We study the dynamic and static input–output behavior of several primitive genetic interactions and their effect on the performance of a genetic signal differentiator. In a simplified design, several requirements for the linearity and time-scales of processes like transcription, translation and competitive promoter binding were introduced. By experimentally probing simple genetic constructs in a cell-free experimental environment and fitting semi-mechanistic models to these data, we show that some of these requirements can be verified, while others are only met with reservations in certain operational regimes. Analyzing the linearized model of the resulting genetic network, we conclude that it approximates a differentiator with relative degree one. Taking also the discovered nonlinearities into account and using a describing function approach, we further determine the particular frequency and amplitude ranges where the genetic differentiator can be expected to behave as such. The systematic design of functional genetic circuits is one of the key challenges in the field of synthetic biology. Usually, the goal is to add a desired function to a cellular organism. As the complexity of these functions has been increasing steadily , it becolinear input/output system can be realized exactly using only zeroth and first order biochemical reactions. We addressed the question of replacing the zeroth and first order biochemical reactions with general genetic interactions in  and D(s): causality is given if the degree of N(s) is not bigger than the degree of D(s).In the field of control theory, one can study linear systems in two different domains. First, in the time domain, by looking at the states of a system and the temporal derivatives thereof which define a system of ordinary differential equations (ODEs). And second, in the frequency domain, by looking at transfer functions which are complex valued functions and describe how different frequency components of an input signal are modified by a system. These two domains are connected via the Laplace transformation and particularly the frequency domain is very useful for the design and analysis of linear systems. An ideal differentiator would be given by the transfer function K is the bandwidth of the filter. One possibility to realize this transfer function is by the circuit depicted in K in the forward path and a weighted integrator in the feedback path. Ideally, one chooses β = 1 to recover (1). Thus, in order to approximate the differentiator, three basic functions are needed: a gain, an integrator and the signal difference between input and feedback. Finding genetic realizations of these basic functions is the main challenge in designing the differentiator. In particular, it is expected that this cannot be achieved in an exact way, thus it is necessary to determine how inaccuracies in the basic parts influence the behavior of the assembled circuit. For an initial guess for finding such functions, a semi-mechanistic model of transcription and translation  and transcribed into mRNA (iM), then, mRNA molecules are translated into proteins (iP). In the following, the subscript i stands for the ith gene (iG) in a network with I distinct genes. Withi are described byiD) or initiation and degradation rates, as well as several environmental parameters i and In the process of protein synthesis, the genetic information is read from DNA . Subsequently, the connection between (3) and (1) will be discussed. We note that the focus on G1 is without any loss of generality as the following requirements can be adjusted with minimal effort to arrive at the equations for G2.Several simplifications and requirements for the processes of transcription, translation and degradation were introduced to finally arrive at the desired model equationsRequirement 1M1 and P1 are subject to first order degradation, i.e.ip and iq are usually dependent on protease and endonuclease levels, we require first order degradation dynamics to assure linearity with respect to mRNA and protein levels.Although degradation rates Requirement 21 and g1 are both approximately linear in D1 and M1, respectivelyThe operation regime is such that f.ig in iM is shown. Alternatively, similar simplifications have been applied by following a linearization approach as pursued in . Note that, when considering both genes G1 and G2, this means that the transcription and translation rate constants α and β are assumed to be equal for both genes. Also, it is required that P1 and P2 was introduced, see , however, avoid using as strict simplifications as the ones outlined in Section 2.A and B are denoted with In the following, complexes of two chemical species if is proportional to the concentration of promoter which is bound to a corresponding RNAP holoenzyme and not blocked by any inhibitors, e.g.P, gene-specific parameters Θi and extract-specific parameters It is a well-established result , 28 thatσ70) first has to bind to RNAP to form the holoenzyme before this complex then binds the promoter region. The sigma factor therein has a very high specificity toward certain promoters, enabling the cell to switch between different transcriptional programs depending on which sigma factor is expressed. Note that compared with (6a), this is a more realistic model for mRNA production but prohibits making the same deductions for the genetic differentiator.In this example, sigma factor 70  can be combined toWe therefore have to consider the reactionsIn most cases, only dissociation constantsxσ denote both the unbound chemical species. If only one sigma factor is present in the system, the amount of Note that in (9) Proposition 1 Given the entities A, B and If none of the entities participates in any other chemical reaction, the steady state of can be expressed in terms of the total amounts of A and B asThe proof can be found in After formation of In such cases, given that the promoter does not interact with other holoenzymes, Proposition 1 can be applied again to simplify the modeling formalism.In contrast to a constitutive promoter, binding of RNAP can also be inhibited by other proteins, leading to a combinatorial promoter with competitive binding mechanism, i.e. by the additional reactionQ) bound to the ribosome binding site of mRNA iM. We assume unregulated ribosomal binding and that the ribosome binding site sequences used for the constructs are of equal strength. Thus, the reactions for forming the complex Similarly to the transcription rate (7), the rate of translation is given byE) and proteases. It is known , and further, whether one can find a suitable combinatorial promotor which satisfies all linearity requirements in order to verify Requirements 3.σ70 dependent constitutive promoter and expresses GFP. By fitting a suitable model to the experimental data and analyzing the corresponding parameters, Requirements 1 and 4 will be verified.First, we analyze the time-scales and linearity of transcription and translation. Therefore, the I/O behavior of these processes are characterized by experimentally probing a simple gene with different input steps as depicted schematically in There are two possibilities to realize a step-like input of varying height at the transcriptional level using promoters like introduced in Section 3.2: either by varying the amount of sigma factor (i.e. the transcription factor) while keeping the DNA concentration constant, or alternatively, changing the DNA concentration itself. While varying DNA amounts is straightforward, the sigma factor input additionally required purified protein which may be biologically unstable and is more difficult to obtain than DNA.Depending on the choice of input, i.e. sigma factor or DNA, different dynamical effects can be expected when probing the system with steps of different height. As discussed before in Section 3.2, the mRNA production rate is proportional to the complex This approach reduces the complexity of the fitting problem by focusing on the identification of promoter binding kinetics only. Thus, for the identification of the I/O behavior of transcription and translation, we first limit ourselves to step inputs in form of varying DNA concentrations and study the sigma factor dependent holoenzyme formation in a separate experiment, discussed in Section 4.2.We choose four different DNA concentrations for probing the system: 1, 3, 5 and 10 nM. Three technical replicates were conducted. The data obtained by this process are depicted in i = 1 and accordingly the amount of GFP with P1. According to Section 3.2 and particularly Equations (2), We denote the index of the gene under study with see e.g. , and appδ is evaluated to be of magnitude γ, practically zero.For the process of translation, we observe that the protein degradation rate Conclusion 1As required in Requirements 4, the degradation of mRNA is much faster than the one of protein.In order to check the linearity Requirements 1 and 2, we study the entities ϵ-linear-range of a function ϵ. For the nonlinearity measure, we follow the methods introduced in (f(x), defined byIn that sense, we now introduce a relative measure of nonlinearity and define the duced in . Let ||ff, we use the linear function mx. Note that we forced the intercept of the linear function to take the value 0 to assure strictly positive values of the linear function on the interval f, the best linear approximation in the interval f(x) and the linear function mx. The value of f on the interval f. Thus, in order to compare this measure across different functions, we normalize (15) by the L2 norm of f, i.e.To approximate Consequently, In the given case, when one allows for a 5% error, i.e. Conclusion 2Linearity of production and degradation terms, as requested in Requirements 1 and 2, can be verified with 95% accuracy withp1 for example, a linearization around some fixed value Given the linear operation regimes indicated in Conclusion 2, one can now derive linear models for transcription and translation which are then valid in the respective regimes. In the control community, the standard approach to approximate a nonlinear model with a linear one is to locally linearize the nonlinear function at on specific value. In case of the nonlinear mRNA degradation rate This approach assures that the linear function evaluated at δ is very small, translation can indeed be seen as integration as long as DNA and mRNA concentrations are in the appropriate operation regime. However, the initial assumption that transcription can be seen as a gain needs to be adjusted as mRNA degradation cannot be neglected, leading to a PT1 element instead of a gain.We thus obtain the linear modelE. coli cell-free extract and mainly focused on possible limitations caused by the promoter and mRNA-binding kinetics. We therefore bypassed nonlinear effects of RNAP holoenzyme formation by changing DNA concentrations instead of using σ70 as input and found that at least during the first 200 min of a TX-TL experiment, resource limitations do have an effect on transcription, translation and mRNA degradation. By studying different step responses, the linear operation regimes were identified. We now turn toward inhibitor binding dynamics and in particular toward the problem of how to realize a signal difference using combinatorial promoters.So far, we studied and characterized time-scales and linearity of the processes of transcription and translation in context of an In order to approximate the derivative of a signal by implementing the scheme depicted in There are various ways to realize a signal difference in biology, a widely used one being sequestration-based mechanisms between the signaling molecules, e.g. binding and degradation of the complex like elaborated in , 8. WhenFollowing such an approach, the desired behavior of the steady state of promoter dynamics is depicted in iHK and ijK of the RNAP holoenzyme and inhibitor, respectively. If for instance we assume that Applying Proposition 1 and assuming that the total amount of RNAP holoenzyme is fixed, the amount of In contrast to that, This means that, while we have to acknowledge that exact realization of the difference of two signals is not possible with combinatorial promoters, some crucial properties can be approximated by choosing dissociation constants and DNA amounts carefully.pTar initiation sequence combined with a tetO inhibitor operator which expresses GFP. The pTar promoter is sensitive toward an RNAP holoenzyme consisting of RNAP bound to σ28, while the operator sequence tetO enables binding and inhibition through Tet repressor proteins (tetR). We denote the concentration of this gene as D2 and GFP concentration as P2.For that purpose and also for detangling the RNAP holoenzyme binding reaction, we study a gene with a σ28 and tetR are produced in the TX-TL system from respective constitutive (i.e. σ70 dependent) DNAs tetRD. While the amount of aTc) which binds to tetR and thus alleviates its association with the promoter. The concentration of σ28 and unblocked tetR. The time-series of this experiment can be found in To avoid usage of purified protein, both According to the experimental setup, several chemical species compete for the same resources, thus Proposition 1 cannot be applied anymore and the binding reactions themselves had to be implemented as fast reactions. For brevity reasons, the binding reactions are not listed here. We focus on mRNA and protein dynamics, i.e. the ODEsFitting these equations to the data, we obtain the parameters listed in σ70 and further, that binding between these two species is very strong. Although σ28 also binds strongly to RNAP, its affinity is still smaller than the one of σ70. The excessive amount of RNAP and the much higher binding affinity of σ70 thus leads to a decoupling of the two binding reactions.The values given in pTar promoter apparently has a very low affinity which leads to low GFP levels compared with the input step experiments. Together with the fact that the repressor tetR binds the pTar promoter very strongly, this leads to the steady-state promoter map depicted in We also note that the binding of Although there is leakage for medium amounts of inhibitor and activator and the level sets are not completely linear, the determined promoter dynamics are comparable to the desired behavior of Conclusion 3 Using combinatorial promoters, the difference between two signals can only be realized to a limited extent.σ28 as input. As pointed out before, the strong binding affinities of the sigma factors lead to a quite linear but bi-modal I/O behavior, as depicted in σ28 until the concentration of RNAP is matched. Consequently, the transcriptional gain txA changes due to the change of input and using the same linear approximation as defined in (16), one now findsGiven these results the transcription dynamics of Section 4.1 can now be extended with the appropriate promoter dynamics and Initially, with Requirements 1 to 4, we expected the process of transcription to behave like a gain, translation to behave like an integrator and combinatorial promoters to provide the difference of two signals. Now, several observations were made which differ from our initial view.First, although mRNA degradation is indeed much faster than protein degradation, the simplification to a simple gain is not justified and the temporal dynamics of mRNA production should be taken into account instead, leading to a PT1 behavior instead of a gain.Second, both production and degradation rates are subject to saturations due to finite amounts of resources of the transcriptional and translational machinery in the cell-free extract. For small inputs, however, these rates can be seen as linear functions of their inputs and the linear operation regimes have been determined explicitly.Third, when realizing the difference of two signals by using combinatorial promoters, one only obtains an approximation of the difference and the quality of the estimate depends on the magnitudes of the inputs.Now that these deviations from our initial requirements have been identified and characterized, their effect on functionality and performance of the synthetic genetic differentiator postulated in  can be sδ = 0, the transfer function of the closed loop system thus is given byThe first model is given by the closed loop of the models G1 with a combinatorial promoter like the one studied in Section 4.2, i.e. sensitive to a σ28 holoenzyme and tetR inhibitor, producing this very same inhibitor, therefore realizing the circuit from σ28 is considered as input signal. In order to capture both positive and negative gradients, the same approach as introduced in  analyzed in terms of its Fourier coefficients. Assume that after time nth Fourier coefficient of signal y(t) which corresponds to frequency ω. Then, the magnitude amplification Λ will be given as the ratio of the magnitudes of the first Fourier coefficients of output and input signal. With the input defined like in (22), the first Fourier coefficient of this signal is simply For the nonlinear model, however, we use a describing function approach as described in  to compaFurther, the phase shift Ω for this frequency and particular input signal is given byA0 of input signal (22) is necessary to produce non-negative sinusoid functions. Due to the nonlinearity of Model 2, the output y(t) does not only depend on the frequency ω but the shape of the input function in general, i.e. also the variables A0 and A. We therefore probed the system for several frequencies and values for A0 and A.The constant part n > 1. This is not the case for general nonlinear systems where higher harmonics can also appear and in principle more than just the first Fourier coefficient should be analyzed. Thus, the way we use the describing function approach in this work relies on the assumption that higher harmonics of the output signal can be neglected. We thus analyzed the power spectrum of the output signals for different values of A, A0 and ω and found that for most combinations, the higher harmonics contributed less than 5% to the overall power spectrum. However, in the case when A approaches A0 and ω is close to the pole of the transfer function, it seems that the assumption does not hold, see For linear systems, an input signals with a single frequency component, like the one of (22), leads to an output with also only one frequency component, namely the same as the one of the input. In other words, higher harmonics are not existent and A0. The values for A are chosen as A, however, the choice of the offset A0 significantly influences the I/O behavior of the nonlinear signal differentiator. Very low values of A0  lead to a very sensitive response, i.e. too high gain of the resulting closed loop and a smaller range of frequencies for which the output approximates the derivative of the input.In A0 (dashed cyan and dark red), Model 2 breaks down as expected due to the previously characterized saturation effects and the resulting loss of sensitivity toward the input signal.A value of A0, the gain is very small for values of A0 and amplitude A of the input signal. The normalized outputFrom the previous analysis, we summarize that for the detailed Model 2, the phase of the output signal is off for too small values of A0, as expected, the phase is off, however, the output signal still has a sinusoid shape for all amplitudes A. In contrast, for higher values of A0, the phase is correct but with amplitude A approaching the offset A0, the output signal becomes more and more distorted. This effect is amplified for higher offset values and is caused by a dilution of the power spectrum as discussed in the previous section.For a small value of For the synthesis of genetic networks that realize arbitrary linear transfer functions, we follow a similar approach as in . TherefoBy observing mRNA and protein levels as response to step inputs of varying height, it was verified in Conclusion 1 that protein degradation is almost nonexistent while mRNA degradation is comparably fast. However, degradation dynamics are not as fast as desired and a quasi steady state assumption for the process of transcription would be oversimplifying. Thus, transcription should be considered as a PT1-element rather than a gain.By fitting an ODE model to the experimental data and analyzing the corresponding parameters, it was also shown that all processes are subject to saturation due to limited amounts of resources. Using the same model and the fitted parameters, the linear operation regimes of the I/O primitives can be characterized as shown in Conclusion 2, leading to more insight into the capabilities and limitations of respective genetic circuits.In a second series of experiments, the dependence of the performance of a combinatorial promoter on the operation regime was emphasized, realizing in Conclusion 3 that the difference of two signals can only be obtained approximately. Based on these insights, DNA concentrations for a simulation study were chosen such that the I/O behavior of the combinatorial promoter is as close as possible to the desired one. In conclusion, the use of combinatorial promoters for comparing the concentrations of two transcription factors is only possible within a limited range of magnitudes and we suggest to use sequestration-based mechanisms in future.For the realization of a genetic signal differentiator using the studied parts, the initial goal was to realize a differentiator with high-pass filter. The corresponding transfer function is given in If protein degradation were significantly larger than zero, this would lead to a transfer function of the formduced in  leads toduced in , the zerA0. With an appropriate choice of A0, the presented network approximates the temporal derivative of an input signal for frequencies up to A0, simulations in the time domain revealed a dependence on the relative amplitude In order to conduct studies beyond the linearized model, a describing function approach is used to evaluate the response of the nonlinear model to sinusoid inputs like in ysz015_Supplementary_DataClick here for additional data file.


Eukaryotic genomes are pervasively transcribed, producing both coding and non-coding RNAs (ncRNAs). ncRNAs are diverse and a critical family of biological molecules, yet much remains unknown regarding their functions and mechanisms of regulation. ATP-dependent nucleosome remodeling complexes, in modifying chromatin structure, play an important role in transcriptional regulation. Recent findings show that ncRNAs regulate nucleosome remodeler activities at many levels and that ncRNAs are regulatory targets of nucleosome remodelers. Further, a series of recent screens indicate this network of regulatory interactions is more expansive than previously appreciated. Here, we discuss currently described regulatory interactions between ncRNAs and nucleosome remodelers and contextualize their biological functions. Many cellular processes occur within the cell nucleus where access to the information encoded with genomic DNA is essential. Eukaryotic genomic DNA is packaged within the nucleus as chromatin– a DNA, RNA, and histone protein structure consisting of repeating elements referred to as nucleosomes . EukaryoRecently, it has been shown that at least 75% of the human genome is transcribed at detectable levels . TraditiLargely due to advances in genomics and RNA-protein interaction probing techniques, the identification of interactions between nucleosome remodelers and ncRNAs has emerged as important in regulating one another and therefore function. In several cases, these interactions have been shown to modulate the activity and targeting of nucleosome remodelers ,14. RemoNucleosomes are an obstacle preventing the transcriptional machinery access to DNA. Nucleosome remodelers are recruited to chromatin through various mechanisms, where they utilize ATP hydrolysis to alter nucleosome occupancy, positioning, or composition to promote or prevent transcription at sites across the genome. Classifications of nucleosome remodelers are primarily driven by the protein domain content of their ATPase subunits. All remodeler ATPases contain a set of ATP-binding/DNA translocation protein domains , which classifies them within the RNA/DNA helicase superfamily 2. However, remodeler ATPases are then further classified into four remodeler subfamilies due to the presence of accessory protein domains found within the ATPase subunit . InitialWhile the DExx/HELICc domains are the catalytic center of nucleosome remodelers, accessory protein domains and remodeler subunits serve important roles in nucleosome remodeler function and regulation. Some subunits contain protein domains that regulate remodeler substrate recognition. For example, accessory bromodomains and chromodomains found in both ATPase and non-ATPase subunits recognize and bind acetylated  and methNon-coding RNAs are broadly defined as any RNA transcript generated from non-protein coding DNA (meaning any transcript outside of the exons within an mRNA transcript). The first described non-coding RNAs were transfer RNAs (tRNAs) , but witAs it has become apparent in recent years that ncRNAs comprise the majority of the eukaryotic transcriptome ,35,36, rTranscripts categorized as lncRNAs are the most numerous ,40 and fXIST [XIST are transcribed from the copy of the X chromosome within female somatic cells destined to become inactive (Xi), which then forms stable trans-associations with chromatin across Xi. The PRC2 complex forms a physical interaction with XIST, which is required for the proper recruitment of PRC2 and deposition of H3K27me3 for initiation of X chromosome inactivation [Xist operates as a guide lncRNA to recruit PRC2 to Xi. The 5’ end of HOTAIR interacts with PRC2 to induce the transcriptional repression of the HOXD locus through a similar mechanism to Xist [These modes of lncRNA functionality are represented with the interactions of the polycomb repressive complex 2 (PRC2) and lncRNAs such as XIST  and HOTAXIST . The PRCtivation . Thus, X to Xist . However to Xist . Here, H to Xist . StudiesCurrently, non-coding transcription around genomic regulatory elements termed enhancers is a focus point within the field. Enhancers are gene-distal loci capable of regulating the transcription of target genes by increasing the recruitment of RNA polymerase II to target genes ,49,50. ESimilar to histone modifying complexes such as PRC2, the action of nucleosome remodelers can be guided by interactions with ncRNAs . These fSChLAP1 and the BAF subunit SMARCB1/SNF5. SNF5, a core subunit of the BAF complex, is required for proper complex assembly and function [SChLAP1 was identified as an aberrantly expressed lncRNA in a screen of lncRNAs in prostate cancer tissues [SChLAP1 was found to directly interact with SNF5 in human prostate cells and overexpression of SChLAP1 led to a decrease in BAF occupancy genome-wide [SChLAP1 negatively influenced BAF occupancy, potentially through acting as a lncRNA decoy to inhibit complex formation through sequestration of SNF5. A well-characterized example of this type of interaction is between that of the BAF complex and several lncRNAs. The BAF (BRM/BRG1-associated factor) remodeling complex, the mammalian homolog of the yeast SWI/SNF and fly BAP complexes, consists of approximately 15 subunits filled by several gene families. BAF complex subunit composition can vary with respect to cell-type, which, when considered with the high number of potential BAF subunits, leads to the possibility that hundreds of cell-type specific BAF complexes exist. The BAF complex localizes to gene regulatory sites, such as enhancers and promoters, where it translocates DNA to alter nucleosome positioning. This activity regulates the accessibility of these locations for the core transcriptional machinery and is critical to appropriate gene expression in a variety of cellular processes, including maintenance of pluripotency, neural, heart, and muscle development, and tumor suppression (reviewed in ). Targetfunction . SChLAP1 tissues . Subsequome-wide . In thisXist, discussed previously, physically associates with Brg1 and inhibits its ATPase activity in vitro [Xist overexpression in an inducible murine embryonic fibroblast system results in decreased Brg1 occupancy across the Xi, arguing that an important function of Xist during Xi inactivation is to inhibit BAF targeting to the Xi through decoy interactions with Brg1 [UCA1, similarly behaves as a decoy to restrict BAF chromatin localization through Brg1 association [UCA1 is overexpressed in bladder cancer tissue, this interaction may result in an inhibition of BAF’s tumor suppressor activities and cancer cell proliferation [lncTC7, a lncRNA highly expressed in cancer stem cells, binds to the BAF subunits Brg1, BAF170, and SNF5 [UCA1 or Xist, lncTC7 functions as a guide lncRNA to promote BAF recruitment to the TCF7 locus. Recruitment of BAF to the TCF7 gene by lncTC7 is proposed to drive Wnt signaling and promote cancer stem cell self-renewal [lincRNA-Cox2 [IL-7-AS [MALAT1 [In mammalian cells, the BAF complex can utilize one of two ATPases, Brahma (BRM)  or Brahmin vitro . Xist ovith Brg1 . Anotherociation . As UCA1feration . lncTC7,and SNF5 . In an a-renewal . Similar[IL-7-AS , and MAL [MALAT1 , each relncKdm2b, interacts with the ATPase of the SRCAP complex, SRCAP [lncKdm2b increases SRCAP ATPase activity in mESC nuclear lysates and promotes complex integrity [Zbtb3, a transcriptional activator that upregulates Nanog expression, therefore maintaining mESC pluripotency [PAPAS [PAPAS-dependent NuRD recruitment results in the loss of histone H4 acetylation and repositioning of nucleosomes at the rDNA promoter to downregulate transcription of rDNA. A final example involves the interaction with Xist and a less well-characterized nucleosome remodeler, ATRX. ATRX localizes to heterochromatic genomic regions via RNA interactions to modulate DNA methylation and/or nucleosome composition and exchanges histone H3 for the histone variant H3.3 [Xist through a non-canonical RNA-binding domain, and this interaction is required for proper targeting of PRC2 to a subset of Polycomb target genes on Xi [Similar interactions between lncRNAs and nucleosome remodeling complex subunits have been reported with other nucleosome remodeling complexes, including the Snf2-related CREBP activator protein (SRCAP) complex, the nucleosome remodeling and deacylation (NuRD) complex, and alpha-thalassemia X-linked mental retardation (ATRX). The SRCAP remodeling complex from the INO80 family regulates chromatin structure by altering the composition of nucleosomes. SRCAP facilitates the exchange of canonical histone H2A with the histone variant H2AZ , which ix, SRCAP . Associantegrity . This inipotency . Targetiipotency . While Aipotency . Upon hyy [PAPAS . PAPAS-dant H3.3 ,79. ATRXlncTCF7 is necessary to bind BAF subunits BAF170, Brg1, and SNF5, with a stable stem-loop secondary structure predicted to mediate the interaction [Xist also suggest an important relationship with RNA secondary structure and protein partner interaction. Xist holds six repeating sequence elements (referred to as elements A–F) that mediate interactions with its binding partners. ATRX binds Xist repeat A with its N-terminal RNA binding domain (RBD) and its C-terminal helicase domain [Xist repeats A, B, E, and F, though the domains or residues of Brg1 that mediate this interaction have not been determined [Xist are highly structured in vivo, forming secondary structures such as stem-loops [What characteristics determine if or how ncRNAs can interact with nucleosome remodeler subunits? The majority of studies that have identified interactions between ncRNAs and nucleosome remodeler have not probed deep enough to answer these questions. However, a small number of studies have characterized the specific interacting regions between ncRNAs and BAF and ATRX. 7SK snRNA is a central member of the 7SK small nuclear ribonucleoprotein complex, a complex with critical roles in transcriptional regulation ,81. Utileraction . Secondae domain . In comptermined . Two sepem-loops ,83. WhenMM9 and CCL2 enhancers [Evf binds Brg1 within its HSA and BRK domains to downregulate Brg1 ATPase activity [Evf behaving as a decoy to influence BAF complex stability or extra-complex interactions associated with targeting. A third study, by Han et al. found that the helicase domain of Brg1 binds the lncRNA Mhrt779 with high affinity in vitro [Myh6, Myh7, and OPN in rat primary cardiomyocytes [lncTCF7 [If canonical RNA binding domains are absent in remodeler subunits, what protein domains could facilitate their interactions with ncRNAs? In 2018, Rahnamoun et al. examined how the enhancer localization of the chromatin associated protein BRD4 was influenced by eRNAs produced at these locations in human colon cancer cells . BRD4 isnhancers . Thus, Bactivity . Functioactivity ,85, and in vitro . This inmyocytes . Togethemyocytes  and sevemyocytes . Thus, Bmyocytes ,29,87. Tmyocytes ,29,87, amyocytes ,29,87 asmyocytes  and lncT[lncTCF7 , it remancRNAs can regulate nucleosome remodeler activity at the post-transcriptional level . While tSMARCD1 (which encodes BAF60A) to downregulate its expression post-transcriptionally [miRNA-based regulation of nucleosome remodelers can result in two outcomes: fine tuning the expression of individual subunits, and mediating subunit composition. The expression level of several subunits from SWI/SNF ,89, ISWItionally ,89. In ntionally . This sttionally ), it is By reducing the abundance of nucleosome remodeler subunits, miRNAs can stimulate the exchange of subunits and alter complex composition. A well-characterized example of this phenomenon is the altering subunit composition of the BAF complex during mammalian neural development. In neural stem cells, the BAF complex exists as a cell-type specific assembly termed neural progenitor BAF (npBAF). During differentiation from neural stem cells to post-mitotic neural cells, npBAF undergoes a series of subunit exchanges to become a neural cell specific assembly referred to as neural BAF (nBAF). Among these exchanges is the swapping of BAF53a with BAF53b. BAF53a and BAF53b belong to the same gene family and are critical for BAF ATPase activity, but do not exist within the same complex . This BACR993309 has been implicated as a post-transcriptional regulator of the INO80 complex subunit D (INO80D) in human epithelial cells [CR993309, overlapping the INO80D gene, bears 99% sequence homology to an approximately 9kb region of the 3’UTR of INO80D [INO80D [INO80D. Cells overexpressing CR993309 transfected with miRNA-5096 show an increase in INO80D expression, suggesting that CRR993309 may act as a ‘miRNA sponge’, or a decoy that sequesters miRNA-5096 to interfere with suppression of its intended target, INO80D [Cytoplasmic lncRNAs are primarily involved in post-transcriptional regulation of target mRNAs. The lncRNA al cells . This inf INO80D . miRNA-5 [INO80D  and resu, INO80D . Other e, INO80D ), and thIn comparison to the numerous mechanisms by which ncRNAs modulate nucleosome remodeler function, the mechanisms by which nucleosome remodelers regulate ncRNAs are currently limited. ncRNA regulation can occur pre-transcriptionally through limiting the transcription of non-coding loci or through co-/post-transcriptional degradation of the nascent transcript; nucleosome remodelers from each major subfamily have emerged as significant players in the pre-transcriptional regulation of ncRNAs. However, as the number and biological significance of ncRNAs continues to grow, so too does the need to identify mechanisms of ncRNA regulation. In this light, nucleosome remodelers represent a logical first avenue for exploratory research defining novel mechanisms of ncRNA regulation for two significant reasons. First, nucleosome remodelers alter chromatin structure through diverse ATP-dependent mechanisms , and some nucleosome remodelers demonstrate the capacity to utilize more than one of these mechanisms in different context ,101,102.Arabidopsis thaliana [Danio rerio, BAF is required for myogenic miRNA expression in skeletal muscle development [S. cerevisiae, which lack distal enhancer elements, SWI/SNF family nucleosome remodelers RSC and yeast SWI/SNF localize to promoters to control gene activation through chromatin accessibility [The relationship between the mammalian SWI/SNF or BAF complex and ncRNAs at gene regulatory sites has been well studied in eukaryotic systems a–c. Univthaliana . BAF funthaliana . In Danielopment . In S. csibility ,110,111.sibility . The selArabidopsis thaliana, the SWR1 homolog SWR1-C is required for proper transcriptional activation of miRNAs critical to flower and leaf development [S. cerevisiae [INO80 family nucleosome remodelers, divided into the SWR1 and INO80 sub-classes, alter chromatin structure primarily through regulating the chromatin localization of histone variant H2AZ d,e. Histelopment , suggestrevisiae ,120,121.revisiae . This corevisiae , as wellrevisiae . These sDrosophila melanogaster [ISWI nucleosome remodelers are typically associated with nucleosome remodeling activity that promotes chromatin organization, utilizing nucleosome sliding to establish regularly spaced nucleosome arrays following chromatin altering events such as replication or transcription . Localiznogaster ,130, humnogaster ,132,133,nogaster  suggest S. cerevisiae and Schizosaccharomyces pombe have one and four representative members of this family, respectively, mammalian cells have at least 9 CHD family nucleosome remodelers , leads to an increase in nucleosome occupancy at promoters genome-wide, which suggests CHD remodelers can modulate nucleosome occupancy as well [CHD nucleosome remodelers are an extraordinary example of nucleosome remodeler diversification in higher eukaryotes. While iewed in ). In higiewed in . Like yeiewed in ,128,130.iewed in . As disciewed in . However as well . Several as well  and CHD8 as well , are kno as well ,138. CurNucleosome remodelers and ncRNAs regulate one another through a complicated, multi-faceted, and partially understood interactions. On one side of this interaction are nucleosome remodelers, whose activity is required for ncRNA expression in some instances but necessary for their suppression to maintain genomic stability in others. On the other side of this interaction are ncRNAs, indispensable to proper nucleosome remodeling targeting and complex composition yet serving as critical limiters for the functions of others. Multiple ncRNAs in seemingly redundant fashion exist to keep in check the abundance of one nucleosome remodeler. In a more complicated fashion, ncRNAs exist to regulate ncRNAs that regulate nucleosome remodelers. In terms of mechanistic diversity, ncRNAs boast an interesting array of methods to regulate nucleosome remodeler function at many levels. While the mechanisms are diverse, individual nucleosome remodeler regulation by specific ncRNA(s) is highly explicit, targeting distinct remodelers or influencing only particular types of genomic elements or regions. In this regard, the popular notion of ncRNAs functioning as a ‘fine tuner’ of cellular processes is an attractive, and inclusive, model. With the high number of ncRNAs awaiting characterization, it will be exciting to see the scope of current mechanisms of nucleosome remodeler regulation and witness the discovery of novel ones. In comparison, ATP-dependent activities by nucleosome remodelers regulate ncRNAs primarily at the transcriptional level. Still, multiple members from each nucleosome remodeler subfamily in evolutionarily diverged systems modulate ncRNA expression through discrete, nucleosome-based mechanisms. This highlights that remodelers have an evolutionarily conserved role in ncRNA regulation that has only grown over evolutionary time, likely due to increasing eukaryotic genome size and complexity.Even with the substantial progress that has been made, important questions and limitations in our understanding remain about the regulatory interactions between ncRNAs and nucleosome remodelers. The functions for many ncRNAs await characterization, but current approaches to elucidate RNA functions are labor intensive and low throughput. The development of new techniques and approaches that can elucidate ncRNA functions in a systematic and genome-wide scale will expediate this research. Until these techniques are developed and applied, the full scope of remodeler subunits regulated by miRNAs and lncRNAs will remain unresolved. Another important question to address is how RNA secondary structure mediates ncRNA interactions with nucleosome remodelers. A handful of studies suggest that secondary structure plays a more influential role in mediating ncRNA-nucleosome remodeler interactions than transcript length or sequence, but only additional secondary structural studies of ncRNA regions that come in physical contact with nucleosome remodeler subunits will address this issue. In addition, it will be important to identify and validate ncRNA binding interactions for novel nucleosome remodeler subunits, as well as identify the RNA binding protein domains and the functional implications of these binding events on remodeling activity. These data will clarify the commonality of ncRNA interactions with nucleosome remodelers and inform our perspective of its impact on remodeler function. While outside the scope of this review, understanding how non-ATP-dependent mechanisms of ncRNA regulation fit into the network of ncRNA and nucleosome remodeler interactions will undoubtedly be important. Activities such as histone post-translational modifications and extra-complex protein interactions alter chromatin structure and transcription in alternative ways and likely shapes the non-coding transcriptome through new mechanisms.The current body of research has shown that ncRNAs and nucleosome remodelers regulate one another through a complex, yet balanced, network of interactions. Concurrently, research has illuminated the depth of what remains to be determined regarding this network. With the vast amount of knowledge left to be unlocked in this field, it is an exciting time for research in chromatin and ncRNA biology.


MED12 mutant uterine leiomyomas. Furthermore, modifications in enhancer architecture are driven by the depletion of AP-1 occupancy on chromatin. Silencing of AP-1 subunits in primary myometrium cells leads to transcriptional dysregulation of extracellular matrix associated genes and partly recapitulates transcriptional and epigenetic changes observed in leiomyomas. These findings establish AP-1 driven aberrant enhancer regulation as an important mechanism of leiomyoma disease pathogenesis.Uterine leiomyomas (fibroids) are a major source of gynecologic morbidity in reproductive age women and are characterized by the excessive deposition of a disorganized extracellular matrix, resulting in rigid benign tumors. Although down regulation of the transcription factor AP-1 is highly prevalent in leiomyomas, the functional consequence of AP-1 loss on gene transcription in uterine fibroids remains poorly understood. Using high-resolution ChIP-sequencing, promoter capture Hi-C, and RNA-sequencing of matched normal and leiomyoma tissues, here we show that modified enhancer architecture is a major driver of transcriptional dysregulation in  MED12 have been implicated as the causal genetic lesion in the majority of uterine leiomyomas. Here, the authors profile the chromatin landscape of matched normal and leiomyoma tissues and find that changes in enhancer acetylation, enhancer-promoter interaction strength, differential enhancer usage and transcription factor AP-1 occupancy are significant drivers of transcriptional dysregulation in MED12 mutant leiomyomas.Somatic mutations in  Leiomyomas are clonal tumors that originate from the smooth muscle layer of the uterine wall, the myometrium2. They are primarily characterized by an increased deposition of a disorganized extracellular matrix (ECM), resulting in benign neoplasms of varying size4. Despite a high prevalence, viable long-term non-surgical treatment options for uterine fibroids do not exist. Many women diagnosed with the disease ultimately undergo hysterectomies as the only curative treatment, with fibroid-related hysterectomies accounting for over 30% of all hysterectomies5.Uterine fibroids affect over 70% of all reproductive age women and are a major cause of gynecologic and reproductive dysfunction, ranging from profuse menstrual bleeding and pelvic discomfort to infertility, recurrent miscarriage, and preterm labor6. In uterine leiomyomas, whole-exome and whole-genome sequencing have identified multiple genetic aberrations that may be required for leiomyoma development and growth. Fibroid tumors stratify into four main subtypes that are dependent on the mutational status of mediator of transcription subunit 12 (MED12), fumarate hydratase (FH), high mobility group AT-hook 2 (HMGA2) translocations, and collagen (COL4A5-COL4A6) gene deletions7. In particular, the characterization of mutations in MED12, a key subunit of the mediator of RNA polymerase II transcription (Mediator) complex that globally regulates RNA polymerase II-dependent transcription, is of considerable interest as they have been found in ~70% of patients diagnosed with uterine leiomyomas, with mutations occurring predominantly in exon 2 of MED128. In addition to whole-exome sequencing, gene expression profiling of leiomyomas has been performed, primarily using RNA microarrays10. Recent work has revealed subtype-specific gene expression profiles, indicating possibly different mechanisms of pathogenesis in fibroid tumors belonging to different subtypes9. Despite multiple gene expression studies identifying aberrant gene regulation, very little is known regarding the mechanisms of gene dysregulation in uterine leiomyomas.High-throughput sequencing techniques have provided major insights into the mechanisms of disease pathogenesis13. In addition, recent studies demonstrate that changes in chromatin structure at enhancer regions also play a major role in disease pathogenesis14. However, the role of enhancer chromatin structure in uterine leiomyomas remains unexplored.Aberrant epigenetic modifications and transcription factor chromatin binding have long been implicated in transcriptional dysregulation and disease developmentcis-regulatory DNA elements (CREs) that spatially organize in close proximity to their respective promoters, thereby enabling direct contact between trans-acting factors and gene promoters16. Alterations in histone modification state as well as transcription factor and co-factor DNA binding affinity lead to changes in enhancer 3D architecture, resulting in alterations to RNAPII-dependent gene transcription. Recent chromosome conformation capture techniques have led to high-resolution maps of enhancer-promoter contacts, allowing for the unambiguous pairing of enhancers with their corresponding promoters18. In addition, promoter capture Hi-C (CHi-C) has been used to demonstrate the dynamism of enhancer-promoter contacts during cell differentiation20. However, the existence of dynamic or altered contacts in human disease as a result of epigenetic changes to chromatin has not been investigated.Enhancers are long-range 21. Additionally, in primary cell culture of cells obtained from leiomyoma patient tissue samples, a rapid loss of MED12 mutant cells was observed, suggesting a very limited viability of this cell population in culture23.Previous studies suggest that culturing of primary cells from tissue samples may partially alter the gene expression profiles of cellsIn this study, freshly procured tissue samples from women who have undergone hysterectomies as a course of treatment for uterine leiomyomas, confirmed to have a glycine-to-aspartate (G44D) or glycine-to-serine (G44S) substitution in exon 2 of MED12, are used to characterize epigenetic changes in the disease. Adjacent, non-diseased areas of the myometrium from the same patients are also collected to represent normal (wild-type [WT]) samples. In an effort to avoid artifactual alterations to the transcriptomic and epigenomic profiles of patient samples, gene expression profiling by RNA-sequencing (RNA-seq) as well as epigenetic profiling by high-resolution chromatin immunoprecipitation-sequencing (ChIP-seq) and promoter capture Hi-C are performed directly from tissue samples with minimal processing. Our integrative analysis of transcriptomic and epigenetic changes, highlighted by the near-native characterization of long-range promoter interactions in uterine fibroids, identifies differential transcription factor occupancy, differential enhancer engagement, and altered enhancer-promoter contacts as key events that drive gene dysregulation in leiomyomas.MED12 mutant disease tissue types are primarily attributable to biological pathways that are important for the development and maintenance of the leiomyoma disease state.We used RNA isolation followed by massively parallel sequencing (RNA-seq) to examine the transcriptome profiles of normal myometrium (WT) and matched leiomyoma (G44D/S) tissue obtained from 15 women. A high degree of similarity between biological replicates of myometrium transcriptome profiles was seen, with a similar observation among biological replicates of leiomyoma tissue samples. Hierarchical clustering of all RNA-seq datasets highlights clustering primarily by disease state Fig. . SignifiP values). Two thousand nine hundred and sixty-five genes exhibited a >2-fold change in expression between myometrium and leiomyoma samples, while 1014 genes were differentially expressed at >4-fold  was carried out. Exonic and intronic read count changes between myometrium and leiomyoma tissue samples were highly correlated, indicating that gene expression changes seen in the disease state were primarily transcription driven and not post-transcriptional . However, 331 genes were identified as possible targets of post-transcriptional regulation. Scanning of the 3′-untranslated regions (UTRs) of genes identified seed sequences for mir-216 and mir-124a, which have both been demonstrated to play a role in tumor growth factor-β signaling, a major pathway implicated in tissue fibrosis 28. In summary, these results highlight perturbations, primarily at the transcriptional level, of a large proportion of the uterine muscle tissue transcriptome, the majority of which are dysregulated protein-coding genes, including ECM-associated genes.While regulation of RNA levels occurs primarily at transcription, which includes epigenetic control of the RNAPII-mediated transcription machinery, RNA levels are also regulated post-transcriptionally through the regulation of RNA processing and degradation29. To provide epigenetic mechanistic insight into the predominantly transcriptional changes in gene expression observed by leiomyoma transcriptome profiling, chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) of H3K27Ac was performed  of all identified H3K27Ac regions are differentially acetylated with >2-fold change in signal and ~10% of sites  exhibit >4-fold change in H3K27Ac signal  is a post-translational modification that is highly correlated with active transcription at enhancers and promotersnal Fig. .Fig. 2Hiχ2(4) = 1205, p < 0.05). While gene promoter acetylation signal accounts for ~17% of all H3K27-acetylated regions in myometrium and leiomyoma, acetylated promoters account for only 8% of all differentially acetylated regions. Conversely, higher than expected numbers of promoter-distal sites were differentially acetylated, with 85% of all differentially acetylated regions occurring at intergenic and intronic regions, compared to 75% of sites in myometrium and leiomyoma. This suggests alterations at cis-regulatory elements such as enhancers in uterine leiomyomas.Analysis of genomic loci at sites of altered H3K27Ac signal revealed changes at predominantly promoter-distal regions, as evidenced by a significant deviation from the expected H3K27Ac signal distribution across the genome , a disintegrin and metalloprotease domain-containing protein 19 (ADAM19), and collagen gene COL12A1, key genes involved in ECM formation and degradation  of contacts were associated with differentially expressed genes, which ontology analysis revealed to be enriched for genes involved in ECM organization , as well as collagen genes COL6A3, and COL12A1  regions with unaltered promoter contact strength and differential H3K27 acetylation; (ii) regions with altered promoter contact strength and unchanged H3K27 acetylation; and (iii) regions with altered promoter contact strength and differential H3K27 acetylation  transcription factor motif was enriched at differentially acetylated enhancers Fig. . Analysisue Fig. . This isn = 2573/2993) showing a loss in binding affinity  also showing a decrease in binding affinity   Fig. .ChIP-seq of AP-1 subunits JUN and FOS in myometrium and leiomyoma tissue samples revealed a depletion of AP-1 on chromatin in leiomyomas, including depletion at a subset of enhancers that make contacts with promoters of dysregulated genes. From these results we conclude that AP-1 occupancy at H3K27-acetylated enhancers in myometrium is important in regulating uterine muscle cell gene regulatory programs and a loss of AP-1 binding at enhancers as a result of decreased AP-1 gene expression may lead to widespread gene dysregulation in leiomyomas.34. Previously published interaction studies in heterologous cell lines expressing mutant MED12 (G44D/S) have suggested that mutations in exon 2 of MED12 may result in a decrease in cyclin C-CDK8 chromatin occupancy at sites of active transcription35. To assess whether exon 2 mutations at glycine 44 of MED12 result in a loss of CDK8 chromatin binding in uterine leiomyomas, ChIP-seq of CDK8 and MED12 was performed  of CDK8 submodule binding sites show changes in binding affinity in leiomyoma tissue samples as compared to myometrium  by CRISPR/Cas9-mediated protein depletion  = 346.7, p < 0.05). Motif enrichment analysis also revealed almost exclusively AP-1 motifs as the top enriched motifs at differential H3K27Ac regions, with AP-1 motifs enriched at H3K27Ac peak centers  and 122 (H3K122Ac), but not at H3K2738. Promoter contacts that do not overlap H3K27-acetylated regions in leiomyoma may be indicative of H3K122- and H3K64-acetylated enhancers and further studies to determine the function of these distinct regions will be required. Promoter contacts that do not overlap with H3K27Ac signal may also be indicative of negative regulators such as silencers, which are associated with a different epigenetic profile from enhancers marked with H3K27Ac39.A survey of enhancers associated with differential gene expression identified three classes of remodeled enhancers on the basis of changing H3K27 acetylation and altered promoter contact strength, of which enhancer regions with stable contacts and differential acetylation were the predominant class of altered enhancers. This is consistent with previous studies showing the lineage-defining characteristics of most promoter contacts, hence their relative stabilityDifferential enhancer usage, whereby two or more enhancers associated with the same gene undergo inversely related changes in H3K27Ac signal and promoter contact strength, was observed in enhancers associated with differentially expressed genes in leiomyomas. This mechanism may allow for the alternation of more densely occupied/stronger enhancers with weaker ones and vice versa, resulting in changes in gene expression. It may also be a mechanism by which different groups of transcription factors and cofactors are brought into contact with promoters to modulate gene transcription in uterine leiomyomas. Taken together, the observations of alterations in H3K27Ac signal at distal sites, altered promoter contacts, and the identification of differential enhancer usage in uterine leiomyomas all suggest that in addition to important promoter-proximal gene transcription regulatory events, enhancer malfunction is also an important mechanism of gene dysregulation in uterine leiomyomas.40. The dimeric composition of AP-1 determines which AP-1-responsive genes are regulated and whether the resulting AP-1 complex is gene activating or repressive, either directly or through the recruitment of gene-activating or repressive chromatin-modifying complexes41. Also, AP-1 has been previously implicated in the development of many fibrotic diseases44.The AP-1 complex is a transcription factor composed of homodimers or heterodimers from the JUN, FOS, and ATF families of transcription factorsJUN and FOS mRNA levels has been observed in uterine fibroids32. However, the significance of this observation and the mechanisms by which AP-1 down-regulation led to changes in transcription were not clearly understood. In this study, a loss of JUN and FOS gene expression leads to a concomitant decrease in AP-1 recruitment at enhancer sites, which is correlated with decreased H3K27Ac signal. Enhancers associated with differentially expressed genes also show perturbations in AP-1 occupancy. In addition, loss of AP-1 chromatin occupancy at enhancers partially overlaps with changes in promoter contact strength. This is consistent with the described role of AP-1 in mediating regulatory chromatin contacts and suggests that loss of AP-1 gene expression and chromatin occupancy may drive the remodeling of enhancer architecture in leiomyomas45. CRISPR/Cas9-mediated silencing of AP-1 subunits in HUtSMCs demonstrates that loss of AP-1 can indeed cause the large-scale gene expression and enhancer acetylation changes seen in leiomyoma tissue samples. As such, AP-1 may serve as a master regulator of gene expression in uterine muscle cells, the loss of which could explain the broad transcriptional changes seen in leiomyoma disease pathogenesis.Down-regulation of 47. In addition, down-regulation of AP-1 is also observed across leiomyomas of different subtypes9. Given the role of AP-1 in widespread enhancer malfunction identified in this study, ongoing work will seek to determine if similar alterations in enhancer architecture are also observed in leiomyomas of non-MED12 mutant origins, with AP-1 providing a possible unifying mechanism of gene dysregulation in uterine leiomyomas of differing subtypes.Given the interest in AP-1 transcription factor activity at enhancers as well as the implication of AP-1 in multiple fibrotic diseases, future studies on AP-1-dependent enhancer malfunction in other fibrotic diseases will be of great interestDespite multiple studies showing decreased expression of AP-1 subunits in fibroids, a mechanism for AP-1 down-regulation remains unknown. The loss in expression of a significant number of subunits would suggest a unifying mode of regulation for AP-1 family genes that is perturbed in uterine fibroids; however, we did not find a singular enhancer or group of enhancers that jointly regulates AP-1 genes. Studies to further elucidate the mechanisms that result in the repression of AP-1 subunit gene expression in leiomyomas will be of great importance in increasing our understanding of leiomyoma disease pathogenesis.JUN and FOS family genes are down-regulated in uterine fibroids, it is unclear if the loss in expression of all AP-1 genes is necessary to cause the enhancer defects observed in leiomyoma cells. Given that incomplete CRISPR/Cas9-mediated depletion of JUN family genes in this study still results in enhancer malfunction, it is possible that a loss of a subset of AP-1 genes results in a combinatorial shift in JUN binding partners. Alternatively, a more complete depletion of AP-1 subunits may result in the observation of greater enhancer malfunction in primary cells that more closely recapitulates changes seen in leiomyomas. Further studies will be necessary to clarify the role of different AP-1 subunits in this disease.Although all 49. Recent work suggests that DNA methylation in leiomyomas is mediated by the long non-coding RNA H19, which regulates TET3 expression50. Consistent with previous reports, we observed an increase in H19 expression in leiomyoma tissue samples relative to myometrium. In addition, we observed increased gene expression of TET1, TET3, DNMT1, and DNMT3a. Interestingly, an increase in H19 expression is seen upon AP-1 depletion in primary uterine smooth muscle cells. This result suggests that AP-1 plays a role, directly or indirectly, in regulating H19 expression and further emphasizes the important role of AP-1 in this disease.Previous studies have implicated aberrant DNA methylation as an important event in uterine leiomyoma pathogenesisMED12 lead to leiomyomas is not well understood. Studies have suggested that MED12 mutations may affect transcription either through the disruption of CDK8 kinase activity or through decreased binding affinity of cyclin C and CDK8 at sites of active transcription35. Our work demonstrates that while CDK8 binding is altered in leiomyoma tissue samples, characterized by both a loss of binding affinity at a subset of sites and a recruitment of CDK8 at new sites, the observed change is not consistent with a global loss of CDK8 recruitment that would result from a mutation-dependent MED12-cyclin C-CDK8 interaction defect. Rather, our results show that changes in the CDK8 submodule cistrome are positively correlated with altered H3K27Ac, suggesting that altered CDK8 binding is more consistent with general changes in the enhancer landscape of leiomyoma cells. This aligns with recently published data in heterologous cell lines as well as leiomyoma tissue samples, which demonstrate that the presence of MED13 is a stabilizing protein in the submodule that compensates for any MED12 mutant-dependent interaction defect52. However, while MED13 may stabilize cyclin C-CDK8 chromatin binding in the presence of a mutation in MED12, it cannot compensate for the loss in kinase activity of the submodule. Therefore, MED12 mutation-driven leiomyoma disease pathogenesis is more likely a result of CDK8 kinase activity defects, and not cyclin C-CDK8 binding defects, and future work will be required to identify the kinase substrate of the CDK8 submodule.The mechanism by which exon 2 mutations in FOS and JUN gene expression in leiomyoma tissues, along with the resulting depletion of AP-1 chromatin occupancy, as likely significant events in leiomyoma disease pathogenesis.In summary, this work highlights enhancer malfunction as an important mechanism of gene dysregulation in uterine leiomyomas. Altered enhancer architecture, consisting of changes in histone acetylation, differential enhancer usage, and promoter contact rewiring, plays a key role in leiomyoma differential gene expression. In addition, AP-1 is shown to be an important complex in the regulation of ECM-associated biological pathways in uterine muscle tissue. This study establishes the loss of MED12 mutant leiomyoma samples with either a G44D or G44S mutation. Tissue samples were either immediately cryopreserved at −80 °C, processed for high-resolution ChIP-seq, RNA-seq, and promoter capture Hi-C, or processed for primary cell culture. Tissue from all 15 patients was used in RNA-seq experiments and tissue from five of the patients was used for ChIP-seq and capture Hi-C experiments. All surgeries were performed at Northwestern University Prentice Women’s Hospital and all patients gave informed consent for their participation in this study. The study was carried out in accordance with a Northwestern University institutional review board-approved protocol. Samples were collected from hysterectomies performed on eight black women, three white women, and one latino woman. The race/ethnicities of three women were unknown. Patient ages ranged from 38 to 51 years with a mean of 46.1 years.Tumor samples were collected from 15 premenopausal women undergoing hysterectomies as a treatment for uterine fibroids. Normal, adjacent myometrium tissue samples were also collected from hysterectomies. Sanger sequencing of PCR-amplified genomic DNA extracted from leiomyomas and matched myometrium was used to positively identify 3 pieces and digested with shaking (100 r.p.m.) at 37 °C for 5 h in collagenase digestion buffer  at a ratio of 1 part w/v tissue with five parts digestion buffer. Digested tissue was filtered with sterile gauze sponges and centrifuged for 5 min at 400 × g. Cells were resuspended in smooth muscle cell culture media , filtered with a 70 μm cell strainer, plated, and grown at 37 °C in a humidified cell culture incubator containing 5% CO2. Primary cells were used in experiments after two passages.Myometrium and leiomyoma tissue were cut into 5 mmThirty milligram of myometrium and leiomyoma tissue was cryopulverized into a fine powder and homogenized in Qiazol lysis reagent using a Kinematica Polytron PT2100 homogenizer. Total RNA was isolated using Qiagen miRNeasy Mini Kit according to the manufacturer’s instructions . After RNA elution, an in-solution DNase treatment was performed to completely digest genomic DNA . RNA quality was verified using the Bioanalyzer Eukaryote Total RNA 600 Nano Kit . Purified RNA samples had a mean RNA integrity number of 8.0. RNA was prepared for sequencing using the KAPA-stranded RNA-seq with RiboErase Kit according to the manufacturer’s instructions . Paired-end sequencing of all RNA libraries was performed on the Illumina NextSeq 500 Platform.g at 4 °C for 5 min and the supernatant discarded. Two washes in phosphate-buffered saline containing 1× Protease inhibitor cocktail  was performed and tissue recovered by centrifugation at 1000 × g at 4  °C for 5 min. Cross-linked tissue was then either frozen at −80 °C or immediately used in ChIP or CHi-C experiments.Myometrium and leiomyoma tissue samples were cryopulverized into a fine powder using a Covaris cryoPREP CP02 impactor followed by a mortar and pestle. Samples were immediately formaldehyde cross-linked  for 10 min at room temperature with rotation and then quenched with 0.15 M glycine for 10 min at room temperature with rotation. Cross-linked tissue was recovered by centrifugation at 1000 × 53. Cross-linked myometrium and leiomyoma tissue samples were dounce homogenized in ChIP lysis buffer 1 , followed by end-over-end rotation at 4 °C for 15 min and then centrifugation for 5 min at 1000 × g, also at 4 °C. Samples were resuspended in ChIP lysis buffer 2 , followed again by end-over-end rotation at 4 °C for 15 min and sample recovery by centrifugation at 1000 × g at 4 °C for 5 min. Samples were then resuspended in MNase digestion buffer , and incubated at 37 °C until 75% mono-nucleosomal chromatin profile is observable by gel electrophoresis of purified MNase-digested DNA (~5–7 min). Digestion was quenched with MNase quenching buffer , after which samples were briefly sonicated in an ice water bath . Collagen and cell debris was removed by centrifugation at 20,000 × g at 4 °C for 20 min. Solubilized chromatin concentration was measured by the BCA assay and ~400 μg of chromatin and 3 μg of antibody was used for overnight immunoprecipitation of histones at 4 °C with end-over-end rotation, while 600 μg of chromatin and 5 μg of antibody was used for overnight immunoprecipation of factors. Antibodies against H3K27Ac , RNAPII, FOS , JUN , and CDK8 and MED12  were used in pulldowns. Ten microliters of protein G magnetic beads  per μg of antibody were added and samples were incubated at 4 °C for an additional 3 h. Immunoprecipitated samples bound to beads were recovered using a magnetic rack , followed by 5 washes with ChIP-RIPA wash buffer  and once with NaCl containing TE buffer . DNA was eluted off the beads twice with 50 μl ChIP elution buffer  at 65 °C for 20 min with shaking. Cross-links were reversed with 400 mM NaCl for 12–16 h at 65 °C, followed by RNase A treatment at 37 °C for 1 h. This was followed by 2 h at 55 °C with 40 μg of proteinase K  supplemented with 16.5 mM EDTA and 66 mM Tris-HCl (pH 8.0). Reverse cross-linked DNA was isolated by phenol–chloroform extraction. KAPA Hyper Prep Kit  was used for end repair, A tailing, and adapter ligation with TruSeq index adapters, all according to the manufacturer’s instructions. A 1.1×–1.8× AMPure XP bead  size selection was carried out after adapter ligation to enrich for sub-nucleosomal DNA fragments, followed by 10–12 cycles of PCR amplification. For H3K27Ac ChIP, a 0.6×–1.0× AMPure XP bead size selection was performed to enrich for mono-nucleosomal DNA fragments. Single-end sequencing of all ChIP libraries was performed on the Illumina NextSeq 500 platform.Chromatin immunoprecipitation was adapted from previously described methods57. Cross-linked myometrium and leiomyoma tissue samples were dounce homogenized in permeabilization buffer  followed by end-over-end rotation at 4 °C for 15 min, after which sample pellet was recovered by centrifugation at 4 °C. Samples were washed in 1.2× NEBuffer2 (cat. # B7002) at 4 °C for 15 min with end-over-end rotation, followed by centrifugation at 4 °C. Samples were then resuspended in 1.2× NEBuffer2 containing 0.2% SDS buffer and incubated at 37 °C for 1 h with end-over-end rotation, followed by the addition of 1.8% Triton X-100 at 37 °C for an additional hour. The resulting chromatin was digested with 400 U of HindIII restriction enzyme (NEB cat. # R0104) for 12–16 h at 37 °C with end-over-end rotation. This was followed by DNA polymerase I-mediated  labeling of DNA ends with dNTPs , for 3 h at 37 °C. Samples were then incubated at room temperature for 4 h with end-over-end rotation in proximity ligation mix , followed by the addition of SDS to a final concentration of 1%. Samples were then treated with RNase A at 37 °C for 1 h and proteinase K at 55 °C for 2 h. Cross-links were reversed with 400 mM NaCl for 12–16 h at 65 °C. Labeled and proximity ligated DNA was isolated by phenol–chloroform extraction and sheared to a size of 300–500 bp using a Covaris S2 focused ultrasonicator, followed by a 0.5×–0.9× AMPure XP bead  size selection. Biotin was removed from unligated ends with T4 DNA polymerase mix ) at 20 °C for 4 h, followed by streptavidin bead-based biotin pulldown  in 1× streptavidin binding buffer . Beads were washed five times in streptavidin Tween washing buffer  and DNA was eluted off the beads twice with 50 μl biotin elution buffer  at room temperature for 30 min, followed by a 1× AMPure XP bead cleanup. KAPA Hyper Prep Kit  was used for end repair, A tailing, adapter ligation with TruSeq index adapters, and 12 cycles of PCR amplification, all according to the manufacturer’s instructions. Previously described custom-designed biotinylated 120-mer RNA baits targeting Ensembl promoters of protein-coding and non-coding transcripts were used to capture promoter associated biotinylated and adapter ligated Hi-C DNA from myometrium and leiomyoma samples using Agilent SureSelect XT Kit  according to the manufacturer’s instructions. TS universal blocker and TS index-specific blocker  were used in place of the SureSelect ILM Indexing Block 3. Six cycles of post-capture, on-bead PCR amplification was performed and paired-end sequencing of all CHi-C libraries was performed on the Illumina NextSeq 500 platform.Promoter capture Hi-C was adapted from previously described methodsJUN, JUNB, JUND, FOS, and FOSB were obtained from the RNAi consortium (TRC). Short hairpin oligonucleotides (Integrated DNA Technologies) targeting AP-1 subunits and non-silencing control were cloned into a modified pLVX-IRES-mCherry vector  with bicistronic transcription driven by the phosphoglycerate kinase (PGK) promoter instead of the cytomegalovirus immediate early (CMV IE) promoter. Lentivirus production was carried out in HEK 293 T/17 cells  with lipofectamine co-transfection of pMD2.G, psPAX2, and shRNA-containing lentiviral construct, a total of 18 μg DNA at a ratio of 3:2:1, according to manufacturer’s instructions . Virus was concentrated 10-fold using polyethylene glycol according to manufacturer’s instructions . 1.0 × 106 myometrium primary cells collected from each patient  and cultured in 10 cm2 dishes with SmGM-2 muscle cell media, were each transduced with 100 μl of either non-silencing control or AP-1 gene silencing concentrated virus and 6 μg/ml of polybrene for 18 h. This amount of virus was experimentally determined to be the minimum amount required to give >80% transduction efficiency as determined by mCherry fluorescence. Fresh SmGM-2 muscle cell media was then added and cells were allowed to grow for 3–4 days. Cells were then harvested and RNA extracted using the RNeasy Mini Kit according to manufacturer’s instructions . Gene knockdown was confirmed by RT-qPCR and western blot of JUN, JUNB , JUND , FOS , and FOSB . Uncropped western blot images are provided in Supplementary fig. Short hairpin sequences targeting AP-1 subunits 2. Media was changed every 48 h and cells were passaged at 90% confluence.HUtSMCs were obtained from American Type Culture Collection  and grown in vascular smooth muscle cell complete media . Cells were grown at 37 °C in a humidified cell culture incubator containing 5% COJUN, JUNB, JUND, FOS, and FOSB gene loci  were combined with tracrRNA  in a 1:1 ratio to a final concentration of 44 μM, incubated at 95 °C for 5 min and allowed to cool to room temperature. SpCas9 enzyme  was diluted to 36 μM with Neon resuspension buffer R  and combined with the crRNA:tracrRNA AP-1 mixes in a 1:1 volume ratio and incubated at room temperature for 20 min, after which electroporation enhancer  was added to a final concentration of 1.8 μM. HUtSM cells that were 90% confluent were trypsinized for 4 min, quenched in FBS-containing media and centrifuged at 300 × g for 5 min at room temperature. Centrifuged cells were resuspended in phosphate-buffered saline and counted. Cells were then centrifuged at 300 × g for 5 min at room temperature and resuspended at a concentration of 15,000 cells/μl in Neon resuspension buffer R. 135,000 cells were combined with crRNA:tracrRNA:cas9:electroporation enhancer mix targeting either a non-silencing control or AP-1  and electroporated in a 10 μl Neon pipette tip  using a Neon electroporation system  according to manufacturer’s instructions. Electroporation of 135,000 cells in a 10 μl Neon pipette tip was repeated ten times and 1.35 × 106 electroporated cells were then cultured in smooth muscle cell complete media. Gene editing was confirmed by sequence trace decomposition58.Custom CRISPR-Cas9 crRNAs targeting AP-1 subunits Cells were harvested and RNA extracted using the RNeasy Mini Kit according to the manufacturer’s instructions . Purified RNA was prepared for sequencing using the KAPA mRNA Hyper Prep Kit according to the manufacturer’s instructions . Paired-end sequencing of all libraries was performed on the Illumina NextSeq 500 platform.g and then resuspended in ChIP lysis buffer 1 , followed by end-over-end rotation at 4 °C for 15 min and then centrifuged for 5 min at 1000 × g, also at 4 °C. Samples were resuspended in ChIP lysis buffer 2 , followed again by end-over-end rotation at 4 °C for 15 min and sample recovery by centrifugation at 1000 × g at 4 °C for 5 min. Samples were then resuspended in ChIP lysis buffer 3  and sonicated in an ice water bath . Triton X-100 was added to a final concentration of 1% and samples were centrifuged at 20,000 × g at 4 °C for 20 min, with recovery of chromatin-containing supernatant. Solubilized chromatin concentration was measured by the BCA assay according to the manufacturer’s instructions . Approximately 500 μg of chromatin and 4 μg of antibody against H3K27Ac  was used for overnight immunoprecipation of histones at 4 °C with end-over-end rotation. 10 μl of protein G magnetic beads  per μg of antibody was added and samples were incubated at 4 °C for an additional 3 h. Immunoprecipitated samples bound to beads were recovered using a magnetic rack , followed by 5 washes with ChIP-RIPA wash buffer  and once with NaCl containing TE buffer , with magnetic recovery of immunoprecipitated samples bound to beads after each wash. DNA was eluted off the beads twice with 50 μl ChIP elution buffer.  at 65 °C for 20 min with shaking. Cross-links were reversed with 300 mM NaCl for 12–16 h at 65 °C followed by 20 μg of RNase A  treatment at 37 °C for 1 h. This was followed by 2 h at 55 °C with 80 μg of proteinase K  supplemented with 16.5 mM EDTA and 66 mM Tris-HCl (pH 8.0). Reverse cross-linked DNA was isolated by phenol–chloroform extraction with phenol/chloroform/isoamyl alcohol [25:24:1]  using 5Prime phase lock gel  according to manufacturer’s instructions, followed by ethanol precipitation  at −80 °C for 1 h. DNA was then pelleted by centrifugation at 20,000 × g at 4 °C for 20 min, followed by one wash in with 70% ethanol and further centrifugation at 20,000 × g at 4 °C for 10 min. DNA pellet was dried and resolubilized in 50 μl of elution buffer . KAPA Hyper Prep Kit  was used for end repair, A tailing, and adapter ligation with TruSeq index adapters, all according to the manufacturer’s instructions. A 0.6 × −0.8 × AMPure XP bead  size selection was carried out after adapter ligation to enrich for mono-nucleosomal DNA fragments from the H3K27Ac ChIP followed by 12 cycles of PCR amplification. Single-end sequencing of all ChIP libraries was performed on the Illumina NextSeq 500 platform.Sixteen percent paraformaldehyde was added to full media-containing HUtSM cell plates to a final concentration of 1% paraformaldehyde and incubated at room temperature for 10 min. Cross-linked cells were then quenched with 0.125 M glycine solution for a further 10 min, followed by multiple washes in cold (stored at 4 °C) phosphate-buffered saline containing 1× protease inhibitor cocktail . Cells were then centrifuged for 5 min at 1000 × q-scores59.All ChIP, CHi-C, and RNA-seq reads in FASTQ format were aligned to the GRCh38 human genome assembly obtained from Ensembl (release 90) and all transcript annotations were also obtained from Ensembl. The R package Gviz was used to visualize browser tracks of ChIP-seq, and RNA-seq BigWig files as well as myometrium and leiomyoma CHi-C n = 15 patients) were aligned to the GRCh38 human genome assembly using the STAR aligner (v2.5.3a) with default settings60. Uniform gene body coverage of reads was verified with the RSeQC python package (v2.6.4)61. RSeQC was also used to normalize BAM files obtained from STAR alignments to 1 × 107 reads when converting to wiggle format, which were then converted to BigWig files using the UCSC browser utility wigToBigWig. Counting of reads per gene was performed using featureCounts from the Subread package  and differential expression analysis of gene counts between myometrium and leiomyoma was performed with DESeq2 (v1.18.1), with the Wald test used to test for significance . Multiple testing correction was applied using the BH method, with the false discovery rate  controlled for at <0.0163. RNA tissue clustering and differential gene expression heat maps were generated in R using pheatmaps (cran.r-project.org). All reads (introns + exons) mapping to an annotated feature were used to compare transcriptome profiles between samples while only exonic reads were used for differential gene expression analysis. Enriched gene ontology terms were identified using Metascape and scatter plots of ontology terms were made using REVIGO65. EISA was performed as previously described25.RNA-seq reads (n = 5 patients) were first adapter trimmed using cutadapt (v1.15)66. Read quality of tissue samples and cells (n = 3) were then verified with FastQC (www.bioinformatics.babraham.ac.uk)66. Reads were then aligned to the GRCh38 human genome assembly using bowtie 67. Peak calling and motif enrichment were performed with HOMER . Differential enrichment of peaks was determined using the R package DiffBind68 (www.bioconductor.org). First, DiffBind was used to derive a consensus myometrium and leiomyoma peak set for each histone or factor ChIP from the individual HOMER-called peak sets of each biological replicate. Differential peak analysis on the consensus peak set was then performed with a peak being identified as differentially enriched or depleted at an FDR cutoff <0.05 and a fold change >|2|. Promoters are defined as regions from −1000 to +100 bp of the transcriptional start site and enhancer regions are H3K27Ac peaks that fall outside promoter regions and are further subdivided into H3K27Ac peaks found in exonic, intronic, and intergenic regions. Pearson’s correlation heat maps of biological replicates were also drawn using DiffBind. Reads per genomic content normalization  of BAM files obtained from Bowtie alignments was performed with DeepTools to produce BigWig files69. Heat maps and profile plots of ChIP-seq peaks were also plotted using DeepTools. Motif enrichment analysis at differential H3K27Ac regions and at JUN/FOS enhancer-bound sites was carried out using the HOMER function findMotifsGenome using default parameters.ChIP-seq reads (n = 5 patients) were processed using the Hi-C User Pipeline , with the bowtie aligner (v1.2.2) being used to align the reads to the GRCh38 human genome assembly70. The capture Hi-C analysis of genomic organization package (CHiCAGO) was used to score CHi-C contact strength, with HiCUP processed BAM files being used as input files for the CHiCAGO pipeline71. Enhancer-promoter (E-P) and promoter-promoter (P-P) interactions in myometrium and leiomyoma samples with a CHiCAGO score >5 were considered to be valid contacts. The edgR package (v3.20.9) was used to identify significantly altered contacts at an FDR <0.172. Only contacts with a mean read count >10 for each tissue type were used in the edgeR analysis. Heat maps of differential contacts were generated in R using pheatmaps (cran.r-project.org). HindIII digest of DNA in CHi-C sample preparation was leveraged as a means to assign overlap between E-P contacts and H3K27-acetylated regions. H3K27Ac peaks that overlapped the same HindIII fragment as the distal other end of an E-P contact were assigned to the same HindIII fragment/region (related to Fig. Promoter capture Hi-C sequencing reads (Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting Summary