Escherichia coli inducible promoters, our incomplete understanding of the relationship between sequence composition and gene expression hinders our ability to predictably control transcriptional responses. Here, we profile the expression dynamics of 8269 rationally designed, IPTG-inducible promoters that collectively explore the individual and combinatorial effects of RNA polymerase and LacI repressor binding site strengths. We then fit a statistical mechanics model to measured expression that accurately models gene expression and reveals properties of theoretically optimal inducible promoters. Furthermore, we characterize three alternative promoter architectures and show that repositioning binding sites within promoters influences the types of combinatorial effects observed between promoter elements. In total, this approach enables us to deconstruct relationships between inducible promoter elements and discover practical insights for engineering inducible promoters with desirable characteristics.A crucial step towards engineering biological systems is the ability to precisely tune the genetic response to environmental stimuli. In the case of  Precisely tuning the genetic response to environmental stimuli is a key step in engineering synthetic biology systems. Here, the authors profile 8269 IPTG-induced promoters to deconstruct the relationship between sequence architecture and gene expression. For example, inducible systems have been designed to function as controlled cell factories for biosynthesis as well as non-invasive diagnostics for gut inflammation5. However, these applications generally rely on synthetic inducible promoters that can elicit precisely programmable responses, a quality that is not exhibited by native promoter systems. As a result, there is a demand for strategies to engineer inducible promoters with desirable characteristics, such as minimal expression in the uninduced state  and maximal difference between the induced and uninduced states . More broadly, the design and analysis of synthetic inducible promoter function provide insight on the biophysical processes driving gene regulation.Inducible promoters are key regulators of cellular responses to external stimuli and popular engineering targets for applications in metabolic flux optimization and biosensing10. Directed evolution is a promising strategy that leverages stepwise random mutagenesis and selection to identify favorable promoters, but is generally limited to optimizing within local, evolutionarily accessible sequence space12. While this black box approach can produce variants with the desired phenotype, it often requires iterative rounds of library screenings12 and does not inform our ability to logically construct promoters. Lastly, rational design is a promising approach based on the application of pre-existing mechanistic knowledge of inducible systems to generate novel variants14. Although there is great potential in rationally designed promoters for achieving specific applications, this approach requires a fundamental understanding of how to engineer these systems.A variety of approaches have been implemented to engineer inducible promoters, however, these strategies have their shortcomings. Previous studies have had great success implementing biophysical models to tune the relative behaviors of regulatory elements and explain promoter expression, but do not tell us how the repositioning of binding sites influences expressioncis-regulatory elements that work in concert with multiple trans-acting factors to determine overall expression output16. As such, a critical step towards learning how to engineer these systems is to interrogate the combinatorial regulatory effects between promoter-based elements. Years of studies on the inducible lacZYA promoter have revealed many sequence-based factors influencing its regulation. First, the binding affinities of operator sites are critical elements in determining the activity of the repressor protein, LacI18. Second, the nucleotide spacing between operator sites is vital as looping-mediated repression is dependent on repressor orientation19. Third, the positioning of the repressor sites relative to the RNA polymerase (RNAP) binding sites determines a variety of repression mechanisms and transcriptional behaviors14. Fourth, the strength of the core promoter modulates RNAP avidity and thus gene expression6. However, while previous studies have characterized these modular sequence components individually, the combinatorial effects of these features on promoter induction have yet to be explored.Inducible promoters consist of E. coli promoters20, we sought to address these obstacles by integrating rational design with a high-throughput screening of large DNA-encoded libraries. The recent development of massively parallel reporter assays (MPRAs) provides a framework for leveraging next-generation sequencing to measure cellular transcription levels of large numbers of DNA sequence variants. This approach enables the measurement of thousands of synthetic sequences in a single, multiplexed experiment, often using transcriptional barcodes as a readout21. Previously, this paradigm has also been used to empirically examine both the individual and combinatorial effects of transcription factor binding sites on gene expression in eukaryotes, improving our ability to design synthetic eukaryotic promoters with programmable responses29. However, there have been few similar high-throughput studies in prokaryotes.Inspired by previous success in studying the combinatorial logic of lacZYA promoter and investigate the relationships between inducible promoter components across four cis-regulatory sequence architectures. We first explore the relationship between operator spacing and repression at the lacUV5 promoter using a variety of transcriptional repressors. Next, we design and characterize 8269 promoters composed of combinations of LacI repressor and RNAP-binding sites, exploring combinatorial interactions between elements and establishing relationships that guide transcriptional behavior. Lastly, we isolate and further characterize promoters with various levels of fold change and leakiness that may be useful in synthetic applications.In this work, we implement a genomically-encoded MPRA to interrogate thousands of rationally designed variants of the lacZYA promoter is a classic model for gene regulation in E. coli, with many studies investigating the relationship between sequence composition and induction properties. This promoter contains two LacI dimer sites positioned at the proximal +11 and distal −82 positions relative to the transcription start site (TSS)31, which flank a set of σ70 −10 and −35 elements . RNAP cooperatively binds these σ70 hexameric sequences and the relative binding affinity of these elements determines promoter strength8. Conversely, the LacI operator sites repress expression from the native lacZYA promoter when bound32. While LacI repressor bound at the proximal site blocks RNAP binding as well as promoter escape, binding at the distal site alone does not inhibit transcription and serves a more nuanced role in repression33. When both the proximal and distal sites are bound, LacI dimers at these sites can engage in homotetrameric protein interaction, tethering these sites together and forming a local DNA loop35. This repression loop further occludes RNAP binding, decreasing gene expression.The 38. Due to the helical nature of B-form DNA, which completes a full rotation roughly every 10.5 bp, as operator sites are placed at various distances from one another along with the DNA their relative orientation along the face of the DNA helix changes as well. As a result, the ability of the distal site to engage in this repression loop fluctuates as it is shifted along with the promoter, with repression strength correlated with helical phasing between the two operator sites37. In our effort to optimize the lacZYA promoter, we sought to validate the effect operator spacing has on repression, as well as explore whether other repressors follow this same phenomenon.Studies exploring the formation of this repression loop have found that it is heavily dependent on the spacing between LacI operator sites Fig. 36–38. DulacZYA-derived promoter, lacUV5: LacI, AraC, GalR, GlpR, LldR, and PurR. While LacI39, AraC41, and GalR46 have been experimentally shown to engage in DNA looping, there is evidence that GlpR47, LldR48, and PurR34 may also be capable of this mechanism. Using reported, natural binding sites for these TFs49  of the variants we designed, measuring an average of 70 unique barcodes per variant .Accordingly, we tested the relationship between spacing and repression for six transcription factors (TFs) at the most commonly utilized TSS Fig. . Further 20 Fig. . In brielacUV5 promoter when placed in the proximal position. To evaluate this, we compared the relative expression between variants with proximal sites to the lacUV5 promoter containing a scrambled LacI site in the proximal position . LacI exhibited the strongest level of repression in the proximal position at 2.62-fold , which may be due to the strong binding affinity of the native proximal operator site30.We first explored the ability of these TFs to repress the ion Fig. . At this37 and AraC41 are known to exhibit a cyclic pattern of repression as the distance between operator sites is increased, there are no direct measurements showing that GalR, GlpR, LldR, or PurR share this phenomenon. First, we looked at the effect of moving the distal site across 33 nucleotides in the absence of a functional proximal site . To see whether these relationships would change when DNA looping was possible, we evaluated the effect of moving the distal site when a proximal site was also present . This enhanced repression by distal sites when a proximal site is present indicates the existence of synergistic interactions between these sites. Furthermore, repression by these distal sites followed a 10–11 bp periodicity as they were placed incrementally further from the proximal site, which may indicate the formation of DNA loops at the lacUV5 promoter. LldR, PurR, and GlpR distal sites did not show significantly enhanced ability to repress when a proximal site was present , indicating these TFs may not participate in looping-based repression. Additionally, distal site and loop-mediated repression differed between TFs tested which is likely due to differences in how these proteins are situated on their binding sites or oligomerize to form DNA loops. Thus, we find different repressor systems exhibit unique relationships between operator spacings and repression, highlighting the need to study these systems individually.To gauge the performance of these repressors at each position in the distal site, we looked at how expression changes as a function of distance from the proximal site. While LacIite Fig. . We obseent Fig. . To direlacUV5 architecture, we next sought to learn how these sites may be manipulated to generate lacUV5 variants with minimal leakiness and maximal fold change, properties that are desirable in synthetic applications. Previously, we found testing large libraries of promoters composed of various combinations of sequence elements allows us to characterize the contribution of individual sequence elements and reveal interactions between them53. Employing a similar MPRA strategy, we designed and assayed a library of 1600 inducible promoters, referred to as Pcombo, composed of all possible combinations of one of ten proximal LacI-binding sites at +11, four −10 elements, four −35 elements, and ten distal LacI sites at −90 18, a variant of the natural O2 site, O2-var, and a series of LacI sites created from different combinations of the monomeric halves of each of these dimeric binding sites  and fully induced conditions (1 mM IPTG). We recovered expression measurements for 1493 variants within this library (93.3%) with an average of 9 barcodes measured per variant. We observed high expression correlation between biological replicates in both the induced and uninduced conditions   in the proximal site yielding 4.61-fold higher fold changes on average than promoters containing weak operators in the proximal site . We attribute this to the importance of the downstream operator in blocking RNAP binding and transcriptional initiation55. As expected, promoters containing Osym in the proximal site generally drove the highest fold change, however, pairing with another Osym in the distal site surprisingly decreased fold change relative to other variants. Notably, while the consensus core promoter containing Osym in both the proximal and distal sites yielded a fold change of 4.63, its counterpart containing the weaker O1 variant in the proximal site drove an increased fold change of 8.97. While the promoter containing Osym in both the proximal and distal sites had 1.77-fold lower uninduced expression compared to its counterpart with a weaker O1 in the proximal site, induced expression was also 3.43-fold lower . We expect the drop in accuracy is related to the observed interactions between the distal and distal+ sites, which will require further studies to parameterize. Nonetheless, we show that this adaptable model framework is robust even across previously unseen sequence architectures.Finally, we explored whether our previously established statistical mechanics model could accurately predict the expression of variants in this library. We extended our model framework to account for the different promoter states available to the Pmultiple architecture , which may be because some of the tested distances were insufficient to enable the formation of DNA loops37  in the absence of IPTG and at 1 mM IPTG with an average of 8 barcodes per variant . Variants with the highest fold change were isolated from Pspacer and Pmultiple libraries, highlighting the potential benefits of exploring beyond canonical regulatory architectures. Although previously we found Pspacer variants exhibited greater uninduced and induced expression than Pcombo variants, we did not observe this phenomenon between these subsets of each library.To gauge how our alternative inducible promoter architectures perform relative to one another, we compared the distributions of fold changes between each library. To focus on inducible variants, we limited our analysis to promoters with fold change ≥2. Of the thousands of promoters tested, relatively few were capable of induction, highlighting the difficulty in engineering these systems. Each architecture generated promoters with similarly wide ranges of uninduced expression, induced expression, and fold changes Fig. . HoweverlacUV5 promoter. From all four architectures, we individually evaluated promoter sequences exhibiting higher fold change with low leakiness by using flow cytometry to measure sfGFP expression in uninduced (0 mM IPTG) and fully induced (1 mM IPTG) conditions . In particular, a Pmultiple variant demonstrated >5-fold higher fold change than lacUV5. Many variants, especially Psteric promoters, exhibited low leakiness while maintaining comparable induced expression. Activity measurements using flow cytometry well-correlated with MPRA measurements    as well as UV5 mutations in the −10 region to increase activity lacUV5 variants composed of each combination of 10 proximal operator sites, 10 distal operator sites, four −10 elements, and four −35 elements was designed. The operator sites were selected to span a wide range of lacI-binding affinities (Supplementary Table 1 and O3) and a variant of the native O2 lac operator with three mutations (O2-var). In addition, Osym and six other synthetic operators  were used with the latter being designed by creating palindromic sequences based on either the left or right halves of each native sequence. Lastly, a scrambled operator (Oscram) composed of a random scrambling of the O1 sequence served as a negative control. The −10 and −35 sites were selected to span a range of binding affinities for RNA Polymerase and obtained from a previous characterization20  of 1600 lacUV5 variants composed of each combination of one of five distal+ operator sites, five distal operator sites, five proximal operator sites, four −10 elements, and four −35 elements was designed. The O1, O3, O2-var, Osym, and Oscram operators from the Pcombo library were selected as the five operator sites for testing. In addition, the same −10 and −35 elements from the Pcombo library were selected. This library was constructed with sequence elements placed in the same positions as the Pcombo library, with the exception of the distal+ sequence being placed immediately upstream of the distal site.A library (Pmultiple) of 2000 lacUV5 variants composed of each combination of five distal operator sites, four −35 elements, four −10 elements, and five spacer operator sites was designed. In order to fit the 17-bp spacer region, two base pairs were trimmed from each end of the spacer operator sites (Supplementary Table A library (Pspacer) of 4400 lacUV5 variants composed of each combination of four −10 elements, five core LacI sites centered at −26, five proximal operator sites, and one of four UP elements in the presence or absence of an extended −10 motif was designed. The same operator sites and −10 elements from the Pmultiple library were selected. Proximal operator sites were tested when centered at both the +11 and +30 positions relative to the TSS. The UP elements selected were obtained from a previous characterization and range in their abilities to enhance transcription73 (Supplementary Table 64.A library (Psteric) of 800 ® Q5® Hot Start HiFi PCR Master Mix (#M0543S) at 11 cycles using primers GU 132 and GU 133 as well, in triplicate. Replicates were pooled, then cleaned with Zymo Clean and Concentrator Kit (#D40140).The library was synthesized by Agilent and then resuspended in 100 µL of elution buffer before cloning into plasmid pLibacceptorV2 (Addgene ID no. 106250). The transcription factor spacing library was ordered separate from the other libraries, which were altogether synthesized and tested in a multiplexed pool. First, the library was amplified with KAPA SYBR FAST qPCR Master Mix (#KK4600) utilizing primers GU 132 and GU 133 at 10 µM to determine Cq values. Afterward, the library was amplified with NEBNext® Q5® Hot Start HiFi PCR Master Mix (#M0543S) for 10 cycles using primers GU 132 and GU 134. Library ends were then digested with SbfI-HF (NEB #R3642S) and XhoI (NEB #R0146S) by incubating at 37 °C for 1.5 h. The plasmid vector, pLibAcceptorV2, was first maxi-prepped with QIAGEN Plasmid Maxi Kit (#12162), concentrated with a Promega Wizard SV Gel and PCR Clean-up System (#A9281), and digested with SbfI-HF (NEB #R3642S), SalI-HF (NEB #R3138S), and rSAP (NEB #M0371S) for 1.5 h at 37 °C. Insert (library) and vector (pLibAcceptorV2) were ligated using T7 DNA Ligase (NEB #M0318S), incubating at room temperature for 1 h. The plasmid was then transformed into DH5α electrocompetent E. coli cells (New England Biolabs C2989K) and plated for 24 h at 30 °C on LB + kanamycin (25 µg/mL) agar plates. These plates were then harvested in 5 mL of LB and 400 × 106 cells (based on OD600) were grown overnight in 450 mL LB + kanamycin (25 ug/mL). This plasmid, consisting of the library cloned into pLibacceptorV2, was isolated and concentrated with Zymo Clean and Concentrator Kit (#D40140).To barcode the library, each library was amplified with NEBNext® Q5® Hot Start HiFi PCR Master Mix (#M0543S) for 25 cycles using primers GU 99 and GU 100 at 10 µM. This amplicon was then digested with BsaI-HF (NEB # R3535) and NcoI-HF (NEB #R3193S) for 1.5 h at 37 °C. pLib was digested with BsaI-HF (NEB # R3535) and NheI (NEB# R3131S). pLib vector was then ligated with the GFP insert using T7 DNA Ligase (NEB #M0318S), incubating at room temperature for 1 h. This plasmid was next transformed into DH5α electrocompetent cells and plated for 24 h of growth at 30 °C as well, yielding pLib_sfGFP plasmid after maxi-prep.To clone RiboJ::sfGFP into the plasmid, RiboJ::sfGFP was first amplified with NEBNext20E. coli MG1655 derivative , and grown overnight for 24 h at 30 °C. The following day, plates were scraped and 800 million cells in 200 mL of LB + kan (25 µg/mL) were inoculated overnight at 30 °C.The pLib_sfGFP plasmid was first digested with SalI-HF (NEB #R3138S) and NheI (NEB# R3131S) to remove the background. This was then transformed into the landing pad strain, an engineered−5, and 10−6 dilutions. Plates grown overnight were then scraped, and 400 million cells inoculated into 200 mL LB + Kan 25 µg/mL for overnight growth at 37 °C. Ultimately, this was plated again at 30 °C to validate integration (GFP instead of mCherry) and then glycerol stocked after colony PCR for further confirmation.For library integration, glycerol stocks of landing pad strain with the integration plasmid were grown overnight in 200 mL + kan (25 µg/mL) at 30 °C. 200 million cells from this overnight culture was inoculated the next day into 250 mL LB + 0.2% arabinose + 25 µg/ml Kan at 30 °C for 24 h to induce recombination. The following day, 800 million cells of induced overnight were inoculated into 80 mL LB + 25 µg/mL Kan at 42 °C for heat cure. This was grown to log phase (OD 0.3–0.7) for about 1.5 h. In total, 200 million cells from this log phase culture were plated at 42 °C for 16 h in undiluted, 10The promoter and barcode region from pLib was prepared for sequencing and downstream mapping of the barcodes to their respective variants. Two PCRs were performed to prepare pLib samples for sequencing, the first of which adds sites for the sequencing primer whereas the second PCR adds the adaptors for Illumina sequencing and a unique index DNA label. Each barcode mapping was performed in duplicate.® Q5® Hot Start HiFi PCR Master Mix (#M0543S) at 11 cycles using primers GU 60 and GU 79 at 5 µM as well in triplicate. Replicates were pooled, then cleaned with Zymo Clean and Concentrator Kit (#D40140), eluting into 10 µL of Ultra-pure H2O.For the first PCR, the library was amplified with KAPA SYBR FAST qPCR Master Mix (#KK4600) with primers GU 60 and GU 79 at 5 µM to determine Cq values. Afterward, the library was amplified with NEBNext® Q5® Hot Start HiFi PCR Master Mix (#M0543S) at ten cycles using primers GU 70 and GU 86 at 5 µM. Since different primers add different indices to each sample, we re-ran the second PCR with a different set of primers to serve as redundancy and allow us to compare sequencing replicates. This process was repeated in a separate PCR, with primers GU 70 and GU 87 also at 5 µM.For the second PCR, Illumina adapters P7, P5, and a unique DNA index were added. The product from the first PCR was amplified with primers GU 70 and GU 86 at 5 µM to determine Cq values. Afterward, the library was amplified with NEBNext20. In brief, paired-end reads are merged using PEAR74 (version 0.9.1). We then extract the first 150 bp of each read, which encodes the promoter variant, as well as the last 20 bp encoding the barcode, and generate a list of barcode-variant associations. Finally, we perform additional filtering steps for quality control purposes.Ultimately, each technical replicate was performed in duplicate, cleaned with Zymo Clean and Concentrator Kit (#D40140), and ran on a 1.0% agarose gel for final confirmation. After quality assessment, samples were sequenced on an Illumina Nextseq 500 using a Paired-end 300-cycle kit (2 × 150 bp). Barcodes were mapped to their respective promoter variants using the pipeline from Urtecho et al.Library pellets were prepared in both Induced and Uninduced conditions. First, glycerol stocks were inoculated in 100 mL of MOPS with 0.2% glucose + kanamycin (25 µg/mL) at 30 °C for 16 h overnight. The following day, the overnight culture was diluted to OD 0.0005, inoculated into 200 mL MOPS + kanamycin (25 µg/mL) with 0.2% glucose, and grown at 37 °C to OD 0.5–0.55 (~5 h) both with 1 mM IPTG and without.g at 4 °C. The supernatant was poured off. RNA was extracted from E. coli pellets using Qiagen RNEasy Midiprep kit (#75142). We performed technical replicates of this extraction (separate RNA extractions of the same culture) with the operator spacing library and biological replicates . Subsequent wash steps concentrated isolated RNA with Qiagen Minelute Cleanup Kit (#74204). Next, isolated RNA was converted to cDNA with Thermo Fisher SuperScript IV (#18090010) following the manufacturer’s directions.To harvest RNA pellets, the culture was first cooled for two minutes in an ice slurry while periodically swirling. For each sample, three 50 mL aliquots of culture were poured into pre-chilled tubes and spun for two minutes at 13,000 × g. The supernatant was then poured out. DNA from each pellet was then isolated with Zymo Research ZR Plasmid Miniprep Kit (#D4015) for use as normalization.To harvest gDNA pellets, 5 mL samples of each culture were then spun down for four min @ 5000 × ® Q5® Hot Start HiFi PCR Master Mix (#M0543S) from 1 µg of gDNA for 14 cycles with primers GU 59 and GU 60 at 5 µM. The product was cleaned with Zymo Clean and Concentrator Kit (#D40140). In all, 1 ng of this sample was amplified again for ten cycles with primers GU 65–68 and GU 70 for indexing, yielding 8 total samples; technical replicates for induced and uninduced cDNA, and induced and uninduced gDNA. Both prepared DNA and RNA library samples were quantified with Agilent Tapestation, then sent for sequencing on HiSeq2500 (SE 50-cycle) to the Broad Stem Cell Research Center at UCLA.The barcoded cDNA was amplified with NEBNextA comprehensive list of all primers used in this paper can be found in Supplementary Table Following RNA-Seq and DNA-Seq of the barcodes, we quantify the relative abundance of each barcode. Demultiplexed RNA and DNA reads for each biological replicate were converted to counts of each barcode via a custom UNIX script that extracts barcode sequences from individual reads and counts the number of observed reads for each barcode. These barcode counts were normalized using the following formula:Normalized read counts were then merged by common barcode to yield a comprehensive data frame containing normalized read counts for each barcode in each replicate. This data frame was then merged with the barcode mapping data to map normalized read counts to their corresponding promoter. Multiple barcodes could map to a single promoter, thereby providing replicability, and any promoter that contained fewer than three barcodes in any sample was removed. After this filtering step, promoter expression for each replicate was calculated using the following formula:To normalize promoter expression between induced and uninduced samples, the expression of each promoter was normalized to the median negative control promoter expression in its respective biological replicate. Lastly, the mean expression of the biological replicates was calculated to obtain final expression values for the induced and uninduced conditions.r2) between fit and actual gene expression values was calculated using log10-transformed values to reduce the effects of large expression outliers.For the Pcombo library, initial guesses for the binding energies of each LacI operator site were used as inputs and refined when fitting a statistical mechanics model to the Pcombo promoter expression data. The coefficient of determination  and SalI-HF (NEB #R3138S).Two promoters were selected from each of the libraries, yielding eight total promoters in addition to two controls (a constitutive promoter and UV5). Individual promoter variants were selected from our library of variants based on the highest fold change (induced over uninduced expression) and fold change:noise ratio (fold change over uninduced expression). These sequences were ordered from IDT as gBlocks® Q5® Hot Start HiFi PCR Master Mix (#M0543S). Each reporter was assembled with Gibson Assembly® Master Mix (NEB #E2611S) using 30 bp overlaps between the plasmid pLibAcceptorV2, the promoter, and RiboJ:sfGFP. Each assembled reporter was separately transformed into E. coli DH5α Chemically Competent E. coli (NEB #C2987H) yielding 10 total transformed E. coli strains containing their respective promoter, RiboJ:sfGFP, and Kanamycin antibiotic resistance. Afterward, the promoter and downstream GFP segment were sequenced from isolated colonies using the same set of primers, GU 142 and GU89, to confirm correct constructs. All products were cleaned with Zymo Clean and Concentrator Kit (#D40140) except for pLibAcceptorV2, which was cleaned with Promega Wizard SV Gel and PCR Clean-up System (#A9281) after DNA isolation with QIAGEN Plasmid Maxi Kit (#12162).After synthesis by IDT, promoters were amplified using primers GU 142, GU89, and NEBNextE. coli strains containing library members were grown overnight for 16 h in 5 mL of Luria Broth and kanamycin (25 mg/µL). Afterward, the plasmid was isolated using Zymo ZR Plasmid Miniprep Kit (#D4054) formed into an electrocompetent MG1655 containing an engineered landing pad within the essQ-cspB intergenic locus20 and plated on LB and kanamycin (25 μg/mL) at 30 °C. Two colonies per promoter were resuspended in LB, and inoculated into 5 mL of LB + kanamycin (25 μg/mL) for overnight growth.essQ-cspB locus using Cre-Lox-mediated cassette exchange. Following overnight growth, cells of this culture were inoculated into 5 mL of LB, kanamycin (25 μg/mL), and 0.2% arabinose (g/mL) and grown for 24 h to induce integration of the reporter cassette. After integration of the reporter cassette through the arabinose-induced Cre system, the residual plasmid was removed through heat-curing. In total, 200 million cells were inoculated into 3 mL of LB and kanamycin (25 μg/mL) and grown at 42 °C for about 1.5 h to reach log phase (OD 0.3–0.7). After this growth, cells were diluted to 10−4 and plated on LB + kanamycin (25 ug/mL) plates overnight at 42 °C to complete the heat-curing process.Each promoter was separately integrated into the ® PBS Phosphate-Buffered Saline 10010023). In total, 1 mL of each sample was filtered into a Falcon 5 mL Polystyrene Round-Bottom Tube with Cell-Strainer Cap. E. coli MG1655 was used as a negative control for GFP expression while a constitutively active library member was used as positive. Data was collected using a BioRad S3 Cell Sorter with ProSort Version 1.6 and analyzed in FlowJo (version 10.0.8r1). E. coli cells were isolated by gating using FSC and SSC  and 25 µg/mL of kanamycin at 30 °C for overnight growth. The following day, cells grown overnight were diluted to an OD of 0.002 in MOPS EZ-Rich Media (TEKNOVA #M2105) with 0.2% glucose (g/mL) and 25 ug/mL of kanamycin at 30 °C. These cells were then transferred to 100-mL flasks all containing 15 mL of MOPS EZ-rich media + 0.2% glucose. 1 mM IPTG + 25 µg/mL kanamycin were added to the “Induced” cultures, whereas 25 µg/mL kanamycin was added to the “Uninduced” cultures. These cultures were then grown at 37 °C for 3.5 h. In all, 5 mL of each sample was spun down, the supernatant was decanted, and the cell pellets were resuspended in 1 mL PBS (GIBCOGlycerol stocks for each promoter were scraped and inoculated into liquid cultures containing MOPS EZ-Rich Media (TEKNOVA #M2105) and 25 µg/mL of kanamycin at 30 °C for overnight growth in 5 mL disposable culture tubes. The following day, each promoter was diluted to OD 0.005 in 500 µL of MOPS EZ-Rich Media (TEKNOVA #M2105) with 0.2% glucose (g/mL) and 25 µg/mL of kanamycin and set up for plate reader analysis in triplicates across an IPTG gradient: 0, 0.001, 0.005, 0.01, 0.1, 1 mM. After samples were grown for five hours at 37 °C, 100 µL aliquots were transferred into 96-well flat-bottom microplates. Measurements were taken for wavelengths 650 nm (measures OD) and 520 nm (measures GFP) on the Tecan Infinite M1000 Pro No. 30064852 plate reader. Data were analyzed in Excel (Version 16.41) with the four reads per time point per well averaged and divided by the OD measurement to calculate the GFP fluorescence.Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


The establishment of the small intestinal (SI) lineage during human embryogenesis ensures functional integrity of the intestine after birth. The chromatin dynamics that drive SI lineage formation and regional patterning in humans are essentially unknown. To fill this knowledge void, we apply a cutting-edge genomic technology to a state-of-the-art human model of early SI development. Specifically, we leverage chromatin run-on sequencing (ChRO-seq) to define the landscape of active promoters, enhancers and gene bodies across distinct stages of directed differentiation of human pluripotent stem cells into SI spheroids with regional specification. Through comprehensive ChRO-seq analysis we identify candidate stage-specific chromatin activity states, novel markers and enhancer hotspots during the directed differentiation. Moreover, we propose a detailed transcriptional network associated with SI lineage formation or regional patterning. Our ChRO-seq analyses uncover a previously undescribed pattern of enhancer activity and transcription at HOX gene loci underlying SI regional patterning. We also validated this unique HOX dynamics by the analysis of single cell RNA-seq data from human fetal SI. Overall, the results lead to a new proposed working model for the regulatory underpinnings of human SI development, thereby adding a novel dimension to the literature that has relied almost exclusively on non-human models. The embryonic development of the small intestine (SI) is critical for a fetus to thrive and grow. The genetic programming of SI cell identity and regional specification during early development is fundamental to ensure proper SI morphogenesis and maturation with specialized functions, including nutrient digestion and absorption, energy balance and pathogen defense. Several seminal studies have identified important molecular regulators associated with gut development , and morcis-regulatory regions known as enhancers. Enhancers harbor binding sites for transcription factors (TFs), activate long-range gene transcription, and are often cell-type specific , including human embryonic stem cells (hESCs), provide a powerful strategy for studying the early developmental events of human SI that would be nearly impossible otherwise ,25. The elopment , includielopment ,29. WhilDifferentiation of H9 hESCs and organoids was performed as previously published, with minor modifications ,30. Brieg. Cells were labeled with EpCAM phycoerythrin (PE)-conjugated antibody (BioLegend) and an EpCAM isotype-PE control (BioLegend), and were sorted in 0.1% BSA 2 mM EDTA-DPBS on a MoFlo Astrios 1  instrument at the University of Michigan BRCF Flow Cytometry Core facility. Events were first selected with light-scatter and doublet discrimination gating, followed by exclusion of dead cells using 1 μM DAPI dilactate (Molecular Probes). EpCAM-PE(+)/DAPI(−) cells were sorted into cold Advanced DMEM/F12 (Invitrogen). Collected cells were reanalyzed for a purity-check and showed greater than 89% viable and 98% EpCAM-PE(+) events. Cells were pelleted at 500 × g for 5 min and flash-frozen for subsequent RNA isolation.Methods for organoid dissociation into single cells followed by selection of the epithelial component with fluorescence activated cell sorting, were based on previously described procedures . All solProcr gene , 5 mM Mg(CH3COO)2, 0.1 mM EDTA, 5  mM DTT, 40 units per ml RNase inhibitor) was added to each sample. The samples were loaded into a Bioruptor and sonicated to get the chromatin into suspension. Samples were stored at −80°C before proceeding to ChRO-seq library preparation.The chromatin isolation for ChRO-seq library preparation was performed as previously described ,33. Brie6 cells per sample in chromatin storage buffer was mixed with an equal volume of 2 × chromatin run-on buffer . The run-on reaction was incubated at 37°C for 5 min. The reaction was stopped by adding Trizol LS  and pelleted with GlycoBlue  to visualize the RNA pellet. The RNA pellet was resuspended in diethylpyrocarbonate (DEPC)-treated water and heat denatured at 65°C for 40 s. In the present study, base hydrolysis of RNA was performed by incubating RNA with 0.2N NaOH on ice for 4 min. Nascent RNA was purified by binding streptavidin beads  before and in between the following procedures: (i) 3′ adapter ligation with T4 RNA Ligase 1 , (ii) 5′ de-capping with RNA 5′ pyrophosphohydrolase , (iii) 5′ end phosphorylation using T4 polynucleotide kinase , (iv) 5′ adapter ligation with T4 RNA Ligase 1 . The resulting RNA fragments were used for a reverse transcription reaction using Superscript III Reverse Transcriptase  to generate cDNA. cDNA was then amplified using Q5 High-Fidelity DNA Polymerase  to generate the ChRO-seq libraries. Libraries were sequenced  using the NextSeq500 high-throughput sequencing system (Illumina) at the Cornell University Biotechnology Resource Center. After chromatin isolation, the ChRO-seq library preparation closely followed the protocol described previously ,34. BrieTotal RNA was isolated using the Total Purification kit . High Capacity RNA to cDNA kit  was used for reverse transcription of RNA. Libraries were generated using the NEBNext Ultra II Directional Library Prep Kit  and subjected to sequencing (single-end 92×) on the NextSeq500 platform (Illumina) at the Cornell University Biotechnology Resource Center. At least 80M reads per sample were acquired.U13369.1). The location of active RNA polymerase was represented by a single base that denotes the 3′ end of the nascent RNA, which corresponds to the position on the 5′ end of each sequenced read. Mapped reads were converted to bigwig format using BedTools and the bedGraphToBigWig program in the Kent Source software package. For visualization purpose, bigwig files from identical stages were merged and normalized to a total signal of 1 × 106. In the RNA-seq studies, reads were mapped to human genome hg38 using STAR (v2.5.3a)   with GENv2.5.3a) .Gene definitions were obtained from GENCODE v25 annotations. To quantify transcription activity of gene loci, ChRO-seq signals present in annotated gene bodies were used with exclusion of reads within 500 base downstream of transcription start site (TSS) to avoid bias generated by the RNA polymerase pausing at the promoters. Genes with gen body <1000 base were excluded from all the gene body related analysis, given that genes with short gene bodies are likely biased when excluding the pause peak. The ChRO-seq reads were normalized by the length of gene bodies to transcripts per million (TPM). To determine promoter activity of genes, stranded ChRO-seq signals at the promoter proximal region (500 bps upstream and 200 bps downstream of TSS) were used. The promoter regions that have significant changing patterns were defined using the following criteria: average TPM > 5 and padj < 0.05 across all the stages by likelihood ratio test (DESeq2). The promoter regions that have significant changing patterns and are annotated as ‘protein-coding’ gene type were subject to clustering analysis (‘degPatterns’ in R platform).The differential analysis of gene bodies in ChRO-seq data was performed using DESeq2 package . For allP < 0.05, fold change > 1.5 compared to all the other stages . Second, genes that have RNA-seq signal > 100 base mean units are identified and filtered according to padj < 0.2, P < 0.05, fold change > 1.5 compared to all the other stages . Third, the output from these two analyses are intersected to arrive at final gene lists for stage-specific markers. To identify genes that label SI spheroids irrespective of regional identity, analyses were done using the same criteria except the fold change criterion. The genes that have short gene bodies (<1000 bp) or annotated under the pseudogene category are excluded from this analysis.First, genes that have ChRO-seq signal in gene bodies (excluding the first 500 bps downstream of the TSS) greater than 50 TPM in the stage of interest are identified and filtered according to padj < 0.2, annotatePeaks.pl function (genome = hg38) of HOMER package  were identified by dREG tool . The ann package  based on2fold change > 2.5 by DESeq2)  in the stage of interest relative to a comparison stage.The stage-specific TREs were defined as TREs of which the ChRO-seq intensity is significantly higher in a stage of interest relative to a comparative stage (padj < 0.05 and log DESeq2) . The ChRThe stage-specific active distal TREs (enhancers) were used in the enhancer hotspot analysis. The enhancer hotspots in this study were identified by the criteria similar (with slight modifications) to the studies describing ‘super-enhancers’ ,42. BriefindMotifsGenome.pl (genome = hg38 and size = given) and the TREs which are shared or unique to the comparative stage were used as background.HOMER tool  was usedP < 0.05, fold change > 1.5 in both ChRO-seq and RNA-seq by DESeq2) in the stage of interest relative to a comparison stage.For a TF of interest, the binding motifs present in the stage-specific TREs were identified and the density of the motif was determined by the number of motifs within the window of +100 and −100 kb around the TSS for all the genes which are actively transcribed in the stage of interest (TPM > 50). The cistrome of a TF is defined using the following criteria: (i) genes have motif density > 0, (ii) genes are actively transcribed (TPM > 50 in the ChRO-seq) and expressed (base mean > 500 in the RNA-seq) in the stage of interest, and (iii) the genes are significantly uptranscribed and upregulated , unless otherwise specifically noted.All the padj and The directed differentiation of hPSCs (here we used H9 hESCs) into SI spheroids with regional specification was carried out as previously described ,28 Figu. The patSOX2, GATA6 and CDX2) indicate the expected specificity   Figure . The ana) Figure . We also) Figure .n = 7651), which exhibit significantly changing patterns of promoter activity across different stages  (n = 2992). These include BEX5 and PROCR  . NotablyR Figure , which wR Figure . AnalysiE Figure , confirms Figure  and demoacZ mice  at E9.5 acZ mice , indicatChRO-seq signal at gene bodies (TPM > 50 in at least one stage) reveals the changing patterns of transcription across stages Figure . We sougn = 239), DE (n = 149), SI spheroid irrespective of regional identity (n = 88), Duo spheroid (n = 9) and Ile spheroid (n = 40)  are lowly transcribed in the DE stage , suggesting that the DE cells used in this study are indeed highly enriched in cells with DE identity, instead of mesendoderm cells that still have dual potential for endoderm and mesoderm. Furthermore, consistent with the literature, CDX2 was identified to faithfully label SI spheroids irrespective of regional identity  Figure , indicat) Figure . The stas Figure  and NODAE Figure . Notablyy Figure . We defis Figure  and G. Ws Figure  and G. Ns Figure , which mPERCC1, which exhibits transient expression in the SI during development and has been linked to congenital diarrheal disorders in humans  and steady-state expression (RNA-seq) Figure . Among ts Figure . Notablys Figure . Regardis Figure . Among tP Figure  and M, sP Figure . These aP Figure  and M.It has been shown in previous studies that dense clusters of highly active enhancers (which we refer to as ‘hotspots’) occur nearby to genes that are especially critical for defining cell identity and status ,50. We sTo identify enhancer hotspots that emerge during SI lineage formation and ileal patterning, we clustered Duo- and Ile-specific enhancers, respectively, into ‘stitched enhancers’ . Among tNext, we performed an analysis to identify the genes likely associated with Duo- and Ile-specific enhancer hotspots. We assigned each of the Duo-specific stitched enhancers (including non-hotspots and hotspots) to its nearest active gene (TPM > 50 in ChRO-seq in Duo spheroids) within a 100 kb window on either end of the boundaries of the stitched enhancer. We found that the overall increase in gene body transcription is significantly greater for the set of genes associated with Duo-specific enhancer hotspots compared to those associated with stitched enhancers that are not hotspots Figure . We alsoCDX2, FOXA1, HOXB cluster, SALL1 and LINC00543)  and steady state expression (RNA-seq) in Duo spheroids relative to DE Figure  and E. M) Figure . Among t) Figure . These g1 Figure , many ofTo discover putative key TF drivers and their candidate cistromes (genome-wide set of TF targets) in this model, we first used the tool HOMER to determine motifs enriched in each set of stage-specific enhancers and then identified genes associated enhancers that harbor specific motifs of interest Figure . ExpecteHOXB cluster  and is thus likely subject to autoregulation as previously described  are associated with FOXA1 and FOXO3 motifs; SP5 (defined earlier as a marker gene of Duo spheroid) is associated with the TCF3 motif  loci is associated with Ile-specific TREs harboring the CDX2 motif  and/or ATF factors  upon acquisition of SI identity (Duo spheroids) and the sequential transcriptional activation of members of HOXA, C, D clusters during ileal regional patterning (Ile spheroids)  and 5′-end HOXB genes   Figure , which a) Figure  and 5. T) Figure , except ) Figure  and B, wHOX gene expression between early Duo and Ile extends beyond this in vitro directed differentiation model, we mined the single cell RNA-seq (scRNA-seq) dataset of primary human Duo and Ile tissue at very early stages of fetal development , with HOXB1 particularly enriched in Duo cells and 5′ HOX genes of all four clusters enriched in Ile cells  as well as enhancer hotspots associated with SI lineage formation and ileal patterning and (iii) candidate key TF drivers and their putative cistromes relevant to these critical developmental events. However, in these analyses we also observed cases where genes near an emerging enhancer or enhancer hotspot do not exhibit changes in transcriptional activity and expressional levels, which may indicate the presence of very long-range chromatin interactions beyond the conservative distance criterion used in this study (100 kb window from TSSs). Recent seminal studies leveraging Hi-C technology observed that chromatin interactions are often in the range of 200 kb but could be up to 500 kb or more in the human genome (ChRO-seq is capable of high-resolution definition (approximately single nucleotide resolution) of regulatory elements, which offers the opportunity to more precisely define the boundary of promoters and enhancers. ChRO-seq also captures active regulatory elements, as opposed to open chromatin regions which represent candidate regulatory elements. By leveraging ChRO-seq, we generated for the first time ever comprehensive chromatin regulatory landscapes across the different stages of directed differentiation from hPSC to SI spheroids with regional specification. Specifically, we defined: (i) early marker genes that label SI lineage formation and regional specification, (ii) the map of n genome ,53. NoneHES4 as marker gene of SI spheroids irrespective of regional identity in the context of gut development; one likely reason for this is that it is absent in the genome of the mouse  that are also known to be downstream of Wnt signaling. Fjx1 has been shown to function through the Wnt/planar cell polarity pathway to determine anterior-posterior axis in Drosophila  and their expression pattern along the anterior-posterior axis. Indeed, consistent with early literature in the mouse developing gut  first occurs during the formation of Duo spheroids and the other HOX clusters  are activated subsequently during the formation of Ile spheroids. Through mining of a novel scRNA-seq dataset  in a pseudo anterior-posterior space , providing in vivo evidence for the role of a 3′ Hoxd gene in regulating the development of the distal intestinal region. Using hPSC-derived SI spheroids, as well as in human primary fetal tissues, our study is able to show activation of distinct HOX clusters during early SI formation and regional specification. More importantly, through ChRO-seq analyses, we also provided additional insights into HOX biology by elucidating candidate TF drivers of specific HOX genes (and associated enhancers and enhancer hotspots), which merits detailed investigation.Although ping gut , our dat dataset ,43, we sor space . The samCDX2, which encodes the well-known master TF regulator of SI development. Notably, the FOXA1 gene locus is actively up-transcribed during the transition from definitive endoderm to SI spheroids and is associated with an enhancer hotspot that emerges during this process. FOXA1 has been reported to regulate allocation of murine SI secretory lineage  ,43. Raw gkaa1204_Supplemental_FilesClick here for additional data file.


Histone acetylation is a critical epigenetic modification that changes chromatin architecture and regulates gene expression by opening or closing the chromatin structure. It plays an essential role in cell cycle progression and differentiation. The human endometrium goes through cycles of regeneration, proliferation, differentiation, and degradation each month; each phase requiring strict epigenetic regulation for the proper functioning of the endometrium. Aberrant histone acetylation and alterations in levels of two acetylation modulators - histone acetylases (HATs) and histone deacetylases (HDACs) - have been associated with endometrial pathologies such as endometrial cancer, implantation failures, and endometriosis. Thus, histone acetylation is likely to have an essential role in the regulation of endometrial remodelling throughout the menstrual cycle. The human endometrium is a dynamic tissue. Its fundamental function is to provide an immunoprivileged site for embryo implantation and a nurturing environment for fetal development . It mainEpigenetic modification is a critical regulator of gene expression and determinator of cellular properties. Histone acetylation is a primary epigenetic change that makes genes accessible or inaccessible to transcriptional factors and thus influences gene expression. Histone acetylation, in conjunction with other epigenetic modulators, has been associated with endometrial cyclic remodelling throughout the menstrual cycle; global histone acetylation levels have been seen to follow a cyclic pattern according to menstrual cycle stage in normal cyclic endometrium . HistoneEpigenetic alteration refers to the chemical or a physical modification that affects gene accessibility, thus regulating how genes are read and expressed, without changing the DNA sequence . These mSome epigenetic modifications bring about life-changing alterations in an organism, while others are just part of normal cellular functions . EpigeneGenes are epigenetically regulated by DNA methylation, chromatin remodelling, and non-coding RNAs . DNA metEmerging research in epigenetics suggests that a range of environmental factors, lifestyle, early life stress, and trauma can influence the establishment and maintenance of epigenetic marks through generations (often known as epigenetic memory) , 16, 17.Approximately 2 m of DNA is packaged inside a nucleus with the help of proteins called histones, forming a DNA-protein complex called chromatin. The fundamental unit of chromatin is called the nucleosome, which consists of two sets of the four core histones – H2A, H2B, H3 and H4  Fig. 1)1). HistoFirst identified in 1964 , histoneHATs add an acetyl group to the ε-amino group of lysine using acetyl CoA as a cofactor, which neutralizes the positive charge on lysine, weakens the histone-DNA interaction, and makes genes accessible .HATs are a diverse set of proteins. So far, about 30 HATs have been identified in humans. HATs are primarily classified into two classes based on their subcellular localization: Type A and Type B. Type A HATs are localized in the nucleus, while Type B is found in the cytoplasm. The Type A HATs function in transcription-related histone acetylation in chromatin, while Type B HATs acetylate newly synthesized histones and influence the structure of the nucleosome , 30.Type A HATs are further grouped into five families based on their catalytic domain , 32. TheDeregulation of HATs is associated with cancer formation . CBP/p302+ dependent enzymes and have similar functional mechanisms whereas class III HDACs are NAD+ dependent. HDACs have relatively low substrate specificity, and one HDAC can act on multiple substrates or multiple HDACs can act on the same substrate [HDACs are post-transcriptional modulators that remove acetyl groups from lysine residues of both histone and nonhistone proteins . In humaubstrate . Additioubstrate . There iubstrate .HDACs have proved to be dynamic enzymes that can modify a variety of proteins. The involvement of HDACs in regulating fundamental cellular functions such as proliferation, cell cycle, regeneration, apoptosis, and differentiation makes them an important target of study for disorders .Class I HDACs are expressed in all tissues. HDAC 1 and 2 have been involved in cellular functions such as proliferation and apoptosis, while HDAC 3 is involved in the DNA damage response , 45. HDAEach month the endometrium goes through controlled cyclic changes, which are divided into three major phases: proliferative phase, secretory phase, and menstruation. This is called the menstrual cycle, and it occurs approximately 400 times in a lifetime .The proliferative phase starts around day 5 of the menstrual cycle and ends around day 14 . The proPost ovulation, around day 14 of the menstrual cycle, there is an increase in progesterone levels produced by the corpus luteum, which gives rise to secretory changes in the endometrium . The firIn the absence of a fertilized embryo, the fall in progesterone levels triggers the breakdown of the decidualized endometrial lining. This process is called menstruation , and corInterestingly, other estrogen and progesterone responsive tissues such as breast, fallopian tubes, and vagina do not shed in response to the decline in hormonal levels . After mThese perfectly timed events of regeneration, proliferation, differentiation, and degradation require strict epigenetic and genetic regulation of endometrial remodelling. Many endometrial pathologies are associated with the deregulation of epigenetic control. Understanding the epigenetic regulation of the endometrial lining is also essential in order to improve artificial reproductive technologies by identifying the receptive endometrium and modulating the endometrial receptivity state .After each menstrual cycle, the endometrial functionalis layer regenerates from the stratum basalis. It grows from an initial thickness of 0.5–1.0 mm to up to 7.0–8.0 mm in the secretory phase . It not The remarkable regenerative capacity of the endometrium is due to the presence of adult progenitor stem cells . These cOver the past decade, several studies have shown that epigenetics are a crucial regulator of stem cell functioning . HistoneThe endometrium has become a popular source of stem cells for reprogramming into induced pluripotent stem cells (iPSCs) . The MSCAberrant epigenetic regulation in eMSCs has been associated with endometriosis pathogenesis . eMSCs aUpregulation of the global acetylation levels of H2AK5, H3K9, and H4K8 during the early proliferative phase in the adult endometrium correlates with transcription activation of several regenerative pathways . AlthougHistone acetylation and DNA-methylation work in a coordinated manner, decide stem cell fate and influence cancer pathogenesis .After endometrium regeneration, estrogen levels from the developing follicle stimulate the proliferation of stromal and epithelial cells during the proliferative phase of the menstrual cycle , 76. EstCancer studies provide us with great insight into the epigenetic regulation of cell proliferation. HDACs can act as both suppressors and inducers of cell proliferation, depending on the gene they regulate. Inhibition of HDAC 1 reduces cell proliferation, and inhibition of HDAC 3 is associated with decreased migration of ovarian cancer cells .Class I HDACs –3, 8 are. The study showed that treatment with HDAC inhibitors VPA and SAHA, in combination with vascular endothelial growth factor (VEGF), increased endothelial cell sprouting [Studies using HDAC inhibitors VPA and SAHA have also shown the association between HDACs and angiogenesis in vitroprouting , suggestprouting , the rolThe rise in progesterone level post ovulation gives rise to morphological and functional changes in the human endometrium called decidualization. These changes include differentiation of fibroblastoid endometrial stromal cells (ESC) into decidual cells, the presence of decidual white blood cells, and vascular modifications in maternal arteries , 88. DecMunro et al., on human endometrial tissue biopsies, a statistically significant increase in H4K8 expression was seen, and H3K9 and H4K14/18 followed a similar trend [Decidualization, being a complicated process, requires dramatic gene expression changes in ESCs . These car trend . These far trend . In a gear trend .IGFBP-1) and prolactin, as regulated by 17β-estradiol (E2) plus progesterone (P4) [TIMP-1 and TIMP-3 expression, while expression of MMPs was decreased. This study associated histone acetylation with the disruption of trophoblast invasion and suggested HDACs are critical for implantation [COL1A1 and COL1A2 (collagenase) genes in humans and col1a1 and col1a2 genes in mice [Trichostatin A (TSA) is an inhibitor of HDAC and is involved in the inhibition of HDACs class I, II, and IV; TSA however, does not inhibit class III HDACs (Sirtuins) . A compaone (P4) . This stantation . A recenantation . In the  in mice . The cumThe highly timed molecular events discussed above, if not appropriately regulated, lead to a spectrum of endometrial pathologies such as endometriosis, endometrial cancers, abnormal uterine bleeding, and infertility.PR expression, which in turn regulates cell differentiation and induces cell cycle arrest [SIRT 1, SIRT 2, SIRT 4, and SIRT 5 are seen to be downregulated, while SIRT 7 is significantly upregulated in ECs compared to non-neoplastic cells. Interestingly, this study showed no significant difference in SIRT 6 and SIRT 3 levels between ECs and non-neoplastic cells [Studies have found the involvement of histone acetylation, in conjunction with other epigenetic modulators, in several human malignancies. High expression of class I HDACs in some tumors has been linked to unfavorable prognosis, with the exception of breast cancer in which it has a favorable prognosis. On the other hand, the upregulation of class II HDACs is associated with a favorable prognosis in human tumors . One of e arrest , 102. Whe arrest . Expressic cells  Fig. 3)PR expresHDAC 1 gene expression was high in ectopic tissues while HDAC 2 expression levels were high in eutopic tissue samples. Expression of HATs P300 and CREBBP did not change significantly when endometriosis tissue samples were compared to standard samples, while PCAF expression was high in the ectopic tissue [Histone acetylation and aberrant levels of HDACs are also associated with endometriosis. HDAC 1 expression is seen to be significantly elevated during endometriosis compared to normal endometrium, and the level also correlates with low acetylation levels of H3 and H4 , 106. Itc tissue . It shouc tissue  HDAC 3 is linked to decidualization defects and implantation failure in mice [HDACs also play a crucial role in implantation and decidualization. Loss of  in mice . HDAC in in mice , 95 (FigSince most of the studies on endometriosis and other endometrial pathologies compare healthy and diseased tissue samples, it is hard to tell if the epigenetic aberrations are the cause or effect of various endometrial pathologies.Histone acetylation is a fundamental regulator of chromatin structure and gene expression. Many studies on endometrial tissues and cell lines have shown the involvement of aberrant levels of histone acetylation and HDACs in endometrial pathologies, especially endometrial carcinomas and endometriosis. The majority of HDACs studied seem to be elevated in endometrial carcinomas compared to the normal endometrium , 85. Hig


Long non-coding RNAs (lncRNAs), including linear lncRNAs and circular RNAs (circRNAs), exhibit a surprising range of structures. Linear lncRNAs and circRNAs are generated by different pathways. Linear lncRNAs perform functions that depend on their specific sequences, transcription, and DNA elements of their gene loci. In some cases, linear lncRNAs contain a short open reading frame encoding a peptide. circRNAs are covalently closed RNAs with tissue-specific and cell-specific expression patterns that have recently been extensively investigated. Pioneering work focusing on their biogenesis and functional characterization indicates that circRNAs regulate cell development via multiple mechanisms and play critical roles in the immune system. Furthermore, circRNAs in exosomes function on target cells. As with linear lncRNAs, specific circRNAs can also be translated. In this review, we summarize current understanding and highlight the diverse structure, regulation, and function of linear lncRNAs and circRNAs. The central dogma of molecular biology describes the relationship between the informational macromolecules, DNA and RNA, and the transfer of the encoded information into proteins . Howevercis or in trans by recruiting transcription factors or chromatin-modifying complexes to DNA targets in the nucleus  and can function as a sponge to recruit miRNAs or transcriptional effectors to regulate target gene expression. Most circRNAs consist of one or more exons, termed extra-coding RNAs (ecRNAs), but some derive from the intron of the parent gene, such as circular intronic RNAs (ciRNAs) and intron retained circRNAs . circRNAs are more stable than linear ncRNAs because their circular structure cannot be degraded by most RNA decay machinery . The fir as 1976  and was  as 1976 . Electro as 1976 . Althoug as 1976 . They ap as 1976 , interacelopment , and proelopment . They caelopment , ciRNAs elopment . Some spelopment , but intelopment . Furtherelopment Although models of regulation are well established in many species, tissues, and cells types, the functions of linear lncRNAs and circRNAs remain elusive. Here, we highlight advances in our understanding of the multiple structures, functions, and regulation of linear lncRNAs and circRNAs.7G cap and 3′ poly(A) tail are the hallmark structures of eukaryotic mRNAs and most annotated linear lncRNAs are transcribed from their own loci and are spliced just like mRNAs  has a unique Box H/ACA snoRNA at both ends. Because SLERT is generated from the TBRG4 gene locus, which is located at a distance from the nucleolus, these ends are critical to protect the SLERT internal sequence from degradation and for translocation to the nucleolus  tails but that have snoRNAs at both ends  Figure. Likewisucleolus . Remarkaucleolus  and theicis-acting linear lncRNA is the X-inactive specific transcript, Xist  is specifically expressed in mouse skeletal muscle cells. linc-RAM is transcriptionally upregulated by MyoD and directly binds to MyoD, which promotes the assembly of the MyoD-Baf60c-Brg1 activation complex on specific regulatory elements of target genes , is a spliced and polyadenylated mammalian transcript derived from the HOXC locus, one of four HOX gene clusters . Using chromatin isolation by RNA purification (ChIRP), HOTAIR was shown to interact with the HOXD cluster. Although newer methods are needed to further dissect the mechanism of HOTAIR function, it is thought to act as a scaffold that coordinates the recruitment of a chromatin-modifying complex to the distant HOXD locus, thereby establishing a repressed chromatin state , and p300  influences the adjacent MyoD gene, whereas an eRNA named distal regulatory region (DDR) eRNA (DRReRNA), which is transcribed from the enhancer of MyoD, acts in trans (in situ hybridization (FISH) experiments indicated that DRReRNA co-localizes with nascent Myogenin transcripts, which are located on mouse chromosome 1. DRReRNA associates with the cohesin complex, which is required for cohesin chromatin recruitment and maintenance on chromosome 1, promoting the activation of the Myogenin gene , which contains many linear lncRNA genes, such as Linx, Jpx, and Ftx. Linear lncRNA transcribed from Ftx or Ftx-embedded miRs had no impact on Xist transcription , provides an example of the translation of a non-coding RNA having a critical impact on the transcriptional activation of a nearby gene, Hand2. HAND2 is a transcription factor that controls the reprogramming of fibroblasts into cardiomyocytes. Termination of Uph transcription independently of its transcript resulted in the loss of Hand2 expression in the mouse heart, which leads to embryonic lethality  encodes a ligand that inhibits Notch1 signaling and plays a role in placental development, nutrient metabolism, and adipocytosis , Rtl1, and Dio3, also expresses multiple ncRNAs, such as linear lncRNA, Meg3 , the C/D-box snoRNA cluster, Rian, the miRNA cluster, Mirg, and the Rtl1-antisense, Rtl1as  was the first linear lncRNA identified in human Burkitt's lymphoma (PVT1 and the myelocytomatosis (MYC) oncogenes are located some distance apart on 8q24 and the PVT1-encoded linear lncRNA and miRNA have oncogenic functions (PVT1 performs an oncogenic function by stabilizing the MYC protein (PVT1 using CRISPR interference (CRISPRi) technology unexpectedly enhanced cell proliferation of glioblastoma cells and induced pluripotent stem cells. Furthermore, recurrent structural rearrangements of the PVT1 locus disrupts PVT1 transcription in cancer genomes, indicating that the PVT1 locus has unknown regulatory mechanisms. These findings suggest that the PVT1 locus harbors four intragenic enhancers of MYC, which promote MYC transcription. Furthermore, the competition for enhancers between the PVT1 and MYC promoters is proposed to control cell development in glioblastoma cells and induced pluripotent stem cells independent of the PVT1 linear lncRNA (Bendr (Bend4-regulating effects not dependent on the RNA) has a regulatory function for the adjacent Bend4 gene. Inserting a polyA signal into the first intron of Bendr had no effect on Bend4 expression, indicating that the regulatory function was independent of Bendr RNA transcription. However, deletion of the ~750 bp Bendr promoter-proximal region reduced expression of the adjacent Bend4 gene by 57%. Therefore, cis activation of Bend4 requires the promoter of a nearby linear lncRNA  and to control human DC differentiation by directly binding to STAT3  in the cytoplasm (lncRNA-ACOD1 binding protein. lncRNA-ACOD1 is induced by multiple viruses but is independent of type I interferon (IFN-I). Upon induction, lncRNA-ACOD1 activates GOT2 and substantially changes cellular metabolism for the benefit of viruses. Down-regulation of lncRNA-ACOD1 dramatically protects mouse and human cells from virus infection. It is considered to be a tactic by which viruses complete their life cycle and propagate  has the ability to bind to pathogenic RNA through its C-terminal domain (CTD) in the presence of ATP and to promote innate immunity  as the binding partner of LncMyoD. The increased level of LncMyoD serves as decoy to outcompete other mRNAs, such as N-Ras and c-Myc, blocking proliferation and creating a permissive state for differentiation , a protein down-stream of RIGI. A linear lncRNA named nfection .MIR100HG, and two MIR100HG-derived miRNAs, miR-100 and miR-125b, play important roles in the cetuximab resistance of cetuximab-sensitive CRC cells and head and neck squamous cell carcinoma cells lines  hypothesis, a large number of linear lncRNAs that serve as miRNAs sponges were identified to play roles in regulating translation . The linls lines . The traignaling . The humd cancer . Howeverntiation .Deep-sequencing technologies have led to the identification of a large number of linear lncRNAs that lack obvious long protein-coding open reading frames (ORFs). However, some linear lncRNAs with putative small ORFs of less than 100 amino acids actually code for proteins that play important roles in cells  Figure.LINC00961. SPAR plays an important role in muscle regeneration. SPAR contains a conserved transmembrane domain at its N terminus with its C terminus extending into the cytosol. Immunofluorescence staining showed that SPAR is localized to late endosomes/lysosomes. Further work revealed that this small peptide interacts with the lysosomal v-ATPase to negatively regulate mTORC1 activation by amino acids  is a short, 90 amino acid peptide encoded in humans and mice by the linear lncRNA, no acids . Using Cno acids .2+-ATPase (SERCA) controls the release and reuptake of Ca2+ from the sarcoplasmic reticulum (SR). Myoregulin (MLN) and dwarf open reading frame (DWORF), which are 46 and 34 amino acids long, respectively, are two functional mammalian polypeptides encoded by linear lncRNAs that modulate the calcium pump, SERCA, and control muscle performance  gene is a linear lncRNA gene that has the ability to produce a conserved 53-amino acid peptide. This peptide modulates the alternative splicing of the PKM pre-mRNA to yield more PKM1, which suppresses CRC cell growth  cells, exhibit distinct metabolic reprogramming patterns, which support rapid proliferation . The glyl growth .cis-acting elements and trans-acting splicing factors  and disrupts circRNA biogenesis. The loss of DHX9 doubles the production of circRNAs , and ATP-dependent RNA helicase, DDX39A , are associated with transporting circRNAs. In humans, UAP56 exports circRNAs larger than 1,200 nucleotides whereas URH49 exports circRNAs smaller than 400 nucleotides .CDR1as  is degraded by the Argonaute 2 (AGO2) protein complex  within circRNAs promotes their degradation  from adipocytes is upregulated. Ubiquitin-specific protease 7 (USP7) is a deubiquitinating enzyme and a high level of USP7 is frequently found in HCC tissues. circ-DB absorbs miR-34a, which targets USP7 mRNA and promotes the expression of USP7 . circSEP3 binds strongly to its cognate DNA locus, forming an RNA:DNA hybrid, or R-loop. The formation of the R-loop results in transcriptional pausing and leads to the formation of alternatively spliced SEP3 mRNA with exon skipping, which in turn drives floral homeotic phenotypes (FECR1) derived from exons 4-2-3 of the FLI1 pre-mRNA can bind to the FLI1 promoter and recruits demethylase TET1 to the promoter region of its own host gene  and mainly accumulates in the nucleus, associating with the elongation Pol II machinery to positively regulate translation of its encoding gene  in the cytoplasm. Upon viral infection, NF90/NF110 is released from the circRNPs, resulting in an antiviral immune response  . They foCDR1as) is a circRNA found in the cytoplasm of neurons. CDR1as contains over 70 conserved seed matches for miR-7 and one binding site for miR-671 , ectopically delivered circ-Ccnb1 interacts with H2AX and Bclaf1, which induces the death of the p53 mutant cancer cells  mRNA. Interestingly, the circRNA, CircPABPN1, which is derived from the PABPN1 pre-mRNAs, can regulate the translation of its linear counterparts. High levels of CircPABPN1 can suppress HuR binding to PABPN1 mRNA, leading to decreased translation. CircPABPN1 also acts as a decoy to competitively bind to HuR and negatively regulate PABPN1 mRNA translation  mediated degradation and promotes the production of IL-13Rα1 in crypt ISCs, resulting in the reception of more IL-13  and is the most rapidly self-renewing tissue with a turnover of 5 days . ISCs lore IL-13   cap structure and the 3′ poly(A) tail that are necessary for mRNA translation, they have the potential to produce proteins. In 1995, an artificial circRNA was demonstrated to be translatable in eukaryotic cells  gene, an E3 ligase that targets the proliferating cell nuclear antigen (PCNA) for degradation , and the Department of Science and Technology of Yunnan Province (2017HB006).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Chondrocyte enhancers were associated with chondrogenesis‐related gene ontology (GO) terms. In silico analysis and integration of DNA methylation data with chondrogenesis chromatin states revealed that enhancers marked by histone marks H3K4me1 and H3K27ac were de‐methylated during in vitro chondrogenesis. Similarity analysis between hMSC and chondrocyte chromatin states defined in this study with epigenomes of cell‐types defined by the Roadmap Epigenomics project revealed that enhancers are more distinct between cell‐types compared to other chromatin states. Motif analysis revealed that the transcription factor SOX9 is enriched in chondrocyte enhancers. Luciferase reporter assays confirmed that chondrocyte enhancers characterized in this study exhibited enhancer activity which may be modulated by DNA methylation and SOX9 overexpression. Altogether, these integrated data illustrate the cross‐talk between different epigenetic mechanisms during chondrocyte differentiation.Epigenetic mechanisms are known to regulate gene expression during chondrogenesis. In this study, we have characterized the epigenome during the in vitro differentiation of human mesenchymal stem cells (hMSCs) into chondrocytes. Chromatin immunoprecipitation followed by next‐generation sequencing (ChIP‐seq) was used to assess a range of N‐terminal posttranscriptional modifications (marks) to histone H3 lysines  in both hMSCs and differentiated chondrocytes. Chromatin states were characterized using histone ChIP‐seq and  ACANaggrecanChIP‐seqchromatin immunoprecipitationCOL2A1collagen type II alpha 1 chainDMEMDulbecco's modified eagle mediumECMextracellular matrixGOgene ontologyhMSChuman mesenchymal stem cellIGVintegrative genome viewerlincRNAlong non coding RNAMACS2model‐based analysis of ChIP‐seq 2miRNAmicroRNAOAosteoarthritisPBSphosphate buffered salineRUNX2runt‐related transcription factor 2SOX9sex determining region Y box 91Chondrogenesis is the process of differentiation of mesenchymal progenitors into chondrocytes. Articular cartilage, present in synovial joints, comprises an extracellular matrix secreted by chondrocytes and has an important function in aiding the mobility of joints. As the only cell type present in articular cartilage, adult articular chondrocytes are responsible for the homeostasis of cartilage.COL10A1 and osteoblast markers such as RUNX2, and low expression of cartilage‐specific genes such as COL2A1 and SOX9.Cis‐regulatory elements such as gene enhancers have been shown to be disrupted in cartilage pathologies. Deletions in a distal regulatory region of the SOX9 transcription factor gene and within the SOX9 gene itself both lead to campomelic dysplasia in humans.During embryogenesis, the skeletal system originates from the mesoderm germ layer. Mesenchymal progenitors differentiate into chondrocytes to form temporary cartilage. During endochondral ossification, these cells generally undergo apoptosis to be replaced by bone. However, cartilage at synovial joints does not ossify and remains throughout the life. Hypertrophic chondrocytes bound for ossification have high expression of Mesenchymal stem cells (MSCs) are able to differentiate into chondrocytes and have been used to study chondrogenesis in vitro. Tissue engineering solutions to cartilage repair include autologous chondrocyte implantation, cartilage autografts, and injection of MSCs into the damaged site.SOX9‐binding motifs. Altogether, our study provides a comprehensive analysis of the global epigenetic changes during MSC chondrogenesis and highlights the role of enhancers in defining cell‐type specificity.In this study, histone ChIP‐seq  was performed in a scaffold‐free in vitro model of human MSC (hMSC) chondrogenesis.22.1Bone marrow aspirates  were purchased from LONZA and hMSCs were isolated by adherence to tissue culture flasks for 24 hours. hMSCs were phenotyped by flow cytometry2.2Cartilage discs were digested at Day 14 of chondrogenesis, a time point at which chondrocytes have been determined to be fully differentiated in a pellet model of chondrogenesis.2.3hMSCs were harvested from monolayer culture using trypsin. Chromatin from hMSCs and differentiated chondrocytes were extracted using the Diagenode iDeal histone ChIP‐seq kit . Extracted chromatin was sonicated using a Diagenode Bioruptor Standard or Bioruptor Pico to an average size of 200‐500 bp, using 15 sonication cycles (30s on/30 seconds off). ChIP‐seq grade premium antibodies were purchased from Diagenode: H3K4me3 , H3K4me1 (Cat. no. C15410194), H3K27ac (Cat. no. C15410196), H3K27me3 (Cat. no. C15410195), and H3K36me3 (Cat. no. C15410192). Chromatin immunoprecipitation was performed following the Diagenode iDeal histone ChIP‐seq protocol using chromatin from 1 million cells and 1μg antibody per ChIP. Immunoprecipitated DNA was purified using Agencourt AMPure XP beads (Beckman Coulter (UK) Ltd, High Wycombe, UK). For one hMSC chondrogenesis replicate ChIP‐seq, DNA sequencing libraries were generated using Diagenode MicroPlex v2 kit and single‐ended reads of 50 bp length were generated on an Illumina HiSeq 2500 . The second experimental replicate was prepared using the NEBNext Ultra II kit  and sequenced using an Illumina NextSeq 500 platform, generating 75 bp single‐end reads. For both replicates, 30‐65 million reads were generated per sample Table .2.4E coli (Invitrogen) and DNA isolated using the PureYield Plasmid Midiprep system (Promega). Plasmid DNA was in vitro methylated using CpG Methyltransferase , with the efficiency of methylation assessed by digestion using HpaII and HhaI methylation‐sensitive restriction enzymes (NEB). The effect of SOX9 on enhancer activity was assessed by transfection with a SOX9 overexpression plasmid (pUT‐FLAG‐SOX9).3 per well in 96‐well plates. After 24 hours, cells were co‐transfected with 100 ng (DNA methylation) or 50 ng (SOX9 overexpression) of the relevant pCpGL‐EF1 plasmid and 6ng (DNA methylation) or 1.5 ng (SOX9 overexpression) of pRL‐TK Renilla control plasmid and 0.3 μL of FuGENE HD reagent (Promega) per well. Cells were lysed 24hrs post‐transfection and luciferase and the renilla activity measured using the Dual‐Luciferase Reporter Assay kit on a GloMax‐Multi reader. Three experiments with six replicates each were performed for each construct and luciferase/renilla activity normalized to for the empty the pCpGL‐EF1vector.Putative enhancer regions were amplified from human genomic DNA using the primers listed in Table 2.5–broad and –no‐model) using input samples as controls. The ngs.plot program (v.2.61)Quality control of sequencing reads was performed using FastQC (v.0.11.5). All reads passed quality thresholds. Reads were aligned to the reference human genome hg38 using Bowtie2 (v.2.2.4).ChromHMM (v.1.12)http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE69109) were aligned to mm10 using Bowtie2 (default settings). Aligned reads were converted to hg38 using the UCSC liftOver tool and narrow peaks were called using MACS2 (v.2.1.0.2) using input samples as control.Mouse SOX9 ChIP‐seq data (GEO 2.6http://www.roadmapepigenomics.org/). For comparisons with human articular chondrocyte enhancers, histone ChIP‐seq data from human fetal and adult articular cartilage were accessed from http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111850.Chromatin state coordinates from our study were converted to hg19 using UCSC liftOver as Roadmap data were aligned to hg19. Similarity analysis between equivalent chromatin states across hMSC, chondrocyte, and Roadmap cell‐types was performed using the Jaccard index and hierarchical clustering. Roadmap chromatin state data are available to download from the project website  using ChIP‐seq. These histone marks were selected to reflect a wide range of regulatory states. H3K4me3 commonly marks active promoters, H3K4me1 and H3K27ac are found at active enhancers, H3K36me3 are located at actively transcribed regions and H3K27me3 marks transcriptionally repressed regions. The genome‐wide profiles of each histone mark were as expected; the density of each histone mark differs across the genome with the active promoter mark H3K4me3 showing a high density of peaks close to transcriptional start sites . This confirms the finding by Lui et al, that dysregulated enhancers in OA are enriched in cell‐type‐specific enhancers.Roadmap chondrocyte enhancers were intersected with the chondrocyte enhancers identified in this study, resulting in a total of 23 158 enhancer regions common to both types of MSC‐derived chondrocytes Table . We next3.4P < .001). We evaluated the effect of DNA methylation in six selected regions  has also been reported to be important in chondrogenesis or cartilage‐related diseases. CpG sites around the ASPSCR1 gene are differentially methylated in human knee OA compared to control.TLE3 is a target of the SOX5, SOX6, and SOX9 trio of transcription factors important in chondrogenesisWWP2 gene and is co‐expressed with an isoform of WWP2. MiR140 expression is unique to cartilage and plays an essential role in chondrocyte proliferation.ZMIZ1 is differentially expressed in OA chondrocytes following exposure to hyperosmotic conditions.LRP5 is involved in MSC differentiation and cartilage degradation.MYEOV is associated with multiple myeloma and may also be involved in abnormal bone homeostasis.Histone modifications are influenced by DNA methylation and vice versa during development.ns Table  that acq3.5Transcription factor binding occurs at gene enhancers to regulate gene expression.SOX9 is a pivotal transcription factor driving chondrogenesis and interacts with promoters and enhancers to promote chondrogenesis.TLE3) have a SOX9‐binding site in the lifted over SOX9 ChIP‐seq data; as predicted, the TLE3 region did not show increased enhancer activity upon SOX9 overexpression.To investigate whether SOX9 binds to motifs found in chondrocyte enhancers we used a publicly available mouse rib chondrocyte SOX9 ChIP‐seq datasetPreviously, analysis of mouse Sox9 ChIP‐seq found that AP‐1 factors, Jun, and Fos were found to co‐localize with Sox9.4There are numerous in vitro models of chondrogenesis and although some models utilize scaffolds for cells to grow, scaffold‐free models are reported to better reflect the conditions during in vivo chondrogenesis during development.Combinations of histone modifications can define regulatory elements and regulate genes through modulating chromatin remodeling to allow or block access to transcription factors. However, histone modifications also rely on other epigenetic mechanisms such as DNA methylation and vice versa.Acan enhancers in transgenic mice resulted in a loss of chondrocyte‐specific expression.Motif discovery at chondrocyte enhancers recovered motifs of transcription factors known to be involved in cartilage development and diseases such as CREB3L1, ELF3, and SOX9. We utilized a mouse Sox9 ChIP‐seq dataset to assess whether enhancers defined in our study contained SOX9‐binding sites. SOX9 has a highly conserved DNA‐binding motif and function.Enhancers are thought to regulate their target genes by forming a loop to physically contact the gene promoter within topologically associating domainsTo conclude, the integration of ChIP‐seq with methylation data revealed that gene enhancers are de‐methylated during an in vitro Transwell model of chondrogenesis. Comparison of chromatin states across hMSCs and chondrocytes generated in this study along with those from the Roadmap Epigenomics project revealed that enhancers marked by H3K4me1 and H3K27ac are more cell‐type specific compared to other chromatin states. Chondrocytes from the Epigenomics Roadmap project and this study showed a more similarity of enhancers with each other than other cell‐types despite being from different models. We have established that chondrocyte enhancers contain motifs to which SOX9 binds in vivo. Additional investigations are needed to elucidate further the epigenetic landscape of chondrocytes originating from other in vitro models and to determine whether these are comparable to the epigenome of human articular chondrocytes. A link between reactivation of developmental pathways and OA has been suggested5Human mesenchymal stem cells are able to differentiate into chondrocytes, the cell type found in cartilage, making them an accessible system to study gene regulation during this process. Epigenetic mechanisms such as histone modifications and DNA methylation together with transcription factor binding play a role in activating and repressing gene expression. In this study, we investigated the genome‐wide histone modification changes during chondrocyte differentiation. Integration of this data with DNA methylation and SOX9 transcription factor ChIP‐seq revealed epigenetic changes at gene enhancer elements. Regions of the genome that transition from non‐enhancers to enhancers in chondrocytes are enriched for SOX9 transcription factor‐binding sites. Luciferase reporter assays revealed that enhancer activity may be modulated by manipulating DNA methylation and SOX9 expression. This study has defined important regulatory elements in chondrocytes which could serve as targets for future mechanistic studies.None.Kathleen Cheung: Collection and/or assembly of data, data analysis and interpretation, manuscript writing. Matthew J. Barter: Collection and/or assembly of data, provision of study material or patients. Julia Falk: Collection and/or assembly of data. Carole Proctor: Conception and design. Louise N. Reynard: Conception and design, provision of study material or patients. David. A. Young: Conception and design, final approval of the manuscript. Click here for additional data file. Click here for additional data file.