuclear extract (2 µg) in binding buffer  along with 0.1 mg/ml poly (dI-dC) at 4°C for 30 minutes. For cold competition, unlabeled wild type, probe or unlabeled consensus was added and preincubated for 20 min. For super shift assays, nuclear extracts were preincubated for 30 min with 2 µg of anti-Lef1 antibody . The samples were separated in 4% nondenaturing polyacrylamide gel containing 0.5× Tris-borate-EDTA at 4°C.To prepare nuclear extract, P0 stage chondrocytes were washed in ice cold PBS and suspended in 1ml of lysis buffer  and 1 µg was digested with 10 U of HpaII or MspI enzymes overnight at 37°C. The target regions were subjected to by RT-PCR with SYBR green using a protocol provided by the manufacturer (MJ Research Chromo4) with primers provided in Table1. Product formation was calculated relative to that of uncut DNA from each stage.Chondrocytes at P4 stages were cultured in medium containing with 5 µM of 5-aza-2′-deoxycytidine  that was freshly diluted immediately before use, for 3 days by replacing the media with fresh 5-Aza containing media every 24 hrs.Chondrocytes at the P0 stage were transfected with plasmids encoding Lef1, Lef1 siRNA and control siRNA and  using Lipofectamine Plus , and cultured in complete medium for 48 hrs and total RNA was isolated as described previously.To detect interaction between distal 3′ UTR with promoter of Col2a1, 3C assay was performed as described previously with minor modifications t-test was used to determine the significance of the results. The level of significance was set at *P<0.05, **P<0.01 and ***P<0.001. Significance was only indicated when appropriate.Data are the mean of SE of at least three independent experiments, unless differently specified in the text. The student’s Figure S1Comparative sequence analysis of the mouse and human Col2a1 loci. Comparison of the genomic sequence of murine and human 3′ UTR region. Positions are assigned with respect to the transcription start site (TSS). Potential binding sites for Lef1 that met the most stringent requirements , using rVISTA 2.0 and TRANSFAC database analysis, are identified and boxed. Primer pair at position +29054 (indicated by bold letter C) and +29243 generate a 189 bp 3′ UTR construct and primer pair at position +29110 (indicated by bold letter A) and +29243 generate a 133 bp 3′ UTR construct.(PDF)Click here for additional data file.Figure S2Differential chromatin accessibility between P0 and P4 stage of chondrocytes. Chromatins prepared from non-crosslinked or formaldehyde (HCHO) crosslinked P0 and P4 stage chondrocytes were treated with Pst1 and digestion efficiency was calculated relative to the product obtained by non-Pst1 flanking primer pairs X+Y (airs X+Y . Data ar(PDF)Click here for additional data file.