Monascus yellow pigments by Monascus ruber CGMCC 10910 under high glucose stress was investigated. The increase of lipid content was the major contributor to the increase of dry cell weight (DCW), and the lipid-free DCW was only slightly changed under high glucose stress, which benefited the accumulation of intracellular hydrophobic pigments. The fatty acid composition analysis in Monascus cell membranes showed that high glucose stress significantly increased the ratio of unsaturated/saturated fatty acid and the index of unsaturated fatty acid (IUFA) value, which would improve the fluidity and permeability of the cell membrane. As a consequence, high glucose stress increased extracellular yellow pigments production by enhancing secretion and trans-membrane conversion of intracellular pigments to the broth. The total yield of extracellular and intracellular yellow pigments per unit of lipid-free DCW increased by 94.86 and 26.31% under high glucose stress compared to conventional fermentation, respectively. A real-time quantitative PCR analysis revealed that the expression of the pigment biosynthetic gene cluster was up-regulated under high glucose stress. The gene mppE, which is associated with yellow pigment biosynthesis, was significantly up-regulated. These results  indicated that high glucose stress can shift the Monascus pigment biosynthesis pathway to accumulate yellow pigments and lead to a high yield of both extracellular and intracellular yellow pigments. These findings have potential application in commercial Monascus yellow pigment production.The biosynthesis of microbial secondary metabolites is induced by a wide range of environmental stresses. In this study, submerged fermentation of The online version of this article (doi:10.1186/s13568-017-0382-5) contains supplementary material, which is available to authorized users. Monascus pigments are secondary metabolites with polyketide structures that are produced by Monascus spp. , which was cultivated on PDA medium at 30 °C for 7 days and then stored at 4 °C. The seed medium contained (g/L): glucose, 20; yeast extract, 3; peptone, 10; KH2PO4, 4; KCl, 0.5; and FeSO4·7H2O, 0.01. The inoculum was incubated in a 250-mL Erlenmeyer flask containing 50 mL of seed medium at 30 °C and was shaken at 180 rpm for 25 h. The conventional fermentation medium contained (g/L): glucose, 50; (NH4)2SO4, 5; KH2PO4, 5; MgSO4·7H2O, 0.5; KCl, 0.5; MnSO4·H2O, 0.03; ZnSO4·7H2O, 0.01; and FeSO4·7H2O, 0.01. Fermentation medium containing a higher initial glucose concentration (up to 200 g/L) was used for glucose concentration stress experiments. The fermentation experiment was conducted at 30 °C with shaking at 180 rpm for 8 days in a 250-mL Erlenmeyer flask containing 25 mL of fermentation media and using 2 mL of inoculum. All experiments were performed in triplicate.All experiments in this study were performed with After fermentation, the spent medium was vacuum filtered through a 0.8 mm mixed cellulose esters membrane, after which the filtrate was diluted. Extracellular pigment production was assessed using a UV–Visible spectrophotometer  scanning from 300 to 550 nm at 1-nm intervals ; the mixture was then incubated for 1 h and then passed through filter paper; finally, the filtrate (intracellular extract) was diluted for determining the intracellular pigment concentration. A UV–Visible absorbance spectrum of intracellular pigments was taken from 300 nm to 550 nm at 1-nm intervals, and the absorbance units (AU) at peak wavelengths of 410 and 470 nm multiplied by the dilution ratio were used as indexes of the intracellular yellow and orange pigments concentrations  and acetonitrile (phase B) were used as the mobile phase using the following gradient program: 0 min, 80% A, 20% B; 25 min, 20% A, 80% B; 35 min, 20% A, 80% B; 36 min, 80% A, 20% B; 41 min, 80% A, 20% B. The PDA was set at 200–600 nm, and the flow rate of the mobile phase was 0.8 mL/min.Analyses of sample compositions were performed using an Alliance e2695 HPLC system  equipped with a 2998 Photodiode Array (PDA) detector  and a Zorbax Eclipse Plus C18 column . The temperature of the column oven was set at 30 °C. A mixture of HLiquid chromatography–mass spectrometry consisted of a HP1100 HPLC system  and a micro TOF-QII mass spectrometer . The C18 column and chromatographic conditions were the same as mentioned above, except for mobile phase A .n-hexane was collected for GC–MS analysis, using an Agilent 6890 GC  coupled to an Agilent 5973 mass selective detector (MSD) , equipped with a HP-5MS column . The front injection was 250 °C with a split ratio of 70:1. Helium gas  was used as the carrier gas at a flow rate of 50 mL/min. The oven temperature program was as follows: 80 °C for 2 min, then raised to 150 °C at a rate of 10 °C/min, and then further to 230 °C at a rate of 3 °C/min, keeping at 230 °C for 5 min. The electron impact energy was 70 eV, and the ion source temperature was set at 230 °C.After 8 days of fermentation, mycelia in the fermentation broth were collected. The fatty acid in cell membrane of the mycelia was extracted, purified and methylated according to the method described by Wang et al. . After t2 before total RNA extraction using the Plant RNA Extraction Kit (TakaRa MiniBEST). cDNA was synthesized using the PrimeScript™RT reagent Kit with gDNA Eraser (TaKaRa). Primers for the amplification of MpFasA2, MpFasB2, MpPKS5, mppR1, mppB, mppC, mppD, mppE, mppR2 (GenBank accession No. KC148521) and the actin gene (GenBank accession No.AJ417880) were listed in Additional file actin gene was used as a reference gene. Gene expression was monitored by RT-qPCR using the SYBR Premix Ex TaqII (TaKaRa). RT-qPCR was performed using a Lightcycler 96  with the following cycling program: pre-incubation at 95 °C for 30 s, followed by a two-step amplification  and dissociation curve analyses .The effects of high glucose stress on the expression of key genes during pigments production were investigated using real-time quantitative PCR. Mycelia were collected and stored in liquid Np < 0.05, p < 0.01 was considered statistically significant.Each experiment was repeated at least in triplicate. Numerical data are presented as the mean ± SD. The differences among different treatments were analyzed using one-way ANOVA. All statistical analyses were performed by using SPSS 22.0, software. 350 at 150 g/L IGC  of cells takes into account both the accumulation of lipids and lipid-free dry cell weight (LFDCW) accumulation  were found in the spent broth , palmitic acid (C16:0), stearic acid (C18:0), eicosanoic acid (20:0), oleic acid (C18:1), linoleic acid (C18:2) and linolenic acid (C18:3), respectively  were monitored by RT-qPCR  as the reference value . The expression levels of the pigment biosynthetic genes first increased, and then decreased during the fermentation under high glucose stress. During the first 3 days, the expression levels of the genes mppE, mppD and regulatory gene mppR2 were significantly up-regulated under the high glucose stress. In the middle and later stages of the fermentation (from the 3rd day to the 8th day), the expression levels of the genes MpFasA2, MpFasB2, MpPKS5, mppB, mppD, mppE, and mppR1 were significantly up-regulated (p < 0.01 or p < 0.05) and they were all higher than the control. But the expression levels of the gene mppC and the regulatory gene mppR2 were down-regulated. These results demonstrated that high glucose stress could regulate gene expression for pigment biosynthesis, and increase production of both intracellular and extracellular pigments . As the genes MpFasA2, MpFasB2, MpPKS5, mppD, and mppB are structural genes for pigment biosynthesis and mppR1 is a regulatory gene , the expression levels of Monascus pigments are mixtures with multi-components  to >150 g/L (high). An interesting phenomenon was observed that the dominating intracellular pigments changed from orange to yellow pigments  . Chen et al.  encode a canonical fungal fatty acid synthase and supply the medium-chain (C8 and C10) fatty acyl moieties for Monascus pigments biosynthetic activities , which can transfer the medium-chain (C8 and C10) fatty acyl group into the polyketide chromophore to complete pigment biosynthesis. The mppR1 and mppR2 genes are regulatory genes for pigments biosynthesis  in the polyketide biosynthesis pathway. The production of orange pigment was enhanced, while that of the yellow pigments decreased in an mppE knockout mutant (ΔmppE). The production of yellow pigments was only enhanced with marked reductions in other pigments in an mppE overexpression strain (OV-mppE)  and a decrease in orange pigments under blue light stimulation  just yielded four kinds of yellow pigments but was very limited in red pigments, whereas production of orange and red Monascus pigments was recovered by MpigE complementation strain (ΔMpigE::MpigE)  may all be involved in this conversion (Fig. The gene pR2 Fig. , which ipR2 Fig. . In addipR2 Fig. . As a copR2 Fig. , and resion Fig. .Fig. 5PuMonascus mycelia and the lipids content in Monascus mycelia. Simultaneously, high glucose stress up-regulated the expression of pigment biosynthetic genes, especially the genes involved in yellow pigments biosynthetic. Thereby, a high proportion of intracellular yellow pigments rather than orange pigments were achieved under high glucose stress. High glucose stress also improved the fluidity and permeability of the cell membrane and enhanced the trans-membrane conversion of intracellular pigments to extracellular water-soluble yellow pigments and secretion into the broth, resulted in a twofold increase of extracellular water-soluble yellow pigments compared to low IGC condition. Further studies are needed to elucidate the molecular pathways through which high glucose stress regulates yellow pigments production. Thus, submerged fermentation under high glucose stress has potential application in the production of Monascus yellow pigments.In summary, high glucose stress improved more reservoirs for intracellular pigments storage by increasing the content of Additional file 1: Table S1. Primers used for RT-qPCR analyzing pigment biosynthesis genes.Additional file 2: Figure S1. UV-Visible spectra of extracellular pigments detected by HPLC-PDA.Additional file 3: Figure S2. UV-Visible spectra of intracellular pigments detected by HPLC-PDA.Additional file 4: Figure S3. LC-MS analysis of extracellular pigments. a Total ion chromatograms, absorption traces of the pigments. Y1-Y4, Mass spectra and their collision-induced fragmented data.


FTO variants. We aimed to test IRX3's roles in the browning program and to evaluate the association between the genetic variants in IRX3 and human obesity.IRX3 was recently reported as the effector of the UCP1 transcriptional activity. Moreover, genetic analysis of IRX3 was performed in 861 young obese subjects and 916 controls.IRX3 expression was examined in beige adipocytes in human and mouse models, and further validated in induced beige adipocytes. The browning capacity of primary preadipocytes was assessed with IRX3 knockdown. Luciferase reporter analysis and ChIP assay were applied to investigate IRX3's effects on UCP1 promoter and increased its transcriptional activity. Moreover, 17 rare heterozygous missense/frameshift IRX3 variants were identified, with a significant enrichment in obese subjects .IRX3 expression was induced in the browning process and was positively correlated with the browning markers. IRX3 knockdown remarkably inhibited UCP1 expression in induced mouse and human beige adipocytes, and also repressed the uncoupled oxygen consumption rate. Further, IRX3 directly bound to IRX3 deficiency repressed the browning program of white adipocytes partially by regulating UCP1 transcriptional activity. Rare variants of IRX3 were associated with human obesity. •IRX3 expression was positively correlated with the browning markers in both mice and humans.•IRX3 deficiency repressed the browning program in human and mouse cell models by regulating UCP1 transcriptional activity.•Rare variants of IRX3 were associated with human obesity.While FTO is the first risk gene for obesity identified via genome-wide association studies, its exact mechanism remains unknown until two recent studies reported that IRX3, as FTO's target, probably inhibited the browning of white fat. However, whether IRX3 gene per se predisposes to obesity in humans and how IRX3 regulates the browning process remains unclear. Herein we combined cellular and genetic evidence to support that IRX3 promotes but not inhibit the browning process in both human and mouse cell models. Mechanistically, IRX3 increased the transcriptional activity of UCP1 through directly binding to its promoter. Thus, our findings provided an intact picture of IRX3's function in browning program and human obesity. Beige aIRX3, a transcription factor belonging to the Iroquois family of homeobox genes, participates in the pattern and development of several tissues, including the central nervous system  stress for one week  or recei2.236b4 or human βactin and analyzed using the 2− ΔΔCT methods. The results are representative of at least three independent experiments.Samples were homogenized with TRIzol reagent  and total RNA was extracted according to the manufacturer's instructions. 1 μg of RNA was reverse transcribed to cDNA with a reverse transcription kit . Real-time PCR was carried out on the QuantStudio Dx system (Thermo Fisher Scientific Inc.) using SYBR Green Supermix . The primers used in this study are provided in Table S1. Quantitative amounts of gene expression were normalized to the housekeeping gene mouse 2.3Western blotting (WB) was performed as previously described . Protein2.4Alpha Diagnostic, ucp1-a, diluted 1:400) or rabbit anti-IRX3  was used to incubate the sections, followed by incubation with HRP-linked secondary antibody . Electron microscopy of browning adipose tissue was performed as previously described (Adipose tissues were stained with hematoxylin and eosin (HE) according to the standard protocols. For immunohistochemistry, rabbit anti-UCP1 antibody  from subcutaneous WAT (sWAT) for the induction to beige adipocytes were obtained from an underaged subjects. The biopsy samples of abdominal sWAT and omental WAT (oWAT) for the comparison of IRX3 expression were obtained from 21 severely obese subjects and 9 lean subjects. Obese patients with secondary or syndromic obesity-related diseases were excluded. Detailed characteristics of the subjects are presented in Table S2. The adipose tissue pieces were obtained during surgical procedures followed by immediate storage in liquid nitrogen and fixation in formalin. The human study was approved by the Institutional Review Board of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Written informed consent was provided from each participant prior to inclusion in the study.2.6SVFs were isolated from the inguinal WAT (IWAT), epididymal WAT (EWAT) or brown adipose tissue (BAT) of male mice with C57BL/6J or 129/Sv background and induced into fully differentiated beige/brown adipocytes or white adipocytes as previously described . Briefly2.7SVFs from murine or human were cultured in DMEM/F12 supplemented with 10% FBS, 1% P/S, 1 mM L-glutamine, and 10 ng/ml murine basic fibroblast growth factor . HEK293T cells were purchased from ATCC.2.8Irx3 shRNA constructs and lentiviral human IRX3 constructs were made using pLKO-PGK-RFP and pLKO-CMV-RFP lentivector cloning, respectively. We verified all constructs using DNA sequencing; the sequences of mouse Irx3 shRNA were ccctatccaatgtgctttcat for Irx3 shRNA-1 and tctgtgtatgattaccttaaa for Irx3 shRNA-2. The sequences for human IRX3 shRNA were ttgtaagcatgtccgtgtata for hIRX3 shRNA-1 and gtttgtttgtccggttgattt for hIRX3 shRNA-2. The cells were infected with the lentivirus to knockdown Irx3/IRX3 24 h after seeding.Lentiviral mouse 2.9After induction to mature adipocytes for eight days, mature adipocytes underwent Oil Red O staining as previously described . In brie2.10After the induction for 8 days, mature beige adipocytes were collected for triglyceride (TG) measurement guided by the manufacturer's protocols .2.112 incubator (Seahorse Bioscience) for 1 h. 75 μl respiratory inhibitors  was loaded into the injection port to reach the final concentration of 1 mg/ml oligomycin, 1.5 mM FCCP, 0.5 mM antimycin A and 0.5 mg/ml rotenone to detect the uncoupled respiration, maximal respiration, and non-mitochondrial respiration, respectively. The final OCR results were standardized to the protein concentration. The results are representative of at least three independent experiments.SVFs were seeded in an XF24 V28 microplate (Seahorse Bioscience) coated with poly-L-lysine. After 24 h, the cells were infected with lentivirus and subsequently differentiated into beige adipocytes for five days. Oxygen Consumption Rate (OCR) was measured as previously described  using an2.12IRX3 PCR amplified from a human cDNA clone (NM_0243362) was sub-cloned into the pEGFP-C1 vector  between the HindIII and BamHI restriction sites. Plasmids of murine Ucp1 promoter and human UCP1 promoter, which was 4 kb proximal to the transcriptional start site (TSS), was constructed into the pGL-4.12 vector (Promega) as previously described . The final results were normalized to Renilla luciferase activity. The results are representative of at least three independent experiments.Luciferase reporter assay was conducted in HEK293T cells. After 48 h transfection with 800 ng pEGFP-C1 vector or pEGFP-C1-IRX3 plasmids, along with 1 ng pRL-TK  and 200 ng 2.14Ucp1 promoter using a commercial kit  in accordance with the manufacturer's instructions. Nuclei were extracted from the mature beige adipocytes induced from preadipocytes within IWAT of C57BL/6J mice. For immunoprecipitation, equal aliquots of cell lysates were incubated with anti-IRX3 antibody or IgG overnight at 4 °C. The precipitated DNA fragments were analyzed by real-time PCR with the following mouse Ucp1 primers: forward primer 5′-GAGTAGCCGAAGGGTTCAGG-3′; reverse primer 5′-TCCCATAGGAAGCGTCATGC-3′′.Chromatin immunoprecipitation (ChIP) assay was carried out to investigate the interaction between IRX3 and the 2.15ClinicalTrials.gov. In stage two, 634 young obese patients and 697 controls were additionally recruited into the study according to the same procedure. This study was also approved by the Institutional Review Board of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Written informed consent was obtained from each participant.In stage one, we used an in-home whole-exome sequencing database that included 227 young obese subjects and 219 lean controls from eastern China as described in our recent study . It has 2.16IRX3  exons with minor allele frequency (MAF) < 1%. Primers used to amplify the IRX3 exons are shown in Table S3.Genomic DNA was isolated from peripheral blood using a commercial blood genomic DNA extraction kit (Qiagen). Sanger sequencing was performed to identify nonsynonymous variants, including frameshift, nonsense, deletion, or missense mutations in 2.17http://www.ebi.ac.uk/Tools/msa/clustalw2/). The comparison included the IRX3 amino acid sequence of human , mouse , rat , chimpanzee , and dog .Sequence alignment of IRX3 across different species related to the variants identified in obese subjects and controls was conducted using a ClustalW2 alignment program .Spearman's correlation analysis was performed to examine the associations between the expression of 33.1IRX3 in the generation of beige adipocytes within WAT, we first detected IRX3 expression during the induction of the browning program. In rodents, the browning program of white fat depots can be activated by prolonged cold stress or β3-adrenergic agonist CL-316,243 . Furthermore, Irx3 knockdown also inhibited Ucp1, Cidea and Pgc-1α expression in differentiated brown adipocytes from SVFs in classical BAT . In addition, we also constructed human UCP1 promoter containing five putative IRX3 binding regions (ACAnnTGT) and five corresponding point mutations with the sequence from ACAnnTGT to ATAnnTAT. Consistently, we found that IRX3 overexpression also promoted human UCP1 promoter activity while point mutations did not abolish IRX3’s activating effects on UCP1 promoter . To further examine the direct binding effects of Irx3 on Ucp1 promoter, we conducted ChIP assay and found that Irx3 bound to the putative binding motif on the Ucp1 promoter in the induced mature beige cells  and 219 lean controls  in the first stage; and 634 young obese subjects  and 697 lean control subjects  in the second stage  had rare nonsynonymous IRX3 variants   B, indicaIRX3 variants were highly conserved among various species, implying the functional importance for the IRX3 protein  IRX3/Irx3 reduction by shRNA in vitro dramatically inhibited the browning program and energy expenditure of beige adipocytes, as reflected by the reduced UCP1 levels and oxygen consumption ability, which is a key mechanism for the development of obesity  two loss-of-function IRX3 mutants identified in human largely abolished this activating effect. This evidence well documented the direct promoting effects of IRX3 on the browning program. Consistently, IRX3/Irx3 expression was induced during the browning process of WAT in humans (pheochromocytoma patients) and β3-adrenergic agonist-stimulated mice, supporting the physiological role of IRX3 in browning. Irx3 expression was also increased during the time course of beige/brown adipocyte differentiation, supporting the promoting effects of IRX3/Irx3 on browning program. Obviously, this conclusion was opposite to previous reports regarding IRX3’s inhibiting effects on browning in mouse models  Ie models . By usins of WAT . Howevere allele , indicatUCP1, the uncoupling protein specialized in thermogenesis of mitochondria. A few transcriptional factors such as PPARγ are required for the commitment and differentiation of brown-like adipocytes by targeting UCP1 promoters. Although early studies focused on the regulating region of the UCP1 promoter located ~ 2.5 kb proximal to the TSS, Kong et al found that the transcription factor IRF4 could mediate the transcriptional activity of the UCP1 promoter outside this region . Here, we demonstrated that the IRX3-specific binding site ACATGTGT was located in − 3470 ~ – 3463 bp of UCP1's TSS, and deletion of this sequence ablated transcriptional activation by IRX3. Besides, ChIP assay further identified that Irx3 directly bound to this motif of Ucp1 promoter, providing the evidence that Irx3 could interact with the Ucp1 promoter to increase its transcriptional activity and might regulate thermogenesis as a novel transcriptional factor. It is also worth mentioning whether IRX3 also recruited PGC-1α or other co-activators to promote UCP1 transcription is largely unknown and warranted further investigation. In addition, we also found potential IRX3 binding site on the promoter of Cidea, another affected gene by IRX3. The transcriptional regulation of Cidea by IRX3 remained to be determined in future study. Another point that we want to mention is that deletion of the whole IRX3-binding domain in UCP1 promoter showed increased luciferase activity than wild-type UCP1 promoter, while point mutations in this domain did not increase UCP1 promoter activity, either did not affect IRX3’s promoting effects on UCP1 promoter activity . These data suggested that the whole IRX3 binding domain might possess an inhibitory effect on UCP1 transcription. We speculate that there might be an inhibitory factor to competitively bind to this IRX3 binding domain to play an inhibitory role on UCP1 transcription activity. Overexpression of IRX3 increased wild-type UCP1 promoter activity but not the binding motif-deleted UCP1 promoter. This result suggested that IRX3 might release the inhibitive effects of this domain on UCP1 transcriptional activity. Taken together, IRX3 might be one activating component in a transcription machinery complex for induction of browning. Detailed mechanism may need more investigations in future study.It is widely known that the browning process is characterized by the induction of s region . In addiFTO and IRX3 could influence obesity organically , National Basic Research Program of China (2015CB553601), Foundation for Innovative Research Groups of the National Natural Science Foundation of China (81621061), National International Science Cooperation Foundation (2015DFA30560), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support , Shanghai Rising-Star Program (17QA1403300), Shanghai Municipal Commission of Health and Family Planning (2017YQ002).This research was supported by grants from the No potential conflicts of interest relevant to this article were reported.J.W., W.W., G.N., and W.G. conceived the project and designed the experiments. Y.Z. and P.L. carried out most of the experiments. N.C. assisted in some experiments. Y. Z., J.W. and R.L. wrote the paper. J.S., W.L., M.Y., S.Z., M.C., Y.S., and T.N. recruited obese and control participants in stages one. A.G., Q.C., T.N., and Q.M. provided rodent biological samples for association analysis. X.Y., J.J., X.D., and B.S. provided white adipose samples of obese subjects who received sleeve gastrectomy surgery for weight loss. B.Y. provided subcutaneous adipose samples of subject. W.G., Y.F.Z., J.H., W.W., and G.N. designed and set up the GOCY study. Y.S. assisted with statistical analysis. S.L. and S·K contributed valuable comments and advice on the manuscript. G.N., W.W., W.G., and J.W. are the guarantors of this work and, as such, had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis.


Ustilago maydis is known for its natural potential to produce a broad range of valuable chemicals, such as itaconate, from both industrial carbon waste streams and renewable biomass. Production of itaconate, and many other secondary metabolites, is induced by nitrogen limitation in U. maydis. The clustered genes responsible for itaconate production have recently been identified, enabling the development of new expression tools that are compatible with biotechnological processes.tad1 and Pmtt1, from the itaconate cluster of U. maydis MB215. For both promoters the specific activation upon nitrogen limitation, which is known to be the trigger for itaconate production in Ustilago, could be demonstrated by gfp expression. The promoters cover a broad range of expression levels, especially when combined with the possibility to create single- and multicopy construct integration events. In addition, these reporter constructs enable a functional characterization of gene induction patterns associated with itaconate production.Here we report on the investigation of two of the native promoters, PUstilago.The promoters are well suited to induce gene expression in response to nitrogen limitation, coupled to the itaconate production phase, which contributes towards the further improvement of organic acid production with  U. maydis, these tools can also be adapted for use in other Ustilaginaceae [Aspergillus niger [Escherichia coli [Penicillium chrysogenum [Potef and Poma [Pcrg1, Pnar1 or the tet-system, come with other drawbacks [Pcrg1 relies on arabinose and is efficiently repressed by glucose and xylose, two of the main carbon sources for industrial production processes [Pnar1 is induced by nitrate and repressed by ammonium [Ustilago life cycle, making them equally problematic for engineering of secondary metabolite production [The family of Ustilaginaceae has sparked great interest as promising industrial production organisms in recent years. The growing biotechnological attention results from their native ability to utilize a range of bio-based substrates , 20, 62 ginaceae , 60. Howus niger , 56, Eschia coli , 38, andysogenum . Modern ysogenum . One parysogenum , 26, 52.nd Poma , 46, 49,rawbacks . Pcrg1 rU. maydis was identified [rdo1, was shown during the itaconate production phase compared to non-induced conditions in rich medium [ria1. By deletion of ria1, the transcription level for all genes within the cluster was lowered by up to ninefold and overexpression triggered expression of most cluster genes [Recently, the gene cluster responsible for itaconate production in entified  and its entified . Two- toh medium . Furtherer genes . TherefoU. maydis that are induced during the non-growing production phase as initiated by nitrogen limitation.Here we investigate a set of promoters from the genes responsible for itaconate production in All strains used and constructed within this work are listed in Table E. coli DH5α (DSM 6897) was used. All E. coli strains were grown at 37 °C shaking at 200 rpm (shaking diameter 25 mm) in lysogeny broth (LB) medium. For recombinant strains 100 mg L−1 ampicillin were added to the medium.As host for cloning experiments, Ustilago maydis cultures were cultivated in YEPS light medium containing 20 g L−1 D–sucrose, 5 g L−1 yeast extract, and 10 g L−1 peptone at 30 °C shaking at 200 rpm (shaking diameter 25 mm).For physiological experiments 96 flat-bottom round-well plates or 48-well flower plates were used as described below.−1 glucose, 0.2 g L−1 MgSO4 · 7 H2O, 0.01 g L−1 FeSO4 · 7 H2O, 0.5 g L−1 KH2PO4, 1 mL L−1 vitamin solution, 10 mL L−1 trace element solution, 19.5 g L−1 2-(N-morpholino)ethanesulfonic acid (MES) as buffer, and differing NH4Cl concentrations. MES buffer was used as stock solution with pH adjusted to 6.5 with NaOH. The vitamin solution contained (per liter) 0.05 g D-biotin, 1 g D-calcium panthotenate, 1 g nicotinic acid, 25 g myo-inositol, 1 g thiamine hydrochloride, 1 g pyridoxol hydrochloride, and 0.2 g para–aminobenzoic acid. The trace element solution contained (per liter) 1.5 g EDTA, 0.45 g ZnSO4 · 7 H2O, 0.10 g MnCl2 · 4 H2O, 0.03 g CoCl2 · 6 H2O, 0.03 g CuSO4 · 5 H2O, 0.04 g Na2MoO4 · 2 H2O, 0.45 g CaCl2 · 2 H2O, 0.3 g FeSO4 · 7 H2O, 0.10 g H3BO3, and 0.01 g KI [−1 NH4Cl and 0.1 g L−1 KH2PO4 were used.As cultivation medium MTM was used containing 50 g L−1 NH4Cl, to ensure that no nitrogen limitation occurs prior to inoculation of the main culture.Precultures for analytical experiments were cultivated for 24 h in 500 mL shake flasks without baffles with 50 mL MTM containing 4 g LAll experiments were performed in triplicates, unless stated otherwise. The arithmetic mean of the biological replicates is shown. Error bars and ± values indicate standard error of the mean.g, 5 min) were filtered through cellulose acetate filters  and subsequently diluted 1:10 with distilled water. For analysis a Dionex Ultimate 3000 HPLC  with an Organic Acid Resin column  kept at 75 °C, with a constant flow rate of 0.8 ml min−1 of 5 mM sulfuric acid as eluent was used. For detection, a Shodex RI101 detector at 35 °C and a variable wavelength UV detector  at 210 nm were used.HPLC analysis was performed as described previously . Centrif−1− NH4+) were mixed with 1 mL reagent solution and afterwards with 0.25 mL hypochlorite solution. The mixture was incubated for at least 12 min at room temperature before measuring the absorbance at 685 nm. The reagent solution contained 32 g sodium salicylate (anhydrous), 40 g trisodium phosphate (TSP) and 0.5 g sodium nitrosylpentacyanoferrate(III) (sodium nitroprusside) dissolved in 1 L of water. For the hypochlorite solution, 50 mL of commercially available bleach (Clorox) containing 5–5.25% sodium hypochlorite were diluted with water to 1 L with a final concentration of ~ 0.25% hypochlorite.Ammonium concentration was determined by a colorimetric assay according to Willis . For thiInitial promoter characterization experiments were performed in 96-well plates using a BioLector  at 30 °C shaking at 1000 rpm (shaking diameter: 3 mm). GFP fluorescence intensity was determined at 488/520 nm (excitation/emission) with the gain set to 70. For biomass determination backscatter intensity in the far red range 620/620 nm with the gain set to 10 was used. Specific promoter activities are expressed as GFP fluorescence over biomass backscatter. Since both these signals are measured in arbitrary units, this specific activity is expressed without dimension. Values are normalized (point-to-point) for the arithmetic mean of the corresponding wildtype. Induction ratios were determined by dividing the maximum specific promoter activity by the average activity from 2–10 h.U. maydis MB215 Ptad1-gfp under different cultivation conditions were analyzed in 48-well flower plates  with an in-house constructed screening system based on the established BioLector setup [Induction profiles of or setup , 54 withU. maydis 521 was taken as reference sequence [The genome of sequence .Ptad1-gfp, Pitp1-gfp, Padi1-gfp, and Pmtt1-gfp were cloned by amplifying the promoter regions of genes UMAG_05076 (tad1), UMAG_05077 (itp1), UMAG_05078 (adi1), and UMAG_05079 (mtt1) with the oligonucleotide primers listed in Table Potef on the vector pUMa43 [UMAG_05077 (itp1), UMAG_05078 (adi1), and UMAG_05079 (mtt1) 2000 bp in front of the respective start codon were used and for UMAG_05076 (tad1) 1500 bp were used  were chosen as primary targets for the investigation of promoter activities. From previous studies it was known that their native activity is strongly influenced by the activity of the transcriptional regulator Ria1 which is itself also encoded in the itaconate gene cluster [The recently characterized genes encoding core catalytic enzymes and transporters, which are required for itaconate production  or multiple (m) integration events by PCR and Southern Blotting. This targeted in-locus integration avoids undesired polar effects, such as gene disruption, and ensures optimal comparability between different strains by avoiding locus- and copy number-dependent expression differences [The plasmids containing the promoter-is MB215 , 31. Theferences . For allPitp1 and Padi1 showed very low fluorescence intensities after 24 h of cultivation, about two-to-three orders of magnitude below that of the strong Potef, Ptad1 showed 48-fold and Pmtt1 fivefold lower intensities. These data indicate pronounced differences of promoter activity within the itaconate cluster from U. maydis, with Padi1 and Pitp1 as relatively weak promoters and Pmtt1 as a strong promoter. The differences are in line with the physiological roles of the regulated genes. The mitochondrial transporter Mtt1 is the limiting step for itaconate biosynthesis in U. maydis [trans–aconitate, which is a potent inhibitor for vital metabolic reactions, such as the conversion of citrate to isocitrate by aconitase in the TCA–cycle [in-trans expression of Padi1 and Pitp1 outside of their original genomic context might influence their activity, since the orientation in the genome suggests a possible bidirectional promoter , however, with great differences in the absolute intensity range. While . maydis , requiriCA–cycle . The in-cis-aconitate. Consequently we investigated the induction time and conditions  and 122 (multiple integrations). Pmtt1 in contrast is a strong, but leaky promoter, with a basal activity during the growth phase and a lower induction ratio of 13 (single integration) and 11 (multiple integrations). The basal activity of Pmtt1 in the growth phase may result from the generally high promoter activity, or from an activation due to expression out of the original chromosomal context.Promoter activities are expressed as GFP fluorescence intensity over biomass (measured as scattered light). The activity of both ium Fig. . ProductPtad1, single, 23–32 h), 0.40 , 0.33 , 1.32 , 1.38 , and 3.00 , the maximum expression level increased by about twofold (Potef), fourfold (Pmtt1), and 13-fold (Ptad1) for multiple construct integrations compared to single integration. Taking into consideration the promoter activities of single-copy Ptad1 and Pmtt1 constructs, and multiple integration, expression levels covering a nearly 50-fold dynamic range with high resolution appears realistic. This value may even be increased by selecting higher integration numbers for Pmtt1.We next investigated the impact of multiple integration on the expression activity. With values of 0.03  and 7.5-fold (3.2 g L−1) lower for the cultures containing more NH4Cl, compared to the culture containing 0.8 g L−1 NH4Cl, and for the phosphate-limited culture the specific promoter activity was negligible (Fig. −1 NH4Cl resulted in similar volumetric production rates [−1 NH4Cl. Apparently, lower nitrogen concentrations result in a stronger induction of itaconate production genes, leading to more efficient production. This may be related to pH in the employed system of batch cultures with a soluble 100 mM MES buffer starting at pH 6.5. Higher nitrogen concentrations may lead to a stronger pH drop during growth, and itaconate formation might be triggered at a sub-optimal pH level with preference over other secondary metabolites. Indeed, U. maydis favors the production of glycolipids over organic acids at a low pH [A. terreus [Higher NHon rates , even tha low pH , 27, 50,a low pH . A stron terreus .U. maydis is induced specifically in response to nitrogen limitation, and the level of induction was strongly dependent on the initial nitrogen concentration. Although the depletion of nitrogen triggers the activation, different other factors, such as pH and the concentration of other nutrients, can be optimized for fine tuning of the promoters activities.In summary, we were able to show that the activation of the itaconate cluster in Ustilago’s itaconate cluster provides a new set of genetic tools that will enable heterologous gene expression under nitrogen limitation. Activity of these promoters is clearly coupled to the production phase, with a broad range of activities that reach up to the level of the commonly used Potef. The investigation of these promoters opens new doors for future metabolic engineering strategies. These strategies aim for an improved match with the different cellular objectives during growth- and production phases compared to Potef and Poma, which are mainly active during growth phase or e.g. Pcrg1 and Pnar1, which are activated or repressed under conditions incompatible with Ustilago’s production phase. In addition, the GFP fusions enable further detailed investigations into the mechanism of induction of secondary metabolite production in U. maydis, and specifically give further insight into the regulation of the itaconate cluster of Ustilago and of the itaconate production pathway.The potential of Ustilaginaceae as production organisms for different industrially-relevant compounds has been convincingly demonstrated in several instances. The presented investigation of the promoters from


Programs of gene expression are executed by a battery of transcription factors that coordinate divergent transcription from a pair of tightly linked core initiation regions of promoters and enhancers. Here, to investigate how divergent transcription is reprogrammed upon stress, we measured nascent RNA synthesis at nucleotide-resolution, and profiled histone H4 acetylation in human cells. Our results globally show that the release of promoter-proximal paused RNA polymerase into elongation functions as a critical switch at which a gene’s response to stress is determined. Highly transcribed and highly inducible genes display strong transcriptional directionality and selective assembly of general transcription factors on the core sense promoter. Heat-induced transcription at enhancers, instead, correlates with prior binding of cell-type, sequence-specific transcription factors. Activated Heat Shock Factor 1 (HSF1) binds to transcription-primed promoters and enhancers, and CTCF-occupied, non-transcribed chromatin. These results reveal chromatin architectural features that orient transcription at divergent regulatory elements and prime transcriptional responses genome-wide. Heat Shock Factor 1 (HSF1) is a regulator of stress-induced transcription. Here, the authors investigate changes to transcription and chromatin organization upon stress and find that activated HSF1 binds to transcription-primed promoters and enhancers, and to CTCF occupied, untranscribed chromatin. Beyond the interactions at gene promoters, distal cis-acting regulatory elements have emerged as prominent determinants of cell type-specific transcription7. Our understanding of transcription is, however, severely hampered by the lack of information on how genes and distal regulatory elements are globally orchestrated in response to developmental or environmental signals, such as stress. Here, we identified the molecular features that determine transcriptional responses in the human genome upon exposure to an acute 30-min heat stress, which is known to rapidly reprogram RNA synthesis10. To profile the dynamic regulation of genome-wide transcription, we mapped the transcriptionally engaged RNA Polymerase (Pol) in human myeloid/erythroid leukemia K562 cells prior to and upon heat shock using Precision nuclear Run-On sequencing (PRO-seq)11. Importantly, global run-on techniques enable strand-specific, nucleotide-resolution measures of nascent RNA synthesis, empowering the accurate mapping of gene expression18 and identification of RNA-producing enhancers across the whole genome with high sensitivity20.The plasticity of transcriptional programs is fundamental for all biological processes from cellular growth and differentiation to coordinated functions of tissues and organisms. The execution of distinct transcriptional steps has been extensively investigated at promoters of single genes, providing the basis for our current comprehension of the ordered interactions of DNA elements, transcriptional regulators, and transcription machinery21. Upon activation, HSF1 rapidly binds to Heat Shock Elements (HSEs) at hundreds of genomic loci, both at genes and intergenic regions, as has been shown in yeast22, round worm23, fly25, mouse26 and human cells28. Despite the central role of HSF1 in coordinating stress-induced transcription, several classes of genes that are activated by stress stimuli are not bound by HSF127. Moreover, HSF1 binds to a number of genes that do not show induction upon stress28. HSF1-driven transcription depends on the cell type, cell cycle phase, and is integrated into the metabolic and pathological state of the organism36. Consistent with the condition-dependent transcriptional reprogramming, the chromatin that is targeted by HSF1 has been shown to reside generally in an open conformation prior to HSF1 activation28, which suggests that the local chromatin environment pre-conditions transcriptional responses. However, the precise chromatin configuration that determines the access of HSF1, or any other transcription factor, to DNA and the exact mechanistic control that underlies the transcriptional outcome at genes and distal regulatory elements have remained elusive.Heat shock factor 1 (HSF1) is the prime regulator of heat stress-induced transcription, and a key player for maintaining protein homeostasis in eukaryotic cells and organismsIn this study, we investigate the mechanisms that coordinate transcription of genes and enhancers in the human genome. By rapidly provoking a transcriptional change by heat shock and directly measuring RNA synthesis from genes and distal regulatory elements, our results provide a global view on transcriptional reprogramming. Nucleotide-resolution maps of nascent transcription complexes, in the context of the local chromatin environment, reveal how transcriptional responses are architecturally pre-set and rapidly adjusted in human cells. Moreover, deciphering of gene networks and their distal regulatory elements uncovers functional mechanisms that induce hundreds and repress thousands of genes, simultaneously establishing a distinct enhancer repertoire and chromatin state in stressed human cells. In particular, our results demonstrate how inhibition of the pause–release of promoter–proximal Pol II clears transcription complexes from the majority of transcribed genes upon stress, and how the consequently increased free Pol II in the cell is directed to the coding strands of the activated genes, as well as to enhancers that are primed by lineage-specific transcription factors.39, we refer to Pol II as the source of the PRO-seq signal analyzed hereon. By utilizing the strand specificity of PRO-seq, the Pol II density was selectively quantified on the coding strand of each gene, scoring the promoter–proximal pause site as a 50-nucleotide (nt) window from a region spanning −100 nt to +400 nt from the annotated transcription start site (TSS), and measuring transcription of the gene body from +500 nt from the TSS to −500 nt from the polyA site  and upon a 30-minute heat shock at 42 °C (HS). Since PRO-seq detects a single de novo-added nucleotide at the active site of each nascent transcript, it provides the locations of transcriptionally competent Pol complexes at a single-nucleotide resolution. PRO-seq does not discern which Pol is producing the RNA, but because our present study focuses on genes and enhancers, which are predominantly transcribed by Pol IIene Fig. . The PROene Fig. , and accene Fig. .Fig. 1RaBAG3  identify the repertoire of RNA-producing distal regulatory elements in NHS and HS conditions and (2) quantify their transcriptional activity at high spatial and temporal resolution . Next, TREs were classified into promoters and distal TREs (dTREs), a class of regulatory elements that includes RNA-producing enhancers. This classification is based on the assumption that at least one of the divergent initiation sites originating from promoters will produce a stable mRNA  that arise from active enhancersions and  quantifyions and  quantifyRNA Fig. , whereas 19 Fig. . The cooOur novel computational tool, dREG-HD, identified from the PRO-seq data 16,723 dTREs in NHS, and 21,768 dTREs upon HS, of which 7764 occurred in both conditions Fig. , demonst50. To investigate the simultaneous coordination of transcription and chromatin, we assessed chromatin state by measuring acetylation of histone H4 with an antibody that recognizes H4 acetylation at lysines 5, 8, 12, and 16. Consistent with previous studies52, chromatin immunoprecipitation coupled to deep sequencing (ChIP-seq) identified prominent acetylation of histone H4 at the promoters of transcribed genes. As shown in Supplementary Fig. Transcription is a dynamic process where the chromatin state and the Pol II machinery influence each other53.Upon stress, the pattern of histone H4 acetylation underwent a drastic change both at genes Fig.  and dTRE41 54, which together enable nucleotide-resolution profiling of the positioning, initiation, pausing, and elongation of Pol II. Consistent with previous reports39, transcribed genes, on average, displayed a comparable intensity of transcription machinery at the coupled initiation regions of coding and divergent strands . Analogous to the polarized Pol II positioning Fig. , the hig58. We found a strong occupancy of NELF-E subunit immediately downstream of the TSS of highly upregulated genes  co-localizes with the paused Pol II to inhibit its release into productive elongationnes Fig. , supportnes Fig. , indicattrans-activator in heat-stressed eukaryote cells59, and it was recently shown to function primarily at the step of the pause-release18. To determine the extent to which HSF1 orchestrated the heat-triggered transcriptional reprogramming of genes and dTREs in human cells, we compared HSF1-binding sites upon acute stress28 with the heat-induced changes in the nascent RNA synthesis. HSF1-binding was detected at 29% of upregulated and 2% of downregulated genes, as measured from −2.5 kb from the TSS to the polyA-site  with the gene’s heat-induction, whereas we detected no correlation  between the HSF1-binding intensity and the magnitude of downregulation  with K562 transcriptional profiles of three distinct ENCODE laboratories using previously published Pol II ChIP-seq data sets  at the HSF1-targeted promoters of downregulated genes  at the HSF1-bound downregulated genes  and T-cell acute lymphoid leukemia 1 (TAL1), occupied the HSF1-bound and unbound dTREs that gained transcriptional activity upon stress  of HSF1 target sites localized neither to promoters nor to dTREs Fig. . These Hess Fig. . In staress Fig. . The HSFess Fig.  and 8. Ttrans-activator can occur without pre-existing or concurrent loading of the transcription machinery. In conclusion, HSF1 targeted both transcription-primed and CTCF-primed chromatin that gained localized, heat-induced acetylation, indicative of assembly of transcription factors to pre-existing transcription-primed or architectural protein-primed platforms at genes and intergenic regions.The closed chromatin did not prevent HSF1 from rapidly gaining access to the HSEs Fig. . Indeed,trans-activator HSF1 localizes to transcription-primed and architectural protein-primed genomic regions. (6) Local SP2-associated chromatin architecture can restrain the activation of primed, HSF1-targeted gene promoters.The human genome encodes a great number of transcripts whose expression needs to be precisely coordinated in space and time. In this study, we took the advantage of the immediate transcriptional change that is provoked by heat shock and monitored at high sensitivity the synthesis of long and short, stable and unstable RNAs that arise from genes and distal regulatory elements. Deciphering the rapidly induced transcriptional response in the context of the chromatin architecture revealed that: (1) Cells elicit a massive response to stress by reprogramming transcription and adjusting the chromatin landscape of genes and enhancers. (2) A gene’s response to stress is defined at the pause-release of promoter-proximal Pol II. (3) Strand-specific assembly of PIC and paused Pol II sets up the orientation of promoters and pre-wires transcriptional activation. (4) Lineage-specific factors prime enhancer transcription. (5) Potent 53. The inhibition of P-TEFb could be counteracted by strong trans-activators, such as HSF1, the activation of which has been coupled with recruitment of P-TEFb to heat-activated genes73.We propose a model Fig. , where ctrans-activators known, and it is capable of inducing transcription to a level where Pol II is tightly packed on genes. We found strong HSF1-binding at the heat-activated genes, but also uncovered new classes of HSF1 target sites occurring at dTREs and untranscribed regions within closed chromatin. Characteristic for HSF1-binding was the simultaneous acetylation of histone H4, indicative of the complex changes that occurred on activated genomic regions in the presence or absence of Pol II. Recently, HSF1 was demonstrated to promote the release of Pol II into elongation18, which provides a mechanistic explanation for why the poised gene promoter and the well-positioned binding of HSF1 upstream of the paused Pol II rapidly launched transcription. At certain gene promoters HSF1-binding did not enforce transcriptional activation. Among the multitude of functional genomic data sets queried, SP2 emerged as the most distinctive factor that occupied HSF1-bound downregulated genes and correlated with the low transcription in stressed cells. Intriguingly, the HSF1-bound promoters of downregulated genes were devoid of HSF2. The recently solved crystal structure of the HSF2 DNA-binding domain revealed that the amino acids that are in direct contact with the HSE are highly conserved among HSFs, while the amino acids that face away from the DNA backbone are diverse74, which mechanistically could explain why HSF1 and HSF2 bind to practically identical HSEs but mediate profoundly different actions at the level of chromatin28. To this end, the selective presence of SP2, and the lack of HSF2, at the downregulated HSF1 target genes emphasizes the importance of the local chromatin environment and interactions of transcription factors beyond the DNA sequence for determining the factor-specific recruitment and the transcriptional outcome thereof.HSF1 is one of the most potent Examining the simultaneous change in the chromatin state and transcription uncovered that hyper-acetylation of histone H4 accompanied the increase in Pol II density at promoters and dTREs. Intriguingly, islands of histone H4 acetylation appeared also at sites that did not initiate transcription, suggesting that distal Untranscribed Regulatory Elements (dUREs) are formed upon heat shock Fig. . The eme2 atmosphere and cultured in RPMI medium (Sigma), containing 10% fetal calf serum, 2 mM L-glutamate, 100 μg ml−1 streptomycin, and 100 U ml−1 penicillin. The K562 cells originated from ATCC, were tested to be mycoplasma free, and displayed morphology, proliferation rate and transcriptional profile characteristic to K562 cells. To avoid provoking transcriptional changes by freshly added media, the cells were expanded 24 h prior to the treatments to confluence of 2 × 105 cells per ml. For treatments, 2 × 107 cells per sample were concentrated in 10 ml culture media and placed in 37 °C incubator (NHS) or a 42 °C water bath (HS) for 30 min. In the water bath, the temperature of the cell suspension reached 42 °C within 4 min.Human K562 erythroleukemia cells were maintained at 37 °C in a humidified 5% CO28, using an antibody that recognized a broad range of histone H4 acetylations (Upstate 06–866). In detail, the cells were cross-linked in 1% formaldehyde for 5 min on ice, quenched in 125 mM glycine for 5 min, washed twice with ice cold PBS, and flash frozen. The cells were lyzed in ChIP lysis buffer , and the chromatin was sheared to 200–500 bp fragments by 10-min sonication , using high throughput settings with 30 s on/30 s off cycles. The lysate was pre-cleared with protein G-coated sepharose beads (Amersham Biosciences), 4 µl of Histone H4ac (Upstate 06–866) antibody incubated over night at 4 °C, and the immunocomplexes washed twice in wash buffers 1 , 2  and 3 . Proteins were degraded with Proteinase K, and RNA with RNaseA, and cross-links were reversed over nigh at 65 °C. DNA was purified twice with phenol:chloroform and once with chloroform, and precipitated using EtOH. Ten biological ChIP replicates of non-treated and heat-treated samples, respectively, were pooled and purified with Qiaquick DNA purification columns, and the libraries were generated with Nextera DNA Sample Preparation kit (Illumina). Total fragmented chromatin (Input) was used as a normalization control. Sequencing was performed with HiSeq2500 (Illumina).Immunoprecipitation of chromatin with acetylated histone H4 was conducted as previously describedhttp://hannonlab.cshl.edu/fastx_toolkit/) to 36 nucleotides with a minimum requirement of 99% probability for correct identification for at least 0.8 fraction of bases in a read. The reads were aligned to the human genome 19 (GRCh37/hg19) with Bowtie75, and the peaks were called with the MACS 1.4 software76 using total fragmented chromatin as control. The complete raw data for histone H4 acetylation is available at Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo) under accession: GSE89382. See The sequenced ChIP-reads were trimmed, clipped and filtered with fastx toolkit , followed by 25x douncing , to isolate the nuclei. After centrifugation , the nuclei were collected and flash frozen in storage buffer . The nuclear run-on reactions were performed at 37 °C for 3 min in the presence of 0.05 mM biotin-A/C/G/UTP (Perkin Elmer), 0.5% sarkosyl, 5 mM Tris-HCl (pH 8.0), 1.5 mM MgCl2, 0.5 mM DTT, 150 mM KCl and 0.1 units per ul Superase RNase inhibitor (Life Technologies). Total RNA was isolated using Trizol LS (Life Technlogies), pelleted by EtOH-precipitation and base hydrolyzed with NaOH to 100–150 nt fragments. For high specificity, the biotinylated nascent transcripts were purified three times with streptavidin-coated magnetic beads (Life Technologies), each round followed by isolation with Trizol (Life Technlogies) and EtOH-precipitation. The 5′ cap of transcripts was removed with tobacco acid pyrophosphate (Epicentre) and the 5′ hydroxyl group repaired with T4 polynucleotide kinase (BioLabs). The libraries were generated using TruSeq small-RNA adaptors and sequenced using HiSeq2500 (Illumina).PRO-seq was conducted as previously described78 and trimmed and filtered to 15–36 bp with fastx (http://hannonlab.cshl.edu/fastx_toolkit/). Reads that did not map to ribosomal RNA genes were aligned to hg19 using Bowtie75 selecting only uniquely mapping reads with up to two mismatches. The complete raw data for PRO-seq in human K562 cells is available at GEO database (http://www.ncbi.nlm.nih.gov/geo) under accession: GSE89230.The PRO-seq reads were adapter-clipped using cutadaptn = 23,698). The density and location of transcriptionally engaged Pol II at each gene’s promoter–proximal region (−100 to +400 nt from TSS) was defined as reads per kb mappable genomic DNA (RPK) at a 50-nt window with the highest read count. The localization and intensity of Pol II at the divergent strand, was scored at the 50-nt window from −800 to +100 region from the TSS. To measure the transcription of a gene’s coding region, the average Pol II density (RPK) across each gene body (+500 nt from the TSS to −500 nt from the polyA site) was measured. The correlation of the two biological replicates in each condition was analyzed using Spearman correlation and plotted as density plots of reads mapping to promoter–proximal regions, gene bodies and dTREs. The reads of the two replicates highly correlated, allowing their combination for further analyses. To compare transcription in NHS versus HS conditions, each PRO-seq data set was normalized, as described and tested previously16, using the 3′ end of long (>150 kb) genes as reference genomic regions where no transcriptional change upon a 30-min heat stress was detected. See To analyze transcription of genes, we filtered RefGen database to obtain a non-redundant list of RNA- and protein-coding genes with 500 nt or more in length  1.25 as cutoffs for calling a significant expression change. Genes that were identified changed with low confidence, constituted mainly of lowly transcribed genes, and were not considered in the downstream analyses. To remove genes whose expression change was called significant due to the activity of adjacent or partially overlapping genes , or due to changed transcription of internal regulatory element(s), we utilized dREG20. dREG is a machine-based learning method that identifies transcribed regulatory elements from the human genome using support vector regression. To call a gene significantly changed, unchanged or unexpressed, we required it to have a dREG-identified TRE (minimum score 0.7), at the TSS.For identification of genes with significantly changed transcription upon stress we utilized DESeq2, which uses the variance in biological replicates to assess significant changes between stressed and unstressed data sets20 to map the broad locations of TREs. Next, we developed a novel strategy called dREG-HD that refines the coordinates of TREs using the peaks of divergently oriented Pol II. The source code of dREG-HD is available at https://github.com/Danko-Lab/dREG.HD. Briefly, we developed an epsilon-Support Vector Regression (SVR) with a Gaussian kernel to predict the level of DNaseI hypersensitivity, which peaks between the divergently oriented paused Pol II, using PRO-seq data. The SVR was trained on randomly selected positions within peaks identified by dREG extended by 200 nt on either side. To optimize model free parameters, we maximized the Pearson correlation between the imputed and experimental DNaseI55 score on holdout sites not used during training. After the initial parameter adjustment, the dREG-HD model was trained using DNaseI55 and PRO-seq19 data in the whole genome in K562 cells. Next, we identified peaks in the imputed DNaseI hypersensitivity profile by fitting the imputed DNaseI signal using a cubic spline and identifying local maxima. We optimized two free parameters that control the (1) smoothness of spline curve fitting, and (2) threshold on the imputed DNaseI signal intensity. Parameters were optimized using grid optimization to achieve an appropriate trade-off between False Discovery Rate (FDR) and sensitivity on the K562 data set. Applying the TRE-caller on a GRO-seq data set, which inherently has a lower resolution, obtained from a different cell line (GM12878)19, completely held out during model training, and resulted in 82% sensitivity for identification of DNaseI peaks within dREG sites at a 10% FDR.To identify active promoters and transcribed enhancers we used the dREG software program19. Briefly, by comparing the signal intensities of 5’ capped RNAs from GRO-cap (reports both stable and unstable transcripts) with that obtained by Cap Analyses of Gene Expression , Core et al.19 grouped TREs into promoters and dTREs. While promoters produce at least one stable transcript from the two possible orientations, dTREs are defined to produce rapidly degraded divergent transcripts in both directions. The transcription profile from a divergent promoter is schematically depicted in Fig. TREs were classified into promoters and dTREs by predicting the stability of divergent transcripts, a strategy introduced by Core and co-workers55 as input. We used the subset of dREG-HD peaks that intersect with GRO-cap pairs19 as training examples. We separately trained and optimized independent classifiers based on the PRO-seq profile and CpG/GC content. The PRO-seq data was initially summarized as read counts in fixed non-overlapping windows centered on the dREG-HD site. First the number of bases passed to the classifier was optimized using a fixed 50 bp non-overlapping window size, and subsequently, the resolution was tuned to the total genomic area covered. We also passed the classifier both GC and CpG content in a single window centered on the dREG-HD site. Free parameters for PRO-seq classifier (window size and number of windows), as well as for CpG/GC classifier (the window size) were defined using five-fold cross-validation to optimize the Area Under the receiver operating characteristic Curve (AUC). After optimization of the free parameters, we trained a single classifier to integrate both PRO-seq and CpG/GC content. All SVM training tasks used an internal ten-fold cross-validation to obtain optimized SVM model parameters  using R package e1071. In a held out test set the final classifier achieved an AUC of 0.918, which is higher than achieved using the commonly used log ratio of histone modifications (log[H3K4me3/H3K4me1] at ± 2000 nt window) that obtained an AUC of 0.875. The clear outperforming of the standard method for enhancer prediction, demonstrated the high sensitivity, specificity and resolution that we obtained by calling dTREs from the nucleotide-resolution profile of PRO-seq.We trained a Support Vector Machine (SVM) classifier to predict whether each TRE encoded at least one stable transcription unit (promoter) or whether both of the transcripts were unstable (dTRE) using PRO-seq data and CpG/GC content80, using 50% overlap as an intersect criterion for an element occurring in both conditions. For each dTRE, the average read count (RPK) and the position of the highest Pol II density in a 50-nt window were separately scored at plus and minus strands along the whole length of the element. In heat maps the dTREs were sorted by increasing distance between the highest Pol II densities at the plus and minus strands. To identify dTREs with significantly induced or reduced Pol II density we compared, in a strand-specific manner, the PRO-seq profiles across the whole length of the dTRE in NHS versus HS. dTREs that were called significantly changed by DESeq2  at either or both of the strands were called as up- or downregulated. dTREs where neither strand displayed high confidence change in Pol II density upon stress were called unchanged.We used our dREG-HD classifier for high-resolution identification of TREs in NHS and HS conditions, sorting them to active promoters and dTREs. In the very rare cases when a dTRE fell on an annotated TSS (identified from RefGen), that region was not considered as a distal regulatory element. The occurrence of dTREs in NHS and HS were compared by bedtools28 (GEO: GSE43579) that have been generated in the same cells and under the same conditions as the PRO-seq and ChIP-seq experiments reported in this study. HSF1-binding on genes was measured along the length of the gene from −2500 nt from the TSS to the polyA site. To sort HSF1 target sites to promoters and dTREs, we intersected the HSF1 peak coordinates with those of dREG-HD-identified TREs, requiring a minimum of one nucleotide overlap. The HSF1 target sites that did not occur on either promoters or dTREs were termed untranscribed. The statistical analyses on HSF1-binding intensity and the transcriptional change, or the change in histone H4 acetylation were conducted using Spearman’s rank correlation.To analyze the recruitment of HSF1 and HSF2 to the genome upon acute stress, we used the published data sets55. Considering the lower resolution, intrinsic background, and lack of strand-specificity in ChIP-seq, we selected 10,000 most actively transcribed genes that were at least 5000 nt in length (each having gene body RPK over 10 in PRO-seq), counted PRO-seq reads from both strands, and measured the total read count of PRO-seq and ChIP-seq from gene body (+2000 nt from TSS to −2000 nt from polyA site). The statistical analyses performed using Spearman’s rank correlation showed excellent agreement, ensuring that our K562 cell line was behaving like the K562 cell line used by the ENCODE.To ensure the applicability of ENCODE data sets for analyses of chromatin composition at HSF1 target sites, the PRO-seq profile of transcription in NHS in our K562 cells was compared to Pol II ChIP-seq data in K562 cells generated by the ENCODE laboratoriesU-test.To compare HSF1-binding intensity with that of SP2, the HSF1-binding (measured over input) was first converted to ENCODE binding score, giving a spread of scores between 1000 and 107, which is according to the ENCODE guidelines. The statistical analysis of SP2 over HSF1 (SP2 ENCODE binding score/HSF1 ENCODE binding score) versus the gene’s transcription upon stress was conducted with Spearman’s rank correlation. The statistical significance of the difference in SP2 binding score on HSF1-bound or HSF1-unbound up- or downregulated genes was analyzed using Mann–Whitney https://github.com/andrelmartins/bigWig/) that queries the total normalized reads at defined positions. The average intensities in composite profiles, and the region-specific intensities in heat map analyses were queried in 20-bp and 10-bp bins, respectively, unless otherwise indicated. The bootstrap estimates in the composite profiles display 12.5–87.5% interval for each group. The queried genomic sites and nucleotide ranges are indicated in each figure. For mapping against exon start sites, the first and last exons were omitted from the analyses due to increased possibility for an alternative TSS and polyA site, respectively. The highly upregulated genes were selected by requiring log2 fold change (HS/NHS) > 2 and change in the RPK (HS-NHS) > 200. The moderately upregulated genes are called significantly induced by DESeq2 but do not meet the above-mentioned criteria. Highly transcribed genes displayed transcription of >500 RPK in NHS conditions and all transcribed genes were any genes that showed transcriptional activity in human K562 cells in NHS conditions.The composite profiles and heat map analyses were generated using the bigWig package . Heat maps were generated by separately querying the plus and minus strand read counts in each 4-bp bin, after which the pair of plus and minus strands were displayed in conjunction in the heat map. For metaprofiles, the plus and minus strand values were combined.The bigWig data sets of TBP ChIP-nexusComputational analyses have been performed using R and Python languages. Custom made scripts can be made available upon request.http://www.ncbi.nlm.nih.gov/geo). All other data that support the findings of this study are available from the corresponding authors upon request.The complete raw data sets for histone H4 acetylation (GSE89382), and nascent RNA synthesis (GSE89230) are publically available at GEO databases (Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Peer Review File


RET, HRAS, and KRAS are the most important genes that are characterized in MTC. From the epigenetic perspective, Ras-association domain family member 1A, telomerase reverse transcriptase promoter methylations, overexpression of histone methyltransferases, EZH2 and SMYD3, and wide ranging increase and decrease in non-coding RNAs can be responsible for medullary thyroid carcinogenesis.Medullary thyroid carcinoma (MTC) is an infrequent calcitonin-producing neuroendocrine tumor that initiates from the parafollicular C cells of the thyroid gland. Several genetic and epigenetic alterations are collaterally responsible for medullary thyroid carcinogenesis. In this review article, we shed light on all the genetic and epigenetic hallmarks of MTC. From the genetic perspective,  Medullary thyroid carcinoma (MTC) is a rare neuroendocrine tumor that originates from the parafollicular cells (C cells) and produces calcitonin. ApproxiA mutation is a stable modification in the DNA sequence of a given gene, which may alter the normal gene function. MutatioRET proto-oncogene is a tyrosine kinas receptor coding gene, and it is an element of the glial cell line-derived neurotrophic factor (GDNF) family which are classified as extracellular signaling molecules[RET gene with 21 exons is localized on chromosome 10 (10q11.2)[ERK), phosphatidylinositol 3-kinase (PI3K)/AKT, p38 mitogen-activated protein kinase (MAPK), and c-Jun N-terminal kinase (JNK) pathways[RET gene results in three different isoforms. The C-terminal region in RET51, RET43, and RET9 have 51, 43, and 9 amino acids, respectively[MTC has been described in two forms: sporadic and hereditary/familial. About one-fourth of MTC patients haveone of three different syndromes, which are FMTC, multiple endocrine neoplasia type 2A (MEN 2A), or type 2B (MEN 2B). Around a half of the patients with MEN 2A or MEN 2B develop pheochromocytomas-30. Moremolecules. Human R(10q11.2),38. Like pathways-44. The pectively. As showpectively,46.RET tyrosine kinase[Glial cell line-derived neurotrophic factor and some other related molecules like neurturin, artemin, and persephin trigger an intracellular signaling pathway through a unique multi-component receptor systems including glycosyl-phosphatidylinositol-anchored co-receptor in addition to ne kinase,40,47-49ne kinase-52 more ne kinase,54.HRAS and KRAS mutations, have been found to be more appropriate diagnostic markers than RET for MTC. This observation has been suggested on the basis of high-throughput mutation profiling study  is associated with stage of the disease and persistence of the disease after total thyroidectomy because it makes the chance of recurrence and metastasis greater than before and reduces chances of free survival[In more aggressive phenotypes of sporadic MTC, the  of cases. Actuall survival,63. A co survival-67.RASSF1A)[TERT) genes[EZH2 and SMYD3[The word ‘epigenetics’ refers to covalent modification of DNA, protein, or RNA, resulting in changes in the function and/or regulation of DNA without modification of their original sequences. In some cases, epigenetic modifications can be stable and can pass on to future generations; mostly, however, they are vigorous modifications in response to environmental stimuli. The majRASSF1A),74 and tRT) genes, overexpand SMYD3,77, and and SMYD3-84.RASSF1A gene promoter hypermethylation is linked to more aggressive thyroid cancers, CpG island methylation of tumor-associated genes— such as p16, TSHR, MGMT, and PTEN—have not shown any significant degree of hypermethylation in MTC[TERT gene and consequent variation of DNA copy numbers within a huge cohort study of MTC cases have been documented[TERT has been increased frequently in many types of cancers, including MTC[TERT gene hypermethylation is related to high DNA copy number, and MTC patients with higher TERT methylation have lower chances of survival[In spite of the fact that on in MTC,85-87. Nocumented. In factuding MTC. Wang anuding MTC have rep survival.et al.[EZH2 and SMYD3, is connected with higher risk of metastases, disease consistency, and finally death of patients: These can be prognostic biomarkers for MTC[RET or RAS mutations.Epigenetic control through histone methyltrasferases in more aggressive forms of MTC have recently been investigated by Sponziello et al.. In facts for MTC,88. RemaRET mutation in genetic alteration of MTC tumorigenesis. The transcriptional activity of RET is controlled by epigenetic processes as well. It has been demonstrated that in colorectal cancer CpGisland methylation of RET gene promoter is a potential prognostic marker for stage II of the disease[RET have worse overall survival compared to those with unmethylated RET promoter[RET expression is regulated by a transcription factor, homeobox B5[RET gene in addition to the higher level of RET transcription. Another regulating mechanism of RET transcription level is acethylation because in human neuroblastoma cells with a low RET mRNA level, histone deacetylase inhibitor and sodium butyrate cause hyper acetylation and increase the transcription of RET gene[The most common mutation is related to the e disease,90. Pati promoter. Signifimeobox B5, which i RET gene.MiRNAs are small non-coding RNAs with the lengths of 20-23 nucleotides; they are classified as epigenetic modifiers. MiRNAs Ten miRNAs were shown to have different expression pattern between sporadic MTC andmiR-183 and miR-375 were linked to the lateral lymph node and distant metastases[miR-183, miR-375, and miR-9-3p (miR-9) between primary tumor tissues and lymph node metastasis tissues was observed[miR-9-3pin in regulation of autophagy[hereditary MTC. In corretastases. Signifi observed. This waautophagy. More thautophagy-83,98. Tautophagy,99,100.RET mutations in codons 609 (C609X), 618 (C618X), 620 (C620X), 786 (E768D), 804 (V804L), 819 (S891A), 918 (M918T), 833 (A883F), 804 (V804M), 806 (Y806C), 632 (Glu632), 633 (Leu633), and 918 (M918T) as well as HRAS, and KRAS mutations are the most important mutations that cause medullary thyroid carcinogenesis. From epigenetic perspective, RASSF1, TERT promoter methylations, histone methyltransferases (EZH2 and SMYD3) overexpression, and wide ranging increase and decrease of non-coding RNAs contribute to medullary thyroid carcinogenesis.From genetic point of view,


Telomerase elongates telomeres and is crucial for maintaining genomic stability. While stem cells and cancer cells display high telomerase activity, normal somatic cells lack telomerase activity primarily due to transcriptional repression of telomerase reverse transcriptase (TERT), the catalytic component of telomerase. Transcription factor binding, chromatin status as well as epigenetic modifications at the TERT promoter regulates TERT transcription. Myc is an important transcriptional regulator of TERT that directly controls its expression by promoter binding and associating with other transcription factors. In this review, we discuss the current understanding of the molecular mechanisms behind regulation of TERT transcription by Myc. We also discuss future perspectives in investigating the regulation of Myc at TERT promoter during cancer development. Terc and accessory proteins   and associated histone acetyl transferases (HATs). It can also recruit P-TEFb, which is a kinase that phosphorylates the C-Terminus of RNA polymerase II, thereby helping in promoter clearance. However, the mechanism employed by Myc to activate the TERT promoter is not known  at positions −165 and +44 nucleotide position relative to the transcription start site (TSS)  mutations commonly occur in low-grade gliomas  domain  ligands inhibit TERT mRNA expression in colon cancer cells via modulation of the Myc/Max/Mad network (Toaldo et al., Alterations in Myc expression are commonly observed during cancer initiation and progression. They occur via chromosomal translocations, amplifications and gene mutations Dang, . TelomerMyc expression is usually measured at the protein level using immunohistochemistry. For measurement of TERT expression, some studies use mRNA expression, while others measure telomerase enzyme activity since TERT mRNA levels positively correlate with telomerase activity (Armstrong et al., Myc and TERT play crucial roles during normal development as well as in carcinogenesis. Therefore, understanding their interplay in both these processes is important. Myc can activate TERT transcription directly or by cooperating with other transcription factors like SP1, ER and ETS. The factors that discriminate Myc-dependent TERT regulation during normal development from cancer are not known and need further investigation. Mutations in the TERT promoter represent the most common non-coding somatic mutations in cancer and it would be interesting to investigate the role of Myc in regulating the mutant TERT promoter. The existing correlation studies between Myc and TERT are not very conclusive and need further analysis.EK conceived, designed and wrote the manuscript. EK and VT revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.