Saccharolobus solfataricus. We discovered a distinct early elongation phase where RNA polymerases sequentially recruit the elongation factors Spt4/5 and Elf1 to form the transcription elongation complex (TEC) before the TEC escapes into productive transcription. TEC escape is rate-limiting for transcription output during exponential growth. Oxidative stress causes changes in TEC escape that correlate with changes in the transcriptome. Our results thus establish that TEC escape contributes to the basal promoter strength and facilitates transcription regulation. Impaired TEC escape coincides with the accumulation of initiation factors at the promoter and recruitment of termination factor aCPSF1 to the early TEC. This suggests two possible mechanisms for how TEC escape limits transcription, physically blocking upstream RNA polymerases during transcription initiation and premature termination of early TECs.Recruitment of RNA polymerase and initiation factors to the promoter is the only known target for transcription activation and repression in archaea. Whether any of the subsequent steps towards productive transcription elongation are involved in regulation is not known. We characterised how the basal transcription machinery is distributed along genes in the archaeon  Saccharolobus solfataricus regulates transcription globally through a rate-limiting promoter-proximal elongation step.Transcription in archaea is known to be regulated through the recruitment of RNA polymerase to promoters. Here, the authors show that the archaeon  The recruitment stage of transcription initiation is the main target for regulation in yeast and bacteria3, however, the initiation rate can also be affected indirectly by downstream events. In metazoans, promoter-proximal pausing of RNA polymerase (RNAP) II with slow turnover times blocks pre-initiation complex (PIC) formation for the following RNAP and is thereby widely rate-limiting for transcription initiation5. Promoter-proximal paused RNAPII can also be subject to premature termination providing an additional way of transcription regulation10. Promoter-proximal RNAP dynamics also limit gene expression in E. coli11. Well-established processes of post-recruitment regulation include Sigma70-dependent pausing and transcription attenuation mediated by premature termination14. Another possible underlying molecular mechanism might be pausing during initial transcription16, though its contribution to genome-wide gene regulation remains to be investigated17.The rate of RNA synthesis is determined by the frequency of transcription initiation and premature termination18. The basal archaeal transcription machinery represents an evolutionarily ancient core of the RNAPII system encompassing RNAP subunits, basal transcription initiation and -elongation factors and core promoter elements21. The mechanisms of initiation have been characterised in great detail in vitro  corresponds to an evolutionarily ancient RNAPII TEC encompassing homologues of a subset of RNAPII elongation factors: Spt4/5 (DSIF in human)29 and potentially the archaeal homologue of elongation factor Elf1 (Elof1 in humans)31. In addition, the transcript cleavage factor TFS (homologous to TFIIS) transiently associates with the TEC and reactivates arrested TECs32. Spt4/5 and TFE bind to RNAP in a mutually exclusive manner and the transition from transcription initiation to elongation requires factor switching between TFE and Spt4/533. Transcription termination in archaea occurs via intrinsic or factor-dependent mechanisms. The latter involves termination factor aCPSF1 (or FttA)34, a ribonuclease that is evolutionary related to the RNAP II termination factor CPSF73 and the integrator subunit Ints11.Archaea form the ‘third domain of life’ next to bacteria and eukaryotes, with the latter likely originating from an archaeal ancestortro Fig. . The bas36. Likewise, our understanding of transcription regulation is limited to factors modulating the recruitment of PICs38 where repression generally involves steric hindrance of RNAP or basal initiation factor binding and activation is achieved by enhancing their binding42. How archaeal RNA polymerase progresses further through the transcription cycle and whether subsequent stages beyond initiation are targeted for transcription regulation in archaea is currently poorly understood.Archaeal promoters seem to comprise fewer promoter elements compared to their bacterial and eukaryotic counterparts, but it is possible that additional unknown sequence elements as well as the physicochemical properties of promoter DNA contribute to promoter strengthSaccharolobus solfataricus (formerly Sulfolobus) is a well-established model organism for archaeal transcription . Importantly, S. solfataricus harbours the full repertoire of known archaeal transcription initiation and elongation factors including Elf1 making it a good model to study mechanisms of transcription regulation beyond initiation.The crenarchaeon S. solfataricus by using a multi-omics approach including chromatin immunoprecipitation-sequencing-based techniques (ChIP-seq) and transcriptomics. Our results provide evidence for a sequential recruitment cascade of elongation- (Spt4/5 and Elf1) and termination (aCPSF1) factors to RNAPs in the promoter-proximal region of the transcription unit. We show that escape of TECs from this region is rate-limiting for transcription and subject to regulation. Thereby we establish early elongation as an important checkpoint to set and regulate promoter strength in archaea.We analysed the genome-wide distribution of RNAP and transcription initiation and elongation factors in S. solfataricus. We developed and adapted ChIP-seq using polyclonal antibodies raised against RNAP subunits Rpo4/7 and recombinant transcription factors. In order to obtain the resolution that separates PICs from promoter-proximal, early TECs, we adapted a ChIP-exo approach for RNAP and initiation factors TFB and TFEβ that includes 5′->3′ exonuclease-trimming of the immunoprecipitated-DNA fragments46.We mapped the genome-wide occupancy of RNAP, initiation-, elongation- and termination factors to shed light on how the individual stages of transcription are subject to transcription regulation in 47. The downstream border for the PIC signal on the template strand was relatively broad and reached well beyond the ~20 bp downstream of the TSS protected in in vitro exonuclease foot-printing experiments of archaeal PICs49 . The profiles showed a distinct footprint for RNAP and initiation factors TFB and TFEβ around the TSS Fig. . The oves49 Fig. .r = 0.92 in both cases, Fig. To investigate any heterogeneity in the recruitment of basal factors, we quantified the ChIP-exo signal on the non-template strand over a window from −30 to +20 relative to the TSS Fig. . Both TFWe expected exceptions to this rule where transcription regulators would interfere with the recruitment of RNAP. E.g., the SSO8620 promoter shows strong TFB- but weak RNAP- and TFEβ signals, which indicates repression of RNAP recruitment to the TBP-TFB ternary complex. Consistent with this notion, the predominant TFB footprint on SSO8620 was significantly narrower compared to TFB footprints on promoters showing unimpaired RNAP recruitment to the PIC   than with RNAP promoter occupancy  Fig.  the decrncy Fig. . Notablyncy Fig. . If the In summary, the ChIP-seq results reveal that the escape of RNAP into productive elongation varies greatly across different TUs.Bd over RNAPPr. We divided the TUs into two subsets with a high (EI > −1) or low escape index (EI < −2.5) and compared the aggregate profiles for RNAP, Spt4/5 and Elf1  for each TU defined as the log-transformed ratio of RNAPlf1 Fig. . Both ellf1 Fig. . In supplf1 Fig. . Escape Spt45 and TECSpt45-Elf1. Our data thereby also provide the first experimental evidence that the archaeal Elf1 homologue is a general part of the archaeal TEC.Notably, Spt4/5 is consistently recruited to the TEC prior to Elf1, independent of whether escape is high or low Fig. . Consecu55 and deep-sequenced. In quantitative terms, the occupancy of promoter-proximal elongation complexes (using Spt4/5Pr as proxy) correlated well with TSS-RNA read counts (Spearman’s r = 0.61)   were isolated, enriched for triphosphorylated 5′-ends using the Cappable-seq method61) Fig.  and signIn summary, our results demonstrate that RNAP accumulates in the promoter-proximal region in the form of early TECs that incorporate Spt4/5 and Elf1.56, and has more recently also been reported for eukaryotic transcription systems57. Premature termination could also contribute to the observed promoter-proximal enrichment of TECs that we observed in archaea. As we found no evidence for any significant sequence bias in the promoter-proximal region including uridine-stretches that could serve as intrinsic terminators, we considered factor-dependent termination mediated by the archaeal termination factor aCPSF159. aCPSF1 is capable of inducing transcription termination on TECs stalled in the promoter-proximal region (+54)34. Intriguingly, aCPSF1 accumulated in the promoter-proximal-region of most TUs (188 out of 212 TUs with peaks passing detection threshold) including thsB, rps8E and dhg-1  and two promoters showing low TEC escape (dhg-1 and CRISPR C), the same promoters with ChIP-seq profiles depicted in Fig. CRISPR C promoter displayed some level of early, broad pausing at 30–40 nt transcript length, about the shortest length that is reliably detectable with the assay. Thus, the in vitro transcription data for the CRISPR C promoter are in line with early pausing of TECs in the promoter-proximal region. The absence of a corresponding pausing pattern for the dhg-1 promoter may suggest that it is difficult to establish the proper context such as chromatinization of the DNA templates that reflects the in vivo situation.To generate templates for in vitro transcription, we inserted target promoter regions encompassing −50 to +100 relative to the TSS into plasmids that were subsequently linearised downstream of the insert to allow for run-off transcripts of 115 nt length. Synchronisation of transcription was achieved by a transcriptionally inhibitory variant of TFB termed TFBc that comprises only the C-terminal cyclin fold domainsers Fig. . The genS. solfataricus using a hydrogen peroxide treatment protocol has been partially characterised61. We expected that besides the induction of transcription for stress genes such as dps-l61, oxidative stress would cause a broader, global transcriptional response such as a widespread attenuation of the transcriptome. Relevant to transcription initiation, the peroxide treatment results in the depletion of the TFEβ-subunit from the cytoplasm  suggesting that the control of rRNA synthesis occurs in part at the level of TEC escape  was generally associated with the accumulation of TFB and TFEβ at the promoter Fig. . The rrnape Fig. . TFB accuoB Fig. . In cont549 Fig. . These pThe link between increased TFB accumulation and reduced TEC escape thus offers a possible mechanistic explanation how TEC escape can affect productive transcription. The accumulation of TFB at the promoter in ChIP-seq experiments could reflect a slower progression from the initial formation of ternary DNA-TBP-TFB complexes towards dissociation of TFB from RNAP during promoter clearance. To test whether RNAP recruitment to DNA-TBP-TFB is impaired, we compared TFB and RNAP ChIP-exo data regarding the fold-changes in PIC signal between exponential growth and oxidative stress . Notably, under exponential growth conditions, TSS duplex stability showed a weaker, non-significant positive correlation with TEC escape  and DNA duplex stability around the TSS. An increased aCPSF1 load was associated with lower TEC escape specifically under exponential growth conditions. TSS DNA duplex stability was associated with increased TEC escape specifically under oxidative stress conditions  under exponential growth and oxidative stress conditions using negative binomial generalised linear models. The models reproduced the observed accumulation of TFB when TEC escape is low , archaeal promoters show a lower apparent complexity in terms of promoter element compositiongth Fig. . DNA dupE. coli RNAP from Sigma70-dependent pause sites65. Likewise, cleavage factor TFIIS facilitates the release of promoter-proximally paused RNAP II in Drosophila and human cell lines67. It is tempting to speculate that TFS, the archaeal TFIIS homologue, might play a role in controlling promoter-proximal TEC dynamics in Saccharolobus. Unfortunately, our TFS ChIP-experiments were not successful and TFS association with promoter-proximal TECs remains to be tested.Besides premature termination, the promoter-proximal accumulation of TECs can be explained by slow elongation or pausing that might result in backtracking. Bacterial cleavage factor GreB facilitates the release of 1. Importantly, altered TEC dynamics will only affect productive transcription if it leads to a reduced initiation frequency as in the case of promoter-proximally pause RNAPII that blocks PIC formation in metazoans5. In S. solfataricus, promoter-proximal TEC accumulation coincides with accumulation rather than depletion of PICs. This indicates functional interaction between PICs and TECs and we propose that promoter-proximal TECs might prevent PICs from completing initiation and clear the promoter. Notably, our ChIP-exo data reveal broader PIC footprints than previously anticipated based on in vitro data. This could be due to wrapping of the downstream DNA around the PIC68 or additional DNA-binding factors in the cross-linked complexes such as chromatin proteins69. But, independent of the mechanism, it indicates a larger spatial overlap and thereby interference between PICs and promoter-proximal TECs possibly forming the basis for their functional interaction.Promoter-proximal enrichment of TECs can be caused by altered dynamics such as slower elongation rates or pausing, or premature termination70 and Elf1 recruitment depends on Spt471. In S. solfataricus, Elf1 is recruited subsequent to Spt4/5 in the promoter-proximal region. The role of Elf1 as an integral part of the TEC genome-wide makes it a likely target for regulation, possibly by post-translational modification of the N- and C-terminal tails of Elf172.The function of Elf1 remains poorly understood and our data provide the first insight into archaeal Elf1. Yeast Elf1 is recruited after Spt4/5 to the TEC with a gradual increase in Elf1 occupancy towards the poly-adenylation siteSaccharolobus shows intriguing parallels to metazoans. Firstly, the early elongation phase of transcription is rate limiting for gene expression. Secondly, Spt4/5 (DSIF) is an integral component of promoter-proximal elongation complexes in humans as well as archaea. Thirdly, CPFS73-related RNases are likely to mediate premature termination of promoter-proximal RNAPs in both eukaryotes73 and archaea. Our discovery that early elongation complex dynamics modulate transcription in Saccharolobus suggests that promoter-proximal regulation is an ancient feature of the archaeo-eukaryotic transcription machinery. Control of promoter-proximal TEC escape efficiency could provide a simple primordial mechanism for gene regulation, from which a more complex process evolved that involves the stable pausing of elongation complexes, and a tightly controlled pause-release by factors including NELF and P-TEFb.The pivotal role of Spt4/5, Elf1 and aCPSF1 in the early TEC dynamics in In Sum, we provide evidence for widespread promoter-proximal transcription regulation in archaea. Our data suggest that the archaeal transcription cycle involves at least two major regulatory checkpoints: (i) recruitment of RNAP and initiation factors to the promoter and (ii) TEC escape into productive elongation likely involving a negative feedback effect on initiating RNAPs upstream as well as premature termination. Together they create a dynamic mosaic of mechanisms that determines the transcription output in archaea. The relative simplicity and biochemical tractability of archaeal transcription complexes provides for the development of in vitro models to elucidate on the molecular mechanisms underlying TEC escape.S. solfataricus TFB, TBP, TFEβ, TFEα and Rpo4/7 have been described previously24. Polyclonal rabbit antisera against recombinant Spt5, Elf1, and aCPSF1 were produced at Davids Biotechnology . All antibodies were purified from antiserum by Protein A-agarose affinity chromatography.Rabbit antisera against S. solfataricus P2 cells were grown in Brock medium74 at 76 °C in a Thermotron air incubator (Infors) to mid-exponential growth phase (O.D.600 021–0.29). For oxidative stress, cells were grown overnight in modified Brock medium without FeCl2 supplemented with 0.2% tryptone to mid-exponential growth phase (O.D.600 0.11 to 0.24) before the addition of 30 µM H2O2 similar to what has been described before75. 105 min after H2O2 addition cells were cross-linked. All cultures were cross-linked by the addition of 0.4% formaldehyde for 1 min before quenching with 100 mM Tris/HCl pH 8.0.Cells were washed three times in PBS buffer before freezing in liquid nitrogen and storage at −80 °C. To prepare lysates for ChIP experiments, cells were resuspended in lysis buffer  supplemented with complete protease inhibitor cocktail (Roche). DNA was sheared in polystyrene tubes in a Q700 cup sonicator (Qsonica) at 4 °C to an average fragment size of 150 bp as judged by agarose gel electrophoresis. Debris was removed by centrifugation before freezing in liquid nitrogen and storage at −80 °C. For ChIP, 500 µl lysate diluted to a DNA content of 20 ng/µl (based on A260 measurements) was supplemented with antibody  and incubated overnight in an end-over-end rotator at 4 °C. After addition of 50 µl of sheep anti-rabbit IgG Dynabeads M-280 (Thermo Scientific) were added and incubation was continued for 1 h. Beads were washed twice with 1 ml lysis buffer, once with lysis buffer containing 500 mM NaCl, wash buffer  and TE buffer. Immunoprecipitated material was eluted from beads by the addition of 200 µl ChIP-elution buffer , de-cross-linked overnight at 65° in the presence of 10 µg RNase A and 40 µg Proteinase K. DNA was purified using Qiaquick PCR purification kit (Qiagen). For Spt4/5, Elf1 and CPSF1 ChIP experiments, the lysate volume was increased to 1 ml and 8 µg of antibody were used in combination with Protein G Dynabeads M-280 (Thermo Scientific) as described above. The yield of the ChIP experiments was determined using Qubit dsDNA HS (Thermo Scientific). Libraries were prepared using the NEBNext Ultra II DNA library prep kit for Illumina (NEB) according to the manufacturer’s protocol. Library quality and quantity was assessed using Agilent High Sensitivity DNA kit (Agilent Technologies) and Qubit dsDNA HS assay kit (Thermo Scientific).S. solfataricus P2 genome (NC_002754.1) using bowtie76 (parameters -v 2 -m 1–fr) retaining only alignments for read pairs with a single best match. The alignments were converted to bam file format using SAMtools77. To ensure comparability between different ChIP-seq samples, we sequenced all samples to high genomic coverage and adjusted the fragment size distribution using a computational approach by subsampling the reads. The fraction size range in a ChIP-seq experiment is shaped in different ways including the chromatin shearing, size selection method and conditions during the library preparation, and gating  during read alignment and calculation of genomic coverage.We generated paired-end ChIP-seq data on Illumina HiSeq platforms with two biological replicates per condition. 75 or 125 nt reads were trimmed from the 3′-ends to 50 nt read length and the read pairs were aligned to the 78 and imported into R. We adjusted the fragment size distribution to fit a normal distribution with mean 120 and standard deviation of 18. To this end, then read pairs were binned into 200 bins according to fragment size in the range of 51–250 bp randomly drawn from each bin using the sample function in R. The number of reads to be drawn was determined by their relative frequency in the target distribution (dnorm) multiplied by maximum total number of reads possible without exhausting any of the bins. This procedure retained on average 46% of the read pairs. Data were exported as bed files with the fragment coordinates and subsequently converted back to bam format for any downstream analysis.In order to adjust the fragment size distribution computationally, bam files were converted to bedpe format using BEDTools bamtobed79 with bin size 50. Data were imported into R and pairwise correlation between unfiltered and sampled data as well as and the pairwise correlation between biological replicates was assessed using the cor.test function . Spearman correlation estimates were considered to be significantly different at significance level α when the confidence intervals calculated from the bootstrapped data set for the same significance level were non-overlapping for both biological replicates.Peaks were identified with MACS283 and converted to bigwig format. For individual gene plots normalised bigwig files were imported into R via the rtracklayer package84 and plotted using ggplot285. Heatmaps were generated using deepTools computeMatrix and plotHeatmap functions79.Bam files were normalised against input using deepTools bamCompare using the SES method for scaling S. solfataricus TUs to ensure robust, TU-specific signal quantification of the ChIP-seq data. We manually curated a previously published map of 2229 TUs based on RNA-seq data86. The dataset contained 1035 TUs with primary TSSs. We also included two previously determined transcription start sites for 5S and 16S/23S rRNA genes87. For the remaining 1192 TUs no experimentally verified TSS data were available, but based on the generally short 5′ UTR length of Sso mRNAs (69% with length of 4 bp or shorter)86, we used the start codon position as rough indicator for TSS position with additional adjustments based on our ChIP-exo data (see below). The start codon for genes Sso0845 (TU 565) and Sso1077/fumC (TU 710) were reassigned to the third and second ATG codon 33 bp and 21 bp downstream, respectively, as TFB and TFE ChIP-seq, TFB ChIP-exo and permanganate ChIP-seq signals as well as RNA-seq data all consistently suggested the reassignment.Because archaeal genomes are densely packed with TUs, we filtered the set of We filtered for those TUs showing TFB occupancy at the promoter with bijective correspondence and pairing of TFB and TFEβ peaks with bijective correspondence. To this end, TFB ChIP-seq peaks were matched to TFEβ peaks and then assigned to TSSs using BEDTools window with the peak summit position within 40 bp maximal distance from the TSS.In order to ensure reliable data normalisation and quantification over the TU body, we only considered TUs with 500 bp minimum length and a minimum coverage of 20 reads in the chromatin input sample within the −250 to +500 interval relative to the TSS. TUs were further filtered against internal TFB peaks within +40 to 500 bp relative to TSS to ensure that RNAP and Spt4/5 occupancy is not influenced by any TU internal promoters.S. solfataricus are often tightly spaced causing the RNAP and Spt4/5 ChIP-seq signal from these promoter pairs to be convoluted to considerable extent. To address this problem, we filtered our TU set further for those where the input-normalised Spt4/5 occupancy at TSS position was at least 1.5x increased compared to occupancy at position −150 relative to TSS.Divergent promoters in The resulting set of TUs was checked for consistent positions of the ChIP-exo data as additional control for TSS position (see below).To determine the escape index, the log2 ratio of input-normalised RNAP occupancy within the TU body (+251 to 500 bp relative to TSS) to the promoter region (−50 to +100) was calculated.86 were used to build a training set. A peak with median position −14 relative to TSS (Inter-Quartile Range −16 to −12) was identified.For TSS positions that were initially estimated from start codon positions only, we used TFB ChIP-exo signal on the non-template strand to get a more precise estimate of TSS position. The ChIP-exo TFB signals for genes with experimentally mapped TSSsFor nine TUs out of 82 TUs without experimentally mapped TSS in total, adjustment of TSS positions by 10 to 33 bp were suggested. For TU 1536 no consistent peak could be identified and it was removed from the data set. Lastly, all suggested adjustments of TSS positions were checked manually for their consistence with ChIP-seq, ChIP-exo, permanganate ChIP-seq and RNA-seq data and TSS positions were adjusted further by max 3 bp to match the (C/T)(A/G) consensus of the Inr promoter element.88 output of the RNA-seq data (see below). Predicted RNA 3′-ends were filtered by the following criteria: (i) a TU length of >300 nt, (ii) no TFB peaks within the surrounding 600 bp to filter out aCPSF1 peaks resulting from promoter-proximal recruitment, (iii) continuous input coverage of >20 reads in the surrounding 500 bp to ensure reliable input normalisation, (iv) a two-fold increase in average Spt4/5 occupancy in the 250 bp downstream of the predicted RNA 3′-end compared to the 250 bp window upstream. The final data set comprised 41 predicted RNA 3′-ends.To test aCPSF1 association with putative transcription termination sites, we used an initial dataset of 1727 predicted RNA 3′-ends based on the Rockhopper 2S. solfataricus BRE-TATA box motif was determined by scanning promoters with mapped TSS within a 24-bp window (positions −42 bp to −19 relative to TSS) using MEME89 in ‘oops’ mode with 8–15 bp motif width.The 90. Cell growth, crosslinking and DNA shearing were carried out as described for ChIP-seq samples, but DNA was sheared to a range of >200 bp to be suitable for ChIP-exo. Immunoprecipitation was carried out as for ChIP-seq samples by incubating 1 ml lysate with 8 µg antibody overnight. The lysates were transferred to a new tube with 50 µl Protein-G Dynabeads (Active Motif) and further incubated for 1 h before following the manufacturer’s recommendations for washing of the beads and library preparation. Library quality was assessed using Agilent High Sensitivity DNA it (Agilent Technologies) and Qubit dsDNA HS assay kit (Thermo Scientific). Libraries were sequenced on the Illumina HiSeq platform with 50 cycles read-length. Reads were aligned to the S. solfataricus strain P2 genome using bowtie v1.2.276 (parameters -v 2 -m 1 –best –strata -S), converted to bam file format SAMtools77 and converted to strand-specific 5′-end coverage data using MACE v1.2 pre-processor function with default settings normalised to 1x genome coverage91. For pooling of replicates we calculated the geometric mean.For ChIP-exo analysis, we used the ChIP-exo Kit (Active Motif) according to manufacturer’s specifications with the following modifications. This kit is based on the modified ChIP-exo protocol adapted for Illumina sequencing92. Gibbs free energy values for each dinucleotide were extrapolated to the optimal growth temperature for Saccharolobus of 76 °C. Initially, windows with sizes of 3–20 bp were tested for correlation of duplex stability with escape indices and a 7 bp from position −3 to +4 relative to TSS yielded the highest Spearman ‘s r. In the nearest-neighbour model, the salt concentration is included in the Gibbs free energy calculation as offset. While the internal salt concentration of Saccharolobus cells is not known and could only be roughly estimated, the Spearman correlation is not influenced by offsets.DNA duplex stability was estimated using a nearest-neighbour modelPr occupancy was included as a second offset term. Thereby, the models effectively identified variables predictive for TEC escape efficiency. As explanatory variables, we tested TFB and TFEβ promoter occupancy (log-transformed), aCPSF1 load on promoter-proximal TECs (log-transformed aCPSF1Pr to Spt4/5Pr ratio), and TSS DNA duplex stability. The full model was structured as follows:We generated negative binomial generalised linear models with log link function using the glm.nb function in the MASS package in R. Raw Spt4/5 TU body coverage was used as the dependent variable in the models while log-transformed raw input coverage over the same region was included as fixed offset effectively performing the input normalisation. We calculated the total occupancy signal within the TU body  for Spt4/5 ChIP-seq data as well as the chromatin input for the subset of TUs with mapped TSSs. These values were corrected for the average fragment length (120 nt) to obtain an estimate of overlapping read pair counts. Log-transformed input-normalised Spt4/5Pr offsets) as well as all explanatory variables added separately. Statistical significance of the included variables in the model was confirmed using the Likelihood-ratio chi-squared test implemented in anova.negbin (car package) by comparing the optimal models against models excluding single variables (p < 0.05). Only statistically significant explanatory variables consistent between replicates were retained in the final model. To validate the model, bootstrapped 95% confidence intervals were calculated for each coefficient and checked whether they were consistently positive or negative using the Boot function from the car package.TUs passing a Cook’s distance of 0.5 for a model of all explanatory variables were removed from the dataset. To identify the optimal model, we used the step function in the MASS package for automatic model building using default settings for AIC minimisation. The null model was used as lower boundary and the full model including all variables as upper boundary. Searches were initiated with the null model  was added and cells were washed three more times in PBS before freezing in liquid nitrogen. Cells were further processed as for ChIP-exo, but λ-exonuclease and RecJF digestion steps were omitted. After reversal of crosslinks and ethanol precipitation, the purified DNA was dissolved in 10% piperidine and incubated at 90 °C for 30 min. Piperidine was removed by three steps of 1-butanol extraction followed by chloroform extraction before ethanol precipitation. P7 primer extension, P5 adaptor ligation and PCR amplification were carried out according to the ChIP-exo protocol. Reads were mapped onto the S. solfataricus strain P2 genome as described above and converted to strand-specific 5′-end coverage data using BEDTools genomecov. Coverage data were loaded into R and corrected for the position by −1 nt so that it corresponds to the modified nucleotides eliminated during permanganate/piperidine cleavage. Coverage data were filtered for genomic positions with Ts. While regions around TSSs are generally characterised by strongly increased signal on T positions over non-T positions (expected for potassium permanganate/piperidine treatment of DNA), we found that these regions were often flanked by broader regions of lower coverage signal that did not show any specificity for Ts and possibly results from incomplete P7 adaptor ligation during the library preparation. For this reason, we performed a background correction on the data. Background signal was calculated as from four neighbouring non-T positions for each T (the two closest non-T residues on either side). The median background signal was subtracted and the resulting T-specific signal was normalised to 1x genome coverage of Ts for both strands.For permanganate ChIP-seq, we adapted our ChIP-exo protocol analogously to the method described by Gilmour, Pugh and co-workers88. Rockhopper 2 quantifies transcripts by applying an upper quartile normalisation. The coverage tracks with raw coverage were corrected for sequencing depth and the fraction of reads mapping sense to protein encoding genes (mRNA) yielding normalised coverage in counts per million (cpm). Reproducibility between biological replicates was assessed based on the correlation of raw count data for 3057 coding genes . RNA was isolated according to manufacturer’s protocol and remaining genomic DNA was removed using the TURBO DNA-free kit (Thermo-Fisher). RNA was quantified using the Qubit RNA BR Assay kit (Thermo Scientific) and quality was assessed using the RNA ScreenTape system (Agilent). Libraries were prepared at Edinburgh Genomics using the TruSeq® Stranded Total RNA Library Prep kit (Illumina) including partial ribosomal RNA depletion with the Ribo-Zero rRNA Removal Kit (Bacteria) (Illumina). 75 bp paired-end reads were generated on a HiSeq 4000 system (Illumina). Coverage tracks were produced using the Rockhopper 2 software package in–rf modeg at 4 °C). Pellets were immediately subjected to RNA isolation using the mirVana miRNA isolation kit (Ambion) with an initial resuspension buffer volume of 200 µl following the protocol for small RNAs (20-200 nt length). Library preparation and deep sequencing was conducted at Vertis Biotechnologie (Germany). In brief, 5′-triphosporylated RNA was capped with 3′-desthiobiotin-TEG-GTP (NEB)) using the Vaccinia virus Capping enzyme (NEB) and biotinylated RNA species were subsequently enriched by affinity purification using streptavidin beads yielding 0.6–1.3% of the sRNA preparation. The eluted RNA was poly-adenylated using E. coli Poly(A) polymerase and 5′-ends were converted to mono-phosphates by incubation with RNA 5′ Pyrophosphohydrolase (NEB). Subsequently, an RNA adaptor (5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3′) was ligated to the newly formed 5′-monophosphate structures. First-strand cDNA synthesis was performed using an oligo(dT)-adaptor primer and the M-MLV reverse transcriptase at 42 °C. The resulting cDNA was finally PCR-amplified (12 cycles) with TruSeq Dual Index sequencing primers (Illumina) and Herculase II Fusion DNA Polymerase (Agilent). The libraries were sequenced on an Illumina NextSeq 500 system with 75 bp read length. In order to remove poly(A)-tails and adaptors, we trimmed the reads using Cutadapt93 in two rounds with the following settings to prevent trimming of naturally occurring A-rich RNAs due to the low GC-content of the S. solfataricus genome: (i) -a “{A15}” -e 0 -m 15 to remove all poly(A) stretches of at least 15 nt length plus downstream regions and (ii) -a “A{15}”X -e 0 -O 5 to terminal shorter poly(A) stretches of minimum 5 nt length. Trimmed and untrimmed reads were split into separate fastq files using awk. Both fastq files were aligned to the S. solfataricus genome using bowtie v1.2.276 (parameters -v 1 -m 1 –-best –-strata -S) with untrimmed 75 nt reads shortened to 71 nt (−3 4). The bam file output was merged, sorted and indexed using SAMtools77. Bam files were imported into the R environment using the rsamtools and GenomicRanges packages and filtered to ensure a unique sequence of the initial 20 bp within the S. solfataricus genome required to map the reads in R using the Biostrings package. TSS-RNA were defined as RNAs with a 5′-end within 20 nt of a mapped or predicted TSS. The two biological replicates showed good reproducibility of TSS-RNA occupancy with a Spearman correlation of 0.98 for 438 mappable promoters.15 ml cell culture was rapidly mixed with 30 ml pre-cooled RNAprotect Bacteria Reagent (Qiagen) placed in an ice bath. Cells were harvested by centrifugation  in combination with donkey anti-rabbit IgG Dylight680 (Bethyl Laboratories). Dps-l antiserum was a kind gift of Mark Young . As loading control, we used sheep Alba antiserum  in combination with donkey anti-sheep IgG Alexa488 (Thermo Fisher). Blots were scanned on a Typhoon FLA 9500 scanner (GE Lifesciences).94.In order to trace promoter-proximal transcription elongation dynamics in vitro, we developed a cell lysate-based synchronised transcription assay. Synchronisation was achieved by supplementing an inactive variant of initiation factor TFB comprising only the C-terminal cyclin folds (TFBc) simultaneously with rNTPs. TFBc forms ternary complexes with TBP and DNA containing TATA/BRE promoter motifs, but it fails to recruit RNA polymerase and does not facilitate transcription initiationS. solfataricus P2 cultures in exponential growth phase were transferred to an ice-water bath for cooling. Cells were harvested by centrifugation for 20 min at 4000 × g at 4 °C and washed three times in ice-cold 20 mM sucrose solution before flash freezing in liquid nitrogen and storage at −80 °C. Cells were resuspended in 6 ml 10 mM MOPS pH 6.5, 10 mM MgCl2, 1 mM DTT, 1 mM EDTA, 0.1% Triton X-100 and incubated for 1 h on ice for cell lysis. Insoluble material was removed by centrifugation for 20 min at 20,000 × g at 4 °C and the supernatant was aliquoted and stored at −80 °C.To generate cell lysates for the in vitro transcription, 4 litre thsB, rps8e, dhg-1 and CRISPR C from S. solfataricus P2 genomic DNA and ligating the products into the vector pGEM-T (Promega)  were provided to initiate transcription together with 5 µM TFBc blocking further pre-initiation complex assembly. At given time points, 30 µl sample were withdrawn and mixed rapidly with 200 µl stop mix . After 5 min incubation, samples were purified by a single acidic phenol/chloroform extraction step. Transcripts were subsequently enriched by affinity purification following a protocol based on95. 200 µl supernatant from the phenol extraction step was mixed with 100 µl salt adjust mix  and 1.6 pmol 3′-biotinlylated antisense oligonucleotide matching the first 25 nt of the transcripts (based on the predicted TSS)  for 20 min at 37 °C followed by 5 min at 65 °C to inactivate the phosphatase. Samples contained 2/3 volume cell lysate, 20 ng/µl linearised plasmids, 2 mM spermidine, 10 mM MOPS pH 6.5 and 10 mM MgClFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


BRCA1 germline mutation carriers are predisposed to breast cancers. Epigenomic regulations have been known to strongly interact with genetic variations and potentially mediate biochemical cascades involved in tumorigenesis. Due to the cell-type specificity of epigenomic features, profiling of individual cell types is critical for understanding the molecular events in various cellular compartments within complex breast tissue. Here, we produced cell-type-specific profiles of genome-wide histone modifications including H3K27ac and H3K4me3 in basal, luminal progenitor, mature luminal and stromal cells extracted from a small pilot cohort of pre-cancer BRCA1 mutation carriers (mut/+BRCA1) and non-carriers (+/+BRCA1), using a low-input ChIP-seq technology that we developed. We discovered that basal and stromal cells present the most extensive epigenomic differences between mutation carriers (mut/+BRCA1) and non-carriers (+/+BRCA1), while luminal progenitor and mature luminal cells are relatively unchanged with the mutation. Furthermore, the epigenomic changes in basal cells due to BRCA1 mutation appear to facilitate their transformation into luminal progenitor cells. Taken together, epigenomic regulation plays an important role in the case of BRCA1 mutation for shaping the molecular landscape that facilitates tumorigenesis. BRCA1 have been strongly linked to increased risks to breast, ovarian and other cancers  and non-carriers (+/+BRCA1) and extract epigenomic features that separate the two groups. Such comparison reveals that the extent of epigenomic changes varies among the four cell types. Despite a limited sample size, we find striking changes that warrant additional study and correlate our results with current literature. These epigenomic alterations potentially change the cell state and lay the groundwork for future tumorigenesis.In this study, we profile two important histone marks H3K4me3 and H3K27ac in a cell-type-specific manner in all four major cell types from pre-cancerous human breast tissue samples using a low-input ChIP-seq technology that we developed . It BRCA1 mutation carriers (MUT) or non-carriers (NC) who underwent cosmetic reduction of mammoplasty, diagnostic biopsies or mastectomy. Genetic testing of BRCA1 mutation was conducted by the hospital , EpCAMlowCD49fhigh basal cells (BCs), EpCAMhigh CD49f + luminal progenitor cells (LPs) and EpCAMhigh CD49f_ mature luminal cells (MLs).The study was approved by the Institutional Review Board at the University of Texas Health Science Center at San Antonio. Informed consent was obtained from all participants. Breast tissues were obtained from adult female cancer-free hospital . The de-hospital , fresh ug for 5 min at room temperature and washed twice with 1 ml PBS (4°C). Cells were resuspended in 1 ml of 1% freshly prepared formaldehyde in PBS and incubated at room temperature on a shaker for 5 min. Crosslinking was quenched by adding 0.05 ml of 2.5 M glycine and shaking for 5 min at room temperature. The crosslinked cells were centrifuged at 1600 g for 5 min and washed twice with 1 ml PBS (4°C). The pelleted cells were resuspended in 130 μl of the sonication buffer  and sonicated with 105 W peak incident power, 5% duty factor, and 200 cycles per burst for 16 min using a Covaris S220 sonicator (Covaris). The sonicated chromatin samples were shipped to Virginia Tech for MOWChIP-seq assay. The sonicated sample was centrifuged at 16 100 g for 10 min at 4°C. The sheared chromatin in the supernatant was transferred to a pre-autoclaved 1.5 ml microcentrifuge tube (VWR). A fraction of the sonicated chromatin sample was mixed with IP buffer  sodium deoxycholate, 0.1% SDS, 1% (v/v) Triton X-100, with 1% freshly added PMSF and PIC) to generate a MOWChIP sample containing chromatin from 50 000 cells with a total volume of 50 μl.The sonication process to generate chromatin fragments is similar to what we described in previous publications ,19. A soWe conducted MOWChIP-seq of the sonicated chromatin samples with 50 000 cells per assay for H3K27ac profiling and 10 000 cells per assay for H3K4me3 profiling, using protocols and microfluidic devices described in our previous publications ,19. We uChIP-seq data sets that had fewer than 10 000 called peaks in a given technical replicate were discarded. After quality control, the technical replicates of the same cell sample were combined for the data analysis. As a result, we obtained three biological replicates for MUT H3K27ac samples, four biological replicates for NC H3K27ac samples, two biological replicates for MUT H3K4me3 samples and one biological replicate for the NC H3K4me3 sample. The fraction of reads in peaks (FrIP) was calculated using the number of mapped reads within peak regions divided by total mapped reads. Normalized-strand correlation (NSC) and relative-strand correlation (RSC) were calculated using phantompeakqualtools v1.2.2 ,23.q < 0.05)  scripts in RStudio. Sequencing reads were trimmed using default settings by Trim Galore! v0.4.1 (Babraham Institute). Trimmed reads were aligned to the hg19 genome with Bowtie v1.1.2 . Peaks w < 0.05) . Blackli < 0.05) . Mapped For Pearson’s correlation, the signal was calculated around the promoter region (TSS ± 2 kb) and plotted with the corr and levelplot functions. For visualization in IGV v2.4.10 (Broad Institute), the signal was calculated in 100 bp windows over the entire genome and output as a bigWig file.To determine peak regions with differential signal, the Bioconductor package DiffBind v2.12.0 was used ,28. A ‘mhigh regions that did not intersect with promoter regions to be enhancer regions. First, consensus H3K27ac peak sets were generated for NC and MUT samples for each cell type after determining the set of peak regions present in NC and/or MUT samples. Peak widths were expanded to be 1000 bp long (summit ± 500 bp). Promoters were defined as TSS ± 500 bp. Any H3K27ac 1 kb regions that intersected with a promoter region was removed and the remaining regions were designated as enhancers. Motif analysis was performed to determine enriched transcription factor binding motifs among the enhancer regions with HOMER v4.10.3  if the BRCA1 mutation carriers  and non-carriers  were collected during breast reduction or mastectomy surgery , with an average r of 0.960 for H3K4me3 and 0.918 for H3K27ac . In comparison, LPs and MLs have similar H3K27ac profiles between MUT and NC .Breast tissues from  surgery , dissoci surgery  35). Du. DuBRCA1 surgery . We sele surgery ,37, and  surgery . Both ma surgery . Averagec Figure . Generalc Figure . In contc Figure . BCs andr of 0.926 among MUTs and 0.946 among NCs) while SCs show slightly more differences from LPs and MLs . With H3K27ac data, LPs and MLs correlate with each other fairly well  while the other pairs generally have low correlation (with average r in the range of 0.668–0.794).In terms of differences among various cell types, BCs, LPs, and MLs have very similar H3K4me3 profiles .We carefully examined differentially modified H3K27ac peak regions  between MUTs and NCs Figure . We founBRCA1 mutation. These differential regions were associated with 783, 3972 and 12 160 genes in LPs, BCs and SCs, respectively. We also conducted gene ontology enrichment analysis using these regions for each of the cell types . For the purposes of this analysis, we have defined the region within 500 bp of the TSS as the promoter region. Using the NCs as the reference, MUT enhancers cover 67%, 90%, 79% and 29% of the NC enhancers in BCs, LPs, MLs and SCs, respectively. Furthermore, while MUT BCs and LPs had a similar number of unique enhancers, 85% of MUT BC enhancers were unique compared to 39% of MUT LP enhancers, supporting the notion that BRCA1 mutation affects BCs more than LPs. The enhancers were then mapped to genomic regions . In addition, we also found that super enhancers are significantly attenuated in all cell types except for BCs, in congruence with our previous homogenate data  binding motifs significantly enriched in each cell population Figure . BCs hadude PAX5   for BCs, LPs, and MLs, respectively  between NC and MUT BCs followed by SCs (∼21%). This further supports that the BRCA1 mutation leads to profound epigenetic changes in BCs and that these changes have the potential to increase breast cancer risk.To further validate the possibility of the basal compartment being a significant contributor in  by GWAS  , H3K27ac is more differentiating than H3K4me3, which is consistent with previous findings by Ma et al.  repository with the accession number of GSE148995.lqac006_Supplemental_FilesClick here for additional data file.


Persistent infections with some types of human papillomavirus (HPV) constitute the major etiological factor for cervical cancer development. Nanog, a stem cell transcription factor has been shown to increase during cancer progression. We wanted to determine whether Nanog could modulate transcription of E6 and E7 oncogenes. We used luciferase reporters under the regulation of the long control region (LCR) of HPV types 16 and 18 (HPV16/18) and performed RT-qPCR. We found that Nanog increases activity of both viral regulatory regions and elevates endogenous E6/E7 mRNA levels in cervical cancer-derived cells. We demonstrated by in vitro mutagenesis that changes at Nanog-binding sites found in the HPV18 LCR significantly inhibit transcriptional activation. Chromatin immunoprecipitation (ChIP) assays showed that Nanog binds in vivo to the HPV18 LCR, and its overexpression increases its binding as well as that of c-Jun. Surprisingly, we observed that mutation of AP1-binding sites also affect Nanog’s ability to activate transcription, suggesting cooperation between the two factors. We searched for putative Nanog-binding sites in the LCR of several HPVs and surprisingly found them only in those types associated with cancer development. Our study shows, for the first time, a role for Nanog in the regulation of E6/E7 transcription of HPV16/18. Alphapapillomavirus genus are considered necessary for the development of cervical cancer [Persistent infections with certain types of human papillomavirus (HPV) belonging to the l cancer ,2. Althol cancer .HPV genomes are divided into three regions: a regulatory one encompassing the origin of replication and binding sequences for a plethora of transcription factors, called long control region (LCR), an early region containing non-structural genes , and a late region containing L1 and L2 structural genes [Initiation and maintenance of tumors requires the continuous expression of E6 and E7 . E7 and Nanog is involved in carcinogenesis and has been associated with angiogenesis , epithelV12 protein, increased expression of E6 and E7 oncogenes is observed [It has been shown that two AP1-binding sites are found in the LCR of HPV18 . Furtherobserved . The priobserved . c-Jun hobserved ,38. SincThe aim of this work was to investigate whether Nanog affects transcriptional activity of the LCR from HPV16/18 and consequently expression of E6 and E7 oncogenes. We determine if Nanog binds in vivo to the LCR of HPV18. In addition, we wanted to know if putative Nanog-binding sites can be found in the LCRs of other HPVs and if their presence could be associated with cancer development.TM; RRID:CVCL_1094). Human embryonic kidney cell line immortalized with SV40 T antigen (HEK-293T) was kindly provided by Dr. Manzo Merino from the National Cancer Institute in Mexico City (INCAN). Mouse epithelial primary tumor lung cell line TC-1 was generously donated by Dr. Lutz Gissmann from the German Cancer Research Center (DKFZ). Cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) containing 10% heat-inactivated fetal bovine serum, 50 U/mL penicillin and 50 μg/mL streptomycin from Gibco®, Waltham, MA  and maintained at 37 °C in a humidified atmosphere containing 5% CO2.Human cervical cancer-derived cell lines HeLa and SiHa were authenticated by SRT DNA profiling at the University of Colorado DNA Sequencing and Analysis Core according to issued report number DP0297. The epithelial carcinoma-derived cell line C-33A was acquired from ATCC, Manassas, VA , and pcDNA 3.1-Nanog (Nanog or N) coding for Nanog were obtained from Promega, (U47295), Invitrogen (#V79020), and Addgene , respectively. The vector expressing a mutant H-RasV12G (H-RasV12 gene or R) was previously described . The LCRTM.Mutations sites were generated using pLCR18luc as a template and the following primers: for site N: 5′-TATGTTGTATGTTACTATATTTG TTGGTTAT GTGGCGGGCAATAAAATATGTTTTGTGGTTCTGTGTGTTATGTG-3′, and for site N/A-E: 5′-CGCATATAGGCCCACCTGGTGCGAGTCATTTTCCTGTCCAGG-3′. We employed the QuikChange Lightning Multi Site-Directed Mutagenesis Kit (Agilent #210513) that includes a derivative of PfuUltra high-fidelity (HF) DNA polymerase. pLCR18luc was used together with a synthetic oligonucleotide primer that was extended during temperature cycling. Products were treated with Dpn I endonuclease to digest parental DNA template and transformed into XL10-Gold ultracompetent cells. Mutations were confirmed by sequencing selected clones using a 3500 xL Genetic Analyzer from Applied BiosystemsHEK-293T cells  were seeded in 6-well plates and transfected with 2.5 μg second-generation plasmids obtained from Addgene; psPax2 for packaging, pMD2.G for envelope, pSin-EF2-Nanog-Pur for transference, or pL-SIN-EF1α-EGFP for transference control . Lipofectamine 3000  was used following the manufacture’s protocol, to improve lentiviral production. Transduction was performed on HeLa cells using one-half of the supernatant harvest obtained from 1 well of a 6-well plate for both Nanog and GFP produced lentiviruses. The transduction was made in the absence of serum and antibiotics and in the presence of the cationic polymer polybrene  at a concentration of 10 μg/mL. After incubation for three hours at 37 °C, media were supplemented with 5% serum. On the third day, medium was changed, and cells were collected the same day or on day 7. Cells were seeded as follows: for 3-day collection, 750,000 cells on P100 plates; for 7-day collection, 350,000 on P100 plates.For luciferase assays, cells were transfected using PolyFect Transfection Reagent  in 6 well plates with 1 μg of LCR16/18 reporter vectors, together with 0.5 or 1μg of Nanog or H-RasV12 expression vectors, or 1 μg of empty vector. The following number of cells were used: TC-1, 250,000; C-33A, 290,000; and HEK-293T, 300,000. For RNA isolation, cells were transfected on 60 mm cell culture dishes with 2.5 μg of plasmids expressing Nanog or H-RasV12 using PolyFect reagent with the following number of cells: for HeLa, 450,000; for SiHa, 900,000. For western blots, reverse transfections were performed as follows: 10 μg of Nanog or H-RasV12 expressing vectors were mixed in 250 μL of DMEM medium with 32 μL of PolyFect and left 30 min at room temperature. Cells were seeded into 60 mm cell culture dishes and DNA-PolyFect mixture added. We used the following number of cells: TC-1, 1,500,000; C-33A, 2,000,000; HEK-293T 1,800,000; HeLa, 1,350,000; SiHa, 2,700,000.Cells were treated with trypsin washed with PBS and lysed with RIPA buffer. Sixty micrograms of protein were loaded onto 12% polyacrylamide gels, transferred to 0.2 μm nitrocellulose membranes and blocked with 5% low-fat milk. Then, antibodies were added and incubated overnight. We used the following antibodies from Santa Cruz, Dallas, TX for Nanog , diluted 1/1000 in 5% low-fat milk; α-tubulin , diluted 1/20,000 in 5% low-fat milk; Anti Ras antibodies from Calbiochem, Burlington, MA  were diluted 1/1000 in 0.2% albumin, and anti -mouse secondary antibody coupled to horseradish peroxidase from R&D, Minneapolis,  diluted 1/3000 in 5% low-fat milk. Chemo-luminescence was detected employing the Immobilon HRP substrate  with a C-DiGiT  scanner.RNA was extracted 48 h after transfection using Trizol  as described in the manual (MAN0016385) and DNA digested with DNAase I . The presence of the 28S and 18S was evaluated on 1% agarose gels. The RNA was quantified on a spectrophotometer, and a PCR was performed employing the GAPDH primers to ensure that no DNA was left. Then 1 μg of RNA per sample was used for retro-transcription using the RevertAid First Strand Kit . cDNA was obtained and the qRT PCR was performed by using Thermo Maxima SYBR Green/ROX 1qPCR Master Mix . The following primers were used: E6-18-F: 5′-GCG ACC CTA CAA GCT ACC TG-3′, E6-18-R: 5′-GTT GGA GTC GTT CCT GTC GT-3′; E7-18-F: 5′-AAC ATT TAC CAG CCC GAC GA-3′, E7-18-R: 5′-TCG TCT GCT GAG CTT TCT AC-3′; E6-16-F: 5′-ACT GCA ATG TTT CAG GAC CC-3′, E6-16-R: 5′-TCA GGA CAC AGT GGC TTT TG-3′; E7-16-F: 5′-CCC AGC TGT AAT CAT GCA TG-3′, E7-16-R: 5′-TGC CCA TTA ACA GGT CTT CC-3′; GAPDH-F: 5′-AAG GTC GGA GTC AAC GGA TTT-3′, GAPDH-R: 5′-CCA TGG GTG GAA TCA TAT TGG AA-3′. PCR cycles were as follows: 95 °C/10 min, 35 cycles: 95 °C/35 s; 60 °C/ 35 s; 72 °C/35 s. Data were processed with the Delta CT method.TM, Waltham, MA, USA, #T2120) was used. Additionally, a reaction accelerator compound  was employed at the moment of detection, and the signal was measured after 5 s.Cells were washed 48 h after transfection, treated with trypsin, and then suspended in 30 μL of lysis buffer, followed by five cycles of freezing on dry ice and heating at 37 °C for 45 s, homogenizing the mix using a vortex after every heating step. The Dual Luciferase Reporter Assay kit was used , and both the luciferase and the β-galactosidase activities were detected in a GloMax 20/20 Luminometer (Promega). For β-galactosidase, a buffer containing 100 mM sodium phosphate pH 8, 1 mM magnesium chloride, and Galacton 1X . Three 100 mm dishes with HeLa cells at 85% confluence were employed per condition. Cells were fixed in 1% formaldehyde, quenched with 125 mM glycine and lysed with a buffer containing 1% SDS, 10 mM EDTA (pH 8.0), 50 mM Tris-HCL (pH 8.0), and 1X protease inhibitors (Roche #04693159001). Chromatin was fragmented with an Ultrasonic Processor , using 1 cycle of 37% amplitude for 30 s, alternating 5 s ON/3 s OFF. The size of the fragments was determined to be between (200–500 bp). Protein was quantified by using the Pierce BCA Protein Assay Kit (ThermoFisher #23225). For each immunoprecipitation, 700 μg of protein was used with the following antibodies: anti-Nanog , anti c-Jun , and anti IgG .Quantitative PCR was performed as described previously. As negative control, primers for a region on chromosome 7 were used for both Nanog and c-Jun immunoprecipitations: F: 5′-TCTAGGCTAGGGTAGTGAAGCAC-3′ and R: 5′-GGGACAGCCAGTGATGAAGAAA-3′. As positive control for Nanog, the following primers from a segment of the Nanog promoter were used: F: 5′-GTCTGGGTTACTCTGCAGCTACT-3′ and R: 5′-CTTAGACC CACCCCTCCTGGC-3′. For c-Jun positive control, the following primers for the cyclin D promoter were used: F: 5′-CCAGGGCAAATTCTA AAGGT-3′ and R: 5′-CACACCTCTGAATGGAAAGC-3′. Finally, for HPV18 LCR, the following primers were used: F: 5′-CTTTTGGGCACTGCTCCTAC-3′ and R: 5′-GCAGTTTTATTACTTAGGGAGTGGA-3′. PCR was performed as described above, except that for annealing, temperatures for primers described were as follows: 60 °C for c-Jun and Nanog negative control; 62 °C for Nanog positive control, 58 °C for c-Jun positive control, and 59 °C for the LCR.The efficiency of each primer was calculated based on the results obtained from a standard curve made from different input dilutions. The occupancy of c-Jun and Nanog was calculated based on the negative control IgG. The formulas employed were extracted from the Diagenode Kit protocol (C01010055).Putative Nanog-binding sites as reported by Mitsui et al. 2003 (C(G/C)ATTAN) , Lin et p-Values under 0.05 were considered significant. Three replicates were considered for each experiment.Statistical analysis was performed by one-way ANOVA and Tukey’s multiple comparison tests . V12) as positive control also showed a significant increase in reporter activity. Expression of Nanog and H-RasV12 was verified by Western blot in reverse transfected cells  and found that exogenous Nanog significantly increased reporter activity in all of them, as shown in ed cells . We thenV12 significantly increased viral oncogene expression to similar levels. Higher levels of Nanog and H-RasV12 proteins in transfected cells was corroborated, as shown in The effect of transfected Nanog on endogenous E6 and E7 oncogene expression was evaluated by RT-qPCR in SiHa and HeLa cell lines, positive for types 16 and 18, respectively. Both cervical cancer-derived cell lines exhibited a significant increase in the levels of E6 and E7 when transfected with Nanog. A 3-fold to 5-fold significant increment in expression of E6 and E7 was observed in comparison with empty vector A. When oTo test the relevance and function of putative Nanog-binding sites found in the LCR of HPV18, we performed site-directed mutagenesis. Reporter plasmids containing the LCR were used to create independent mutations of either the N or N/A-E sites, as indicated in b) agent as negative control showed 4.2-fold and 2.8 enrichment of both Nanog and c-Jun, respectively. In contrast, cells transduced with lentivirus coding for Nanog (NL) displayed 7.5- and 5.8-fold enrichment for Nanog and c-Jun, respectively. We thus conclude that Nanog is capable of binding to the HPV18 LCR in vivo and, when overexpressed, increases its binding to the LCR together with c-Jun.We first corroborated that Nanog increases the expression of c-Jun, as previously described . HeLa ceTo determine whether Nanog and c-Jun together are able to increase the activity of HPV18 LCR more than N or J separately, we performed a luciferase assay. TC-1 cells were transfected with the HPV18 LCR and co-transfected with V, N, R, J, or N and J together (NJ). We observed a 5.9-fold increase in luciferase activity in the condition where NJ was co-transfected, compared with N alone (3.5-fold) or J alone (3.0-fold) B. TherefIn order to evaluate if Nanog-binding sites shown here to activate transcription of E6/E7 are found in the regulatory regions of other viral types, we analyze LCR sequences from several HPVs. In addition, we performed a comparative analysis of the putative-binding sites in HPV associated or not with cancer development. We performed an in-silico analysis of LCR sequences from different HPV types, searching for putative Nanog-binding sites that could contain elements related to sequences previously described .Betapapillomavirus genus associated with skin cancer (5 and 8) or to the Alphapapillomavirus genus associated with either low-grade lesions (6 and 11) or high-grade lesions and cancer . Surprisingly, our analysis clearly shows that while the LCR of HPV types associated with cancer contain at least one putative Nanog-binding site, no such sites were found in the LCR of both HPV types 6 and 11, not associated with cancer. The majority of putative Nanog-binding sites were found positioned at equivalent distances to the E6/E7 transcription start site. In addition, we also found an equivalent site in the LCR of MmPV2. Sequences corresponding to putative Nanog-binding found in the LCR of different papillomavirus genomes are shown in We searched LCR sequences from HPV types belonging either to the Our results support the notion that Nanog increases expression of E6/E7 from HPV types 16 and 18 by activating transcription from the LCR. In addition, we showed that Nanog binds in vivo and activates transcription from the LCR of HPV18 in cooperation with AP-1. Importantly, we only found putative Nanog-binding sites in the LCRs of HPV types belonging to both the alpha and beta genus that have been associated with cancer development.Expression of E7/E6 has been shown to be regulated by several transcription factors associated with cancer, pluripotency, and regulation of stem cells. Nanog has been shown to be expressed in malignant tumors of the uterine cervix, and high expression associated with poor tumor prognosis ,47.In this work, we studied the effect of Nanog in the transcriptional regulation of E6/E7 from HPV types 16 and 18. In order to find out if Nanog affects viral oncogene expression, we first performed reporter gene assays using the respective LCRs  and threNext, we demonstrated that ectopically expressed Nanog significantly enhances transcription of E6 and E7 oncogenes from HPV16/18  in two tTo determine whether Nanog binds directly to the HPV18 LCR, we mutated two putative Nanog-binding sites B, in accAlternatively, we tested an HPV18 LCR mutated at both AP1-binding sites to explore the participation of AP1 in the increase in LCR transcriptional activity mediated by Nanog. Contrary to what happened with wt HPV18 LCR, Nanog was unable to activate LCR with mutated AP1 sites, indicating that transcriptional activation of the LCR mediated by Nanog requires AP1 binding. It has been reported that Nanog is upregulated by c-Jun and that c-Jun is positively regulated by Nanog ,51 The cMoreover, to determine if Nanog can bind to the HPV LCRs, we performed chromatin immunoprecipitation assays. We showed that Nanog binds to the LCR of HPV18 in vivo, and its overexpression increases the levels of c-Jun bound to the LCR A. This rA role of Nanog in transcriptional regulation of E6/E7 in transformed cells is supported by the fact that at least one DNA sequence similar to those shown here is present in the LCR of 10 different HPV types associated with cancer. In contrast, no similar sites are found in the LCR of HPV types 6 and 11, implicating a possible role of Nanog in the transformation process. In addition, a sequence related to site NE was found in the LCR of MmPV2. It is important to note that in types 18, 35, and 45, Nanog and AP1 sites (GTATTAGTCA) are found overlapped and in conserved positions. In the case of MmPV1, where no Nanog-putative-binding sites could be documented, we noticed the presence of two putative AP1-binding sites that could cooperate to activate E6 and E7 transcription. Since all our experiments were performed using transformed cells, it could be important to determine if similar effects are observed in cell lines that maintain episomal copies of the viral genome.Our results contribute to the understanding of the relationship that stem cell transcription factors could have on the transcriptional activation of E6 and E7 from hr-HPV, during the transformation process. Thus, it is possible that infection of stem cells with hr-HPV can potentiates transformation.


Manihot esculenta) identified peaks of transcriptionally engaged RNA polymerase at both the 5′ and 3′ end of genes, consistent with paused or slowly moving Polymerase. In addition, we identified divergent transcription at intergenic sites. A full genome search for bi-directional transcription using an algorithm for enhancer detection developed in mammals (dREG) identified many intergenic regulatory element (IRE) candidates. These sites showed distinct patterns of methylation and nucleotide conservation based on genomic evolutionary rate profiling (GERP). SNPs within these IRE candidates explained significantly more variation in fitness and root composition than SNPs in chromosomal segments randomly ascertained from the same intergenic distribution, strongly suggesting a functional importance of these sites. Maize GRO-Seq data showed RNA polymerase occupancy at IREs consistent with patterns in cassava. Furthermore, these IREs in maize significantly overlapped with sites previously identified on the basis of open chromatin, histone marks, and methylation, and were enriched for reported eQTL. Our results suggest that bidirectional transcription can identify intergenic genomic regions in plants that play an important role in transcription regulation and whose identification has the potential to aid crop improvement.Control of gene expression is fundamental at every level of cell function. Promoter-proximal pausing and divergent transcription at promoters and enhancers, which are prominent features in animals, have only been studied in a handful of research experiments in plants. PRO-Seq analysis in cassava ( Gene expression in plants is a highly regulated process controlling the production of coding and noncoding RNA molecules. Proper regulation of expression is central to the development and phenotypic plasticity. The dynamics of transcriptional regulation have been extensively studied in several model organisms, including humans, yeast, and fruit flies . These sNascent RNA sequencing techniques such as Global nuclear Run-On sequencing (GRO-seq) . PromoteEnhancers are key eukaryotic regulatory elements that control spatiotemporal gene expression and are especially important during development . While tOnly a few studies on a limited number of species (Manihot esculenta) and maize (Zea mays), and to reveal if promoter-proximal pausing and bidirectional transcription at intergenic regions are present in these species. To do so, we quantified nascent transcription in cassava and maize using PRO-seq and re-analyzed a maize GRO-seq dataset (The objective of this study was to characterize nascent transcription in cassava (2 and stored at −80°C. Most of the nuclei extraction protocol took place in a cold room (4°C) with all reagents on ice. A fraction of the nuclei preparations were stained with DAPI  and visualized under a fluorescence microscope to test for concentration and nuclei integrity.Cassava accession “Nase 3” (synonymous with “IITA-TMS-IBA30572” and “Migyera”) cuttings were grown in tubes containing enriched medium. Tubes were placed in growth chambers with 12 hours of light at 30°C for 6 weeks before tissue collection. Stem and leaves of approximately 25 gr were ground with liquid nitrogen to a fine powder using a mortar and pestle. The resulting powder was transferred to a coffee grinder containing cold 1X NIB buffer. We then used the CelLytica PN Plant nuclei isolation/extraction kit (Sigma-Aldrich) following the instructions for the “Highly pure preparation of Nuclei.” The resulting solutions were frozen in liquid NMaize inbred line B73 seeds were put into growth chambers. Shoots were collected 9 days after germination. Around 10 grams of plant tissue were ground with liquid nitrogen to a fine powder. Five grams of ground tissue were transferred into 50 ml fresh SEB extraction buffer , incubated on ice for 20 minutes, and then filtered through 2 layers of 100um nylon mesh. Triton X-100 was then added to a final concentration of 0.5% and incubated on ice for another 10 minutes. Then the lysate was centrifuged at 2000 rcf for 15 minutes at 4°C and the supernatant was recovered. The pellet was suspended in another 25 ml SEB extraction buffer and centrifuged again at 2000 rcf for 15 minutes. We added 10 ml nuclei storage buffer  in the pellet and centrifuged at 2000 rcf for 5 minutes at 4°C. This step was repeated using 1 ml nuclei storage buffer. Finally, the pellet was resuspended and stored in 105 µl nuclei storage buffer. The protocol was conducted in a cold room (4°C).The PRO-seq protocol was performed as described by http://hannonlab.cshl.edu/fastx_toolkit/), and the 3′ molecular barcode was removed. Reads were trimmed to a maximum length of 36 bp, and the reverse complement was calculated because the HiSeq apparatus starts sequencing from the 5′ end. All downstream alignments were performed using Bowtie2 . Low-quality alignments were filtered and only reads mapping once to the genome were considered for further analysis (Supplementary Table S1). Bedtools (https://github.com/ENCODE-DCC/kentUtils).The fastq files were scanned for any residual adapter sequence (5′-TGGAATTCTCGGGTGCCAAGG -3′) using fastx_clipper from the FASTX_toolkit ( Bowtie2 . BecauseBedtools  was usedhttps://github.com/corcra/bed-metric.git) . Normalized BigWig files representing the mapped reads were used to visualize each strand of the genome separately in the Integrative Genomics Viewer (IGV) (https://github.com/bdo311/metagene-maker). Since the cassava genome is not readily available to work with HOMER, feature annotations were created separately, and the HOMER config files were modified. We approximated the transcription start site (TSS) as the beginning of the 5′ UTR because the cassava genome annotation lacked precise TSS annotations. The same approach was used for Arabidopsis and Maize genomes.The percentage of the cassava genome transcribed was calculated using bedtools . A saturation curve, which calculates the number of unique positions covered as a function of read depth was obtained using the bed-metric scripts , jatropha (Jatropha curcas), castor bean (Ricinus communis), willow , flax (Linum usitatissimum), and poplar (Populus trichocarpa) as previously described by The GERP scores for conservation were calculated using different species from the Malpighiales clade, including rubber  of the Genetic Gain (GG) collection, which comprises 709 elite and historically important clones. Along with these, we analyzed 2302 clones developed as the cycle 1 of IITA’s Genomic Selection (GS) breeding program. In total, 3011 clones were used as the source for phenotypes. For further details on the populations used, see We analyzed data from the IITA cassava breeding program in Nigeria, including a fraction . The GBS markers were combined with the Cassava HapMap v2.0 variants from 241 deep-sequenced cassava accessions (Genotyping-by-sequencing (GBS) libraries were prepared as described previously (cessions . The impr2 > 0.9 and were closer than 100 kb to IRE markers). We also built 10 random sets of 9665 regions with the same average length and approximate distribution in the genome as these elements. As with the IREs, random sets were forced to be outside any annotated gene , and markers from the ROG in close physical distance and high LD were removed.Genomic partitioning analyses are imprecise in highly related populations because of high LD between partitions. We mitigated this problem by eliminating markers from the rest of the genome (ROG) in high LD with the 9665 cassava IREs  and ROG genomic partitions using the software LDAK5 following the ideas of Genomic relationship matrices (GRMs) were calculated for focal l ∼ Ng ∼ Nh ∼ N or ROG (RGRM) partitions. The incidence matrices loc.yrZ, and Z relate observations to the levels of trials and clones, respectively. Variance components were estimated using the “emmremlMultikernel” function implemented in the “EMMREML” R package . For clarity, the four main libraries used through this study will be referred to as PRO-cassava, PRO-maize, GRO-maize  in the cassava genome. We identified ∼2000 peaks in intergenic regions using Homer’s  of the NextGen Cassava Breeding Project ^3 = 7.51e-4.Finally, to test the functional relevance of the plant IREs, we estimated the percentage of the SNP heritability  IREs using dREG and again observed a clear pattern of bi-directional transcription . Our PROIt is unknown whether dREG can accurately predict plant enhancers. The traditional way to identify enhancer candidates is based on open chromatin sites as assessed by DNase-seq or ATAC-seq, together with histone modification marks. To test if dREG was able to tag previously reported enhancer candidates in maize, we analyzed the transcription pattern of 1495 (Supplementary File S3) enhancer candidates identified in a separate maize study . MoreoveTo further characterize the IRE identified by dREG and explore whether they house more phenotypically relevant variants, we compared our results to a list of previously identified expression Quantitative Trait Loci (eQTLs) in maize kernels , which is likely required for a kinase-regulated release of paused Pol II . Some of the transcriptional activity observed in the IREs might be driven by RNA polymerase dependent DNA methylation (RdDM). Thus, we expect the IRE regions to not only capture enhancers but also regulatory RNAs  and transposons. This would partially explain the high methylation profiles and low evolutionary conservation at the IRE sites.IRE regions were shown in cassava to have low levels of evolutionary conservation, a bi-directional pattern of transcription, and a specific DNA methylation profile. It is worth mentioning that PRO-seq cannot differentiate between Pol II and other polymerases  leads to a superset or subset of sites relative to the other. We clearly show, however, that bi-directionally transcribed regions in maize have the characteristic of active enhancers of affecting gene transcription.In mammals, there are various enhancer states enriched with different histone marks: poised or inactive enhancers, primed enhancers, and active enhancers . PreviouRecently, The ability of PRO-seq to detect functional intergenic regions has also been explored in rice. While building a fitness consequence map of the rice genome A. thaliana: they validated 10 of the 14 enhancer candidates tested using a reporter assay. None of the Arabidopsis GRO-seq data, however, displayed strong evidence of transcription in the regions identified by Transcription of genomic enhancers was first described in 1992  there haThe results reported herein cassava and maize suggest that plant transcriptional regulation may be more similar to that of mammals and other metazoans than previously thought. The lack of transcription previously observed in putative Arabidopsis enhancers may have been the result of different growth conditions, tissues, or even low read depth. Recent research, however, has shown that bidirectional transcription could be observed in 113 noncoding regions in Arabidopsis when using exosome mutants  outside of genes in species with larger genomes. This hypothesis has recently been supported in a study that analyzed CRES in 13 plant species, where distal CRES were most abundant in larger and more complex genomes  under accession GSE114758. All other data needed to evaluate the conclusions in the paper are present in the paper or the supplementary materials. Custom scripts for the NGS analysis, metagene plots, and genomic partitioning, among others, have been made publicly available through the GitHub repository:


RXFP1 expression remain largely unknown. This study aimed to identify regulatory elements of RXFP1 that may function differentially in fibrotic fibroblasts. We identified and evaluated a distal regulatory region of RXFP1 in lung fibroblasts using a luciferase reporter system. Using serial deletions, an enhancer upregulating pGL3-promoter activity was localized to the distal region between -584 to -242bp from the distal transcription start site (TSS). This enhancer exhibited reduced activity in IPF and SSc lung fibroblasts. Bioinformatic analysis identified two clusters of activator protein 1 (AP-1) transcription factor binding sites within the enhancer. Site-directed mutagenesis of the binding sites confirmed that only one cluster reduced activity . Co-expression of FOS in lung fibroblasts further increased enhancer activity. In vitro complex formation with a labeled probe spanning the functional AP-1 site using nuclear proteins isolated from lung fibroblasts confirmed a specific DNA/protein complex formation. Application of antibodies against JUN and FOS resulted in the complex alteration, while antibodies to JUNB and FOSL1 did not. Analysis of AP-1 binding in 5 pairs of control and IPF lung fibroblasts detected positive binding more frequently in control fibroblasts. Expression of JUN and FOS was reduced and correlated positively with RXFP1 expression in IPF lungs. In conclusion, we identified a distal enhancer of RXFP1 with differential activity in fibrotic lung fibroblasts involving AP-1 transcription factors. Our study provides insight into RXFP1 downregulation in fILD and may support efforts to reevaluate relaxin-based therapeutics alongside upregulation of RXFP1 transcription.Relaxin/insulin-like family peptide receptor 1 (RXFP1) mediates relaxin’s antifibrotic effects and has reduced expression in the lung and skin of patients with fibrotic interstitial lung disease (fILD) including idiopathic pulmonary fibrosis (IPF) and systemic sclerosis (SSc). This may explain the failure of relaxin-based anti-fibrotic treatments in SSc, but the regulatory mechanisms controlling  Pulmonary fibrosis is a hallmark of fibrotic interstitial lung diseases (fILD). Although the pathogenesis of fILD is not fully understood , fibroblRXFP1-null mice develop early onset peribronchiolar and perivascular fibrosis, with relaxin knock out mice developing early pulmonary and systemic organ fibrosis ) was used as a control  for transfection efficiency. Plasmids were transfected into primary lung fibroblasts using Lipofectamine 2000 according to the manufacturers’ instruction. At 40 hours post-transfection, the cells were washed with PBS, lysed in 1 × passive lysis buffer and analyzed using the Dual-Luciferase Reporter Assay System (Promega Corporation) and a SpectraMax L Microplate Reader . Relative expression levels of pGL-RXFP1 reporters were normalized against pB or pP vector luciferase activity. Four independent fibroblast lines from each group  were used in this study. Transfection studies were performed with a minimum of duplicates in each experiment.Fibroblasts were seeded at 5,000 cells/well in 24 well cell culture plates and cultured overnight prior to transfection with either the pGL3-http://www.fruitfly.org/seq_tools/promoter.html) was used with a minimum promoter score of 0.85 [DNA sequences upstream of both distal and proximal transcriptional start sites (TSS) were used to identify putative promoter and TATA elements. The Neural Network Promoter Prediction method . Primer pairs Nuclear proteins were prepared using fibroblasts at 80–90% confluency and the Nuclear Extract Kit (Active Motif), according to the manufacturer’s protocol.RXFP1 enhancer (TGACTCACAGATTGTTCTAGA-3’5’-TACATTTAGCATGCA) was used as a probe for EMSA. The probe was biotin labeled at the 3’ end using a Pierce™ Biotin 3’ End DNA Labeling Kit (Thermo Fisher Scientific Inc.). EMSA were performed using the LightShift™ Chemiluminescent EMSA Kit (Thermo Fisher Scientific Inc.). Briefly, 2μl 10X binding buffer , 1μl poly (dl·dC) at 1μg/μl, 1μl 50% glycerol, 1μl 1% NP-40, 20fmol biotin-labeled probe and 7μg of nuclear proteins were mixed in a 20μl final volume and incubated at room temperature for 20 minutes. After incubation, 5μL of 5X loading buffer was added to each binding reaction and 20μL was immediately used for polyacrylamide gel electrophoresis . The gel was transferred to a nylon membrane and DNA was crosslinked to the membrane with a hand-held 254nm UV lamp. After treated with blocking buffer, the membrane was incubated with streptavidin-horseradish peroxidase conjugate followed by an incubation with Luminol/Enhancer and peroxide solution prior to autoradiography with a CCD camera. For competition binding reaction, unlabeled wildtype, described above, or mutated AP-1 binding site probe at the underlined lowercase nucleotides (gggCACAGATTGTTCTAGA-3’5’-TACATTTAGCATGCATG) was added in 50-fold excess to the reaction mixture. Supershift assays were performed by incubating monoclonal antibody (Ab) to specific AP-1 transcription factors c-Jun , c-Fos (9F6), FOSL1, and JUNB (C-11) from Santa Cruz Biotechnology with nuclear proteins for 10 minutes on ice and 10 minutes at room temperature prior to the binding reaction described above. Rabbit IgG  was used as a negative control. A total of 4 independent control and 4 IPF fibroblast lines were used for the binding analyses. Supershift analyses were conducted in nuclear protein isolated from 2 IPF and 2 control fibroblast lines.A 37-base pair (bp) double-stranded oligonucleotide containing the AP-1-binding motif (underlined) of the 5’- AAACACTGGACTGGGTTTGG-3’ and R: 5’- GGAAAGTAGGCCCCTTGAGA-3’) spanning the putative AP-1 binding site 2 on the RXFP1 enhancer. ChIP assay was performed using rabbit IgG as a negative control. Densitometry analysis of the PCR amplification was performed using ImageJ [ChIP assay was performed as described , 28. Brig ImageJ . PositivLung fibroblasts were cultured to confluence and total proteins were prepared using radioimmunoprecipitation assay (RIPA). Proteins were separated by SDS-PAGE gel electrophoresis, transferred to polyvinylidene difluoride (PVDF) membrane (Bio-Rad), and blocked in 5% milk prior to primary antibody binding. Antibodies specific for JUN  and FOS  were used. Antibody for GAPDH  were used to determine sample loading. Membrane was developed using HRP-substrate  and imaged using Amersham Imager 680 . The ImageJ software  was used to perform densitometry analysis of the protein bands . A totalRXFP1, JUN, and FOS genes (where JUN and FOS are both AP-1 transcription factors) were obtained from the publicly available Lung Genomics Research Consortium (LGRC) gene expression dataset  [FOS gene expression was analyzed using microarray and was available for 108 controls and 160 IPF patients. JUN gene expression was only available from the RNA sequencing (RNAseq) dataset for 22 controls and 22 IPF patients. The expression levels on RNAseq were shown in Fragments Per Kilobase of transcript per Million mapped reads (fPKM) and were square root transformed for normality prior to analysis.Lung tissue-specific expressions of cs.org/) . FOS gent-test was used for two-way comparisons. Gene expression levels of control and IPF groups were compared using the Mann-Whitney U test. Correlation of FOS and JUN gene expression levels with RXFP1 expression levels was analyzed using linear regression modeling as described [p value < 0.05 significance threshold was used.All data were expressed as the mean ± SD. Student’s escribed . All anaRXFP1 is located on chromosome 4  and historically was thought to be solely comprised of a 132kb (kilobase pair) genomic sequence (designated as the “Short” form of RXFP1). Subsequently the GENCODE project reported one extended RXFP1 transcript with 204.6kb additional sequences  upstream of the previously reported transcript as shown in the University of California Santa Cruz (UCSC) genome browser (https://genome.ucsc.edu/). This is designated as the “Long” form of RXFP1 (https://www.gencodegenes.org/). As shown in RXFP1 [RXFP1, we analyzed the core promoter regions of each transcript using a pGL3 luciferase reporter system and primary lung fibroblasts isolated from donor lungs, as controls, and IPF lungs. A 233bp DNA element spanning -142 to +90 of the distal TSS  for the Long form , and a 194bp fragment covering -145 to +48 of the proximal TSS  for the Short form  were tested . As shown in To determine whether a functional promoter is associated with each of the two forms of RXFP1 gene in lung fibroblasts.We further analyzed the two promoter regions for chromatin characteristics associated with active transcriptional regulation including H3K4Me1, H3K27Ac, H3K4Me3 and DNAse sensitivity clusters using the Encyclopedia of DNA Elements (ENCODE) histone ChIP data tracts in the UCSC genome browser . ConsistGiven the lack of promoter activity in the core proximal regulatory region, we extended our search for potential regulatory elements to both the proximal (PE) and distal (DE) regulatory regions. We analyzed the likelihood of a functional promoter by identifying a TATA box in a 3.1kb region (-2158bp to +971bp) and a 1.4kb region (-1202bp to +161bp) within the DE and PE regions, respectively. These regions possess potential regulatory functions based on the UCSC genome browser. Consistent with the lack of proximal promoter activity, there was no TATA box within 200bp upstream of the proximal TSS. However, a potential site was identified in the proximal region at -1095 to -1114. For the distal region, a TATA box was identified at -16 to +3 in addition to another site between -1946bp to -1927bp .These extended regions were further characterized in lung fibroblasts from control, IPF, and SSc patients for promoter activity using pB. Similar to the proximal core promoter, there was no increased activity for the PE in any fibroblast lines compared to the pB vector . The 3.1We performed serial deletions of the 1.4kb PE to rule out any repressor element interfering with promoter activity. Deleting 274 bp or 700 bp upstream sequences did not result in any significant promoter activity increase compared to the pB vector , furtherRXFP1 enhancer). This was confirmed with the additional deletion of 1233bp 3’ sequences (DE-D2toD3) of the D2 clone that fully restored the 3.1kb enhancer activity (106 ± 6%).Since the extended distal region retained promoter activities among control and fibrotic fibroblasts, we tested whether the extended distal region was associated with enhancer function using pGL3promoter, which contains a SV40 promoter and used for testing enhancer activity of targeted sequences. Using control fibroblasts, we consistently observed greater than 50-fold enhancer activity in the distal region while there was no activity for the proximal extended region compared to the pP vector . The DE https://genome.ucsc.edu/) (RXFP1 enhancer (pP-D2toD3) and the TFBS element showed similar enhancer activities in control and SSc lung fibroblasts  . Thereforoblasts .The enhancer activities were significantly reduced in SSc compared to control fibroblasts. We performed serial deletion using the pP-TFBS to further map the enhancer region . A 91bp We further refined the enhancer region by serial deletion of the 515 bp TFBS-D1 clone (-584 to -68) from both 5’ and 3’ ends and tested the enhancer activity in control lung fibroblasts. Among all deletions, a 343bp element  resulted in a 5.8-fold (±0.6) increased activity compared to TFBS-D1 (p<0.001). Thus, the enhancer appears to reside in this 343bp region .To identify transcription factors that may mediate the enhancer activity, we mined the UCSC genome browser and identified binding sites for multiple transcription factors . Since AWe further tested whether AP-1 factors directly bind to the enhancer using a labeled probe spanning the functional site 2 of AP-1 and nuclear proteins isolated from control and IPF lung fibroblasts. Compared to probe alone, addition of nuclear proteins resulted in a complex formation (complex A) which was competed out with 50-fold unlabeled wildtype probe, but not unlabeled AP-1 site mutated probe in the binding reaction . NuclearRXFP1 distal regulatory region in IPF fibroblasts. However, quantitative analysis of the RXFP1 gene expression in each fibroblast did not show any correlation with observed binding level of JUN to the AP-1 site from the ChIP assay . To estimate the positive binding, we used an arbitrary cutoff of a 25% increase in PCR amplification for the pulldown ratio (JUN/IgG) >1.25 based on the densitometry analysis of the PCR amplification products with primers spanning the AP-1 binding site 2. For control fibroblast lines, 4/5 of them had pulldown signal while only 1/5 IPF lines was positive  althoughIP assay .FOS and RXFP1. The demographic and clinical characteristics are shown in FOS compared to controls    data from 22 controls and 22 patients with IPF. The expression levels of JUN showed reduced levels in IPF compared to controls   (FOS in IPF and positive correlation with RXFP1 expression levels were confirmed in the RNAseq dataset (Microarray whole lung tissue gene expression was performed for 108 controls and 160 IPF in the LGRC dataset  for FOS p<0.001) . The exp= 0.002) . Since J= 0.001) . The expp<0.001)  in IPF.  dataset .To determine whether AP-1 expression is reduced in IPF fibroblasts, we analyzed the protein expressions of JUN and FOS in independent lung fibroblast lines (5 control and 5 IPF). The levels of FOS protein were reduced in IPF fibroblasts than controls . Lower lRXFP1, which had reduced activities upon introduction into fibrotic lung fibroblasts compared to controls. The enhancer activity was partially mediated by AP-1, with lower expression of JUN and FOS in lungs from patients with IPF compared to controls and lower binding of JUN to the enhancer region in IPF fibroblasts. Furthermore, expression levels of JUN and FOS were positively correlated with RXFP1 expression in lung tissue from patients with IPF. This is the first study to systemically analyze the regulatory elements of RXFP1, thus providing molecular insights into transcriptional regulation of this important protein in lung fibroblasts.We have identified a strong enhancer within the distal regulatory region of RXFP1 in fibroblasts of these patients, which would abrogate their responsiveness to relaxin [RXFP1 expression in their lungs have better pulmonary function, supporting the pathophysiologic relevance of this locus in fILD [In vitro silencing of RXFP1 results in insensitivity to exogenous relaxin, an effect which is reversed by enhancement of RXFP1 expression in both control and IPF lung fibroblasts [RXFP1 may serve as a therapeutic option that would help to restore the responsiveness to relaxin-based therapies in fibrotic tissues [RXFP1 in fibroblasts from patients with fILD may be one of the strategies to restore RXFP1 expression and the responsiveness to relaxin-based antifibrotic therapies in patients with IPF and SSc.A number of studies support relaxin as a potent anti-fibrotic agent , 33, 34. relaxin , 17–19.  tissues . Our stuRXFP1 gene distal enhancer in lung fibroblasts. The reduced JUN and FOS gene expression in IPF lungs and FOS protein expression in IPF lung fibroblasts suggest lower AP-1 expression may be one of the mechanisms associated with the decreased RXFP1 expression in fibrotic lungs. By upregulating these transcription factors in IPF fibroblasts we may be able to restore RXFP1 expression and thus responsiveness to relaxin-based therapeutics in fibrotic fibroblasts. Interestingly, we did not observe a direct correlation of JUN binding by ChIP and RXFP1 expression by quantitative PCR analysis in lung fibroblasts, suggesting other mechanisms are associated with RXFP1 gene regulation. This could be also due to the fact that ChIP represents a semi-quantitative measurement of DNA binding. Similarly, the binding of nuclear proteins to the AP-1 site was not different in IPF compared to control in our EMSA analysis although EMSA is also limited by being a semi-quantitative method.AP-1 is ubiquitously expressed in different cells and tissues and plays important roles in multiple cellular processes including proliferation, differentiation, senescence, and cell death , 37. TheFosl2 mice develop spontaneous lung fibrosis with Fosl2-expressing macrophages promoting lung fibrosis [FOSL2 and RXFP1 were negatively correlated . Due to the heterogeneity of primary fibroblasts isolated from lungs [RXFP1 regulation is important.Our study does have some limitations. First, the analysis of om lungs , the intom lungs , 37. We RXFP1 with differential activity in fibrotic lung fibroblasts involving AP-1 transcription factors. Our study provides insight into the reduced expression of RXFP1 in patients with IPF and may support efforts to restore the effectiveness of relaxin-based therapeutics in fILD through the upregulation of RXFP1 transcription.In conclusion, we identified a distal enhancer of S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Fighttps://genome.ucsc.edu/). (B) Transcription factors identified using the UCSC Genome Browser for the 608bp enhancer region. The narrowed 343bp enhancer was boxed in red. Nucleotide locations of the two AP-1 sites are labeled. The colored boxes with specific numbers correspond to specific transcription factors and detailed in (C).(A) chromatin characteristics associated with active transcriptional regulation including H3K4Me1, H3K27Ac, H3K4Me3 and DNAse sensitivity clusters using the Encyclopedia of DNA Elements (ENCODE) histone ChIP data tracts in the UCSC genome browser ((PDF)Click here for additional data file.S2 FigRXFP1 enhancer. The unlabeled wildtype (wt) or mutated (mut) AP-1 site probe at 50-fold of labeled wt RXFP1 probe were used as unlabeled competitor for the specific binding competition assays. Supershift analyses with antibodies specific for JUN, FOS, JUNB and FOSL1 or control IgG are shown. The AP-1 specific complex is labeled as “complex A”. The supershifted complex with antibody specific for JUN is labeled as “complex B”. Unbound free labeled probe band is marked as “free probe”.(A) Original photos for the EMSA analysis showing in (PDF)Click here for additional data file.S3 FigRXFP1 gene expression was analyzed using a quantitative PCR (qPCR) with a Taqman probe (Hs01073141_m1) and standard protocol. The qPCR was analyzed using a QuantStudio 5 System (Applied Biosystem Inc.). Correlation of JUN binding analyzed by the ChIP analysis on (PDF)Click here for additional data file.S4 FigFOS in control and IPF (n = 22 for each) analyzed using bulk RNA sequencing from the publicly available Lung Genomics Research Consortium (LGRC) gene expression dataset . Correlation of FOS (right) gene expression levels with RXFP1 was analyzed in IPF lungs (22 subjects) using linear regression and the R2 and p-value are shown. (B) Correlation of FOSL2 gene expression levels with RXFP1 was analyzed with microarray in IPF lungs (160 subjects) using linear regression and the R2 and p-value are shown.(A) Lung tissue expression levels of (PDF)Click here for additional data file.S5 FigAgarose gel images from the gel electrophoresis for the Chromatin Immunoprecipitation (ChIP) analysis shown in (PDF)Click here for additional data file.S6 FigThe bands specific for FOS, JUN and GAPDH are shown.(PDF)Click here for additional data file.