The abnormally increased rate of hepatic gluconeogenesis contributes to hyperglycemia in diabetes. Gluconeogenesis is regulated on multiple levels, such as hormonal secretion, gene transcription, and posttranslational modification. We review here the molecular mechanisms underlying the transcriptional regulation of gluconeogenesis in response to nutritional and hormonal changes. The nutrient state determines the hormone release, which instigates the signaling cascades in the liver to modulate the activities of various transcriptional factors through various post-translational modifications like phosphorylation, methylation, and acetylation. AMP-activated protein kinase (AMPK) can mediate the activities of some transcription factors, however its role in the regulation of gluconeogenesis remains uncertain. Metformin, a primary hypoglycemic agent of type 2 diabetes, ameliorates hyperglycemia predominantly through suppression of hepatic gluconeogenesis. Several molecular mechanisms have been proposed to be metformin's mode of action.Hepatic gluconeogenesis,  However, the abnormally increased rate of hepatic gluconeogenesis contributes to hyperglycemia of both type I and II diabetes. This underscores the importance of maintaining normal gluconeogenic rates to avoid disease pathophysiology. Therefore, identification of the molecular mechanisms regulating hepatic gluconeogenesis is crucial to the development of improved therapeutic strategies for the treatment of diabetes.Glucose functions as a primary fuel to active tissues like brain and blood cells in mammals and its blood levels are maintained within a relatively narrow range . The livGluconeogenesis is modulated by various external factors, such as nutrient and energy conditions, exercise, and stress reaction, through mediating the secretion and activity of certain molecules –4. The rIn this review, we would like to discuss the molecular mechanisms underlying the transcriptional regulation of gluconeogenesis in response to hormonal changes.Gluconeogenesis is a process that transforms non-carbohydrate substrates  into glucose Figure . Both laAlthough gluconeogenesis is theoretically the reversal of glycolysis, there are three key irreversible glycolysis kinase reactions catalyzed by glucokinase (GK), phosphofructokinase-1 (PFK-1), and pyruvate kinase (PK) , which aHepatic gluconeogenesis is hormonally regulated, and numbers of hormones  have known stimulatory effects Figure . Of thesInsulin and glucagon are the most important hormones regulating hepatic gluconeogenesis. They demonstrated antagonistic effects on blood glucose levels. Under fasting or feeding, the blood circulating levels of the two hormones will change, subsequently affecting the expression of gluconeogenetic genes.Insulin is a polypeptide hormone secreted by pancreatic β cells, and its major physiological function is to regulate glucose transport, lipid synthesis, gluconeogenesis, and glycogen synthesis via the PI3K/Akt signaling pathway . Under fGlucagon is also a polypeptide hormone secreted by pancreatic α cells, and its primary function is to prevent hypoglycemia. After overnight fasting, the markedly increased glucagon released from the pancreas recognizes and then binds to the hepatic glucagon receptor (GCGR), which is a member of G protein-coupled receptors characterized by being membrane-bound and G protein-coupled , formingGlucocorticoid is a kind of sterol hormone secreted by the zona fasciculata of the adrenal glands and its blood levels are under tight regulation by the hypothalamic–pituitary–adrenal axis. It is well-established that glucocorticoid itself can promote hepatic gluconeogenesis. Also, glucocorticoid is important for other hormones  to promote gluconeogenesis in adrenalectomized mice . These mGlucocorticoid functions through binding to intracellular glucocorticoid receptor , which is a member of the nuclear hormone receptor superfamily. Without ligand binding, GR resides primarily in the cytoplasm in a complex with heat shock proteins (HSP 90 and 70) and co-chaperones Hsp40, p23 and others, leading the GR to preserve a low affinity state . Upon liThyrotro-pin-releasing hormone (TRH), secreted from the hypothalamus, acts upon the pituitary gland and binds to G protein-coupled TRH receptors on the thyrotrope, resulting in an increase in intracellular cAMP, and subsequent thyroid-stimulating hormone (TSH) release. Then TSH binds to a G protein-coupled TSH receptor (TSHR) on the thyroid follicular cell, stimulating the production, and release of thyroid hormone (TH), and TH in turn regulates TRH/TSH. This is the fundamental pathway of the hypothalamic-pituitary-thyroid axis.Studies show that the hormones TSH and TH are closely related to glycometabolism. TSH interacts with its receptor TSHR to activate the PKA via cAMP, PKA in turn inhibits the activation of SIK2 by dephosphorylating at Ser587. The dephospho-CRTC2 enters into the nucleus and interacts with CREB, promoting the expressions of PEPCK and G6Pase genes, and a subsequent increase in hepatic gluconeogenesis . In addi2+ influx, leading to a decreased concentration of mitochondrial Ca2+ and an increased PC activation in hepatocyte. Meanwhile, the Vlb receptor induces Ca2+ release from the endoplasmic reticulum (ER), which triggers exocytosis including insulin and glucagon in the pancreas. Under stringent extracellular Ca2+ deprivation, AVP still can increase the release of insulin and glucagon; the gluconeogenesis, however, is restrained. This suggests that the action of AVP is sensitive to extracellular Ca2+ and may be a Ca2+-independent pathway of VAP-induced insulin and glucagon release , is produced in hypothalamus and released by neurohypophysis in a condition of high plasma osmolality, low plasma volume, and low blood pressure . AVP is  release .Bile acids are known mainly as intestinal detergents for the absorption of dietary lipids and fat-soluble vitamins in the gut. In recent studies, bile acids have been proposed to participate in diverse physiologic processes, they also function as hormones or signaling molecules to regulate metabolism of lipids, glucose, and energy expenditure . Bile acHormone release instigates signaling cascades in the liver to modulate activities of various transcription factors, which in turn control the expressions of key rate-limiting enzymes in the gluconeogenic pathway, specifically PEPCK and G6Pase Figure .CREB is a nuclear transcription factor binding to a conserved cAMP-response element (CRE) on gene promoter. CREB can be directly phosphorylated by protein kinase A (PKA) . Under fFoxO proteins belong to a subgroup of the forkhead family of transcription factors that possess a conserved forkhead box-type DNA-binding domain. Four major FoxO proteins  are identified in mammals, and they are commonly regulated by the insulin/Akt signaling pathway. FOXO transcription factors modulate gene expression in stress resistance, metabolism, apoptosis, and longevity . AlthougCREBH belongs to ER-bound transcription factor families, and it is mainly expressed in the liver. CREBH was recently identified as an important physiological regulator of hepatic gluconeogenesis . During C/EBPs are key regulators of numerous cellular processes, including cell proliferation, differentiation, tumorigenesis, and gluconeogenesis and lipid synthesis . All C/ENRs are a superfamily of transcription factors. A typical nuclear receptor consists of a highly variable NH2-terminal A/B domain that contains a transactivation function known as AF-1, a central conserved DNA binding domain (DBD or C domain), a short D domain that contains the nuclear location signal, a large conserved carboxyl-terminal E domain (LBD) that is responsible for the binding of cognate ligand or hormone and contains ligand-regulated transcriptional activation function 2 (AF-2) . NRs usuHomodimeric NRs, such as glucocorticoid receptor (GR) and hepatocyte nuclear factor 4α (HNF4α), normally reside in the cytosol via interaction with chaperon proteins . Upon binding ligand, the receptor disassociates from the chaperone and homodimerize, leading to the exposure of nuclear localization sequence and entry into the nucleus. HNF4α is hepatocyte-specific transcription factor mainly regulating hepatic differentiation , which iHeterodimeric NRs  reside in the nucleus and form heterodimers with retinoid X receptor (RXR), which is generally bound to the target promoter through interactions with corepressor complexes NCoR and SMRT in the absence of ligand. Ligand binding results in the replacement of corepressors with coactivator complexes and facilitates gene transcription. PPARα, highly expressed in liver and brown adipose tissue, recognizes, and binds to specific DNA sequences AGGTCA (PPAR response elements) with the RXR. PPARα has been identified as a direct activator of numerous gluconeogenic genes . GlucagoMonomeric NRs, such as retinoic acid receptor-related orphan receptor alpha (RORα), bind as monomers to half-site response elements which typically consist of a consensus AGGTCA half-site. RORα has been shown to directly bind to the promoters of PEPCK and G6Pase and regulates their transcriptions. Mice treated with RORα antagonists or targeted deletion of the RORα gene exhibit decreased PEPCK expression and glucose production. Similarly, the expression of G6Pase and PEPCK is suppressed in HepG2 cells overexpressing Rev-erbα, the physiological repressor of RORα. In contrast, silencing Rev-erbα significantly promotes G6Pase and PEPCK expression. Furthermore, there is evidence that steroid receptor coactivator 2 (SRC2) functions as a coactivator of RORα to enhance the rate of transcription of G6Pase; both RORα and SRC2 are required for the stimulatory effect of PGC-1α on G6Pase transcription.Yin Yang 1 (YY1), belonging to the polycomb group protein family, is widely expressed in various tissues. YY1 binds to CCATNTT consensus sequences to activate or silence gene transcription via chromatin modification. During states of fasting or insulin resistance, YY1 transcriptionally upregulates GR expression, which then enhances the expression of gluconeogenic enzymes PEPCK and G6Pase, promoting hepatic glucose production .Nuclear factor-Y (NF-Y) is a trimeric protein complex composed by NF-YA, NF-YB, and NF-YC subunits, and it can specifically recognize and bind the CCAAT box. We recently observed that the hepatic NF-Y expression is markedly induced by cAMP, glucagon and fasting, and knockdown of NF-YA weakens gluconeogenic gene expression and glucose production in mice. NF-Y enhances the expressions of PEPCK and G6Pase either by binding to their promoters directly or interacting with CREB .Transcription factor 7-like 2  is demonstrated as a strong candidate for type 2 diabetes in recent extensive genome-wide association studies . TCF7L2 Transcriptional cofactors regulate gluconeogenesis by directly binding to transcriptional factors to either activate or inhibit transcriptions Figure .PGC-1α is a transcriptional coactivator that involves the regulation of energy metabolism. The expression of PGC-1α is markedly induced in the liver upon prolonged fasting and in multiple diabetes animal models . Under fCRTC2 is a member of the CRTC transcriptional coactivators family known as transducers of regulated CREB activity (TORC). There are three CRTC isoforms  in mammals: CRTC1 is expressed predominantly in the brain, CRTC3 is expressed highly in adipose tissue, whereas CRTC2 is found mainly in liver. CRTCs contain an N-terminal CREB-binding domain, a central regulatory domain, and a negatively charged C-terminus TAD. The activity and cellular localization of CRTCs are phosphorylation-dependently regulated. Under feeding, liver kinase B1 (LKB1) constitutively activates salt-inducible kinases (SIKs) through phosphorylation of a threonine residue in the activation loop of their kinase domain, leading to the subsequent phosphorylation of CRTCs , and phoP300 and CBP are histone acetyltransferases transferring an acetyl group to lysine residue, and the acetylation level of non-histone proteins has been identified as a key mechanism for regulating hepatic gluconeogenic gene transcription . P300/CBSMILE is a member of the CREB/ATF (activating transcription factor) family of bZIP transcription factors. Under feeding or insulin-resistant conditions, insulin can promote hepatic SMILE expression . SMILE cDAX-1 and SHP  are members of an orphan nuclear receptor family of unknown endogenous ligands. However, unlike other nuclear receptors, both DAX and SHP lack a DNA-binding motif, and they primarily function as transcriptional corepressors through interaction with other proteins . SHP is PTMs including phosphorylation, (de)methylation, (de)acetylation, ADP-ribosylation and ubiquitination, and modulate gene transcription in a cell- and tissue-specific manner. The role of PTMs in the regulation of gluconeogenesis has received close attention. The aforementioned phosphorylation of transcriptional activators, such as FoxO1, CREB, and CRTC2, exemplifies a typical mechanism whereby PTMs directly regulate gluconeogenesis. In addition, Lysine acetylation and arginine methylation have been identified as evolutionarily conserved PTMs in gluconeogenic regulators and have emerged to play major roles in mediating gluconeogenesis.AMPK is an evolutionarily conserved serine/threonine heterotrimeric protein kinase, which consists of a catalytic α-subunit and two regulatory subunits, β-subunits and γ-subunits. AMPK is activated when cellular energy levels are low, switching off ATP-consuming anabolic pathways and switching on ATP-generating catabolic processes. It has been reported that acute hepatic overexpression of a constitutively active form of AMPKα2 suppresses the expression of key hepatic gluconeogenic genes, G6Pase, and PEPCK, resulting in a depressed hepatic glucose output . Compounin vivo clamp studies  consisting of three major types (type I-III) have been identified in mammals . Type I PRMT1 catalyzes asymmetric methylation of FoxO1 at arginines 248 and 250, thereby repressing phosphorylation of the adjacent serine 253 , promotiPRMT4, also known as coactivator-associated arginine methyltransferase 1, modulates hepatic gluconeogenesis through interaction with CREB in hepatocytes. PRMT4 can mediate asymmetric dimethylation of histone H3 at arginine 2 (H3R2), thereby promoting its acetylation by CBP/p300, leading to an increased CREB-dependent transcription of gluconeogenic genes . HoweverPRMT5 regulates gluconeogenesis either as a coactivator of CREB or a corepressor of SHP. In fasting conditions, the elevated glucagon promotes the interaction of PRMT5 with CRTC2, by which PRMT5 is recruited to the gluconeogenic promoters and mediates symmetric dimethylation of H3R2, leading to enhanced chromatin accessibility at promoters, stimulating CREB phosphorylation, and gluconeogenic gene expression . In addiPRMT6 can directly mediate asymmetric dimethylation of CTRC2 at multiple arginine residues, resulting in an enhanced interaction between CRTC2 and CREB on gluconeogenic promoters .Histone methylation, occurring on specific lysine and arginine residues in histones H3 and H4, has been associated with activated or repressed transcription. Lysine residues on histone tails can be mono-, di-, or trimethylated, while arginine residues can be mono and asymmetrically or symmetrically di-methylated. The site and degree of histone tail methylation have a distinct effect on gene regulation. For instance, histone H3 lysine-4 (H3K4) and H3K36 di- or trimethylation (Me2 or Me3) and H3K27 mono-methylation are positively correlated with transcriptional activity, whereas H3K9 and H3K27 Me2 and Me3 are found to mark the repressed genes .Histone methylation is a dynamic and reversible process controlled by a balance between histone methyl-transferases and demethylases. Since the amine oxidase LSD1 (KDM1) is identified as the first histone lysine-specific demethylase, a number of histone demethylases have been identified, such as H3K9-specific demethylase JHDM2A (KDM3A), H3K27-specific demethylases UTX (KDM6A), and JMJD3 (KDM6B) . LSD1 hiAccumulated evidence has suggested that histone demethylation suppresses the expression of gluconeogenic genes and glucose production. For example, in one study in H4IIE rat hepatoma cells, Hall et al. observed that insulin induces demethylation of arginine-17 on histone H3 at promoters of PEPCK and G6Pase, which correlated with the disruption of the PEPCK and G6Pase gene transcription complex, resulting in the repression of gene transcription . In a stAcetylation of histone by HAT reduces the affinity between histone and DNA and allows transcription factors more accessible to DNA. Initially, acetylation is primarily identified on histone or chromatin-associated factors. Recent studies have shown that many transcriptional regulators are modulated by acetylation on lysine residues or that they are themselves acetyltransferase , 100. ThIn contrast to HAT, HDACs remove acetyl groups, resulting in chromatin remodeling and transcription inhibition. In mammals, there are eighteen HDACs and they are divided into four classes (class I-IV) based on their sequence identity and catalytic activity . Class IClass I HDACs increase the expression of nuclear receptor HNF4α and the transcriptional activity of FoxO1 in hepatocytes, inducing PEPCK expression, and hepatic gluconeogenesis .Class IIa HDACs play an important role in the regulation of hormone-induced FoxO . Under fAmong Class III HDACs, SIRT1 is the most studied in gluconeogenesis. During fasting, SIRT1 mediates deacetylation and activation of transcription factors FoxO1 and PGC-1α, resulting in an elevated expression of gluconeogenic genes and hepatic glucose output . AdditioAdditionally, SIRT2, SIRT3, SIRT6, and SIRT7 have been implicated as potential regulators of blood glucose metabolism , 113–115Metformin, a primary hypoglycemic agent for type 2 diabetes, ameliorates hyperglycemia predominantly through suppression of hepatic gluconeogenesis . Severalin vivo. There is evidence that metformin could promote the assembly of a functional AMPKαβγ heterotrimeric complex, making it more easily phosphorylated at T172 of subunit α1 by upstream kinase LKB1, as well as hindering the dephosphorylation of the T172 by protein phosphatase  from the matrix to the mitochondrial intermembrane space to generate a proton gradient that is the energy source in ATP synthesis. Metformin could decrease intracellular ATP production through the disruption of mitochondrial complex I (Mechanistic studies suggest that the primary site of metformin action appears to be the respiratory chain complex I Figure , which complex I , leadingomplex I . Since gomplex I . Howeveromplex I , 119, whomplex I . Recent omplex I , which complex I . More reomplex I , which iRecently, an additional mitochondrial target implicated in metformin has been proposed , 132. MeHepatic gluconeogenesis is largely controlled by transcriptional regulation of key rate-limiting enzymes, and these transcription regulators are mediated by hormonal signals in response to nutritional changes during the starvation-feeding cycle. Significant progress has been made to understand the expression regulation of gluconeogenic enzymes PEPCK and G6Pase by various transcriptional factors and their posttranslational modifications including phosphorylation, methylation and acetylation. Although PEPCK and G6Pase are regarded as the central operators of gluconeogenesis, recent investigations question their roles in the regulation of gluconeogenesis. Targeted deletion of the PEPCK gene in mice only led to a 40% reduction in gluconeogenic flux despite a 90% reduction in hepatic PEPCK expression . MoreoveHepatic gluconeogenesis is a complex and genetically heterogeneous process modulated on multiple levels . AlthougAll authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


DNA sequence elements in the core promoter can play a central role in regulation of gene expression. Core elements (e.g. INR and TATA box) are located within ~50bp of the transcription start site and both upstream and downstream elements are known. Although all can affect the level of gene expression, their mechanism of action has yet to be fully defined. The studies described here are focused on two core promoter elements, INR and BRE, in the human genome. The locations of the two elements were determined in a large number of human promoters and the results were interpreted in terms of overall promoter function.A total of 13,406 promoters were collected from the reference version of the human genome and found to contain 62,891 INR sequences and 32,290 BRE. An INR sequence was found in the core region of 1231 (9.2%) promoters and a BRE in 2592 (19.3%); 121 promoters (0.9%) have both INR and BRE elements. Counts support the view that most human promoters lack an INR or BRE element in the core promoter. Further analysis was carried out with the aligned aggregate of promoters from each chromosome. The results showed distinct INR distributions in separate chromosome groups indicating a degree of chromosome specificity to the way core promoter elements are deployed in the genome. The rare promoters with both INR and BRE elements were found to be enriched among the genes with divergent transcription. Enrichment raises the possibility that core promoter elements can have a function in chromosome organization as well as in initiation of transcription. Control of gene expression is central to the functioning of all living organisms. The need to modulate expression of an invariant set of genes is at the center of development, homeostasis and the ability of organisms to adapt to environmental change. It is not surprising therefore that regulation of gene expression is one of the most actively-studied areas in all of biology and that we know so much about it. Although gene expression is controlled at multiple levels, investigators continue to emphasize the first step in the overall process, the transcription of gene DNA into mRNA by DNA-dependent RNA polymerase II (RNAPII). This enzyme and the means to control its activity are found in all three domains of life , and it Regulation of RNAPII activity in humans and other higher metazoans is thought to involve two classes of mechanisms called canonical and non-canonical –10. CanoCore promoter sequences are found within ~50bp of the transcription start site (TSS) and are recognized by general transcription factors such as TFIID and TFIIB , 11. TogTranscription factor sites are quite different from core promoter elements. For instance, transcription factor (TF) sites can be hundreds of base pairs removed from the TSS. Each site is recognized by its own TF, and more than 1000 TF-TF site pairs have been identified in the human genome –18. It iThree observations have suggested the need for non-canonical in addition to canonical mechanisms for the regulation of RNAPII transcription. First, in the case of mammalian genomes, the concept of a core promoter is not considered to apply . InsteadINR and BRE core promoter elements are best known for the way they function in canonical promoters. INR has the sequence YYANWYY and is recognized by the general transcription factor TFIID. In canonical promoters INR spans the TSS . Two TFII have been examining core promoter elements in the human genome with the goal of defining their role in non-canonical promoters. The major resource for the study is a database of more than 13,000 human gene promoters, and analysis is focused on INR and BRE elements. Here I report results showing the distribution of the two elements in the aggregate of promoters present in each human chromosome. Further analysis is focused on promoters that contain both INR and BRE elements in the core promoter region.https://genome.ucsc.edu/). Downloading was performed one chromosome at a time beginning at the left chromosome end. Promoters were examined individually and accepted for the database only if they were located upstream and near to an annotated, functional gene. Downloaded promoters were all 1000bp in length beginning 570bp upstream from the transcription start site and extending to 430bp downstream. Downstream sequences were included in case analysis revealed a role for them in control of gene expression. Each accepted promoter was downloaded initially into a computer file called the megafile which contains all the promoters downloaded from a single chromosome. The megafile was then split computationally into 1000bp individual promoters with each given a number preceded by “list”. Promoters were downloaded from all 24 human chromosomes, 22 autosomes plus the X and Y chromosomes. Megafile and list files are available online at: (http://www.people.virginia.edu/~akr4xc/Human%20Gene%20Promoters%20-%20Home.html).Promoters were identified by the FirstEF algorithm  and down(C|T)(C|T)A(A|T|G|C)(A|T)(T|C)(T|C) and (G|C)(G|C)(A|G)CGCC, respectively. Otherwise identification of the INR and BRE locations was similar and the method is described below only for INR. The locations of INR sequences were determined by use of a Python script based on re.find beginning with one entire chromosome sequence. The script returned the position of the first nucleotide in each of the INR sequences identified. Depending on the chromosome, a few hundred to a few thousand INR positions were returned. To align the promoter sequences together, all but the right-most three positions of the location number was stripped away using the RIGHT function in Microsoft Excel. The remaining list contained only the position of each INR within its own promoter sequence. A histogram was plotted of the list numbers using the histogram function in SigmaPlot 13.0 and rendered graphically also with SigmaPlot.INR and BRE locations were determined beginning with the megafile for each chromosome. Sequences employed for INR and BRE were The locations of INR and BRE sequences within the core promoter were determined using a similar method to that described above for the entire promoter. In the case of INR, re.find was employed to identify all the promoter INR sequences in a chromosome and Excel RIGHT was used to remove all but the INR position within its own promoter. INR sequences within the core region were then identified using the Excel routine Conditional Formatting>Highlight Cells>Between. As described in the text, -20bp-+10bp with respect to the TSS was used for the core region of INR and -60bp-+10bp was used for BRE. Methods described above were used for computing a histogram and for rendering it graphically.Core promoters containing both INR and BRE elements were identified one chromosome at a time beginning with the list of core promoters containing an INR and the list of BRE-containing core promoters. The two lists were aligned and the list numbers of promoters containing both elements in the core were added to a separate file that ultimately contained the identities of INR+BRE core promoters from the entire database. The same method was used to identify core promoters with two or more BRE elements.NCBI BLAST was used to identify the genes driven by the 121 promoters found to have both INR and BRE sequences in the core region. The same method was used to identify the genes driven by the control promoters described below. In all cases, the entire promoter sequence was used to probe the 2013 version of the reference human genome (hg18). A unique gene or gene pair was returned in every case. A transcript was considered to be a part of a divergently-transcribed pair if its start site was within 2000bp of a transcript synthesized in the opposite direction. Not counted were un-transcribed candidate genes and pseudogene transcripts.The count of divergently-transcribed gene pairs with INR+BRE core promoters was compared to three control counts: (1) 121 genes chosen at random from the human genome; (2) 121 genes chosen at random from those containing an INR, but not a BRE in the core promoter; and (3) 121 genes chosen at random from those with a BRE, but no INR in the core promoter. In each of the three control gene sets, the number of genes from each chromosome was matched to the number present in the INR+BRE set.The main resource for the study was a database of 13,406 human promoters. Promoters were identified by the FirstEF algorithm  and down(C|T)(C|T)A(A|T|G|C)(A|T)(T|C)(T|C) and (G|C)(G|C)(A|G)CGCC [A|G)CGCC , 21. TheSimilar counts were made using just the core promoter regions of each promoter . The corIn view of the small number of INR elements in individual promoters, INR sequences were considered as a group in all the promoters of each chromosome. Hundreds to thousands (depending on the chromosome) of INR locations were therefore considered at once permitting a comprehensive view of core element locations. For each of the 1000 positions in a promoter, the number of INR elements starting at each location was added and the sums were plotted as a histogram . The barThe flat distribution  shows liThe distribution of BRE elements was examined in the same way described above for INR. Like the results with INR, the results with BRE demonstrated a dependence on chromosome. Two types of distribution were found, central peak and skewed peak distributions . CentralBecause INR and BRE elements are both thought to function primarily within the core promoter, locations of the two elements were examined specifically in the core. Core promoters were considered to extend from -20-+10bp with respect to the TSS in the case of INR and -60-+10bp for BRE. Alignments were created as described above for a set of promoter sequences. INR or BRE elements beginning at each bp in the target region were counted in an entire chromosome and the counts were plotted as a histogram. Representative results are shown in Figs As in the case of the whole promoter studies, both INR and BRE element core distributions were found to differ in different chromosomes. Two distinct distributions were observed in the case of INR, a unimodal and a bimodal distribution. Examples of the two are shown in Three distributions were recognized in the case of BRE, flat, trimodal and multi-modal Figs . Little Although the number of promoters with both INR and BRE elements is small, it was considered that analysis of this class might be revealing about how the two elements function together in initiation of transcription. The INR-BRE separation distance was therefore determined in the core region (-60-+10) of all promoters in the database that have both INR and BRE elements . As a control, the spacings were compared with those between random numbers in the same 70bp interval. As expected, short spacings were more abundant than long ones in both INR-BRE and control distributions. The INR-BRE count exceeded the control only for separations of ~25-30bp . Other hThe distance between adjacent BRE elements was determined using the same strategy employed for INR-BRE. Beginning with all promoters in the database, those with more than one BRE in the core promoter were identified and the number of bp between adjacent BRE elements was plotted as a histogram. A total of 413 BRE-BRE pairs were examined. As a control, a similar plot was calculated using the separations between random numbers in a 70bp interval.The results  showed tExamination of the genes encoded by promoters containing both INR and BRE elements revealed an unexpected correlation with divergent transcription . For thiThe results showed 63 divergent and 58 non-divergent genes with INR-BRE promoters Tables –S4. LoweThe promoter database employed in this study was created with the goal of including the maximum number of promoters with emphasis on those driving annotated, functional genes. The prediction algorithm used, FirstEF , emphasiThe nature of the database employed here can be characterized by comparing it with other possibilities using the proportion of promoters found to have INR or BRE elements in the core. In the case of INR, the value obtained here, 9.2%  is at thA regular feature of the results reported here is the very different distribution of INR promoter elements in individual chromosomes. Chromosome dependence was also observed in the case of BRE distributions Figs  and 2. TThe character of all three INR distributions was unexpected. The V distribution was reported for all human INR promoter elements , but theThe distributions observed for BRE elements more closely resemble the results expected for a core promoter component. In a majority of chromosomes (16 of 22) there is a maximum in the distribution of BRE elements at or near the core promoter  raises the possibility that a repeating unit of transcription factors may be involved.Although the number of promoters with both INR and BRE elements is very small, it was considered attractive to examine them as such rare combinations might have unique properties. In fact, the observed number of INR+BRE promoters was lower than the abundance expected by chance. The statistical expectation (i.e. the measured INR promoter frequency (9.2%) X the BRE frequency (19.3%) is 1.8%. In contrast, the measured value (0.9%) is ~2-fold lower suggesting that INR+BRE-containing promoters are depleted overall compared to the chance expectation. The presence of an INR or a BRE in a promoter may therefore suppress the presence of the other. The observed spacing between INR and BRE elements in INR+BRE promoters  INR and BRE perform in promoters that have them.Finally, I performed an analysis of the small number of core promoters that have both an INR element and a BRE . A correlation was found relating such promoters with divergent transcription in the adjacent genes. More than 50% of INR+BRE promoters were found to be located in a position where they could drive divergently-transcribed gene pairs. The result raises the possibility, for the first time, that core promoter elements can have a function in chromosome organization as well as in initiation of transcription.S1 Powerpoint(PPTX)Click here for additional data file.S2 Powerpoint(PPTX)Click here for additional data file.S3 Powerpoint(PPTX)Click here for additional data file.S4 Powerpoint(PPTX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


Active CREs are imbedded in open chromatin that are accessible to nucleases. Several techniques, including DNase-seq, which is based on nuclease DNase I, and ATAC-seq, which is based on transposase Tn5, have been widely used to identify genomic regions associated with open chromatin. These techniques have played a key role in dissecting the regulatory networks in gene expression in both animal and plant species.Regulation of transcription depends on interactions between Arabidopsis thaliana. Genomic regions enriched with MH-seq reads are referred as MNase hypersensitive sites (MHSs). MHSs overlap with the majority (~ 90%) of the open chromatin identified previously by DNase-seq and ATAC-seq. Surprisingly, 22% MHSs are not covered by DNase-seq or ATAC-seq reads, which are referred to “specific MHSs” (sMHSs). sMHSs tend to be located away from promoters, and a substantial portion of sMHSs are derived from transposable elements. Most interestingly, genomic regions containing sMHSs are enriched with epigenetic marks, including H3K27me3 and DNA methylation. In addition, sMHSs show a number of distinct characteristics including association with transcriptional repressors. Thus, sMHSs span distinct classes of open chromatin that may not be accessible to DNase I or Tn5. We hypothesize that the small size of the MNase enzyme relative to DNase I or Tn5 allows its access to relatively more condensed chromatin domains.We develop a technique, named MNase hypersensitivity sequencing (MH-seq), to identify genomic regions associated with open chromatin in MNase can be used to identify open chromatin regions that are not accessible to DNase I or Tn5. Thus, MH-seq provides an important tool to identify and catalog all classes of open chromatin in plants.Supplementary information accompanies this paper at 10.1186/s13059-020-1927-5. Decips (CREs) , 5. The s (CREs) –9. In ths (CREs) , 11 and s (CREs) –18.Micrococal nuclease (MNase) generates double-strand breaks at unprotected DNA and “nibbles” the exposed DNA until it encounters an obstruction, such as a nucleosome , 20. SinArabidopsis thaliana. Briefly, the A. thaliana chromatin was fixed and lightly digested by MNase. The resulting small DNA fragments (20 to 100 bp) were collected and sequenced using the Illumina platform. Genomic regions enriched with MH-seq reads are referred as MNase hypersensitive sites (MHSs). We found that MHSs cover the majority (87–92%) of the open chromatin identified previously by DNase-seq and ATAC-seq. Surprisingly, a significant proportion (22%) of MHSs were not covered by DNase-seq or ATAC-seq reads, which are thereafter referred to “specific MHSs” (sMHSs). We demonstrate that sMHSs are enriched for H3K27me3 and DNA methylation and represent distinct classes of open chromatin domains that may not be accessible to DNase I or Tn5. sMHSs showed a number of distinct characteristics compared to the MHSs covered by DNase-seq or ATAC-seq reads, including association with transcriptional repressors. Thus, MH-seq provides a new tool to identify and catalog all classes of open chromatin in higher eukaryotes.We developed a technique, named MNase hypersensitivity sequencing (MH-seq), to identify genomic regions associated with open chromatin in A. thaliana in the current study, was cross-linked using formaldehyde and then digested with a small amount of MNase  of sequence reads. After adapter trimming and quality filtering, 110.7 M reads were used for further analyses. As expected, the majority of the sequence reads (90%) were of length of 30–60 bp  using Bowtie 2 [Arabidopsis genome. Approximately 50% MHSs were located within 1 kb upstream of a transcription start site (TSS) and 1 kb downstream of transcription terminal site (TTS) . We found that 89.7% of the 44,552 peaks identified by the second dataset overlap with the original data although the second dataset has a higher percentage of reads mapped to chloroplast genome (48.4% versus 24.7%) and a higher background, which was measured by the fraction of reads in peaks (0.34 versus 0.61). Overall, MHSs and DNase I hypersensitive sites (DHSs) [Arabidopsis genome.We developed a MHS library from 2-week-old seedling tissue of Bowtie 2 . We deves (DHSs)  shared aA. thaliana [We first used the genome-wide nucleosome positioning dataset from thaliana  to examiTo further confirm the association of MHSs with regulatory proteins, we examined the association of MHSs with genomic regions bound to different TFs. The TF-binding sites were previously detected by chromatin immunoprecipitation (ChIP) using seedling tissue and antibodies specific to each TF. The immunoprecipitated DNAs were used for microarray hybridization (ChIP-chip) or sequencing (ChIP-seq), which resulted in genome-wide binding datasets of 22 TFs  ; 0.25 between MH-seq and ATAC-seq ; and 0.42 between DNase-seq and ATAC-seq .We compared datasets from MH-seq, DNase-seq (based on nuclease DNase I) from both single-cut  and doubp value < 2.2 × 10− 16, Mann–Whitney U test). Furthermore, many MHSs contained multiple peaks with an average length of 47 bp , suggesting that the sMHS-associated chromatin domains were more resistant to DNase I and Tn5 digestion compared to those associated with cMHS. Despite these differences, sMHSs and cMHSs showed a similar distribution pattern along the chromosomes  and cellular developmental process (FDRe = 2.1 × 10− 2) and underrepresented in cellular process (FDR = 7.4 × 10− 6) and metabolic process (FDR = 2.1 × 10− 4).Surprisingly, 15,354 MHSs 22%) were not covered by DNase-seq or ATAC-seq reads, which are thereafter referred to “specific MHSs”  and genic  regions showed identical patterns , cell differentiation (FDR = 1.1 × 10− 9), and sequence-specific DNA-binding TF activity (FDR = 1.2 × 10− 12) when compared to genes associated with cMHSs. Interestingly, genes associated with class 2 sMHSs showed the same tissue specificity as cMHSs and no enriched function groups were identified after multiple testing correction (FDR < 0.01).We were intrigued by the potential function of sMHSs associated with H3K27me3 and DNA methylation. We examined the expression level and pattern of neighboring genes associated with sMHSs in classes 1 and 2. A sMHS-cognate gene was defined as the closest gene located within 1 kb up- and downstream of the sMHS. Genes associated with these sMHSs generally showed lower expression levels compared to genes associated with cMHSs  can be detected by DNase-seq in flower buds. Indeed, 7.5% (382 of 5078) of the class 1 sMHSs were associated with DHSs Fig. a. These ddm1 mutant showed that only few genes  changed the expression level in ddm1 . For example, 65% of cMHSs contained motifs associated with the WRKY TFs, which are mostly involved in the regulation of pathogen responses and salicylic acid signaling [The distinct patterns of nucleosome organization, epigenetic modifications, and transcription of cognate genes associated with sMHSs and cMHSs inspired us to explore potential TFs that bind to these two different types of genomic regions. sMHS and cMHS sequences were searched for enrichment of known motifs that bind to a total of 529 TFs in bidopsis . Interesignaling , 43. In ignaling , and cMHignaling –48.Fig.p value = 1.06e−30), STZ , RHL41 , AZF1 , and AZF2 . RBE is a putative zinc finger transcriptional repressor [AGAMOUS, a key gene in flower development [MIR164c and represses its expression in flowers [To further understand the function of sMHSs, we used AME  to identepressor  and was elopment . RBE bin flowers . RHL41,  flowers . AZF1, A flowers . A recen flowers .p value = 0.047, t-test) : some TFs tend to bind toward the edges within NDRs, some at the center, and some at other positions . Giving MNase has been used in various types of chromatin studies. It was most commonly used to study nucleosome occupancy and positioning in eukaryotic genomes, which can be accomplished by sequencing mononucleosomal DNA fragments generated from MNase digestion . SequencSeveral previous studies showed that open chromatin regions identified by DNase I and Tn5 largely overlap in both animal and plant species , 63, 64.Alternatively, the sizes (molecular weight) of the three enzymes may be responsible for their differential accessibility to chromatin. MNase is a small protein with only 17 kDa . By contGenes associated with sMHSs showed lower expression levels in leaves than in flower buds Fig. b, suggesMNase, an enzyme with a small molecular weight, can be used to identify open chromatin regions that are not accessible to DNase I or Tn5. The open chromatin identified by MNase, but not DNase I or Tn5, shows distinct features associated with epigenetic marks H3K27me3 and DNA methylation. MH-seq provides an important tool to identify and catalog all classes of open chromatin in plants.A. thaliana ecotype Columbia (Col-0) were germinated in half-strength MS medium and grown under a 16-h light/8-h dark cycle at 23 °C for 2 weeks. Two-week-old seedling tissues were collected and immediately fixed with 1% of final concentration of formaldehyde under vacuum for 10 min at room temperature, then followed by 5 min neutralization by adding 0.125 M as the final concentration of glycine. Approximately 2.0 g of fixed seedling tissues was ground into a fine powder in liquid nitrogen and nuclei prepared following published protocols [2, and 1 mM CaCl2). The suspended nuclei were divided into 5 of 1.5-ml Eppendorf tubes  after aliquoting 100 μl of nuclei as an undigested control. The aliquoted nuclei were cleaved at 37 °C for 10 min using a series of MNase  in 0 unit (U), 0.04 U, 0.2 U, 0.4 U, 0.8 U, and 1.2 U, respectively. Each MNase cleaved nuclei aliquot was split into two separate parts, one cross-linked part was stored at 4 °C overnight, and the second part was incubated at 65 °C overnight for reverse cross-linking. The cross-linked or reverse cross-linked nuclei were extracted by adding 1 volume of phenol:chloroform mixture. The recovered reverse cross-linked DNA was separated by running 2% of agarose gel in 1× TAE buffer. To exclude nucleosome-related DNA fragments, we specifically recovered DNA fragments with less than 100 bp  with Bowtie2 [The MHS libraries were sequenced in single end mode to 100 nt on the Illumina Hiseq 2000 sequencing platform. Raw reads were trimmed using Cutadapt  and alig Bowtie2  using pahttps://github.com/zhangtaolab/Jazz/) to identify MHSs. A MHS is defined as a genomic region that is significantly enriched with MH-seq reads compared to the genomic background. The threshold of the read count is determined using a cut-off value of the Bayes Factor (BF) of two competing hypotheses. A zero-inflated Poisson (ZIP) model is applied to determine the local threshold and identify peaks with each MHSs. This method can reduce the impact of excessive windows with zero read counts and reduce the false positives.We developed Jazz software (BEDTools  was usedhttps://www.ncbi.nlm.nih.gov/). A complete list of the data used can be found in Additional file k from 2 to 16 were calculated. The optimal value of k (k = 5) was determined as the smallest value of k that the TWSS changed slower   and ATACddm1, RNA-seq data of wild type and ddm1 were obtained from previous study [q value < 0.05. Gene expression data of 79 tissues were obtained from TraVA [http://travadb.org). Shannon entropy of each gene was calculated using R package “entropy”. GO enrichment analysis were conducted using PANTHER [https://www.arabidopsis.org).Genes were assigned to MHSs if they are located within 1 kb up- and downstream of the MHS. If there are two genes on both sides of MHSs, the closest genes were selected. No directionality were required as enhancers can function in both directions. Gene expression data of leaves and flower buds were obtained from previous study of our group . Brieflyus study . Differeom TraVA   with default parameters. Differentially enriched motifs were detected using Motif Enrichment Analysis (AME) [The candidate TF-binding sites were detected using Find Individual Motif Occurences (FIMO)  against is (AME)  with defN. benthamiana mediated by agrobacterium. The bioluminescent signals were detected by NightSHADE LB 985 .The enhancer validation was conducted using a previously described method . The DNAAdditional file 1:Figure S1. DNA fragments recovered for MH-seq. Figure S2. Genomic locations of all MHSs and overlap between TF-binding sites and MHSs. Figure S3. Positions of nucleosomes, MNase and DNase I cuts on MHSs. Figure S4. Examples of cMHSs and sMHSs. Figure S5. Distribution of MHSs along chromosome 1 of A. thaliana. Figure S6. Histone modifications and DNA methylations associated with sMHSs and cMHSs. Figure S7. Clustering analysis of all MHSs based on epigenetic marks H3K27ac, H2A.Z, H3K27me3 and DNA methylation . Figure S8. Expression patterns of genes associated with sMHSs [Figure S9. CG, CHG and CHH DNA methylation profile at genes in class 2. Figure S10. Changes of DNA methylation and gene expression levels of class 2 in ddm1. Figure S11. Positioning of TF-binding motifs within cMHSs and correlation between number of TF families and percentage change. (PDF 10155 kb)th sMHSs . Figure Additional file 2:Table S1. TFs and their genome-wide binding sites in A. thaliana. (DOCX 21 kb)Additional file 3:Table S2. sMHSs selected for functional validation based on agrobacterium-mediated transient assay. (XLSX 14 kb)Additional file 4:Table S3. Top 30 most enriched TFs and their binding motifs within sMHSs. (XLSX 14 kb)Additional file 5:Table S4. List of data sets used in current study. (DOCX 34 kb)Additional file 6.Review history (DOCX 278 kb)


Maintenance of transcription programs is challenged during mitosis when chromatin becomes condensed and transcription is silenced. How do the daughter cells re-establish the original transcription program? Here, we report that the TATA-binding protein (TBP), a key component of the core transcriptional machinery, remains bound globally to active promoters in mouse embryonic stem cells during mitosis. Using live-cell single-molecule imaging, we observed that TBP mitotic binding is highly stable, with an average residence time of minutes, in stark contrast to typical TFs with residence times of seconds. To test the functional effect of mitotic TBP binding, we used a drug-inducible degron system and found that TBP promotes the association of RNA Polymerase II with mitotic chromosomes, and facilitates transcriptional reactivation following mitosis. These results suggest that the core transcriptional machinery promotes efficient transcription maintenance globally. The cell cycle presents a challenge to maintenance of transcription programs. First, the genome becomes highly condensed . SecondlTo effect transcriptional regulation, TFs at enhancers must cross-talk with the RNA Polymerase II (Pol II) machinery at promoters. Previous studies have shown that certain transcription start sites (TSSs) remain sensitive to permanganate oxidation during mitosis , suggestThrough live-cell imaging of endogenously tagged proteins in mouse embryonic stem cells (mESCs), we report that TBP stably binds to interphase chromatin for minutes. Moreover, TBP maintains a stable association with mitotic chromosomes, and occurs on a global scale at promoters of active genes. Using an endogenous knock-in of a drug inducible protein degradation system, we found that TBP recruits a small fraction of Pol II molecules to mitotic chromosomes, and that a subset of the recruited Pol II retain activity in mitosis. Lastly, nascent RNA analysis indicates that TBP promotes efficient reactivation of global transcription programs following mitosis.Previous studies have shown that mitotic chromosomes globally retain accessibility . FocusinGiven the persistence of accessibility at promoters of mitotic mESCs and previous reports of TBP binding in mitosis , we set To test whether over-expressed Halo-TBP could recapitulate the behavior of endogenous Halo-TBP we analyzed the enrichment on mitotic chromosomes of stably over-expressed  and the endogenous knock-in  in live versus fixed cells as previously described . Under lTo examine TBP interaction with mitotic chromosomes, we imaged individual Halo-TBP KI molecules at a frame rate of 133 Hz and high illumination intensity using stroboscopic photoactivatable single particle tracking (spaSPT) , 2018. WWe next examined how stable is TBP binding to chromatin by performing single particle tracking (SPT) with long exposure times (500 ms) as previously described . This imAs an orthogonal approach to SPT, we performed FRAP (Fluorescence Recovery after Photobleaching) analysis to independently measure TBP binding dynamics during interphase and mitosis. We plotted the FRAP recovery at the bleach spot over time for Halo-TBP in interphase and in mitotic cells, along with Halo-3xNLS and H2B-Halo . When no2 normalized reads in binding sites for asynchronous and mitotic samples and found that while there is a slight decrease in the average number of reads in binding sites for mitotic samples, the peak counts are largely very similar   at the T2 of untreated vs TBP-degraded samples, and averaged the levels at the TSS of all genes (cis-elements (DNA sequence and shape) and/or chromatin-modifying factors.One potential function of TBP binding in mitosis is to maintain promoter accessibility and chromatin architecture. To test this hypothesis, we performed ATAC-seq analysis on asynchronous and synchronized mESCs with and without TBP degradation in three biological replicates . Analyzill genes , bottom.ll genes . For monll genes . The effll genes , bottom.To test the role of TBP in recruiting Pol II to mitotic chromosomes, we endogenously knocked-in HaloTag at the Rpb1 locus, the catalytic subunit of RNA Polymerase II, in the mAID-TBP (C94) cells. We successfully produced heterozygous knock-in of Halo-Pol2 II, as shown by Western blot analysis , and theTo examine the dynamics of Pol II in interphase and mitotic cells, we performed spaSPT with subsequent model fitting using Spot-on and extracted the fraction of bound Pol II molecules . As withIn mitotic cells, the fraction of bound Pol II molecules decreases to an average of 15.4% (S.D. 3.2%) , about hTo determine the dynamics of Pol II binding to mitotic chromosomes, we performed SPT with long exposure times (500 ms) and measured the dwell times for Halo-Pol II in interphase and mitosis with and without TBP . Pol II To cross-validate the SPT residence time analysis, we performed FRAP analysis on Halo-Pol II during interphase and mitosis, with and without TBP degradation. We plotted the FRAP recovery over time  and measTo test the hypothesis that TBP recruitment of Pol II to mitotic chromosomes helps prime active genes for efficient reactivation following mitosis, we performed two orthogonal experiments: one based on imaging and another on sequencing newly transcribed RNA products as a time course after mitosis. Using spaSPT and Spot-On to measure fraction of bound Pol II, we marked Halo-Pol II C64 cells at the metaphase stage of mitosis and imaged the same cells every 15 min until 60 min after first staging. Repeated imaging of interphase cells with and without TBP degradation showed no change in bound Pol II over time . In contGapdh) and an ES cell-specific gene (Nanog) is shown on Second, we extracted the nascent chromatin-associated RNA (chr-RNA) of asynchronous (A), mitotic cells (M), and 30 and 60 min after release from mitotic arrest  in two replicates  and 2. AGapdh and Nanog is also shown on To examine the role of TBP in promoting efficient reactivation, we degraded TBP in asynchronous (A) and in mitotic cells (M)  and sequ2 ratio of reads in the 30- and 60 min conditions relative to mitotically arrested samoles , for both untreated and TBP-degraded samples. In this way, we can observe the change in RNA levels relative to mitotic cells as a function of time after release from mitosis. Globally, the untreated M30/M sample shows no overall change in RNA levels whereas M60/M samples show massive increase in both upstream and downstream transcription at the TSS  obtained from the KOMP repository (https://www.komp.org/pdf.php?cloneID=8669) and tested negative for mycoplasma. ES cells were cultured on gelatin-coated plates in ESC media Knockout D-MEM  with 15% FBS, 0.1 mM MEMnon-essential amino acids, 2 mM GlutaMAX, 0.1 mM 2-mercaptoethanol (Sigma) and 1000 units/ml of ESGRO (Chem- icon). ES cells are fed daily and passaged every two days by trypsinization. Mitotic cells were synchronized by adding 100 ng/mL of Nocodazole for 6 hr followed by shake-off. To assess the purity of synchronization, cells collected from shake off were placed on gelatin-coated glass bottom microwell dishes (MatTek #P35G-1.5–14 C) and imaged for H2B-GFP using epifluorescence microscopy. Mitotic cells were counted by visual inspection. For endogenously-tagged mAID-TBP cells, TBP degradation was performed by addition of indole-3-acetic acid (IAA) at 500 μM final concentration for 4–6 hr. Flavopiridol and triptolide treatments were performed at 1 μM final concentration for 30–60 min.For all experiments, we used the mouse ES cell line JM8.N4 (RRID: AB_945758), N20 Pol II antibody , 8WG16 Pol II antibody (RRID:AB_785522), or Flag antibody .Cas9-mediated endogenous knock-ins were performed as previously described . BrieflyLive-cell imaging experiments including FRAP were performed on cells grown on gelatin-coated glass bottom microwell dishes (MatTek #P35G-1.5–14 C) and labeled with the Halo-ligand dye JF549 at 100 nM concentration for 30 min. To remove unbound ligand, cells were washed 3x with fresh media for 5 min each. Epi-fluorescence time lapse imaging was performed on Nikon Biostation IM-Q equipped with a 40x/0.8 NA objective, temperature, humidity and CO2 control, and an external mercury illuminator. Images were collected every 2 min for 12 hr. Confocal live-cell imaging was performed using a Zeiss LSM 710 confocal microscope equipped with temperature and CO2 control. For fixed imaging, labeled cells were fixed with 4% PFA for 10 min in room temperature and were washed with 1x PBS prior to imaging. Standard immunofluorescence was performed on labeled cells using 4% PFA. Quantification of chromosome enrichment was performed using Fiji.FRAP was performed on Zeiss LSM 710 confocal microscope with a 40x/1.3 NA oil-immersion objective and a 561 nm laser as previously described . Bleachioffk (ln2/offk) extracted from the two-exponential function fitting. Photobleaching correction is performed by subtracting the apparent offk of H2B-Halo from the apparent offk of Pol II samples as previously described  and were repeated 3x for biological replicates, with each experiment consisting of 10 cells for technical replicates each. Data are represented as mean over experimental replicates  ± SEM. Imaging experiments were conducted as described previously  on a cusescribed .−6.25; deflation loops: 0; Blinking (frames); 1; maximum number of competitors: 3; maximal expected diffusion constant (µm2/s): 20. The fraction of bound molecules was determined as previously described using the following fit paramerters: TimeGap, 7.477; Gaps Allowed, 1; Jumps to consider, 4; Model fit type, CDF; Localization error, 0.045 (https://gitlab.com/tjian-darzacq-lab/spot-on-matlab). The general statistics for image analysis  for all spaSPT data are listed in Cells were labeled with photo-activatable PA-JF549 or PA-JF646 (as indicated) at 25 nM for 30 min and washed 3x with fresh media for 5 min each to remove unbound ligand. Cells were imaged in ESC media without phenol-red. A total of 20,000 frames were collected for at 7.5 ms frame rate (133 Hz) and were repeated 3-4x for biological replicates, with each replicate consisting of eight cells of technical replicates. Data are represented as mean over experimental replicates  ± standard error of means. Single molecules were localized and tracked using SLIMfast, a custom-written MATLAB implementation of the MTT algorithm , using tr, 0.045  using Sp2). MNase (5 μL of 0.2 U/μL stock) was added to the lysate and samples were incubated at 37C for 15 min. Digested was quenched by adding EDTA  and EGTA . Samples were sonicated using Branson digital sonifier at 40 s on time at 30% power with 2.5 s on and 5 s off cycles. Lysates were cleared by centrifugation and used as input for ChIP by adding α-TBP antibody (Abcam #ab51841 RRID:AB_945758) and incubating overnight. Protein-G magnetic beads were used to immuno-capture bound fragments, which were then washed as described previously  as described. Nuclei were subjected to consecutive biochemical fractionation with incubations for 15 min each with centrifugation and collection in between each fractionation. Nuclei were incubated 3 times for 15 min with Buffer B , then 2 times for 15 min each with Buffer B+ . Following fractionation, RNA from the insoluble chromatin was extracted with Trizol and prepared for sequencing as previously described  Unlike many other TFs, TBP remains strongly bound to chromatin during mitosis.2) TBP binds to promoters of active genes during mitosis.3) TBP is partly required for Pol II recruitment to chromatin both in interphase and mitosis.4) In the absence of TBP, Pol II reactivation after mitosis is impaired at a large subset of genes.The reviewers deemed the results interesting and appreciated the novel approaches employed. However, the reviewers also identified and discussed a number of major concerns, and decided to invite resubmission of a revised paper addressing these.Reviewers noted that the novelty of the results presented needs to be better placed into the context of the published literature. Segil et al., (1996) showed that a sub-population of TBP is visible on chromosomes of mitotic cells and that TBP and TAF12 were clearly present in their highly purified mitotic chromosome preparation. Christova et al. (2002) showed that TFIID (TBP and TAF5) remains associated with active class II gene promoters during mitosis (the authors did not cite this study). Fairley et al. (2003) showed that TBP and Brf1 remain associated with purified metaphase chromosomes (the authors did not cite this study). Xing et al. (2008) reported that TBP-PP2A mitotic complex bookmarks genes by preventing condensin action. This same team  showed also that TBP fractionated with mitotic chromosomes . All these five studies demonstrate unequivocally that TBP is bound to mitotic chromosomes independently of the methodology used without any conflicting results.In the light of all these studies, the reviewers agreed that the current manuscript should provide more insight in order to advance the field in a significant way.Major concerns:1) Need for further insights about bookmarking, transcriptional memory, and transcriptional reactivation after mitosis. The entire manuscript, including the title, is written under the overall interpretation/assumption that TBP is required for 'bookmarking' of genes that were active during interphase, so that these same genes can be transcribed again after mitosis. Thus, TBP is deemed to confer a 'transcriptional memory' to preserve cellular identity after cell division. Reviewers discussed this interpretation and concluded that more data is necessary to sustain these conclusions.The results show that TBP is required for 'reactivation' of many genes after mitosis , but there is no evidence that TBP is part of the initial 'bookmarking' event that confers 'transcriptional identity/memory'. In the absence of TBP, there does not seem to be any loss of 'transcriptional memory' i.e. when mouse ES cells are depleted of TBP, they still seem to express the same set of genes, and Pol II is not suddenly redirected to new sets of genes, or impeded from transcribing the genes that were expressed in the previous interphase. In other words, TBP seems to act downstream of a primary bookmarking event that defines the sets of active promoters in this cell type, and, as it would be expected for a core subunit of a general transcription factor, TBP is required for reactivation of Pol II at previously active genes.These issues can be addressed experimentally with technologies already used in the manuscript. What would be the impact of TBP depletion on ATAC-seq results shown in Figure 1A, B? Would TBP depletion erase the 'enhancer memory' or 'nucleosomal memory' of murine ES cells? Would entire sets of active promoters be occluded upon TBP depletion while others would be 'licensed' during the course of a single TBP-less mitosis? Reviewers discussed that the bookmarking mechanism would be the mechanism that enabled global promoter nucleosome architecture to be basically the same in asynchronous versus mitotic cells sown in Figure 1B, and there is currently not proof that TBP is involved in this mechanism.A second issue within this same framework revolves around the interpretation of the impact of TBP depletion on recruitment of Pol II during interphase and mitosis. The results show that TBP remains bound to chromatin during mitosis. In contrast, the levels of chromatin-bound Pol II are much decreased during mitosis. In both stages of the cell cycle, TBP depletion mildly reduces levels of chromatin-bound Pol II. What is not addressed in the manuscript is the mechanism by which Pol II is removed from chromatin during mitosis and how it relates to TBP action. Where in the genome is the fraction of Pol II that is bound to chromatin during mitosis? Which pool of Pol II is sensitive to TBP versus sensitive to mitotic chromosome condensation? Presumably, Pol II would be depleted from gene bodies during mitosis, indicative of a shutdown in elongation and transcription, but a fraction of it would remain bound to promoters. After all, promoter nucleosome architecture is preserved in mitosis . If so, it would then be the pool of promoter-bound Pol II that is sensitive to TBP depletion? This could be answered with ChIP-seq for Pol II in asynchronous cells versus mitotic cells -/+ TBP. Another way of investigating this would be to look at the impact of TBP depletion on tagged Pol II after treatment with flavopiridol (an inhibitor of elongation) or tryptolide (an inhibitor of initiation). One possibility is that TBP depletion would preferentially affect the pool of Pol II that remains bound to chromatin  after flavopiridol treatment.Also on the same topic, the data does not support the corresponding heading . This same team  showed that TBP fractionated with mitotic chromosomes, whereas Pol II did not . In contrast, in the present study the authors show that in the heterozygous Halo-Pol II knock-in cell-line, the bound population of Halo-Pol II is 12.1% during mitosis . The authors should clarify this contradiction. Moreover, the effect of TBP degradation on Pol II recruitment and binding is quite mild, it changes from 12.1% in untreated mitotic cells to about 10.8% after TBP degradation. Is this change really significant? This very minor change does not support the claim that "TBP recruits RNA Polymerase II to mitotic chromosomes". The same comment stands for “Remarkably, such stable TBP binding leads to recruitment of Pol II to mitotic chromosomes, despite the general inhibition of transcriptional activity”. The authors should tone down their overstated conclusions.Finally, when analyzing the effects of mitotic TBP binding/depletion on the transcriptional reactivation rate , could the authors see any difference between Group 1, 2, 3 genes ?2) Need for further insight about which TBP-containing complex impacts on Pol II reactivation after mitosis. As TBP is involved in Pol I, Pol II, and Pol III transcription, the authors should show, which TBP-containing complex, SL1, TFIID and/or TFIIB, is playing the reactivation 'bookmarking role'. This would provide more novelty over published work.3) Asynchronous versus mitotic cells. Reviewers did not find sufficient details and controls about how the asynchronous versus mitotic cell populations were generated. Authors should demonstrate the degree of mitotic synchronization efficiency and the number of mitotic cells in the 'asynchronous' population. Otherwise the interpretation of all the ChIP-seq results becomes questionable. Alternatively, the authors should consider FACS sorting after synchronisation as described by Kadauke et al. (2012). Reviewers noted that the novelty of the results presented needs to be better placed into the context of the published literature. Segil et al., (1996) showed that a sub-population of TBP is visible on chromosomes of mitotic cells and that TBP and TAF12 were clearly present in their highly purified mitotic chromosome preparation. Christova et al. (2002) showed that TFIID (TBP and TAF5) remains associated with active class II gene promoters during mitosis (the authors did not cite this study). Fairley et al. (2003) showed that TBP and Brf1 remain associated with purified metaphase chromosomes (the authors did not cite this study). Xing et al. (2008) reported that TBP-PP2A mitotic complex bookmarks genes by preventing condensin action. This same team  showed also that TBP fractionated with mitotic chromosomes . All these five studies demonstrate unequivocally that TBP is bound to mitotic chromosomes independently of the methodology used without any conflicting results.In the light of all these studies, the reviewers agreed that the current manuscript should provide more insight in order to advance the field in a significant way.We agree with the reviewers and we have revised the Introduction section (second paragraph) to include a discussion of these important studies characterizing TBP binding to mitotic chromosomes. As the reviewers point out in their summary above, however, our manuscript goes far beyond what previous studies have done. We believe that our ability to examine the dynamics of the endogenous TBP, and our functional analyses on TBP’s role in Pol II dynamics and activity during and after mitosis provide significant advances in the field.Major concerns:1) Need for further insights about bookmarking, transcriptional memory, and transcriptional reactivation after mitosis. The entire manuscript, including the title, is written under the overall interpretation/assumption that TBP is required for 'bookmarking' of genes that were active during interphase, so that these same genes can be transcribed again after mitosis. Thus, TBP is deemed to confer a 'transcriptional memory' to preserve cellular identity after cell division. Reviewers discussed this interpretation and concluded that more data is necessary to sustain these conclusions.The results show that TBP is required for 'reactivation' of many genes after mitosis , but there is no evidence that TBP is part of the initial 'bookmarking' event that confers 'transcriptional identity/memory'. In the absence of TBP, there does not seem to be any loss of 'transcriptional memory' i.e. when mouse ES cells are depleted of TBP, they still seem to express the same set of genes, and Pol II is not suddenly redirected to new sets of genes, or impeded from transcribing the genes that were expressed in the previous interphase. In other words, TBP seems to act downstream of a primary bookmarking event that defines the sets of active promoters in this cell type, and, as it would be expected for a core subunit of a general transcription factor, TBP is required for reactivation of Pol II at previously active genes.The reviewers raise an important issue on the whether TBP can be truly considered a bookmarking factor. Our study on TBP does not make the assumption that the observed binding of TBP during mitosis constitutes the initial bookmarking event, as defined in the traditional sense. Rather, we focus on the mechanisms of TBP stably binding to promoters during mitosis and that this binding has the functional consequence of recruiting Pol II for efficient reactivation of transcription. With discoveries showing that most TFs associate with mitotic chromosomes, the term bookmarking is evolving from an all-or-none to a continuum definition. Where TBP and the Pol II machinery fit in this continuum may remain to be decided.To respond to this main concern further, we have broken it down by concept and addressed each one individually. Overall, we find these suggestions helpful in further understanding the mechanisms of TBP and Pol II action during and after mitosis, and we think the results support the main conclusions presented in the manuscript on the role of TBP in reactivating global transcription. However, we have toned down our interpretation that “TBP is required for bookmarking … to preserve cell identity” throughout the manuscript because as the reviewers point out, we do not have direct evidence on maintenance of cell identity per se to fully support this interpretation.These issues can be addressed experimentally with technologies already used in the manuscript. What would be the impact of TBP depletion on ATAC-seq results shown in Figure 1A, B? Would TBP depletion erase the 'enhancer memory' or 'nucleosomal memory' of murine ES cells? Would entire sets of active promoters be occluded upon TBP depletion while others would be 'licensed' during the course of a single TBP-less mitosis? Reviewers discussed that the bookmarking mechanism would be the mechanism that enabled global promoter nucleosome architecture to be basically the same in asynchronous versus mitotic cells sown in Figure 1B, and there is currently not proof that TBP is involved in this mechanism.We agree with the reviewers that this ATAC-seq experiment in cells degraded of TBP is an interesting experiment. We therefore performed this experiment and included the analyses in the revised manuscript . In summary, TBP depletion shows some minor effects on promoter chromatin architecture, suggesting that the major determinants of promoter chromatin architecture are largely independent of TBP. We also found that most sequence specific TF binding sites remain unaffected upon TBP depletion, as would be expected .A second issue within this same framework revolves around the interpretation of the impact of TBP depletion on recruitment of Pol II during interphase and mitosis. The results show that TBP remains bound to chromatin during mitosis. In contrast, the levels of chromatin-bound Pol II are much decreased during mitosis. In both stages of the cell cycle, TBP depletion mildly reduces levels of chromatin-bound Pol II. What is not addressed in the manuscript is the mechanism by which Pol II is removed from chromatin during mitosis and how it relates to TBP action. Where in the genome is the fraction of Pol II that is bound to chromatin during mitosis? Which pool of Pol II is sensitive to TBP versus sensitive to mitotic chromosome condensation? Presumably, Pol II would be depleted from gene bodies during mitosis, indicative of a shutdown in elongation and transcription, but a fraction of it would remain bound to promoters. After all, promoter nucleosome architecture is preserved in mitosis . If so, it would then be the pool of promoter-bound Pol II that is sensitive to TBP depletion? This could be answered with ChIP-seq for Pol II in asynchronous cells versus mitotic cells -/+ TBP. Another way of investigating this would be to look at the impact of TBP depletion on tagged Pol II after treatment with flavopiridol (an inhibitor of elongation) or tryptolide (an inhibitor of initiation). One possibility is that TBP depletion would preferentially affect the pool of Pol II that remains bound to chromatin  after flavopiridol treatment.specific Pol II interactions during mitosis is TBP-dependent. These results are discussed in the manuscript.We agree with the reviewers that the question of which Pol II species is associating with mitotic chromosomes and how much of it is TBP-dependent are important issues to address. We had previously performed ChIP-seq for Pol II see  but we cAlso on the same topic, the data does not support the corresponding heading . This same team  showed that TBP fractionated with mitotic chromosomes, whereas Pol II did not . In contrast, in the present study the authors show that in the heterozygous Halo-Pol II knock-in cell-line, the bound population of Halo-Pol II is 12.1% during mitosis . The authors should clarify this contradiction. Moreover, the effect of TBP degradation on Pol II recruitment and binding is quite mild, it changes from 12.1% in untreated mitotic cells to about 10.8% after TBP degradation. Is this change really significant? This very minor change does not support the claim that "TBP recruits RNA Polymerase II to mitotic chromosomes". The same comment stands for “Remarkably, such stable TBP binding leads to recruitment of Pol II to mitotic chromosomes, despite the general inhibition of transcriptional activity”. The authors should tone down their overstated conclusions.Part 1 . For several reasons, we disagree that our previous result  is necessarily a contradiction to our claims in the current manuscript. First, from the current study, we found that the small fraction of Pol II molecules that bind during mitosis has an average residence time of 5-8 seconds, making Pol II much more dynamic in mitosis compared to interphase . This increase in dynamics likely affected Pol II during the mitotic chromosome purification in our 2016 study. The second reason relates to Part 2 of this comment. We agree that the majority of the ‘bound’ Pol II that we observe is largely non-specific interactions between Pol II and DNA/chromatin in general, and that the small fraction that changes when TBP is degraded is likely the fraction that specifically interacts with promoter DNA. Since promoters make up a very small fraction of the genome, it is not surprising that the number of Pol II molecules that specifically interacts with promoters in mitosis would be small. It is likely that what we would observe in the biochemical fractionation of mitotic chromosomes from our previous study is the stripping away of non-specific chromatin associations by our stringent washing and that our Western blots were not sensitive enough to detect the small fraction of specific binding events that the single molecule imaging was able to reveal.Part 2 . After improving the imaging method (see response to Major concern point #3), our data now show that the average fraction of bound Pol II in mitosis is 15.39% (SEM 0.56) while after TBP degradation, the fraction bound decreases to 11.84% (SEM 0.51). After statistical analyses, this difference has a p-value < 0.05 (t-test). These results suggest that TBP does recruit a subset of Pol II molecules to mitotic chromosomes. However, we agree with the reviewers that we should be more careful in not overstating our conclusions, and we have made changes to the manuscript to reflect this. We have changed the heading of the section to “TBP recruits a subset of RNA Polymerase II molecules to mitotic chromosomes” and point out in the text that non-specific interactions dominate the fraction of bound Pol II in mitosis.Finally, when analyzing the effects of mitotic TBP binding/depletion on the transcriptional reactivation rate , could the authors see any difference between Group 1, 2, 3 genes ?2 ratio of reads in the 30- and 60-minute conditions relative to mitotically arrested , for both untreated and TBP-degraded samples using the 3 groups as defined in TBP ChIP-seq data. In general, we see no differences in the amount of RNA in groups 1 and 3, whereas group 2 shows much lower levels. Based on our GO term analysis of these groups, this seemed like the expected result. We have included this analysis as a new supplemental figure , and added the appropriate reference in the Results section of the main manuscript.For the new analyses included in the revised manuscript, we performed differential peak analysis on the TBP ChIP-seq data using DiffBind  and found that the majority of the peaks in asynchronous and mitotic cells are not significantly different from each other , suggesting that the differences that we observed by k-means clustering , are minor level changes. Indeed, in our GO term analysis of the genes in these groups, we see similar classes of genes and we concluded that “the differences in TBP binding that we observe by ChIP-seq largely reflect the cyclical expression of genes during the cell cycle.” With this in mind, we performed the analysis requested by the reviewer, comparing the RNA data on Groups 1, 2, and 3 from the TBP ChIP-seq. We plotted the heatmap of the log2) Need for further insight about which TBP-containing complex impacts on Pol II reactivation after mitosis. As TBP is involved in Pol I, Pol II, and Pol III transcription, the authors should show, which TBP-containing complex, SL1, TFIID and/or TFIIB, is playing the reactivation 'bookmarking role'. This would provide more novelty over published work.This is an interesting question posed by the reviewers. We reasoned that our chrRNA-seq data may shed some light into this question. Upon further analysis of our chrRNA-seq data, we observe that tRNA genes show a large decrease in reactivation upon TBP depletion in mitosis, suggesting that RNA Polymerase III activity is also affected by TBP degradation in mitosis. We performed a similar analysis on ribosomal RNA genes (RNA Polymerase I-transcribed genes), but we see little change upon TBP depletion during mitosis. However, this result may reflect the stability of ribosomal RNA in mitotic cells. Because our assay relies on enrichment of nascent RNA through chromatin association, it is possible that effects of TBP degradation on nascent rRNAs may be masked in our assay. These results are now summarized in Figure 6—figure supplement 3 and in the Results section of the manuscript. However, we chose not to elaborate on these results as they are somewhat outside the scope of the current manuscript, but we will likely pursue these effects in future studies.3) Asynchronous versus mitotic cells. Reviewers did not find sufficient details and controls about how the asynchronous versus mitotic cell populations were generated. Authors should demonstrate the degree of mitotic synchronization efficiency and the number of mitotic cells in the 'asynchronous' population. Otherwise the interpretation of all the ChIP-seq results becomes questionable. Alternatively, the authors should consider FACS sorting after synchronisation as described by Kadauke et al. (2012).We apologize for this oversight. Our mitotic preparation methods are exactly the same as in the previously published 2016 study. To further quantify the purity of our mitotic preps using orthogonal methods, we imaged H2B-GFP after Nocodazole synchronization and shake off, and individually scored each cell as either mitotic or interphase (n=1075 cells). This data is now summarized in the new Figure 3—figure supplement 1, which shows that our mitotic prep typically achieves ~96% purity.