Drosophila melanogaster shavenbaby (svb) locus and related enhancers in nuclear microenvironments of high Ubx concentrations. Related enhancers colocalize to the same microenvironments independently of their chromosomal location, suggesting that microenvironments are highly differentiated transcription domains. Manipulating the affinity of svb enhancers revealed an inverse relationship between enhancer affinity and Ubx concentration required for transcriptional activation. The Ubx cofactor, Homothorax (Hth), was co-enriched with Ubx near enhancers that require Hth, even though Ubx and Hth did not co-localize throughout the nucleus. Thus, microenvironments of high local transcription factor and cofactor concentrations could help low-affinity sites overcome their kinetic inefficiency. Mechanisms that generate these microenvironments could be a general feature of eukaryotic transcriptional regulation.Transcription factors bind low-affinity DNA sequences for only short durations. It is not clear how brief, low-affinity interactions can drive efficient transcription. Here, we report that the transcription factor Ultrabithorax (Ubx) utilizes low-affinity binding sites in the  Genomic regions near coding genes, called enhancers, direct specific patterns of gene expression . EnhanceOne possible mechanism that could mitigate their kinetic inefficiency is to increase the local concentrations of transcription factors. At the scale of a single enhancer over a few hundred base pairs long, multiple low-affinity binding sites for the same transcription factor in close proximity could increase the frequency of binding events by trapping the protein. Furthermore, interactions between transcription factors and cofactors with multiple binding sites within an enhancer could generate ‘microenvironments’  of high shavenbaby (svb) locus, which contains multiple enhancers that drive specific patterns of svb gene expression in developing Drosophila embryos. Each of three characterized svb enhancers contains clusters of low-affinity binding sites for the Hox gene Ultrabithorax (Ubx). These enhancers also require a Ubx cofactor Homothorax (Hth) to function  staining in fixed embryos and high-resolution confocal imaging using Airyscan . We found that Ubx protein was not distributed uniformly, but rather exhibited regions of high and low Ubx intensities  clusters per nucleus that were stronger than one-quarter of the maximum Ubx intensity within that nucleus , a transcription factor unrelated to Ubx, displayed non-uniform sub-nuclear concentrations, but its distribution only partially overlapped with that of Ubx . AbdominWe also examined whether Ubx simply occupies regions containing actively transcribed DNA. Both active RNA Polymerase II  and the methylated histone H3K4me3, which marks actively transcribed DNA, only partially overlapped with Ubx . In contDrosophila embryos. Single-molecule imaging has been mostly performed in cell lines previously because live-imaging studies of transcription factor dynamics in embryos requires overcoming several new challenges, including imaging at lower signal-to-noise ratios, compensating for rapid morphological changes during embryonic development, and determining how to deliver fluorescent dyes. We overcame these challenges by generating a HaloTag-Ubx transgene that allowed precise control of fusion protein levels (hsp70) or from a 20x UAS promoter by crossing with a GAL4 driver line.To understand if the heterogeneous distribution of Ubx is dynamic or stable over the timescale of seconds to minutes, as well as to rule out the possibility that our observations of Ubx microenvironments are an artifact of the fixation protocol , we examn levels  and coupn levels . The traHaloTag-Ubx transgene by incubating the embryos at 30°C transformed anterior segments to the fate of more posterior segments, indicated by the presence of additional trichomes. This result indicates that the HaloTag-Ubx protein retains the expected Ubx behavior (20x UAS promoter with the nos::GAL4 (nanos promoter driving GAL4) driver line, which drives HaloTag-Ubx expression in all cells at early developmental stages. We injected the HaloTag ligand of Janelia Fluor 635 (JF635)   into thenjection , consistnjection . We examnjection . We founnjection . These rObservation of embryos at late stage 6 showed that total HaloTag-Ubx concentration continued to increase as the embryo ages . EmbryosHaloTag-Ubx transgene where Arg3 and Asn51 of the homeodomain were mutated to Ala (R3A and N51A), abrogating DNA binding  and compared the localization of actively transcribed svb loci to Ubx protein concentration (svb transcription sites (r from the point of maximum FISH intensity for each svb transcription site (r = 0 (maximum FISH intensity) and averaged across all sites measured. To adjust for background fluorescence, we located the minimum intensity in the averaged Ubx distribution (r = 2–4 μm) and subtracted that value from the distribution. The first micrometer of the radially averaged 3D distribution is shown, with the shaded area representing the variance  and decreased approximately 250 nm away from the site. Thus, active svb transcription sites colocalized with areas of high Ubx concentration spanning approximately a few hundred nanometers.The heterogeneous distributions of Ubx we observed are consistent with the hypothesis of nuclear ‘microenvironments’ , wherebynhancers . We markntration . We obseon sites . To quanion site . Ubx intvariance . Within svb loci because Ubx drives svb expression, then we would expect that transcription at a locus not regulated by Ubx should not co-localize with high Ubx concentrations. Indeed, we observed that active transcription sites driven by a synthetic enhancer containing binding sites for a TALEA transcription factor . As these transcription sites are not close to maxima of Ubx intensity, the variance in these distributions incresed with distance from the site of transcription.If Ubx protein co-localizes with actively transcribed n factor  did not svb on the X chromosome, we observed what appeared to be two transcription sites within 200 nm of each other  did not colocalize with the svb locus  within single nuclei is approximately 250 nm, near the resolution limit of AiryScan images , 0.51 ± 0.19 for E3N , and 0.68 ± 0.10 for 7 hr   . These rE3N enhancer decreased Ubx enrichment near transcription sites to 0.44 ± 0.27 .Increasing the binding affinity of a site should increase its sensitivity to Ubx and allow transcriptional activation at lower Ubx concentrations. We found previously that replacing a single low-affinity Ubx site with one of a higher affinity led to higher levels of expression and sometimes drove promiscuous transcription , suggestE3N, active transcription was observed in regions of increased Ubx enrichment . Deletion of two low-affinity Ubx sites from the 7 hr enhancer did not alter Ubx enrichment around transcription sites . But, deletion of three Ubx-binding sites in the 7H enhancer increased relative Ubx enrichment, consistent with the pattern we observed for the E3N enhancer .In contrast, we reported previously that deletion of low-affinity binding sites reduced transcription . Removinsvb enhancers to local Ubx concentration. Lower affinity enhancers require higher Ubx concentrations to drive transcription. Conversely, higher affinity enhancers can drive transcription at lower local Ubx concentrations.Across all manipulations, we observed an inverse correlation between binding site affinity and the distribution of Ubx intensities at transcription sites . Thus, tsvb elements.Taken together, these data suggest that enhancers may be dynamically sampling local nuclear environments. A lower fraction of nuclei showing transcription from enhancers with binding site deletions  may occu7H and E3N  and 0.66 ± 0.13 and 0.39 ± 0.24 for E3N . These results suggest that transcription from co-factor-dependent enhancers requires microenvironments that contain high concentrations of both transcription factors and their co-factors. This observation provides further support for the model that transcription factor microenvironments are present as multiple highly differentiated transcription domains containing unique combinations of transcription factors.Co-factors can stabilize low-affinity binding interactions through cooperative and scaffolding interactions with transcription factors. A co-factor-dependent enhancer would require sufficient concentrations of both the factor and the co-factor to drive transcription. The homeodomain proteins Extradenticle (Exd)/Pbx and Homothorax (Hth)/MEIS  interact and E3N . In vivo and E3N . ConsistH or E3N . The relsvb enhancers placed on the third chromosome often co-localized with the native svb locus on the X chromosome suggests that unique microenvironments may have relatively long half-lives. One challenge for the future is to determine how rapid dynamics of individual molecules generates apparently stable sub-nuclear domains.Biological systems often generate locally high concentrations of interacting molecules to increase the efficiency of biochemical reactions . This apMany mechanisms might work in concert to create these observed microenvironments. First, clustered binding sites for the same transcription factor  could leDrosophila cells generating distinct chromatin environments at the kilobase-to-megabase scale  or the first intron of svb or fkh. DIG-labeled RNA products were detected with a DIG antibody: Invitrogen, 9H27L19 (1:200 dilution) and biotin-labeled RNA products are detected using a biotin antibody: Pierce, PA1-26792 (1:200).Flies were reared at 25°C and embryos were fixed and stained according to standard protocols . PrimaryThe following primary antibodies for proteins were used at the indicated concentrations:Ubx: Developmental Studies Hybridoma Bank, FP3.38-C (1:20)Hth: Santa Cruz Biotechnology (dN-19), sc-26186 (1:50)Eve: Developmental Studies Hybridoma Bank, 2B8-C (1:20)AbdA: Santa Cruz Biotechnology (dN-17), sc-27063 (1:50)En: Santa Cruz Biotechnology (d-300), sc-28640 (1:50)RNA PolII RPB1 (Ser5 phosphorylated): BioLegend, (920304), (1:200)Histone H3K27me3: Active Motif, 39157 (1:200)Histone H3K4me3: Cell-signaling technology C42D8 (1:200)LacZ: Promega anti-ß-Gal antibody (1:1000)Drosophila embryos mounted in ProLong Gold mounting media  were imaged on a Zeiss LSM 880 confocal microscope with Airyscan  using 3D Airyscan in SR mode to obtain images with 1.7-fold higher resolution compared to diffraction-limited confocal imaging  using a Zeiss LSM 800 confocal microscope  using standard settings (method supplements: imaging setup for expanded embryos).hsp70 and a 20x UAS promoter was made by Rainbow Transgenic Flies Inc. The lines were made homozygous for the transgene.Transgenic fly lines containing HaloTag-Ubx under the control of both a nos::GAL4 driver line were injected following previously established protocols  with the appropriate settings (method supplements: imaging setup for live embryos).Embryos from the homozygous HaloTag-Ubx transgene line were exposed to 30°C to induce the heat shock promoter and cuticle preps were prepared following previously established protocols .To obtain the distributions of Ubx and Hth around a transcription site, the processed Airyscan stacks obtained from the Zeiss LSM 880 confocal microscope were analyzed in Fiji  using naAll Airyscan images were acquired using a Zeiss Plan-Apochromat 63x/1.4 Oil DIC M27 objective due to its well-characterized point spread function. First an embryo at the appropriate developmental stage (stage 15 for most embryos) and proper orientation was located. The band of mRNA expression in high Ubx regions of the first abdominal (A1) segment was then found. Within that band, areas containing transcription sites in nuclei of high Ubx expression were imaged. Images with both Ubx and Hth were acquired in the same manner by locating the proper area using the mRNA and Ubx. When Ubx was imaged together with RNA polymerase II, a histone marker, or other transcription factors, Ubx expression levels were used to locate the region of interest.The optimal setting suggested by Zeiss for the number of pixels in the x-y direction (40 nm per pixel) and displacement in the z-stack (190 nm) were used for all Airyscan images. The images from different fluorophores were acquired sequentially with the appropriate laser lines  and spectral filters. The laser power and gain were adjusted to maximize the signal to noise ratio within the dynamic range of the Airyscan detector. The acquired stacks were processed with Zen 2.3 SP1  in 3D mode to obtain super-resolved images.To allow easier handling of expanded gels, the gels containing embryos were cast into eight-well silicone isolators without adhesives ) and allowed to polymerize. The gels were transferred into a six-well glass-bottom cell culture plate  and expanded using ultrapure water containing 500 nM DAPI. Before imaging, the water was removed and the gel encased in 3% low melting temperature agarose , taking care not to allow the agarose to flow under the gel and float the gel away from the cover glass bottom. Water was then added back into the wells to prevent drying.A long working-distance water immersion objective, the Zeiss LCI Plan-Neofluar 25x/0.8 Imm Korr DIC M27, was selected for index-matching with the gel and its ability to image up to 400 µm above the surface of the coverslip. Stage 15 embryos in the correct orientation were located using the DAPI and Ubx staining. Regions of low to high Ubx expression were imaged sequentially using the appropriate laser lines  with the proper spectral filters. The laser power settings and the gain were selected to maximize signal to noise within the dynamic range of the detector. The full field of view of the microscope was imaged with 2048 × 2048 pixels and with a z-step of 1 μm. The final images presented were processed in ImageJ .635 tagged Halo-Ubx molecules with laser power and camera gain set to maximize signal from individual Ubx molecules without oversaturating the EMCCD detector. The 512 × 512 pixel videos were acquired at an exposure time of 100 ms per frame for up to 200 s. Images were processed using ImageJ to generate the time-averaged images and the intensity-over-time traces presented in the figures.All videos were collected under a Nikon CFI Plan Apo NCG 100X Oil NA 1.41 objective with an Andor iXon 897 EMCCD camera . Embryos at stage 5 and late stage 6 in the correct orientation were found and imaged. We selected an area in the middle of the embryo with enough dye-labeled Ubx molecules to observe single molecules and we avoided regions close to the injection site to avoid oversaturating the camera . The samples were illuminated with a 633 nm laser to image the JFr from the center in three dimensions for all the values of r ranging from zero to a desired outer limit (4 μm in this case).To extract radially averaged protein distributions, we used Fiji to identify transcription sites inside nuclei by thresholding at a level that is roughly 50-fold above the background intensity. The center of a transcription site was defined as the pixel of maximum intensity in 3D in the mRNA channel inside a nucleus with high levels of Ubx expression. The radially averaged distribution out to a radius of 4 μm from transcription site for the transcription factor in 3D was computed using the 3D ImageJ Suite. The suite generates the distribution by computing the average intensity on the surface of a sphere with a radius r = 0) equal to 1. The distributions were averaged and background offset in Matlab. To adjust for background Ubx intensity outside of the nucleus, the entire averaged distribution was offset by a constant value to bring the minimum intensity present in the distribution to zero to generate the distribution plots. The shaded area around the line represents the variance. The first μm of the distributions, where contributions from outside of the nucleus were minimal, are shown in the figures. The relative enrichment of Ubx or Hth for each enhancer variant is the intensity at r = 0 in the distribution and the cited uncertainty is the variance at the location of zero Ubx or Hth intensity .The individual distributions from each transcription site were normalized to have the intensity at the center  and four reviewers, one of whom is a member of our Board of Reviewing Editors. The following individuals involved in review of your submission have agreed to reveal their identity: Robert P Zinzen (Reviewer #2); Stephen J. Small (Reviewer #3).Thank you for submitting your article "Nuclear microenvironments modulate transcription from low-affinity enhancers" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.svb regulatory elements with carefully "tuned" Ubx affinities and output is another powerful tool that reveals the importance of the different Ubx domains. Overall, the reviewers found that this study uses a set of advanced imaging methods to fundamentally reshape our view of the nuclear environment in the context of developmental gene regulation.The reviewers of your manuscript found that the observations on the inhomogeneity of intranuclear Ubx transcription factor concentration and the activation of homologous enhancers with different Ubx affinities were highly significant and convincingly demonstrated. A strength of this study is that the authors use complementary techniques on fixed and living cells to observe the distribution of Ubx and other TF, and see similar trends. The use of Essential points to be addressed:svb enhancers" might be interpreted to mean that the selected enhancers are shaping the nuclear gradients. A clearer understanding of the authors' interpretation is necessary, and better statement of what they propose is establishing the different concentration of Ubx. Another related point: are the authors proposing that the enhancers get "stuck" in areas of appropriate concentration, or do they randomly sample the different intranuclear environments, activating when they are experiencing sufficient Ubx levels?1) There was some uncertainty whether the authors are proposing that the change in affinity of the Ubx motifs reshapes the intranuclear concentration gradients, or whether they are just responding differently to the existing inhomogeneities in the nucleus. The statement "Manipulation of binding site number and affinity changes the level of Ubx enrichment around 2) Pertinent to the above point, the in vivo halo-tagged imaging was found to be a very helpful complement to the analysis of fixed material, but the paper should do a better job of explicitly tying the results from the in vivo imaged spots to later work with the fixed, expanded embryos. The halo-tagged construct was not adequately described in the paper (only a brief outline in 3) An intriguing point of this paper is that as Ubx motifs are degraded, the active spots become localized only to regions of higher Ubx activity. In addition to the shown mean values that are higher for the low affinity enhancers, it is also clear from the embryo images that the weaker site enhancers are found in fewer nuclei altogether. Is this because the low affinity enhancer is able to respond to Ubx concentrations that are only found in a small percentage of nuclei? The authors are asked to show how the distribution of active enhancers in all nuclei changes as a function of Ubx motif affinity, as well as the mean values in nuclear regions where it is active. In addition, the mean levels of Ubx found in proximity to the active transcription loci have a very small variation at close range, and increase with increasing distance for a small range. Then the variation in concentration appears to be constant . The authors are asked to explain the shape of the Ubx distributions noted for the reporter constructs.svb enhancer sequences. Do the reporters activate and colocalize with the endogenous svb locus in the fixed specimens? Double FISH with intronic probes should be able to discern this point.4) The reporter constructs with different Ubx affinities were derived from  Essential points to be addressed:1) There was some uncertainty whether the authors are proposing that the change in affinity of the Ubx motifs reshapes the intranuclear concentration gradients, or whether they are just responding differently to the existing inhomogeneities in the nucleus. The statement "Manipulation of binding site number and affinity changes the level of Ubx enrichment around svb enhancers" might be interpreted to mean that the selected enhancers are shaping the nuclear gradients. A clearer understanding of the authors' interpretation is necessary, and better statement of what they propose is establishing the different concentration of Ubx.D. melanogaster is unlikely to change Ubx distribution in general.We agree that the original statement could imply that changing the enhancer architecture changes the distribution of Ubx in the nucleus. We have modified this statement to clarify that we do not expect this to happen. Our interpretation is that the mutated enhancers respond to existing Ubx concentration gradients within the nucleus. Even if the aggregated effects of transcription factor interacting with binding sites could shape their overall distribution, changing only one to three low affinity binding sites out of the thousands of possible Ubx binding sites in the genome of Another related point: are the authors proposing that the enhancers get "stuck" in areas of appropriate concentration, or do they randomly sample the different intranuclear environments, activating when they are experiencing sufficient Ubx levels?Our interpretation is that enhancers sample various areas of the nuclear environment and initiate transcription only when Ubx and cofactor concentrations are sufficient, but this is only our working model. In the future, we plan to characterize the dynamics of these interactions using additional live imaging strategies, but this technology is not yet fully operational.2) Pertinent to the above point, the in vivo halo-tagged imaging was found to be a very helpful complement to the analysis of fixed material, but the paper should do a better job of explicitly tying the results from the in vivo imaged spots to later work with the fixed, expanded embryos.It is not entirely clear what the reviewers are requesting here. We first observed microenvironments in fixed embryos and then expanded fixed embryos to examine the microenvironments at higher resolution. We then used live imaging to characterize the temporal dynamics of transcription factor binding and found that the dynamics were consistent with the observations of fixed specimens. We explicitly state that the live imaging was performed, in part, to test whether the observations of microenvironments in fixed specimens was an artifact of fixation. The consistency between the live-imaging and fixed specimens suggests that microenvironments are real. All following experiments were performed with fixed, unexpanded embryos.The halo-tagged construct was not adequately described in the paper . These observations suggest that the mutant protein is expressed and stable so that it is not degraded immediately post translation and can be imported into the nucleus.The DNA-binding deficient Ubx involves only two mutations to alanine in the DNA binding pocket and has previously been reported by Richard Mann and colleagues. With the binding deficient mutant, we still observed that the mutant Ubx is labeled using JFWe also performed an experiment with just an H2B-derived NLS fused to the HaloTag, which is unstable, and we observed no fluorescence signal beyond background noise and auto-fluorescence. There was no enrichment in the nucleus. This control experiment demonstrates the effect of an unstable protein that is degraded immediately, in sharp contrast with the Ubx binding-deficient mutant. We have added 3) An intriguing point of this paper is that as Ubx motifs are degraded, the active spots become localized only to regions of higher Ubx activity. In addition to the shown mean values that are higher for the low affinity enhancers, it is also clear from the embryo images that the weaker site enhancers are found in fewer nuclei altogether. Is this because the low affinity enhancer is able to respond to Ubx concentrations that are only found in a small percentage of nuclei? The authors are asked to show how the distribution of active enhancers in all nuclei changes as a function of Ubx motif affinity, as well as the mean values in nuclear regions where it is active.svb transcription . In this case, transcriptional repression may prevent svb expression at peak Ubx concentrations. It would be of interest to explore in greater detail the mechanisms of this activity as a part of our future work.We agree with the interpretation that weaker enhancers require higher Ubx concentration to activate transcription. As a result, fewer microenvironments within a nucleus would contain sufficient concentrations of Ubx to activate transcription, lowering the probability that an enhancer exploring different areas within a nucleus would be transcriptionally active at any given moment. However, due to the heterogeneous distributions of Ubx in the nuclei of embryos at stage 15, the average concentration of Ubx over an entire nucleus is a poor representation of the specific conditions within microenvironments. While we observed that these weakened enhancers are active in microenvironments with higher Ubx concentrations, transcription sites are not preferentially located in nuclei of high average Ubx concentration. In fact, we did not observe transcription sites in most nuclei with some of the highest average levels of Ubx. It is known that other transcriptional inputs, both positive and negative, regulate In addition, the mean levels of Ubx found in proximity to the active transcription loci have a very small variation at close range, and increase with increasing distance for a small range. Then the variation in concentration appears to be constant . The authors are asked to explain the shape of the Ubx distributions noted for the reporter constructs.The convergence of the variance in Ubx intensity to zero at the transcription site is an effect of intensity normalization. The intensity of Ubx directly over the transcription site is normalized to one for each of the transcription sites. We average the distributions for all observed nuclei and then subtract the residual Ubx intensity 4 μm from the transcription site as the average background to generate the plots in The reviewers made an important observation concerning the relatively constant variance in the Ubx distributions after an initial increase. Because a transcription site may sit slightly off from the local maximum of Ubx concentration, the Ubx intensity distribution close to each site fluctuates within the first 200 nm . This leads to an initial increase in variance as the distributions move away from the transcription sites. However, most transcriptions sites are close to a maximum of Ubx concentration, so Ubx intensity decreases monotonically after initial fluctuations. Because of the relatively uniform shapes of the individual distributions, the variance stops increasing after a short distance from the transcription site. We believe that there is no a priori reason that this must be the case. For example, the individual Ubx distributions around the transcription sites of the synthetic enhancer, which is not under the control of Ubx, fluctuate at random out to the full distance plotted in 4) The reporter constructs with different Ubx affinities were derived from svb enhancer sequences. Do the reporters activate and colocalize with the endogenous svb locus in the fixed specimens? Double FISH with intronic probes should be able to discern this point.svb locus (on the X chromosome) and other transcription sites (on chromosome 3). Enhancers related to svb, which are 7H and E3N, preferentially colocalized close to the svb site in nuclei expressing both, despite being on different chromosomes. The average distance between transcription sites is about 200 nm. The transcription sites for forkhead (fkh), also on chromosome 3, did not preferentially colocalize with svb, with an average distance of 1 μm between transcription sites. In fact, nuclei showing two transcription sites from the svb locus (two bright spots next to each other) also have an average of 200 nm between those pairs of sites. We have added a new section in the Results and a new figure (We conducted double FISH experiments and measured the distances of transcription sites between the endogenous w figure  describi


Competition ChIP is an experimental method that allows transcription factor (TF) chromatin turnover dynamics to be measured across a genome. We develop and apply a physical model of TF-chromatin competitive binding using chemical reaction rate theory and are able to derive the physical half-life or residence time for TATA-binding protein (TBP) across the yeast genome from competition ChIP data. Using our physical modeling approach where we explicitly include the induction profile of the competitor in the model, we are able to estimate yeast TBP-chromatin residence times as short as 1.3 minutes, demonstrating that competition ChIP is a relatively high temporal-resolution approach. Strikingly, we find a median value of ~5 TBP-chromatin binding events associated with the synthesis of one RNA molecule across Pol II genes, suggesting multiple rounds of pre-initiation complex assembly and disassembly before productive elongation of Pol II is achieved at most genes in the yeast genome. Precise estimation of the chromatin-binding on-rates and off-rates of general transcription factors (GTFs) and other classes of transcription factors (TFs) would allow more quantitative understanding and modeling of pre-initiation complex (PIC) formation24in vivo TF-chromatin on-rates and off-rates at single-copy loci7991012Cellular processes including transcription are inherently dynamic. Currently, the dynamics of transcription and other molecular processes in the cell are poorly understoodet al.772Competition ChIP is another high-spatial resolution method in which the endogenous copy of a TF contains one protein tag and an alternative copy, a competitor, is transcriptionally induced with an alternative protein tag14151571415Comparing TBP-chromatin residence times with nascent RNA transcription rates1415141514151920CB) relative to the endogenous TBP concentration (labelled CA) takes ~60–70 minutes to reach steady state levels as shown by the dashed line in Competition ChIP  and off-rate (kd)  yielded a simulated ratio of occupancies versus time that was mildly but noticeably displaced or shifted  to the right of the induction curve (We applied kinetic theory to model the ate (kd)  at sites on-rate . Additio on-rate  rose andon curve  while TBon curve . Intuition curve , and, ast → ∞) as outlined in t = 0) and steady state or equilibrium (t → ∞). More specifically, the mathematical solution of the kinetic theory equations (t → ∞). This is depicted in 14Along with background subtraction and normalization  of TBP c15quations  shows tht = 0) and steady state or equilibrium (t → ∞)   ratio of fractional occupancy of the competitor In order to fit TBP competition ChIP two-color Agilent tiling microarray data (t → ∞) . The kinimen = 4  displaysmeter t0 , which q binding , the resrudely ) . We useded above .in vivo kinetic equations of competitive binding between species A and B .We then simultaneously numerically solved and fitted the  Methods  to norma Methods . We  , relatively reliable residence times could be estimated. The distribution of genome-wide TBP t1/2 values (Stratifying TBP-containing promoters in 2-minute bands of e right)  with core right) , showing24.5 min . This nu minutes  and long minutes  to be es2 values  reveals 2 values  shows thr, for 602 Pol II and 264 Pol III genes were obtained using linear regression to a subset of the data t1/2 and t0, we calculated the percent error in t0 (100 times the standard error in t0 divided by t0) genome-wide  whose inverse represents the number of TBP residence times or binding events associated with productive elongation of Pol II and transcription. Strikingly, we found low transcriptional efficiencies for Pol II genes  determined by the likely TBP-chromatin residence time from AA experiments and characteristic values of transcription rate across the yeast genomeEarlier estimates of relative TBP turnover, ome-wide , which rrrelated , r is al signals , which set al.t0 . With esII genes . The med proceed . This ist1/2) to the only other regulatory factors whose dynamics have been characterized on a genomic scale (in yeast): previously derived Rap1λ) and their inverse turnover rates (λ−1) or relative residence times. Notably, we showed that the relative turnover (λ), derived using a Poisson statistical turnover model14kd) plus a time-dependent function (t1/2) was correlated with Rap1 relative residence time (λ−1) at non-RP Pol II genes but not at RP Pol II genes (λ−1) showed slight positive correlation , whose presence yielded statistically shorter TBP residence times compared to its absence . Given tWe developed and applied a physical model of competitive binding using chemical kinetic theory to TBP competition ChIP-chip data and derived TBP-chromatin residence times genome-wide in yeast. While competition ChIP was believed to be a low time resolution approach given the 60–70 minutes that it takes to induce the competitor to a concentration approaching steady state levels, we found that we could reliably extract residence times as short as 1.3 minutes. Consistent with live cell imagingin vivo occupancy variable in order to fit the associated kinetic theory using these occupancy variables. Second, this scaling requires quantifying soluble competitor and endogenous TF levels  and obtain biologically meaningful results, we learned a number of critical lessons. First, normalized ChIP-chip or ChIP-seq data must be scaled to the relevant ls as in  as well et al.et al.r) derived by van Werven et al.et al.Comparison of reliable TBP-chromatin residence times, which ranged from 1.3 minutes to 53 minutes, across different promoter classes revealed highly dynamic TBP at Pol II genes and less so at Pol III genes similar to previous studies using competition ChIP9et al.et al.We also assessed the effect that the occupancies of 202 mapped TFset al.TRt1/2, of 0.2 molecules) suggesting multiple PIC assembly and disassembly events before synthesis of one RNA molecule at Pol II genes. Taken together, these findings suggest increased dynamic coupling of TFs and GTFs at similar stages of PIC assembly, Pol II recruitment and elongation, and transcription; the dynamics of factors that are more involved in the early stages of transcription initiation including Pol II elongation  and endogenous (SAvi) TBP concentrations hybridized on an Agilent whole-genome microarray. SHA and SAvi were replicated by swapping Cy3 and Cy5 dyes to take into account dye-specific variations in the intensity of the optical signal. We geometrically averaged the two dye-swapped ratios , as described in t = 0 minute log2(Rm) data as shown in t = 0 minute data. Rm values were normalized  with the added term B was fitted to B was subtracted from α was determined as the asymptotic in vivo concentration ratio of HA/Avi  over the asymptotic in vivo competitive binding.The raw data generated by van Werven d probes . To quan of 2.23 . We theo and Avi . To deteach peak . B was sin vivo competitive binding dynamics between endogenous Avi (subscript A) and competitor HA (subscript B) TBP is described by mass-action differential equations linear in the TBP-chromatin association rate The model for In the above equations, we have assumed that the association and dissociation rates for endogenous and competitor TBP are the same, and we have absorbed the experimentally undetermined endogenous concentration  minutes . Equatio minutes  could noNonlinearModelFit function was used for fitting. The ratio in vivo differential equations using NDSolve in Mathematica. Exceptions to the fitting procedure where we had to change the starting estimate of We fitted the analytical solution of ideal induction to the normalized, scaled ratio data by developing a procedure for estimating the starting values for nonlinear regression . The algtive to  , and hention to  . To calcks.test function to determine the p-values reported in t1/2, TR, and TRt1/2. In other words, loci across the genome were partitioned into two sets for each transcription factor: those that showed a significant enrichment of the transcription factor above the background as determined by Venters et al.t1/2, TR, or TRt1/2 test statistics using permTS in the perm library in R, which gave the mean difference of the test statistic between the two sets along with the p-value for the mean difference. The p-value was adjusted using the Benjamini-Hochberg correctionp.adjust function in R to derive FDR estimates. In TRt1/2 was plotted against the FDR for TR, and transcription factors were listed in descending order of TRt1/2 mean differences. The blue dots (representing TFs that affect TR more significantly than TRt1/2) were chosen with a TR FDR < 0.06 and TRt1/2 FDR > 0.1. Red dots (representing TFs that were significant in permutation tests for both TR and TRt1/2) were chosen with TR FDR < 0.1 and TRt1/2 FDR < 0.1. Finally, black dots  were chosen with TR FDR > 0.1 and TRt1/2 FDR < 0.1, or TR FDR > 0.45 and TRt1/2 FDR < 0.3.Throughout the main text and the supplement, quoted correlations are Spearman correlation coefficients unless otherwise stated. Kolmogorov-Smirnoff (KS) test was conducted in R using the How to cite this article: Zaidi, H. A. et al. RNA synthesis is associated with multiple TBP-chromatin binding events. Sci. Rep.7, 39631; doi: 10.1038/srep39631 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Disease-associated variants of the structural ncRNAs were disproportionately enriched in immune-specific cell types. We also identified riboSNitches in lymphoid eRNAs and investigated the potential pathogenic mechanisms by which eRNAs might function in autoimmune diseases. Collectively, our findings offer valuable insights into the function of eRNAs and suggest that eRNAs might be effective diagnostic and therapeutic targets for human diseases.Enhancer RNAs (eRNAs) are a novel class of non-coding RNA (ncRNA) molecules transcribed from the DNA sequences of enhancer regions. Despite extensive efforts devoted to revealing the potential functions and underlying mechanisms of eRNAs, it remains an open question whether eRNAs are mere transcriptional noise or relevant biologically functional species. Here, we identified a catalogue of eRNAs in a broad range of human cell/tissue types and extended our understanding of eRNAs by demonstrating their multi-omic signatures. Gene Ontology (GO) analysis revealed that eRNAs play key roles in human cell identity. Furthermore, we detected numerous known and novel functional RNA structures within eRNA regions. To better characterize the  Thus, enhancers play key roles in the precise spatiotemporal control of gene expression required for development, differentiation and homeostasis3. In 2010, two studies simultaneously presented genome-wide evidence of widespread RNA polymerase II (RNAPII) binding at non-coding enhancers which transcribed a novel class of enhancer RNAs (eRNAs)4. Later studies revealed the existence of two types of eRNAs (1D-eRNA and 2D-eRNA) distinguished by their size, polyadenylation state, and transcriptional directionality5. 1D-eRNAs are long (>4 kb), polyadenylated RNAs that undergo unidirectional transcription, whereas 2D-eRNAs are relatively short (<2 kb), are not polyadenylated, and undergo bidirectional transcription.Enhancers are distal-acting DNA regulatory elements that can recruit transcription factors (TFs) and coactivators to up-regulate the transcription of their distal target genes by forming promoter-enhancer looping interactions8. An increasing number of studies support the idea that the eRNA transcription is not transcriptional noise but is responsible for biological functions and the regulation of transcriptional programmes10. However, whether the act of transcription or the transcripts per se convey the functionality remains open for debate. Some studies have found that the act of transcription outweighs the importance of the eRNA transcripts11 and that the inhibition of eRNA transcription does not affect enhancer-promoter looping with 3 C12, whereas an increasing number of studies have presented evidence that eRNA transcripts are important for proper enhancer-promoter looping and that the eRNA transcripts themselves play a functional role in regulating the transcription of target genes17.Since the discovery of eRNAs, extensive efforts have been devoted to revealing their potential functions and underlying mechanisms. Several studies have reported that eRNAs are a strong hallmark of active enhancers and that the transcription of eRNAs correlates with the activity of active enhancers20. The differential transcription of active enhancers across cell types and tissues helps explain the diversity of cell types and tissues sharing the same genome. However, the existing knowledge of eRNAs is greatly inadequate, and the mechanisms of eRNA activity remain a mystery. Therefore, it is of interest to investigate the differences in eRNA transcription and the function of eRNAs across cell types and tissues. Furthermore, regulatory RNA structures play an important role in gene regulation and function, and many novel structured RNAs have been identified24. In addition, it has been reported that genetic variation can induce changes in RNA structure28 and that variation in enhancers is closely associated with human diseases31. Thus, investigating eRNA transcription from a structural perspective should help identify and validate structural RNA elements that are involved in diverse cellular processes and thereby increase our understanding of eRNA function.Nonetheless, it is clear that not all enhancers are transcribed at the same time and that the active enhancers transcribing eRNAs may represent only a small fraction of all enhancerscis-regulatory effects of non-coding variation, we performed a comprehensive association analysis between the structural ncRNAs in eRNA regions and genetic variants associated with inflammatory autoimmune diseases. We observed that these disease-associated variants are disproportionately enriched in structural ncRNAs. Importantly, this enrichment is biased towards immune-specific cell types. Furthermore, we detected riboSNitches in lymphoid eRNA regions and investigated the potential pathogenic mechanisms by which eRNAs can function in autoimmune diseases. Collectively, our findings offer valuable insights into the function of eRNAs and reveal their potential as effective diagnostic and therapeutic targets for human diseases.In this study, we created a catalogue of eRNA regions using genome-wide chromatin immunoprecipitation-sequencing (ChIP-seq) and RNA sequencing (RNA-seq) data across 50 human cell and tissue types. We characterized these eRNA regions and extended our understanding of their functionality by analysing their multi-omic signatures, including genomic, epigenetic, transcriptomic, and chromatin interaction characteristics. Gene Ontology (GO) analysis revealed that eRNA regions are associated with genes that control and define cell identity. Furthermore, we detected and identified numerous known and novel functional RNA structures within eRNA regions. To better characterize the ab initio” assembly pipeline using histone modifications and GENCODE (V19)32 annotation, and we identified 2,373 intergenic candidate enhancers in human embryonic stem cells (ESCs)  Fig.  and S3. To discriminate between enhancers that are classified into eRNA regions and weakly-transcribed enhancers, we used high-throughput mRNA-seq data to count the RNA-seq tags at a given candidate enhancer . We detected polyadenylated 1D-eRNA regions in 837 of the 2,373 (35.3%) intergenic candidate enhancers in H1-hESCs. Then, we investigated the size, distance to the nearest transcription start site (TSS), evolutionary conservation, and chromatin 3D structure of both the eRNA regions and the weakly-transcribed enhancers Fig. . Relativ33; H3K4me2 and H3K4me3 play important roles in the positive regulation of transcription36; transcription initiation factor TFIID subunit 1 (Taf1) is the largest component and the core scaffold of the TFIID basal TF complex and can be recruited to tissue-specific enhancers33; and chromodomain-helicase-DNA-binding protein 1 (Chd1) is an ATP-dependent chromatin-remodelling factor that can regulate RNAPII transcription and that plays key roles in maintaining open chromatin and pluripotency in ESCs38. In addition, we investigated the enrichment of TF binding motifs in eRNA regions  of the human genome. To assess the rate of discovery of new eRNA regions, we performed a saturation analysis as described in our recent study40 and predicted saturation at a count of approximately 37,222 eRNA regions  and coverage of approximately 90.0 Mb (standard deviation = 3.5 Mb)  Fig. . Our resA substantial portion of these eRNA regions were typically much more cell selective than weakly-transcribed enhancers Fig. ; the aveTo investigate whether the activity levels of eRNAs correlate with the mRNA levels of nearby genes, we used RNA-seq data and calculated their expression levels Fig. . We foun41. They provided the most comprehensive map of the human epigenome landscape. We compared our identified enhancers with their comprehensive enhancer sets in 11 common cell/tissue types. On average, 76.0% of our enhancers overlapped with those defined by the Roadmap Epigenome Consortium in the corresponding cell/tissue  overlapping with enhancer regions42. Compared to these e-lncRNAs, our eRNAs demonstrate higher enhancer and transcriptional activity based on the different multi-omic signatures, including 2 TFs, 1 cofactor, 3 histone modifications, DHSs, and RNAPII  from the Rfam database (V12.0)45 to search for known structural ncRNAs in eRNA regions across 10 human lymphoid cell types . The enhancers and ncRNAs that control lymphoid cell states are likely to be understood better than those in other cell types, making lymphoid commitment an excellent model for identifying structural ncRNA components49.To further extend our understanding of eRNA function, we investigated eRNA transcripts from a structural perspective and explored the consensus RNA secondary structure profiles within eRNA regions. We used the Infernal software package50  in lymphoid eRNA regions Fig. . These h50 Fig. . Other s54, REAPR22, and EvoFold55, to identify novel structural ncRNAs in lymphoid eRNA regions . We selected the predictions identified by at least two methods as our results . These lncRNA transcripts, which were confirmed by both CPAT60 and CPC61 analyses, were devoid of protein-coding potential. This comprehensive annotation covered a total of 22,339 lncRNA genes, of which 4,378 (19.6%) novel lncRNA genes are not annotated in existing lncRNA databases, including GENCODE32 (V19) and LNCipedia62 (V2.1)  Fig. . Because.1) Fig. . When ma.1) Fig. .62  values > 1 in at least one sample Fig. . The siz62 Fig. . Notably62 Fig. . Analysi62 Fig.  revealed62 Fig. .64, is crucial to interpreting RNA structures as they pertain to individual phenotypes. Thus, we collected a list of 68,295 SNPs from the NHGRI-EBI GWAS catalogue65 and the ClinVar database66 and compiled a list of 504 SNPs related to autoimmune disease based on annotations and literature reports. In addition, we generated a list of SNPs in strong linkage disequilibrium (LD) with autoimmune disease-associated SNPs using the LD cutoff of r2 > 0.8. Of these 504 SNPs, 204 (40.6%) occurred in 197 structural ncRNA loci in lymphoid eRNA regions. Moreover, 17,002 (39.5%) were in strong LD (r2 > 0.8) with these 504 SNPs in 9,324 structural ncRNA loci in lymphoid eRNA regions. In total, 17,506 SNPs were defined as autoimmune disease-associated SNPs. Notably, these autoimmune disease-associated SNPs, regardless of whether they were annotated or in strong LD with annotated SNPs, showed a greater disproportional enrichment in these structural ncRNA loci than in random structural ncRNA loci .Single nucleotide polymorphisms (SNPs) and other mutations can alter RNA structure, affect its molecular function, and thereby cause phenotypic effects. The accurate prediction of riboSNitches, which are RNAs with large structural disparities caused by a single nucleotide variant (SNV)26 with the default parameters to predict the effects of SNPs on local RNA secondary structure . We found that lymphoid structural ncRNA loci were more likely than random loci to be disrupted by SNPs .To analyse the structural ncRNAs in which these autoimmune disease-associated SNPs resided, we used RNAsnpNPs Fig. . AdditioTo gain further insight into the relationship between SNPs and structural ncRNAs, especially riboSNitches, we focused on several structural ncRNAs that underwent large structural changes when disrupted by autoimmune disease-associated SNPs Fig. .Figure 568, whereas SNP rs19942273 is related to AA, in which the pathway “Telomere Extension by Telomerase” plays an important role68.Mutations in the telomerase RNA component (TERC), an RNA component of telomerase and the template for telomere replication by telomerase, can cause dyskeratosis congenita (DC) and might also be associated with some cases of aplastic anaemia (AA). DC is a disorder that is related to congenital bone marrow disease, whereas AA is a rare disease in which the bone marrow and the haematopoietic stem cells that reside there are damaged. Three SNPs  were found in a locus of the CM “Telomerase-vert” that was located within TERC in lymphoid eRNA regions, and they had a significant effect on the structure of TERC Figs  and S8B.r2 > 0.8) with GWAS or ClinVar SNPs, have significant effects on structural ncRNAs within lymphoid eRNA regions. SNP rs6972403, which is in strong LD with SNP rs4722404, fell into a locus of the heat shock protein 83 3′ UTR (Hsp83_3_UTR) structural ncRNA 71, which is a chronically relapsing inflammatory allergic response occurring in the skin that causes itching and flaking. We found that SNP rs6972403 disrupted the structure of Hsp83_3_UTR and caused a large structural disparity  gene, whose reduced expression has also recently been demonstrated to influence the risk of Behcet’s disease76. SNP rs6677319, which occurs in the lncRNA lnc-IL12RB2-1  gene, the up-regulation of which has recently been demonstrated to influence the risk of Crohn’s disease79. These two SNPs (rs11757571 and rs6677319) and an additional two SNPs (rs11950065 and rs11762252)  Fig.  can causTaken together, we found that autoimmune disease-associated SNPs, whether they were GWAS or ClinVar SNPs or in strong LD with GWAS or ClinVar SNPs, could significantly disrupt the structure of many ncRNAs in lymphoid eRNA regions. These examples of riboSNitches suggested that the significant impact of disease-associated SNPs on structural ncRNAs plays an important role in the function of the structural ncRNAs or their associated genes in disease-relevant cells. The large structural disparities caused by these SNPs might contribute to autoimmune-related diseases Fig. .Figure 680. eRNAs regulate enhancer-promoter looping through the recruitment of cohesin17, direct chromatin accessibility16, assistance in recruiting mediators to promoters81, the facilitation of paused RNAPII transition into productive elongation82, and the stimulation of histone acetylation and transcription by binding directly to CBP83. Consistently, the knockdown of eRNAs affects the transcription of enhancer-associated genes81. Although extensive efforts have been devoted to revealing the functions and underlying mechanisms of eRNAs, the debate on these questions continues.Recent studies have provided increasing evidence of a direct role for eRNAs in enhancer functionab initio” assembly pipeline using three histone modifications and classified them into eRNA regions and weakly-transcribed enhancers. Then, we characterized the eRNA regions in ESCs and demonstrated that the eRNA regions show higher activity than weakly-transcribed enhancers. Further exploration of the enrichment of the multi-omic signatures and TF binding motifs at eRNA regions suggested that eRNAs may participate in various regulated gene transcription programmes.In this study, we first identified candidate enhancers by an “1.Next, we created a catalogue of 23,878 eRNA regions covering 55.2 Mb (1.8%) of the human genome across 50 different human cell/tissue types. Saturation analysis suggests that we identified more than 60% of the total estimated eRNA regions. Both eRNA regions and their associated genes demonstrated heightened cell selectivity. Further GO analysis revealed that eRNAs play key roles in promoting gene transcription in the respective cell/tissue type. Moreover, the level of eRNA expression was positively correlated with the level of mRNA synthesis at nearby genes in a cell type-specific manner. Our results further confirmed previous findings that eRNA synthesis occurs specifically at enhancers that are actively engaged in promoting mRNA synthesisThird, we explored the function of eRNAs by detecting known and novel functional RNA structures within these domains across 10 human lymphoid cell types. We discovered 18,767 unique structural ncRNAs with known secondary structure in lymphoid eRNA regions, which are a substantial portion of the functional ncRNAs provided by the Rfam database (V12.0). Additionally, we identified 1,158 structural ncRNAs in lymphoid eRNA regions, and 75.7% of these structural ncRNAs were novel. Furthermore, we identified 3,230 lncRNA genes and 3,239 lncRNA transcripts located within lymphoid eRNA regions, with 529 (16.4%) novel lncRNA genes and 645 (20.0%) novel lncRNA transcripts that were previously unannotated in existing lncRNA databases. Taken together, we identified a large number and variety of known and novel functional ncRNAs in lymphoid eRNA regions.Finally, we found that genetic variants associated with a broad spectrum of inflammatory autoimmune diseases were disproportionately enriched in the structural ncRNAs in the eRNA regions of autoimmune disease-related cell types. To interpret the structural ncRNAs in which these autoimmune disease-associated SNPs resided, we predicted the effects of SNPs on the local RNA secondary structure and identified 1,764 of these structural ncRNAs within lymphoid eRNA regions as riboSNitches. Further examples of riboSNitches suggested that the significant impact of the autoimmune disease-associated SNPs on the structural ncRNAs plays an important role in the function of the structural ncRNAs or their associated genes in disease-related cells. The large structural disparities caused by these SNPs might contribute to these diseases.Collectively, our eRNA catalogue provides a valuable resource for further study of the functions and underlying mechanisms of eRNAs. Our findings offer new insights into the functional roles of eRNAs in promoting gene transcription. The novel riboSNitches detected among the diverse structural ncRNAs within eRNA regions provide potentially effective diagnostic and therapeutic targets for human diseases. Thus, eRNAs play key roles in human cell identity and disease.39. TFs, cofactors, histone modifications, DHSs, and DNA methylation in H1-hESCs were collected from the ENCODE Project84. Gene annotations were obtained from GENCODE (V19)32, and ncRNA annotations were obtained from NONCODE (http://noncode.org/)85, LNCipedia62, and GENCODE (V19). RNA-seq data collected from the Human BodyMap project are listed in Table All raw ChIP-seq data for histone modifications  and raw RNA-seq data across 50 cell/tissue types were downloaded from the Roadmap Epigenomics Project86 and TopHat287 to map the raw ChIP-seq data on histone modifications  and the RNA-seq data, respectively, to the human reference genome NCBI37/hg19. We employed the “callpeak” function of MACS288 to call peaks of H3K4me1 and H3K27ac, and the P-values of H3K4me1 and H3K27ac were set to 10−6 and 10−9, respectively. The control for peak calling was obtained by merging all the replicates of the input using BEDOPS89 with the parameter “-everything”. For peaks of H3K4me1 and H3K27ac with two or more replicates, we chose only those that appeared in over 70% of the replicates and merged all the peaks from multiple replicates into a single peak file.We used Bowtie232 based on the method used by Kim et al.1  The enrichment of H3K4me3 within a 2-kb window centred on the peak had to have a z-score less than 3. (5) An H3K4me1 peak had to be present within 2 kb of the H3K27ac peak. (6) We included intergenic H3K27ac peaks. (7) We excluded H3K27ac peaks that overlapped rRNA genes annotated by GENCODE (V19). Fig. We first set up the workflow to locate candidate enhancers using histone modifications and gene annotation from GENCODE (V19)l.1 Fig. . We bega33, and we denoted the tag number of the i-th candidate enhancer in H1-hESCs by 1. A total of 837 enhancers belonged to a cluster with a median tag number of 123, whereas the other 1,536 enhancers were clustered into the other cluster with a median tag number of 4. We defined the 837 candidate enhancers with a significant level of transcription as eRNA regions and the other 1,537 candidate enhancers as weakly-transcribed enhancers. The minimal tag number of the enhancers classified as eRNA regions was 31, whereas the maximal tag number of the weakly-transcribed enhancers was 30; thus, the threshold number of poly(A) RNA tags for eRNA regions detection was 30. The eRNA regions in other cell types and tissues were detected with a corrected threshold, N was the total number of RNA tags in the other cell types or tissues. The corrected threshold alleviated the impact of sequencing depth in different cell types and tissues. For each cell type or tissue, the orientation of eRNA transcription was assessed on the basis of the direction given by the most RNA-seq tags.Then, we counted the number of poly(A) RNA tags centred at candidate enhancers in −1 kb to 1 kb regions91 to calculate confidence intervals for the degree of overlap between evolutionarily constrained bases in eRNA regions and in weakly-transcribed enhancers.We estimated the extent of constraint on eRNA regions and weakly-transcribed enhancers by calculating the distribution of the PhastCons conservation scores in each region. Additionally, we used the genome structure correction (GSC) statisticshttp://homer.salk.edu/homer/interactions/)92 to process the Hi-C data in H1-hESCs93. We used the liftOver tool of the UCSC Genome Brower94 to transform the Hi-C data from reference NCBI36/hg18 to reference NCBI37/hg19. The resolution of the Hi-C data processed with HOMER was set to 1 kb, and ChromSDE95 was used to predict the chromatin 3D structures of enhancers.We used HOMER  compared with the background sets. We used the binomial distribution test to calculate the P-values of TF enrichment for eRNA regions relative to the whole genome, and we used Fisher’s exact test to calculate the P-values of TF enrichment for eRNA regions relative to weakly-transcribed enhancers. Finally, we employed the standard normal distribution to transform the P-values to corresponding z-scores.We determined the enrichment of 466 TFs in eRNA regions and weakly-transcribed enhancers using FIMO40.To assess the rate of discovery of new eRNA regions, we performed a saturation analysis on the element count and genomic coverage of enhancer RNAs using a Weibull distribution http://david.abcc.ncifcrf.gov/home.jsp)101. For each cell type/tissue, the four top scoring categories were selected for display, and the threshold P-value for scoring as a top category was set to 10−3.For GO analysis, a subset of 14 human datasets that represent the diversity of cell types and tissues were selected from the total 50 cell types and tissues. For each cell type/tissue, the genes that were associated with eRNA regions in that cell type/tissue and no more than three other cells/tissues in the subset were analysed using DAVID 22, and EvoFold (version 1.0)55, were applied to detect ncRNAs using these genome-wide alignments. For RNAz and REAPR, we used scripts from RNAz to slice multi-alignment files into windows of size 120 and slide 40, and we filtered the spliced files using the default parameters. The filtered files were scored with RNAz and REAPR on both strands with the default parameters. Predictions with final P-values higher than 0.5 were retrieved for the subsequent pipeline. For the EvoFold55 analysis, sequences with > 20% gaps relative to the human genome were first removed. Then, alignments with sequences from fewer than six species were eliminated. EvoFold was then applied to the concatenated alignments and their reverse complements in 120 long overlapping windows, and each was offset by 40. Predictions with fewer than 10 pairing bases or an average stem length less than 3 and predictions that overlapped repeats or retrogenes were eliminated. Finally, the set was reduced to single coverage by removing the lowest-scoring candidates if overlap occurred, and the remaining candidates were ranked according to score.To detect novel structural ncRNAs in lymphoid eRNA regions, we first extracted the 100-way multiple alignments file of the eRNA regions in different cells from the stitched hg19 100-way multi-alignment file. Three programs, RNAz (version 2.1)56 search against the Rfam database using the default parameters. The whole predicted set was clustered by NoFold57 into the RNA empirical structure space (RESS). The top-ranked CMs were used to rank the most similar known structures to each novel structure in the cluster.Predictions detected by at least two of the aforementioned methods were retained for the subsequent analysis, and the corresponding predicted structures were selected first on the basis of the EvoFold rank and then by REAPR and RNAz. The predicted loci in the candidate set were identified by a local BLASTN46. Briefly, we first used the STAR aligner102 to align paired-end reads to the human genome by following a 2-pass alignment strategy103 with the parameter “–twopassMode Basic”. All the paired-end alignments overlapping at least one putative intron were retrieved and pooled. Second, filtered alignments collected from all samples were pooled and used as input for References Annotation Based Transcript (RABT)104, and the RefSeq human reference annotation was employed to optimize the performance of RABT104. To correct the transcript predictions derived from RABT, we filtered out the following set of transcript predictions: a) those with a total exonic sequence length less than 200 bp, b) predictions with FPKM = 0, and c) monoexonic predictions. Third, using cuffcompare, we compared our transcript predictions with all the genes from GENCODE with biotype annotations other than protein-coding or lncRNA105. We retained only those transcripts flagged as “novel” by cuffcompare. For the remaining transcripts, we calculated their coding potential using two methods, CPAT60 and CPC61, to exclude possible unannotated protein-coding or pseudogene loci. We ran CPAT on several non-coding gene sets using default parameters and the pre-defined threshold provided by the CPAT training procedure on human genes. Approximately 95% of our transcript predictions were predicted to be non-coding. Similarly, we computed CPC scores for our lncRNA candidates. In addition, the transcripts with open reading frame length > 300 were filtered from the results for both methods. Transcripts that did not show coding potential according to either CPAT or CPC were retained to generate a set of candidate lncRNAs. Fourth, the resulting set of candidate lncRNAs was used to build the final gene set and annotate novel lncRNAs as follows: candidate lncRNAs were merged with previous lncRNA annotations from both GENCODE V1932 and LNCipedia62 (V2.1) using cuffmerge105. The merged lncRNA annotation was further filtered to remove transcripts that had significant exonic overlap with GENCODE protein-coding genes. Specifically, we employed cuffcompare again and retained only those non-coding transcripts with class codes labelled “x”, “‘i”, “j”, “u”, and “o”.To annotate newly identified lncRNAs, we followed the pipeline used in a recent study32 or LNCipedia62 databases). A total of 80% of the non-coding genes  were classified as fully intergenic. A total of 4,654 non-coding loci generated at least one novel isoform. Finally, a total of 3,359 non-coding transcripts that overlap with the eRNA region master list were then extracted from the final annotated results for the downstream analysis.At the gene level, the final annotation used throughout the manuscript includes 20,345 protein-coding genes from GENCODE and 18,268 non-coding genes. A gene transfer format (gtf) file representing this reference annotation was used for all downstream analysis. A gtf annotation file for the non-coding genes is available as Table 26 to predict the effects of SNPs on the RNA secondary structure of structural ncRNAs using the default parameters with mode 1 for short RNA sequences  and with mode 2 for large RNA sequences. For each autoimmune disease-associated SNP collected from the dbSNP106, GWAS SNP65, and ClinVar107 databases, RNAsnp considered a window of + /−200 nt around the SNP position to generate the wild-type (WT) and mutant (MT) subsequences and compute their respective base pair probability matrices. Then, the structural difference between WT and MT RNA sequences was calculated on the basis of Euclidean distance for all sequence intervals within the subsequence. Finally, a local region predicted with the maximum Euclidean distance and the corresponding P-value was reported. The riboSNitches were predicted to be those RNAs showing large structural disruptions caused by autoimmune disease-associated SNPs according to RNAsnp with P-values < 0.2, and they are shown in Table We used RNAsnp (version 1.1)The enhancer RNAs identified across 50 human cell/tissue types have been deposited with the Gene Expression Omnibus under the accession ID GSE99453.Supplementary MaterialSupplementary Table1Supplementary Table2Supplementary Table3Supplementary Table4Supplementary Table5Supplementary Table6Supplementary Table7