The thyroid hormone plays a key role in energy and nutrient metabolisms in many tissues and regulates the transcription of key genes in metabolic pathways. It has long been believed that thyroid hormones (THs) exerted their effects primarily by binding to nuclear TH receptors (THRs) that are associated with conserved thyroid hormone response elements (TREs) located on the promoters of target genes. However, recent transcriptome and ChIP-Seq studies have challenged this conventional view as discordance was observed between TH-responsive genes and THR binding to DNA. While THR association with other transcription factors bound to DNA, TH activation of THRs to mediate effects that do not involve DNA-binding, or TH binding to proteins other than THRs have been invoked as potential mechanisms to explain this discrepancy, it appears that additional novel mechanisms may enable TH to regulate the mRNA expression. These include activation of transcription factors by SIRT1 via metabolic actions by TH, the post-translational modification of THR, the THR co-regulation of transcription with other nuclear receptors and transcription factors, and the microRNA (miR) control of RNA transcript expression to encode proteins involved in the cellular metabolism. Together, these novel mechanisms enlarge and diversify the panoply of metabolic genes that can be regulated by TH. T4 is the major form in circulation and is converted to the more biologically active T3 in many tissues. THs can positively or negatively regulate the transcription of anabolic and/or catabolic gene subsets that affect energy homeostasis and metabolism [Thyroid hormones  and THRB (NR1A2) that encode the TRα and TRβ receptor isoforms, respectively. THRA and THRB are located on human chromosomes, 17 and 3. Interestingly, THRA generates two major isoforms (THRA1 and THRA2) by alternative splicing to encode TRα1 and another isoform, TRα2, that has no T3-binding capacity [THRB generates two major isoforms (THRB1 and THRB2) from different transcriptional promoters to encode the TRβ1 and TRβ2 proteins [THRs are ligand-dependent nuclear transcription factors that are generated from two different genes, capacity ,13. THRBproteins ,13. The proteins ,15. THRsproteins ,13. In tproteins ,19,20. Oproteins . These fproteins ,13, the proteins ,13. The proteins ,24,25,26proteins .In addition to Type 1 regulation of transcriptional activity by THRs, there are alternative mechanisms for transcriptional regulation by TH. A recent consensus paper tried to categorize these alternative mechanisms, which are now referred to as Types 2–4 . BrieflyType 2 transcriptional regulation. Other modulator proteins or transcription factors can interact with TRs bound to TREs as well as vice versa with the latter referred to as Type 2 transcriptional regulation. Lin et al. [3-independent manner to repress TRβ1′s silencing activity in an unliganded state and transcriptional activity in the liganded state. Yap et al. and Bhat et al. [n et al.  found tht et al. ,29 showet et al. , demonstt et al.  demonstrt et al. . Type 3 transcriptional regulation. Liganded TRβ associated with the PI3K-regulatory subunit p85α in the cytosol to activate phosphoinositide 3-kinase (PI3K), phosphorylate protein kinase B (PKB/AKT), and mammalian target of rapamycin (MTOR), and the latter’s substrate ribosomal protein S6 kinase beta-1 (p70S6K), to induce the hypoxia-Inducible Factor 1A (HIF-1α) transcription factor and the expression of its target genes, glucose transporter 1 (GLUT1), phosphofructokinase platelet (PFKP), and monocarboxylate transporter 4 (MCT4) [PV/PV mutant bound significantly more to the PI3K-regulatory subunit p85 in the mutant TRβ (THRBPV/PV) mice than the wild-type mice [PV mutant also bound to β-catenin, a transcriptional co-activator that regulates cell proliferation and cell survival, which was highly elevated in the thyroid tumours of THRBPV/PV mice [3-dependent. The increased physical interaction between β-catenin and unliganded TRβ led to the activation of β-catenin-regulated downstream target genes. 4 (MCT4) ,34. Moreype mice . This reype mice ,36. Furt/PV mice . The intType 4 transcriptional receptor. THs have been shown to bind to integrin αvβ3 membrane receptor proteins independent of THRs [3 and activates PI3K via Src kinase, whereas, the S2 domain regulates mitogen-activated protein kinases MAPK1 and MAPK2 and binds both T4 and T3. Interestingly, each S1 and S2 domain mediates specific downstream effects, S1 directs Src and the PI3K-mediated trafficking of intact TRα from the cytoplasm to the nucleus and causes the transcription of HIF1A. In contrast, S2 activates MAPK1 and MAPK2, which then promotes the nuclear uptake of TRβ1 from the cytoplasm and leads to the proliferation of tumour cells [FGF2), matrix metalloproteinase 2 (MMP2), HIF1A, and cyclooxygenase 2 (COX2), which have been associated with cancer development and angiogenesis [ of THRs . αvβ3 me of THRs . Integriur cells ,39,40. Gogenesis .Type 1 transcriptional regulation is the predominant mechanism for TH-mediated regulation of transcription; however, the presence of cytosolic THRs has raised the possibilities for other THR interactions with cytoplasmic proteins . MoreoveCombined DNA binding and interaction with other transcription factors (Types 1 and 2). TH and Sterol regulatory element-binding protein 1 (SREBP1) have cross-talk as many target genes involved in lipogenesis are co-regulated by both receptors. SREBP1 (transcription factor that regulates gene transcription of hepatic lipogenesis pathway) expression induced ATP binding cassette subfamily D member 2 (ABCD2) through an sterol regulatory element (SRE) that overlapped with a direct repeat (DR-4) element. Both TRα and TRβ bound to this motif and modulated the SREBP1-dependent transcriptional activation of the ABCD2 gene [ACC) gene promoter [ChREBP) promoter and physically interacted to enhance ChREBP gene expression in the liver; however, this effect seemed to be tissue-specific since THR alone was sufficient to increase the ChREBP transcription in white adipose tissue [CD2 gene ,48. Simipromoter . THR ande tissue .Secondary regulation of target gene expression through the transcriptional induction of transcription factor expression. Direct transcriptional regulation of the transcription factor, kruppel-like factor 9 (KLF9), by TH stimulated the expression of several downstream target genes in human and mouse hepatic cells [KLF9 stimulated mouse deiodinase 1 (DIO1) expression via two CACCC sequences located on both sides of the hepatocyte nuclear factor (HNF)4α-response element, as HNF4α and KLF9, in conjunction with HNF4α and GATA binding protein 4 (GATA4), synergistically activated the mouse DIO1 promoter [ic cells ,52. In ppromoter .ChREBP (which controls the activation of glucose-induced lipogenesis in the liver) as well as SREBP1 expression, leading to an increased transcription of their downstream target genes. Both THR and LXR co-regulated the expression of ChREBP, as TH stimulated the ChREBP gene transcription maximally when THR was bound to the LXR-response element 2 (LXRE2), and LXR to LXRE1 [UCP1) gene transcription due to the TH-mediated induction of the ChREBP expression in brown adipose tissue [TRβ1 regulated the LXRα expression in a ligand-dependent manner to regulate hepatic lipogenic pathways . Howeverto LXRE1 . Recentle tissue . We haveNon-specific downstream effects on gene expression mediated by TH. Recently, DNA methyltransferase DNMT3a was shown to be a direct target of TH and suggested that it may be an evolutionarily conserved mechanism for modulating global changes in DNA methylation [PGC1α), CREB binding protein (CREBBP), p300, CD44 antigen homing function and Indian blood group system (CD44), secreted phosphoprotein 1 (SPP1), and matrix metallopeptidase 9 (MMP9) [THRSP/SPOT14) mRNA [hylation . Consist9 (MMP9) . These e14) mRNA ,59. The Transcriptional regulation by TH metabolites. T3 (the most active form of TH) was previously thought to be responsible for most, if not all, of the TH effects on the metabolism in the cell. However, there has been accumulating evidence suggesting that other THs and TH metabolites that were originally thought to be less active (T4), or even inert 3,5-diiodo-l-thyronine (T2), as well as 3-iodothyronamine (T1 AM), have significant biological activities [2 administration prevented the induction of insulin resistance by high-fat diet feeding [2 rapidly stimulated hepatic fatty acid oxidation, decreasing hepatic triglyceride levels, and improving the serum lipid profile. Interestingly, T2 could mediate these effects in the absence of TRβ and thus suggested that they may involve a Type 4 transcriptional mechanism. These T2 actions have also been associated with improvements in healthy ageing [2 may have beneficial physiological actions in humans [4 has been shown to interact with integrin on the cell membrane to activate janus kinase/signal transducers and activators of transcription (JAK/STAT) signalling [3 and T4 [tivities . Recentl feeding . Moreovey ageing , and sugn humans . T4 has gnalling . T1Am of3 and T4 . Since tTH regulation of mitochondrial DNA. Early studies by Segal et. al. demonstrated the TH rapidly increased the 2-deoxyglucose uptake in chick embryo heart cells even in the presence of cycloheximide (a translation inhibitor), removing any involvement of the genomic pathway [ pathway ,64,65. W pathway ,39,41,44 pathway . Truncat pathway ,67. MechBesides the previous types of mechanisms that we have described, we recently discovered two novel mechanisms of transcriptional regulation by TH that further increase and diversify the number of target genes regulated by TH.Metabolic regulation of transcription factors by TH: TH activation of SIRT1. Sirtuin 1 (SIRT1) is metabolically regulated by the nicotinamide adenine dinucleotide (NAD+)-dependent redox-sensitive protein deacetylase [3 and enhanced the ubiquitin-dependent TRβ1 turnover [CPT1A) gene, which contains a bona fide TRE in the promoter [PDK4) and CPT1A [cetylase . SIRT1 hcetylase . Since Tcetylase ,3, it wacetylase ,7. Interturnover . A co-oppromoter . Additiond CPT1A ,71. Thus+ concentration. SIRT1 deacetylated forkhead box (FOX)O1 transcription factor and stimulated the expression of the gluconeogenic genes: phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase catalytic subunit (G6PC) [2, has also been shown to rapidly stimulate hepatic fatty acid oxidation via the activation of SIRT1, leading to the deacetylation and activation of PGC1α and the inactivation of SREBP1c with increased transcription of genes involved in mitochondrial biogenesis and decreased transcription of genes involved lipogenesis [2 has shown protection against intracellular damage in diabetic nephropathy and non-alcoholic fatty liver disease (NAFLD) in a SIRT1-dependent manner [We recently showed that the TH stimulation of oxidative phosphorylation led to the metabolic activation of SIRT1 by increasing the intracellular NADt (G6PC) . Later at (G6PC) . In thist (G6PC) . Interesogenesis . Of notet manner . While tt manner ,74.+, as well as Type 3 by virtue of the THR interaction with SIRT1 and by SIRT1’s effect on THR acetylation. The SIRT1-TRβ1 interaction may be protective for reducing TH-mediated transcriptional activation when general transcription needs to be reduced during times of nutrient or energy stress . Of noteSecondary regulation of transcription by TH: TH induction of PGC1α/ERRα. Although this mechanism might seem to be a subset of secondary transcription by TH, it is complicated by the role of the secondary (PGC1α) and tertiary (ERRα) transcription factors co-regulating genes that may also be partially regulated by THR. It is noteworthy that this cascade is mediated by the cross-talk of three different nuclear hormone receptors.3 for 10 days than those seen in mice only 6 hours after one injection with T3 [NRF1), transcription factor A, mitochondrial (TFAM), and CPT1A Thus, this pathway plays a critical role in mitochondrial biogenesis, activity, and autophagy . Of note, SIRT1 also deacetylated and activated both PGC1α and ERRα [Recent ChIP-Seq analyses of THR with other DNA interacting proteins such as FOXO1 and ERRα have shown that the THRs stimulated binding of these proteins to the promoters of their target genes. Additionally, THRs bound to DNA sequences in the promoters of target genes that were different than TREs and also bound to non-promoter regions ,7,10,26, with T3 ,77. Thesand ERRα ; therefoULK1)  gene transcription by TH suggesting that TH induction of ERRα played a pivotal role in mitophagy, in addition to mitochondrial biogenesis and function. TH stimulation of oxidative phosphorylation and ROS may be another stimulus for mitophagy. The TH-dependent increase in ROS led to activation of calcium/calmodulin-dependent protein kinase kinase (CAMKK)2 to phosphorylate 5′ AMP-activated protein kinase (AMPK), which in turn, phosphorylated ULK1 and led to its mitochondrial recruitment and initiation of mitophagy [ULK1 expression via PGC1α-ERRα increased the phosphorylation and activation of both DRP1 and FUNDC1 to promote mitochondrial fission and mitophagy, respectively [We recently showed that TH-PGC1α-ERRα was required to induce the Unc-51 like autophagy activating kinase 1  and glycerol-3-phosphate dehydrogenase 2 (GPD2)—respectively, were regulated by TH via miR-206 [CDX2) (a positive regulator of hepatocyte differentiation), also decreased the sterol O-acyltransferase 2 (SOAT2) (an enzyme that generates cholesterol esters that are packaged into LDL lipoproteins) expression. These findings suggested that miR-181d may play a significant role in the reduction of serum cholesterol by TH by decreasing LDL lipoproteins [MicroRNAs (miRs) are short non-coding RNA molecules approximately 20–22 nucleotides in lngth that are involved in the post-transcriptional silencing of RNA . The exp miR-206 . These f miR-206 . Yap et proteins .TIAM1) expression in order to reduce the hepatoma cell migration and invasion [PIM1) expression, cell proliferation, and hepatoma formation [MMP3 expression in hepatocellular carcinoma (HCC) [THRA1 and interferon regulatory factor 1 (IRF1) gene expressions, as well as EMT-related genes, the phosphorylation of MTOR, and the STAT3 and AKT activation-pathways that increase the motility and invasion of hepatoma cells [TH stimulated miR-21 and decreased the T-lymphoma invasion and metastasis-inducing protein 1  . In contma cells . Furtherma cells . This tyStudies from the last two decades have shown that TH regulates the transcription of specific gene subsets during various metabolic conditions. Although the direct regulation of transcription by THRs has been extensively studied , TH-mediated induction of metabolic effects to activate other transcription factors by SIRT1, as well as the temporal and physical co-regulation of transcription by THRs in conjunction with other transcription factors (Type 1 and 2). Since SIRT1 activation and interaction with THR leads to the deacetylation of THR or other transcription factors, it might be considered a special subset of Type 3 transcriptional regulation. The THR-PGC1α-ERRα transcriptional pathway could be considered an extension of Type 1 for secondary transcriptional regulation after the primary induction of a transcription factor by TH. The post-transcriptional regulation by miRs to silence the mRNA expression of the target genes adds further complexity and fine-tuning of metabolic processes within the cell and can be considered as Types 1, 2, or 3 transcriptional regulation depending upon the mechanism, followed by the special post-transcriptional gene regulation. These different mechanisms may play important roles during acute vs. chronic responses to different nutrient conditions and hormonal states. It is also possible that different mechanisms (or combination of mechanisms) may predominate during different phases of target gene expression regulation by TH. These new mechanisms further increase the number and diversity of genes that can be directly and indirectly regulated by TH. Understanding the molecular mechanisms by which TH regulates the mRNA expression of target genes involved in nutrient and energy homeostasis will help us to better manage their pathophysiologic derangements in hypo- and hyperthyroidism. They may also enable us to employ TH and TH mimetics to treat metabolic conditions such as obesity, hypercholesterolemia, and non-alcoholic fatty liver disease.


Enhancers are distal cis-regulatory elements that control gene expression. Despite an increasing appreciation of the importance of enhancers in cellular function and disease, our knowledge of enhancer-regulated transcription is very limited. Nascent RNA sequencing technologies, such as global nuclear run-on sequencing (GRO-seq) and precision run-on sequencing (PRO-seq), not only provide a direct and reliable measurement of enhancer activity, but also allow for quantifying transcription of enhancers and target genes simultaneously, making these technologies extremely useful for exploring enhancer-mediated regulation.Hdac3) knockout mouse livers, NRSA showed that HDAC3 regulates RNA polymerase recruitment through both proximal (promoter) and distal (enhancer) regulatory elements. Integrating ChIP-seq with PRO-seq data, NRSA prioritized enhancers based on their potential contribution to mediating HDAC3 regulation.Nascent RNA sequencing analysis (NRSA) provides a comprehensive view of enhancer-mediated gene regulation. NRSA not only outperforms existing methods for enhancer identification, but also enables annotation and quantification of active enhancers, and prediction of their target genes. Furthermore, NRSA identifies functionally important enhancers by integrating 1) nascent transcriptional changes in enhancers and their target genes and 2) binding profiles from regulator(s) of interest. Applied to wildtype and histone deacetylase 3 (NRSA will greatly facilitate the usage of nascent RNA sequencing techniques and accelerate the study of enhancer-mediated regulation.The online version of this article (10.1186/s12864-018-5016-z) contains supplementary material, which is available to authorized users. RNA transcription in eukaryotic cells is actively regulated in multiple stages, including RNA polymerase recruitment, transcription initiation, elongation, and termination. The RNA polymerase pause immediately downstream of a transcription start site (TSS) constitutes another critical step in transcriptional regulation –5. NasceLike promoters, enhancers are key regulatory components bound by transcriptional regulators to enable temporal and spatial control of gene expression. Active enhancers contain transcription initiation sites –10. EnhaCombinations of histone modifications, such as high levels of H3K4me1 and H3K27ac in the absence of H3K4me3, are commonly used to predict active enhancers –21. AlthIn contrast, enhancer-templated RNA (eRNA), a group of non-coding RNAs bidirectionally transcribed from enhancers, is a more direct and reliable indicator of enhancer activity –35. BecaHere, we describe nascent RNA sequencing analysis (NRSA), a novel tool with the dedicated goal of comprehensive analysis of enhancer-mediated regulation from nascent transcriptome data. NRSA not only identifies and quantifies enhancers as do dREG, Vespucci, Fstitch, and groHMM, but also annotates and assigns enhancers to their potential target genes. These additional functions are critical for studying enhancer-mediated regulation and downstream effects Fig. . MoreoveHdac3-deleted mouse livers at post-natal day 14 (P14). Histone deacetylases (HDACs) catalyse the removal of acetyl groups from histone tails and non-histone proteins, repressing gene transcription by modulating chromatin structure [Hdac3 in mouse livers primarily increased RNA polymerase promoter-proximal pausing without promoting transcription elongation. More interestingly, NRSA identified 1650 novel enhancers and further prioritized these enhancers based on their potential contribution to HDAC3-regulated RNA polymerase recruitment. NRSA and Hdac3 Wildtype(WT)/Knockout(KO) PRO-seq data are freely available at http://bioinfo.vanderbilt.edu/NRSA/.When first applied to public GRO/PRO-seq data, NRSA demonstrated its power for highly reliable identification of enhancers. NRSA was then applied to a new PRO-seq dataset acquired from wildtype (WT) and tructure . HDAC3 ptructure –46. The NRSA was first applied to public K562 and GM12878 GRO-seq data to assess its performance in enhancer identification. 358 enhancers were detected in K562 data . A similar number of enhancers  was detected in each replicate, with the majority of these (1238) shared in common between replicates, suggesting high reproducibility of enhancer identification Fig.  Additio Additio. The incHdac3 KO mouse livers  and used NRSA to explore the functional role of Hdac3 in transcriptional regulation. In total, 11,273 genes were found to be transcriptionally active, using the criteria of promoter-proximal density greater than zero and gene body density greater than 4 reads/kb (see Implementation).HDAC3 is known to repress gene transcription –46; howeHdac3 KO vs. WT conditions, the PRO-seq measurements were first normalized by the total number of uniquely mapped reads in each library. After normalization, transcriptional changes were calculated for all active genes at multiple stages, including initiation, elongation, and pausing. The gene body transcriptional changes were consistent with our previously published mRNA expression microarray data generated from Hdac3 WT and KO mouse livers at P17 [Hdac3 caused a global accumulation of polymerase in promoter-proximal regions , while only 180 genes showed significant loss (FDR < 0.05 & log2FC < − 0.6)  Fig. . Further.6) Fig. , and 15 .6) Fig. .Fig. 4ImHdac3 knockout demonstrated a different transcriptional regulation mechanism  enhancers were found to be upregulated (FDR < 0.05 & log2FC > 1) upon Hdac3 deletion, while only 212  enhancers were downregulated (FDR < 0.05 & log2FC < − 1)  Fig. . Consistcription , 57.Fig.Hdac3 deletion, while only minor increase was observed at unchanged enhancers  and experimentally validated enhancer-promoter interactions from 4DGenome (see Implementation). For 582 upregulated enhancers, 582 enhancer-gene associations were found by the closest method, 387 were identified by the 50 kb distance constraint, and 3599 interactions were identified by 4DGenome. Among these associations, 204 were found by all three strategies  upon Hdac3 deletion . Fscn1 is near this enhancer; with a separation of less than 50 kb. In addition, the enhancer-Fscn1 interaction has been experimentally validated by both Hi-C and ChIA-PET technologies [Fscn1.After upregulated enhancers are detected, the next challenge is to identify those contributing to HDAC3 regulation. NRSA integrates ChIP-seq and PRO-seq data to prioritize enhancers, with the goal of identifying enhancers not only bound by HDAC3 but also regulating the transcription of target genes. We ranked enhancers by binding affinity, functional activity, and combined score. For the 582 upregulated enhancers, H3K9ac signal was elevated upon TSS Fig. . The enhnologies . These rPRO-seq directly measures nascent RNA transcription by creating high-resolution maps of all transcriptionally engaged RNA polymerases on a genome-wide scale. This technology has several advantages as compared to traditional RNA-seq or ChIP-seq analyses: (1) PRO-seq measures the recruitment of all types of RNA polymerases including RNAPI, RNAPII, and RNAPIII, and provides directional information; (2) With high sensitivity and low background, PRO-seq enables a detailed study of the individual steps of RNA transcription, including RNA polymerase recruitment, promoter-proximal pausing and transcription elongation; (3) On a genome-wide scale and at the same time, PRO-seq allows for study of all transcriptional events, including transcription of protein-coding RNAs, long non-coding RNAs, regulatory elements such as eRNAs, and microRNAs ; and (4)The most challenging task in performing enhancer research is to identify target genes of enhancers and to select functionally important enhancers. NRSA detects potential targets for enhancers using four different strategies: 1) closest TSS, 2) TSS within a user-defined distance, 3) TSS-enhancer associations defined by FANTOM5, and 4) experimentally validated TSS-enhancer interactions from 4DGenome. It is useful and necessary to integrate analysis of GRO/PRO-seq data with other genomic data, which will further facilitate the usage of these technologies. NRSA provides tools to smoothly integrate GRO/PRO-seq data with other genomic data to prioritize enhancers. This function, designed to find enhancers not only bound by the regulator of interest but also affecting transcription of their target genes, is currently limited to ChIP-seq data. Integration with other types of genomic data around enhancer centers or other chromatin locations of interest is still under development.Drosophila melanogaster (dm10), C. elegans (ce10), and Zebrafish (danRer10). NRSA also can be applied to data generated by other nascent RNA-sequencing technologies able to detect proximal pausing of RNA Pol II and divergent transcription around promoters and enhancers, such as native elongating transcript sequencing (NET-seq) or mammalian NET-seq (mNET-seq) profiles [The current version of NRSA supports five organisms, including human (hg19), mouse (mm10), profiles . Howeverprofiles . NormaliAkap2  was ranked 2nd in Additional file Akap2, suggesting this hit represents an alternative TSS for Akap2 rather than an enhancer. In addition to histone markers and RNA-seq, experimental approaches that study the impact of genome editing on expression of neighboring genes also can help discriminate between enhancers and novel genes [Although GRO/PRO-seq provides a method to identify and quantify eRNA transcription, enhancers and alternative transcription start sites can be difficult to distinguish, as both possess a unified bidirectional-transcription structure . To avoiel genes , 68.NRSA is a powerful tool to study enhancer-mediated regulation from nascent transcriptome data, which will greatly facilitate the usage of nascent RNA sequencing techniques and accelerate the study of enhancer-mediated regulation.PRO-seq, GRO-seq, and GRO-cap data from K562 cells were acquired from published studies  . EP300, http://genome.ucsc.edu/) was used to convert genome coordinates from the assembly mm9 to mm10.H3K4me1 and H3K27ac ChIP-seq data in the mouse liver were obtained from GEO (accessions: GSM769015 and GSM1000140) . ChIP-seHdac3 were crossed with transgenic mice expressing albumin promoter-driven Cre (Alb-Cre). Offspring from these mice were bred in our animal facility to generate mice expressing WT (+/+) and conditional/null (fl/fl and fl/−) alleles in conjugation with Alb-Cre. Animals were housed under specific pathogen-free conditions at and in accordance with guidelines set forth by Vanderbilt University Medical Center. All experiments were conducted according to the protocol developed by the Vanderbilt University Institutional Animal Care and Use Committee (IACUC). The protocol number is M/12/021.C57BL6 mice containing a conditional allele (fl) and a null allele (−) for Alb-Cre+Hdac3+/+ (WT) and Alb-Cre+Hdac3fl/− or fl/fl (KO) mice were euthanized by isoflurane overdose following the American Veterinary Medical Association. The entire liver was immediately removed and snap frozen in liquid nitrogen within 1 min after sacrifice. Each P14 mouse liver weighs about 250 mg. On the day of the experiment, liver tissues from 3 mice were transferred onto ice and thawed. Up to 1000 mg of liver tissue combined from 3 mice was homogenized, using the rubber head of a 5 ml syringe plunger to gently push the tissue through a 70 μm cell strainer, and collected in 3 ml of isotonic buffer . At this time, all nuclei were released due to breakdown of hepatocytes. The homogenized liver was quickly mixed with 6 ml of cushion buffer  and overlaid on another 2 ml cushion buffer. In the end, the liver nuclei were in a total volume of 10 ml, and the physiological concentration of nucleotides was diluted by at least tenfold. All of these procedures were performed on ice with ice-cold buffers, taking about 5–6 min, during which time the formation of functional preinitiation complex is unlikely to occur and the incorporation of nucleotide by transcriptionally engaged RNA polymerases can be kept to a minimum.On P14, 2, and protease inhibitors), and resuspended at 20 million nuclei per 100 μl storage buffer. In order to avoid repeated freezing and thawing of liver nuclei, resuspended nuclei were directly subjected to nuclear run-on assay on the same day of isolation. Overall, 18 WT mice, as well as 18 Hdac3 KO mice, were divided into 2 biological replicates, respectively.The homogenization of liver tissue and preparation for centrifugation were each performed by two technicians; 6 samples from 18 mice were processed within 20 min. Liver nuclei were precipitated by centrifugation at 77,000×g at 4 °C for 1 h. Pooled nuclei from 9 mice were washed with cold PBS, cold nuclei storage buffer  , GRO/PROTo determine transcriptional rates for known genes, NRSA first defines the promoter-proximal region by examining each 50 bp window with a 5 bp sliding step along the coding strand spanning ±500 bp from known TSSs , 5, 74, http://homer.salk.edu/) [http://bioinfo.vanderbilt.edu/NRSA/). Pairs of bidirectional transcripts have been used to define active enhancers [To detect and quantify intergenic enhancers, NRSA uses HOMER (lk.edu/)  to call nhancers , 34, 78.nhancers , which rNRSA generates two types of output files, tables and figures of publishable quality Table . Tables Hdac3 KO mouse livers, we first used NRSA to identify up- and down-regulated enhancers by comparing transcriptional changes of eRNAs between Hdac3 KO and WT mice. The enrichment of H3K9ac signal was plotted around the centers of upregulated enhancers, and the 50 most significantly changed enhancers were selected for plotting the enrichment of HDAC3, and NCOR1 binding. All graphs were generated by NRSA within regions ±1000/2000 bp of enhancer centers with 20 bp bin size.To investigate the regulation and function of identified enhancers in Mus musculus, this version of NRSA links enhancers to their target genes based on the three strategies other than FANTOM5. To be noted, enhancers are only assigned to active genes, while inactive genes are ignored.To fully understand the function of enhancers, one fundamental and challenging step is to link enhancers to their target genes. The common strategy is assigning enhancers to the nearest gene , 63, 78 Es = wBs + (1 − w)Fs,where Es is the combined score for the enhancer, Bs is the upstream binding affinity score and Fs is the downstream functional activity score, and w is a weight to balance the relative impact of binding and functional evidence. The binding affinity score is modeled by an exponential distribution of the relative distance between ChIP-seq peaks and the enhancer center [0 is a constant. The downstream functional activity score is estimated by simultaneous transcriptional changes in enhancers and their target genes:To identify functionally important enhancers, NRSA introduces an integrative algorithm to prioritize enhancers based on the assumption that enhancers bound by a transcriptional regulator(s) of interest and affecting target gene transcription are highly likely to mediate the function of the regulator(s). Integrating GRO/PRO-seq data with ChIP-seq peaks of the regulator, NRSA combines the binding and the functional evidence to rank each enhancer: r center \documentIen _ g = 0, Cen and Cg are the normalized transcriptional changes of the enhancer and associated genes from GRO/PRO-seq data. If multiple genes are predicted to be associated with this enhancer by a strategy, average transcriptional change or the maximal transcriptional change of associated genes is used.KIT have been identified. The regulatory function of one enhancer (E2 in the paper) on KIT expression was validated by CRISPRi and chromosome conformation capture experiments [KIT : Linux, MacOS.Programming language: Perl, R.Other requirements: HOMER, bedtools v2.24.0 or higher.License: NRSA is free of charge to academic and non-profit institutions.Any restrictions to use by non-academics: Please contact authors for commercial use.Additional file 1:Table S1. Identified enhancers using K562 GRO-seq (S1a), GM12878 GRO-seq (S1b), two replicates of U2OS GRO-seq (GSM1634453 (S1c) & GSM1634455 (S1d)), and K562 PRO-seq (S1e). (XLSX 1158 kb)Additional file 2:Figure S1. Examples of enhancers identified by NRSA, dREG and groHMM in K562 GRO-seq data. (PPTX 117 kb)Additional file 3:Figure S2. Transcriptional levels of common and unique enhancers identified in K562 GRO/PRO-seq data. (PPTX 88 kb)Additional file 4:Figure S3. The effect of Hdac3 deletion on gene body transcription. (a) Heatmap of log2-transformed fold changes of RNA polymerases ±5 kb from TSSs with 200 bp bin size for all active genes comparing Hdac3 KO to WT mouse livers. Genes were ranked according to changes of gene body read densities. gb up: up-regulated in gene body regions; gb down: down-regulated in gene body regions. (b) Comparative analysis of up-regulated (top) and down-regulated (bottom) genes on P14 and P17 by Gene Set Enrichment Analysis (GSEA). Differentially regulated genes were determined based on gene body densities on P14 and by microarray on P17. (PPTX 194 kb)Additional file 5:Table S2. Transcriptional changes in gene body regions (S2a) and promoter-proximal regions (S2b) for active genes in Hdac3 KO vs. WT mouse livers. (XLSX 2301 kb)Additional file 6Figure S4. The relationship of transcriptional changes between promoter proximal levels (x-axis) and gene body levels (y-axis) in mice-liver Hdac3 knockout (green), triptolide (purple) and flavopiridol (blue) treatment. (PPTX 711 kb)Additional file 7:Table S3. Identified enhancers using Hdac3 WT and KO PRO-seq data derived from the mouse liver. (XLSX 381 kb)Additional file 8:Figure S5. The length distribution of identified active enhancers in the mouse liver. (PPTX 60 kb)Additional file 9:Table S4. Transcriptional changes of active enhancers upon Hdac3 deletion. (XLSX 264 kb)Additional file 10:Figure S6. Venn diagram of enhancer-gene associations determined by the closest TSS, within 50 k distance (50 kb) and 4DGenome (4D) methods. (PPTX 34 kb)Additional file 11:Table S5. Rank of upregulated enhancers upon Hdac3 deletion by NRSA. (XLSX 123 kb)Additional file 12:Figure S7. Gene body transcriptional changes between biological replicates after normalization. gb: gene body regions: gbd: read density in gene body regions; WT1: wildtype liver replicate 1; WT2; wildtype liver replicate 2; KO1: Hdac3-deleted liver replicate 1; KO2: Hdac3-deleted liver replicate 2. (PPTX 47 kb)Additional file 13:Figure S8. PRO-seq transcriptional levels around transcription termination sites (TTSs). (PPTX 108 kb)Additional file 14:Figure S9. Histograms showing histone modification enrichment and GRO-cap transcriptional levels around intergenic bidirectional transcripts vs. unidirectional transcripts. (PPTX 175 kb)Additional file 15:Figure S10. Illustration of strategies to identify enhancers and enhancer centers. (PPTX 42 kb)Additional file 16:Table S6. Rank of the previously reported three enhancers regulating KIT. (XLSX 10 kb)


Epigenetic modifications play critical roles in modulating gene expression, yet their roles in regulatory networks in human cell lines remain poorly characterized. We integrated multiomics data to construct directed regulatory networks with nodes and edges labeled with chromatin states in human cell lines. We observed extensive association of diverse chromatin states and network motifs. The gene expression analysis showed that diverse chromatin states of coherent type-1 feedforward loop (C1-FFL) and incoherent type-1 feedforward loops (I1-FFL) contributed to the dynamic expression patterns of targets. Notably, diverse chromatin state compositions could help C1- or I1-FFL to control a large number of distinct biological functions in human cell lines, such as four different types of chromatin state compositions cooperating with K562-associated C1-FFLs controlling “regulation of cytokinesis,” “G1/S transition of mitotic cell cycle,” “DNA recombination,” and “telomere maintenance,” respectively. Remarkably, we identified six chromatin state-marked C1-FFL instances  could act as prognostic biomarkers of acute myelogenous leukemia though influencing cancer-related biological functions, such as cell proliferation, telomere maintenance, and DNA recombination. Our results will provide novel insight for better understanding of chromatin state-mediated gene regulation and facilitate the identification of novel diagnostic and therapeutic biomarkers of human cancers. A variety of posttranslation modifications of histones were reported including histone acetylation and methylation. Aberrant histone modification patterns during tumorigenesis frequently occurred and could trigger pathogenic misregulation of gene expression or genome instability . For exaNetwork motifs, as building blocks of complex networks, provide a unifying language to describe regulatory networks . They caIL-6/JAK/Stat3, in which IL-6 activates JAK and STAT3 was involved in tumor proliferation, tumor microenvironment shaping, and metastasis [STAT3 can promote the apoptosis in human cancer cells.FFL motif is a best design for signal transduction because it excels in the noise-reduction function . Living tastasis . DisruptThe close cooperation between TF regulations and chromatin modifications gives rise to an interesting question—how regulatory network collaborate with multiple chromatin states for controlling gene expression. To address this question, we collected 269 ChIP-seq data including 140 TFs and genome-wide chromatin states across human cell lines, and then constructed directed regulatory networks with nodes and edges labeled with multiple chromatin states. We characterized the association of diverse chromatin states and network motifs. Diverse chromatin states compositions of targets in C1-FFL or I1-FFL control a large number of distinct biological functions. Furthermore, we identified six chromatin state-marked C1-FFL instances that could act as prognostic biomarkers of acute myelogenous leukemia (LAML). Our results suggest that multiple chromatin states play crucial roles in controlling distinct biological functions of regulatory networks and the modified FFLs could serve as important prognostic biomarkers in human cancers.By integrating multiomics data of embryonic stem cells (H1-hESC), lymphoblastoid (GM12878), myelogenous leukemia (K562), and hepatocellular carcinoma (HepG2), we constructed chromatin state-marked transcriptional regulatory network, in which nodes and edges were assigned with specific chromatin states in each of the four cell lines. In the directed transcriptional regulatory network, there are 15 types of chromatin states of genes, which are defined by combinations of histone marks (acetylation or methylation). For example, the active/weak promoter state is characterized by combinations of active epigenetic marks, such as H3K4me2, H3K4me3 and H3K9ac, poised promoter state by both active mark H3K4me3 and repressed mark H3K27me3. The strong/weak enhancer state is characterized by combinations of H3K4me1 and H3K27ac. Different chromatin states provide the central role of chromatin in mediating regulatory signals and controlling DNA access. The number of regulatory interactions ranged from 113,468 to 188,148 in the four cell lines Fig. .Fig. 1DiWe found that regulators and targets within regulatory networks showed diverse chromatin states, reflecting their active or repressed states. Apart from the active promoters, a number of repressed states of genes present in regulatory networks were also observed Fig. . One posTo gain insights into dynamic association between chromatin states and regulatory networks, we sought to systematically search for three-node motifs by taking into account both the network topological structures and chromatin states of nodes and edges. Comparing with random networks, we determined the over-represented association between three-node motifs and gene chromatin states, which were defined as chromatin state-marked network motifs. We found a large number of network motifs marked by diverse chromatin state compositions in the four cell types, including 10,974 in H1, 6,918 in GM12878, 11,340 in K562, and 7349 in HepG2, referring to a total of eighteen types of motif structures Fig. . We obseTAF1 and SMC3 (active state) are marked by active epigenetic marks H3K4me3 and H3K27ac and showed high-level expression of TAF1 and SMC3. TAF1 and SMC3 together regulate two targets UQCRH (active state) and C3orf33 (weak active state) forming two C1-FFL  marked by “active promoter” states in which TAF7 together with ZNF143 regulate protein arginine PRMT5 contributing to functions associated with the self-renew of stem cell, such as regulation of cell cycle process  marked by “active promoter” (top), “poised promoter,” (intermediate) and “active promoter” (bottom) which, captured functions associated with DNA repair and telomere maintenance . Telomere maintenance is associated with stem cell renewal. In particular, it is consistent with essential roles of the “poised promoter” state in stem cell maintenance and subsequent differentiation [PAX5 (“active promoter”) and ELF1 (“weak transcribed”) together regulated downstream target TECPR1 (“weak transcribed”) forming a C1-FFL (PAX5-ELF1-TECPR1) which mediated autophagosome maturation  marked by “active promoter” states of FOSL2, GABPA and WRAP53, a weak transcribed state of CD46 controlling telomere maintenance and viral life cycle, respectively. WRAP53, a novel regulator of p53, promotes cancer cell survival and is a potential target for cancer therapy [CD46 acting as a complement regulatory protein contribute to escape of hepatoma cells from complement-dependent cytotoxicity [CHD2-TAF1-MAD2L1) marked by an “active promoter” state of CHD2, a “weak/poised enhancer” state of TAF1 and an “active promoter” of mitotic arrest deficient 2 (MAD2L1) controlling cycle cell checkpoint [Notably, diverse chromatin states seem to be associated with distinct biological functions. Importantly, these functions are related to specific cellular context Fig. . As an eess Fig. . PRMT5 aem cells . Two H1-nce Fig. . The CDKem cells . The “pontiation . In GM12ion Fig. . Two Hep therapy . Hepatit therapy . CD46 actoxicity . Anothereckpoint .Fig. 6DiHCFC1-NFYA-ABL1 and THAP1-USF1-BRCA2) marked by “active promoter” states captured functions associated with nuclear division and regulation of cytokinesis, respectively  was positively associated with the longer overall survival of AML patients  showing a “weak transcribed” state, forming a C1-FFL controlling DNA recombination in K562. Low expression of YY1-EGR1-INO80C was significantly correlated with AML patient’s poor prognosis  induced and rapidly regulate EGR1 transcription (active promoter) [CCT4 (strong enhancer) forming a C1-FFL in K562 (ELK1-EGR1-CCT4), which mediated positive regulation of telomere maintenance. ELK1 could promote osteosarcoma progression by the inactivating Hippo pathway [ELK1-EGR1-CCT4) correlated with patient’s poor prognosis  , both to pathway . Loss of pathway . Amplifi pathway . The lowWe constructed regulatory networks labeled with chromatin states in human cell lines and performed a systemic analysis of network motifs marked by chromatin states. We found that dynamic association between diverse chromatin states and network motifs. Diverse chromatin states could help regulatory network to control distinct biological functions that are essential for cell identity in human cell lines. Notably, we identified six chromatin state-marked FFL signatures as network-based prognostic biomarkers in LAML. The detection of FFL motifs will help understanding the mechanism of transcriptional regulation and network evolution . PreviouIn addition, in order to validate the stability of positive or negative relationships between TFs and their targets, Spearman’s rank correlation coefficient is calculated between a TF and a target using Human Body Map 2.0 Project RNA-seq data for 16 different human tissues. As a comparison, we found a high consistency between the results from the Spearman’s rank correlation coefficient and those from Pearson correlation coefficient (PCC), with an average of 82.6% . Furthermore, in order to validate the effectiveness of the method, we used the FANMOD approach , a tool We systematically examined the association between epigenetic regulation and transcriptional regulation in human cell lines. Our results underline important roles of diverse chromatin states in fine regulation of gene expression and distinct biological functions. Remarkably, we identified six chromatin state-marked FFLs acting as important prognostic biomarkers of LAML. Chromatin state-mediated FFLs will provide novel insight for the identification of novel diagnostic and therapeutic biomarkers of human cancers.P < 10−5 [ChIP-seq dataset: we obtained 269 ChIP-seq data sets referring to 140 TFs in H1, GM12878, K562, and HepG2 from ENCODE (GSE32465 and GSE31477) . Reads fP < 10−5 . MACS2 cP < 10−5 .DNase-seq dataset: for each of these cell lines, sequence reads of DNaseI-seq experiments from ENCODE Project Consortium  were mapRNA-seq dataset: we extracted whole cell long polyA-selected RNA-seq data sets from ENCODE (GSE26284) . MoreoveUCSC hg19 Known Gene annotation and 3 kb promoters (2.5-kb upstream to 0.5-kb downstream) were used . For eacPCC was calculated between a TF and a target using Human Body Map 2.0 Project RNA-seq data for 16 different human tissues. Positive relationships between a TF and their targets were determined by positive PCCs, and vice versa .S of a type of chromatin state s on a given region (such as a promoter or a TFBS) was calculated by: rs represents the number of bases in a given region overlapping with a specific chromatin state s (s in 1:15), n represents the number of bases in the region, cs represents the total number of bases of the chromatin state s and t represents the total number of bases of all chromatin states. A chromatin state with the highest enrichment score was selected as the chromatin state of a promoter or a TFBS [We obtained genome-wide 15 types of chromatin states based on recurrent combinations of histone marks using a multivariate Hidden Markov Model (HMM) for these four cell lines from UCSC genome browser . Firstlyr a TFBS .6 times, keeping the same number of appearances of all two-node subgraphs as in the real network. It can avoid assigning a high significance to a network pattern only because it contains a highly significant subpattern. We repeated this procedure 1000 times to generate 1000 randomized networks, and assigned a P-value to the chromatin state-marked network subgraph as the fraction of randomized networks that lead to a greater or equal number of frequency than those observed in the real network. Besides, z-score was calculated by observed frequency of the chromatin state-marked subgraph appears in the network subtracting the mean of its appearances in the randomized network and dividing by the standard deviation of its appearances in the randomized network. A normalized z-score by z-scores normalized to length 1 was also used for evaluating the significance as previously proposed [P < 0.01 (ii) normalized z-score > 0 [We mapped the chromatin states of gene promoters and TFBS to the nodes  and edges , respectively, for forming the directed regulatory network labeled with chromatin states. In order to identify over-represented chromatin state-marked network motifs, frequency of occurrence of a specific three-node network subgraph in which nodes marked by specific chromatin states was estimated in the real network. Next, the randomized networks were used to calculate the significance level of the specific chromatin state-marked network subgraph. A degree-preserving randomized network was obtained by swapping edges between random pairs of nodes 10proposed . We idencore > 0 . (iii) Thttps://portal.gdc.cancer.gov/) on January 2015. We assigned each patient a risk score according to a linear combination of the expression level of nodes in the FFL instances weighted equally. Patients with higher risk scores are expected to have poor survival outcomes. We next divided the LAML patients into high-risk and low-risk groups using the median risk scores. The Kaplan–Meier method and log-rank test were used to evaluate the effect of FFLs on overall survival.Gene expression and survival data of 197 patients with LAML were downloaded from TCGA Data Portal  annotation terms using R package clusterProfile (version: 2.0.0) . We used


Experimental evolution of microbes often involves a serial transfer protocol, where microbes are repeatedly diluted by transfer to a fresh medium, starting a new growth cycle. This has revealed that evolution can be remarkably reproducible, where microbes show parallel adaptations both on the level of the phenotype as well as the genotype. However, these studies also reveal a strong potential for divergent evolution, leading to diversity both between and within replicate populations. We here study how in silico evolved Virtual Microbe “wild types” (WTs) adapt to a serial transfer protocol to investigate generic evolutionary adaptations, and how these adaptations can be manifested by a variety of different mechanisms.We show that all WTs evolve to anticipate the regularity of the serial transfer protocol by adopting a fine-tuned balance of growth and survival. This anticipation is done by evolving either a high yield mode, or a high growth rate mode. We find that both modes of anticipation can be achieved by individual lineages and by collectives of microbes. Moreover, these different outcomes can be achieved with or without regulation, although the individual-based anticipation without regulation is less well adapted in the high growth rate mode.All our in silico WTs evolve to trust the hand that feeds by evolving to anticipate the periodicity of a serial transfer protocol, but can do so by evolving two distinct growth strategies. Furthermore, both these growth strategies can be accomplished by gene regulation, a variety of different polymorphisms, and combinations thereof. Our work reveals that, even under controlled conditions like those in the lab, it may not be possible to predict individual evolutionary trajectories, but repeated experiments may well result in only a limited number of possible outcomes. In order to see microbial evolution in action, we often rely on experimental evolution under controlled laboratory conditions. The Long-term Evolution Experiment (LTEE)  and simiEscherichia coli might have survived precisely because they diverted resources away from growth. Indeed, E. coli does exactly this during the stationary phase by means of the stringent response, regulating up to one third of all genes during starvation  is the concentration of substrate transported, and [e] is the concentration of available energy carrier metabolites. KS and KE are the Michaelis-Menten constants for the substrate and energy carrier respectfully. Depending on the direction of transport (importing or exporting) [S] is either the external or the internal concentration of the substrate. Note that for any gene on the genome of a Virtual Microbe, KE are all freely evolvable parameters.Metabolism Similar to the transport, metabolic rates are catalysed by proteins by Michaelis-Menten kinetics with rate v: KR and Biomass production Virtual microbes convert building block B to a biomass product P, which is consumed for cell growth and maintenance Growth(B) and protein production Prod(B), and determines strength with which individuals compete to reproduce. Biomass is next converted to cell volume with a fixed rate, and used for protein expression depending on the demands by the evolved genome. In other words, high rates of expression demand more biomass product for proteins, leaving less biomass product to invest in cell volume or maintenance (see cell volume growth). In total, the rate of change of P then becomes Pri is the basal expression rate of gene i, either up or down-regulated if transcription factors are bound to its operator sequence Regi .Cell volume growth We assume that cell volumes a maximum cell size MaxV and that there is a continuous turnover d of the cell volume at steady state, ensuring the necessity to keep on metabolising even if there is no possibility to reproduce . Volume then changes as Transcriptional regulationThe rates at which genes are expressed is a function of the basal expression rate of the gene and the concentrations of binding TFs and their molecular ligands. The intrinsic basal expression rate of a gene is encoded by a strength parameter in a gene’s promoter region. This basal expression rate can be modulated by TFs that bind to an operator sequence associated with the gene. Binding sites and TF binding motifs are modelled as bit-strings and matching depends on a certain fraction of sequence complementarity. If a minimum complementarity is chosen <1 a match may occur anywhere within the full length of the operator binding sequence and the TF binding motif. The maximum fraction of complementarity achieved between matching sequences linearly scales the strength with which a TF binds the target gene. In addition to binding strength following from sequence complementarity, TFs encode an intrinsic binding affinity for promoters Kb, representing the structural stability of the TF-DNA binding complex.Kl, altering the regulatory effect they exert on downstream genes. These effects are encoded by parameters effbound and effapo for the ligand-bound and ligand-free state of the TF, respectively, and evolve independently. Ligand binding to TFs is assumed to be a fast process, relative to enzymatic and transcription-translation dynamics, and modeled at quasi steady state. We determine the fraction of TF that is not bound by any of its ligands L: τ in a particular state σ (bound or apo) is bound to a particular operator o: cτo between the tf binding motif and the operator sequence [cite Neyfahk]. The binding polynomial in the denominator is the partition function of all TFs TFs can, themselves, be bound to small ligand molecules with binding affinity Vi the fraction of time that the operator is either unbound or bound by a TF in either ligand bound or unbound state and Ei the regulatory effect of that state . Finally, protein concentrations Pr is the evolvable parameter promoter strength and degr a fixed protein degradation rate which is not evolvable.Now, the operator mediated regulation function for any gene is given by Toxicity and death Virtual Microbe death is a stochastic process depending on a basal death rate, which is potentially increased when internal metabolite concentrations reach a toxic threshold. A cumulative toxic effect is computed over the current life time τ of a microbe as M, with m at time t with toxicity threshold toxm. This toxic effect increases the death rate d of microbes starting at the intrinsic death rate rs scales the toxic effect. Virtual Microbes that survive after an update cycle retain the toxic level they accumulated so far. Apart from toxicity and stochastic death, cells can also starve. When insufficient biomass product is available to keep up the slowly decaying volume of the cell, the cells decrease in volume. If the cell volume drops below a minimally viable volume, this cell is automatically for death.Reproduction When an empty grid point is available, the 8 (or less) neighbouring competitors get to compete to reproduce into the grid point. During the ‘in silico serial transfer protocol’ (see below), all cells are continuously mixed, so 8 (or less) random competitors are sampled. When cells compete for reproduction, the cells are ranked according to cell size. The “winner” is then drawn from a roulette wheel with weights proportional to this ranking. Upon reproduction, cell volume is divided equally between parent and offspring, and the genome is copied with mutations (see below). Molecule and protein concentrations remaining constant. Toxic effects built up during the parent’s lifetime do not carry over to offspring.Genome and mutations The genome is a circular list of explicit genes and their promoter region, organised like “pearls on a string”. Genes can be enzymes, transporters, or transcription factors. At birth, the genome is subject to various types of mutations. Large mutations include duplications, deletions, inversions, and translocations of stretches of genes  and very low values (10−5), conditions can be very poor, very rich, and/or potentially toxic. To avoid total extinction, we subdivided the 40x40 grid into four 20x20 subspaces, in which these fluctuations are independent  Escherichia coli REL606 clone that is used in the LTEE [the LTEE . FinallyGrowth rate and yield measurementsYield was approximated by taking sum of all cell volumes. We measured yield both within a single serial transfer cycle (“daily yield”), and as the extended yield when we tested for long-term survival. As all WTs had slightly temporal growth rate dynamics, we estimated the growth rates as the average building block production during the first half of the protocol.Characterising coexistenceUsing the neutral lineage markers (also see Additional file Further configuration of Virtual Microbes Apart from the parameters within the confines of this article (Tables https://bitbucket.org/thocu/virtual-microbese Tables , 3 and 4Additional file 1 Supplementary materials.


Mycobacterium tuberculosis (Mtb) RNAP holoenzyme comprising an RNAP core enzyme and the ECF σ factor σH (σH-RNAP) at 2.7 Å, and solve another crystal structure of a transcription initiation complex of Mtb σH-RNAP (σH-RPo) comprising promoter DNA and an RNA primer at 2.8 Å. The two structures together reveal the interactions between σH and RNAP that are essential for σH-RNAP holoenzyme assembly as well as the interactions between σH-RNAP and promoter DNA responsible for stringent promoter recognition and for promoter unwinding. Our study establishes that ECF σ factors and primary σ factors employ distinct mechanisms for promoter recognition and for promoter unwinding.Bacterial RNA polymerase employs extra-cytoplasmic function (ECF) σ factors to regulate context-specific gene expression programs. Despite being the most abundant and divergent σ factor class, the structural basis of ECF σ factor-mediated transcription initiation remains unknown. Here, we determine a crystal structure of  M. tuberculosis ECF σ factor bound RNA Polymerase (RNAP) holoenzyme and the ECF σ factor-RNAP transcription initiation complex.Extra-cytoplasmic function (ECF) σ factors allow bacteria to rapidly respond to stress conditions. Here the authors provide insights into the mechanism of bacterial stress-induced transcription by determining the crystal structures of the  After forming a complex with the RNA polymerase (RNAP) core enzyme, σ factors guide RNAP to promoter DNA, open double-stranded DNA (dsDNA) to form a transcription bubble, facilitate synthesis of initial short RNA transcripts, and later assist in promoter escape6.Transcription initiation is the first and the most tightly regulated step of bacterial gene expression70- and σ54-type factors based on their distinct structures and mechanisms. The σ70-type factors can be further classified into four groups according to their conserved domains4. Group-1 σ factors (or primary σ factors) contain domains σ1.1, σ1.2, σNCR, σ2, σ3.1, σ3.2, and σ4; group-2 σ factors contain all domains except σ1.1; group-3 σ factors contain σ2, σ3.1, σ3.2, and σ4; while group-4 or extra-cytoplasmic function (ECF) σ factors only contain σ2 and σ47. The genomes of a majority of bacteria harbor one primary σ factor for expression of most genes , and multiple alternative σ factors for expression of genes with cellular- or environmental-context-dependent functions9. The ECF σ factors are the largest family of alternative σ factors. On average, bacterial genomes encode six ECF σ factors; the given number for a particular bacterium will vary according to its genome size and environmental complexity10. ECF σ factors enable bacteria to rapidly respond to a variety of stresses12 and are known to be essential for the pathogenicity of several disease-causing bacteria14. Mycobacterium tuberculosis has 10 ECF σ factors ; deletion of ECF σ factors from M. tuberculosis results in attenuated disease progression  or in alleviated virulence 16.Bacterial σ factors are classified into two types—σA is capable of recognizing at least five conserved functional elements in the DNA sequences of gene promoters, including the “−35 element” (TTGACA)17, the “Z element”18, the “extended −10 element” (TG)19, the “−10 element” (TATAAT)17, and the “discriminator element” (GGG)20. Distinct domains of σA are responsible for interacting with these DNA elements: the domain σ4 forms sequence-specific interactions with exposed bases in the major groove of the −35 dsDNA21; the σ2.5 and σ3.1 domains reach into the major groove of the extended −10 element and make base-specific contacts23; and the σ2 and σ1.2 domains recognize and then unwind the −10 element dsDNA24.The σ2 (W256/W257 in Thermus aquaticus σA or W433/W434 in E. coli σ70) forms a chair-like structure that functions as a wedge to separate the dsDNA at the (−12)/(−11) junction23. The group-2 σ factors use the same set of residues to unwind promoter DNA; but the melting residues of group-3 σ factors are not conserved25. Subsequently, the base moieties of the unwound nucleotides at position −11 and −7 of the nontemplate strand—A(−11)(nt) and T(−7)(nt)—are flipped out and inserted into pre-formed pockets by σ2 and σ1.224. Domain σ1.2 also recognizes the discriminator element by flipping out the guanine base of G(−6)(nt) and inserting it into a pocket24. Although σ3.2 does not read the promoter sequence directly, it is essential for transcription initiation. Domain σ3.2 reaches into the RNAP active site cleft and “pre-organizes” template single-stranded DNA (ssDNA)24. Domain σ3.2 also blocks the path of the extending RNA chain (>5 nt)27 thereby contributing to both initial transcription pausing28 and promoter escape30.During the process of promoter unwinding, a tryptophan dyad of σ31. In contrast to the high tolerance to sequence variation at the −35 and the −10 promoter elements exhibited by the primary σ factor, the ECF σ factors have stringent requirements for sequence identity in the −35 and the −10 elements and for spacer length between these two elements through an unknown mechanism32. Although both the primary and ECF σ factors recognize the −35 element via σ4 and recognize the −10 element via σ2, the protein sequences of these two domains are not well conserved, and the consensus sequences of the two corresponding DNA elements vary. Crystal structures of individual σ2 or σ4 domains of ECF σ factors complexed with cognate DNA have suggested that these ECF domains bind the −35 and the −10 elements differently than does the primary σ factor, implicating a unique means of promoter recognition by ECF σ factors34. Another striking difference was revealed by a sequence analysis showing the surprising fact that ECF σ factors do not contain σ3 domains (σ3.1 and σ3.2), but instead contains a linker—highly variable in both length and sequence—to connect the σ2 and σ4 domains4. This fact immediately raises the question of how these σ factors perform multiple steps of transcription initiation that the σ3 domain performs in the primary σ factor.Each category of known ECF σ factors recognizes promoters bearing a unique sequence signature at the −35 and the −10 elementsE. coli σE2/−10 ssDNA binary complex suggests that bacterial ECF σ factors probably recognize and unwind promoters through a unique mechanism. Specifically, E. coli σE employs a flexible “specificity loop” to recognize a flipped master nucleotide of the −10 element and probably unwinds at a distinct position compared with that of σ70 by using non-conserved melting residues34. In contrast to the large collection of structural information of primary σ factor, no structure of bacterial RNAP complex with ECF σ factor is available. Therefore, it is largely unknown how ECF σ factors form a holoenzyme with RNAP and how ECF σ factors work alongside RNAP to recognize and to unwind promoter DNA. Here we report the crystal structure of an ECF σ factor-RNAP holoenzyme comprising M. tuberculosis RNAP and σH at 2.70 Å resolution. We also report the crystal structure of an ECF σ factor-RNAP transcription initiation complex comprising M. tuberculosis σH-RNAP holoenzyme, a full transcription bubble of promoter DNA, and an RNA primer at 2.80 Å resolution. The crystal structures present detailed interactions among RNAP, ECF σ factors, and promoter DNA. The structures together with data from biochemical assays collectively establish the structural basis of RNAP holoenzyme assembly, promoter recognition, and promoter unwinding by the ECF σ factors.A recent crystal structure of M. tuberculosis σH-RNAP holoenzyme were unexpectedly obtained during an initial attempt to crystallize M. tuberculosis σH-RPo  [https://www.rcsb.org/structure/5TW1] as the searching model35. The Fo–Fc map shows unambiguous density for σH residues 22–195  and σH4 (residues 140–195) fold into independent helical domains  and makes extensive electrostatic interactions with the latter  but showed little effect on promoter with sub-optimal spacer lengths (16 and 18 bp)  Fig. , suggestH-RNAP is the interaction between RNAP and the linker region connecting σH2 and σH4. The linker is the least conserved region among the ECF σ factors and shares no sequence similarity with the linker of σA  σH4 inserts into the major groove and reads the sequence of the −35 element  thent Figs.  and 4b; sm Figs.  and 4e; NA Figs.  and 5a–cNA Figs. ; and (6)ft Figs. .Fig. 4ThH-regulated promoters have a distinct consensus sequence at their −35 elements  out of six positions  makes two H-bonds with R186 through its O6 and N7 atoms; the G−33(nt) makes one H-bond with S182 through its O6 atom; and the A−31(nt) makes one H-bond with M181. Moreover, M181 forms extensive van der Waals interactions with nucleotides at positions −31 and −30 of the template strand. The interactions are important, as alanine substitutions of R186 or S182 resulted in substantial loss of transcription activity  at this region42. We observed a similar DNA conformation  makes no base-specific interactions. Previous crystal structure of 188 Fig. . Consist188 Fig. .Mtb σH-RPo, σH-RNAP contacts phosphate backbones of the spacer region between the −35 and the −10 elements at three positions  R77−14 Fig. . These ions Fig. .A) is unwound, despite the fact that the −10 nucleotides in the synthetic nucleotide scaffold were designed to be complimentary /(−12) of promoters targeted by σA; Fig. A34. In the structure, N88 blocks the pathway of upstream dsDNA and serves as a wedge to disrupt the stacking of base pairs at the positions −11 and −10. The base pair at position −10 is subsequently forced open by σN88 via a competitive H-bond between the Watson-Crick atom of the T−10(nt) and N88  and T−9(nt)) are stabilized by two adjacent pockets of σH2; these pockets are where the sequence identities are “read”  and T−9(t)) are also trapped in a cleft created by the RNAP-β lobe and σH2. Specifically, the base moieties of A−10(t) and T−9(t) form a stack with βR395, σY90, and σY94, and the phosphate moieties are stabilized by βK428, βN419, and σQ98. The functional importance of these residues for promoter DNA unwinding was underscored by our finding that their substitution with alanine resulted in defects in transcription /(−11) junction for σA; the −11/−10 junction for σH corresponding to the (−13)/(−12) junction for σA; Supplementary Fig. E. coli or W256/W257 in T. aquaticus) is essential for promoter unwinding at the (−12)/(−11) junction by σA44, but the residues at the corresponding positions of σH (R84/I85) are not conserved  resulted in substantial loss of transcription activity, confirming that σH opens promoter through a different mechanism than σA and supporting the mechanism proposed for E. coli σE  ju σE Fig. 34.H-regulated promoters contain a “G−11T−10T−9” consensus sequence at the −10 element  of the −11 G:C pair makes one H-bond with N92 and extensive Van der Waal interactions with I91, alanine substitution of N92 or I91 resulted in modest or substantial decrease of transcription, respectively  is accommodated by a shallow protein pocket on σH2 wherein I85 forms a stack with the base of T−10(nt) at the pocket bottom and W81 supports the sugar moiety of T−10(nt) on one side of the pocket , likely contributing to the sequence specificity known to occur for this position. Alanine substitution of I85 or W81 causes severe defects in transcription activity   is flipped out and inserted into a protein pocket formed by the “specificity loop” of σH234. The thymine base of T−9(nt) stacked on top of W81 makes one H-bond with the main-chain atom of σH residue R73 and two H-bonds with the side-chain atoms of σ residues T76 and N77 on the specificity loop  completely abolished promoter activity   severely decreased transcription activity from the consensus promoter /σH2 interaction for transcription. The fact that both the primary and the ECF σ factors use the specificity loop to read the sequence identity (position −9 of σH-regulated promoters corresponding to position (−11) of σA-regulated promoters; Supplementary Fig. 34. Outside of these crucial positions, σH2 forms fewer interactions with nontemplate nucleotides . In the structure, the base moiety of nucleotide at position −8 is disordered (no electron density) and the base moieties of −7 and −6 nucleotides are sandwiched between residue T405 of the RNAP-β subunit and residue Y83 of σH2  (corresponding to the (−7) position of σA-regulated promoters) and inserts into a shallow pocket created by σH2 and the RNAP-β gate loop  forms a stacking interaction with R282, two H-bonds with E285 on the RNAP-β gate loop, and has van der Waals interactions with D38 and Q39 of σH2. Mutations of the RNAP-β gate loop (βR282A or βE285A) cause severely reduced transcription activity , we tested whether this position exhibits sequence preference. Results of in vitro transcription assays showed that promoters with C or G at this position have much higher transcription activity compared with T or A (nt) nucleotide in a protein pocket24; however, the protein pocket is mainly formed by residues from σA1.2 and σA224, and the structural features that determine sequence specificity are located on σA  is inserted into the “G” pocket in σH-RPo (nt) does in the σA-RPo complex (nt) forms a stacking interaction with βW211, the nontemplate T+1(nt) in σH-RPo was pushed out from the base-stacking. Instead, the nucleotide immediately upstream of +1 makes the stacking interaction with βW211 in σH-RPo 26. These interactions provide explanations for the effects of σA3.2 on de novo RNA synthesis48. The structure predicts that the σA3.2 finger has to be displaced by an RNA molecule of >4-nt in length and that the σA3.2 loop in the RNA exit channel has to be cleared during the promoter escape process. The interactions observed in the σA-RPo structure underscore the key role of the σA3.2 on abortive production, pausing, and promoter escape in transcription initiation50. In our crystal structures of σH-RPo, we show that σH3.2 guides the template ssDNA into the active site cleft and forms interactions with template ssDNA , T−9(nt), and G−5(nt) (corresponding to the positions (−12), (−11) and (−7) of σA-regulated promoters)—were flipped out and inserted into three respective protein pockets on σH (nt) and T(−7)(nt) on σA24. This extra pocket for T−10(nt) on σH was also suggested in a previous structure of a E. coli σE2/−10 element binary complex  pocket are generally conserved between ECF σ factors but are distinct from σA, suggesting that other ECF σ factors likely also recognize the nucleotide at this position using similar protein pockets  (corresponding to position (−7) of σA-regulated promoters) than does σA-RNAP for T(−7)(nt)  is sandwiched between σH2 and the RNAP-β gate loop, while the thymine base of T(−7)(nt) resides in a pocket on σA1.224. Our mutation study of the G−5(nt) pocket residues demonstrated that the RNAP-β gate loop functions to recognize this particular nucleotide  and G−5(nt) on σH -RNAP do not exist in the absence of promoter DNA  is disordered; and the RNAP-β gate loop is too far away from the σH2 to form the G−5(nt) pocket  of CRE in a same way as does σA-RNAP  to unwind promoter DNA; (2) σH and σA unwind promoter DNA at positions differing by one base pair ((−13)/(−12) junction for σH vs. −(12)/(−11) junction for σA); and (3) σH traps and reads two unwound nucleotides (T(−12)(nt) and T(−11)(nt) immediately after the unwinding points), whereas σA only traps and reads one unwound nucleotide (A(−11)(nt)). Although it is unclear whether trapping of the flipped nucleotides initiates or facilitates the event of promoter unwinding, such interactions play crucial roles during RPo formation.Our crystal structures suggest that σE. coli σE2 for the T(−12)(nt) in a crystal structure of E. coli σE bound to the −10 element ssDNA, and predicted that E. coli σE unwinds promoter dsDNA at the (−13)/(−12) junction34. Our crystal structure of σH-RPo clearly confirms the unwinding position proposed in the study by Campagne et al.. Sequence alignment of multiple ECF σ factors and σA revealed that most of the ECF σ factors do not contain the tryptophan dyad of σA at corresponding positions, but instead share a conserved (−12) pocket  juH-regulated promoter showed that substitution of the consensus sequence at almost every position on the −35 element and −10 element abolished transcription activity  unwinds promoter DNA less efficiently than does σA. This putative sub-optimal unwinding efficiency could be compensated by employing a very-high-affinity consensus sequence of promoter DNA to facilitate its loading25. Our proposed induced-fit mode of nontemplate ssDNA binding by σH-RNAP at the position immediate downstream of unwinding—i.e. T−9(nt)—also require the consensus promoter sequence to induce formation of correct conformation of the “specificity loop”  for E. coli σ70 corresponding to T−9(nt) for Mtb σH—as demonstrated in the case of E. coli σ70-RNAP55.We have shown that σop” Fig. ; RNAP isIn conclusion, we demonstrate the structural basis of RNAP holoenzyme formation and transcription initiation by the ECF σ factors, thereby deepening our understanding the basic mechanisms of transcription initiation used by the largest and most diverse group of bacterial initiation factors. Our work will facilitate the rational design of orthogonal transcription units based on ECF σ factors and should help computational chemistry and other efforts to design selective antibacterial agents through the inhibition ECF σ factor-mediated transcription initiation.MtbσH, the M. tuberculosis σH gene amplified from M. tubercolusis genomic DNA (see Supplementary Data NcoI and XhoI restriction sites. The pTolo-EX5-MtbσH derivatives bearing single or double mutations were generated through site-directed mutagenesis (Transgen biotech).The plasmids used in this study are listed in Supplementary MtbσH derivatives encoding chimeric σH were generated by replacing the DNA fragment encoding Mtb σH3.2 (aa 96–144) with DNA fragments encoding Ec σA , Mtb σE3.2 (aa 150–189), Mtb σL3.2 (aa 78–122), or Mtb σM3.2 (aa 98–137) in pTolo-EX5-MtbσH (Tolo Biotech).The pTolo-EX5-Mtb-rpoA-rpoZ plasmid was constructed by replacing Mtb rpoD with Mtb rpoZ in parent plasmid pACYCDuet-Mtb-rpoA-sigA plasmid using KpnI and NdeI  of ClpB gene amplified from M. tuberculosis genomic DNA was cloned into pEASY-Blunt simple vectors, resulting in pEASY-Blunt-pClpB  cells (NovoProtein) carrying pTolo-EX5-MtbσH were cultured in Luria-Bertani broth (LB) at 37 °C, and the expression of N-terminal sumo-tagged Mtb σH was induced at 18 °C for 14 h with 0.5 mM isopropyl β-d-1-thiogalactopyranoside (IPTG) at OD600 of 0.8. Cells were harvested by centrifugation , re-suspended in lysis buffer (20 mM Tris-HCl (pH 8.0), 0.5 M NaCl, 5% (v/v) glycerol, 0.5 mM β-mercaptoethanol, and protease inhibitor cocktail (bimake.cn)) and lysed using an Avestin EmulsiFlex-C3 cell disrupter . The lysate was centrifuged  and the supernatant was loaded on to a 2 mL column packed with Ni-NTA agarose . The protein was washed by lysis buffer containing 20 mM imidazole and eluted with lysis buffer containing 250 mM imidazole. The eluted fraction was digested by tobacco etch virus protease and dialyzed overnight in dialysis buffer (20 mM Tris-HCl (pH 8.0), 0.2 M NaCl, 1% (v/v) glycerol, and 0.5 mM β-mercaptoethanol). The sample was loaded onto a second Ni-NTA column and the cleaved protein was retrieved from the flow-through fraction. The sample was diluted to the dialysis buffer with 0.05 M NaCl and further purified through a Heparin column  with buffer A (20 mM Tris-HCl (pH 8.0), 0.05 M NaCl, 1% (v/v) glycerol, and 1 mM dithiothreitol (DTT)) and buffer B (20 mM Tris-HCl (pH 8.0), 1 M NaCl, 1% (v/v) glycerol, and 1 mM DTT). Fractions containing M. tuberculosis σH was concentrated to 5 mg/mL and stored at −80 °C. The M. tuberculosis σH derivatives were prepared by the same procedure.For preparation of M. tuberculosis σH, BL21 (DE3) strains carrying pTolo-EX5-MtbσH were cultured in SelenoMet base medium supplemented with nutrient mix (Molecular Dimensions) at 37 °C. The amino-acid mixture containing selemethionine was added into the culture at OD600 of 0.4 and the protein expression was induced with 0.5 mM IPTG at OD600 of 0.8 for 14 h at 18 °C. The SeMet-labeled Mtb σH was purified as described above.For preparation of selenomethionines (SeMet)-labeled M. tuberculosis RNAP core enzyme was expressed and purified from E. coli BL21(DE3) carrying pETDuet-Mtb-rpoA-rpoZ and pACYCDuet-Mtb-rpoB-rpoC as described56. The protein sample was concentrated to 5 mg/mL and stored at −80 °C.The H-RPo* for crystallization of σH-RNAP holoenzyme was prepared from synthetic oligos .Nucleic acid scaffolds for assembly of σH-RPo was prepared from synthetic oligos .Nucleic acid scaffolds for crystallization of σM. tuberculosis σH-RPo and σH-RPo* were reconstituted from M. tuberculosis RNAP core enzyme, σH (or SeMet-σH), and nucleic acid scaffolds. The RNAP core enzyme, σH, and nucleic acid scaffolds were mixed at a 1:4:1.2 molar ratio and incubate at 4 °C overnight. The mixture was loaded on a HiLoad 16/60 Superdex S200 column  equilibrated in 20 mM Tris-HCl (pH 8.0), 0.1 M NaCl, 1%(v/v) glycerol, and 1 mM DTT. Fractions containing Mtb σH-RPo were collected, concentrated to 7.5 mg/mL, and stored at −80 °C.The H-RNAP holoenzyme was obtained during an attempt for obtaining the σH-RPo* with the fork transcription bubble DNA scaffold (no RNA oligo in the scaffold). The initial screen was performed by a sitting-drop vapor diffusion technique. Crystals grown from optimized reservoir solution A , and 10% PEG4000 mixed with 1 μL 7.5 mg/mL protein complex) for 3 days at 22 °C were harvested for X-ray diffraction data collection. Crystals were soaked in stepwise fashion to reservoir solution A containing 18%(v/v) -(−)-2,3-butanediol (Sigma-Aldrich) and cooled in liquid nitrogen. The crystals of σH-RNAP derivative containing SeMet-labeled σH were obtained by analogous procedure.The structure of σ57. The structure was solved by molecular replacement with Phaser MR58 using the structure of M. smegmatis core enzyme in a M. smegmatis transcription initiation complex (PDB: 5TW1)35 [https://www.rcsb.org/structure/5TW1] as the search model. Only one molecule of RNAP core enzyme was found in one asymmetric unit. The electron density maps show clear signal for σH. Cycles of iterative model building and refinement were performed in Coot59 and Phenix60. Residues of σH were built into the model at the last stage of refinement. No density of nucleic acid was observed in all stages of refinements, suggesting that the nucleic acids dissociated during crystallization resulting in a crystal of σH-RNAP holoenzyme. The final model of Mtb σH-RNAP holoenzyme was refined to Rwork and Rfree of 0.218 and 0.258, respectively. Analogous procedures were used to refine the structures of σH-RNAP holoenzyme with SeMet-labeled σH.Data were collected at Shanghai Synchrotron Radiation Facility (SSRF) beamlines 17U and 19U1, processed using HKL2000H-RPo was performed by a sitting-drop vapor diffusion technique. Crystals grown from reservoir solution B  for 15 days at 22 °C were harvested for X-ray diffraction data collection. Crystals were soaked in stepwise fashion to the reservoir solution B containing 18%(v/v) -(−)-2,3-butanediol (Sigma-Aldrich) and cooled in liquid nitrogen. Data were collected at SSRF beamlines 17U and 19U1, processed using HKL200057. The structure was solved by molecular replacement with Phaser MR58 using the structure of M. tuberculosis σH-RNAP as a search model. Only one molecule of σH-RNAP was found in one asymmetric unit. The electron density map showed clear signals for nucleotides in transcription bubble and downstream DNA duplex after initial rigid-body refinement, and clear signals for nucleotides in upstream DNA duplex after iterative cycles of model building and refinements in Coot59 and Phenix60. The nucleotides were built into the model at the last stage, and the final model of Mtb σH-RPo was refined to Rwork and Rfree of 0.220 and 0.255, respectively.The initial screen of σM. tuberculosis RNAP σH-holoenzyme were performed as follows: reaction mixtures contained (20 μL): 80 nM M. tuberculosis RNAP core enzyme; 1 μM M. tuberculosis σH; 40 mM Tris-HCl, pH 7.9; 75 mM KCl; 5 mM MgCl2; 2.5 mM DTT; and 12.5% glycerol. Reaction mixtures were incubated for 10 min at 37 °C, and then supplemented with 2 μL promoter DNA , 3 mM ATP, 3 mM GTP, and 3 mM CTP), and RNA synthesis was allowed to proceed for 10 min at 37 °C. Reactions were terminated by adding 8 μL loading buffer , boiled for 2 min, and stored in ice for 5 min. Reaction mixtures were applied to 15% urea-polyacrylamide slab gels (19:1 acrylamide/bisacrylamide), electrophoresed in 90 mM Tris-borate (pH 8.0) and 0.2 mM EDTA, and analyzed by storage-phosphor scanning .Transcription assays with M. tuberculosis RNAP σA-holoenzyme were performed essentially as above except that σA instead of σH were added and N25 promoter DNA were used  containing 160 nM M. tuberculosis RNAP core enzyme, 1 μM M. tuberculosis σH, 40 mM Tris-HCl, pH 7.9, 75 mM KCl, 5 mM MgCl2, 2.5 mM DTT, and 12.5% glycerol were incubated for 10 min at 37 °C, and then supplemented with 2 μL promoter DNA ) and were allowed to proceed for 20 min at 37 °C. The reactions were terminated and the transcripts were separated and visualized as above.Transcription assays using All biochemical assays were performed at least three times independently. Data were analyzed with SigmaPlot 10.0 (Systat Software Inc.).Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting SummarySource Data


Arabidopsis thaliana TF GROWTH-REGULATING FACTOR9 (GRF9) and demonstrate that it exerts its negative effect on leaf growth by activating expression of the bZIP TF OBP3-RESPONSIVE GENE 3 (ORG3). While grf9 knockout mutants produce bigger incipient leaf primordia at the shoot apex, rosette leaves and petals than the wild type, the sizes of those organs are reduced in plants overexpressing GRF9 (GRF9ox). Cell measurements demonstrate that changes in leaf size result from alterations in cell numbers rather than cell sizes. Kinematic analysis and 5-ethynyl-2'-deoxyuridine (EdU) incorporation assay revealed that GRF9 restricts cell proliferation in the early developing leaf. Performing in vitro binding site selection, we identified the 6-base motif 5'-CTGACA-3' as the core binding site of GRF9. By global transcriptome profiling, electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP) we identified ORG3 as a direct downstream, and positively regulated target of GRF9. Genetic analysis of grf9 org3 and GRF9ox org3 double mutants reveals that both transcription factors act in a regulatory cascade to control the final leaf dimensions by restricting cell number in the developing leaf.Leaf growth is a complex process that involves the action of diverse transcription factors (TFs) and their downstream gene regulatory networks. In this study, we focus on the functional characterization of the  Arabidopsis thaliana, commonly known as thale cress. We show that GRF9 negatively controls final leaf dimensions by restricting cell number in the leaf primordium, while the size of the leaf cells remains unaltered. Using molecular techniques, we discovered that GRF9 acts within a regulatory cascade with another transcription factor, called ORG3. Furthermore, we show that GRF9 and ORG3 negatively control the organ size of flower petals, demonstrating that both transcription factors have a more general role in regulating organ size. Our study is one of the first reports to demonstrate a negative role of GRF transcription factors for organ growth.Leaves are central plant organs that determine photosynthetic efficiency and thereby biomass production, yet the molecular mechanisms driving and controlling their growth and development are still far from being completely understood. Leaf growth—like other biological processes in different organisms—is controlled by an extensive network of genes that encode proteins of diverse cellular functions. Proteins controlling the activity of genes, called transcription factors, are particularly important for staging developmental processes. Here, we report how GRF9, a member of the so-called Growth-Regulating Factor (GRF) family of transcriptions factors, controls growth of leaves in the widely-studied model plant  Arabidopsis thaliana, phase one starts by the initiation of leaf primordia from cells located within the peripheral zone of the SAM, a process that lasts 2–3 days  ethanol and then in sterilisation solution (6% [w/v] sodium hypochlorite) for 10 min and thereafter washed three times with autoclaved ddH2O. After sterilization, the seeds were sown on half-strength MS medium , supplemented with 1% (w/v) sucrose and appropriate antibiotics, and solidified with 0.7% (w/v) phytoagar. Two-week-old Arabidopsis seedlings were carefully removed from plates and transplanted to soil  or, if necessary, directly subjected to various treatments. Unless otherwise indicated, Arabidopsis plants were grown in controlled conditions in a growth chamber with 16-h day length provided by fluorescent light at 80 or 120 μmol m-2 s-1, a day/night temperature of 20/16°C and relative humidity of 60/75%.After imbibition, the seeds were stratified at 4°C for 3 days. The seeds were germinated at 22°C under a 16-h day (140 μmol mGRF9-CELD: The GRF9 coding sequence (CDS) was PCR-amplified from Arabidopsis seedling cDNA using primers GRF9-CELD-fwd and GRF9-CELD-rev and inserted into pJET1.2  from where it was then transferred via NheI and BamHI sites into plasmid pTacLCELD6XHis  glycerol and 0.025% [v/v] Nonidet P-40. 40,000 fluorescent units h-1 of the CELD activity of GRF9-CELD protein and 2 pmol of biotinylated probes were used per assay. The washing buffer contained 25 mM HEPES/KOH, pH 7.0, 50 mM KCl and 1 μM ZnCl2. The cellulase activity of GRF9-CELD protein bound to immobilised biotinylated probes was assayed by incubation in 100 μl of the CELD substrate solution (1 mM methylumbelliferyl β-D-cellobioside (MUC) in 50 mM Na-citrate buffer, pH 6.0) at 40°C for 3 h. A biotin-labelled double-stranded oligonucleotide without a GRF9 binding site was used as a control for background activity.The DNA-binding activity of GRF9-CELD was measured essentially as described previously ,95 usingGRF9-IOE seedlings grown on MS medium (3 and 4 h after induction with 10 μM estradiol or 0.15% [v/v] ethanol for control) or detached mature leaves from 4-week-old soil-grown GRF9-IOE plants (6 h after 10 μM estradiol treatment or 0.15% [v/v] ethanol for control) were used for Affymetrix ATH1 micro-array hybridisations . Labelling, hybridisation, washing, staining, and scanning procedures were performed by ATLAS Biolabs . Up-/down-regulated genes at each time point were obtained by calculating the ratio of the gene expression values in treatment versus control values. In addition, all time points were considered as three replicates and the differentially expressed genes were obtained using the limma package  ethanol as control), or from 2-week-old GRF9ox and grf9-1 seedlings grown on MS medium (WT as control). Values represent the means of replicates obtained from three sets of seedlings .Gene expression as determined by Affymetrix ATH1 microarray hybridizations (first two columns) or qRT-PCR (other columns). RNA for expression analysis was obtained from 2-week-old (PDF)Click here for additional data file.S9 Figorg3-1 (SALK_025676) and (B) org3-2 (SAIL_737_H11) mutants. (a) Right gene-specific primer and T-DNA left boarder primer, and (b) left and right gene-specific primers for genotyping . M, DNA size marker. Primer sequences are given in  (C) Semi-quantitative RT-PCR using ORG3-specific primers performed on total RNA isolated from 1-week-old org3-1, org3-2, WT, ORG3ox1 and ORG3ox2 seedlings. ACTIN2 was used as a control. (D) Expression of ORG3 measured by qRT-PCR in org3 knockout and ORG3ox plants. (E) Expression of GRF9 and ORG3 measured by qRT-PCR in grf9-2 org3-1 (lines 3 and 7) and GRF9ox-1 org3-1 (lines 33 and 34) double mutants. Values in panels D and E represent the means of three technical replicates ± SD. (F) DNA genotyping results of double mutant lines using (a) right gene-specific primer and T-DNA left boarder primer, (b) left and right gene-specific primers for genotyping , and (c) 35S-up and reverse GRF9-IOE specific primers. Genes tested by the chosen primer combinations are underlined. M, DNA size marker.Genotyping of (A) (PDF)Click here for additional data file.S10 Figorg3-1 plants. (B) Petal size and (C) petal cell area. Data represent means ± SD from at least 32 petals . The asterisk indicates a significant difference from WT . Bar = 0.5 mm.(A) Mature petals of WT and (PDF)Click here for additional data file.