Previous studies have analyzed patterns of transcription, transcription factor (TF) binding or mapped nucleosome occupancy across the genome. These suggest that the three aspects are genetically connected but the cause and effect relationships are still unknown. For example, physiologic TF binding studies involve many TFs, consequently, it is difficult to assign nucleosome reorganization to the binding site occupancy of any particular TF. Therefore, several aspects remain unclear: does TF binding influence nucleosome (re)organizations locally or impact the chromatin landscape at a more global level; are all or only a fraction of TF binding a result of reorganization in nucleosome occupancy and do all TF binding and associated changes in nucleosome occupancy result in altered gene expression? With these in mind, following characterization of two states (before and after induction of a single TF of choice) we determined: (i) genomic binding sites of the TF, (ii) promoter nucleosome occupancy and (iii) transcriptome profiles. Results demonstrated that promoter-proximal TF binding influenced expression of the target gene when it was coupled to nucleosome repositioning at or close to its binding site in most cases. In contrast, only in few cases change in target gene expression was found when TF binding occurred without local nucleosome reorganization. Although transcription factors (TFs) can recognize cognate binding sites on the nucleosomal surface, functional binding is almost always associated with chromatin modification, remodeling and finally displacement or compaction of nucleosomes. Specific epigenetic signals determine the site and state of this reorganization based on the bound TF, leading to cellular differentiation. Modification of the cellular phenotype from a group or ‘community’-like  state to a more solitary  form  is a basic developmental feature that has been well studied . Metain vivo toward binding of TFs , a nucleosome-free region (NFR) followed by a ‘+1’ nucleosome downstream of TSS, in addition to an array of positioned nucleosomes throughout the gene body . This bag of TFs . This mog of TFs  or deep g of TFs .in vivo  and using these, the correlation between target site occupancy, nucleosome reorganization and their combined effect on the transcriptome was examined. Results that suggest a model where TF binding coupled with nucleosome reorganizations that influence transcription are: (i) almost always associated with nucleosome repositioning that is at close proximity to the TF binding site (TFBS) and (ii) constrained to specific loci, and not spread over the whole genome.This understanding prompts further interesting questions regarding both the nature of TF/nucleosome interactions and to what extent this influences transcriptional response. For example, in earlier studies, induced physiological perturbations activated multiple TFs , it is n5 A549 cells in each, on achieving 80% confluency the first plate was transfected with the clone pcDNA3-NME2-Myc to induce non-metastatic 2 (NME2). The second plate was left untreated (un-induced condition). After 48 h, ChIP was performed using cells from each plate independently and cells were fixed with 1% formaldehyde for 10 min, lysed and sheared (∼300 bp) using a Misonix 3000 sonicator. Twenty-five per cent of lysate was used to isolate input chromatin using phenol–chloroform and ethanol precipitation. The remaining lysate was pre-cleared using protein-A sepharose beads and further divided into two equal portions: one part was immunoprecipitated using antibody specific to the Myc epitope  and a negative control of immunoprecipitation was prepared by adding isotypic control (IgG) to the second portion. Immunoprecipitation was done by incubating overnight at 4°C with 2 μg of antibody. The un-induced experiment was treated in a similar fashion before immunoprecipitation with nm23-h2-L-16 antibody ; IgG was used as the respective isotypic control. Immune complexes were collected using herring sperm DNA-saturated protein-A Sepharose, washed and eluted using Chelex-100 after proteinase K treatment. Reads were aligned (mapped) to the unmasked human reference genome  using the MAQ  (Chromatin immunoprecipitation (ChIP) assays were performed following the Fast ChIP protocol . Brieflyalities)  after clThe resulting sequence read distribution was processed with ChIP-Seq peak locator utility, CisGenome . CisGeno2 and 0.3 μg/μl of random hexamers was incubated at 95ºC for 5 min and chilled to 4ºC. 5 μl dNTP  and 50 units of Klenow were incubated first at 22ºC for 10 min, then 37ºC for 30 min and finally 95ºC for 5 min and chilled as above. Fifty units of Klenow were added again and the mix first kept at 22ºC for 10 min and then 37ºC for 30 min. Reaction was stopped by phenol–chloroform followed by purification using Qiagen columns. Purified product was fragmented and labeled as per Affymetrix Chromatin Immunoprecipitation Assay protocol before hybridization to Gene Chip Human Promoter Tiling array 1.0R (Affymetrix) as per manufacturer's protocol. The control DNase-treated cells were also subjected to labeling reactions as above before hybridization. Nucleosome positions were identified using iChIP (Bioconductor package).A549 cells  were grown in T-75 flasks till 80% confluency in Dulbecco's modified Eagle's medium, trypsinized, pelleted and washed with ice-cold 1X phosphate buffered saline. Five times the packed pellet volume of 1X hypotonic buffer was added, cells homogenized, IGEPAL  was added to final concentration of 0.6% lysate, vortexed and centrifuged at 12 000 revolutions per minute. Pellet (nuclei) was resuspended in MNase digestion buffer and incubated on ice. OD  was measured and 1 unit of MNase/OD was added to the suspension. Following incubation at 37ºC for 30 min the reaction was stopped using stop-buffer, treated with proteinase K (1 mg/ml) overnight at 37ºC before phenol–chloroform purification and ethanol precipitation to pellet down the mononucleosomal DNA. Precipitated DNA was washed with 70% alcohol and dissolved in water. DNase-treated cells were taken as control. Mixture containing 3 μg of DNA, 50 mMTris-Cl, 5 mM MgClA549 cells depleted for NME2 were generated using commercially available short hairpin RNAs  and stable cell clones were selected in presence of puromycin. For analysis of nucleosome repositioning, we considered a distance of 300 bp (+/− 150 bases), in other words, a nucleosome was denoted as repositioned in the NME2-induced condition when detected beyond 300 bp of a nucleosome found in the cells before NME2 induction. In order to avoid arbitrary assignments, in cases where a gene belonged to more than one category  7500 fast platform. Equal concentration of ChIP DNA was taken from the test (from NME2-induced cells) and contro‘l (un-induced) samples. The results were analyzed using comparative computed tomography method. Primers were designed against the obtained peak region using ABI's Primer Express software (Supplementary Table S1).Western blot for NME2 was done using antibody from Abcam, Cambridge, MA, USA . Actin was probed by antibody from Sigma .http:/www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE40194, GSE40300, GSE40363 and GSE18182.The gene expression, nucleosome positioning and ChIP-seq data have been submitted to Gene Expression Omnibus . Details of this analysis including further experiments performed by us which confirmed that NME2 can induce anti-metastatic changes in lung adenocarcinoma-derived A549 cells are provided as supplementary material . Next, we determined NME2 occupancy, nucleosome positions and transcriptomes before and after induction of NME2 levels in A549 cells. As NME2 is endogenously expressed in A549 cells, we used the term ‘NME2 induced’ to refer to the cellular state following increase of NME2 levels. To ascertain whether NME2 protein level after induction in A549 cells was still in the physiological range, we analyzed protein expression of NME2 in normal lung lysates and tumor lung, and compared with NME2-induced A549 cells. Our comparative analysis clearly showed that NME2 level within induced A549 cells were within physiological range .We analyzed expression of 1722 regulatory factors, including ones involved in cancer metastasis , in tranet al. (submitted for publication) As expected, in more than 50% cases the NME2 motif occurred within 50 bp of the center of peak ) in NME2-induced cells; out of these we found at least one NME2 binding site (in the induced condition) within 10 kb of the TSS in as many as 1235 genes.ChIP followed by massively parallel sequencing (ChIP-seq) was performed in replicate for both conditions, before and after inducing NME2 Figure . Out of r = −0.73; P < 0.05 before NME2 induction and r = −0.80; P < 0.05 after NME2 induction, Pearson correlation; Figure We next asked whether altered state of chromatin in regulatory regions influenced NME2 occupancy and consequent gene expression changes. To test this, nucleosome positions were determined in the A549 cells before and after NME2 induction. Mono-nucleosomes were isolated by MNase digestion and we mapped nucleosome positions to putative promoter regions (−7.5 kb to 2.5 kb of TSSs) using tiled microarrays Figure  and founP < 0.05))  Figure . It is iWe next checked target-site nucleosome occupancy before and after NME2 induction Figure . On analNdisplacement . Following this we determined nucleosome positions in the NME2-depleted cells. All the 870 NME2 binding sites that were nucleosome-free in the NME2-induced condition and occupied in the un-induced state Figure  were alsWe found that occupancy of about a fifth  of the transcription target sites was concurrent with repositioning of nucleosomes in the NME2-induced condition. Interestingly, these repositioning events resulted in altered expression of all the 791 genes Figure . In contOur findings suggest that TF binding when closely associated with nucleosome repositioning results in altered gene expression changes. Interestingly, in most cases when TF binding did not impact local nucleosome reorganization it was not associated with altered transcriptional state of target gene. As we used human cancer cells that are metastatic, and expression of the TF NME2 decreased their metastatic potential, these findings also help in understanding how TF binding-induced nucleosome level changes influence the transcriptome during metastasis.It was recently reported that repositioning of the +1 nucleosome resulted in changes to NFR in genes that were differentially regulated during meiotic development in yeast . Though Non-specific binding of NME2 in the induced condition could be a confounding factor. To address this, first, we checked and found that in NME2-depleted cells a large number of genes were oppositely expressed with respect to their status in NME2-induced cells; it is unlikely that non-specifically activated/repressed genes as a result of NME2 induction would be differentially expressed on depleting NME2 . Second, the differentially expressed genes in NME2-induced cells correlate significantly with transcriptome changes that are clinically relevant . Therefore, though all NME2 binding events do not lead to increase/decrease in transcription it is unlikely to be due to spurious binding—it is possible that many of these associations are required for functions other than transcription.We found only 11.4% of nucleosomes to be repositioned in promoter proximal regions as a result of NME2 induction. Therefore, it is interesting to consider that overall chromatin level changes may be relatively small. On the other hand, and perhaps more interestingly, there may be shift in nucleosome occupancy on TF binding, leading to site-specific ‘open’ or ‘closed’ regions that facilitate regulatory events. Our findings (discussed above) further support this: nucleosomes repositioning along with engagement of TF at specific sites were in almost all cases associated with transcriptional change in the corresponding gene. In addition, in both cases before and after NME2 induction, enriched promoter nucleosome occupancy correlated with decreased expression of genes. Together these support a model where nucleosome occupancy generally determines the suppressed state of the transcriptome, and reorganization induced by DNA binding factors (themselves or when associated with chromatin modifiers) results in transcriptional activation at specific loci. Although further studies using other TFs will be required to substantiate this, it appears to be consistent with an earlier study which observed decreased presence of nucleosomes in promoters of genes that were expressed during heat shock in yeast . HoweverEpigenetic signaling directs the location of TFs to cognate sites in given chromatin territories. Following this, TFs are believed to be one of the key recruiters of chromatin modification and remodeling machineries . Recent Supplementary Data are available at NAR Online.


The activity of a single gene is influenced by the composition of the chromatin in which it is embedded. Nucleosome turnover, conformational dynamics, and covalent histone modifications each induce changes in the structure of chromatin and its affinity for regulatory proteins. The dynamics of histone modifications and the persistence of modification patterns for long periods are still largely unknown.In this study, we present a stochastic mathematical model that describes the molecular mechanisms of histone modification pattern formation along a single gene, with non-phenomenological, physical parameters. We find that diffusion and recruitment properties of histone modifying enzymes together with chromatin connectivity allow for a rich repertoire of stochastic histone modification dynamics and pattern formation. We demonstrate that histone modification patterns at a single gene can be established or removed within a few minutes through diffusion and weak recruitment mechanisms of histone modification spreading. Moreover, we show that strong synergism between diffusion and weak recruitment mechanisms leads to nearly irreversible transitions in histone modification patterns providing stable patterns. In the absence of chromatin connectivity spontaneous and dynamic histone modification boundaries can be formed that are highly unstable, and spontaneous fluctuations cause them to diffuse randomly. Chromatin connectivity destabilizes this synergistic system and introduces bistability, illustrating state switching between opposing modification states of the model gene. The observed bistable long-range and localized pattern formation are critical effectors of gene expression regulation.This study illustrates how the cooperative interactions between regulatory proteins and the chromatin state generate complex stochastic dynamics of gene expression regulation.The online version of this article (doi:10.1186/1756-8935-7-30) contains supplementary material, which is available to authorized users. Eukaryotic gene activity is strongly related to the epigenetic state –4. CovalBesides cell differentiation, covalent-histone modifications are proposed to mark transcription initiation, for example, involving ATP-dependent nucleosome remodeling, establishment of the pre-initiation complex, and allowing RNA polymerase II to start and proceed transcriptional elongation –14.A prominent feature of core histones is the large amount and diversity of covalently modified residues they can possess , 15. TheA large body of evidence indicates that transcription regulation is dynamic and that it is inherently stochastic at various levels to give rise to large cell-to-cell heterogeneity  in transet al. [Insight into the mechanisms underlying the dynamics of histone modification pattern formation is largely lacking, mainly because many factors act in concert and simultaneous measurement or control of these factors is still experimentally challenging. In such a case, mathematical models can be effective to provide additional mechanistic insight into the molecular behavior of the system. Such models help to stimulate intuitive understanding and suggest new experiments to test the predicted behavior. Several computational efforts have enhanced our understanding of histone modification spreading and bistability –34. For et al.  showed tet al. –36. AlthStochPy [In this work, we study how the biochemical mechanisms of histone regulatory proteins give rise to spreading of histone modifications along the body of a gene . We focuStochPy . We studWe consider a nucleosomal chromatin region of 10 kb corresponding to the length of an average human gene  with an accessible protein-DNA binding domain. Such 5’-NFRs often demarcate the transcription start site. They are typically enriched in transcription factor binding sequences  and knowFirst, we monitor only the methyltransferase explicitly and we assume the acetyltransferase as background activity. The modification mechanism of a single nucleosome is given in Figure Drosophila suppressor of variegation 3-9 (Su(var)3-9)), thereby inducing spreading of H3K9Me3 [We distinguish several mechanisms for transferase movement along the nucleosomal array: diffusion Figure C, modifi H3K9Me3 . Here, w H3K9Me3 , 31. In StochPy (Stochastic modeling in Python) [The boundaries of the nucleosomal model array are reflective, thus a transferase at the border perceives the boundary as another nucleosome that is occupied by another transferase. The transferase at the boundary can either dissociate from the nucleosome or move away from the boundary. The kinetic parameters of the model can be found in Table  Python) , which uAs an illustration, we show in Figure We introduce four characteristics to analyze how modification patterns are established, maintained, and removed with the three mechanisms of transferase movement. The first characteristic is the stationary methylation and methyltransferase occupancy pattern along the array in steady state. Such a simulated stationary pattern corresponds to a histone modification pattern as observed with ChIP-seq analysis. The second characteristic is the activation dynamics. This characteristic is plotted as the methyltransferase occupancy on the nucleosomal array starting from the fully acetylated array state. As third characteristic we compute the steady-state probability distribution of the number of methyltransferases and the three modification states on the array. Finally, we test the relaxation dynamics. This fourth characteristic determines the stability of a modification pattern. To test this we simulate the system from an initial state in which all nucleosomes are methylated and bound to a methyltransferase, subsequently, we determine the decline in the number of methylated nucleosomes. In this fourth test we do not allow any influx of new methyltransferases at the initiation site and we assume that the initiation site is inaccessible, for instance due to nucleosomal repositioning.In Figure In the combined diffusion and recruitment mechanism, the parameters for diffusion and recruitment of the separate mechanisms are combined. Here, the number of methyltransferases on the array at steady state increases drastically Figure Ci. SeparThe relaxation dynamics of the combined mechanism shows an unanticipated result Figure Civ. In cWe additionally investigated the amount of methyltransferases that are minimally required as initial ‘seeds’ for the induction of a stationary modification pattern in the combined mechanism. We observed in simulations that started with a single methylated nucleosome occupied by a methyltransferase that, in 80% of the 400 simulations Figure  a stableIn the previous section, we studied the response properties and patterns of histone modifications by tracking only the methyltransferase and assuming that the antagonistic enzyme (the acetyltransferase) is active in the background. Here we extend our model. We additionally track the acetyltransferase explicitly such that the methyl- and acetyltransferase compete for nucleosome binding. Our parameter settings change such that the two opposite transferases have equal diffusion and recruitment properties and that they each have their own initiation site, at nucleosome position 5 and 45 for the methyl- and acetyltransferase, respectively Figure A. The me-1 for the initiation rate (kinitiation).Simulations performed with a single transferase indicated that an elevated influx of transferases at the initiation site causes a nearly permanent occupation of the initiation site. This situation leads to stable modification peaks around each initiation site Figure  column irecruitment is higher than kinitiation and as a result, the entire system remains mostly in one modification state. When an initiation event occurs, in principle, the modification that is outnumbered is able to overtake the opposite modification but the probability of this event is relatively low. This simulation, therefore, indicates that spontaneous boundary formation by opposing modifications may eventually lead to one modification dictating the opposite modification. In Figure The recruitment mechanism with RE = 0.5 (so a low initiation rate) yields a modification pattern close to the initiation sites Figure C. This iinteraction. When the chromatin interaction is formed, the respective transferase present on the nucleosomes that initiates contact via the interaction either: (i) hops to the other nucleosome it contacts, or (ii) exchanges position with another transferase in case one is present on the other nucleosome  increases the modification extent of the array causing spontaneous state switching between an almost completely methylated and acetylated nucleosomal array  and with five interaction sites, chromatin connectivity allows acetyl- and methyltransferases to pass over each other and enables the nucleosomal array to switch between alternative states  make the state switching more rapid and induce a more stable chromatin composition  active conversion, driven by positive feedback between two selected nucleosomes, and (ii) noisy conversion of a nucleosome state into a different state. The Dodd model indicates the importance of the feedback-to-noise ratio in the histone modification reactions and the necessity for cooperativity to establish a bistable epigenetic system. Angel et al. [Arabidopsis epigenetic switch for flowering after an environmental alteration. This Arabidopsis model is composed of a two-compartment system in which the nucleation site is modeled as a more interactive region than the surrounding regions. The model allowed the authors to fit the experimentally measured spreading of histone H3 lysine 27 methylation (H3K27me) on the flowering locus. Satake and Iwasa [Arabidopsis during environmental changes and to explain stability of the switch in a stochastic environment. In their study, the rates of modification were formalized as reactions that are dependent on the total amount of modified histones. Thus, in this model a positive feedback-loop was implemented independent of the position of the histones or the mechanism of feedback regulation. In these previous models the molecular interactions of the regulatory proteins (transferases) producing histone modifications were not the object of interest and therefore not mechanically incorporated. Our model extends those studies and considers the kinetic descriptions of nucleosome modifications, that is, diffusive propagation and recruitment of modification enzymes, and chromatin connectivity. As a result, our simulations concern physically realistic parameters, concentrations of modifications enzymes, and time-scales of kinetic and diffusive processes.Previous model efforts have described the dynamics of histone modification state switching. Dodd et al.  developel et al.  implemennd Iwasa  also useet al. [All our investigated mechanisms involving transferase propagation  show that stable patterns of histone modifications can be formed in a dynamic environment. The different mechanisms result in different types of modification patterns. For instance, the diffusion mechanism produces localized patterns around a DNA binding site. These patterns resemble the enrichment of transcription factors commonly found at promoter and enhancer sites of active genes , 9. Addiet al.  showing demodification and 1/krecruitment). A change in these parameters can cause large differences in the stability of the modification patterns on the chromatin. Biological alterations in these parameters can be caused by changes in the concentrations of the involved enzymes or changes in their binding affinities. In addition, a change in the binding affinity could be caused by, for example, the presence of another histone modification at the same nucleosome, which could influence the stability of the interaction of the transferase with the nucleosome. Furthermore, we showed that the combined diffusion and recruitment mechanism has a high capability of initiation from a small amount of seeds. These experiments were shown in a more or less neutral background, in which acetylation is not explicitly modeled. This means that although a high seeding capacity is shown, the effects of a heterochromatin protein in an actively acetylated gene could have a different outcome. However, in a favorable environment a fast switch from active to inactive chromatin can be established by the activity of only a few methyltransferases.An important factor for the stability of global modification patterns in the model is the recruitment efficiency. Here, we define this efficiency in terms of two parameters, the lifetime of the modification and the time before recruitment  boundary formation: (i) opposing transferases cannot pass each other on the chromatin causing confinement of the transferases, and (ii) the acetyltransferase and the methyltransferase have the same kinetic properties, which generates a balanced competition of the transferases for the nucleosomes. Interestingly, the system as we show it would be a plausible explanation for early stages in the differential expression of genes that are affected by Position Effect Variegation (PEV). In PEV, genes that are close to an inactive region of the chromatin (mostly telomeric or centromeric) can be differentially expressed in a subpopulation of cells dependent on the dynamic position of the boundary of the heterochromatin region , 37.Similar to computational efforts on histone modification spreading introduced by other authors , we findOur study shows that histone modification patterns can be established within minutes and that nearly irreversible transitions can result, which provides stable epigenetic patterns. Introduction of two opposing enzymes causes dynamic histone modification boundaries whereas chromatin connectivity can introduce both bistable epigenetic state switching and stable histone modification patterns. We demonstrate in our model that with biologically plausible parameter regimes both epigenetic stochastic switching and stable pattern formation are noticed to provide cell-to-cell heterogeneity and robustness in gene expression.The reaction parameters in the model are derived from physical and biological constants. We have taken some assumptions to derive these parameters. The association and background modification reactions in the model are based on diffusion limited association reactions. The reaction rate constant is dependent on the concentration of the enzyme, the diffusion constants of the DNA and the enzyme, the diameter of the binding site and the enzyme, and on the volume of the nucleus.on is the second-order diffusion limited rate constant, nE is the number of modification enzymes per nucleus with volume Vnucleus, DE and DS are the diffusion coefficients of the enzyme and the DNA site, and rE and rS are the radii of the enzyme and the DNA site. Since we fix the total number of modification enzymes the relevant rate constant to consider is Here E) in a cell are between 0.5 and 5 μm2s-1 [DS) is expected to be a few orders of magnitude lower than the diffusion constant of the enzyme, therefore we neglect this constant. We assume a transferase of about 5 nm in diameter and a DNA binding site of 15 bp, which measures 5 nm. The volume of an average human nucleus is 500 pL. The concentration of specific histone modification transferases in the nucleus has not been reported so far. Transcription factors are estimated to be present in a wide copy range from tens to thousands per cell [-1. This value is used for the binding at the initiation site, the rate of demodification and the rate of modification for the acetyltransferase in the first section of the results .The diffusion constant of enzymes (D5 μm2s-1 . The difper cell . Here weoff) is 0.1 s-1.The transferase nucleosomal release rate is based on FRAP analysis of transcription factors and histone modification transferases . The reset al. [in vitro setting and their diffusion coefficients have been measured. Measured diffusion coefficients range from 2 × 10-4 μm2s-1 to 0.5 μm2s-1. For our model we assume the lower end of the range because most of the measurements were performed on stretched, nucleosome free DNA while the measurements on chromatin gave varying results, depending on the measured transferase [slide) of 0.6 s-1.Gorman et al. , 41 studtransferase =1,000 s-1). The transferase neighbor modification reactions however are much slower due to the persistence length of the DNA over short distances (kneighbor =0.2 s-1). From these assumptions we calculated the model parameters (Table We assume that the transferase modification reaction is fast once the transferase is bound to the nucleosome (khttp://stochpy.sourceforge.net/. Simulation data are saved and imported into Mathematica (Wolfram Research) for further analysis.The model with different mechanisms and parameter settings was simulated with the NucleosomeTool plug-in designed for StochPy . StochPySimulations with one transferase are run for at least 1,000 time trajectories of at least 400 s simulated time. This accounts for 34 to 89 million data points per simulated mechanism. Dynamics plots  investigates functional and dynamic organization of the mammalian genome exploring the use of engineering (synthetic) mammalian cell systems allowing to modulate the epigenetic state performing quantitative single-cell measurements and computational stochastic modeling. Her main research focus is on epigenetic state switching and transcriptional dynamics/stochasticity also in the context of deregulated behavior such as UV-induced DNA damage repair and age-related diseases.FJB (Associate and Extraordinary Professor) uses theory and experiments to study the mechanisms and principles of cellular adaptation to new conditions developing simulation software for combined simulation of stochasticity of molecular reactions and cell growth. Main research topics include metabolic regulation by gene expression, mixing of transcription stochasticity and cell-growth stochasticity, and genome-wide adaptation of metabolism.LCMA and TRM are PhD students using systems biology approaches to understand complex biological behavior.Additional file 1: S1: Description of NucleosomeTool plug-in. (DOCX 36 KB)Additional file 2: S2: Scripts for NucleosomeTool usage. (DOCX 18 KB)Additional file 3: S3: Scripts S1 Python scripts to regenerate Figures Additional file 4: S4: Scripts S2 Python scripts to regenerate Additional file kinteraction =0.01 s-1, right column figures  show interaction at frequency kinteraction =0.1 s-1. The other parameters used in these simulations are listed in Table Additional file 5: Figure S1: The influence of chromatin connectivity on the diffusion mechanism, related to Figure recruitment =1.2 s-1). Each subfigure shows a row of four simulations of 500 s each as an illustration of the model behavior. Top panels of each subfigure show the position (y-axis) of the methylation (red) and acetylation (green) over time (x-axis), initiation sites indicated by red and green arrowheads . Bottom panels show the total amount of each modification over time, corresponding to the top panel. Left column figures  show interaction at kinteraction =0.01 s-1, right column figures  show interaction at kinteraction =0.1 s-1. The other parameters used in these simulations are listed in Table Additional file 6: Figure S2: The influence of chromatin connectivity on the recruitment mechanism (RE =0.5), related to Figure recruitment =4.8 s-1). Each subfigure shows a row of four simulations of 500 s each as an illustration of the model behavior. Top panels of each subfigure show the position (y-axis) of the methylation (red) and acetylation (green) over time (x-axis), initiation sites indicated by red and green arrowheads . Bottom panels show the total amount of each modification over time, corresponding to the top panel. Left column figures  show interaction at kinteraction =0.01 s-1, right column figures  show interaction at kinteraction =0.1 s-1. The other parameters used in these simulations are listed in Table Additional file 7: Figure S3: The influence of chromatin connectivity on the recruitment mechanism (RE =2), related to Figure recruitment =0.24 s-1). Each subfigure shows a row of four simulations of 500 s each as an illustration of the model behavior. Top panels of each subfigure show the position (y-axis) of the methylation (red) and acetylation (green) over time (x-axis), initiation sites indicated by red and green arrowheads . Bottom panels show the total amount of each modification over time, corresponding to the top panel. Left column figures  show interaction at kinteraction =0.01 s-1, right column figures  show interaction at kinteraction =0.1 s-1. The other parameters used in these simulations are listed in Table Additional file 8: Figure S4: The influence of chromatin connectivity on the combined mechanism RE =0.1), related to Figure , relatedrecruitment =0.48 s-1). Each subfigure shows a row of four simulations of 500 s each as an illustration of the model behavior. Top panels of each subfigure show the position (y-axis) of the methylation (red) and acetylation (green) over time (x-axis), initiation sites indicated by red and green arrowheads . Bottom panels show the total amount of each modification over time, corresponding to the top panel. Left column figures  show interaction at kinteraction =0.01 s-1, right column figures  show interactions at kinteraction =0.1 s-1. The other parameters used in these simulations are listed in Table Additional file 9: Figure S5: The influence of chromatin connectivity on the combined mechanism (RE =0.2), related to Figure Additional file 10: S5: Summarized list of model reactions. (DOCX 19 KB)


Saccharomyces cerevisiae, and use this distinctive two-dimensional nucleosomal “fingerprint” as the basis for a new nucleosome-positioning algorithm called NucleoATAC. We show that NucleoATAC can identify the rotational and translational positions of nucleosomes with up to base-pair resolution and provide quantitative measures of nucleosome occupancy in S. cerevisiae, Schizosaccharomyces pombe, and human cells. We demonstrate the application of NucleoATAC to a number of outstanding problems in chromatin biology, including analysis of sequence features underlying nucleosome positioning, promoter chromatin architecture across species, identification of transient changes in nucleosome occupancy and positioning during a dynamic cellular response, and integrated analysis of nucleosome occupancy and transcription factor binding.Transcription factors canonically bind nucleosome-free DNA, making the positioning of nucleosomes within regulatory regions crucial to the regulation of gene expression. Using the assay of transposase accessible chromatin (ATAC-seq), we observe a highly structured pattern of DNA fragment lengths and positions around nucleosomes in  Chromatin accessibility modulates the ability of transcription factors (TFs) and transcriptional machinery to interact with DNA. Within regions of increased accessibility, nucleosomes and TFs compete for access to regulatory DNA . While sSaccharomyces cerevisiae and Schizosaccharomyces pombe have provided base-pair resolved maps of nucleosome positions , a method for rapid, sensitive, genome-wide profiling of chromatin accessibility . Here, wS. cerevisiae ATAC-seq protocol to determine ATAC-seq fragmentation patterns at positions of base-pair resolved nucleosomes in S. cerevisiae generated using chemical mapping techniques . ATAC-seq read depth for S. cerevisiae is highly correlated with DNase-seq , whereas long fragments are enriched at nucleosome-associated DNA . To furtchniques . Using ANase-seq  but showBy aggregating ATAC-seq transposition centers around well-positioned, base-pair resolved nucleosome positions determined by chemical mapping , we obseWe reasoned that standard methods for inferring nucleosome centers, which assume that fragment midpoints are normally distributed around the nucleosome core , could bZ-score ≥ 3, log-likelihood ratio > 0) (see Methods), compared to 17,015 positions determined across these same regions using chemical mapping. Fewer calls are made by NucleoATAC relative to chemical mapping because ATAC-seq coverage varies greatly across the genome based on accessibility; however, ATAC-seq coverage by short fragments can be used to distinguish between genuine nucleosome depletion and absence of nucleosome calls due to low accessibility . We found that no characteristic nucleosome fingerprint is observed when aggregating ATAC-seq insertions generated from genomic DNA or predicted by transposase bias signal  around NucleoATAC-called nucleosomes, suggesting residual intrinsic Tn5 insertion bias has little effect on aggregate NucleoATAC nucleosome calls.NucleoATAC identified the positions of 13,344 nucleosomes across broad open chromatin regions in the yeast genome . As positional concordance between NucleoATAC and chemical mapping calls increases as a function of both NucleoATAC and chemical map confidence metrics , discrepancies between the two methods are likely partially due to either lower quality chemical mapping calls or inconsistently positioned nucleosomes.We also sought to determine whether the cross-correlation analysis in NucleoATAC might be applicable to MNase data sets as well, using V-plots derived from paired-end MNase data sets . NucleoATAC applied to MNase data sets is also able to capture the rotational positioning of nucleosomes B,C and oSchizosaccharomyces pombe, a species highly diverged from S. cerevisiae  would approximate the S. pombe nucleosome V-plot. Indeed, the resulting V-plot is similar to a V-plot made from ATAC-seq reads using S. pombe chemically mapped dyads    and similarly concordant with the chemical mapping calls  may be applied to diverse organisms of interest.Because histones are among the most evolutionarily conserved proteins, we hypothesized that the same structured V-plot “nucleosome fingerprint” pattern is present for different species. To test this possibility, we first developed ATAC-seq for revisiae  for whicrevisiae . Subtle  lengths  and vari lengths A, suggesization) B,C. We ang calls ; Fig. 4De V-plot . These rS. pombe, the local maxima in the fragment-size distribution for ATAC-seq data from the human lymphoblastoid GM12878 cell line . To quantify the strength of the 10.5-bp oscillations for each dinucleotide, we computed the power spectrum density at frequency 1/10.5 bp A, providS. pombe having the shortest linker length and human the longest, consistent with previous observations . We observe that the pattern observed in aggregate is present for the majority of individual TSS for each organism, although a small fraction of promoters in S. pombe do show a larger nucleosome-free region, and a fraction of promoters in S. cerevisiae and human lack a clear nucleosome-free region.We aggregated NucleoATAC nucleosome signals around transcription start sites (TSSs) E to expl mapping  but not revisiae . Others revisiae , as the P < 10−4) . Furthermore, promoters that had both increased expression and increased accessibility were significantly enriched for terms relating to salt or osmotic stress response when compared to genes with just increased expression , suggesting that up-regulation of key genes in the response to osmotic stress is modulated through changes in chromatin architecture at the promoter.To investigate the ability of NucleoATAC to infer nucleosome positioning and occupancy changes during a dynamic process, we performed ATAC-seq on yeast exposed to osmotic stress (0.6 M increase in the NaCl concentration over 60 min). Because osmotic stress induces transient gene expression changes that peak after 15 min , we idenP < 10−5 by bootstrap sampling) (P < 10−5 by bootstrap sampling) (P < 10−5 by bootstrap sampling)  B. In conampling) C. While ampling) C. To expampling) D. Changeampling) D.P < 10−5 by bootstrap sampling), as has been previously observed . The strongest association for a TF other than Hog1 is Sko1, a factor previously characterized as a master regulator of the osmotic stress response , and the correlation between expression increase and −1 nucleosome depletion is also stronger   D. These To demonstrate the ability of NucleoATAC to enable detailed investigation of the profiles of human nucleosomes around TFs, we assessed NucleoATAC calls around CTCF motifs. We observe high consistency in the distance of NucleoATAC calls from CTCF binding sites A, concorTo further explore the relationship between nucleosomes and TF occupancy, we examined both the nucleosome signal and insertion pattern for NFKB sites with different nucleosome occupancy scores C. Sites S. cerevisiae can be applied across species, assuming similar nucleosomal constraints on Tn5 insertion as suggested by the fragment-size distribution. These high-resolution maps are highly concordant to those identified by chemical cleavage in yeast and capture the rotational positioning information from nucleosomes in both yeast and humans. As expected, we observe that, for S. cerevisiae and S. pombe, WW (W = A or T) and SS (S = G or C) dinucleotides show strong 10- to 11-bp periodicity, but we also observe that all other dinucleotides exhibit considerable periodicity in-phase with the SS dinucleotides. We find human nucleosomes show periodicity in some dinucleotides but that the extent of periodicity is lower and phasing is less consistent than for both yeast species, suggesting that in vivo human nucleosome positions are much less constrained by their inherent sequence preference. We also observe a depletion of AT content immediately flanking nucleosomes called by NucleoATAC, in contrast to MNase-based studies that have observed an enrichment of AT content in these regions  signal. In contrast, ATAC-seq simultaneously assays nucleosome depletion (through the presence of short fragments) and nucleosome positioning (from longer fragments). Combining measurements of chromatin accessibility, nucleosome positioning, and nucleosome occupancy allows an integrative analysis of chromatin architectural changes, as demonstrated by the observation of specific types of changes in nucleosome positioning and occupancy during the osmotic stress response in cterized , we showHigh-resolution nucleosome calls and occupancy tracks also enabled investigation of the effects of nucleosome occupancy on TF binding within regulatory regions. Some factors show intermediate nucleosome occupancy at a substantial fraction of binding sites, suggesting cell-to-cell heterogeneity in TF occupancy. This heterogeneity may be linked to oscillation in nuclear localization of TFs ; examiniNucleoATAC provides a powerful new framework for analyzing ATAC-seq, MNase, and other paired-end functional genomics data. By using a two-dimensional fragment size versus midpoint representation of sequencing data, NucleoATAC is built on the understanding that different fragment lengths provide unique information content. Our 2D signal processing approach can likely be extended by applying additional methodologies from the image analysis field, opening exciting possibilities for future applications for calling genomic features other than nucleosomes.ATAC-seq coupled with NucleoATAC allows for the interrogation of high-resolution nucleosome positions in regulatory regions from limited cellular populations, allowing rapid, cost-effective, and high-resolution nucleosome inference. We believe future efforts will include fine mapping of chromatin structure in rare developmental and disease cellular populations, providing a detailed understanding of the molecular determinants of chromatin structure across dynamic cellular processes in human cells.S. cerevisiae, except for the osmotic stress time-course, for which S. cerevisiae strain BY4741 was used. Strain 972 h- was used for S. pombe. S. cerevisiae and S. pombe samples were spheroplasted prior to incubation with Nextera Transposase; PCR was performed as previously described , S. pomng MACS2  with theThe start of sequencing reads generated from ATAC-seq are offset from the center of the Tn5 binding site by 4 bp . Thus, AWe sought to model the fragment-size distribution as a mixture of nucleosome-free fragments and nucleosome-associated fragments in a way that captured the highly structured, nonparametric nature of the nucleosomal distribution. Because fragments <115 bp very likely arise from the nucleosome-free distribution, we parameterized the fragment-size distribution below that size-cutoff as an exponential distribution, which provided a good fit to this region of the distribution. This fit distribution was used to extrapolate the nucleosome-free fragment distribution for sizes larger than 115 bp. The subtracted difference between the extrapolated nucleosome-free model and the observed fragment distribution was used as the nucleosome-associated fragment distribution. The fragment-size distribution was then modeled as a mixture of the nucleosomal and nucleosome-free insert size distributions:The yeast V-plot used for cross-correlation was generated by aggregating reads around dyad calls from chemical mapping that met two criteria: (1) They had an NCP/noise ratio (positioning metric defined previously ) in the x along the genome is given byF is the matrix of fragment center and size information for fragments of size 105 to 250 bp with centers between x − 60 and x + 60 and V is the V-plot matrix. This raw signal is then normalized using a background signal that is intended to represent the expected signal from the cross-correlation, given (1) the number of fragments observed, and (2) the Tn5 sequence preference. The background signal at position x is defined asB represents a matrix with relative probabilities of generating fragments of different sizes and center positions such that ∑B = 1. The scaling factor ∑F, the sum of all reads in the signal matrix, ensures that the background signal represents the expected signal given the observed number of fragments. To determine B, the probability of observing individual insertion sites was first modeled as follows. Tn5 has a sequence preference across ∼21 bp that it contacts  and log-likelihood ratios >0 were considered. These cutoffs were chosen based on three factors: (1) analysis of ATAC insertion profile and V-plot around calls; (2) concordance between calls and chemical mapping; and (3) concordance between calls for individual replicates. As can be seen in Supplemental Figure 10, increasing the stringency of thresholds leads to more consistent calls that are more concordant with chemical mapping. The choice of thresholds for different applications should be based on the desired balance between more comprehensive calls and higher confidence calls.For each dyad, a S. cerevisiae and S. pombe were used as the gold standard data sets. Concordance metrics were adapted from In order to assess the quality of a set of nucleosome position calls, we used several metrics that measure the concordance of the calls with a “gold standard” data set. The chemical mapping data sets from Several changes to the NucleoATAC workflow were made for application to MNase data. Nucleosome occupancy was not computed via the method outlined for ATAC-seq, as short fragments were removed via size-selection for both samples analyzed; rather the number of MNase fragments centered within 60 bp of a position was used as the occupancy. Additionally, a sequence bias model was not used, as modeling the sequence bias of a processive enzyme is not straightforward. A background model was still used; however, the model simply represents fragments positioned at random given the fragment-size distribution.For comparison of dinucleotide frequencies between species, a higher confidence threshold was used—only calls with log likelihood ratios ≥5 were used . For both correlation and power spectrum density analysis, dinucleotide frequencies between 19 and 60 bp from the dyad calls  were normalized by division with the mean frequency of the dinucleotide in that window. The power spectrum density at frequency 1/10.5 was calculated as the square of the fast Fourier transform at that frequency.For differential accessibility analysis between time points, we compared the number of insertions in promoters (−400 to +100 bp relative to TSS) between time points. As variation in the degree of enrichment of fragments within open chromatin regions can affect differential accessibility measurements between ATAC-seq samples, counts were normalized as follows: Quantile normalization was applied to the counts for promoters with genes showing no expression change as determined by a previous study . . ChIP-Seq peaks were intersected with motif occurrences called using FIMO (For  TIF-Seq  and geneDatabase . For S. ing FIMO  and the ing FIMO .http://www.ncbi.nlm.nih.gov/geo/) under accession number GSM1155960  under accession number GSE66386. NucleoATAC source code is freely available as a python package at https://github.com/GreenleafLab/NucleoATAC, as well as in the Supplemental Material.The raw data for J.D.B. and W.J.G. are listed as inventors on a patent for the ATAC-seq method. W.J.G. is a scientific cofounder of Epinomics.


Arabidopsis thaliana, for which many known TFBSs are now available. We developed a method to identify the locations of TFBSs in the promoter sequences of genes in A. thaliana. We found that the distribution is nearly bell-shaped with a peak at 50 base pairs (bp) upstream of the transcription start site (TSS) and 86% of the TFBSs are in the region from −1,000 bp to +200 bp with respect to the TSS. Our distribution was supported by chromatin immunoprecipitation sequencing and microarray data and DNase I hypersensitive site sequencing data. When TF families were considered separately, differences in positional preference were observed between TF families. Our study of the positional distribution of TFBSs seems to be the first in a plant.Binding of a transcription factor (TF) to its DNA binding sites (TFBSs) is a critical step to initiate the transcription of its target genes. It is therefore interesting to know where the TFBSs of a gene are likely to locate in the promoter region. Here we studied the positional distribution of TFBSs in  Binding of a transcription factor (TF) to its DNA binding sites is a critical step to initiate the transcription of its target genes. Typically, a TF binding site (TFBS) is 5 to 15 base pairs (bp) long within the promoter of its target gene and a TF protein usually can recognize a set of similar DNA sequences with varying degrees of binding affinity. In view of the importance of TFBSs in gene regulation, it is useful to know how the TFBSs of a gene are spatially distributed in its promoter region. Such a study for a large number of genes may shed light on how many TFBSs on average regulate a gene and where to find the TFBSs of a new TF.Saccharomyces cerevisiae TFBSs are enriched in the region from 200 bp to 100 bp upstream (from −200 bp to −100 bp) of the transcription start site (TSS) and has a sharp peak at 115 bpArabidopsis thalianaThe spatial distribution of TFBS has been studied in yeast and human1Arabidopsis thaliana. We therefore studied the distribution in this plant. Another requirement for studying the spatial distribution of TFBS is to have a large number of target genes. For this purpose we developed a method for predicting the target genes of a TF whose TFBS is known.To study the spatial distribution of TFBSs in an organism requires a large number of known TFBSs in that organism. In plants such data are available only for in vivo cross-linked DNA-protein complexes, which contain the TF protein, DNA fragments, and other chromatin-associated proteins. The DNA fragments are then sequenced to determine the TFBSs (peaks) of the TF over the genome. Many ChIP-seq studies have been made on A. thaliana568A. thaliana and we compared the distributions obtained from the three types of data.A common experimental approach to identify the TFBSs of a TF in a genome is chromatin immunoprecipitation-sequencing (ChIP-seq) assayet al.Arabidopsis lyrata, Brassica oleracea, and Brassica rapa. Among the annotated 33,602 genes in A. thaliana (TAIR10), 17,573 genes were found to have orthologs in all of the above three species. We found that 547 of the 586 TFBSs passed the conservation test and that 15,781 of the 17,573 genes contained at least one of the 547 TFBSs in their promoter region . As in Yu r region , which i−4). Note that this occurrence refers to the chance occurrence of the sequence without requiring that it passes the conservation test. This distribution can be considered the random distribution of TFBS sequences in the promoter region. As expected, this distribution (the probability density) is rather flat except for a small peak at −50 bp with respect to the TSS (the grey line in −4) is only 1.3-fold higher than that at the position −2,000 bp (~4.1 × 10−4).First, we consider the occurrence of a TFBS sequence in the promoter region of a gene. The presence of a TFBS sequence at a nucleotide site in a promoter was predicted by FIMOSecond, we consider the positional distribution of the TFBS sequences that have passed the conservation test. We call this the positional distribution of TFBSs. Compared to the random distribution  is >0.8 . The distribution under this additional condition, which is denoted by the green line in When the distribution is given for each TF family separately, some distributions turn into sharply bell-shaped, showing a peak upstream of the TSS , very clOne may also get some insight into the positional preference of TFBSs from the positional distributions of DNase I hypersensitive sites (DHSs)12As the ChIP dataset provided 27 TFs in 13 TF families, we grouped the ChIP sites into families. As it requires a fairly large number of sites to obtain a reliable distribution, we selected the 7 TF families that had >1000 sites to examine positional preferences between different TF families. Overall, the positional distributions of the ChIP sites in a family have a flatter shape compared to the positional distributions of TFBS , becauseA. thaliana to study the positional distribution within the promoter sequences of ~15,800 genes. The distribution inferred (kurtosis = 0.36) is somewhat sharper than the normal distribution (kurtosis = 0). It has a peak at 50 bp upstream of the TSS and the majority of TFBSs (86%) lie in the region from −1,000 bp to +200 bp with respect to the TSS. When the 41 TF families were considered separately, different positional preferences were found between families and 11TF families showed a flatter distribution (kurtosis < 0) than the normal distribution. However, even for the most flat distribution 79% of the TFBSs are in the region from −1,000 bp to +200 bp. Thus, it is a good region to look for the TFBS(s) of a TF in A. thaliana.We used ~500 known TFBSs in In this study, we only used the TFs whose TFBSs have been verified by experiment, so most of the TFBS sequences (518/586 = 88%) were assigned to unique TFs. Two TFBS sequences that are assigned to two TFs in the same TF family usually have substantial differences. Nevertheless, two TFs in the same family can share a target gene for two reasons. First, two sequences on the promoter of the gene are predicted to be similar to the two TFBSs, respectively. This is the same as the situation where two TFs in different TF families share a target gene. In this case, there are two TF binding sites. Second, a single sequence on the promoter of the gene is predicted to be similar to both TFBSs, according to the prediction method such as FIMO, which we used. This can happen because a TF usually recognizes a set of similar sequences and all prediction methods allow for such sequence variation. In this case, we count only one TF binding site.Our distribution of TFBS in Arabidopsis and the published distributions in yeast and human all show positional preference of TFBSs with respect to the TSS and all have a probability peak upstream of the TSS. However, the probability for a TFBS to lie downstream of the TSS is negligible in yeast but substantial in Arabidopsis and human. As mentioned above, the method used to infer the distribution in Arabidopsis may produce a bias for the region downstream of TSS. For human the distribution was based on ChIP-chip data and as mentioned above this type of data might also have a bias for the downstream region of TSS. Therefore, the distributions in Arabidopsis and human should be reexamined when more suitable data becomes available.For the human studyA. thaliana for which no data about the TF binding specificity is available, a more detailed study should be conducted in the future to check the accuracy of the present inferences and to expand the study to other TFs.As the amount of TFBS data used in this study is still limited and as there are still TF families in et al.For Arabidopsis TFBSs, we collected 478 TFBSs for 359 TFs from four databases . The promoter sequence of a gene is defined by the region from −2,000 bp to +200 bp relative to the TSS of the gene. Then, we used the following three species to test the conservation of a TFBS in Arabidopsis thaliana (TAIR10): Arabidopsis lyrata (v.1.0), Brassica oleracea (v2.1) and B. rapa (IVFCAASv1). The orthologous relationships between A. thaliana and the three reference species were obtained from the Ensembl Plants orthologous definition and we determined a one-to-one relationship by two criteria: (i) the sequence identity between the target and the query is >50% and (ii) among the potential orthologs it has the highest average sequence identity with the Arabidopsis gene. If no such ortholog was found for one of the three species, we discarded the gene from our analysis. For a gene under study, the promoter sequences in the three reference species were identified by alignment to the promoter sequence of the orthologous gene in A. thaliana. We required that a TFBS similar sequence is located within 100 bp from the TFBS of Arabidopsis thaliana in the promoter sequence alignment of the all four species and is on the same strandA. thaliana and the three other species, 79% of the TFBSs have identical positions in the alignment and in less than 10% of the cases the TFBS position has moved more than 10 bp.For each of the collected TFBSs, we examined whether a TFBS similar sequence is present in the promoter sequence of a gene, using the software FIMOiana TAIR: ArabidoTo assess whether a TFBS is regulated by its putative cognate TF, we looked for transcriptome (RNA-seq) data. For Arabidopsis transcriptomes, we collected three data sets: 5 transcriptomes from stomatal developmentIn the 21 transcriptomes, a gene was defined as expressed and retained in subsequent analyses, if its raw RPKM value was >1 in at least two transcriptomes. Under this criterion, there were 32,799 expressed genes in the 21 transcriptomes. For each transcriptome, the gene expression levels were then normalized by upper quantile normalizationet al.A. thaliana. This dataset included 55,500 peaks (FDR < 0.01) with an average length of 311.7 bp. The second set was from the DHS peaks in roots of 7-day-old seedlings of A. thalianaTwo datasets of DNase I hypersensitive sites (DHSs) were collected. First, a processed dataset of DHS peaks was downloaded from Zhang A. thaliana, we downloaded integrated data of binding profiles for 27 TFsFor the chromatin immunoprecipitation (ChIP) data in For the positional distributions of DHS and ChIP-seq peaks, we calculated the distance between the mid position of the peak and the TSS of its nearest gene in either the forward or the reverse strand.How to cite this article: Yu, C.-P. et al. Positional distribution of transcription factor binding sites in Arabidopsis thaliana. Sci. Rep.6, 25164; doi: 10.1038/srep25164 (2016).


DNA after they completed their function. Metabolic genes in particular are dynamically regulated and continuously adjusted to cellular requirements. Transcription factors controlling metabolism are therefore under constant surveillance by the ubiquitin–proteasome system, which can degrade DNA‐bound proteins in a site‐specific manner. Several of these metabolic transcription factors are critical to cancer cells, as they promote uncontrolled growth and proliferation. This review highlights recent findings in the emerging field of nuclear proteolysis and outlines novel paradigms for cancer treatment, with an emphasis on multiple myeloma.The recruitment of transcription factors to promoters and enhancers is a critical step in gene regulation. Many of these proteins are quickly removed from  AR androgen receptorChIP chromatin immunoprecipitationCREB cAMP response element‐binding proteinEBPδ enhancer‐binding protein deltaGR glucocorticoid receptorHIF‐1α hypoxia‐inducible factor‐1 αIKZF1 Ikaros family zinc finger protein 1IKZF3 Ikaros family zinc finger protein 3MM multiple myelomaNCoR1 nuclear receptor corepressor 1NRF‐2 nuclear respiratory factor‐2pVHL von Hippel–Lindau tumor suppressor proteinROS vreactive oxygen speciesT‐ALL T‐cell acute lymphoblastic leukemiaTGF‐β transforming growth factor‐βUPS ubiquitin–proteasome systemOver the past decade, extensive work has confirmed the existence of a link between cancer cell‐intrinsic metabolism and tumor signaling pathways. It is now clear that dysregulation of oncogenic signal transduction pathways alters metabolic flux; conversely, mutations in enzymes regulating metabolic flux can initiate cellular transformation and contribute to tumor maintenance and progression Recent evidence has emerged that a direct cross‐talk between metabolism and epigenetics is a key regulatory mechanism in cancer development. Specifically, proteome modifications provide a fast mechanism to respond to environmental changes and to adjust homeostasis. Regulated protein catabolism via the ubiquitin–proteasome system (UPS) controls a wide variety of cellular functions, from transcriptional regulation and stress response to cell cycle regulation.This review focuses on the mechanisms by which the turnover of transcription factors regulates gene activity and metabolism, and how these functions are interconnected Fig. . After aThe proteasome and the upstream ubiquitination pathway are an evolutionary conserved and highly regulated proteolytic system that precisely controls selective protein degradation in order to tightly maintain proper protein content and quality. The UPS acts as a disposal pathway and optimizes cellular functions by removing misshaped proteins. The UPS also has a ‘surgical’ role in controlling cell function by adjusting the abundance of regulatory proteins involved in numerous biological pathways, such as signal transduction, stress response, immunity, cell cycle regulation, and transcriptional regulation.Protein degradation through the UPS pathway is a complex process, involving multiple steps and enzyme machineries that begins when proteins are targeted for degradation by sequential conjugation, usually at lysine residues, to isopeptide‐linked ubiquitin molecules. Ubiquitination is achieved through the coordinated actions of E1 ubiquitin‐activating enzymes, E2 ubiquitin‐conjugating enzymes, and E3 ubiquitin ligases. The interaction between the E3 ubiquitin ligase and its target protein provides a high level of specificity in this dynamic process Once at least four ubiquitin molecules have been conjugated to a substrate in a particular chain formation, the proteasome recognizes the poly‐ubiquitinated proteins as substrates and degrades them in vivo with a half‐life of 20–30 min, and c‐Myc proteolysis is mediated very rapidly by the ubiquitin–proteasome pathway The expression of a gene is not only controlled by the type of transcription factor that binds to its promoter but also by the duration of this interaction. Many transcription factors, particularly those involved in the regulation of cell proliferation, are unstable regulators. Upon completion of their tasks, for instance the recruitment of additional enzymes or cofactors, they are quickly catabolized by the UPS in order to vacate the promoters and allow new regulatory proteins to bind. The stability of the c‐Myc protein, for example, is actively regulated Interestingly, a global study of the budding yeast proteome categorized half‐lives of proteins based on their function. Based on the integration of proteomic data with previous measurements of mRNA levels and translation rate, two functional clusters of coregulated genes can be distinguished: a first group (‘production cluster’) encodes for abundant and generally stable proteins that appear to be optimized for maximum protein production and maintenance efficiency . The second group, called ‘regulatory cluster’, is enriched in proteins produced in smaller quantities and with short half‐lives, whose elimination is regulated by the UPS. This second cluster seems to be optimized for regulatory flexibility and therefore mainly consists of cell cycle proteins and transcriptional regulators Saccharomyces cerevisiae, diverse subsets of genes were identified in connection with their enrichment in particular biological and functional processes. Constitutive cellular processes like translation , and central metabolism (glycolysis) were mainly regulated by a first subset of genes with stable proteins, whereas a second group of genes including many transcriptional regulators, signal transduction genes, chromatin modifying enzymes, as well as genes involved in mitosis or cell cycle, encodes short‐lived proteins The first quantitative genome‐scale prediction of protein abundance and turnover in mammalian cells confirmed a link between protein stability and function The UPS influences transcription through proteolytic and nonproteolytic activities, including regulation of transcription factors and RNA polymerase activity, as well as epigenetic histone ubiquitination The concept that the spatio‐temporal regulation of gene expression is influenced by the binding as well as the removal of transcriptional regulators is well established. Ubiquitination of transcription factors and their proteasome‐mediated turnover are crucial steps in this process. A review from the Tansey laboratory suggests two distinct strategies in which cells control the nuclear abundance of transcription factors by ubiquitin‐mediated proteolysis. The first mechanism takes place off chromatin and represents a constitutive turnover in which the transcription factor is maintained in a constitutively unstable form and in restricted abundance by the UPS in situ, and allows a ‘fresh’ pool of transcription factors to bind to the cleared promoter region and catalyze a new round of transcription The second proteolytic method occurs on chromatin at promoter and enhancer sites and during the process of transcriptional activation et al. show that the ability of a transcription factor to function can be associated with its ubiquitin‐dependent degradation. With a half‐life of only 5 min, MATα2 is a short‐lived protein known to be rapidly ubiquitinated by at least two distinct E3 ubiquitin ligases Saccharomyces cerevisiae. The authors reported that several mutations within the second degradation target site and homeodomain of MATα2 result in stabilization of this protein, but impair the ability of MATα2 to interact with DNA Saccharomyces cerevisiae requires the F‐box protein Dsg1/Mdm30 that targets Gal4 for UPS‐mediated proteolysis in cells grown under activating conditions . Dsg1/Mdm30‐mediated Gal4 turnover surprisingly stimulates the expression of GAL genes, while in Dsg1/Mdm30‐deficient cells, Gal4 stabilization prevents the transcriptional activation of GAL genes. The authors concluded that the proteolytic degradation of Gal4 under activating conditions is essential for its activator function Growing evidence supports the concept that the function of a particular transcription factor is more strongly linked to its binding dynamics than to static gene occupancy. It is well established that transcriptional activators are fine‐tuned by the UPS and ultimately turned off after their activation et al. in vivo. Another example of how the UPS closely impacts transcription is the direct protein–protein interaction of the yeast Asr1 ubiquitin ligase with the carboxy‐terminal domain of RNA polymerase II. Ubiquitination by Asr1 alters the subunit composition of the enzymatic complex and is associated with inactivation of polymerase function in vitroUbiquitin‐mediated proteolysis of activators is a critical component of transcription regulation, as evidenced by the deep physical connections between the transcription machinery and the UPS. First, ubiquitin ligases have been demonstrated to interact with genomic sites of activator function. Von der Lehr GAL1 promoter by the Gal4 transactivator upon induction with galactose in vivo, and promotes the association of a specific coactivator/histone acetyl transferase in order to facilitate the recruitment of TFIID for transcriptional initiation GAL10 gene in a similar temporal and spatial manner Saccharomyces cerevisiae, ChIP combined with global transcriptome analyses identified genomic regions regulated by the proteasome, providing evidence that proteasome association correlates with a vast array of highly transcribed genes Furthermore, the proteasome is intimately involved in the regulation of gene expression. This notion is supported by chromatin immunoprecipitation (ChIP) studies in yeast showing that proteasome subunits actually bind to chromatin Even though proteolysis and transcription are functionally interconnected and might be key determinants of cellular metabolism, the genome‐wide degradation pattern of transcription factors is still poorly understood. Chromatin immunoprecipitation assays are widely used to produce a static snapshot of transcription factor occupancy at specific DNA regulatory sequences, but they fail to reflect the transcription factor‐binding dynamics across the genome Proteolytic activities of the proteasome occur in the immediate vicinity of DNA and participate in accessing, modifying, or controlling chromatin in order to regulate transcription. Different models for recruitment of proteasome subunits to chromatin have been proposed. Active proteasomes can be connected to the chromatin by direct contact with activators; indirectly through their interactions with intermediate proteins, or they can also bind to chromatin in response to the presence of ubiquitinated substrates at specific loci −/− mice exhibit enhanced muscle mass and oxidative capacity of muscle fibers, which present a higher mitochondrial content and activity Like transcriptional coactivators, transcriptional repressors and corepressors are also restricted by the UPS and failure to eliminate repressors results in aberrant transcriptional silencing. When mapping DNA‐associated protein turnover on a genome‐wide scale, we found that promoter‐linked proteolysis has a positive effect on the transcription of a significant number of genes, one of the most prevalent groups being the nuclear‐encoded mitochondrial genes Mitochondria play a central role in the metabolism of most eukaryotic cells. Mitochondrial dysfunction has been implicated in the development of a variety of pathological conditions and illnesses, including not only age‐related neurodegenerative diseases such as Alzheimer's dementia, Parkinson's disease, or amyotrophic lateral sclerosis, but also type 2 diabetes and cancer The majority of genes encoding mitochondrial proteins is located in the nucleus C. elegans demonstrated that dysregulation of mitochondrial respiratory complexes induces elevated levels of mitochondria‐derived ROS The UPS is tightly linked to mitochondrial function. Degradation events control the fusion and fission cycle of these organelles, trigger mitophagy, and also eliminate misfolded or mislocalized mitochondrial proteins Among other key signaling events triggered by dysfunctional mitochondria is the altered production of metabolites that have an effect on the epigenetic code. Components such as α‐ketoglutarate, fumarate, or succinate can modify the methylation status of histones and DNA, and alter the stability of transcription factors through prolyl‐hydroxylases The E3 ubiquitin ligase seven in absentia 2 (Siah2), together with proteins of the NCoR1 corepressor complex, such as transducin β‐like 1 (TBL1) and TBL‐related 1 (TBLR1) cis‐sequences and allows replacement by p300‐bound active ARs. This example highlights the complexity of nuclear degradation, which is not only protein‐specific but also specific in terms of the chromatin location at which it occurs.In addition, a recent study identified a role of Siah2 in the development and progression of castration‐resistant prostate cancer via its role in cell cycle regulation and DNA repair pathways. Bhaskara et al. Given the importance of Siah2 in the regulation of mitochondrial metabolism and its tumor‐promoting role, it is worth mentioning that the NCoR1/HDAC3 repressor complex also controls cell proliferation Given its fundamental and ubiquitous role in maintaining cellular homeostasis, it is perhaps surprising that pharmalogical inhibition of the proteasome can be effective in the treatment of cancer, with relatively limited general side effects. Historically, proteasome‐targeted therapy has first shown positive results in the treatment of the hematological malignancy multiple myeloma (MM) Carfilzomib, the second FDA‐approved proteasome inhibitor for treating relapsed and resistant MM previously treated with Bortezomib, has improved proteasome‐targeting selectivity and efficacy, with a lower toxicity profile However, one of the major remaining questions is how proteasome inhibitors actually kill MM cells. Knowing which pathway disruption is critical to MM treatment would help us devise more specific modes of intervention that target a defined E3 ligase rather than use ‘blunt’ proteasome inhibition. Originally, the proposed mode of action of Velcade was to block inflammatory signals that are critical for MM cells by stabilizing the NF‐κB inhibitor IκB. However, Velcade actually seems to stimulate this pathway Diffuse large B‐cell lymphoma (DLBCL) is a malignancy that can be differentiated based on its metabolic profile into a ‘respiratory’ and a ‘nonrespiratory’ subgroup Gene expression profiling and correlation with outcome in clinical trials for Velcade in MM patients shows that many of the pathways associated with progressive disease and response to treatment regulate protein biosynthesis and mitochondrial functions. Results of Velcade chemotherapy in solid tumors, including lung, breast, and prostate cancers, have been somewhat disappointing. However, as the uncontrolled synthesis of antibodies and the associated high energy demand increases their dependence on mitochondrial activity, MM cells may be more sensitive to metabolic treatments Beyond its impact on basic molecular and biological processes, site‐selective transcription factor degradation also holds great medical promise. Transcription factors are generally deemed ‘undruggable’. The ability to modify transcription factor‐specific E3 ubiquitin ligases with small molecules would allow us to facilitate or block the elimination of DNA‐binding regulatory proteins. Because the process of ubiquitination is located upstream of the proteasome and because E3 ubiquitin ligases mediate the specificity of substrate ubiquitination, small molecule modifiers of these enzymes represent more specific and valuable drug candidates than therapies based on proteasome inhibition. Small molecules that inhibit or stimulate specific E3 ubiquitin ligases represent the most appealing target for drug development and are currently being tested for utility in hematological malignancies Another example of a promising therapeutic strategy is the targeting of the E3 enzyme Mdm2. Inhibitors of the ubiquitin ligase are being tested, as the enzyme plays an essential role in regulating p53 turnover. Not only does Mdm2 promote proteasomal degradation of p53, but it also inhibits the transactivation domain of the tumor suppressor protein Recently, new roles have emerged for FBXW7, a substrate‐targeting subunit of the SCF (Skp1‐Cul1‐Fbox) ubiquitin ligase complex, in leukemia. Indeed, a comprehensive screen of FBXW7 mutations in various human malignancies detected that more than 30% of T‐cell acute lymphoblastic leukemia (T‐ALL) patients carry FBXW7 mutations. As FBXW7 mediates the ubiquitin‐dependent proteolysis of several oncoproteins including c‐Myc, cyclin E1, c‐Jun, and Notch, FBXW7 is considered a tumor suppressor. In particular, FBXW7 plays a pivotal role in maintaining quiescence in the small pool of leukemia‐initiating cells by mediating c‐Myc degradation If NCoR1 is indeed a critical substrate of the proteasome and a determinant of the sensitivity to proteasome inhibitors, it would be desirable to have drugs that specifically block its degradation. Given the importance of the E3 ubiquitin ligase Siah2 in the regulation of NCoR1, it is reasonable to consider Siah2 as a target of interest in MM In conclusion, these observations define E3 ubiquitin ligases as the most attractive druggable components of the UPS. It is possible to identify drugs that either stimulate or inhibit specific E3 ligases and thereby degrade or stabilize target transcription factors. Given the selective sensitivity of transcription factors to degradation at certain target genes, one would expect such drugs to interfere with the expression of defined gene sets. These drugs would likely be more effective and feature less side effects than proteasome inhibitors and moreover also have the potential capacity to increase degradation as shown with Thalidomide.Transcription factors are short‐lived proteins under constant surveillance by the UPS. As such, protein turnover is a critical component of genome regulation. Metabolic genes are continuously being adjusted and it is no surprise that these two highly connected systems, degradation and transcription, converge in the control of mitochondrial genes Fig. . The comLM and AC co‐wrote the manuscript.