Trypanosoma cruzi and Trypanosoma brucei are protozoan parasitescausing Chagas disease and African sleeping sickness, displaying unique features of cellular and molecular biology. Remarkably, no canonical signals for RNA polymerase II promoters, which drive protein coding genes transcription, have been identified so far. The secondary structure of DNA has long been recognized as a signal in biological processes and more recently, its involvement in transcription initiation in Leishmania was proposed. In order to study whether this feature is conserved in trypanosomatids, we undertook a genome wide search for intrinsic DNA curvature in T. cruzi and T. brucei.T. cruzi, which have been mapped in divergent switch regions, whereas in T. brucei, the high RIIC scores correlated with sites that have been involved not only in RNA polymerase II initiation but also in termination. In addition, we observed regions with high RIIC score presenting in-phase tracts of Adenines, in the subtelomeric regions of the T. brucei chromosomes that harbor the variable surface glycoproteins genes.Using a region integrated intrinsic curvature (RIIC) scoring that we previously developed, a non-random distribution of sequence-dependent curvature was observed. High RIIC scores were found to be significantly correlated with transcription start sites in T. cruzi and T. brucei genomes, a link between DNA conformational signals and gene expression was found. High sequence dependent curvature is associated with transcriptional regulation regions. High intrinsic curvature also occurs at the T. brucei chromosome subtelomeric regions where the recombination processes involved in the evasion of the immune host system take place. These findings underscore the relevance of indirect DNA readout in these ancient eukaryotes.In both The online version of this article (10.1186/s13104-017-2908-y) contains supplementary material, which is available to authorized users. Trypanosoma cruzi and Trypanosoma brucei  are flagellated protozoan parasites that cause Chagas disease and African sleeping sickness, respectively. They infect the poorest rural populations in developing countries in tropical and subtropical regions leading to tens of thousands of human deaths every year [ery year .T. cruzi has a complex life cycle alternating between two extracellular forms in the triatomine insect: the epimastigote and the infective metacyclic trypomastigote, and two forms in the mammalian host: the intracellular amastigote and the infective bloodstream trypomastigote. In contrast, T. brucei is exclusively extracellular, alternating between the procyclic form in the tsetse fly and the bloodstream form in the mammalian host [T. brucei forms a dense coat of variant surface glycoproteins (VSG) that are expressed one at a time from telomeric expression sites and are derived from a repertoire of up to 2000 genes [T. brucei, T. cruzi and Leishmania but also within RNAPI promoters involved in transcribing protein coding genes in T. brucei (VSG and procyclins) [Although trypanosomes share many characteristics at the molecular and biochemical levels, they exhibit different life cycles. ian host . In orde00 genes , 4. The 00 genes . This mRcyclins) .trans-splicing of mRNAs, the extensive editing of mitochondrial transcripts [T. brucei or genes encoding surface antigens in T. cruzi [d-Glucopyranosyloxymethyluracil), predominantly present in repetitive DNA sequences in telomeres and subtelomeres of trypanosomatids, has been more recently localized in RNA polymerase II transcription initiation and termination sites [trans-splicing to all the protein coding genes [T. brucei [Trypanosomes have additional unique gene organization and expression features, including the organization of genes in directional clusters, the constitutive transcription of large polycistronic gene units, the amplification of genes in response to environmental stimuli, the nscripts , 8 and tnscripts , 9. SubtT. cruzi , 11. Theon sites . Canonicng genes . Nonetheng genes , 15 and ng genes , 17 haveng genes . Transcrng genes , 20. A bng genes . A link . brucei . In spit. brucei ), the mo. brucei , 25.Leishmania [Leishmania, due to different base composition content and derived intrinsic curvature (IC) may exist, here we studied whether high RIIC regions could also be associated to a biological phenomenon in T. cruzi and T. brucei. We found that high RIIC scores were indeed associated with regions involved in transcriptional regulation, such as DSSRs in T. cruzi and regions enriched in base J in T. brucei. A concentration of high curvature regions in the T. brucei subtelomeres, overlapping regions of silent VSGs, was also observed, and a canonical signal for DNA bending consisting on A tracts repeated in phase was discovered therein. Our findings suggest a link between DNA conformational signals and gene expression in trypanosomes.Intrinsically curved DNA structures are often found around origins of DNA replication, DNA recombination loci and in regions that regulate transcription. In eukaryotes, this feature is common not only to the promoters of genes transcribed by RNA polymerase I, but also to many promoters of RNA polymerase II (reviewed in ). We havishmania . Though Leishmania [T. cruzi and T. brucei. The T. cruzi CL Brener Esmeraldo-like haplotype [T. brucei 11 Megabase-sized chromosomes [T. cruzi chromosome 9 and T. brucei chromosome 5 is shown in Fig. T. cruzi and T. brucei chromosomes, respectively. Wiggle format files containing the positions and magnitude of plotted intrinsic curvature are provided as Additional file T. cruzi the high RIIC regions are apparently associated with strand switch regions, this observation is not so obvious for T. brucei. Nevertheless, it seems that also in T. brucei, regions of high curvature are not randomly distributed. Indeed, a clear concentration of regions of high curvature can readily be observed at subtelomeric positions.Since we have previously shown that sequence-dependent DNA curvature may have a role in transcription initiation in ishmania , we inveaplotype  and the omosomes  were useomosomes . The outomosomes . Concurromosomes . A samplT. cruzi, a necessary first step consisted of identifying SSRs in the T. cruzi genome. We restricted the definition of SSRs to those separating polycistronic units containing at least six CDSs and excluded SSRs containing sequencing gaps. Using these stringent criteria, we identified 115 SSRs. Following a scan of the T. cruzi genome for high RIIC regions, we computed their overlap with the two types of SSRs  compared to Leishmania .Here we present data demonstrating that regions of high curvature are not randomly distributed along T. cruzi is suggestive of a putative association of DNA intrinsic curvature with transcription initiation as previously observed in Leishmania. Such curvatures may either facilitate the binding of the RNA polymerase and/or help enable the formation of the open DNA complex. Although an enrichment of histone acetylation in the DSSR has been described in T. cruzi [Leishmania, one can only speculate that a high percentage of the 172 internal high RIIC scoring regions found in T. cruzi may also serve as internal TSSs. If this consideration holds true, and some of the 172 internal sites are actual TSS regions, then the number of true positives would increase while the number of false positives will decrease resulting in a more significant association .Considering the additive contribution to the DNA curvature of each sequential nucleotides in a given region, we compiled the data by using the region integrated intrinsic curvature score, we have previously developed. The enrichment of high RIIC in DSSRs in T. cruzi , no studT. brucei and the one previously seen in Leishmania is evident. In the latter, the association of high RIIC with transcription initiation is clear [T. brucei, a high of RIIC is seen with base J, which in this organism is considered a marker of transcription boundaries [T. brucei [T. brucei [A difference between the pattern observed here in is clear . Besidesis clear –35. In cundaries , while tundaries  . Using ishmania . Here weThese findings point out to the existence of putative conserved process- and species specific-DNA architectural signals in kinetoplastids. Further studies would be necessary to deeply analyze the recognition signal commonalities and differences that may be involved in different steps of the transcriptional mechanisms.T. brucei, the remarkable concentration of regions with high intrinsic curvature at the chromosome subtelomeric regions, where the species-specific genes for the gene family of the highly variable surface glycoproteins are located, may suggest a putative involvement of this structural signal either facilitating the recombinational process or mediating the chromatin silencing and/or granting the vast antigenic variability needed for the efficient evasion of the immune host system that T. brucei has developed.In addition, in Globally, the data here presented, while establishing particularities within kinetoplastids, underscore the relevance of indirect DNA readout in these ancient eukaryotes.T. brucei strain TREU 927 and the T. cruzi CL Brener Esmeraldo-like contigs were downloaded from TritrypDB (version 2.1). T. brucei chromosome regions were classified as ‘core’ and ‘subtelomeric’ regions as described in [The genome data for ribed in .The bend.it algorithm , kindly T. brucei subtelomeric and VSG array regions have a significantly higher RIIC score than the rest of the genome, their RIIC score was compared to a density function representing the population of RIIC scores for equal-length regions in the genome as in [T. cruzi SSRs associated with high RIIC, two criteria were established. Namely, a SSR was not considered if it presented internal sequencing gaps and/or was defined by polycistronic units of less than 6 genes.The RIIC score was calculated as the area under the curvature plot using the Riemann sum. To assess if the me as in . A regiome as in  with minp value of 0.01 to define significance. The statistical correlation of these regions was tested using the Mathews correlation coefficient working on the previously generated contingency matrix.To test the association between high RIIC containing and H4K10ac containing regions a contingency matrix was built for each chromosome. The matrix was constructed by classifying genomic regions as containing both signals, only one signal or none and counting each instance. The independence of both variables was tested using the Fisher exact test in R using a T. brucei chromosome 9 spanning bases from 1 to 319,439 (the VSG array region). A total of 103 sequences surrounding 30 bp each high IC peak were selected for motif discovery. The randomly shuffled sequences were also submitted to the MEME suite as control.For the analysis of motif-based sequence associated to the regions of high intrinsic curvature, the MEME suite was used. The search was performed on a fragment of Additional file 1: Table S1. Association between high RIIC regions and transcription markers per chromosome.Additional file 2. Wiggle format files containing the positions and magnitude of plotted intrinsic curvature and region integrated intrinsic curvature for T. cruzi and T. brucei chromosomes.Additional file 3: Figure S3. Graphical representation of sequence dependent curvature in T. cruzi chromosomes. The chromosome number is depicted at the top of each page. Upper panel: Bar plots of chromosome positions with an IC value greater than 13 degrees per helical turn. Middle panel: Bar plots of chromosome positions with an RIIC value greater than the selected cutoff. Lower panel: both chromosome DNA strands are depicted in grey, overlaid with CDS features shown in blue. Features labeled as ncRNA, snRNA or snoRNAs are shown in green. tRNAs are shown in red. Assembly gaps are shown in brown.Additional file 4: Figure S2. Graphical representation of sequence dependent curvature in T. brucei chromosomes. The chromosome number is depicted at the top of each page. Upper panel: Bar plots of chromosome positions with an IC value greater than 13 degrees per helical turn. Middle panel: Bar plots of chromosome positions with an RIIC value greater than the selected cutoff. Lower panel: both chromosome DNA strands are depicted in grey, overlaid with CDS features shown in blue. Features labeled as ncRNA, snRNA or snoRNAs are shown in green. tRNAs are shown in red. Assembly gaps are shown in brown. Subtelomeric VSG clusters are underlined.Additional file 5: Figure S3. Graphical representation of transcription markers’ signals and sequence dependent curvature RIIC score in T. brucei. For each T. brucei chromosome, the graphical representation of regions with RIIC value greater than the selected cutoff (lower panel) are shown above the schematic representation of both chromosome DNA strands depicted in grey, overlaid with CDS features shown in blue. Modified histone locations (H4K10ac) from [ac) from  and baseac) from  are indi


TATA‐containing and TATA‐less based on the presence of TATA box in their promoters. Experiments on both classes of genes have revealed a disparity in the regulation of gene expression and cellular functions between the two classes. In this study, we report characteristic differences in promoter sequences and associated structural properties of the two categories of genes in six different eukaryotes. We have analyzed three structural features, DNA duplex stability, bendability, and curvature along with the distribution of A‐tracts, G‐quadruplex motifs, and CpG islands. The structural feature analyses reveal that while the two classes of gene promoters are distinctly different from each other, the properties are also distinguishable across the six organisms.Eukaryotic genes can be broadly classified as  AFEaverage free energyTLStranslation start siteTSStranscription start siteGene expression is the most fundamental biological process, in which the genetic information is used to create a phenotype. The initiation of transcription is the first and most crucial step in the regulation of gene expression. Promoters are the genomic sequences where the transcriptional machinery assembles, and the core promoter activity is conferred by the presence of short sequence motifs at specific positions relative to the transcription start site (TSS). TATA box, Inr (Initiator), BRE (TFIIB recognition element), DPE (downstream promoter element), MTE (motif ten element), TCT (polypyrimidine initiator), and Sp1 (specificity protein 1) are well‐characterized sequence motifs reported in several eukaryotes  have been analyzed. The similarities and differences in structural features of the two classes of promoters are discussed in this report.An earlier study has reported differences in DNA bendability of TATA‐containing and TATA‐less promoters in 11 yeast species based on the translation start site (TLS) data of yeast that was available at that time S. cerevisiae, C. elegans, D. melanogaster, zebrafish, mouse, and human relative to TSS information were obtained from different sources. TSS information of S. cerevisiae, C. elegans, and D. melanogaster  was retrieved from Xu et al., 2009 http://www.modencode.org/) and Graveley et al., 2011 S. cerevisiae, C. elegans, D. melanogaster, and vertebrates  are downloaded from SGD (http://www.yeastgenome.org/), Wormbase (https://www.wormbase.org/), Flybase (http://flybase.org/), and UCSC genome browser (https://genome.ucsc.edu/), respectively.Promoter regions of six eukaryotes S. cerevisiae have been defined as those which contain the TATA consensus sequence TATAWAWR in the upstream location −200 to −50 relative to the TLS. In the present study, we have considered TATA‐containing promoters as those sequences which contain TATA box within −150 to −1 region relative to TSS. Table S. cerevisiae, C. elegans, D. melanogaster, zebrafish, mouse, and human.Based on several criteria such as a maximum sequence length (8 bp), minimal consensus sequence, confined upstream location, and conservation across orthologous upstream regions, TATA box was defined as TATA(A/T)A(A/T)(A/G) (TATAWAWR) The three DNA structural features, DNA duplex stability, bendability , and intrinsic curvature have been chosen, as they are biologically relevant, and the information content of each feature is different. The stability of a double‐stranded DNA molecule can be expressed as the sum of free energy or average free energy (AFE) of its constituent base paired dinucleotides. The dinucleotide energy values corresponding to the 16 dinucleotide steps (or 10 unique dinucleotides) are taken from the unified parameters obtained from melting studies on 108 oligonucleotides 3−5N1−7G3−5N1−7G3−5N1−7G3−5 or C3−5N1−7C3−5N1−7C3−5N1−7C3−5N indicates the loop regions and can have any nucleotide. CpG islands (CGIs) are short interspersed DNA chunks that deviate significantly from the average genomic pattern by being GC‐rich, CpG‐rich, and predominantly nonmethylated. CpG islands were calculated using Takai and Jones, 2002 algorithm A‐tracts consist of stretches of minimum four consecutive A : T base pairs without a TA dinucleotide step. A‐tracts of length more than five can act as antinucleosomal sequences S. cerevisiae, C. elegans, D. melanogaster, zebrafish, mouse along with human are considered for this analysis as they are good representative model systems for understanding aspects of eukaryotic transcription at different levels. These six systems differ in their genomic GC content and nucleotide composition, are well studied, and their experimentally validated TSS data have been published. The promoter sequences of six eukaryotes are classified as TATA‐containing and TATA‐less promoters based on the presence or absence of TATA box in the −150 to −1 promoter region relative to TSS , bendability  and curvature of TATA‐containing and TATA‐less promoter sequences of yeast, invertebrate and mammals are computed as reported in earlier studies S. cerevisiae, C. elegans, and zebrafish, the TATA‐containing promoters show low stability regions at approximately −150 to −1 region relative to TSS compared to TATA‐less promoters. In mouse and human, a significant difference in the two classes of promoters is observed across the whole region. The TATA‐containing promoters are less stable in entire −500 to +500 region with two sharp peaks at −30 and −1 region. The stability of DNA is directly dependent on AT/GC content. The lower stability of TATA‐containing promoters in mammals is due to their lower GC content as compared to TATA‐less promoters  at ~ 100–200 bp upstream of the start codon in 11 yeast species has been reported earlier S. cerevisiae and other invertebrates are consistent with this earlier result. DNA bendability describes the anisotropic bending of duplex DNA in the presence of various binding factors. Bendability or flexibility can have two completely different roles. More flexibility is important for several DNA‐binding proteins such as the TATA‐binding protein in vivo. Rigid or less bendable regions disfavors formation of nucleoids in prokaryotes and nucleosomes in eukaryotes, making these regions ‘nucleosome depleted’ and assist in the assembly of the transcriptional machinery. The rigidity of DNA in promoter regions provides greater scope for sliding of DNA‐binding proteins along its length The bendability and curvature profiles also show differences between TATA‐containing and TATA‐less promoters. The bendability profiles of the TATA‐containing and TATA‐less promoters have been analyzed using two models, DNase 1 sensitivity and nucleosome positioning preference model as seen in Fig. S. cerevisiae, C. elegans, and D. melanogaster show that both classes of promoter sequences are curved in the vicinity of TSS with TATA‐containing promoters being slightly more curved . These plots, as well as corresponding The characteristic differences in the structural properties of TATA‐containing and TATA‐less promoter regions in six eukaryotes can arise due to the differential base composition and prevalence of some selected oligonucleotides. Hexanucleotide composition has been calculated in the −150 to −1 core promoter region as well as −500 to −1 regions for the two classes of promoters. The unique hexamer words in promoter regions of TATA‐containing and TATA‐less promoter are calculated and compared Figs  and S2.C. elegans show preponderance of TTTTTT, whereas mammals show preference for GGGCGG, CGCCCC, CGGGGC, CCCCGC, GGCGGC, and CGCCGC. Zebrafish core promoters in TATA‐less class show a different trend, with a high frequency of hexamers CTCTCT and GAGAGA. The −500 to −1 regions in the TATA‐containing class in mammals have preference for AAAAAA (as well as TTTTTT in human) while GC‐rich hexamers (SP1 elements) are overrepresented in TATA‐less promoters .The TATA‐containing promoters show biases for TA containing hexamer steps  in the −150 to −1 region  have been calculated. CpG islands have been computed using a 500nt window, so their occurrence is only given for the −500 to +500 region.Table S. cerevisiae, D. melanogaster, and zebrafish, while in C. elegans, A7‐tracts are more prevalent in TATA‐less promoters. In mouse and human TATA‐containing promoters, A‐tracts are comparatively more frequent than in TATA‐less promoters  are significantly enriched with CpG islands with ~ 51% and ~ 42% containing these elements, while only ~ 25% and ~ 19% of TATA‐containing promoters contain these elements (Fig. S. cerevisiae to mammals and may be due to lower mutation rates in TATA‐containing promoters. In the case of mammals, it has been reported that TATA‐containing promoters tend to evolve more slowly in core promoter as well as upstream regions than the promoters that lack a TATA box Overall the composition and structural features of TATA‐containing and TATA‐less promoter regions are found to be distinctly different even in regions outside the TATA elements. The differences become more prominent going from S. cerevisiae, C. elegans, and D. melanogaster. In mouse and human, stability and curvature are distinguishing features of TATA‐containing and TATA‐less promoters. Significant differences are also observed in the distribution of sequence motifs, such as A‐tracts, G‐quadruplexes, and CpG islands, in TATA‐containing and TATA‐less promoters in mouse and human. The TATA‐less promoters in mammals are characterized by high prevalence of G‐quadruplexes and CpG islands. Overall the work reported in this article gives a broad picture of DNA structural and compositional features of two classes of promoters in different eukaryotes and provides interesting insight into their architecture.The sequence‐dependent structural properties of di and tri nucleotides in DNA, lead to variations in the structure at a higher level and play a role in protein binding, DNA melting, nucleosome organization, and gene regulation. The structural features of TATA‐containing and TATA‐less promoters are distinctly different in lower eukaryotes. The TATA‐containing core promoters are less stable, more flexible, and more curved compared to TATA‐less promoters in YRV and MB conceived and designed the project. YRV acquired and analyzed the data. YRV and MB interpreted the data and wrote the paper.Figure S1. Cumulative distribution function of structural features for TATA‐containing and TATA‐less promoters in the six eukaryotic systems: S. cerevisiae, C. elegans, D. melanogaster, zebrafish, mouse, and human.Click here for additional data file.Figure S2. Hexanucleotide composition of TATA‐containing and TATA‐less promoter of different eukaryotic systems.Click here for additional data file. Click here for additional data file.


PLUTO affects local 3D chromatin structure and transcription of PDX1, encoding a key β cell transcription factor, and that both PLUTO and PDX1 are downregulated in islets from donors with type 2 diabetes or impaired glucose tolerance. These results implicate lncRNAs in the regulation of β cell-specific transcription factor networks.Recent studies have uncovered thousands of long non-coding RNAs (lncRNAs) in human pancreatic β cells. β cell lncRNAs are often cell type specific and exhibit dynamic regulation during differentiation or upon changing glucose concentrations. Although these features hint at a role of lncRNAs in β cell gene regulation and diabetes, the function of β cell lncRNAs remains largely unknown. In this study, we investigated the function of β cell-specific lncRNAs and transcription factors using transcript knockdowns and co-expression network analysis. This revealed lncRNAs that function in concert with transcription factors to regulate β cell-specific transcriptional networks. We further demonstrate that the lncRNA   •A loss-of-function screen reveals functional β cell lncRNAs•Cell-specific lncRNAs and transcription factors regulate common gene networks•PLUTO influences interactions between an enhancer cluster and PDX1The lncRNA •PLUTO and PDX1 are deregulated in type 2 diabetes and impaired glucose tolerance PLUTO, is downregulated in type 2 diabetes and controls PDX1, encoding a key β cell transcription factor.Akerman et al. studied the function of human β cell lncRNAs with RNAi, CRISPRi, and co-expression networks. This revealed β cell lncRNAs and transcription factors that control common regulatory networks. One lncRNA,  Transcriptome surveys have uncovered tens of thousands of mammalian transcripts longer than 200 nucleotides that have low protein-coding potential . A smallPancreatic β cells regulate glucose homeostasis by secreting insulin and play a central role in the pathogenesis of major forms of diabetes mellitus. Recently, more than 1,100 lncRNAs were identified in human pancreatic islets and purified β cells  as well PLUTO, which controls PDX1, a master regulator of pancreas development and β cell differentiation and, thereby, modulates the PDX1-dependent transcriptional program. Finally, we show that PLUTO and PDX1 are downregulated in islets from organ donors with type 2 diabetes or impaired glucose tolerance, suggesting a potential role in human diabetes.In the current study, we have focused on a set of lncRNAs that show restricted expression in human pancreatic β cells and have tested the hypothesis that they regulate β cell gene expression. Our studies have uncovered a regulatory network in which lineage-specific lncRNAs and transcription factors (TFs) control common genes. Furthermore, we show that lncRNAs frequently regulate genes associated with clusters of islet enhancers, which have previously been shown to be the primary functional targets of islet-specific TFs. We performed a detailed analysis of a specific lncRNA named cis-regulatory elements. We thus designed lentiviral vectors that contain RNA polymerase II-transcribed artificial microRNAs (hereafter referred to as amiRNA) with perfect homology to the target sequence to elicit target cleavage. The amiRNAs contain an artificial stem sequence targeting our lncRNA of choice as well as flanking and loop sequences from an endogenous miRNA to allow their processing as pre-miRNA by the RNAi pathway . We thus transduced EndoC-βH1 cells with lentiviruses expressing each amiRNA. This was done in duplicate or in triplicate for lncRNAs that only had one efficient amiRNA. 80 hr post-transduction, RNA was harvested and hybridized to oligonucleotide microarrays (−3 (ANOVA) (The lncRNAs selected for knockdown were derived from a short list of 25 lncRNAs that showed (1) a markedly enriched expression in human islets and fluorescence-activated cell sorting (FACS)-purified β cells relative to the exocrine pancreas and a panel of non-pancreatic tissues, (2) expression in the EndoC-βH1 β cell line, and (3) a chromatin profile in human islets that was consistent with an active promoter C and S1Droarrays A. For ea (ANOVA) B.cis-regulatory mechanism, although other such lncRNAs did not appear to affect neighboring genes and may thus function through trans-regulatory mechanisms . These results show that the observed phenotypes are unlikely to be caused by unspecific effects of amiRNAs and indicate that the sequence-specific inhibition of selected islet lncRNAs can result in transcriptional changes comparable in magnitude to the inhibition of well-established islet transcriptional regulators.Gene silencing using the RNAi pathway can theoretically lead to nonspecific gene deregulation. In our experimental model, a significant nonspecific result would occur when two unrelated amiRNAs elicited changes in a common set of genes that were not observed in the panel of control non-targeting amiRNAs. To assess the likelihood that two unrelated amiRNA sequences elicit such an effect, we studied the five sets of control (non-targeting) amiRNAs, compared all ten possible combinations of two versus three control amiRNAs, and determined the number of differentially expressed genes C. LikewiRFX6, PDX1, CACNA1D, ATP2A3, ROBO1 and 2, PDE8A, ATP6AP1, KCNJ15, TRPM3, ERO1LB, and HADH . Congruent with the broad transcriptional phenotype, we observed reduced insulin content and, consequently, impaired glucose-stimulated insulin secretion for HI-LNC12, HI-LNC78, and HI-LNC71 knockdowns  lncRNAs  , and HI-LNC15-dependent changes, which correlated with those occurring after knockdown of NKX2-2   . This fi−27) . Interes−27) B. Some p= 10−32) C. The reHI-LNC12, 15, 30, 78, 80, 85, and 71  were enriched in genes associated with pancreatic islet enhancer clusters B. Among −7) correlation of expression with an adjacent protein-coding gene in the panel of human islet samples and are thus candidate cis-regulatory lncRNAs  . Our resHI-LNC71, a nuclearly enriched transcript  that is transcribed from a promoter that is located ∼3 kb upstream of PDX1, in an antisense orientation  that shows only limited sequence homology with human PLUTO. Pluto is also transcribed from the opposite strand of Pdx1 but is initiated from a promoter within the first intron of Pdx1 and, like PLUTO, spans a broad regulatory domain upstream of Pdx1  in mouse pancreatic islets  assays revealed that two far upstream enhancers  were cultured with CMRL 1066 medium containing 10% fetal calf serum (FCS) before shipment, after which they were cultured for 3 days with RPMI 1640 medium containing 11 mM glucose and supplemented with 10% FCS.Glucose-stimulated insulin release was assayed in EndoC-βH1 or EndoC-βH3 cells as described previously .RNA was isolated with Tripure (Roche) and treated with DNase I (Sigma). qPCR was performed with SYBR green or Taqman probe detection . See TabLentiviral vectors carrying amiRNAs targeting TFs, lncRNAs, and non-targeting control sequences were transduced into the EndoC-βH1 human β cell line as described previously .PLUTO exon 1 or two unrelated intergenic control regions and transfected in EndoC-βH3 cells . Gene-based differential expression analysis was done using Transcriptome Analysis Console . Enhancer cluster genes were defined by genes that were associated with clustered islet enhancers that show top 50 percentile binding by TFs  as defined previously . PancreaRNA-seq data have been described previously . The samPDX1 intron. Normalized values are expressed as a fraction of non-targeting amiRNA control sample. See 3C and 4C-seq was carried out as described previously  For realhttp://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&hubUrl=http://www.imperial.ac.uk/medicine/beta-cell-genome-regulation-laboratory/data/HILNCs/HILNCs.txt&hgS_loadUrlName=http://www.imperial.ac.uk/medicine/beta-cell-genome-regulation-laboratory/data/.LncRNAs were annotated through de novo assembly of ∼5 billion stranded paired-end RNA-seq reads from 41 human islet samples, filtered for expression in FACS-purified β cell cells, lack of enrichment in the pancreatic exocrine fraction to exclude acinar contaminants, and the presence of H3K4me3 enrichment in the vicinity of the 5′ end. A more detailed description of the annotation process is provided in the Supplemental Information. Annotations are available in The WGCNA(v2) tool was used to build a co-transcriptional network based on mRNAs from 64 human islet RNA-seq samples.J.F. and I.A. conceived the idea, designed the experiments, and wrote the manuscript. J.F. supervised and I.A. coordinated the project. I.A., Z.T., H.W., J.Y., C.A., E.S., A.S., L. Pasquali, and D.M.Y.R. contributed to data analysis. I.A., A.B., M.B., C.S.C., R.G.F., J.G.H., and N.C. performed the experiments. D.M.Y.R., I.M., and N.N. annotated lncRNAs. L. Piemonti, T.B., C.B., J.K.C., and F.P. provided samples. I.A., J.F., Z.T., A.B., D.M.Y.R, L.G., C.B., J.K.C., F.P., P.R., A.S., L.G., C.A., and E.S. discussed the results. All authors read and approved the manuscript. Z.T., A.B., D.M.Y.R., C.S.C., and N.N. contributed equally.


Androgen-Receptor-Associated lincRNAs (ARA-lincRNAs). A genome-wide analysis showed that protein-coding gene neighbors to ARA-lincRNAs had a significantly higher androgen-induced change in expression than protein-coding genes neighboring lincRNAs not associated to AR. To find relevant epigenetic signatures enriched at the ARA-lincRNAs’ transcription start sites (TSSs) we used a machine learning approach and identified that the ARA-lincRNA genomic loci in LNCaP cells are significantly enriched with epigenetic marks that are characteristic of in cis enhancer RNA regulators, and that the H3K27ac mark of active enhancers is conspicuously enriched at the TSS of ARA-lincRNAs adjacent to androgen-activated protein-coding genes. In addition, LNCaP topologically associating domains (TADs) that comprise chromatin regions with ARA-lincRNAs exhibit transcription factor contents, epigenetic marks and gene transcriptional activities that are significantly different from TADs not containing ARA-lincRNAs. This work highlights the possible involvement of hundreds of lincRNAs working in synergy with the AR on the genome-wide androgen-induced gene regulatory program in prostate cells.Cell signaling events triggered by androgen hormone in prostate cells is dependent on activation of the androgen receptor (AR) transcription factor. Androgen hormone binding to AR promotes its displacement from the cytoplasm to the nucleus and AR binding to DNA motifs, thus inducing activatory and inhibitory transcriptional programs through a complex regulatory mechanism not yet fully understood. In this work, we performed RNA-seq deep-sequencing of LNCaP prostate cancer cells and found over 7000 expressed long intergenic non-coding RNAs (lincRNAs), of which ∼4000 are novel lincRNAs, and 258 lincRNAs have their expression activated by androgen. Immunoprecipitation of AR, followed by large-scale sequencing of co-immunoprecipitated RNAs (RIP-Seq) has identified in the LNCaP cell line a total of 619 lincRNAs that were significantly enriched  in the anti-Androgen Receptor (antiAR) fraction in relation to the control fraction (non-specific IgG), and we named them  The AR transcription factor mediates the effect of androgen hormone and modulates the gene transcriptional program in prostate cells. When not interacting with androgen the AR is maintained in the cytoplasmic cell portion associated with a protein scaffold  and afteNRCP lncRNA strongly binds to STAT1 transcription factor and NRCP silencing decreases the interaction of STA1 with RNA polymerase II  can interact with a number of proteins and regulate gene expression . LncRNAserase II , causingactivity . The aboactivity  and CLIPactivity  have notactivity . Neverthde novo TADs formation events can contribute to prostate cancer and other diseases  , an actidiseases .cis-regulatory enhancer function of lncRNAs on the neighbor genes . Cells were cultured for 24 h in androgen-deprived RPMI 1640 (Sigma) with 10% (w/v) charcoal-stripped fetal bovine serum (Sigma) as described in LNCaP cells were obtained from the ATCC and grown as recommended in media supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin (Gibco). Three to four biological replicates were obtained (Oligo-dT-primed reverse transcription (RT) was performed using 1 μg of total RNA according to the Super Script III kit protocol (Invitrogen). The relative levels of the transcripts were determined through quantitative real-time PCR (qPCR) (primers are shown in Supplementary Table 7 LNCaP cells treated for 6 h with 0.1 nM R1881 (Sigma) or with vehicle (ethanol) and 5 μg of each antibody. The following antibodies were used from Millipore: Normal Rabbit IgG (12-370) and Anti-Androgen Receptor (06-680). The RNAs were extracted using Trizol, treated with TURBO DNase (Ambion) at 37°C for 30 min, purified using an RNeasy Micro Kit (Qiagen) and quantified with RiboGreen (Invitrogen). RIP-Seq was performed in biological duplicates (n = 2), RIP-qPCR in biological triplicates (n = 3); co-precipitated RNAs were detected either by high-throughput sequencing as described below or by RT-qPCR with two to three technical replicates for each biological replicate (primers in Supplementary Table A native RIP  was perf+ RNA was extracted from LNCaP cells treated for 24 h with 1 nM of the synthetic androgen R1881 (Sigma) (n = 2) or with an equivalent volume of vehicle (ethanol) (n = 2) using FastTrack MAG Maxi mRNA Isolation Kit (Invitrogen) as described in GSE79301 and GSE100710.For RNA-seq experiments poly(A)GSE100710.For RIP-seq experiments , RNA samhttp://verjolab.usp.br/tracks/hg/hg19/genes/. We generated transcriptome assemblies for each of the four samples separately, and then used Cuffmerge .For differential expression (DE) analyses we performed count-based analysis as described in http://verjolab.usp.br/tracks/hg/hg19/genes/.To classify the transcripts as either previously annotated lincRNAs or putative novel lncRNAs we used the following pipeline: first we retained all transcripts from the above assembly with FPKM ≥ 1 that were previously annotated as lncRNAs in the comprehensive reference transcriptome (custom GTF); next, we retained novel transcripts that were identified in these analyses (class code “u” identified by Cuffmerge – putative novel intergenic transcripts) with FPKM ≥ 1. All transcripts with a length < 200 nt were removed. We discarded all transcripts with coding potential, as assessed by the Coding Potential Calculator (CPC version 0.9-r2) , thus reGene expression fold changes obtained from our RNA-seq data (androgen treated cells relative to control samples) were calculated with DESeq2 as described above and the entire list of expressed genes was pre-ranked and imported into GSEA program  to perfoRIP-seq data from each of two independent biological replicates of each condition (two antiAR replicates and two control non-specific IgG replicates) were trimmed of low-quality reads and clipped of sequencing adaptors, aligned to hg19 version of the human genome and assembled with Cufflinks 2.2 as described for RNA-seq above. We used as reference for the alignment and assembly of the RIP-seq reads the comprehensive_LNCaP_transcriptome GTF file.AR-Associated lincRNAs (ARA-lincRNAs), we used the approach of Soh et al. , which was used in the comparisons described further below. First, lincRNAs with read counts in the RIP-seq datasets (antiAR or IgG) equal to zero were excluded. The remaining lincRNA-genes with non-zero read counts were sorted in an ascending order using the absolute value of log2 (antiAR/IgG) and the first 619 top-ranked lincRNAs, corresponding to the lincRNAs with log2 (antiAR/IgG) normally distributed around zero, were assigned as the set of NonA-lincRNAs. We chose a size of 619 in order to make it identical to the size of the set of identified ARA-lincRNAs.In order to search for features with biological relevance in the set of ARA-lincRNAs, we created a control set of p-value = 0.79 in the comparison with the 619 ARA-lincRNAs). A selected subset of 258 NonA-lincRNAs with expression significantly increased by androgen have an average expression FPKM = 3.55 in the RNA-seq analysis ; this subset was further sub-divided into a selected set of 177 NonA-lincRNAs that had a protein-coding neighbor that also showed an increase in expression with androgen, and those 177 NonA-lincRNAs have an average expression FPKM = 3.86 . A remaining set of 81 NonA-lincRNAs that had a protein-coding neighbor showing a decrease in expression with androgen has an average expression FPKM = 2.78 .This selected control set of 619 NonA-lincRNAs exhibited a very similar (non-significantly different) pattern of average expression in LNCaP in the presence of androgen as that of ARA-lincRNAs. Thus, the entire set of NonA-lincRNAs have an average expression FPKM = 2.27 in the RNA-seq analysis  RNA-seq experiments. We matched the lincRNA-protein-coding pairs using BedTools  with theThe gene ontology analysis on the set of protein-coding neighbor genes was performed using the functional annotation tool DAVID 6.8  with theThe public ChIP and DNAseI datasets used in our analyses were from the following papers: . These aTo find the relevant epigenetic signatures at the ARA-lincRNAs TSS we used a machine learning approach employing the Random Forest algorithm implemented in the python Scikit-Learn (v. 0.18.1) package  with datBriefly, we trained the models with a different number of trees , setting as True the following parameters: warm start, out of bag score and gini criterion. The mean accuracy score for the models was obtained through a fivefold cross validation scheme . After sTopological Associating Domains (TADs) in LNCaP cells at 100 kb bin resolution were determined by To measure the ARA- or NonA-lincRNA TSS distance from the closest TAD boundary we normalized the TADs length, making the maximum boundary distance (100%) the one corresponding to the intersection between the lincRNA TSS coordinate with the exact middle of the TAD, and the minimum distance (0%), that corresponding to the lincRNA TSS overlapping the TAD boundary coordinate.Figure 1A), thus creating a high-quality catalog of the lincRNAs complement expressed in LNCaP prostate cancer cells. We detected a total of 7022 lincRNAs, of which 3979 lincRNAs (57%) were identified as putative novel transcripts expressed in LNCaP cells and not present in the reference transcriptome ; in addition, 3043 known lincRNAs (43%) present in the reference transcriptome were detected as expressed in LNCaP , of which 683 match lincRNAs annotated in GENCODE v19  for 24 h, or with vehicle; deep sequencing was achieved, with approximately 80–90 million strand-specific reads per library, in duplicate. RNA-seq reads were aligned to the genome and assembled using a comprehensive reference transcriptome that includes the lincRNAs from four papers in the literature  , respectively, and lincRNAs are less spliced than protein-coding genes with an average of 2.3 and 7.9 exons per transcript, respectively ; these findings are in line with previous results from the literature on lncRNAs   (Supplementary Table Table 1). In addition, several known androgen-stimulated protein-coding genes such as FASN, NDRG1, PSA, TMPRSS2, KLK2, and KLK4   of hallmark genes in androgen response  and of genes belonging to the prostate cancer signature  ; overall, these results again add confidence to the set of novel lincRNAs identified by RNA-seq analyses of LNCaP cells upon treatment with androgen hormone.We conducted a gene set enrichment analysis (GSEA) with the protein-coding genes detected as expressed in our RNA-seq assay. Briefly, a rank-ordered list was generated for all protein-coding genes, comparing androgen treatment with the control, and this list was used as input to a pre-ranked GSEA analysis. As expected, the analysis demonstrated a significant enrichment (Table 2) . A detailed list of all 424 lincRNAs is given in Supplementary Table Numerous studies demonstrated that lincRNAs contribute to cancer development and progression . We havePCA3, PVT1, and GAS5 lincRNAs as differentially expressed genes in the patient cancer samples compared with adjacent non-tumor tissues; these lincRNAs have been recently shown to be associated with prostate cancer (Table 3). Among them, 67 lincRNAs were differentially expressed in patient tumor samples compared with non-tumor tissue (Table 3), and the majority was up-regulated in the tumors (65 out of 67). A total of 23 of these lincRNAs differentially expressed in patient tumor samples were novel lincRNAs detected here, and Supplementary Table We detected e cancer . InteresPCGEM1   of AR wiPCGEM1  and eKLK1 (eKLK3  and one  (MALAT1 . SupplemAR-Associated-lincRNAs (ARA-lincRNAs) . Among these ARA-lincRNAs, 267 were novel lincRNAs not present in the comprehensive reference dataset of public lincRNAs. The list of 619 ARA-lincRNAs (comprising 1506 transcript isoforms) is given in Supplementary Table Figure 2B, green) and the average number of exons per transcript is 1.6 (median 1.0) . To our knowledge, this is the first report of hundreds of lincRNAs significantly associated to the AR in the presence of androgen. All ARA-lincRNAs can be viewed in a custom track at the UCSC human genome browser2.A total of 619 lincRNAs were identified as significantly enriched  in the anti-Androgen Receptor (antiAR) fraction in relation to the control fraction (non-specific IgG), and we named them cis, regulating genes in their neighborhood (Non-significantly AR-associated lincRNAs (NonA-lincRNAs) having the same number of elements as the ARA-lincRNAs set and corresponding to the lincRNAs with log2 (antiAR/IgG) ratio normally distributed around zero, i.e., lincRNAs that were equally associated to AR or IgG  . This was used as a control set  and the average number of exons per transcript is 1.7 (median 1.0) , very similar to the characteristics of the set of ARA-lincRNAs (see above). In contrast, the median distance  between the ARA-lincRNA and the closest expressed protein coding gene is 21 kb, while for the NonA-lincRNAs the median distance is significantly longer , namely 49 kb .Assuming that lincRNAs can act in hborhood , we searhborhood . FurtherFigure 3), which were chosen based on their neighbor protein coding genes , the amount of lincRNA associated to the antiAR fraction was significantly higher than that associated to the non-specific IgG control , even though the absolute value of the % input of antiAR-associated lincRNA was different for each biological replicate (each symbol). In LNCaP cells without androgen  there was an important decrease in the amount of ARA-lincRNA associated to AR for eight out of the nine lincRNAs tested, with the exception of ARA-lincRNA_0204.2 , and in the absence of androgen a significant enrichment was detected in the antiAR fraction compared with the IgG control for only three out of the nine ARA-lincRNAs tested .We confirmed by RIP-qPCR in LNCaP cells in the presence of androgen that for each of three different biological replicates , while the NonA-lincRNAs control group did not show any significantly enriched term. Protein-coding genes neighbor to ARA-lincRNAs are enriched for GO terms such as chromatin organization and cell adhesion, both relevant for prostate cancer development and maintenance  among the protein-coding genes near ARA-lincRNAs. These terms relate to anabolic processes preceding cell division, which are increased in cancer  terms among the protein-coding genes that are neighbors to ARA-lincRNAs, and separately also those that are neighbors to NonA-lincRNAs. In this analysis, the protein-coding genes neighboring ARA-lincRNAs exhibited statistically significant  enriched GO terms , and the second having 81 lincRNAs whose neighbor protein-coding genes had a reduced expression upon androgen treatment . To characterize this epigenetic profile, we used 23 public ChIP-seq datasets from LNCaP cells treated with androgen, comprising the genomic coordinates for binding sites of several histone marks and transcription factors, as well as the coordinates of Polymerase II and DNAseI accessibility . These ARA-lincRNAs with androgen-activated expression were further separated into two groups, the first comprising 177 lincRNAs whose protein-coding gene neighbors also had their expression activated by androgen . A set of significantly enriched epigenetic marks were at the top of the rank when looking at the TSS of ARA-lincRNAs whose neighbor protein-coding genes had an androgen-induced increase in expression . A different set of marks was enriched at the TSS of ARA-lincRNAs whose neighbor protein-coding genes had an androgen-induced inhibition of expression . Also, three other significantly enriched epigenetic marks were found at the TSS of the neighbor protein-coding genes .To access the relative relevance among the chromatin mark features that permitted the separation of ARA-lincRNAs from NonA-lincRNAs, we ranked the epigenetic marks identified by the machine learning algorithm according to the Gini importance score (Gini-index) generated by the Random-Forest training process . The H3K27ac mark, an important mark present in active enhancers . In contrast, the ARA-lincRNAs that have protein-coding gene neighbors with inhibited transcription upon androgen addition showed an enrichment of the H2A.Z histone variant at their TSS . The H2A.Z histone variant acts establishing and maintaining the interaction between enhancer and promoters, in addition to Polymerase II recruitment to eRNAs transcribed regions .It is noteworthy that PolII and DNAseI accessibility marks were significantly more abundant at the TSS of ARA-lincRNAs compared with NonA-lincRNAs, for ARA-lincRNAs with neighbor protein-coding genes with activated or inhibited expression upon androgen stimulation . Both proteins are important drivers of the proliferation process and cell survival triggered by androgen stimulus   when compared with the control group comprising the protein-coding genes that are neighbors to NonA-lincRNAs . These results show that in LNCaP cells, the presence of ARA-lincRNAs could enhance the change in expression of neighbor protein-coding genes induced by androgen, causing an enhanced androgen-activated expression. ARA-lincRNAs transcription and binding to AR could direct the remodeling of epigenetic marks at these loci; alternatively, we cannot rule out the possibility that pre-existing epigenetic marks and chromatin structure could be exploited by ARA-lincRNAs that are transcribed at these loci, in order to fine-tune the androgen-activated expression.We detected that the protein-coding genes with androgen-activated transcription that are neighbors to ARA-lincRNAs have a higher average expression ratio , when compared with the control group, comprising the protein-coding genes that are neighbors of NonA-lincRNAs , however, the difference in the average expressions ratios was not statistically significant, possibly related to the fact that this analysis included a small set of only 81 ARA-lincRNAs whose neighbor protein-coding genes have a reduced expression, compared with the 171 ARA-lincRNAs in the androgen-activated set.When the expression of the protein-coding genes that had androgen-inhibited transcription was analyzed, the average expression ratio was lower for the neighbors of ARA-lincRNAs  by cross-matching the genomic TAD coordinates with the genomic coordinates of all androgen-activated ARA- and NonA-lincRNAs. Next, we computed the number of significantly enriched epigenetic marks, available in the public ChIP-Seq datasets for LNCaP cells  that were present in the TADs containing ARA-lincRNAs or NonA-lincRNAs and compared the two sets. We found a significantly higher  average abundance of the H3K27ac, H4K5ac, LSD1, H3K4me1, and H3K36me3 epigenetic marks in TADs containing ARA-lincRNAs compared with TADs containing NonA-lincRNAs .In order to take into account the influence of the genomic architecture on the chromatin profile in the neighborhood of ARA-lincRNAs, we used public data of LNCaP TADs . We cata2 ratio (H3K27ac/H3K27me3) indicated that ARA-lincRNA promoters exhibited an H3K27ac/H3K27me3 ratio significantly higher  than NonA-lincRNA promoters , and therefore the promoters of ARA-lincRNAs in LNCaP cells are more similar to active enhancers than those of NonA-lincRNAs.To gain further insight into the possible ARA-lincRNAs epigenetic similarity with enhancers, we used the ratio between H3K27ac and H3K27me3 marks at their TSS to investigate if ARA-lincRNA promoters were similar to poised or to active enhancers . The cumFigure 6B). For all groups tested we found a significant  correlation of expression fold-change between ARA-lincRNAs and their closest protein-coding gene neighbor. The correlation coefficient increased from ρ = 0.17 to ρ = 0.58  when the analysis was performed with increasingly more restricted sets of genes comprising ARA-lincRNAs with a more stringent canonical enhancer signature, i.e., with more abundant H3K4me1 and H3K27ac marks at their promoters. This effect was not observed when the same approach was applied to NonA-lincRNAs . This result shows that the cis-regulatory potential of enhancer-like lincRNAs in LNCaP cells stimulated with androgen is higher for lincRNAs associated to the AR compared with non-associated.Because the expression of lincRNAs with enhancer signature at promoters is known to have a higher correlation with neighbor genes than genes with non-enhancer signature , and conp-value < 0.05, t-test) average expression for all protein-coding genes inside TADs containing ARA-lincRNAs  compared with all protein-coding genes inside TADs containing NonA-lincRNAs .Next, we investigated if the expression of all protein-coding genes inside TADs containing ARA-lincRNAs was significantly different from that of all protein-coding genes inside TADs containing NonA-lincRNAs. Analyzing our RNA-seq data from LNCaP cells, we found a significantly higher  are significantly closer  to TAD-border regions than NonA-lincRNA promoters (the control group) .Topologically associating domains boundaries are important chromatin regions flanking TAD elements. These regions have a small count of contacts with chromatin outside of their TADs and bind to several architectural protein clusters . To examFigure 6E), indicating a higher density of these transcriptional regulators at loci enriched with ARA-lincRNAs compared with loci with NonA-lincRNAs, and suggesting that ARA-lincRNAs might act as scaffolds. In fact, a large number of histone marks were localized closer to ARA-lincRNA promoters than to NonA-lincRNA promoters  ; also, marks representing enhancer signatures (H3K27ac and H3K4me2) are distributed more frequently in closer vicinity to ARA-lincRNAs than to NonA-lincRNAs .Interestingly, we found no difference between the distance of AR ChIP-seq peaks and the TSS of lincRNAs when ARA- and NonA-lincRNA groups were compared , when we re-analyzed the prostate cancer data from Interestingly, a total of 424 lincRNAs were detected as significantly differentially expressed in at least 75% of the patient cancer samples compared with adjacent non-tumor  at its N-terminal domain (NTD) . Intrinsase MDM2 . Our worcis regulation of neighbor protein-coding genes  with an average 2.1 kb length. These are features that are common to known enhancers, which are typically un-spliced and either short (1–3 kb), bidirectional, and non-polyadenylated, or long (>3 kb), unidirectional, and can be polyadenylated or non-polyadenylated .It is known that besides promoting increased transcription of a number of target genes, AR can cause transcriptional repression of a set of genes , and thecis and are related with the metastatic phenotype of cancer cells in vitro. Interestingly, we found that ARA-lincRNAs have a tendency to localize closer to the boundaries of a TAD, suggesting that they may participate in the protein complex that defines and maintains the border structural organization. We also found that protein-coding genes localized within a given TAD containing ARA-lincRNAs were more highly androgen-responsive than those within TADs not containing ARA-lincRNAs, which suggests that the tight and more frequent contact of regulatory elements within a TAD sub-compartment (HOTAIR (Chromatin architecture in eukaryotes is highly complex, and chromosome looping results in partitioning the genome into contact domains, which are associated with distinct patterns of histone marks , and defpartment  could sppartment  and that (HOTAIR . Our datcis-regulatory RNA enhancers that may cooperate with AR to control the androgen regulatory program of prostate cells. Further investigation of the molecular mechanisms involved with such interplay between lincRNAs and AR is warranted.Enhancer RNAs typically promote the engagement of transcription factors and bridging factors such as the Mediator and WDR5 complexes, where the activating RNAs direct the recruitment of specific complexes to target loci and modulate chromatin architecture, leading to transcriptional activation of the target genes . OverallFB, LdS, and SV-A conceived and designed the experiments and wrote the paper. FB, MA, AA, VM, and AV performed the experiments. FB and LdS analyzed the data. ER, JS, and SV-A contributed reagents/materials/analysis tools.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.


SET2 deletion. A majority of these promoters have overlapping lncRNA transcription that targets H3K36me3 and deacetylation by Rpd3S to the mRNA promoter. We previously reported a similar mechanism for H3K4me2-mediated repression via recruitment of the Set3C histone deacetylase. Here we show that the distance between an mRNA and overlapping lncRNA promoter determines whether Set2–Rpd3S or Set3C represses. This analysis also reveals many previously unreported cryptic ncRNAs induced by specific carbon sources, showing that cryptic promoters can be environmentally regulated. Therefore, in addition to repression of cryptic transcription and modulation of elongation, H3K36 methylation maintains optimal expression dynamics of many mRNAs and ncRNAs.H3K36 methylation by Set2 targets Rpd3S histone deacetylase to transcribed regions of mRNA genes, repressing internal cryptic promoters and slowing elongation. Here we explore the function of this pathway by analysing transcription in yeast undergoing a series of carbon source shifts. Approximately 80 mRNA genes show increased induction upon  H3K36 methylation by Set2 targets Rpd3S histone deacetylase to transcribed mRNA genes, repressing internal cryptic promoters and modulating elongation. Here, the authors provide evidence that the Set2-Rpd3S pathway also regulates dynamic expression of mRNAs and lncRNAs. Eukaryotic transcription is regulated by post-translational modifications of histones, including acetylation, methylation, phosphorylation and ubiquitination1The Set2 methyltransferase directly binds elongating RNApII and co-transcriptionally methylates H3K36 refs , 5, 6, 74571011121Nucleosomes methylated on H3K36 are deacetylated by the Rpd3 small complex, Rpd3S refs , 16, 17.1915162122Here we describe a new function for the Set2–Rpd3S in modulating mRNA gene expression dynamics during induction. Microarray analysis shows that a majority of Set2-repressed genes are overlapped by long non-coding RNA (lncRNA) transcription, from either an upstream or an antisense promoter. This non-coding transcription targets H3K36me3 and the Rpd3S HDAC to the Set2-repressed promoters. In addition, new Set2–Rpd3S-repressed cryptic promoters are seen that are induced by specific carbon sources. Therefore, Set2–Rpd3S regulates both mRNA promoters and cryptic non-coding RNA (ncRNA) promoters. We previously showed that overlapping lncRNA transcription could also target H3K4me2 and the Set3C HDAC to repress mRNA inductionSET2 and set2Δ cells during carbon source shifts  or stable unannotated transcripts (SUTs), or possibly read-through transcripts from the adjacent gene) either coming from an upstream or antisense promoter. In contrast, only 12% of yeast genes overlap with lncRNAAAD10  was used to quantitate histone H4 acetylation normalized to total nucleosome levels as measured by histone H3. Relative to the lncRNA promoters, the AAD10 and SUL1 promoters had reduced acetylation in wild-type cells  was inserted between the two promoters in Δ941 cells  showed no effect on the upstream transcript and no derepression of AAD10  and 64 Set3-repressed genes  and gene-specific primers. For The sequences of oligonucleotides used in this study are listed in 32P] dATP with only one primer. Hybridization was done in a buffer containing 1% BSA, 7% SDS, 1 mM EDTA (pH 8.0) and 300 mM sodium phosphate buffer (pH 7.2).RNA was isolated from cells with hot phenol and 10 μg total RNA from each sample was separated on an agarose gel, blotted to membranes and hybridized to radioactive probes using standard techniquesChIPs were done as previously described with the modifications described belowhttp://steinmetzlab.embl.de/buratowski_lab/index.html. The raw microarray data can be downloaded from ArrayExpress with the accession code E-MTAP-4268.High-resolution tiling arrays were done as previously describedhttps://www.ebi.ac.uk/arrayexpress/) with the accession code E-MTAP-4268.Microarry data sets that support the findings of this study have been deposited in the ArrayExpress repository .Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures 1-5, Supplementary Tables 1-2 and Supplementary References.


Proximity ligation assays such as circularized chromosome conformation capture and high-throughput chromosome capture assays have shed light on the structural organization of the interphase genome. Functional topologically associating domains (TADs) that constitute the building blocks of genomic organization are disrupted and reconstructed during the cell cycle. Epigenetic memory, as well as the sequence of chromosomes, regulate TAD reconstitution. Sub-TAD domains that are invariant across cell types have been identified, and contacts between these domains, rather than looping, are speculated to drive chromatin folding. Replication domains are established simultaneously with TADs during the cell cycle and the two correlate well in terms of characteristic features, such as lamin association and histone modifications. CCCTC-binding factor (CTCF) and cohesin cooperate across different cell types to regulate genes and genome organization. CTCF elements that demarcate TAD boundaries are commonly disrupted in cancer and promote oncogene activation. Chromatin looping facilitates interactions between distant promoters and enhancers, and the resulting enhanceosome complex promotes gene expression. Deciphering the chromatin tangle requires comprehensive integrative analyses of DNA- and protein-dependent factors that regulate genomic organization. Genomic DNA in each human diploid cell, which is 2 m in total length, is folded around histone proteins to form the compact nucleosome, which in addition to DNA and the histone octamer core, contains the tail regions of histone proteins. The predominant histone octamer core contains two of each of the four histones, H2A, H2B, H3, and H4, around which ≈147 bp of DNA is wound. The nucleosome also contains the histone H1, which binds outside the nucleosome “bead” to the linker DNA (20–80 bp) that connects adjacent nucleosomes. In the last decade, the emergence of new techniques, such as proximity ligation assays, have provided novel and fundamental insights into chromatin organization at different stages of the cell cycle. The mechanisms by which regulatory elements such as enhancers and CCCTC-binding factor (CTCF) sites control genome organization have also been elucidated. This review focuses on recent insights into genome organization, regulatory mechanisms of genome organization, and the significance of genome organization to gene regulation, development, and disease.The tangled mass of chromatin is spatially organized in the nucleus within very distinct topological domains. Larger chromosomes and chromosomes with more heterochromatin regions are seen to be present on the nuclear periphery while smaller chromosomes and those with more euchromatin regions are localized at the center of the nucleus ,2. The eWithin each of the A and B compartments, there are sub-domains called topologically associating domains (TADs) where interactions are at their highest levels . TADs arGenome organization is partly driven by the high affinity of active chromatin regions for other active regions, which also explains why interactions between euchromatin and heterochromatin are infrequently observed. The interactions listed by their order of strength from strongest to weakest are: within A compartments > within B compartments > between A and B compartments . ApproxiWithin a TAD, the activity of individual DNA sequence elements is correlated , which iTfap2c (Transcription factor AP-2 gamma) and Bmp7 (Bone morphogenetic protein 7) locus in mouse embryos results in the shifting of a TAD boundary, activation of Tfap2c, and silencing of Bmp7 [Evi1oncogene [Several inversions and deletions have been described that disrupt or modify TAD boundaries. For instance, a 0.3 Mb inversion at the  of Bmp7 . The losoncogene . Howeveroncogene .1 phase of the cell cycle [2 phase, the determinants of replication are lost, which suggests that other factors in addition to the chromatin organization determine the establishment of replication domains. However, during the G0 phase, both TADs and replication domains are maintained.Interphase TADs are disrupted during mitosis, requiring reconstitution of the genome architecture after each round of mitosis. Replication domains, defined as 400–800-kb DNA regions that replicate synchronously, are established during the Gll cycle . At the The intimate relationship between TADs, replication domains, and transcriptional regulation is also evident in the spatial organization of the replication domains. Approximately 50% of the genome replicates synchronously  . ConstitThe concept that early acting replication domains correspond to the regions with high euchromatin content has been recently dispelled. In addition to constitutively acting genes, specific developmental genes also replicate early in specific cell types. Moreover, gene expression can be altered without any change in the replication timing ,34. GenePre-replication complexes (pre-RCs) are recruited to specific chromatin sites where they further recruit additional components to form pre-initiation complexes ,37. AlthAlthough chromosomes are reorganized into chromatids during mitosis, the chromatin status of specific regions is retained , which cThe distribution of lamina-associated domains (LADs) could also regulate the chromosome position and shape in the nucleus. LADs tether chromatin to the nuclear lamina through important tethering proteins; lamin B receptor (LBR) and lamin A/C. The LBR and lamin A/C are both constituent proteins of the lamina that are sequentially expressed during cell differentiation. Deletion of both proteins causes dislocation of LADs from the nuclear lamina to the nuclear center . Chromoscis-acting DNA regulatory elements that promote TF. Enhancers do not always affect the promoters that are closest to them in the linear DNA sequence, and may only be active in specific tissue types and at specific development stages. The first enhancer sequence, which was identified in an intron of the mouse immunoglobulin heavy chain gene, increased the transcription of various nuclear factors. Enhancers may potentially function by turning on transcription of a specific locus in more cells or by increasing the number of RNA molecules transcribed from a specific locus [Enhancers are important ic locus .Enhancers are typically located several kb or Mb away from the gene promoters that they act on. The mechanisms of enhancer-mediated gene transcription at a distance have recently been elucidated ,55,56. CMembers of a TF family may share a common DNA binding domain but may be recruited by other unique elements for distinct purposes. For example, the FOXM1 (forkhead box M1) transcription factor is recruited via protein–protein interactions with CHR (cell cycle homology region) elements rather than by interaction with the consensus RYAAAYA binding motif, to specifically regulate late cell cycle processes . AnalysiA genome-wide analysis of enhancer–promoter interactions in human primary resting CD4+ Tcells identified 2373 such interactions involving 2067 enhancers and 1619 promoters., with 91% of these being single promoter–enhancer interactions . The majCTCF and cohesin act cooperatively in genome organization ,64,65,66Numerous databases, such as the Encyclopedia of DNA Elements (ENCODE) database, are available for predicting regulatory sequences through in silico analysis. Whereas CTCF sites are defined by a consensus sequence, enhancer regions are not. Additionally, regulatory sequences may function differently based on the tissue type and/or the developmental stage of the organism. Therefore, it will be necessary to experimentally validate computationally predicted regulatory sequence elements. Cross-disciplinary perspectives that combine structural and functional genomics will be required for a genome-wide understanding of the mechanisms of gene regulation .Although proximity ligation assays have contributed to our understanding of genome organization and chromatin folding, much more remains to be discovered. Further research is required to understand how TADs reconstitute with high fidelity following every cell division. Additional studies are also required to understand the spatial and temporal relationships among replication origins, gene regulatory elements, and epigenetic modifications that regulate if and when two genomic regions are brought into close proximity. Long non-coding RNAs, which are associated with the genome architecture, may be critical to answering some of these questions. Finally, conceptual and experimental models that go beyond single-factor binding models will be required to define both the underlying molecules and mechanisms that govern genome organization.


Mutations in binding motifsreduced occupancy by transcription factors in a chromatin environment.Characterization of mutations revealed that the CRISPR/spCas9 systemmainly induced deletions in short regions of <20 bp and preferentiallydeleted nucleotides around the fifth nucleotide upstream of Protospacer adjacentmotifs (PAMs). These results indicate that the CRISPR/Cas9 system issuitable for mutating the binding motifs of transcription factors, and,consequently, would contribute to elucidate the direct roles of transcriptionfactors.Transcription factors play roles in gene transcription through direct binding totheir motifs in genome, and inhibiting this binding provides an effectivestrategy for studying their roles. Here, we applied the CRISPR /spCas9(CRISPR-associated protein 9) system to mutate the binding motifs oftranscription factors. Binding motifs for erythroid-specific transcriptionfactors were mutated in the locus control region (LCR) hypersensitive sites(HSs) of the human β-globin locus. Guide RNAs targetting binding motifswere cloned into lentiviral CRISPR vector containing the  Transcription factors regulate the transcription of target genes by binding to motifspresent in regulatory regions of the genome. The roles of transcription factors havebeen elucidated by inhibiting their expressions in a cellular environment ,2 or bytrans-activating crRNA (tracrRNA) [Recently, an effective genome editing tool was developed from bacterial adaptiveimmune system type II, clustered, regularly interspaced, short palindromic repeats(CRISPR) ,9. In thracrRNA) . The crRracrRNA) . DNA cleracrRNA) ,11,12. IracrRNA) ,13, whicracrRNA) ,11. ThesracrRNA) . TheCRIracrRNA) –22.Streptococcus pyogenes uses5′-NGG-3′ sequences as PAMs [spCas9 derived from  as PAMs ,23. Thes as PAMs ,which s as PAMs ,24.Here as PAMs . The loc as PAMs . To clea as PAMs . WefounGuide sequences of sgRNA were cloned into lentiCRISPRv2 vector (Addgene #52961) as sugge6 293FT cells in six-well plate and mediumcontaining the mixture was changed with complete medium the next day. At 72 hafter transfection, lentiviral supernatants were harvested and filtered througha 0.45-μm membrane. MEL/ch11 cells were infected with lentiviralsupernatants in the presence of 6 μg/ml polybrene (Sigma) andmedium was replaced with fresh one the next day. Cells were selected using 2μg/ml puromycin (Sigma) for lentiCRISPRv2 vector or 500μg/ml hygromycin (Invitrogen) for pLH-spsgRNA2 vector at 72 hafter transduction.MEL/ch11 and 293FT cells were cultured in DMEM medium (Gibco) supplementedwith 10% FBS (Gibco) and 1% penicillin-streptomycin (Gibco). Toproduce lentivirus, 0.5 μg lentiviral vector and 1.5 μg packagingmix  were diluted in 125 μl Opti-MEM(Gibco), and 6 μl Lipofectamine 2000 (Invitrogen) in another 125μl Opti-MEM. After incubation for 5 min at room temperature, they weremixed and incubated for an additional 20 min. The DNA-Lipofectamine 2000 mixturewas added to 1 × 105 per well. To screen clones with mutations in sequencestargetted by sgRNA, genomic DNA was isolated using a QIAamp DNA Mini Kit(Qiagen) from 1 × 106 cells, and amplified by quantitative PCR(qPCR) using the following parameters: 20 ng genomic DNA, 4.5 μM forwardprimer, 4.5 μM reverse primer, and 1× Power SYBR Green PCR mastermix (Applied Biosystems) in 10 μl of reaction volume using the 7300real-time PCR system (Applied Biosystems). Dissociation curves of qPCR wereanalyzed using qPCR program and PCR products were visualized on agarose gels.Mutations were clarified by DNA sequencing analysis. The sequences of primersused for PCR are presented in Supplementary Table S1.Clonal cells were grown in 96-well plates after antibiotics selection, and thentransferred to 24-well plates when the cell numbers reached 1 ×105 cellsper ml in 5 mM HMBA (Sigma) for 72 h to activate the β-globin locus. Tocross-link protein to genomic DNA, 1 × 107 cells wereincubated in culture medium containing 1% formaldehyde (Sigma) at25°C for 10 min with shaking, and then glycine (Invitrogen) was added toa final concentration of 0.125 M. After PBS washing, cross-linked cells wereincubated in cell lysis buffer  at 4°C for 10 min and centrifuged at 600g for 5 min to isolate nuclei. The nuclei weretreated with 100 U of MNase (Worthington) at 37°C for 15 min, incubatedin nuclei lysis buffer  at 4°C for10 min and then sonicated to digest chromatin to mainly mononucleosomes. Afterpreclearing with protein G agarose beads (Millipore), chromatin was made toreact with antibodies at 4°C for 3 h, and protein–DNA complexeswere collected using protein G agarose beads. DNA was purified by phenolextraction and ethanol precipitation and then dissolved in 150 μl ofTris/EDTA buffer. DNA obtained by ChIP was analyzed by qPCR using TaqManchemistry and the following parameters: 2 μl DNA, 2 μM TaqManprobe, 4.5 μM primers, and 1× TaqMan universal master mix II(Applied Biosystems) in 10 μl of reaction volume using the 7300 real-timePCR system (Applied Biosystems). Antibodies used for ChIP were normal goat IgG(sc-2028), GATA-1 (sc-1233), and TAL1 (sc-12984) from Santa Cruz Biotechnology,and KLF1 (ab2483) from Abcam. The sequences of probes and primers used for ChIPassay are presented in Supplementary Table S2.Clonal MEL/ch11 cells were cultured at 1.5 × 10http://crispr.mit.edu) and chose that theirputative cutting sites are located in binding motifs  and both side regions A. FigureTo screen clones in which the LCR HS3 KLF1 motif was targetted, A2F-1 primer wasdesigned in the outside region of target sequences because of high GC content intarget sequences. Deletions in target sequences were detected by comparing sizesof PCR products C and byOn the other hand, we found some clones in which genomic DNA was not amplified bythe A1 or A3 primer set in addition to A2 primer set E. It indThe mutations of target sequences can be analyzed using various methods. Mismatchcleavage assay is most commonly used to analyze genome edited byCRISPR/Cas9 . This asTo determine whether the deletion of binding motifs results in the loss oftranscription factor binding, we carried out ChIP assays on three mutant clones,that are HS3ΔGA#2, HS3ΔTA#1, and HS3ΔKL#2. TheHS3ΔGA#2 and HS3ΔTA#1 clones showed decrease in approximately50% in the occupancies of GATA-1 and TAL1 in the LCR HS3, respectively,as compared with control cells A,B. KLF1To more strictly inhibit GATA-1 binding in the LCR HS3, we tried to mutateanother GATA-1-binding motif located near the first GATA-1 motif. Guidesequences targetting the second GATA-1 binding motif (HS3_GA_R2)were cloned into pLH-spsgRNA2 vector, which has the hygromycin resistance geneas a selectable marker, and then transduced into the HS3ΔGA#2 clone usingthe procedure described in The CRISPR/Cas9 technique has evolved over several years and has beenutilized to knockout gene expression in many studies –22. TodNext, we determined the locations of deleted nucleotides with respect to theguide sequences of sgRNA in 48 clones containing deletions of under 20 bp D. The focis-elements, such as transcription factor binding motifs.Due to this restriction to short regions, other cis-elementsnear target sequences might be maintained during genome editing using theCRISPR/spCas9 system, which would allow study of the individual roles ofcis-elements. In addition, the deletion of binding motifsfor transcription factors using the CRISPR/Cas9 system would be usefulfor inhibiting the expressions of target genes without side effects, because themutations of gene coding regions could result in the expression of abnormalpolypeptides by nucleotide deletion or frameshift in translation. In recentstudies, the ALAS2 gene transcription was severely inhibited bya GATA-1 motif deletion generated using the CRISPR/Cas9 system [BCL11A gene using theCRISPR/Cas9 system suggested the therapeutic possibility for theβ-hemoglobinopathies by failing BCL11A expression and resulting ininduction of the fetal γ-globin expression [In summary, we mutated the binding motifs of transcription factors using theCRISPR/spCas9 system in a genome context, and observed reductions intranscription factor occupancies in a chromatin environment. Our findings implythat the CRISPR/spCas9 system can be used to study the direct roles oftranscription factors for target genes without disturbing the expressions ofother transcription factors. Furthermore, DNA breaks caused by theCRISPR/spCas9 system resulted mainly in deletions or insertions of lessthan 20 nts, which indicates that this system is suitable for mutating short9 system ,31. Enhapression . Overall