Our data suggest that Set2 is intrinsically regulated through multiple mechanisms and emphasize the importance of a precise temporal control of H3K36 methylation during the dynamic transcription elongation process.Set2-mediated H3K36 methylation ubiquitously functions in coding regions in all eukaryotes. It has been linked to the regulation of acetylation states, histone exchange, alternative splicing, DNA repair and recombination. Set2 is recruited to transcribed chromatin through its SRI domain's direct association with phosphorylated Pol II. However, regulatory mechanisms for histone modifying enzymes like Set2 that travel with elongating Pol II remain largely unknown beyond their initial recruitment events. Here, by fusing Set2 to RNA Pol II, we found that the SRI domain can also recognize linker DNA of chromatin, thereby controlling Set2 substrate specificity. We also discovered that an auto-inhibitory domain (AID) of Set2 primarily restricts Set2 activity to transcribed chromatin and fine-tunes several functions of SRI. Finally, we demonstrated that AID mutations caused hyperactive Set2  Histones are subjected to various post-translational modifications (PTMs) such as methylation, acetylation and phosphorylation. It was proposed that individual histone modifications or combinations of modifications mainly serve as a signaling platform for the regulation of chromatin structure and almost all DNA-related processes . MethylaC-terminal domain of the largest subunit, Rpb1 (CTD)  is capable of di-methylating H3K36. However, the binding of Set2 to the hyper-phosphorylated Pol II not only stabilizes the Set2 protein but also increases its resident time at transcribed regions, which appears to be essential for the formation and maintenance of H3K36me3 (H3K36 methylation is tightly regulated through multiple mechanisms. Set2 directly associates with elongating Pol II through its Set2-Rpb1 interaction domain (SRI), which binds to the phosphorylated b1 (CTD) –21. Thisb1 (CTD) . Disruptb1 (CTD) –25. Simib1 (CTD) ,26. Inteb1 (CTD) , suggestI domain . It was H3K36me3 . Lastly,H3K36me3 –29. SimiH3K36me3  and FACTin vitro. However, these mutations caused distinct patterns of CTD binding and protein stability in vivo. Finally, we showed that AID mutants display synthetic genetic interactions with histone chaperone FACT, underscoring the importance of precise temporal control of K36 methylation during the dynamic elongation process.All Set2 homologs contain a highly conserved catalytic SET domain, WW domain, coiled-coil domain, SRI and a large middle region of unknown function. A recent study reports that the proline-rich motif within the middle region of human Setd2 inhibits the binding of the WW domain to Huntingtin (Htt) protein . The midPlasmids and yeast strains were constructed based on standard procedures and are listed in Supplementary Tables S1 and S2, respectively. Protein purification, ChIP, CTD peptide pull-down and other standard procedures for nucleosome preparation and Electrophoretic mobility shift assay (EMSA) are described in the Supplementary Data.2, 2 mM Dithiothreitol (DTT) and 5% glycerol) supplemented with either 0.5 μl of radio-labeled 3H S-Adenosyl methionine  or 10 μM cold S-Adenosyl methionine (Sigma)  was then added and incubated at 30°C for 1 h. 3H-incorporation was measured via filter-binding assays. For salt-titration experiments as shown in Figure 2, 10 mM DTT and 25% glycerol) was used, and NaCl concentration from each substrates  was taken into the consideration of the final salt condition, was adjusted by adding 5 M NaCl.Standard histone methyltransferase assays (HMT) reactions were carried out in a 20 μl system using 1× HMT buffer  . To dete600 = 1.0. 1% formaldehyde was used for crosslinking at RT for 15 min. Standard ChIP procedures were described previously (STE11 promoter (A)(P1397/1398), 87 and 330 of STE11 5′ (B)(P1399/1400), 925 and 1111 of STE11 coding region (C)(P1401/1402), 1839 and 2129 of STE11 3′ (D)(P1403/1404), 4193 and 4477 of the ORF-free region of ChrI (Z) (P1740/1741). Band intensity was quantified using the UN-SCAN-IT V6.1software Two hundred milliliters of each yeast culture were grown to reach ODeviously . A totalin vivo , suggesting that additional mechanisms within wild-type Set2 prevent such an aberrant activity.We next sought to test if the H3K36 methylation status at individual genomic loci follows a similar trend as the bulk levels that we measured. ChIP was performed to monitor the level of K36me at a model gene,  3′ ends , suggest 3′ ends  or when ΔCTK1 cells. Similar to a previous report . Collectively, these results strongly suggested that Set2/p-CTD contact beyond just the recruitment of Set2 to Pol II is necessary for K36me3.To evaluate if phosphorylation of CTD by CDTK-I was still required for Set2 activity when it was fused to Pol II, we transformed the CTD-Set2-FL plasmid into s report , the ende Figure , Lane 8.e Figure , Lane 6,e Figure . Anothere Figure . We demoin vivo observation that Set2–618 caused a total loss of K36me2 and K36me3 even when it can be ChIP to coding region . Remarkably, we found that SRI in this construct strongly stimulated SET activity on nucleosomes Figure  but inhiin vivo studies, we decided to further analyze the AID regions and identify the minimal deletion that can compromise AID functions. To this end, we constructed a series of internal mutations in which each individual hypothetical alpha-helix motif (A–E) or combinations were deleted . We then switched to a strong constitutive ADH1 promoter to drive higher expression levels, which led to stable accumulation of most mutants to near wild-type level, except for ΔC and ΔD . When K36 methylation was measured, all mutants showed levels of K36me2 comparable to that in wild-type cells . ΔA. ΔASET2 2 Figure . To map 2 Figure . StrikinSTE11 region  as well, although the functional consequence of this abnormal pattern is unknown.We next investigated the histone modification changes caused by AID mutations at a specific genomic locus. Consistent with the bulk level changes, ΔB led to slightly increased K36me3 while maintaining the same level of K36me2 at the n Figure . Despite3 Figure . TherefoSET2 caused increase AcH4 levels at the STE11 coding region . Indeed, we detected subtle cryptic transcription phenotypes in these mutants that were highly consistent with the Northern results . The severity of the phenotype among all mutants seemed to correlate with the levels of H3K36me3. Despite this cryptic transcription phenotype being very mild, this result is counter-intuitive considering that AID mutations cause hyperactive Set2. One possible cause of this surprising phenotype might be attributed to the instability of these AID mutant proteins. Previous studies suggested that Set2-mediated K36me not only helps Rpd3S bind to nucleosomal substrates but also involves activating Rpd3S to deacetylate neighboring nucleosomes . AID mutations appeared to make cells grow even slower than when wild-type Set2 was introduced . Since most of these AID mutants are stably folded and highly active when they are expressed in an insect cell system, we speculate that their instability may be not due to commonly interpreted mis-folding of mutant proteins. We showed that the CTD-Set2-FL fusion cannot function properly without CTD phosphorylation (Figure in vitro (Two key regulatory domains: SRI and the newly identified AID, work in concert to control the catalytic SET domain. We showed that besides binding to Pol II, SRI also recognizes the linker DNA of chromatin Figure  and has n Figure  and H. Iin vitro . Therefoin vitro  may be iin vitro  while SEin vitro . WithoutEmerging lines of evidence suggest that auto-inhibition is a prevalent regulatory mechanism among chromatin regulators. This tight ‘on-site’ repression appear to be important to prevent uncontrolled activation and enable prompt responses to cellular stimuli . ISW1 chSupplementary Data are available at NAR Online.


The importance of TEADs in MYH7 expression was also validated with RNA interference using rat embryonic cardiomyocyte H9c2 cells. These results indicate that T3-bound TRs interfere with transactivation by TEADs via protein-protein interactions, resulting in the negative regulation of MYH7 promoter activity.MYH7  gene expression is known to be repressed by thyroid hormone (T3). However, the molecular mechanism by which T3 inhibits the transcription of its target genes (negative regulation) remains to be clarified, whereas those of transcriptional activation by T3 (positive regulation) have been elucidated in detail. Two MCAT  sites and an A/T-rich region in the MYH7 gene have been shown to play a critical role in the expression of this gene and are known to be recognized by the TEAD/TEF family of transcription factors (TEADs). Using a reconstitution system with CV-1 cells, which has been utilized in the analysis of positive as well as negative regulation, we demonstrate that both T3 receptor (TR) β1 and α1 inhibit TEAD-dependent activation of the MYH7 promoter in a T3 dose-dependent manner. TRβ1 bound with GC-1, a TRβ-selective T3 analog, also repressed TEAD-induced activity. Although T3-dependent inhibition required the DNA-binding domain (DBD) of TRβ1, it remained after the putative negative T3-responsive elements were mutated. A co-immunoprecipitation study demonstrated the  Cardiac muscle contraction in vertebrates is mediated by two molecular motors, MYH6 and MYH7, which are also referred to as myosin heavy chain α and β, respectively via its ligand-binding domain (LBD), silencing is relieved by the dissociation of NCoR or SMRT from TR. T3-bound TR (T3/TR) subsequently recruits co-activators including the p160 family, cAMP response element-binding protein-binding protein (CBP)/p300, and MED1, all of which interact with T3/TR via their LXXLL motifs (L is leucine and X is any amino acid) and activate the transcription of target genes (transactivation). The p160 family and CBP/p300 exhibit intrinsic histone acetyltransferase (HAT) activity while MED1 is a constituent of the Mediator complex that precisely mediates information of liganded NHRs and DNA-binding transcription factors to RNA polymerase II TRs belong to the nuclear hormone receptor (NHR) superfamily, and are encoded by two separate alleles, TRα and β. The TRα gene generates TRα1, Δα1, α2, Δα2, and α3 through alternative splicing while the TRβ gene generates TRβ1, β2, β3, and Δβ3 with the use of the different promoters. Among them, TRα1, β1 and β2 are regarded as the major functional TRs via the remaining TRα1 per se is not a transcriptional activator for the TSHβ gene and raises the question about the definition of nTRE via protein-protein interactions In contrast to positive regulation by liganded NHR, little is known about the molecular mechanism of negative regulation by T3, and a strategy to investigate it has not yet been established via the TEA domain, which is highly conserved among TEADs As in the case of the TSHβ gene, MYH7 expression was also maintained in mice completely deficient of both the TRβ and TRα genes in vivoin vitrovia the interaction of TR-DBD with the TEA domain of TEADs, which causes the inhibition of MYH7 promoter activity. Although distinct transcription factors, i.e., TEADs and GATA2, maintain the basal activities of MYH7 and TSHβ genes, respectively, the tethering mechanism is thought to be a common aspect in the T3-dependent negative regulation of these two genes.Interestingly, Renilla luciferase (hRluc)-based reporter systems . The human MYH7 promoter encompassing nt −293/+125 was fused with the CAT reporter gene and hRluc reporter gene, generating MYH7-CAT and MYH7-hRluc, respectively. Del-1-CAT  and Del-2-CAT (deletion of the A/T-rich region and both MCATs) were generated using PCR amplification of −254/+125 and −198/+125 of the human MYH7 promoter, respectively. Using site-directed mutagenesis , we mutated distal MCAT (Mut-1 and Mut-dM), the A/T-rich region (Mut-2 and Mut-AT), distal MCAT plus the A/T-rich region (Mut-3), proximal MCAT (Mut-4 and Mut-pM), both MCATs plus the A/T-rich region (Mut-5 and Mut-MAM) and all of the reported nTREs (MYH7-mnTRE-CAT). Expression plasmids for human TRβ1 (pCMX-hTRβ1), human TRα1 (pCMX-hTRα1), and the deletion constructs of hTRβ1 (C1 and C2), and GST-DBD have been described previously Because the firefly luciferase-based reporter gene may be artificially suppressed by T3/TR 2/air (1∶19) in Dulbecco's modified Eagle's medium (DMEM) containing 10% (v/v) fetal calf serum (FCS), penicillin G (100 units/ml), and streptomycin (100 µg/ml). CV1 cells were trypsinized and plated in 60 mm dishes for 24 h prior to transient transfection using the calcium-phosphate technique. Cells were transfected at a density of 2×105 cells per well with 2.0 µg of the MYH7-CAT reporter gene, 0.4 µg of expression vectors for the TEAD family transcription factors and/or TRs, and 1.8 µg of the β-galactosidase expression vector, pCH111 . The total amount of the expression plasmid was adjusted with the pCMX empty vector . After cells were exposed to calcium phosphate/DNA precipitates for 20 h, the medium was replaced with fresh DMEM containing 10% FCS depleted of thyroid hormone 5 cells per well were transfected with 4.0 µg of the MYH7-hRluc reporter gene and 0.4 µg of expression vectors for TRβ1 using Lipofectamine reagent  according to the manufacturer's protocol. After 5 h incubation, the medium was replaced with fresh DMEM containing 5% FCS depleted of thyroid hormones or medium supplemented with T3. Renilla luciferase activities were measured with the Renilla Luciferase Assay System (Promega Corp.) using a Lumicounter 700 . CAT and Renilla luciferase activities were normalized for transfection efficiency determined by the β-galactosidase assay Monkey kidney-derived CV-1 cells 5′-gctgtggaatgtgaggcctggcctgggagatatttttgctgc-3′ and antisense; 5′-gcagcaaaaatatctcccaggccaggcctcacattccacagc-3′), PG2-probe , and PG3-probe  were labeled with γ-32P-ATP using T4 polynucleotide kinase . The 6myc-TEAD-1 protein was produced with an in vitro translation kit (Promega Corp.). γ-32P-labeled probes and TEAD-1 proteins were incubated for 30 min on ice in 20 µl binding buffer containing 10 mM Tris-HCl (pH 7.6), 50 mM KCl, 0.05 mM EDTA, 2.5 mM MgCl2, 8.5% glycerol, 1 mM dithiothreitol, 0.5 µg/ml poly (dI-dC), 0.1% Triton X-100, and 1 mg/ml nonfat dry milk. A 100-fold molar excess of cold oligonucleotides for PG1, PG2, PG3, M1 , M2 , M3 , M4 , M5 , and non-specific oligo-DNA  were used as cold competitors. DNA–protein complexes were resolved by electrophoresis on a 5% polyacrylamide gel at 100 V for 80 min at room temperature. Anti-c-myc antibody  was added for the supershift assay. The gel was dried, and labeled bands were visualized using the BAS-1000 autoradiography system .The PG1-probe  and 6myc-TEAD-1 (pcDNA3-6myc-TEAD-1) were co-transfected into CV-1 cells using the calcium phosphate method. After cells had been exposed to calcium phosphate/DNA precipitates for 24 h, the medium was replaced with fresh DMEM containing 5% FCS depleted of thyroid hormone E. coli (DH5α) that had been transformed with pGEX-4T-1-TEA (GST-TEA) or GST-DBD was induced with 0.1 mM isopropyl-1-thio-β-galactopyranoside for 4 h. The E. coli pellet was sonicated, and fusion proteins were mixed with glutathione-Sepharose beads  for purification. Receptor proteins , 6myc-TEAD-1, and TEAD-1 mutants (TEAD-1ΔC and ΔTEA) were translated in vitro using rabbit reticulocyte lysate (Promega) in the presence of 35S-methionine. Radiolabeled proteins were incubated with GST fusion proteins in binding buffer  for 3 h at 4°C and washed three times with the binding buffer. The bound fraction was analyzed by 10–14% SDS-PAGE and visualized using a BAS-1000 autoradiography system .Six µl of Lipofectamine 2000 reagent (Life Technologies) plus 300 µl of Opti-MEM-I was mixed with 300 µl of Opti-MEM-I containing 1.5 µl of 50 µM siRNA, Rn Tead1 5, or Rn Tead1 6 . The mixture was incubated at room temperature for 20 minutes and then applied to a monolayer of H9c2 cells cultured in a 6-cm dish. After a 48-h culture, the cells were harvested and used for Western blotting or RT-PCR.5′-CGAGTCCCAGGTCAACAA-3′) and reverse primer (5′-GGCTTCACAGGCATCCTTA-3′). The cDNA for glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was also amplified with the forward primer (5′-TGAACGGGAAGCTCACTGG-3′) and reverse primer (5′-TCCACCACCCTGTTGGCTGTA-3′). The thermal cycling conditions were 10 min at 95°C, followed by 50 cycles of 10 sec at 95°C for denaturing, 10 sec at 62°C for annealing, and 7 sec at 72°C for extension. PCR signals were analyzed using LightCycler software version 3.5 (Roche Diagnostics).H9c2 cells cultured in 10% FCS were incubated, and total RNAs were purified by the acid guanidinium thiocyanate-phenol-chloroform extraction method. One µg total RNA was mixed with random hexanucleotides and 200 units of Moloney murine leukemia virus reverse transcriptase  for first-strand cDNA synthesis. Using the SYBR Green I kit and a LightCycler , precipitated cDNA was quantified by real-time PCR using the following primers: forward primer . P<0.05 was considered significant.Each CAT or 32P-radiolabeled oligo DNAs encompassing the distal MCAT site and an A/T-rich element (PG1), as well as the A/T-rich elements alone (PG2)  is illustrated in ne (PG2) . As showne (PG2) , lane 5,ne (PG2) , lane 7 ne (PG2) , lane 7 per se is not the transcriptional activator for basal activity of the MYH7 promoter. The TRβ-selective T3 analog, GC-1 Because previous studies showed that TRβ1, but not TRα1, may mediate negative regulation of the MYH7 gene The above findings demonstrated that both TRα1 and TRβ1 repress the MYH7 promoter activated by TEAD-3 upon T3-binding with their LBDs . BecauseThe finding that DBD are required for T3-dependent inhibition  raised tvia protein-protein interactions and modulate their transactivation function in a ligand-dependent manner (tethering mechanism) 32S-labeled TRα1 and TRβ1 . As shown in p<0.05) reduced MYH7 promoter activity (lanes 1 and 3), which may have been because of the small amount of endogenous T3 produced by H9c2 cells To validate the importance of TEADs for basal activity of the MYH7 promoter, we performed RNA interference against endogenous TEAD-1 in H9c2 cells, which are derived from the rat embryonic heart We established a reconstitution system with CV1 cells to investigate the involvement of TEADs in negative regulation of the MYH7 gene by T3/TR –4. AlthoIn the presence of TRβ1, TEAD-induced activity of the MYH7 promoter was repressed by GC-1, a TRβ-selective T3 analog In contrast to rodents, reports on cardiac MYH7 expression in human subjects with hypo- and hyperthyroidism are limited. In large vertebrates including primates or rabbits per se is not a transcriptional activator (via a protein-protein interaction via a tethering mechanism in which liganded TR attenuates the transactivation function of TEADs via a protein-protein interaction (Although we cannot exclude the involvement of the far upstream DNA sequence in the MYH7 gene , the resctivator . Indeed,ctivator , which ictivator . Althougeraction  as in thvia inhibition of TRβ1 expression in vitro study revealed that GATA family transcription factors did not directly bind with this region via positive TREs of the MYH6 promoter, which is located 4.0 kb downstream of the MYH7 gene in rat chromosome 15 In addition to our tethering model  or nTRE via its LXXLL motifs, but also functions as a co-activator for GATA2 Given that T3-dependent negative regulation of the MYH7 gene is mediated by interactions between TRs and TEADs , what isvia TEAD-3 Microarray analyses have indicated that approximately 40% of cardiac genes are repressed by T3


In contrast, M2 macrophages support wound healing and resolution of inflammation. In the past decade, interest has grown in understanding the mechanisms involved in regulating macrophage activation. In particular, epigenetic control of M1 or M2 activation states has been shown to rely on posttranslational modifications of histone proteins adjacent to inflammatory-related genes. Changes in methylation and acetylation of histones by methyltransferases, demethylases, acetyltransferases, and deacetylases can all impact how macrophage phenotypes are generated. In this review, we summarise the latest advances in the field of epigenetic regulation of macrophage polarisation to M1 or M2 states, with particular focus on the cytokine and chemokine profiles associated with these phenotypes.Macrophages function as sentinel cells, which constantly monitor the host environment for infection or injury. Macrophages have been shown to exhibit a spectrum of activated phenotypes, which can often be categorised under the M1/M2 paradigm. M1 macrophages secrete proinflammatory cytokines and chemokines, such as TNF- Macrophages are immune cells of myeloid lineage that originate from the embryonic yolk sac and are an integral component of the host's immune response. They act as sentinel cells, which constantly sample their microenvironment and their primary function is to monitor tissues for potential threats . There are a range of resident macrophage populations, including microglia (brain), Kupffer cells (liver), alveolar macrophages (lungs), splenic macrophages, osteoclasts, bone marrow macrophages, histiocytes (connective tissue), intraocular macrophages (eye), subcapsular sinusoidal macrophages (lymph nodes), and intestinal macrophages , 2.All macrophage populations monitor their tissue of residence and respond to pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs) by initiating the acute inflammatory cascade. After clearance of the pathogenic factors, resident macrophages replenish their populations by proliferation and promote the resolution of inflammation by clearing apoptotic cells/debris and support wound healing and tissue repair programs , 3.In addition to these generic roles, macrophages demonstrate tissue-specific functional properties . For exaβ, CSF-1, and IL-34 in the brain help define peritoneal macrophages and microglia populations, respectively [To carry out their tissue-specific functions, macrophages respond to local signals released in their niche. For instance, it has been shown that retinoic acid in the peritoneal cavity and TGF-ectively , 4. CSF-ectively , 4. Unde Spic has been shown to be the signature TF for red pulp splenic macrophage development, Gata6 is responsible for Tgfb2, Cebpb, and Rarb expression in peritoneal macrophages, Runx3 in intestinal macrophages, Car4 in alveolar macrophages, Clec4f in Kupffer cells, and Mef2c in microglia [ Ciita, which is detected at low levels in large peritoneal macrophages [Examples include the ability of peritoneal macrophages to signal via eicosanoid products, microglia to undergo oxidative metabolism, alveolar macrophages to metabolise lipids, and red pulp splenic macrophages to respond to interferon activation . Recentlicroglia . In someThese transcriptional differences result in selective expression of proteins by different macrophage populations, for instance, VCAM-1 and CD31 in splenic macrophages, CX3CR1 and Siglec-H in microglia, ICAM-2 and CD93 in peritoneal macrophages, CCR2 in monocytes, CLEC4F in Kupffer cells, or CD11a and EpCAM in alveolar macrophages , 6. ThesMacrophages are constantly sampling their ever changing environment and have therefore evolved regulatory epigenetic programs that define their core functions and also allow them to respond to environmental cues rapidly. In vitro ectopic expression of PU.1 in fibroblast cell lines has been shown to induce the expression of macrophage-specific genes, such as Cd68, Emr1, and Lyz2, illustrating its significance for the establishment of the macrophage lineage [The lineage determination of macrophages is accomplished by the constitutive expression of the ETS-domain TF and PU.1. PU.1 can bind to its motifs on DNA and facilitate stable opening of chromatin and recruitment of additional TF Figures . It is f lineage .α, C/EBPβ, and RUNX. Evidence from Heinz et al. suggests that cross-talk between PU.1 and C/EBPβ supports the organisation of the macrophage enhancer landscape [However, recent evidence has shown that PU.1 does not establish the macrophage lineage on its own . In factandscape .The basic unit of DNA organisation in cells, the nucleosome, consists of 147 base pairs of DNA wrapped around a dimer of tetramers of the core histone proteins, H2A, H2B, H3, and H4. The nucleosomes pack the DNA efficiently in the nucleus, but at the same time they obstruct DNA from being transcribed. In order for transcription to occur, the core histones undergo modifications and unravel in a process called nucleosome remodelling. Key histone modifications that regulate gene expression include H3 and H4 methylation and acetylation .During macrophage differentiation, lineage-specific enhancers and promoters are marked epigenetically with histone modifications . EnzymeshiH3K27achi and H3K4me3hiH3K27achi, respectively [Regulation of gene expression in macrophages has been reported to occur mainly at the level of enhancers. Active enhancers present deposition of H3K27ac  and therectively . In contectively .The combination of cell-specific TF and chromatin modifications shapes the epigenetic landscape of macrophages, thereby defining the spectrum of responses that these cells are capable of carrying out . Each cehi enhancers, enriched at LPS-induced genes, where they acetylate H3 and H4 histones [γ-stimulated macrophages, STAT1 is recruited to selected H3K4me1hi enhancers and induces the expression of IFN-γ responsive genes [After the establishment of the macrophage lineage by pioneer TF, some genes are additionally bound by second-tier TF, also referred to as “primers” . Exemplihistones , 22. Thehistones . For exave genes .Gene activation kinetics upon TLR4 ligation revealed that the induced genes fall into two groups; the primary response genes, induced independently of new protein synthesis shortly after macrophage activation, and the secondary response genes, the expression of which is elevated many hours after ligation , 24. AltElegant work by Ramirez-Carrozzi et al. provided an insight into the mechanistic framework of primary and secondary response gene expression in macrophages during LPS activation . High Cpγ and IL-4 retained the induced H3K4me1 marks on the enhancers and preserved these elements in poised conformation [Recently, Ostuni et al. discovered another subset of enhancers termed latent enhancers, which are neither PU.1 nor H3K4me1 marked under basal conditions but are induced upon macrophage activation . Latent ormation .The existence of epigenetic regulation of macrophages is not only essential for the induction of their activation and participation in the inflammatory process, but is also required for the inhibition of the immune response and avoidance of excessive inflammation and tissue damage . CorepreThe remainder of this review will discuss the epigenetic changes that occur upon activation of macrophages and influence polarisation to the M1 or the M2 phenotype. We focus particularly on the role of four broad families of enzymes that are responsible for altering the condensation of chromatin during inflammatory conditions, resulting in gene induction or suppression and how these enzymes interfere with the methylation and acetylation motifs at the promoters of genes to enhance or inhibit their transcription and dictate the overall responses of macrophages to various environmental stimuli.H1/TH2 paradigm of T helper cell activation and embodies the activation status of macrophages primed with IFN-γ, LPS, viral products, or GM-CSF  or IL-4, IL-10, glucocorticoids, or M-CSF . In many studies, M2 macrophages have been further divided into M2a (IL-4 induced), M2b (IgG induced), and M2c (IL-10 and glucocorticoid induced) despite the fact that a consensus has been reached to instead define macrophage phenotypes based on the activator used [Macrophages are activated and respond to mount an effective immune response against potentially harmful agents, such as PAMPs, DAMPs, or tumours. The outcome of macrophage activation depends on the inflammatory stimulus. Historically, macrophage activation states have been summarised under the “M1/M2 paradigm.” The M1/M2 paradigm comes as a reflection of the Ttor used .Induction of the M1 or M2 phenotype in macrophages is associated with a complex network of signalling intermediate effectors and TF. JNK, PI3K/Akt, Notch-Jagged, and cytokine-induced JAK/STAT pathways have all been implicated in skewing macrophage responses to one state or the other, leading to TF-mediated gene expression , 83.Recently, microRNAs have been reported to play a pivotal role in macrophage polarisation and much attention has since focused on their indirect contribution to immunopathologies . SoH1 responses, whereas M2 macrophages clear parasitic infections and promote tissue remodelling. The two activation states can be characterised by certain markers. For example, Nos2 and Il12 are referred to as M1 activation markers, with Arg1, Ym1, Fizz1, and Mgl assigned to the M2 class.M1 activated macrophages acquire a proinflammatory phenotype and secrete high levels of IL-12 and IL-23 and T cell recruiting chemokines, such as CXCL9 and CXCL10, but low levels of IL-10. M2 activated macrophages secrete CCL17, CCL22, and CCL24 and IL-10 and express IL-1ra on their surface . M1 macr in vivo; macrophages display a high degree of plasticity, and activation states can often coexist or change during disease progression upon exposure to microenvironment nascent mediator release [It is important to keep in mind that these polarisation states are not stable release . In supp release .H1-TH2 paradigm, M1 macrophages have been associated with antitumour activity [The presence of polarised macrophages has been linked to pathologies in animal models . Resemblactivity , whereasactivity , 98. Intactivity , whereasactivity . in vitro and in vivo.It is clear that, in order to pharmacologically intervene in diseases where macrophages play a fundamental role, there is a need to understand the mechanisms by which these cells acquire new phenotypesThe domain that primarily catalyses lysine methylation is called Suppressor of variegation-Enhancer of zeste-Trithorax (SET) and additional protein sequences define HMTs into eight distinct subfamilies. KMT2, KMT3, and KMT7 subfamily members leave positive marks on H3K4 and H3K36, whereas KMT1, KMT5, KMT6, and KMT8 leave repressive marks on H3K9, H4K20, and H3K27. KMT4 is the only HMTs subfamily with no SET domain and is responsible for H3K79 methylation. SET7/9 can additionally methylate nonhistone proteins, such as p53, NF-kB, DNA methyltransferase 1, and others .γ [Changes in histone conformations have been extensively reported to occur during priming of macrophages with LPS, IL-4, and IFN-γ . Epigeneγ . TNF transcription [ Ifn and downstream interferon-stimulated gene (ISG) suppression in dendritic cells (DCs) and macrophages [Macrophage polarisation is differentially regulated by different KMT subfamilies and in some cases by different members within one subfamily . HMTs macription , whereasrophages . CXCL10 [ Tnfaip3 promoter to induce the expression of the TLR-antagonising protein A20, which then suppresses IL-6 secretion by peritoneal macrophages [The KMT2 members are among the most well-studied HMTs and are associated with M1 macrophage polarisation. The expression of MLL is enhanced in M1 polarised human macrophages and is responsible for H3K4 trimethylation of signature gene promoters, such as CXCL10 . In controphages . Pigp, an essential component of the GPI-GlcNAc transferase, is one of several hypomethylated genes following MLL4 ablation in macrophages, leading to defective CD14 GPI-anchoring on the cell surface [HMTs can also interfere with upstream macrophage activation signalling. Genetic deletion of MLL4 is linked to impaired CD14 surface expression on LPS-stimulated macrophages. Austenaa et al. demonstrated that surface . Tnf, Il6, MHC-II, and CD40/80 expression [ ALOX15 [ Tnf and Cxcl10 [KMT3 enzymes have been reported to contribute to M2 polarisation. LPS-stimulated macrophages downregulate SMYD2 to prevent the H3K36 dimethylation-mediated repression ofpression . Another[ ALOX15 . Acting d Cxcl10 .In addition to histone methylation, DNMT enzymes carry out DNA cytosine methylation and are divided into four distinct families: DNMT1, DNMT2, DNMT3 , and the chromomethylase family, which is exclusive to plants .DNA methylation occurs in intragenic, intergenic, and CpG islands in promoter regions in mammals . Methylaα and IL-6 [γ-lyase [DNA methylation of CpG islands in gene promoters has been shown to shift macrophages towards both M1 and M2 phenotypes by inactivating state-specific signature genes. Methylation of CpG islands impacts negatively on the expression of genes. For example, DNMT1 hypermethylates SOCS1 gene promoter during LPS activation of macrophages and prolongs the secretion of proinflammatory mediators, such as TNF-and IL-6 . Hyperme[γ-lyase . Cfb, Serping, and Tnfsf15 promoters were hypomethylated, whilst Arg1, Nrp1, Cxcr4, Plxnd1, Cdk18, and Fes were significantly hypermethylated in inflamed tissues, skewing macrophage phenotype to the M1 lineage [Genome-wide methylated DNA sequencing of recruited macrophages in ischemic muscles of hyperlipidemic and type 2 diabetes mellitus mice revealed that lineage .γ via methylation of CpG sites on its promoter [α and PPAR-γ CpG sites were shown to be hypomethylated providing a possible explanation for the anti-inflammatory phenotype of macrophages.Similarly, DNMT3B is activated in adipose tissue macrophages of obese mice and silences the M2 TF PPAR-promoter . Additiopromoter . LXRα anIn contrast to HMTs, DMTs enzymes remove methyl groups from histones exhibiting dynamics of chromatin remodeling and constant regulation of gene expression. There are seven subfamilies of DMTs with high specificity for their substrates . H3K4 mo Il12b, Mdc, and Ip10 promoters in DCs and macrophages stimulated with LPS [ Il12b and Mdc genes in DCs and macrophages upon stimulation with LPS and release them from active repression [DMTs modulate polarisation of macrophages to both M1 and M2 states . The KDMwith LPS . Recruitwith LPS . Similarpression . TNF [ in silico study, where it was predicted to target the CD40, chemokine, and IFN signalling pathways [The most widely studied demethylase in the macrophage polarisation field is the KDM6 member, JMJD3. JMJD3 has been reported to affect multiple cellular processes under inflammatory conditions in macrophages, such as transcription of inflammatory genes, oxidative stress, chromatin remodelling, cell proliferation, and differentiation . JMJD3 e TNF . JMJD3'spathways . Anotherpathways  and it hpathways .α and IL-6, and a reduction in the M2 markers, Arginase-1, and CD206, creating a hostile microenvironment for neurons [ Irf4 to establish a M2 phenotype [In contrast, other reports have shown that deficiency of JMJD3 in microglia leads to enhanced proinflammatory mediator secretion, such as TNF- neurons . This re neurons  and promhenotype .2+- and 2-oxoglutarate-dependent dioxygenases [Recently, a novel family of Feygenases , named tygenases ), were fygenases  and epigygenases , and medygenases , 115.ζ targets TET2 to the Il6 promoter to indirectly recruit HDAC2, which deacetylates H3 and H4 histones and suppresses transcription.Although TET2 and TET3 are expressed in macrophages, their levels are not increased upon LPS stimulation, implying that they may not play a critical role in DNA demethylation during macrophage activation . HoweverTherefore, TET enzymes warrant further investigation as they may regulate macrophage polarisation either directly through conversion of cytosines in gene promoter DNA sequences or indirectly via recruitment of histone modifying enzymes.Histone acetyltransferases are enzymes that transfer acetyl groups to core histones, which has subsequent effects on chromatin remodelling and gene expression. They are a diverse group of proteins with several catalytic domains that dictate their subunit specificity and functions. HATs are divided into two families : Gcn5 N-HATs have been shown to be involved in initiating gene expression in macrophages during inflammation . However M. corti were shown to downregulate Tnf, Il6, Nos2, H2-Eb1, and Ciita expression in LPS-primed microglia. These factors suppressed H3K4me3 and H3K9/14Ac in these genes and promoted RNA polymerase II recruitment to the Arg1 promoter, causing compromised immune responses of microglia in murine neurocysticercosis [Soluble/secreted factors from the parasitecercosis . Ifnα promoter upon IRF5 phosphorylation and displace the SMRT/Sin3a repressive complexes. IRF5 is subsequently acetylated by p300/CBP facilitating H3 histone acetylation of target genes, including Tnf and Il6 [HATs may also interact with the opposing histone deacetylase enzymes to enhance acetylation and eventually activation of antiviral gene promoters. For example, p300/CBP was shown to be recruited to the inactive and Il6 .+-dependent HDACs) and HDAC11, which constitutes a class of its own [The enzymes that oppose HATs functions are referred to as HDACs. Histone deacetylation is a dynamic process and may be the result of other posttranslational modifications. HDACs functions may induce further epigenetic changes and alternative gene expression . To date its own .HDACs effects during macrophage activation  have pri Il6 [β, which binds with lower efficiency to the Arg1 promoter in macrophages [There are many plausible explanations for the HDACs class I and II effects on macrophage activation status. Roger et al. reported that HDACs inhibition enhances the recruitment of the repressive complex Mi-2b to the promoters of M1 activation state genes, such as Il6 . Another Il6 , 61. Introphages .Use of a selective class I HDACs inhibitor, valproic acid (VPA), has been shown to reduce expression of M1-associated genes in macrophages, including CD40, CD80, and proinflammatory cytokines , implyin IFNA promoter from bound repressive complexes upon interaction with p300/CBP and subsequent acetylation and transcription initiation [ Cox2 and Pai1 and reduced Edn1, indicating that HDAC1 may have a protective role in inflammatory diseases [ Ciita expression in macrophages [Class I HDAC1 releases theitiation . Halili diseases . HDAC2 wrophages . In atheβ production [β in an atherosclerosis model [Studies of HDAC3 demonstrate that it promotes macrophage responsiveness to LPS via IFN-oduction . This deis model , 67, wheis model . Il12b, Il6, and Edn1 in TLR4-stimulated macrophages [−/− mice. Deletion of this histone modifier resulted in improved levels of HDL and LDL due to ABCA1 and ABCG1 upregulation and macrophage polarisation to the M2 phenotype [ Edn1 and Il12b [−/− BMDM showed normal LPS-induced expression of HDACs-dependent genes.Class IIa HDACs have also been shown to promote an M1 phenotype. In particular, a HDAC7 isoform that lacks the N-terminal 22 amino acids was reported to interact with the newly transcribed hypoxia-inducible factor 1-alpha and induce the expression ofrophages . Anotherhenotype . In a rend Il12b . However−/− mice, haploinsufficiency of SIRT1 led to augmented macrophage oxLDL uptake and increased foam cell formation [Studies with the Class III HDACs have revealed that members of this enzyme subfamily behave diversely. SIRT1 levels in macrophages are dampened under inflammatory conditions, suggesting that this HDACs and the epigenetic changes it is responsible for are not required for cell activation . Indeed,ormation .α and IL-1β levels, and impaired epithelial integrity [Deficiency of another member of the Sirtuin family, SIRT2, exacerbated DSS colitis in mice, exemplified by higher TNF-ntegrity . Subsequntegrity , 77. Int IkB promoter acetylation status and subsequent Il1β expression [Sirtuins can form a complex network of enzymes acting in concert to regulate inflammatory responses initiated by macrophages. Strikingly, SIRT6 was shown to compensate for the loss of SIRT1 in macrophages by controlling thepression .β suppression in DCs and macrophages during LPS stimulation [β cleavage and maturation in a caspase 8-dependent manner, demonstrating that HDACs inhibition may prove to be more challenging than originally thought.The functions of the only HDACs Class IV member are only now being unravelled. A couple of reports found that HDAC11 regulates the IL-12/IL-10 ratio in antigen presenting cells , 79. HDAmulation . In factResearch in the field of epigenetic regulation of macrophages during inflammation has flourished in the last decade. We now know that methylation and acetylation sites on core histones adjacent to inflammation-related genes are heavily affected by epigenetic enzymes, which contribute to the establishment and maintenance of M1 or M2 phenotypes and therefore dictate the magnitude and type of immune response mounted.HMTs are strongly associated with M2 activation by repressing M1 phenotype signature genes and promoting the transcription of M2 genes. Repression may occur in the basal state or during inflammation. In contrast, DMTs have been linked with the M1 phenotype as a result of demethylation of repressive sites on histones. These findings imply that although demethylation of core histones can be mainly linked to derepression of genes, methylation can be associated with both gene expression when it is positive  or inhibition when negative . However, the timing of histone methylation/demethylation in close proximity to gene promoters is crucial.Accumulation of the negative H3K9me2 motif at primary response gene promoters is sufficient to actively suppress transcription initiation . WhetherIn fact, negative regulation of gene expression may involve networks of HMTs and corepressor complexes that work cooperatively to control the derepression of a cluster of primary response genes. In accordance with this, a different level of cell-specific and stimulus-induced gene repression in DCs and macrophages was shown to be achieved by deposition of H3K9me3 at upstream broad enhancers of inflammation-related genes . This meAs a generic model, it seems plausible to hypothesise that HMTs need guidance by a “molecular beacon” at the promoter DNA sequence in order to bind and methylate their substrates. This can be mediated by corepressor complexes, such as NcoR, SMRT, and mSin3A, as described earlier, that provide the anchor sites for the HMTs adjacent to gene promoters and enhancers. Further investigation into the patterns of interactions between corepressor complexes and HMTs is needed and might reveal that gene regulation in macrophages in the basal state is not as random as it may seem and might be heavily dependent on the corepressor/activator complex balance at gene promoters.Proinflammatory mediator expression during inflammation may be downregulated either by negative methylation effects of recruited HMTs at gene promoters or indirectly via positive methylation (and transcription initiation) of M2 signature or anti-inflammatory genes . Hence, To conclude, the epigenetic changes that occur in macrophages during activation by various environmental stimuli have attracted much interest. The strategic location of macrophage populations complemented by the ability to shape their functions according to the needs of the host makes the enzymes responsible for their polarisation, a potential target for therapeutic interventions. Although there are still many open questions regarding the mode of action and the interactions of these enzymes, the knowledge we have acquired over the last decade hints for more effort to understand the molecular pathways involved in the regulation of gene expression of macrophages during inflammation and the design of therapies to tackle acute and chronic inflammatory diseases.


The organization of DNA into chromatin is dynamic; nucleosomes are frequently displaced to facilitate the ability of regulatory proteins to access specific DNA elements. To gain insight into nucleosome dynamics, and to follow how dynamics change during differentiation, we used a technique called time-ChIP to quantitatively assess histone H3.3 turnover genome-wide during differentiation of mouse ESCs. We found that, without prior assumptions, high turnover could be used to identify regions involved in gene regulation. High turnover was seen at enhancers, as observed previously, with particularly high turnover at super-enhancers. In contrast, regions associated with the repressive Polycomb-Group showed low turnover in ESCs. Turnover correlated with DNA accessibility. Upon differentiation, numerous changes in H3.3 turnover rates were observed, the majority of which occurred at enhancers. Thus, time-ChIP measurement of histone turnover shows that active enhancers are unusually dynamic in ESCs and changes in highly dynamic nucleosomes predominate at enhancers during differentiation.DOI:http://dx.doi.org/10.7554/eLife.15316.001 In animal, plant and other eukaryotic cells, DNA wraps around histone proteins to form structures called nucleosomes. This compacts long strands of DNA to fit them inside a cell. However, nucleosomes also act as barriers that can prevent access to the DNA. This affects the activity, or “expression”, of genes because gene expression requires proteins called transcription factors to bind to specific DNA regions. Therefore, nucleosomes must be disrupted or removed in order to access their DNA and allow their genes to be expressed.Transcription factors can bind to DNA sequences called enhancers to activate nearby genes. Groups of enhancers, called super-enhancers, also exist to further bolster the activity of certain genes, particularly those involved in determining cell identity. Recent work has shown that nucleosomes are frequently lost and then replaced by new ones (in a process referred to as turnover) in DNA regions that include enhancers. Measuring the rate of turnover of nucleosomes can thus provide information about which DNA regions regulate gene expression.Embryonic stem cells can transform or “differentiate” into any type of cell in the body. During this transformation process, different genes are switched on or off in the cell in order to give it a new identity. It is not known how nucleosome turnover changes when this happens.Deaton et al. have now developed a new method called time-ChIP that can measure the rate of nucleosome turnover across the entire DNA of a cell. Using this technique to analyze mouse embryonic stem cells revealed that nucleosome turnover occurs rapidly at enhancers. Furthermore, nucleosomes at super-enhancers are particularly dynamic and turn over more quickly than in any other DNA region.Deaton et al. next analyzed how turnover changes after the mouse embryonic stem cells have developed into neural stem cells. This revealed that the regions of DNA where high turnover occurs change as the cells differentiate, in part because this transformation activates a different set of enhancers. However, the most rapid turnover still takes place at enhancers.Overall, these observations suggest that the high rate of nucleosome turnover at enhancers makes DNA accessible to transcription factors. The next step is to use the new time-ChIP method to study how nucleosome turnover changes during the processes that pattern gene expression as an animal develops from an embryo.DOI:http://dx.doi.org/10.7554/eLife.15316.002 The organization of the genome into chromatin acts as a mechanism for regulating access to the information encoded in DNA. The simplest repeating unit of chromatin is the nucleosome consisting of a histone octamer, made up of two copies each of H2A, H2B, H3 and H4, around which approximately 147 bp of DNA is wrapped. Variants of the histone proteins exist that have distinct incorporation dynamics and functions. These include a number of histone H2A variants such as H2A.Z, H2A.X and macroH2A and histone H3 variants such as H3.3 and CENP-A .As nucleosomes can act as barriers to reading the DNA sequence, they must be disrupted in order for many processes requiring DNA access to occur. Transcription and the binding of proteins to regulatory regions are known to be dependent upon increased accessibility to nucleosomal DNA. Therefore, assessment of nucleosome turnover can provide important information concerning which genomic regions are involved in regulation and the extent to which nucleosome dynamics contribute to their function.Nucleosome dynamics is a generally underexplored property of chromatin. Previous studies examining nucleosome turnover genome-wide have made use of two main strategies – induction of epitope-tagged histone transgenes  and metaTo circumvent these limitations, we have employed a newly developed method called time-ChIP  to assess histone turnover genome-wide during differentiation of mouse embryonic stem cells (ESCs) to neural stem cells (NSCs). Time-ChIP is based on SNAP technology , where hTime-ChIP allows labeling of existing, incorporated histones, is specific for particular histone variants and allows us to study histone dynamics in vivo. We focused on measuring turnover of the histone variant H3.3. In contrast to canonical H3, histone H3.3 is deposited independently of replication at places where nucleosomes are disrupted such as promoters, the bodies of active genes, and regulatory elements . TherefoTo examine H3.3 turnover at developmental loci and regulatory elements, and understand how turnover changes during cell specification, we applied time-ChIP to neural differentiation of mouse ESCs. We found that time-ChIP could be used a priori to identify regions important for gene regulation; specifically, enhancer regions stood out for their rapid turnover. In ESCs, consistent with previous work, we found high H3.3 turnover at active enhancers  with evehprt locus   to the mTo validate that the SNAP tagged histone showed appropriate localization, we analyzed the 0 hr sample. As expected, H3.3 was enriched at enhancers and the promoters and bodies of active genes. H3.3 also showed high enrichment at transcription termination sites (TTSs) . To ensuTo further validate our approach we examined the transcription start sites (TSSs) of active genes as these are elements where one would expect high H3.3 turnover. We plotted H3.3 time-ChIP data for the 0 hr, 3 hr, 6 hr and 12 hr time points over the transcription start sites (TSSs) of active genes . As MNasOur data allows assessment of the relative H3.3 turnover between different genomic sites. Regions of high turnover will have fewer reads in later time points than regions with slower turnover when compared to the number of reads seen for these regions at 0 hr. To examine how turnover compared between different regions, it was necessary to develop a method to quantify turnover rate. To do this we used data from the 0 hr, 3 hr, 6 hr and 12 hr time points to calculate a 'turnover index'. The genome was divided into 1 kb bins and, for each bin, signal from each time point was fitted to a simple linear regression model. The slope of the linear regression was weighted to account for significance of the data fit by the linear model and multiplied by minus 1 . We calcHaving established time-ChIP as a method for assessing H3.3 turnover and shown that it offers reproducible measurements genome-wide we then went on to examine H3.3 turnover in ESCs in more depth.We were interested in how turnover relates to regulation, so, without prior assumptions, we used TI to pick out the 1000 1 kb bins with the highest H3.3 turnover in ESCs  = 0.003) . The majIf TI changes with the nature of a regulatory region, we anticipated that TI would be high in regions of high expression when these were examined over the entire genome. TI in fact did correlate with gene activity and was higher over the promoters, bodies and TTSs of highly expressed genes compared to genes with little or no expression . ESC enhWe examined enhancers in more detail, and found that TI distinguishes isolated enhancers from the grouped enhancers that modulate expression of genes involved in cell identity; super-enhancers or locus control regions . PlottinHaving observed increased TI associated with activation, we were interested in examining regions associated with repression. Heterochromatin, the most extensively repressed segment of the genome, does not have H3.3 , indicatHoxb locus shows low levels of H3.3 throughout the region with average TI values of 0.03  expressing H3.3_SNAP by injecting H3.3_SNAP ESCs into blastocysts, harvesting E13.5 embryos and deriving NSCs from embryonic brain. NSC lines were screened to identify transgenic lines  and thesTo characterize turnover in neural stem cells, time-ChIP was performed with cells harvested at 0 hr, 3 hr, 6 hr and 12 hr post-labeling. As expected, genes that were activated during neural differentiation and lost PcG binding showed a dramatic increase in H3.3 enrichment and TI . RegionsSimilar to what we observed in ESCs, the promoters, gene bodies and TTSs of highly expressed genes show high TI in NSCs as did putative NSC enhancers (identified using H3K27ac ChIP-seq data from embryonic brain). Average TI values were lower for putative NSC enhancers than those observed for active enhancers in ESCs . This miWe conclude that H3.3 turnover is broadly similar in ESCs and NSCs with the exception of PcG-bound regions, which seem to stabilize during differentiation as evidenced by a loss of H3.3.Differences in H3.3 TI between pluripotent ESCs and lineage committed NSCs might identify regulatory regions important for differentiation. To explore this, we identified regions where TI changed upon differentiation. We compared TI between ESCs and NSCs for each 1 kb bin in the genome. Bins changing turnover were defined as those with a difference in TI between ESCs and NSCs in the top 2.5% with a p-value of ≤0.01. Using these criteria, 34,070 1 kb bins were identified which changed TI during differentiation  with morWe observed TI differences in unannotated regions; 8732 of 34,070 differences, when regions overlapping UCSC genes were excluded. Such regions could represent unidentified elements with roles in cell identity and differentiation. This is supported by the fact that 1608 of these regions are located within 50 kb of a gene changing expression during differentiation , while nucleosomes that show increased signal as MNase concentration decreases are deemed accessible  metric that reflects the change in signal at a particular locus in response to decreasing MNase concentration  Mieczko. The metOlig1  and NSCs (18 hr) and most H3.3 enriched loci are still detectable at the first time point as evidenced by the strong correlation between ChIP for H3.3_SNAP_HA and the H3.3 0 hr time-ChIP sample . We are hprt locus. 24 hr after transfection selection was initiated with 300 ug/ml G418 and after 7–9 days colonies were picked and screened for presence of H3.3_SNAP_HA. The resulting A2lox.H3.3_SNAP ESCs were maintained on mitomycin-C inactivated embryonic fibroblast feeders in DMEM supplemented with 15% fetal bovine serum (Hyclone) and 1000 U/ml of leukemia inhibitory factor (EMD Millipore). H3.1_SNAP ESCs were generated in the same manner using a p2lox plasmid containing H3.1 cDNA and a C-terminal SNAP_3xHA tag.A p2lox plasmid containing H3.3 cDNA and a C-terminal SNAP_3xHA tag was introduced into A2lox.Cre ESCs  by nucleRosa26 locus of H3.3_SNAP cells). The most highly chimeric lines were selected with puromycin to generate pure populations of H3.3_SNAP NSCs and expression of H3.3_SNAP_HA was tested after 24 hr doxycycline induction by HA staining and flow cytometry. The best H3.3_SNAP NSC line (line 3_1) was then chosen for further experiments.H3.3_SNAP NSCs were generated by injecting H3.3_SNAP ESCs into blastocysts and harvesting the resulting embryos at E13.5. Neurospheres isolated from embryonic brain were grown in suspension for 1–2 passages and then plated on poly-l-ornithine coated plates and grown as a monolayer in DMEM:F12 containing B-27 and N-2 supplements (Gibco) and EGF+FGF-2 (10 ng/ml). NSC lines were screened for chimerism based on puromycin resistance (which is present in the 8 cells were incubated in 50 µl complete medium supplemented with 10 µM CP-Biotin (NEB) for 40 min at 37°C then washed twice with 10 ml media. Cells were then resuspended in 10 ml complete medium and incubated for 20 min at 37°C to remove any unbound CP-biotin. After a final wash, one-fifth of the labeled cells were harvested as the 0 hr time point and the rest plated for harvest at 3 hr, 6 hr or 12 hr.Prior to labeling, H3.3_SNAP or H3.1_SNAP was induced with 500 ng/ml doxycycline for four cell doubling times (48 hr for ESCs and 72 hr for NSCs). For labeling, 0.5–2 × 102 for 20 min at 25°C. Reactions were stopped by the addition of EDTA/EGTA and chromatin was solubilized by the addition of 0.05% NP-40. The MNase digested chromatin was centrifuged at 12,000 g for 5 min. 400 µl of the resulting supernatant was added to 15 µl streptavidin magnetic beads (Pierce) which had been pre-blocked with 50 mg BSA and 200 µg/ml yeast tRNA and incubated for 1 hr at 4°C. Beads were washed 3 × 5 min with 20 mM Hepes pH7.7, 20 mM KCl, 500 M NaCl, 3 mM CaCl2, 5 mM EGTA, 0.5% NP40, 1 mM DTT containing complete protease inhibitors (Roche). DNA was eluted with 250 µl 10 mM Tris pH8, 100 mM NaCl, 10 mM EDTA, 0.5% SDS and incubated for 10 min at at 30°C in the presence of RNase A . DNA was purified by phenol-chloroform and chloroform extraction followed by column purification . DNA was quantified using picogreen (Molecular Probes).Nuclei were extracted from each sample, counted, and equal numbers of cells resuspended in 500 µl 20 mM Hepes pH7.7, 20 mM KCl, 0.5 mM EDTA, 300 mM NaCl, 1 mM DTT containing complete protease inhibitors (Roche). Nuclei were digested with 120–140U MNase (Worthington) in the presence of 3 mM CaClThree biological replicates were performed for time-ChIP experiments based on what is standard for ChIP-seq experiments. For each replicate, cells were cultured at different times. They were then labeled, harvested at various time-points post-labeling. Pulldown was performed separately for each replicate.7 cells were harvested, and washed in cold PBS. Nuclei were isolated and chromatin was prepared and fragmented by MNase as described above. Chromatin was added to 15 µl protein A dynabeads (Life Technologies) that had been preincubated with anti-HA  and incubated overnight at 4°C. Washing, elution and DNA purification was carried out as described above.H3.3_SNAP_HA was induced with doxycycline for 48 hr, approximately 0.5–1 × 10Cells were cross-linked in 1% formaldehyde at room temperature and ChIP carried out as described in Illingworth et al.  with minRNA was isolated using TRIZOL and Purelink RNA mini kit (Ambion – Life Technologies). For RT-qPCR, RNA was reverse transcribed using SuperScript III Reverse Transcriptase (Life Technologies) and the resulting cDNA analyzed by qPCR using iTaq Universal SYBR Green Supermix (Bio-rad) on an ABI 7500 fast qPCR machine. Primer sequences are listed in Protein extracts were denatured at 95°C for 5 min in 3x reducing loading buffer (NEB), separated by SDS-PAGE using a 4–12% NuPAGE Bis-Tris gel (Life Technologies), then transferred to PVDF membrane . Membranes were blocked, incubated with antibodies and washed according to standard protocols. Primary antibodies used were anti-HA.11 , anti-H3 , anti-H3.3 , anti-H3K4me3 , anti-H3K27me3  and anti-TBP . Secondary antibodies were LI-COR fluorescent secondary antibodies or HRP-conjugated. Membranes were imaged on the LI-COR Odyssey Sa or using Pierce ECL (Thermo Scientific) and film.Cells were grown on coverslips and immunofluorescence was performed according to standard protocols. Primary antibodies used were anti-Sox2 (Millipore) and anti-Nestin . Secondary antibodies were anti-rabbit-IgG-Cy3 and anti-mouse-IgG-488. Coverslips were mounted in Vectashield with DAPI.5 nuclei per reaction using 64U, 16U, 4U or 1U MNase (Worthington) in 10 mM Tris pH 7.4, 15 mM NaCl, 60 mM KCl, 1 mM CaCl2 for 10–15 min at 25°C. Reactions were stopped with EDTA/EGTA and 0.5% SDS and 125 mM NaCl was added to the samples. RNase A and proteinase K treatment along with cross-link reversal at 65°C were performed. DNA was purified by phenol-chloroform extraction and column purification.MNase titration was carried out as described in Mieczkowski et al. In brief, cells were harvested, cross-linked in 1% formaldehyde at room temperature and nuclei isolated. Digestions were performed on 2.5 10Sequencing libraries were prepared as described in Bowman et al. . For timSequenced 50-bp paired-end tags were mapped to the mouse (mm 9) genome using the Bowtie aligner v. 0.12.9 . For timFor each time point the number of reads aligning to the spike-in control was compared to the number of reads aligning to the mouse genome. The greater the number of spike-in reads relative to mouse genomic reads, the smaller the quantity of genomic DNA sequenced from the time point sample. We used this relationship to calculate the relative number of 'sequencing reads' for each time point relative to the 0 hr time point .The genome was divided into 1 kb bins and, for each sample, the number of tags per bin was calculated as reads per million to normalize for library size. Data for each time point was then input subtracted. For each 1 kb bin, TI was calculated by fitting the number of reads for each time point to a simple linear regression model. A Kendall rank correlation test was used to assess how well points fit the linear model. The slope of the line fitted by linear regression was weighted by multiplying by 1-p-value from the Kendall test. Such weighting was introduced to reduce the TI values for the bins where the statistical significance of the fit was low, and thus it ensures a conservative estimation of TI . FinallyTo identify regions with the highest H3.3 turnover a sliding window calculation was performed that calculated the mean TI over a window size of three 1 kb bins. This allowed us to identify high TI bins whose neighboring bins also had high turnover. Regions corresponding to repeats were manually removed and the 1000 bins with the highest TI value were chosen as the set of highest TI regions for the cell type in question. An FDR threshold was calculated for these regions by taking the lowest TI value in this set and seeing how frequently it occurred in 1000 randomly selected 1 kb bins. This random selection was performed 100 times and for each test; FDR = # bins with TI value above threshold/# total bins. The highest value from these 100 tests was chosen as the final FDR.TI difference for each 1 kb bin was calculated (mean ES TI – mean NS TI) and p-values were computed using the empirical Bayes method (limma package in R) on TI data from each of the three replicates. Bins with a TI difference in the 0.975 quantile with p-values ≤ 0.01 were selected as changing TI during differentiation.http://gat.readthedocs.org/en/latest/usage.html) . To determine whether unannotated regions of interest overlapped particular gene sets in a statistically significant manner we used GAT (ge.html) .MACC was calculated as described in Mieczkowski et al.To determine regions of ChIP-seq tag enrichment (peaks), we analyzed tag counts in a 1 kb window over the chromosome length with the step of 200 bp. Statistical significance of enrichment of ChIP vs input was estimated using negative binomial distribution, with the estimate of the mean based on the tag counts in input, and the size parameters selected based on manual inspection of resulting peak calls. Regions of significant enrichment were generated by merging adjacent significantly enriched windows separated by 1 kb or less. In ESCs, PcG targets were defined as having overlapping peaks of Ring1b Cbx2, Cbx7, Ezh2 and H3K27me3. For NSCs, PcG targets were defined as having overlapping peaks of Ring1b and H3K27me3.Expression data for TSS plots in High throughput sequencing data has been deposited in GEO and is accessible using the following links.Time-ChIP: GSE78876http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78876ChIP-seq: GSE78899http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78899MNase titration and RNA-seq: GSE78984http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78984 In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been favorably evaluated by Jessica Tyler (Senior editor) and three reviewers, one of whom is a member of our Board of Reviewing Editors. Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.Thank you for submitting your work entitled "Enhancer regions show high histone H3.3 turnover that changes during differentiation" for consideration by The reviewers all applauded the new methods developed in the manuscript and the promise that these methods will lead to advances in the field. However, it was felt that such advances and novel discoveries were not contained in the manuscript submitted. Most of the results confirm observations previously made with other approaches. It was suggested that the current manuscript would be more appropriate for a high impact methods journal. The reviews also contain several comments and technical points that we help you will find helpful.Reviewer #1:This manuscript uses a novel technique to measure histone H3.3 turnover to generate a turnover index. The experiments are carefully done, well presented and well written. The results are largely predictable (e.g. high turnover at accessible act sites and low turnover at less accessible inactive sites).eLife the manuscript really needs to emphasize novel findings from this method that we did not know already.1) While the method is commendable my sense is that to have a high enough impact for 2) Why study only the replacement variant of H3. Comparison to H3.1 would have increased the impact of the work. It might also allow some measurements on the rate of turnover during the cell cycle.3) Other H3 tags have been shown to interfere with modification of the histone. This study needs some general controls for the stability of the nucleosomes containing the tagged histone and for the presence of histone modifications on the tagged vs. endogenous histone.Reviewer #2:In the manuscript entitled "Enhancer regions show high histone H3.3 turnover that changes during differentiation" Deaton et al. developed a new approach to study variant histone H3.3 turnover and performed a thorough systematic investigation in mouse embryonic stem cells (mESCs) and neural precursors. They correlate such turnover with ESC promoters, with gene expression and with other metrics that allow regulatory region identification . The authors also employed their newly developed method called time-ChIP to assess histone turnover genome-wide after differentiation of mESCs to neural stem cells (NSCs), albeit differentiation occurred in mouse embryo brains. The authors found high histone turnover in the regions that are involved in gene regulation particularly, enhancers and super-enhancers. The turnover is quantitated by a turnover index (TI) in those regions. The results were further corroborated by MNase accessibility metric (MACC), a measure of chromatin/DNA accessibility. The overall approach is solid but we did not see a significant advantage over previously published approaches, i.e., the TET-OFF approach to H3.3 turnover by Zhao's lab. Also, the authors claim that their dual approach would be useful in identifying novel regulatory regions, but they did not pursue this avenue of investigation in a tangible way.Strengths: This study offers two potentially useful tools to study nucleosome dynamics: first, to quantify the histone turnover rate (TI) and second, the MNase accessibility metric (MACC); the latter study appears to also be submitted elsewhere. The results are clean and the data presented in this study are largely convincing. The approach may indeed provide insight into identification of novel regulatory regions but the authors need to prove it.Weaknesses: This study does not break new conceptual ground or add significantly to our present understanding of nucleosome dynamics linked with transcription and silencing. It has long been known from a number of studies that gene expression positively correlates with the occupancy of H3.3 in the regulatory regions  and generally associates with high nucleosome dynamics.Reviewer #3:This paper describes the application of a novel technique (time-ChIP) involving an induced histone variant coupled to a SNAP tag. The system is used in this paper to determine the genome-wide histone turnover of the H3.3 variant in a developmental context . The authors conclude that regions of the genome experiencing active regulation  experience faster histone turnover relative to silenced or inactive regions. Additionally, the authors describe a relationship between polycomb repressive complex and the stabilization of chromatin during neuronal differentiation. Finally, the authors propose this technique as a potential method of identifying previously uncharacterized active regulatory regions.At first glance, the study seems interesting and relevant to the field of nucleosome regulation. However, there were several analytical steps that lacked appropriate details which significantly impaired the full interpretation of the analysis and conclusions. It is difficult to recommend for publication without additional details of the methodology to make an informed decision.A more detailed description of how the spike-in control was used is critical. How was this spike-in used to normalize or control the data?The authors state that they achieved greater DNA recovery the closer to the pulse they collected chromatin which is expected and serves as a good internal control. However, if the genome-wide normalization is based on the relative chromatin enrichment, the TI calculation could merely be mirroring the increased DNA recovery and not, on any sites, actual turnover. Further details on how the data was specifically normalized would clarify this concern. Additionally, it would be important to know the robustness of this TI calculation given alternative normalization approaches. For example, the authors show there is minimal turnover at silent genes. If this is expected to be a relative constant, how do their findings hold up if normalized to turnover at silent genes?What is the rationale for the use of 1kb windows for the calculation of the TI index? This time-ChIP data is presumably at nucleosome resolution since it is MNase-based. It would be of far greater value to see the extent of histone turnover extending into genes rather than what appears to be arbitrary 1 kb blocks. If there is a reason why the data is kept at 1kb blocks, the authors should also explain the limits of the spatial resolution of their data.The use of linear regression slopes as the TI index raises concerns that the authors are assuming a linear relationship in histone turnover. What about regions that experience little turnover for long periods followed by spurts of high turnover? Would the TI metric pull that data out or would it be removed from consideration because of the weighting schema used to punish poorly fit regression models? Again this speaks to the concerns regarding the robustness of the TI metric.How is the MACC calculated? There is insufficient information for the nature of this calculation, which is concerning since an entire section of the paper is based on this calculation. A reference to a submitted paper that was not provided is insufficient to judge the nature of the analysis and findings.[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]eLife. Your revised article has been favorably evaluated by Jessica Tyler (Senior editor), a Reviewing editor, and two reviewers.Thank you for resubmitting your work entitled "Enhancer regions show high histone H3.3 turnover that changes during differentiation" for further consideration at The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:This is a highly interesting study and certainly contributes to our understanding of nucleosome dynamics at enhancers and super-enhancers. However, it is mainly a techniques paper that states that there is high turnover at enhancers/super-enhancers and extends the work of Zhao while adding a large amount of new detail. We point out that Zhao's 2014 Epigenetics paper used the TET-OFF strategy referred to by the author in his rebuttal but Zhao also has a very nice Genome Bio paper, PMC3983652, that was cited by the author and which incorporates many and earlier time points. Although performed in MEFs, that study also shows H3.3 at high resolution and demonstrate high H3.3 turnover at enhancers and low at H3K27me3 rich regions using linear regression models in a TET-ON H3.3 system. The author's new strategy is probably more accurate versus attempting to measure turnover by regression.1) While the Discussion does in fact mention that Zhao came to the same conclusion on enhancers and cites the right paper, it comes too late in the manuscript. Hence, the Abstract makes an inappropriate novelty claim regarding high H3.3 turnover at regions known to be involved in gene regulation. Since Zhao has already shown this then they should acknowledge it in the Abstract and Introduction, i.e., "As previously shown, we found high H3.3 turnover at active regulatory regions including enhancers. However, we also find that even higher turnover occurs at super-enhancers[…]".2) The authors have also satisfied the technical concerns that were raised in the previous review.3) The revised version now included the requested statistical information. [Editors’ note: the author responses to the first round of peer review follow.]Reviewer #1:1) While the method is commendable my sense is that to have a high enough impact for eLife the manuscript really needs to emphasize novel findings from this method that we did not know already.We respectfully dispute the statement concerning ‘novel findings’; while many in the chromatin field take as a given that regulatory regions such as enhancers will have high turnover, there is scant data on this point, and the data that exists is not particularly compelling. The previous work uses protein induction strategies that have time delays, lessening precision. In addition, the difference between super-enhancers and enhancers is novel, as is the extent to which turnover is increased over gene bodies upon differentiation. We have examined turnover during differentiation, which has not to our knowledge been previously studied, and find that enhancers show the most frequent changes in turnover. Enhancers are central and highly studied, these are key data on a key topic.2) Why study only the replacement variant of H3. Comparison to H3.1 would have increased the impact of the work. It might also allow some measurements on the rate of turnover during the cell cycle.We have the H3.1 data on enhancers, and had not included it in the initial version, as we do not have read depth to allow assessment of any other feature of the genome. (The reason H3.1 is difficult to study intensively right now is necessary read depth and attendant cost.) The data are noisy due to read depth even at enhancers. We now include these data  and it b3) Other H3 tags have been shown to interfere with modification of the histone. This study needs some general controls for the stability of the nucleosomes containing the tagged histone and for the presence of histone modifications on the tagged vs. endogenous histone.Looking at modifications on the tagged histone is tough due to the low expression level, however we can see K4me3 on H3.3 and K27me3 on H3.1 and have included these data . All of Reviewer #2:Weaknesses: This study does not break new conceptual ground or add significantly to our present understanding of nucleosome dynamics linked with transcription and silencing. It has long been known from a number of studies that gene expression positively correlates with the occupancy of H3.3 in the regulatory regions  and generally associates with high nucleosome dynamics.See above for the rebuttal on novelty; again the data are scant and no one has rank ordered turnover in active genes vs. enhancers vs. super-enhancers previously and these are high impact issues.Reviewer #3:At first glance, the study seems interesting and relevant to the field of nucleosome regulation. However, there were several analytical steps that lacked appropriate details which significantly impaired the full interpretation of the analysis and conclusions. It is difficult to recommend for publication without additional details of the methodology to make an informed decision.A more detailed description of how the spike-in control was used is critical. How was this spike-in used to normalize or control the data?The spike-in is a control to verify that the proportion of sequencing reads resulting from pull-down of SNAP-tagged histones was consistent with the experimental data for each time point, one cannot normalize due to the nature of the experiment. We apologize for not having made this clear, and have expanded upon this in the text .The authors state that they achieved greater DNA recovery the closer to the pulse they collected chromatin which is expected and serves as a good internal control. However, if the genome-wide normalization is based on the relative chromatin enrichment, the TI calculation could merely be mirroring the increased DNA recovery and not, on any sites, actual turnover. Further details on how the data was specifically normalized would clarify this concern.Although we used a spike in control (as described above) we did not perform any normalization of the sequencing data. Rather, we look at the relative number of reads for different genomic regions at each time point when assessing turnover. Thus these data are internally compared to each other; the turnover rates reflect that internal comparison. For example, high turnover regions such as enhancers will have fewer reads in later time points compared to regions with slower turnover and the TI calculation reflects this. We apologize for not making this clear initially and have expanded upon this in the text .Additionally, it would be important to know the robustness of this TI calculation given alternative normalization approaches. For example, the authors show there is minimal turnover at silent genes. If this is expected to be a relative constant, how do their findings hold up if normalized to turnover at silent genes?As discussed above the read counts are not normalized and we compare relative turnover rates between different genomic regions. By its nature, the TI metric is internally “normalized” across the genome as we look at turnover at silent sites as well as active regions, and the metric provides a direct comparison between these rates. The silenced regions, such as those occupied by Polycomb, have lower TI.What is the rationale for the use of 1kb windows for the calculation of the TI index?1kb was used as the bin size due to read depth, particularly when looking at regions with lower amounts of H3.3 and when examining H3.1 turnover. Enhancers can be done in smaller windows. We have added The use of linear regression slopes as the TI index raises concerns that the authors are assuming a linear relationship in histone turnover. What about regions that experience little turnover for long periods followed by spurts of high turnover?We examine large numbers of stably pluripotent or differentiated cultures, so do not expect complicated behavior in the regions examined. We agree that a non-linear analysis might well be needed if one is doing an acute response; we do not have data that could be used to test this supposition though.How is the MACC calculated? There is insufficient information for the nature of this calculation, which is concerning since an entire section of the paper is based on this calculation. A reference to a submitted paper that was not provided is insufficient to judge the nature of the analysis and findings.We apologize for this oversight, we had thought the paper would be published by the time of submission and should have made the information available. The paper is now published and is cited:Mieczkowski et al. (2016). MNase titration reveals differences between nucleosome occupancy and chromatin accessibility. Nature communications 7, 11485.[Editors’ note: the author responses to the re-review follow.]The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:[…]1) While the Discussion does in fact mention that Zhao came to the same conclusion on enhancers and cites the right paper, it comes too late in the manuscript. Hence, the Abstract makes an inappropriate novelty claim regarding high H3.3 turnover at regions known to be involved in gene regulation. Since Zhao has already shown this then they should acknowledge it in the Abstract and Introduction, i.e., "As previously shown, we found high H3.3 turnover at active regulatory regions including enhancers. However, we also find that even higher turnover occurs at super-enhancers […]".We have edited the Abstract  and the Introduction […]”) to refer to both the Kraushaar and Ha papers from Zhao’s group.


Altered chromatin structure is a hallmark of cancer, and inappropriate regulation of chromatin structure may represent the origin of transformation. Important studies have mapped human nucleosome distributions genome wide, but the role of chromatin structure in cancer progression has not been addressed. We developed a MNase-Transcription Start Site Sequence Capture method (mTSS-seq) to map the nucleosome distribution at human transcription start sites genome-wide in primary human lung and colon adenocarcinoma tissue. Here, we confirm that nucleosome redistribution is an early, widespread event in lung (LAC) and colon (CRC) adenocarcinoma. These altered nucleosome architectures are consistent between LAC and CRC patient samples indicating that they may serve as important early adenocarcinoma markers. We demonstrate that the nucleosome alterations are driven by the underlying DNA sequence and potentiate transcription factor binding. We conclude that DNA-directed nucleosome redistributions are widespread early in cancer progression. We have proposed an entirely new hierarchical model for chromatin-mediated genome regulation. Despite the central role of chromatin as the ultimate substrate for all nuclear events, the structure of chromatin remains poorly characterized. The human genome is packaged into chromatin, whose fundamental subunit is ~147bp of DNA wrapped around a histone octamer to form the nucleosome –4. LittlGenome-wide nucleosome distribution information is critically important for understanding genomic processes, yet this information is lacking for a variety of human cell states. Genome-wide measurements of the locations of genome binding factors by Chromatin immunoprecipitation (ChIP), polymorphisms by exome sequencing, or DNA methylation by bisulfite conversion, have become routine and robust assays of genomic structure and organization. A literature search on any of these assays returns thousands of results, while searches on “nucleosome distribution” returns an order of magnitude fewer results. Only a handful of seminal papers have measured genome wide human nucleosome positions in a limited number –2 of celA complete understanding of the distribution of nucleosomes across the genome in cancer is currently lacking, yet it is critically important for understanding cancer etiology in basic biological and clinical contexts. We have previously shown extensive nucleosome distribution changes at a subset of genes in patients with low-grade LAC,. MononucWe have developed a solution-based sequence capture method enabling the enrichment of the 2000 bp surrounding the TSS of ~22,000 open reading frames in the human genome. Due to the importance of promoter composition in gene regulation, we designed the method to map nucleosomes at the regions surrounding the TSS. This capture method reduces the sequence space of the human genome from 3.4 Gb in total to ~50Mb of TSSs, a 98.5% reduction. This enrichment is analogous to that achieved for well-documented exome sequencing experiments . Using tThis technique represents a unique source of nucleosome distribution information at the TSS, and has not been previously executed on a genome-wide scale. The relative enrichment or reduction of sequences from this assay allows us to determine changes in nucleosome distribution among a variety of sample types. This approach offers several clear advantages. Our approach measures nucleosome distribution at all TSS in the human genome. The targeted enrichment is a cost-effective approach to whole genome studies and allows for comprehensive nucleosome distribution mapping to be completed on several samples. This nuclease protection assay is highly relevant to diffusible molecules such as transcription factors and the paired end sequencing approach provides information on protected fragment size. We can therefore use this assay to analyze subnucleosomal-sized fragments for an additional layer of genomic regulatory information , 13. UsiIn this study, we measured genome-wide chromatin structure in primary patient tumors using a newly developed approach that we present here for the first time. At the outset, we used matched tumor and normal tissue from grade one and three LAC patients on which we have previously reported . The worT values differed at an average of 9 cycles, a minimum of 100-fold enrichment  to data from a human lymphoblastoid cell line, and found a positive global correlation of 0.37 . We haveOur previous study demonstrated that nucleosome redistributions occurred at 50% of the ~900 TSS studied. It was important to determine whether the widespread nature of these changes was limited to the loci studied in the previous investigation, or whether these changes were part of a larger genome wide nucleosomal reorganization. To investigate genome-wide changes in nucleosome distribution at the TSS, we first calculated the difference between the normal and tumor datasets for each patient. Sorted difference maps of these data show that the grade one nucleosome distribution differences are widespread and dispersed throughout the genome, while these differences are greatly diminished in both grade three patients Figure . AdditioIn order to determine if the genome wide nucleosome distribution changes in grade one tumors were similar between patients, we quantified the overlap in genes with nucleosome distribution alterations between the grade one patients. We first calculated the correlation between normal and tumor for each grade one patient for every gene, and then identified overlapping genes in the least correlated 20% . We found that 1,804 genes with the greatest degree of change between normal and tumor overlapped between the grade one patients Figure . Additiok-means to align and cluster all genes based on nucleosome occupancy for a patient tumor and matched normal tissue . Clusters 2 and 3 were enriched for genes with very general cell and molecular processes.We next wanted to determine whether the 1,804 TSSs with altered nucleosomal structure shared in common between low-grade patients grouped into any particular cluster. We found that the majority (76%) of the 1804 shared genes were located in clusters 1 (32%) and 4 (44%) . Upon testing whether genes in each cluster were enriched for any particular gene ontology (GO) process, we found that each cluster had statistically significant GO enrichment Figure  28]. In. In28]. ATM, CASC1, CDKL2, CCR10, and HKR1  had a higher correlation with the DNA-encoded nucleosome positions than the matched normal sample, indicating that those loci are moving to positions favored by the underlying DNA sequence Figure . In contWe then determined the DNA-directed nature of grade one tumor nucleosome distribution changes at individual loci. We co-plotted the nucleosome distribution data with the DNA-based model of nucleosome occupancy at the representative loci analyzed earlier. The agreement between the grade one nucleosome redistributions and the positions directed by the underlying DNA sequence was evident when we plotted the measured and predicted nucleosome distributions at specific loci Figure . The corIn order to determine whether transcription factor binding occurred in the context of nucleosome redistributions in LAC, we first calculated regions of difference between normal and tumor throughout all TSSs for grade one and grade three patients. We found ~18,000 regions of difference in the grade one and ~6,000 regions of difference in the grade three samples by this method Figure . The thrUsing transcription factor binding site (TFBS) data identified by ChIP-seq in a lung adenocarcinoma cell line (A549) we quantified binding sites for nine transcription factors Ctcf (GSM803456), Bcl3 (GSM1010775), Yy1 (GSM1010794), Sin3a (GSM1010882), Taf1 (GSM1010812), P300 (GSM1010827), Creb1 (GSM1010719), Ets1 (GSM1010829) and Atf3 (GSM1010789) at the regions of difference in grade one and grade three patients . In ordeTo investigate whether the nucleosome distribution changes in the grade one tumors exposed DNA at loci for genomic licensing, we measured the proportion of subnucleosomal MNase protected fragments at regulatory factor binding sites. It has been shown that subnucleosomal fragments (< 100±20 bp) derived from MNase digestion of DNA may act as a proxy for protection by DNA-binding proteins, such as transcription factors., 13 To tTo determine whether the widespread nucleosome redistributions were a feature unique to LAC or if nucleosome alterations are a common characteristic of adenocarcinoma types, we mapped the nucleosome distribution in CRC patients. We performed mTSS-seq on matched normal tissue and tumors of stage two (S2), stage three (S3) and stage four (S4). We calculated the correlation between normal and tumor nucleosome distribution for each patient, and found widespread changes in patients with early-CRC (S2 and S3). There were 2,133 genes shared in common between these patients early CRC patients. We next compared these 2,133 common CRC genes with the 1,804 common LAC genes, and found 709 genes with altered nucleosome distribution shared between LAC and CRC. The nucleosome distribution at the ATM, HKR1, NOP16, and KIF2B genes for early LAC and all CRC patients showed that the nucleosome redistributions identified are consistent between the early CRC CRC patients, and are absent in the advanced (S4) CRC patient Figure . These pcis and trans-acting factors governing nucleosome redistributions in the progression of CRC, we first compared the experimentally determined nucleosome distributions for the common CRC genes to the computationally predicted model. We found that the early CRC tumors had a higher correlation than normal with the predicted model at over 58% of genes. The S3 CRC tumor and matched normal data compared to the predicted model at ATM and HKR1 genes showed a greater agreement between the predicted model and the tumor than between predicted model and the normal data , and these alterations are not seen in high grade tumors . We showed that these widespread nucleosome distribution alterations also occur in early CRC (stage two and three), and these changes are relatively absent in more advanced CRC (stage four). There is a high overlap of genes with nucleosome distribution alterations between LAC and CRC. Moreover, we have shown that the redistributions in CRC have a strong agreement with genetically encoded nucleosome distribution signals, indicating that the nucleosome distribution changes are DNA-directed as in LAC. The discovery of increased transcription factor binding events in advanced tumors was also observed in CRC patients. Utilizing a high-resolution, genome-wide technology to identify widespread chromatin structural changes in early tumors across multiple cancer types while defining the functional regulation through analysis of Taken together these results clarify structure-function relationships in the human genome, and support a hierarchical mechanism for chromatin mediated genomic regulation . This stPrimary samples from surgically removed tumors of lung adenocarcinoma patients, and corresponding normal tissue were obtained from the University of Massachusetts Medical School (UMMS) Tissue Bank, and prepared as previously described . PrimaryMNase digested DNA sequencing libraries were prepared using the NEBNext® Ultra™ DNA Library Prep Kit for Illumina® (NEB #E7370S/L), starting with thirty nanograms of input mononucleosomal DNA. Following end prep and adaptor ligation, libraries were cleaned-up with AMPure® XP Beads  without size selection due to the original input of a size population of ~150bp. Universal and indexed sequences were added through 8 cycles of PCR, using NEBNext® Multiplex Oligos for Illumina® . The NEBNext® Multiplex Oligos kit contains indices 1-12 which correspond to the identical product if using Illumina® TruSeq primers. The libraries were quantity and quality checked using the Qubit Fluorometer High Sensitivity Kit and Agilent High Sensitivity DNA kit on the Agilent 2100 Bioanalyzer. The average size of material across all libraries was 275bp, and the average total material in this region was more than 90%; there were no adapter or primer dimers.We used a custom designed Roche Nimblegen SeqCap EZ Library SR to capture ~2kb regions flanking the TSS for every gene in the human genome, using the HG19 build. The TSS sequences were repeat masked, so only unique probes were included. We performed the sequence capture according to the manufacturer's protocol. Following a 72 hour capture hybridization, we performed a 15 cycle PCR amplification using the TruSeq primers 1 (AAT GAT ACG GCG ACC ACC GAG A) and 2 (CAA GCA GAA GAC GGC ATA CGA G). We then performed a quantitative real-time PCR to confirm that regions within the sequence capture were successfully enriched, and that regions excluded from the capture were depleted post-capture. We selected three regions within the 2kb TSS of genes that we knew to be in the SeqCap design (on-target), and for the same three genes we selected regions outside of the 2kb TSS that were not in the SeqCap design (off-target). The on-target and off-target regions and primer sequences can be found in The multiplexed samples were loaded at 12pM on two lanes of an Illumina HiSeq 2500 system, HiSeq Flow Cell v3. For the HiSeq, the suggested range is 10-20pM. Kits used were the TruSeq PE Cluster Kit v3 -cBot – HS and the TruSeq SBS Kit v3.http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74340).There are two measures for data quality: the first is clusters that pass filter (PF) and the other is a quality score, which is given as a percentage of reads > Q30. The reads are based on the reads that pass the chastity filter not the Q30 filter. In addition, each lane was spiked with 1% PhiX as the control. The software performs real-time reporting of error rates for the PhiX spike-in lanes. The sequencing was a paired-end 50 bp run on the HiSeq, using HiSeq Control Software (HCS) version 2.0. The LAC lane had cluster density of 695K/mm(2), a PF of 94%, and 96.6% of the reads having a quality score >Q30. The CRC lane had cluster density of 736K/mm(2), a PF of 94%, and 96.1% of the reads having a quality score >Q30. The samples that were sequenced by on the MiSeq were run on 3 lanes, and was paired-end 150bp sequenced (http://genome.ucsc.edu) [Casava software was used to demultiplex the indices in each lane. Illumina adapters were clipped from reads with cutadapt  and aligcsc.edu) , 57.