Plants monitor environmental factors, such as temperature and day length, and also endogenous factors, such as their age and phytohormones, to decide when to flower. These cues are utilized to control expression levels of genes required for flowering. Thus, flowering time control is a unique model for understanding how gene activity is precisely regulated at the transcriptional level. In Arabidopsis, a remarkable number of non-coding RNA molecules have been identified by advanced sequencing technology. Recent progress in the flowering field has revealed several non-coding RNAs that play a major role in determining flowering time. Here, we introduce how two types of non-coding RNA species, microRNA (miRNA) and long noncoding RNA (lncRNA), contribute to flowering via regulation of target gene activity involved in this vital developmental transition. Plants sense multiple environmental and endogenous signals to determine when to flower. For sessile plants, the ability to monitor and integrate multiple signals is essential to succeed in reproduction. Hence, a complex gene regulatory network has been constructed to enable plants to flower appropriately in time and space. Genetic and molecular analyses of flowering-time mutants in Arabidopsis has established the current model, in which five major genetically defined pathways regulate the transition from the vegetative to reproductive phase Fig. . The phoRecently, the fifth pathway named the aging pathway, has been described by several groups , FLOWERING LOCUS T (FT), SUPRESSOR OF OVEREXPRESSION OF CO1 (SOC1) and LEAFY (LFY) are important regulatory hubs in the control of flowering time. FLC expression is regulated by the vernalization pathway and the autonomous pathway, respectively. FLC encodes a MADS-box transcription factor and acts as a dominant repressor of flowering through its control of FT and SOC1. FT and SOC1 are both targeted by the photoperiod pathway at the transcriptional level as well. The activation of FT is an especially crucial step in the decision to flower, since the protein encoded by FT is a key component of the systemic flowering signal “florigen”. After integration of environmental and endogenous inputs at the leaves via transcription of FT, the FT protein travels to the apical meristem where it initiates floral development  and long noncoding RNAs (lncRNAs). The function of these non-coding RNAs in the control of flowering will be described. In addition, we will discuss the future challenges in this field.Caenorhabditis elegans. The lin-4 and let-7 miRNAs were identified as silencers of genes important for the transition from one larval stage to another  RNAs that repress specific target mRNA activity at the transcriptional and/or translational level. In the early 1990s, miRNAs were discovered from the developmental timing studies in The mechanism for miRNA biogenesis in plants has been well studied in Arabidopsis Fig. . The firThe processing of pri-miRNAs occurs in two steps. The pri-miRNA is first cleaved into a precursor miRNA (pre-miRNA). The pre-miRNA is then cleaved into the mature miRNA:miRNA* duplex. DICER-LIKE 1 (DCL1), a homolog of the RNase III-like enzyme Dicer protein in animals, catalyzes the cleavage of both the pri-miRNA and the pre-miRNA. The first processing step to generate pre-miRNA is assisted by a dsRNA binding protein HYPONASTIC LEAVES 1 (HYL1) , or through a HST independent mechanism . Among numerous miRNAs, only three families so far are shown to be involved in flowering time control: miR172, miR156, and miR159. In the aging pathway, miR172 and miR156 play a major role and act sequentially in the process to acquire reproductive competency , TOE2, TOE3, SCHLAFMÜTZE (SMZ) and SCHNARCHZAPFEN (SNZ).The Arabidopsis genome contains five loci that generate miR172 species, ion Chen . Howeverion Chen . In Arabearly activation tagged, dominant (eat-D) as an extremely early-flowering mutant from an activation-tagging screen. In contrast to the early-flowering phenotype of 35S:miR172, overexpression of TOE1, one of the targets of miR172, causes a late-flowering phenotype. This observation suggests that TOE1 acts as a repressor of flowering. Consistent with this, toe1 loss-of-function mutants show a slightly early-flowering phenotype, and this phenotype is enhanced by mutations in the TOE2 gene, which is closely related to TOE1. However, toe1 toe2 double mutants still flower later than miR172 overexpressors, indicating the existence of other factors that function redundantly with TOE1 and TOE2 to repress flowering. Recently, it was shown that SMZ and SNZ overexpressors flower later than wild type, and toe1 toe2 smz snz quadruple mutants flower much earlier than toe1 toe2 double mutants, although smz snz double mutants do not affect flowering time significantly    mutants that act in the photoperiod pathway  to SPL16, including two genes, SPL13A and SPL13B that encode the same protein. Among this family, 11 members have a target site for miR156. The early-flowering phenotype of the SPL3 overexpressor was the first evidence for the involvement of SPL genes in flowering time control  and AtMYC2 were found more frequently in the promoters of miRNAs compared with the promoters of protein coding loci  tail. They are often transcribed from either strand of a protein-coding locus, but also arise from intergenic regions. LncRNAs are the least well-understood among various RNA species and have been called the “dark matter” of the genome. However, recent studies with advanced sequencing technology illuminate the importance of lncRNAs in transcriptional control , which inhibits assembly of the preinitiation complex at the promoter . For example, the HOTAIR lncRNA, which is expressed from an intergenic region of the HoxC cluster, is involved in the silencing of the HoxD cluster located on a different chromosome  complex containing H3K4 methyltransferase is responsible for adding this “active” mark on FLC chromatin, and this mark is recognized by the RNA polymerase II-associated factor 1 (PAF1) complex for active transcription. During cold, a dramatic change in chromatin status from “active” to “repressive” occurs at the FLC locus, accompanied by an increase in H3K9 and H3K27 methylation. Three key players act in this process: VERNALIZATION INSENSITIVE 3 (VIN3), VERNALIZATION 1 (VRN1) and VRN2 12) protein, which is a component of the PRC2 complex. VRN1 encodes a plant specific DNA binding protein and acts in an alternative PRC1 like complex in plants.Chromatin modifications at the FLC transcription. Recently, the rapid accumulation of FLC antisense transcripts were found as one of the earliest events in the vernalization process  accumulates earlier than VIN3 induction  was identified as a regulator for epigenetic silencing of FLC  tail, which is similar to transcripts produced by RNA polymerase V (PolV). However, mutations in PolIV or PolV subunits does not affect COLDAIR accumulation induced by cold. On the other hand, PolII occupancy is transiently increased by cold, suggesting that PolII is responsible for the transcription of COLDAIR. Intriguingly, CURLY LEAF (CLF), the H3K27 trimethyltransferase in the PRC2 complex of plants can bind to COLDAIR lncRNA. The authors also showed that reduced COLDAIR transcripts by RNAi compromises the vernalization response. These observations point to a similarity in the action of COLDAIR lncRNA and HOTAIR lncRNA as scaffolds for histone modifiers. It will be interesting to further explore the role of COLDAIR in recruitment and maintenance of the PRC2 association with the FLC locus.More recently, the second lncRNA named FLC Fig. . COLDAIRFLC are both clearly induced by exposure to cold, marking the very beginning of the vernalization process. This induction is even earlier than VIN3, which is thought to be a key determinant of long exposure to cold (Fig. Xist RNA, an antisense lncRNA named Tsix has been shown to repress Xist RNA transcription in cis, suggesting the presence of another layer of regulation (Tsai et al. COOLAIR and COLDAIR, is a promising research endeavor to acquire further information on the regulatory mechanism and molecular signals that are initiated during cold exposure. In addition, it will provide us clues to understand the sensing of cold temperatures, which is currently the least described step in the vernalization process.Two lncRNAs identified from the studies of epigenetic regulation of old Fig. b. AlthouThe advancement of sequencing technology has expanded the list of non-coding RNA species, while studies of flowering time have significantly contributed to our understanding of the function and action of miRNAs and lncRNAs as described in this review. With regard to the function of lncRNAs in plants, there are few reports except for the studies of the vernalization process. The advantage of using non-coding RNA to regulate flowering time might be related to the quantitative nature of flowering time. Using RNA as a medium may facilitate quantitative control of the key transcripts in the determination of flowering time. It might be useful to perform a regulatory process quickly without protein translation. Further studies of flowering time control promise to bring us further general insights into the regulatory mechanisms of gene expression.


Cells respond to external cues by precisely coordinating multiple molecular events. Co-regulation may be established by the so-called single-input module (SIM), where a common regulator controls multiple targets. Using mathematical modeling, we compared the ability of SIM architectures to precisely coordinate protein levels despite environmental fluctuations and uncertainties in parameter values. We find that post-transcriptional co-regulation as exemplified by bacterial small RNAs (sRNAs) is particularly robust: sRNA-mediated regulation establishes highly synchronous gene expression thresholds for all mRNA targets without a need for fine-tuning of kinetic parameters. Our analyses reveal that the non-catalytic nature of sRNA action is essential for robust gene expression synchronization, and that sRNA sequestration effects underlie coupling of multiple mRNA pools. This principle also operates in the temporal regime, implying that sRNAs could robustly coordinate the kinetics of mRNA induction as well. Moreover, we observe that multi-target regulation by a small RNA can strongly enhance ultrasensitivity in mRNA expression when compared to the single-target case. Our findings may explain why bacterial small RNAs frequently coordinate all-or-none responses to cellular stress. Since their discovery more than thirty years ago, it has become clear that small RNAs (sRNAs) play a crucial role in regulating gene expression. sRNAs downregulate gene expression post-transcriptionally by pairing with target mRNAs through base complementarity. Complex formation with small RNAs competitively inhibits mRNA translation and/or induces mRNA degradation max, KD, n and kdeg correspond to the maximal transcription rate, the dissociation constant of promoter binding, the Hill coefficient and the mRNA degradation rate, respectively. The steady state solution [mi] = Vmax,i/kdeg,i/ implies that the repressor concentration for half-maximal inhibition equals KD,i. Thus, the threshold repressor concentrations eliciting half-maximal mRNA downregulation depend in a linear manner on the repressor affinity for the promoter. Moreover, the distance between the mRNA dose-response curves scales with α′ = KD,2/KD,1 , where a single regulator controls multiple targets , top areKD,1 cf. . TherefoIn Eq. 1, repressor pools are not depleted by binding to promoter sites; this assumption is likely to be fulfilled in transcriptional networks, where abundant transcriptional regulators cf.,  target o1 and m2) is competitively inhibited by the sRNA (s). Additionally, the effective turnover rate of the mRNA pools is controlled by the sRNA, since the degradation rates of the inhibitory complexes, c1 and c2, may differ from those of the free mRNA species. By employing the law of mass-action, the dynamics of post-transcriptional regulation can be described by the following set of differential equations (2)syn), five degradation rate constants (kdeg), and four rate constants describing complex association and dissociation .Post-transcriptional co-regulation of multiple mRNAs by a shared small RNA was analyzed using the model depicted in 1 and c2, was additionally assumed to be very high . As outlined in the introduction, this seems to be a reasonable assumption for many sRNA circuits. In case of strong binding, all mRNA will be bound into the inhibitory complexes (c1 and c2) as long as the sRNA is present in excess, i.e., (3)syn,m1+vsyn,m2>vsyn,s) to understand principles governing the accumulation of free, translationally active mRNA.Simplifying assumptions were made to derive analytical expressions for the dose-response behavior and parameter sensitivity of the system: It was assumed that the system has reached steady state, thus  neglecting the temporal aspects of the response. As the steady state of the full system (Eq. 2) cannot be solved analytically, the affinity of the mRNA-regulator complexes, c1 and c2 as long as vsyn,m1+vsyn,m2>vsyn,s. Taking into account the binding equilibria of the complexes /kon,i = [s]⋅[mi]/[ci]), one can solve for the steady state concentration of the free mRNA species (5)syn,m1/kdeg,m1, vsyn,m2/kdeg,m2). The mRNA synthesis ratio β relates the production terms of the two mRNAs (6a).2 relative to m1 (6b)2 is larger than that of m1  and/or (ii) sRNA-mediated destabilization of m2 is large relative to m1 . Thus, α>1 indicates that sRNA-mediated inhibition of m2 is stronger than that of m1. For equal inhibition of both mRNAs (α = 1) the steady state solution simplifies to (7)syn,m1+vsyn,m2>vsyn,s; cf. Eq. 3). Using numerical simulations of the full system (Eq. 2), we confirmed that Eqs. 5 and 7 approximate the steady state of the system very well as long as complex formation is sufficiently strong.At steady state, transcription balances RNA degradation, giving rise to the following set of algebraic equations (4)Equation 7 describes a threshold-linear response with a switch from no expression to linear expression at S = 1. Thus, for equal inhibition of both mRNAs (α = 1), each mRNA behaves similar to the case where a single mRNA is strongly inhibited in a threshold-linear manner by a small RNA i] = vsyn,mi/kdeg,mi. Thus, the remaining fraction of active mRNA quantifies the percent inhibition of each mRNA by the sRNA. Plotting f as a function of the stimulus S .To analyze the parameter sensitivity of mRNA co-regulation, it is instructive to analyze the remaining fraction of active mRNA (9)imulus S  yields dFor equal inhibition strength (α = 1), the mRNAs show perfect co-regulation, since the dose-response curves completely overlap regardless of the synthesis ratio β ; Eq. 7. In the context of stress responses, it is most likely the all-or-none transition from no expression to a significant level that matters. In the following, we will therefore use the stimuli of 10% mRNA expression to systematically analyze alignment in this lower part of the dose-response. Synchronous switching is comprehensively quantified in re large . In thisre large . It shousyn,s = const.; vsyn,m2 = β • vsyn,m1 with β = const.). Thus, a constitutively expressed sRNA synchronizes gene expression thresholds arising from transcriptional co-regulation of the two mRNAs. Moreover, parameter-independent coordinated switching is also maintained for selective transcriptional regulation of one mRNA 2 transcription is regulated, the expression of m1 can respond infinitely strong if the system is close to the threshold (S≈1). The effect is enhanced if m2 is more abundant than m1 (β>1). The former condition explains our observation that gene expression thresholds are coupled, while the upper parts of the dose-response curves are less well coordinated.Besides transcriptional induction of the sRNA, the circuitry in Taken together, we comprehensively characterized the parameter space to show a shared sRNA establishes synchronous gene expression thresholds irrespective of the mode of transcriptional regulation in the circuit. Numerical simulations confirm the synchronization effect, but also reveal that the effect is less pronounced if the low-affinity mRNA binds too weakly to the sRNA (non-stoichiometric mode of inhibition): in this case, large sRNA concentrations are required to fully repress the low-affinity mRNA, and, as a result, the threshold ratio would be higher than predicted by the analytical approximation.1 is governed by the terms T1 and T2. For low stimuli, the term T1 dominates, and m1 will not be significantly affected by sRNA-mediated regulation, .i.e., (13)2. As the stimulus increases, sequestration is less efficient and m1 is subject to inhibition as well (term T2 dominates). Thus, sequestration of the sRNA by m2 shifts the onset of m1 downregulation to higher stimulus levels. Since complete inhibition of both mRNAs is fixed to S = 1, a small fold-change in the input S may be sufficient to switch m1 from low to high levels, indicating a strong increase in ultrasensitivity.Our simulations in 1 downregulation by setting T1 = T2≈0, and solving for S. One obtains (14)1. For very low β, Sonset approaches the stimulus level where the sRNA concentration equals the sum of mRNA concentrations (Sonset≈1), indicating a perfect all-or-none switch. We have thus shown that the switching performance of a threshold-linear sRNA system can be strongly enhanced by the presence of a high affinity competitor, which may either represent another target mRNA or a binding decoy.Under the assumption that α≫1, we can estimate the onset of mH≈6 for a ten-fold affinity difference of the two mRNAs (α = 10 and β = 0.1). For comparison, one estimates nH≈2 in the single-target case.Equation 14 represents the extreme case of very large affinity differences (α≫1). In Very strong affinity differences between the two mRNAs may imply that the limit of strong binding may no longer hold for the low-affinity mRNA. We therefore performed numerical simulations under the assumption that the low-affinity mRNA no longer shows a threshold-linear response in the single target case . Importantly, the presence of a high-affinity competitor enhanced ultrasensitivity under these conditions as well (not shown).To conclude, co-regulation by a shared sRNA gives rise to particularly interesting dynamic behavior if a high-affinity, high-abundance mRNA and a low-affinity/low-abundance mRNA co-exist. In this scenario, co-regulation induces robust synexpression and also enhances ultrasensitivity of the low-affinity mRNA .Our analyses in pression . We assuUpon step-like upregulation of both mRNA synthesis rates, while sRNA synthesis rate remained constant , the multi-target system behaved similarly to the single-target case When comparing systems with and without sRNA-mediated regulation, we observe that sRNA regulation can synchronize mRNA accumulation beyond the delay phase as well , left. IUpon coordinated downregulation of both mRNA synthesis rates, we observe a beneficial effect of sRNA-mediated regulation as well , right: The opposite regulatory case of step-like sRNA synthesis with constant synthesis of both mRNAs is shown in Summarizing both modes of regulation , our numireg and miunreg refer to the sRNA circuit and to the corresponding sRNA-less system, respectively. The synchronization factor thus quantifies synchrony as the absolute difference between the response times of m1 and m2, and compares sRNA and sRNA-less systems by taking the ratio. Low values of the synchronization factor (<1) imply that sRNA-mediated regulation promotes synchrony of mRNA expression.To confirm that synchronous rise and shut-down of mRNAs (properties ii and iii) are robust features of the system, we systematically analyzed the parameter space for these scenarios . We compFor mRNA upregulation kinetics beyond the delay phase, we observe that sRNA-mediated regulation synchronizes expression over a broad range of parameter values . As explFor sRNAs to be effective in synchronizing the temporal dynamics of gene expression in a physiological setting, they should be able to coordinate mRNAs that are induced early and late during cellular stress responses. Numerical simulations were thus performed under the assumption that mRNA synthesis rates sequentially increase in a step-like manner at different times . Indeed,Taken together, we conclude that shared sRNAs improve temporal synchronization of gene expression by a combination of stoichiometric inhibition and degradation enhancement effects.Single-input modules (SIMs), where a common regulator controls multiple targets, may exert synexpression or hierarchical prioritization . Here, wSmall RNAs were previously shown to optimize cellular stress responses by establishing threshold-linear behavior and temporal switching Two central and experimentally testable predictions can be derived from our model: (i) mRNA pools targeted by the same sRNA may be coupled by sequestration effects, i.e., transcriptional upregulation of one target mRNA should affect the expression of other targets (Eq. 11). (ii) multiple target mRNAs should exhibit synchronous thresholds, independent of precise sRNA binding affinities. The first prediction is well supported by the existing literature, since mRNA targets of bacterial small RNAs and eukaryotic miRNAs appear to communicate with each other by sequestration effects Our analyses revealed that coupling of mRNA pools by sRNA sequestration effects is the molecular mechanism underlying robust synchronization of mRNA thresholds. Other post-transcriptional regulators like microRNAs or RNA-binding proteins are not degraded alongside with their targets, and thus employ a more catalytic mode of action when compared to bacterial sRNAs. Our unpublished observations suggest that coupling effects are also possible in catalytic system, although the phenomena are less pronounced than in 1 induces the transcription of m2 . Strong negative feedback is known to render biochemical networks insensitive to perturbations 1 partially insensitive to the m1 transcription rate .(DOCX)Click here for additional data file.Protocol S2Analysis of simultaneous switching for independently regulated mRNAs .(DOCX)Click here for additional data file.Protocol S3Regulation by a shared sRNA synchronizes the temporal induction of mRNA expression (related to ).(DOCX)Click here for additional data file.Protocol S4Feedback and adaptation arising from regulation by a shared sRNA (related to ).(DOCX)Click here for additional data file.


Sirt6−/−, and double Sirt6−/− RelA−/− cells reveals the epistatic relationships between Sirt6 and RelA in shaping diverse temporal patterns of gene expression. Genes under the direct joint control of Sirt6 and RelA include several with prominent roles in cell senescence and organismal aging. These data suggest dynamic chromatin relocalization of Sirt6 as a key output of NF-κB signaling in stress response and aging.The sirtuin Sirt6 is a NAD-dependent histone deacetylase that is implicated in gene regulation and lifespan control. Sirt6 can interact with the stress-responsive transcription factor NF-κB and regulate some NF-κB target genes, but the full scope of Sirt6 target genes as well as dynamics of Sirt6 occupancy on chromatin are not known. Here we map Sirt6 occupancy on mouse promoters genome-wide and show that Sirt6 occupancy is highly dynamic in response to TNF-α. More than half of Sirt6 target genes are only revealed upon stress-signaling. The majority of genes bound by NF-κB subunit RelA recruit Sirt6, and dynamic Sirt6 relocalization is largely driven in a RelA-dependent manner. Integrative analysis with global gene expression patterns in wild-type,  Sirtuins (Sirt) are a family of enzymes that modify chromatin and other proteins to affect gene activity. Loss of Sirt6 leads to a progeria-like phenotype in mice, but the target of SIRT6 action has been elusive. Here we show that Sirt6 binds to thousands of gene promoters in a stress-inducible fashion, guided by the stress-responsive transcription factor NF-κB. Both the departure and arrival of Sirt6 alter gene expression, shaping the temporal dynamics of NF-κB transcriptional response and directly controlling the expression of other key regulators of aging. These findings provide the first view of how an oscillatory transcription factor can drive a progression of chromatin changes over time. Sir2) encodes an NAD-dependent histone deacetylase that links chromatin regulation to genomic stability, gene silencing and lifespan in yeast. Sir2 deacetylates lysines in the amino terminal tails or histones H3 and H4 and in the globular core of histone H3 Silent Information Regulator-2  NF-κB comprises a family of transcription factors that control the expression of genes involved in cells survival, senescence, inflammation, immunity and aging Sirt6 in mice results in a degenerative phenotype resembling premature aging Disruption of Sirt6−/− MEFs were also similarly treated as a negative control -chip assays with high-density oligonucleotide arrays to analyze the binding patterns of Sirt6 and RelA in mouse embryonic fibroblasts (MEF) before and after TNF-α addition. Because we and others have observed that histone acetylations and Sirt6 occupancy are clustered in promoter regions upstream of the transcriptional start site (TSS)  control . We idenromoters . Sirt6 bPfk1 and Ldha, as previously reported Pdk1, Pdk2 and Hif1α. In addition, Sirt6 has been shown to be required for Ras-mediated epigenetic silencing of the pro-apoptotic Fas gene Ralb, Raf1, Rhoc, Rab4b and Rab30, Rasgrp2 and Nkiras1.The dynamic relocalization of Sirt6 at each time point is biologically coherent and enriched for specific functions and sequence motifs. Promoters bound by SIRT6 are enriched for genes with roles in the cell cycle, regulation of small GTP-ase activity, immune system development, nucleic acid binding and anti-apoptosis  . It has Nfkbia, Nfkb1, and Nfkb2 as candidate target genes coregulated by RelA and Sirt6, these genes showed inducible SIRT6 binding to these targets upon stimulation with TNF-α We next searched for transcription factor binding motifs enriched for among SIRT6 bound promoters using motif module map RelA−/− MEFs were also similarly treated as a negative control  . SpecifiRelA haplo-insufficiency to rescue the lethality and degenerative phenotypes of Sirt6 knockout mice The direct physical interaction of RelA and Sirt6 raises the hypothesis that RelA can drive the stress-responsive relocalization of Sirt6 genome-wide, thereby allowing Sirt6 to deacetylate histones occupying the promoters of NF-κB target genes. The importance of RelA in Sirt6 function has been demonstrated by the ability of −286, hypergeometric distribution)  . This stibution) . To prov targets , most of of RelA . TogetheSirt6−/− or Sirt6-/-RelA−/− cells in the temporal response to TNF-α. We focused on genes with promoters bound by Sirt6 and RelA in order to understand the consequences of Sirt6 and RelA binding. A further goal is to identify candidate genes that may drive the aging process because genetic evidence suggests a critical role for genes jointly regulated by Sirt6 and RelA Sirt6−/− cells compared to wild-type and correspondingly reduced in Sirt6−/− RelA−/− cells. Hierarchical clustering of the gene expression patterns organized the genes into three distinct classes based on their differential expression in Sirt6−/− cells (Sirt6−/− cells at baseline without TNF-α treatment and at every single time point after TNF-α-treatment. All-Up includes genes such as candidate aging regulators Shc1(encoding p66) Cdkn2a (encoding the cell cycle inhibitor p16 that increases in expression with age) Wnt2Gadd45bMdm2Cdkn2a , which has been previously linked to aging Mapk9 and Fbxo4. Since RelA inhibits Sirt6-dependent repression of these genes, they show an opposite epistatic relationship: They are derepressed in Sirt6−/− cells, but are not reverted in Sirt6−/−RelA−/− cells . Additionally, we found that Sirt6 is located at the promoter of Cdkn1a independent of TNF-α stimulation. As a negative control, we did not detect Sirt6 at the promoter of Dll1, consistent without ChIP-chip results. We also demonstrate the reproducibility of these experiments by perfoming ChIP on two additional independently derived non-littermate wildtype MEF lines on two targets, Nfkbia and Nfkb2 and show the inducibility of Sirt6 recruitment to the promoters of these genes in response to TNF-α stimulation . In Sirt6 knockout cells, no difference was seen at baseline, but TNF-α stimulation led to ∼2-fold greater induction of these genes when compared to wildtype at time 1.5 hours (p<0.05). In Sirt6 RelA double knockout cells the inducibility of the “TNF-Up” genes was abolished, demonstrating that these gene expression changes are dependent on RelA . Furthermore, induction with TNF-α in Sirt6 knockout cells led to a further two-fold superinduction of Gadd45b gene expression when compared to Sirt6 knockout at time 0 hours (p<0.05). In double knockout cells, Gadd45b is no longer responsive to TNF stimulation, indicating that the induction requires NF-κB . In Sirt6−/− RelA−/− cells at time 0 gene expression was reduced to by 60% as in Sirt6 knockout cells, but was induced nearly 1.5 fold with TNF-α stimulation (p<0.05) (Lmna cause Hutchinson-Gilford progeria syndrome (HGPS) Lmna that impedes nuclear function. Lmna expression is decreased in Sirt6−/− cells but induced in Sirt6−/−RelA−/− cells in response to TNF-α, consistent with a role for Sirt6 in modulating NF-κB-dependent expression to prevent aging. The L530P/L530PLmna mice display a progeroid phenotype that strikingly resembles the phenotype of Sirt6−/− mice: reduced size, loss of subcutaneous fat, reduced bone mineral density and lethality by 4–5 weeks of age. These results raise the possibility that some of the characteristics of Sirt6−/− mice could be driven by deregulated LmnA expression. Since Lmna expression is reduced rather than enhanced in Sirt6 knockout, however, Sirt6 and RelA may regulate a factor that represses Lmna expression. Whether Sirt6 alters Lmna splicing, lamin A processing, or nuclear architecture remains to be determined.Finally, we tested the progeria causing gene, (p<0.05) . MutatioTogether these data demonstrate that the interplay of chromatin localization of Sirt6 and RelA regulates the expression of many genes, many of which have roles in regulating aging, stress response and NF-κB feedback, via both direct and indirect mechanisms .Deletion of Sirt6 results in a premature aging-like degenerative syndrome in mice, and heterozygosity of the NF-κB subunit RelA can rescue the degenerative and early lethality phenotype Dynamic relocalization of sirtuins is an important feature of both yeast Sir2 and mammalian Sirt1. Yeast Sir2 represses recombination or transcription at telomeres, silent mating type loci and ribosomal DNA. Upon the addition of DNA damage agents, Sir2 leaves telomeric sites and relocates to sites of DNA damage We now show that Sirt6 binds thousands of promoters in the mouse genome and that similar to RelA, this binding pattern is largely reconfigured in response to stress induced by TNF-α. 29% of Sirt6 targets are shared by RelA, and of these shared targets, 49% require RelA for Sirt6 occupancy. This suggests that in response to TNF-α or one of the many stressors that activate NF-κB, the stress-responsive nature of Sirt6 binding could be driven by its interaction with the stress-responsive transcription factor RelA. However, Sirt6 targets a large number of genes in the absence of RelA, which begs the question of whether another NF-κB family member or related transcription factor may also contribute to Sirt6 localization . In addiCDKN2A (encoding p16), Shc1, Wnt, and LMNA. The interplay of Sirt6 and RelA occupancy can shape several patterns of stimulus-dependent gene expression programs, as revealed in single and double knockout MEFs. Although MEF cultures can be heterogeneous, we have previously shown that the NF-κB driven gene expression program is independent of anatomical origin and is instead responsive to stress and aging Sirt6 and RelA share a large number of direct targets and co-regulate expression of these genes. Genes under the joint control of Sirt6 and RelA include several key regulators of aging, including While the majority of genes with joint occupancy of Sirt6 and RelA showed the expected epistatic relationships (Sirt6 --| RelA ◊ gene), there is a surprising diversity of stimulus-dependent NF-κB responses when Sirt6 is absent . In otheAll animals were handled in strict accordance with good animal practice as defined by the relevant national and/or local animal welfare bodies. All animal work was approved by the Stanford University Institutional Animal Care and Use Committee.Antibodies specific for RELA (Abcam), SIRT6 (Abcam) and Actin (Santa Cruz) are from the indicated sources. MEFs were generated from 13.5-day-old embryos using standard methods and propagated in DMEM (Invitrogen) plus 15% FBS. MEFs were passaged a total of four times before TNF-α treatment.RelA −/− and Sirt6 −/− MEFS were generated from littermate embryos derived on the same day as wildtype embryos. Biological replicates from wildtype embryos were generated from independently derived non littermate embryos, grown at separate times and treated with TNF-α on separate days. See Each amplified DNA sample was labeled according to the manufacturer's ChIP-chip protocol (Nimblegen). Briefly, each DNA sample (1 µg) was denatured in the presence of Cy5- or Cy3-labeled randon nonamers and incubated with 100 units (exo-) Klenow fragment (NEB) and dNTP mix (6 mM each in TE buffer) for 2 hours at 37°C. Reactions were terminated by addition of 0.5 M EDTA (pH 8), precipitated with isopropanol, and resuspended in water. 12 µg of Cy5-labeled ChIP sample and 6 µg of Cy-3 labeled total sample were mixed, dried and resuspended in 40 µL of buffer including hybridization buffer (Nimblegen), alignment oligonucleotides (Nimblegen) and Component A (Nimblegen). The labeled DNA samples were next denatured and hybridized to “MM8” arrays overnight at 42°C. Samples were co-hybridized with input DNA as a reference, and the microarrays (Nimblegen) contained probes tiling a total of 4 kilobases of 21,249 mouse promoters. Fluorescence intensity raw data were obtained from scanned images of the oligonucleotide tiling arrays using NIMBLESCAN 3.0 extraction software (Nimblegen). For each spot on the array, log2-ratios of the Cy5-labeled test sample versus the Cy3-labeled reference sample were calculated. To normalize across samples, the biweight mean of this log2 ratio was subtracted from each point. ChIP-chip time courses were done twice or more for most time points; representative data from single time courses are shown.Sirt6−/− or RelA−/−, respectively).Identification of targets: Raw Chip-chip data were normalized using RMA normalization algorithm in NimbleScan software. Peaks were called out using NimbleScan peak-calling package. We determined whether canonical NF-κBtargets identified by ChIP-seq p<0.05, hypergeometric distribution) of sets of genes sharing the presence of cis-motifs, termed motif modules Sirt6−/− MEFs. All motifs attributed to any of the five NF-κB family members are referred to as “NF-κB”.Motif module map: To analyze cis-regulatory motifs enriched among Sirt6 and RelA direct targets, we identified promoters of genes bound by Sirt6 or RelA and tested for their enrichment . We used a Benjamini threshold of <0.05.Sirt6 antibody was conjugated to magnetic Protein A beads (Invitrogen) using the manufacturer supplied protocol. Prior to IP, a volume of sonicated chromatin equivalent to four times the amount used for the IP was reserved as the input sample. Three technical replicates were done for each time point shown, using an equivalent of 10,000 cells of chromatin each time. ChIP was performed using sonicated chromatin on an automated microfluidic device  from mouse embryonic fibroblasts following TNF-α treatment (20 ng/mL) for the indicated times. RNA was labeled with Cy5 and hybridized to whole genome mouse bead arrays (Illumina). Genes that were induced with an absolute detection value of 100 in any sample during the time course were selected and normalized to the untreated wild-type sample. All genes and samples were next organized by hierarchical clustering Total RNA was extracted with TRIzol. RT-qPCR was performed using total RNA (50 ng), Taqman One Step RT-PCR master mix, and one of the following Taqman assays: GAPDH (Mm99999915g1*), NFKB1 (Mm00476361_m1*), NFKB2 (Mm00479807_m1*), NFKBia (Mm00477798_m1*), Cdkn2a/p14 (Mm01257348_m1), Cdkn2a/p16 (Mm00494449_m1*), Gadd45b (Mm00435123_m1*) and Lmna (Mm00497787_g1). All Taqman reagents were from Applied Biosystems. Reactions were in triplicate for each sample and preformed a minimum of three times. Data were normalized to GAPDH levels.http://smd.stanford.edu) and GEO (http://www.ncbi.nlm.nih.gov/geo/) with accession GSE28641.Primary microarray data are available at Stanford Microarray Database  Immunoblot of RelA in wild-type and RelA−/− MEFs. (C) Two biological replicates of the Sirt6 ChIP-qPCR demonstrating that Sirt6 is recruited to the promoters of Nfkbia and Nfkb2. MEF cells independently derived from two WT mouse embryos were treated with TNF-α (20 ng/ml) for 0 or 30 minutes. ChIP with α-Sirt6 antibodies was preformed and percent Sirt6 occupancy is shown relative to input. Mean of two technical replicates is shown.Western analysis of Sirt6 and RelA protein levels. (A) Immunoblot of Sirt6 in wild-type and (EPS)Click here for additional data file.Figure S2RelA−/− MEFs following TNF-α treatment for 3569 gene promoters that are bound by Sirt6 but not RelA. Sirt6 occupancy at many promoters in wildtype cellsis altered in RelA−/− cells at baseline and upon TNF-α stimulation indicating that our assay is not sensitive enough to detect co-occupancy or that RelA may exert indirect effects on Sirt6 localization.Sirt6 occupany at promoters not shared by RelA. Heat map displaying the Sirt6 ChIP binding data in wild-type and (EPS)Click here for additional data file.Figure S3RelA KO MEFs using different FDRs demonstrate that an FDR of ≤0.1 fails to retrieve known and authentic RelA target genes such as Bcl2a1a, Ill6, Cxcl1 and Nfkbiz while an FDR of ≤0.2 retrieves these targets.Comparison of FDR of ≤0.1 and ≤0.2 in RelA Chip-chip signals. RelA ChIP-chip signal time course in WT and (EPS)Click here for additional data file.Table S1List of Experiments Preformed. Description of cell genotype, TNFα treatment time, experiment type and analysis, number of technical and biological replicates, and figure number for all experiments preformed.(XLSX)Click here for additional data file.Table S2List of 5050 Sirt6 targets. Gene name and NCBI Gene ID for the Sirt6 targets identified by ChIP-chip.(XLSX)Click here for additional data file.Table S3List of 2734 RelA targets. Gene name and NCBI Gene ID for the RelA targets identified by ChIP-chip.(XLSX)Click here for additional data file.Table S4List of ChIP-qPCR primers. Primer sequences used for validation of ChIP-chip.(XLSX)Click here for additional data file.


Metabolic homeostasis and circadian rhythms are closely intertwined biological processes. Nuclear receptors, as sensors of hormonal and nutrient status, are actively implicated in maintaining this physiological relationship. Although the orphan nuclear receptor estrogen-related receptor α  plays a central role in the control of energy metabolism and its expression is known to be cyclic in the liver, its role in temporal control of metabolic networks is unknown. Here we report that ERRα directly regulates all major components of the molecular clock. ERRα-null mice also display deregulated locomotor activity rhythms and circadian period lengths under free-running conditions, as well as altered circulating diurnal bile acid and lipid profiles. In addition, the ERRα-null mice exhibit time-dependent hypoglycemia and hypoinsulinemia, suggesting a role for ERRα in modulating insulin sensitivity and glucose handling during the 24-hour light/dark cycle. We also provide evidence that the newly identified ERRα corepressor PROX1 is implicated in rhythmic control of metabolic outputs. To help uncover the molecular basis of these phenotypes, we performed genome-wide location analyses of binding events by ERRα, PROX1, and BMAL1, an integral component of the molecular clock. These studies revealed the existence of transcriptional regulatory loops among ERRα, PROX1, and BMAL1, as well as extensive overlaps in their target genes, implicating these three factors in the control of clock and metabolic gene networks in the liver. Genomic convergence of ERRα, PROX1, and BMAL1 transcriptional activity thus identified a novel node in the molecular circuitry controlling the daily timing of metabolic processes. The molecular basis for coordinated control of circadian rhythms and metabolism is not well understood. Although integral components of the molecular clock such as the transcription factor BMAL1 can directly regulate some metabolic genes, the output from the circadian oscillator is believed to be in large part mediated through the action of transcription factors whose patterns of expression are rhythmic in metabolic tissues. The estrogen-related receptor α  and its corepressor PROX1, two major metabolic regulators, could be well suited for this function. Indeed, we show that proper maintenance of daily glucose, insulin, bile acid, lipid, and locomotor rhythms in mice are dependent on the presence of ERRα. Ablation of PROX1 in synchronized HepG2 cells revealed the importance of PROX1 in regulating the rhythmic expression of clock and metabolic genes. Using genome-wide analysis of promoter occupancy and gene expression analyses, we identify ERRα and PROX1 as novel transcriptional regulators of the molecular clock and show that the direct participation of BMAL1 in the clock output pathway related to metabolic control is highly extensive. ERRα and BMAL1 thus serve as key bidirectional regulators connecting the peripheral clock and cellular energy metabolism. In most living organisms, metabolic and behavioral processes are orchestrated in a timely fashion approximating a 24 hr daily cycle. In mammals, light/dark (LD) cycles regulate the diurnal activity of the master pacemaker within the suprachiasmatic nuclei (SCN) and in turn synchronize autonomous molecular clocks in peripheral tissues The peripheral clock in metabolic tissues such as liver is reset by physiological cues such as food availability The orphan nuclear receptor estrogen related receptor α  plays a critical role in the control of cellular energy metabolism In this report, we first show that proper maintenance of diurnal glucose, insulin, bile acid, cholesterol, non-esterified fatty acid (NEFA) and triglyceride levels as well as locomotor rhythms in mice is dependent on the presence of ERRα. Analyses of ChIP-on-chip and gene expression datasets as well as functional studies further demonstrate that ERRα PROX1 and BMAL1 are involved in both transcriptional regulatory loops and rhythmic control of components of all major metabolic pathways in the liver. In particular, we show that BMAL1 directly targets a significantly large number of genes linked to diverse biological processes, including cellular metabolism. Our results thus demonstrate that BMAL1 plays a more comprehensive role in the circadian output pathways than previously anticipated and uncover a novel node in the intricate transcriptional network necessary to sustain proper metabolic and circadian rhythms.+ and K+ transporters in the kidneys ad libitum across the day. As expected glucose) calculated using the trapezoidal rule. In contrast, glucose tolerance tests at ZT 0/24 revealed that ERRα-null mice have improved glucose handling demonstrated by significantly decreased basal blood glucose levels after a 6 hr fast and at each subsequent time-point post-glucose administration , and presented an earlier activity offset . SubsequArntl , Clock, Cry1, Per2, Nr1d1 (known as Rev-erbα), Nr1d2 (known as Rev-erbβ), Csnk1d and Bhlhe40 (known as Dec1). Putative ERR binding elements (ERREs) within the major binding peaks are denoted by an asterisk , Ppargc1a (encoding PGC-1α) and Nr0b2 (encoding SHP) in ERRα-null mice. No significant change in expression patterns of Ppargc1b (encoding PGC-1β), Prox1 and Nr1h4 (encoding FXR) were found , Pklr, Pcx and Pdk1 were detected. Of note, expression of the gluconeogenic genes Pck1 and G6pc are significantly up-regulated in ERRα-null liver at ZT 4 during the fasting phase of the LD cycle where ERRα expression is normally at its lowest , Acacb (encoding ACC2) and Mlycd with no significant change in the mRNA profiles of Insr, Pik3c2g and Prkag2  respectively, are generally down-regulated in the absence of ERRα  of ∼17,000 genes, the same platform previously used to identify ERRα occupancy in the genome of mouse livers taken at ZT 4 in LD conditions r arrays . Furtherpromoter . Cry1, aet genes . Our datPck1, Hmgcr, Nr0b2 and miR-122a, were found to have altered cyclic expression patterns in ERRα-null mice  located adjacent to the well characterized, duplicated ERR binding sites (TNAAGGTCA) Esrra promoter which was further enhanced in the presence of PGC-1α  6 and the mRNA oscillation was abrogated in Clock mutant mice  target genes are common to PROX1 and BMAL1 and that 891  target genes are shared by ERRα and BMAL1 . OverallEsrra is itself a direct transcriptional target of BMAL1/CLOCK. While control of the expression of transcription factors involved in metabolic homeostasis provides a sensible molecular mechanism linking core clock genes and metabolism, our results also revealed that BMAL1 directly targets an unexpectedly large number of genes associated with a wide-array of biological processes, including cellular metabolism. In fact, the extensive transcriptional crosstalk between ERRα, BMAL1 and the metabolic coregulator PROX1 firmly positions these three factors at the center of the coordinated control of circadian rhythms and cellular metabolism . In this context, the finding of the potential regulation of Per2 by ERRα is also particularly interesting. PER2 has recently been shown to interact with several nuclear receptors and serve as a coregulator of nuclear receptor-mediated transcription and it has thus been proposed that PER2 could confer more precise oscillator information to metabolic genes As a direct target of BMAL1/CLOCK and a major regulator of energy metabolism, ERRα may also play a central role in clock-controlled output pathways linked to metabolic homeostasis. Indeed, the importance of ERRα as an integrator of the molecular clock and metabolism is evident Diurnal serum chemistry profiling revealed an important role of ERRα in maintaining metabolic homeostasis. Altered diurnal glucose, insulin, bile acid, cholesterol, NEFA and triglyceride levels were observed in ERRα-null mice. Of particular interest, fed ERRα-null mice exhibit time-dependent hypoglycemia and hypoinsulinemia with no apparent impairment in insulin secretion as determined by glucose tolerance tests. We thus hypothesize that the observed hypoglycemia in ERRα-null mice is a result of enhanced glucose uptake due to increased insulin sensitivity. In complete agreement with our findings in ERRα-null mice, a report published during preparation of this manuscript showed that administration of a novel highly selective ERRα inverse agonist (compound 29) in diet-induced murine models of obesity and an overt diabetic rat model resulted in improved insulin sensitivity and glucose tolerance accompanied by reduced circulating glucose, free fatty acid and triglyceride levels in situ hybridization experiments revealed that 19 of 49 nuclear receptors are expressed in the mouse SCN Nuclear receptors have been previously implicated as components of the output pathways of the molecular clock but their involvement in these processes has usually been inferred through the regulation of one to a few specific genes Bmal1, Clock, Cry1, Per2, Rev-erbα and Rev-erbβ and that the presence of ERRα is necessary to maintain the diurnal rhythm of many of these genes in the liver. Of particular interest, ERRα was found to display very potent repressor activity on BMAL1 , lipid  and carbohydrate  metabolism as well as in the TCA cycle/OXPHOS  . This finding thus provides a molecular mechanism for the loss of rhythmic expression of Slc2a2 in liver-specific BMAL1-deficient mice which has been proposed to account for the observed circadian hypoglycemia in these mutant mice BMAL1 is an important regulator of metabolism. In particular, liver-specific disruption of , Atp5g) . Of partPROX1 locus is associated with fasting glucose levels and increased risk for type II diabetes Our study provides evidence for a direct participation of PROX1 in transcriptional regulation of the molecular clock. In particular, clock gene synchronization in HepG2 cells revealed that ablation of PROX1 results in altered oscillation of core clock genes. In this context, we also show that PROX1 is required to maintain the rhythmic expression of genes involved in glucose homeostasis. Taken together with the recent identification of PROX1 as an important regulator of the ERRα/PGC-1α axis involved in the regulation of broad transcriptional programs implicated in the control of energy homeostasis in the liver We have shown that BMAL1 targets the promoter region of a substantial number of metabolic genes. However, a comparative analysis of BMAL1, ERRα and PROX1 targets indicates that the presence of BMAL1 alone is insufficient to regulate the expression of metabolic gene networks . In contWe have identified the orphan nuclear receptor ERRα as a novel transcriptional regulator of both the molecular clock and its output pathways that shares extensive transcriptional cross-talk with the core clock protein BMAL1. As such and given the known property of ERRα to translate signals propagated by physiological sensors such as PGC-1α and SIRT1 into metabolic gene expression networks, ERRα may serve as the key bidirectional regulator connecting the peripheral liver clock and cellular energy metabolism. Similarly, we show that the ERRα corepressor PROX1 can act both upstream and downstream of the endogenous clock. Furthermore, our study showed that the direct participation of BMAL1 in the clock output pathways is highly extensive, suggesting that other core clock proteins might play a similar role. Therefore, additional investigations of the functional relationship between ERRα, PROX1 and core clock genes in diverse tissues are bound to reveal other key molecular mechanisms and physiological phenotypes linked to the daily timing of biological processes.Animal use followed the guidelines of the Canadian Council on Animal Care. The animal use protocol was approved by the local Facility Animal Care Committee (FACC) at McGill University.Clock mutant mice Clock mutant mice kept in DD conditions were collected every 4 hrs from circadian time (CT) 2 to CT 22 and immediately frozen in liquid nitrogen until further processing.Male wild-type and ERRα-null mice ERRα and PROX1 ChIP assays were performed as previously described http://fatigo.org/) and NCBI gene descriptions. The mouse liver BMAL1 ChIP-on-chip bed file can be found in Duplicate mouse liver BMAL1 ChIP-on-chip experiments using tiled extended promoter arrays covering −5.5 to +2.5 kb from transcriptional start sites of ∼17,000 genes (mm8) from Agilent were performed as previously described www.ingenuity.com). Canonical pathways analysis identified significant pathways from the Ingenuity's Pathways Analysis library of canonical pathways. Fisher's exact test was used to calculate a p-value determining the probability that the association between the genes in a dataset and the canonical pathway is explained by chance alone. Networks of genes were algorithmically generated based on their connectivity by overlaying the target genes onto a global molecular network developed from information contained in the Ingenuity Pathways Knowledge Base.Analysis of the ChIP-on-chip target genes for significant biological pathways and networks were done using Ingenuity Pathways Analysis software v7.6 , 100 ng CMV β-galactosidase, 200 ng pSG5 expression vector (empty vector or mCLOCK), 200 ng pCS2 (5× myc) expression vector (empty vector or mBMAL1), with 300 ng of HA-PGC1α or pcDNA3. Cells were harvested and assayed for luciferase activity 24 hrs post-transfection. Experiments were performed in triplicate and each experiment was replicated multiple times.HepG2 cells were cultured in DMEM (Invitrogen) supplemented with 10% FBS and pen/strep and maintained at 70% confluency. HepG2 were transfected with either On-Target Smartpool control (siCtrl) from Dharmacon or a specific siRNA pool against PROX1 (siProx1) using HiPerfect reagent (according to the manufacturer's instructions).HepG2 cells treated with either control siRNA (siCtrl) or siProx1 were grown and maintained in high glucose (25 mM) Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS). 48 hrs later, the cells were then starved in DMEM containing 0.5% fetal bovine serum for 24 hrs. Subsequently, 50% horse serum was added (T = 0) for 2 hrs, and then the medium was changed back to starvation medium. RNA was isolated from cells harvested every 4 hrs during a 24 hr period for qRT-PCR analysis.st strand buffer, DTT, RNase inhibitor, and Superscript II RNase H Reverse Transcriptase. cDNA was amplified using specific primers  primer, dNTPs, 5× 1 primers  and S7 aHPRT1 levels and expressed as mean values +/− s.d. at the indicated time points relative to the mean value of siCtrl samples at T = 0 set at 1. For all genes tested, primer efficiencies were taken into account based on a serial dilution of cDNA.Relative fold expression levels of the analyzed genes in serum shocked HepG2 cells were normalized to For microRNA quantification, RNA was isolated from mouse livers using the Qiagen miRNeasy kit. microRNA levels were detected and normalized to snoRNA412 levels using Taqman miRNA RT-PCR following the manufacturer's instructions . Real-Time PCR reactions were carried out in a Corbett Research Rotor-Gene instrument.Nuclear extracts were prepared from livers of WT and ERRα-null mice collected during a 12∶12 h LD schedule. Briefly, the livers were homogenized in cell lysis buffer  containing protease and phosphatase inhibitor cocktails (Roche) and the nuclei collected were prepared in lysis buffer  containing protease and phosphatase inhibitor cocktails (Roche). Equal amounts of protein were pooled from 4 WT and 4 ERRα-null liver nuclear extracts at the indicated time points and a total of 20 µg protein extract at each time point were used for immunoblot analysis. Immunoblot detection was done using a custom made anti-ERRα  antibody Whole cells lysates from the HepG2 knock-down samples were prepared in lysis buffer  containing protease and phosphatase inhibitor cocktails (Roche). 50 ug of total protein lysate was used for immunoblot analysis. Immunoblot detection was done using anti-PROX1  antibody and anti-RPLP  antibody was used as a loading control.2 periodogram analysis was used for measurement of the free-running period.Wheel-running activity data were collected from male adult age-matched WT (n = 9) and ERRα-null (n = 10) mice. Animals were put in running wheel cages  set in a light-proof ventilated cabinet. Activity under a 12 hr light (∼200 lux), 12 hr dark cycle (LD) was recorded over 5 days following entrainment of the animals to this schedule. Animals were then kept in DD conditions for 20 days and the recordings of day 3 to day 20 in DD were used for defining circadian parameters in free running conditions. All data were analyzed using the Clocklab program . Chiad libitum) and fasted conditions. For fasting experiments, mice were housed in cages with woodchip bedding instead of corncob bedding and food was removed from cages 24 hrs prior to the start of blood collection every 4 hrs during a 24 hr period. Blood was isolated by cardiac puncture from mice anesthetized with isofluorene gas. Blood was allowed to clot for 45 min in serum separation tubes  with subsequent centrifugation at 5000 rpm for 30 min. Serum was isolated and stored at −80°C until further processing. Total cholesterol, triglyceride, bile acid and NEFA levels were detected by enzymatic colorimetric rate assays performed at IDEXX Laboratories . Specifically, cholesterol CHOD-PAP (Roche), triglyceride GPO-PAP (Roche), total bile acids assay (Diazyme) and VetSpec NEFA (Catachem Inc.) kits were used and samples were run on a Roche Hitachi H917 chemistry analyzer. Radioimmunoassay detection of insulin with rat insulin RIA kits (Millipore) was performed at the Animal Health Diagnostic Center at Cornell University.Serum metabolic measurements of insulin, bile acid, total cholesterol, NEFA and triglycerides were conducted on male WT and ERRα knock-out mice (n = 4) under basal (fed Blood glucose levels were measured using a OneTouch Ultra2 glucose meter (LifeScan) on male WT and ERRα knock-out mice. For glucose tolerance tests, mice were housed in cages with woodchip bedding and fasted for 6 hrs prior to intraperitoneal injection of a 20% glucose solution in 0.9% NaCl at 2 mg/g of body weight. Glucose levels were measured prior to glucose administration and at the indicated time points post-injection.Figure S1Arbp levels are shown as a function of ZT. Data shown are relative to WT expression levels at ZT 4 arbitrarily set to 1. ZT 0 values are a duplicate of ZT 24 shown for clarity. Error bars represent ± SEM. Student's t test was used to compare WT and ERRα KO liver expression data at the indicated time points, *P<0.05.Circadian expression of genes involved in transcriptional regulation (A), glycolysis/gluconeogesesis (B) and the TCA cycle and Oxphos (C). Male WT and ERRα-null mice (n = 4) kept in LD conditions were sacrificed at 4 hr intervals over a 24 hr period from ZT 4 to ZT 24. qRT-PCR analysis was performed on RNA isolated from mouse livers and relative expression data normalized to (PDF)Click here for additional data file.Figure S2Binding profiles of BMAL1 on extended promoters of a subset of clock gene targets obtained from ChIP-on-chip. Putative BMAL1/CLOCK binding sequences (E-boxes) are shown.(PDF)Click here for additional data file.Figure S3Mouse liver ERRα and BMAL1 standard ChIP assays on shared ChIP-on-chip target genes involved in AMPK signaling, glycolysis/gluconeogenesis and insulin receptor signaling.(PDF)Click here for additional data file.Figure S4Hprt1 levels are shown as a function of time. Data shown are relative to siCtrl expression levels at T = 0 arbitrarily set to 1. Error bars represent ± SEM. Student's t test was used to compare siCtrl and siProx1 expression data at the indicated time points, *P<0.05.HepG2 cells treated with either control siRNA (siCtrl) or siRNA against Prox1 were grown in DMEM containing 10% fetal bovine serum (FBS) for 48 hrs then starved in DMEM containing 0.5% fetal bovine serum for 24 hrs. On the day of serum shock, 50% horse serum was added (T = 0) for 2 hrs, and then the medium was changed back to starvation medium. Cells were harvested every 4 hrs during a 24 hr period for total RNA extraction. qRT-PCR analysis of molecular clock genes (A) and involved in glucose homeostasis (B) was performed and relative expression data normalized to (PDF)Click here for additional data file.Figure S5Venn Diagram illustrating the overlap in ERRα, PROX1, and BMAL1 ChIP-on-chip target genes known to display circadian rhythmic expression profiles under basal conditions in mouse liver.(PDF)Click here for additional data file.Table S1Mouse liver BMAL1 enriched ChIP-on-chip target genes.(XLS)Click here for additional data file.Table S2Comparison of mouse liver ERRα, PROX1 and BMAL1 enriched ChIP-on-chip target genes.(XLS)Click here for additional data file.Table S3Overlap between mouse liver ERRα, PROX1 and BMAL1 enriched ChIP-on-chip target genes with genes previously known to display hepatic circadian rhythms.(XLS)Click here for additional data file.Table S4Mouse primers used for ERRα/PROX1 ChIP quantitative PCR analysis.(PDF)Click here for additional data file.Table S5Mouse primers used for BMAL1/CLOCK ChIP quantitative PCR analysis.(PDF)Click here for additional data file.Table S6Mouse primers used for qRT-PCR.(PDF)Click here for additional data file.Table S7Human primers used for qRT-PCR.(PDF)Click here for additional data file.Dataset S1Mouse liver BMAL1 ChIP-on-chip enriched binding events in BED format which can be loaded into the UCSC mm8 genome browser.(TXT)Click here for additional data file.


Moreover, in a transgenic reporter assay, tissue-specific expression of the Scl promoter in brain was increased by incorporation of 350 bp flanking fragments from the +45 element. Our data suggests that the +45 region functions as a boundary element that separates the Scl/Map17 and Cyp transcriptional domains, and raise the possibility that this element may be useful for improving tissue-specific expression of transgenic constructs.The Scl gene encodes a transcription factor essential for haematopoietic development. Scl transcription is regulated by a panel of cis-elements spread over 55 kb with the most distal 3′ element being located downstream of the neighbouring gene Map17, which is co-regulated with Scl in haematopoietic cells. The Scl/Map17 domain is flanked upstream by the ubiquitously expressed Sil gene and downstream by a cluster of Cyp genes active in liver, but the mechanisms responsible for delineating the domain boundaries remain unclear. Here we report identification of a DNaseI hypersensitive site at the 3′ end of the Scl/Map17 domain and 45 kb downstream of the Scl transcription start site. This element is located at the boundary of active and inactive chromatin, does not function as a classical tissue-specific enhancer, binds CTCF and is both necessary and sufficient for insulator function in haematopoietic cells  The Stem Cell Leukemia (Scl) gene, also known as Tal1, encodes a basic helix-loop-helix transcription factor with important roles in the development of the haematopoietic in vitro and in vivo.In order to understand the mechanisms involved in the regulation of Scl expression, we have systematically dissected the cis-regulatory elements operating at the murine Scl locus. Comparisons of Scl flanking genes during vertebrate evolution revealed a limited region of conserved synteny, spanning the Scl and Map17 genes, likely to contain regulatory elements responsible for the conserved pattern of Scl expression Briefly, nuclei from 416B, mES cells and primary mice thymocytes were isolated and treated with DNaseI. DNA from treated nuclei and genomic DNA (Input) was isolated, labelled with Cy3/Cy5 and hybridised to DNA microarrays as previously described 416B cells were co-transfected with the constructs described bellow and the PGK-Neo construct and selected for Neo resistance. Luciferase assays were performed on selected cells as described previously The ChIP assays were performed as previously described Hybridization of DNAse1 HSS and ChIP samples to Scl tiling path microarrays as been described before in vivo DMS footprinting experiment we performed ligation-mediated PCR techniques previously published For EMSA was performed using standard techniques. The probe DNA sequence used was the 57 base pair sequence identified from the DMS footprinting experiments. The probe was end-labeled using polynucleotide kinase, 30 pmol of oligos, and 2 µCi [γ-32P] ATP. The cold probe of either wild type sequence or mutated sequence was prepared as standard probe with the exclusion of 2 µCi [γ-32P] ATP. The cold probe was added in 10× excess as compared to the hot probe. For supershift the nuclear extract with probe was incubated with 50 ng of specific CTCF antibody .Insulator transfection assays were performed in K562 cells as described previously F0 transgenic reporter assays were performed using techniques previously reported We have previously described and validated a high throughput array-based approach to DNaseI hypersensitive site (HSS) mapping (ADHM) that uses tiled microarrays to map HSS over large genomic distances A striking feature of the DNase I profiles from the 3 cell types is the presence of a 55 kb region of DNase I accessibility spanning the Scl/Map17 genes. At its 3′ end, the extent of this domain is marked by a DNaseI HSS 45 kb downstream of the Scl promoter 1a (+45 element). This +45 element is DNase I accessible in all cell types studied, and marks a boundary between the Scl and Map17 genes, which are expressed in developing haematopoietic stem and progenitor cells, and the Cyp family of genes, which are not. Analysis of publicly available DNaseI HSS data from the ENCODE consortium in vivo, we next studied different constructs containing the LacZ reporter gene under the control of the SV40 promoter (SV/LacZ) using transgenic mice reporter assays. The SV40 promoter does not confer tissue-specific expression of the reporter gene but variable ectopic expression can be observed in F0 transgenic embryos. We have previously used this approach to characterize a range of Scl regulatory elements that direct Scl expression to specific developmental domains in vivo.To investigate whether the +45 region functions as a transcriptional enhancer, luciferase reporter constructs were generated using a 1.76 kb genomic fragment encompassing the complete +45 element and their activity was assessed in 416B cells hematopoietic progenitor cells . The precis-elements, the +19 enhancer, the Map17 promoter (+32) and the +40 enhancer. In contrast, the +45 element itself did not display H3K9ac in any of the three cell types studied. To examine the distribution of repressive histone marks, we repeated ChIP-Chip experiments using an antibody to trimethylated histone 3 lysine 27  footprinting was used to map DNA-protein interactions across the 350 bp core of the +45 element in 3 different cell types at single nucleotide resolution  was not seen in any of 8 LacZ expressing embryos (p<0.01). These data demonstrate that the presence of flanking 350 bp fragments increased the proportion of LacZ expressing embryos as well as significantly increased the tissue-specific expression from the Scl promoter in vivo. This is consistent with the barrier function of an insulator, by preventing position effect variegation due to integration site of the transgene.To corroborate the results of our in vivo and in vitro assays, and that protein-DNA contacts extend to 57 base pairs around a CTCF-consensus binding site. Furthermore, we have shown that this element functions as an insulator in enhancer-blocking assays when tested in haematopoietic cell lines and we have used a novel transgenic reporter assay to demonstrate that the element can both enhance the efficiency and specificity of midbrain-specific expression compared to conventional transgenic analysis. Moreover, analysis of genome-wide data from the ENCODE consortium confirmed that the +45 element is a ubiquitous CTCF-bound insulator region. This element is identified as a DNaseI HSS bound by CTCF in a variety of Scl/Map17 expressing and non-expressing tissues, including haematopoietic tissues (bone marrow and spleen), brain, kidney, liver, heart and lung.We have used array-based DNaseI hypersensitive site mapping to identify a novel regulatory element 45 kb downstream of the mouse Scl promoter 1a. We have shown that this element binds CTCF using both in vitro and in vivo assays, we have shown that it has CTCF-dependent insulator function. ChIP-chip assays for CTCF in human cells have now also identified the equivalent human sequence as a CTCF binding site The spatial location of this Scl insulator in relation to neighbouring genes has clear similarities with other recently identified insulator sequences. Mapping genome-wide DNase I sites and chromatin structural elements has confirmed that many genes appear to be flanked by HSS that appear ubiquitous in a range of cell types. Data from high throughput protocols have shown that 86% of ubiquitous DNaseI HSS are within 2 kb of a transcription start site (TSS), and of the remaining ubiquitous DNase HSS which are distal to TSS, over 70% have been shown to bind CTCF in ChIP-chip assays. Furthermore, analysis of the recently identified CTCF consensus binding site, shared by this Scl insulator, has shown that more than 80% of evolutionary conserved sites are greater than 10 kb distal to a TSS. In this study, we have used DNase-Chip technology to show that the +45 element is a DNase HSS in both Scl-expressing and Scl-non-expressing cells, and using a range of Several insulators have been identified to date, the chicken 5′ HSIV being the first identified in vertebrates Until recently, there has been no clear consensus for the genomic sequence of the CTCF transcription factor-binding site. However, using high throughput techniques a new consensus CTCF sequence has been proposed. Intriguingly, a number of well-characterised insulators such as the chicken HSIV, the mouse ß-globin HS2 and the human myb HS2 have relatively little sequence similarity in vivo DMS footprinting to map the extent of the protein-DNA interactions at a CTCF-dependent insulator. This shows that in vivo, the protein-DNA interaction extends to 57 base pairs of DNA, i.e. beyond the CTCF-consensus binding sequence, suggesting that the structure of DNA surrounding the consensus binding site is likely to play a key role in the CTCF protein-DNA interaction. Moreover we demonstrate that the 57 bp core fragment is both necessary and sufficient for insulator function in vitro.The CTCF protein is a large 11 zinc-finger complex that has been shown to contact DNA over many base pairs at other previously-mapped insulator elements in vivo, we elected to develop a transgenic mouse reporter model of insulator function. Using this approach we have demonstrated that the +45 insulator can be used to improve both the frequency of transgene expression (i.e. the proportion of transgenic embryos which express LacZ) and reduces ectopic transgene expression. The Scl promoters drive expression to the brain but not to haematopoietic tissues in vitro (haematopoietic) and in vivo (brain) assays was necessitated by the restricted in vivo activity of Scl and Map17 promoters, none of which drive expression in haematopoietic cells in vivo when assayed in transgenic mice. Taken together, these data raise the possibility that the +45 element could be a useful tool allowing effective insulation of transgenic constructs from adjacent chromatin.In order to further dissect the +45 insulator function we performed the classic enhancer-blocking assay in haematopoietic cell lines. Introduction of the full +45 element, the 350 bp core region or the 57FPR between the enhancer and the Neo reporter gene lead to a reduction in Neo resistant colonies, similarly to the 1.2 kb cHSIV insulator. The number of colonies was unaltered or even increased when the CTCF binding site in the +45 element was deleted or mutated  indicating the CTCF-dependent enhancer-blocking activity. This may be explained by the fact that different cell lines were used for these assays (murine 416B and human K562). To check the insulator function of the +45 element In conclusion, we have presented data that identifies and functionally defines a putative novel insulator sequence located 45 kb upstream of the Scl transcription start site at the boundary of chromatin domains. Our results suggest that the +45 element functions as a CTCF-dependent insulator, that may prevent inappropriate activation both of the Cyp genes during haematopoietic differentiation and also of Scl and Map17 during hepatic development.Information S1Information S1 materials for the manuscript “Mapping and Functional Characterisation of a CTCF-dependent Insulator Element at the 3′ Border of the murine Scl Transcriptional Domain” by Follows et al, including Supporting Figures S1 to S4.(PDF)Click here for additional data file.


E. coli RNA Polymerase (RNAP) One of the paramount goals of synthetic biology is to have the ability to tune transcriptional networks to targeted levels of expression at will. As a step in that direction, we have constructed a set of  One of the most fundamental tuning parameters governing expression of a given gene is the strength of its promoter. But what are the sequence rules that govern promoter strength? Recent high throughput mutagenesis experiments present an improved method for constructing an energy function that maps sequence to protein-DNA binding energy. We use this energy function combined with a thermodynamic model to deliberately design different promoters with over three orders of magnitude difference in their mean expression, and measure the resulting level of expression at both the mRNA and protein level to test this design strategy. The designed promoters are used in an alternate regulatory architecture and can now serve as the basis for the systematic examination of how both the mean and noise in gene expression depend upon the regulatory parameters that have been subject to evolutionary and/or human change. The quantitative dissection of the networks that control such expression as well as the construction of designed networks has been a central preoccupation of regulatory biology. As sketched in In this paper, we focus on two sets of these transcriptional parameters: namely, the strength with which polymerase binds the promoter, and the number of transcription factors present when that promoter is controlled by simple repression. We begin by focusing on the simplest case where there are no repressor proteins present in the cell. Our interest in such “constitutive” promoters (those not regulated by transcription factors) stems from the goal of creating a set of promoters in which we can systematically vary both the mean and the noise to test recent models of transcriptional kinetics E. coli genome is zero  transcription events. Hence, precise control of protein-DNA binding is an essential prerequisite for quantitative control of transcription. Despite the complexity of protein-DNA interactions and numerous molecular mechanisms involved in transcription initiation We use this correspondence between promoter sequence and RNAP binding affinity to generate a suite of promoters with a wide range of binding affinities. We then show how a simple thermodynamic model of transcription, which postulates that transcriptional activity is proportional to the probability of finding the RNAP bound at the promoter, accurately predicts the scaling of the expression with RNAP binding energy. In addition, these measurements allow us to determine the proportionality between RNAP binding probability and transcriptional output for our gene. With this information, we can make absolute predictions for the transcriptional output of our designed promoters under other regulatory conditions. We test and confirm these predictions by measuring the transcriptional output of some of our promoters in the architectural context of simple repression  proportional to E. coli genome calculated from the energy matrix of To test the predictive power of the binding energy model, we measured protein expression and mRNA copy numbers for constitutive expression from each of our unique promoters. Based on E. coli genome . To obtain this value, we began at the chromosomal origin of replication and applied the matrix sequentially to each 41 bp segment (both forward and reverse strands) around the chromosome, and computed the mean of the resulting The energy matrix from To see how this plays out in practice, consider a hypothetical sequence whose binding energy is computed to be E.coli (MG1655) with a complete deletion of the lacIZYA genes lacUV5 promoter expressing LacZ (our reporter gene). These constructs are then integrated into the galK region using recombineering lacUV5 promoter which expresses LacZ and a complete deletion of the LacI protein. Our designed promoters span roughly All strains used are wild-type ybcN site. The cassettes contain LacI expressed from an unregulated tet promoter with unique ribosomal binding sequences to produce varying LacI copy numbers. The exception is the data point at average LacI copy number of lacUV5 promoter.For the strains involving simple repression, we took our constitutive expression strains and created as many as 8 different strains with the LacI cassettes from Ref. Cultures were grown overnight (at least 8 hours) in LB and diluted 1∶4000 into Our assay for measuring LacZ activity is the same as described in Ref. E.coli tRNA, Our assay is based on that used in Ref. E. coli. The images are taken with an EMCCD camera (Andor Ixon2). The phase image is used for cell segmentation and the fluorescence images are used in mRNA detection. A total of Samples are imaged on a E. coli, at high copy number mRNA can be difficult to distinguish and count by eye).The FISH data is analyzed in a series of Matlab (The Mathworks) routines. The overview of the workflow is as follows: identifying individual cells, segmenting the fluorescence to identify possible mRNA, quantifying the mRNA which are found . Finally, all local LacZ gene.First we perform several steps to process the raw intensity images. The images are flattened, a process to correct for any uneven elements in the illumination profile, using a fluorescence image of an agarose pad coated with a small drop of fluorescein (such that the drop spreads evenly across most of the pad), each pixel of every fluorescence image is scaled such that the corresponding pixel in the flattening image would be a uniform brightness . This can be achieved by renormalizing each pixel in the data images and dividing by the ratio of the intensity of the corresponding pixel in the flattening image to the intensity of the brightest pixel. For instance, if one pixel in the flattening image was half as bright as the brightest pixel, the signal at that pixel's position in the raw intensity images would be doubled. We then subtract from every pixel the contribution to our signal associated with autofluorescence. The value for the autofluorescence is obtained by averaging over the fluorescence of every pixel in a control sample  and protein level (using the Miller assay of LacZ activity described in the methods). As would be expected from a simple model in which each mRNA produces a “burst” of translated protein molecules characterized by a fixed “burst size” (EPS)Click here for additional data file.Text S1Energy matrix for RNAP  binding affinity. Energy matrix for RNAP (TXT)Click here for additional data file.Text S2Energy matrix for RNAP  binding affinity. Energy matrix for RNAP (TXT)Click here for additional data file.Text S3Source code to adapt energy matrix from Kinney et. al. This code converts from the arbitrary units of SI text S1 to the values in units of E. coli genome is zero. The basis for this conversion is the reference of (TXT)Click here for additional data file.Text S4Promoter sequence for constitutive expression strains. This spreadsheet contains the colloquial name and promoter sequence for each of the unique constitutive expression strains generated for this study. The following column contains the calculated energy for each promoter using the energy matrix in SI text S1 (from E. coli chromosome using the energy matrix given in  S1 from . The fin(TXT)Click here for additional data file.Text S5List of FISH probe sequences. A list of all 72 probes and their sequences used in the mRNA FISH protocol.(TXT)Click here for additional data file.


Evolutionary related multisubunit RNA polymerases (RNAPs) transcribe the genomes of all living organisms. Whereas the core subunits of RNAPs are universally conserved in all three domains of life—indicative of a common evolutionary descent—this only applies to one RNAP-associated transcription factor—Spt5, also known as NusG in bacteria. All other factors that aid RNAP during the transcription cycle are specific for the individual domain or only conserved between archaea and eukaryotes. Spt5 and its bacterial homologue NusG regulate gene expression in several ways by (i) modulating transcription processivity and promoter proximal pausing, (ii) coupling transcription and RNA processing or translation, and (iii) recruiting termination factors and thereby silencing laterally transferred DNA and protecting the genome against double-stranded DNA breaks. This review discusses recent discoveries that identify Spt5-like factors as evolutionary conserved nexus for the regulation and coordination of the machineries responsible for information processing in the cell. ► Spt5–NusG is the only universally conserved transcription factor in the three domains of life. ► Highly versatile multifunction factor. ► Core function is to enhance transcription processivity. ► KOW domains serve as recruitment platform for broad range of factors. ► Instrumental in promoter proximal stalling in eukaryotes. ► Recruits chromatin remodelling and RNA processing factors in eukaryotes. ► Couples transcription and translation in prokaryotes. ► Recruits rho termination factor in bacteria. Life is strictly dependent on the faithful replication of the genetic information and its expression through the synthesis of RNA and protein. This is reflected in the evolutionary conservation of the machineries that carry out these functions and in their regulation. Replication, transcription, and translation are not independent of another but are coordinated in a dynamic fashion that adjusts the needs of one with the yield of the other, and overall enables maximum energy efficiency and protection of genome integrity.TATA binding protein) or TFIID [TBP plus TAFs (TBP-associated factors)], TFIIA (transcription factor IIA), TFIIB, TFIIE, TFIIF, and TFIIH.Transcription of all cellular genomes is carried out by multisubunit RNA polymerases (RNAPs), all of which are derived from one ancestral enzyme present in the last universal common ancestor (LUCA) .1,2 ThisThe fact that bacteria and archaea/eukaryotes make use of nonhomologous factors for initiation strongly suggests that the ancestral RNAP of LUCA did not utilise any of these factors but rather initiated transcription either factor-independently, or aided by a factor that subsequently was lost in evolution.bona fide homology suggests—despite the compelling similarity of the mechanism—that the LUCA RNAP did not utilise any of the factors.During elongation, multisubunit RNAPs frequently pause and move in a retrograde direction along the template by backtracking, which can regulate transcription and contribute to its fidelity, but also be detrimental to productive transcription.During termination of transcription, the stable elongation complex has to dissociate in order to release the transcript and template and make RNAP available for the next round of transcription. The elongation complex has to undergo substantial conformational changes during termination (such as an opening of the RNAP clamp) that are not necessarily energetically favourable. In bacteria, this energy is provided by RNA secondary structure formation by Watson–Crick base pairing of intrinsic terminators or by the rho helicase by virtue of ATP hydrolysis.In summary, it appears that a highly conserved RNAP system is almost exclusively aided by evolutionary nonrelated transcription factors. One important exception to this observation is Spt5/NusG, and the key functions that this factor have in gene expression might explain its universal evolutionary conservation.NusG N-terminal) domain and one (bacteria and archaea) or multiple (eukaryotes) KOW domain(s)Pyrococcus furiosus RNAP clamp with Spt4/5 confirms this binding site.Eukaryotic and archaeal Spt5 and their bacterial homologue NusG display an extensive sequence and structural homology and associate with their cognate RNAPs via evolutionary conserved binding sites.Spt5-like factors bind to the RNAP clamp, a structurally well conserved flexible module that closes over the DNA binding channel by a rotation of up to 30° relative to the RNAP core. The position and possibly movement of the clamp can be altered by the archaeal/eukaryote-specific RNAP stalk  has the potential to affect RNAP in two opposite ways . When Spcription . The stiThe archaeal RNAP requires only two basal transcription factors for promoter-directed transcription, TBP and TFB.et al. used a Förster resonance (fluorescence resonance energy transfer) system to identify the TFE binding site on RNAP in the complete archaeal transcription initiation complex.The answer to this question is the transcription initiation factor TFE. TFE is homologous to TFIIE alpha in the eukaryotic RNAPII systemSaccharomyces cerevisiae has revealed a relatively sharp transition between initiation and elongation factors (including Spt4/5) at approximately 150 bp downstream of the transcription start site.All RNAPs bind DNA in a nonsequence-specific manner, which can critically quench or reduce the concentration of “free” RNAP available for promoter-directed transcription. Association of RNAP with Spt4/5 has the potential to reduce this effect, and since the repression by Spt4/5 is negated by TFE at the promoter, the overall outcome is an increase in promoter specificity of RNAP. In addition, or alternatively, the transition between the initiation and elongation phases of transcription—referred to as promoter escape—may be aided by the factor swap. One of the intrinsic conflicts during transcription initiation is the apparent need for high-affinity interactions between RNAP and promoter DNA to ensure efficient RNAP recruitment and the ability to readily release the promoter-bound RNAP necessary to enable a high firing rate of the promoter.In summary, the mechanism of competition binding between initiation and elongation factors for RNAP is universally conserved in evolution.rfb operon—indicative of NusG recruitment immediately after transcription initiation where the swap of sigma and NusG has occurred.rfb operon. The net outcome of the second swap (between NusG and RfaH) in the very early stages of transcription elongation is the efficient transcription of genes distal to the ops sequence.In the bacterial system, NusG is an essential general transcription factor that has pleiotropic effects on transcription elongation.rfb operon.In summary, both NusG and RfaH interact with the RNAP using a conserved mechanism, but the two factors have opposing regulatory roles. NusG in concert with rho represses the expression of “foreign” genes (see below), while RfaH inhibits rho action on RNAP and thereby enhances the expression of “foreign” genes such as the S. cerevisiae, whereas Spt4 is dispensable at permissive temperatures.negative elongation factor) and kinases such as P-TEFb (positive transcription elongation factor b) and Bur-1/Bur-2, factors that are involved in chromatin remodelling such as Spt6 and FACT, and RNA processing factors such as the mRNA capping enzyme and the cap methyl transferaseIn eukaryotes, Spt4/5 is associated with RNAPII on most, if not all, genes;Drosophila melanogaster, suggested that its transcription was not regulated at the level of RNAPII recruitment. Rather, TECs were paused  proximal to the promoter approximately 40 bp downstream of the transcription start site.The classical paradigm of transcription control—regulation is chiefly controlled by the recruitment of the RNAPII to the promoter—was established using the yeast transcription machinery. Early observations of the regulation of the hsp70 heat shock gene in a metazoan system, In summary, the Spt4/5 complex is associated with many, if not all, class II genes in eukaryotes, and it plays a pivotal role for a eukaryote-specific mechanism that regulates the processivity of transcription—promoter proximal stalling. In addition, like its prokaryotic counterpart, it is involved in the recruitment of accessory factors that regulate gene expression, alter the availability of the chromatin template, and are involved in the processing of the nascent transcript. The function of Spt4/5 in eukaryotes is not restricted to class II genes since Spt4/5 also directly associates with RNAPI and modulates its processivity both positively and negatively.In both prokaryotic domains, bacteria and archaea, transcription and translation are coupled.Escherichia coli strain MDS42, 14% of the genome of the parental strain MG1655 has been deleted. In contrast to MG1655, NusG (and NusA) is not essential for cell viability in MDS42.4×) resistance to the highly selective inhibitor of the rho termination factor (bicyclomycin), it is possible that the function of NusG that makes it indispensable in the wild-type strain is due to its role as a rho cofactor for rho.Bacterial genomes contain a considerable load of horizontally transferred or “foreign” DNA including prophages, insertion sequence elements, and K-island clusters.Transcription and replication occur simultaneously, and collisions between the TEC and the replication fork not only are inevitable but also occur on a regular basis.Spt5-like transcription factors are universally conserved in evolution and serve multiple functions in the three domains of life.The most basic function of Spt5-like proteins that is common to all transcription systems is the stimulation of transcription processivity carried out by the universally conserved Spt5 NGN domain. The exact composition of early genomes is unclear; they could have consisted of either DNA or RNA, either in single-stranded or in double-stranded form. The catalytic centre of multisubunit RNAPs can utilise single-stranded DNA as template