Pseudomonas aeruginosa surprisingly encodes all three classes, allowing it to thrive in different environments. Here we study an aspect of the complex RNR regulation whose molecular mechanism has never been elucidated, the well-described induction through oxidative stress, and link it to the AlgZR two-component system, the primary regulator of the mucoid phenotype. Through bioinformatics, we identify AlgR binding locations in RNR promoters, which we characterize functionally through EMSA and physically through AFM imaging. Gene reporter assays in different growth models are used to study the AlgZR-mediated control on the RNR network under various environmental conditions and physiological states. Thereby, we show that the two-component system AlgZR, which is crucial for bacterial conversion to the mucoid phenotype associated with chronic disease, controls the RNR network and directs how the DNA synthesis pathway is modulated in mucoid and non-mucoid biofilms, allowing it to respond to oxidative stress.Ribonucleotide reductases (RNR) catalyze the last step of deoxyribonucleotide synthesis, and are therefore essential to DNA-based life. Three forms of RNR exist: classes I, II, and III. While eukaryotic cells use only class Ia RNR, bacteria can harbor any combination of classes, granting them adaptability. The opportunistic pathogen  Pseudomonas aeruginosa is a ubiquitous environmental Gram-negative bacterium, but it can also be a dangerous and adaptable opportunistic pathogen. In particular, it is known to cause severe chronic lung infections in immunocompromised patients and other at-risk groups. In cystic fibrosis (CF) patients, this infection is associated with a poor prognosis, leading to severely impaired lung function and an increased risk of respiratory failure, and is the primary cause of morbidity and mortality1. P. aeruginosa initially colonizes the CF lung in a non-mucoid form . However, at later stages of lung colonization, P. aeruginosa switches its phenotype to a mucoid alginate-overproducer variant, leading to rapid pulmonary deterioration3.P. aeruginosa from phagocytosis, antibiotic penetration, and desiccation5, but it is also an energy-intensive process and is therefore closely regulated and activated only when a chronic infection reaches a critical point. It involves a large number of enzymes and precursor substrates. Of particular relevance is the algD (and consecutive genes) operon, encoding the main enzymes for alginate production, and the algC gene from the algC-argB operon, a multifunctional enzyme required for several pathways including alginate biosynthesis and LPS production6. These genes are controlled by products of the algU/mucABCD operon; their transcription is directed by the alternative sigma factor AlgU (sigma E), which is commonly sequestered by the anti-sigma factor MucA. It has been reported that several types of cellular stress can induce proteolytic degradation of MucA, releasing AlgU and transiently activating alginate synthesis7, but the stable mucoid phenotype is generated through the selection of mutations in the regulatory genes, usually in mucA9.Alginate production protects algD and algC operons, the AlgU sigma factor regulates the transcription of the fimS-algR operon, which encodes the AlgZR two-component system11. In this system, FimS is the membrane kinase that can detect an unknown environmental signal and accordingly modulate the phosphorylation of AlgR. In turn, AlgR is the transcriptional factor that, depending on its phosphorylation state, regulates all aspects of alginate biosynthesis , as well as several aspects of anaerobic metabolism, type IV pili formation, rhamnolipid biosynthesis, type III secretion, and cyanide and nucleotide synthesis13. Furthermore, it has recently been reported to bind with high affinity to 157 loci in the P. aeruginosa genome12. Many of the functions regulated by the AlgZR system are important for biofilm formation and chronic infection14. When AlgR is phosphorylated, it controls functions related to cell attachment and initial biofilm formation, while a high excess of non-phosphorylated AlgR induces late biofilm and chronic infection traits, including alginate biosynthesis and the mucoid phenotype12. Several observations have also linked this system with the P. aeruginosa ribonucleotide reductases (RNR) network12.Apart from the 15. There are three known RNR classes , and all use a free-radical-based catalysis; however, they rely on different metallo-cofactors for the initiation of the radical reduction step, and each one exhibits a different behavior towards oxygen. Class I RNR can be enzymatically active only under aerobic conditions, class II RNR is oxygen-independent and requires vitamin B12 for enzyme activation, and class III RNR requires strict anaerobic conditions to be active. While almost all eukaryotic organisms encode exclusively class Ia RNR, prokaryotes are known to encode more than one, in all possible combinations16. P. aeruginosa encodes all three RNR classes: class Ia (nrdAB), class II (nrdJab) and class III (nrdDG)17. Their different requirements and relationships with oxygen give them different roles throughout the Pseudomonas life cycle and in the biofilm structure19.Ribonucleotide reductases are the enzymes responsible for reducing the ribonucleotides (NTP) to the corresponding deoxyribonucleotides (dNTP), thereby forming the building blocks for DNA synthesis and repairThe RNR activity is known to be extensively regulated at both the transcriptional and post-translational levels; it is delicately modulated to keep a balanced nucleotide pool and globally regulated according to the life cycle, stress situations, and environmental conditions. However, much remains unknown about which factors allow bacteria to activate the different classes under different circumstances.P. aeruginosa was the nrdJ gene (PA5497)6, which encodes a class II ribonucleotide reductase that plays a crucial role during biofilm formation and infection. In addition, a recent study that aimed to identify AlgZR-regulated genes using ChIP-seq showed a particular region for AlgR binding (AlgR-box) in a short DNA fragment within the intergenic region between the class I RNR operon first gene  and the PA1157 gene12. All of these observations point to the existence of a relationship between the AlgZR system and the RNR network.Several years ago, one of the genes found in a transcriptomics experiment to be regulated by AlgR in In this study, we aimed to uncover this relationship. We demonstrate that AlgR regulates both RNR classes I and II in a differential way, depending on its phosphorylation state. We explore how this differential regulation allows bacteria to adapt to different situations when living in a free form, during colonization of surfaces and in mucoid or non-mucoid biofilms. Finally, we unravel for the first time the molecular mechanisms behind the well-known activation of ribonucleotide reductase activity that occurs under oxidative stress.6. Furthermore, AlgR has been reported to bind upstream to the class Ia RNR operon12, facing the neighboring gene PA1157. We aimed to explore a possible regulation by AlgZR on the RNR network, and clarify if the already detected binding site regulates the RNR class Ia operon nrdAB or the PA1157.Previous studies have suggested a regulation by the two-component system AlgZR on class II RNRnrdA (pETS134), nrdJ (pETS180), nrdD (pETS136) or PA1157 (pETS206) promoters to the green fluorescent protein (GFP). The promoter of the algD (and consecutive genes) operon, main responsible for alginate biosynthesis, was used here as a positive control, as it is well-known to be regulated by non-phosphorylated AlgR (pETS205)21.We initially used plasmids carrying a transcriptional fusion of the P. aeruginosa PAO1 strain with its isogenic algR mutant strain , the regulation of the PalgD promoter expression by AlgR is consistent with what has been extensively reported. AlgR acts as an activator of its transcription, although it is typically almost fully inactive in the non-mucoid phenotype14.As shown in Fig. nrdAB) and class II operon (nrdJab). Complementation with a fimS-algR overexpression construct  increased the expression of both promoters beyond the levels of the wild-type. The PA1157 promoter was not affected by either the absence or overexpression of the AlgR regulator. Thus, there is no evidence of the AlgR binding in the PA1156-PA1157 intergenic region regulating the PA1157 gene as previously reported12. Instead, it controls the adjacent nrdAB genes. No change was found in PnrdD promoter expression (pETS136), so class III RNR is not regulated by AlgZR under the studied conditions.Studying the control on the RNR operons, we detect similar positive regulation by AlgR on the class I operon  abolishes its in vitro and in vivo phosphorylation by the FimS (AlgZ) kinase in response to environmental signals, while keeps protein structure apparently intact20. Hence, we used the wild-type AlgR (pUCP-AlgR) and its variant AlgRD54N (pUCP-D54N) overexpression plasmids to determine the influence of phosphorylation in regulating nrdA and nrdJ transcription  showed evidence of dependence on the phosphorylation state of AlgR, presenting a higher increase in expression with phosphorylatable AlgR. Further results in other growth models demonstrated this effect (see below). The transcription of nrdD and PA1157 were, as expected, unresponsive to either pUCP-AlgR or pUCP-D54N overexpression.Studying the RNR genes, both pUCP-AlgR and pUCP-D54N were able to increase nrdA, PnrdJ and PnrdD, applying the 1e-4 p-value threshold, a single binding site was identified on PnrdA and PnrdJ, while no hits were retrieved from PnrdD. As further results showed that PnrdJ included more than one binding site (see below), a less stringent search was conducted for this probe, in which all hits obtained from applying a 1e-3 p-value threshold was considered. All the identified boxes are represented in Fig. To localize the AlgR binding sites in the class Ia and II RNR promoter regions, a thorough bioinformatics search was conducted. First, to characterize the AlgR-box consensus sequence, we used MEME (MEME suite), starting from three different sources of information , to obtain three count matrices characteristic of the AlgR binding site , class II (PnrdJ) and class III (PnrdD) were analyzed, with the corresponding positive control  and a negative control. The PalgD band showed, as expected, evidence of two strong binding events. We also identified one binding activity in the PnrdA promoter and two binding events in the PnrdJ promoter, while no evidence of an AlgR-DNA interaction was found for PnrdD . Initially, long DNA probes spanning all the predicted promoter regions for class I  and other proteins21. However, there is no published visual evidence of this process, and no studies have demonstrated if AlgR can alter the DNA structure by itself, in the absence of other factors.It has been reported that AlgR control, usually performed through binding hundreds of base pairs upstream of the basal promoter, often implies DNA bending-AlgR complexes, compared to free DNA probes, using Atomic Force Microscopy (AFM) , although several series of images showed that the PnrdA complex was easier to obtain and more stable. One single binding site is observed in the PnrdA promoter, while the PnrdJ-AlgR complexes show bindings in two locations. There is also a binding event on two sites in the PalgD probe, containing its two strong boxes RB1/RB2.To explore the physical effect of AlgR binding on RNR promoters, we observed previously formed DNAFM) Fig. . AlgR bidinB) promoter region23. No bending is observed due to LexA binding on PdinB  strain, which forms very characteristic mucoid colonies.The AlgZR system is required for fimbrial biogenesisalgD promoter  PalgD transcription. In the ∆mucA strain, the great increase in non-phosphorylated AlgR levels causes a very significant increase in PalgD transcription (>6000 RFU). All results agree with our previous observations and published data27, serving as a control for this technique.We first analyzed the regulation of the Pter Fig. . The basnrdA promoter . The overexpression of wild-type AlgR protein complements the mutation, while, in this model, AlgR D54N overexpression causes a reduction of the operon transcription. This first evidence that accumulation of AlgR can inhibit PnrdJ transcription is supported by the fact that, unlike for PnrdA, the activity of the promoter is severely reduced in the mucoid phenotype (∆mucA). Additionally, mutation of both AlgR boxes in the PnrdJ promoter causes not a reduction, but a significant increase in the transcription of the promoter, indicating a more complicated underlying mechanism.The results are more complex for the class II RNR Pter Fig. . As prevter Fig. , mutatinnrdJ transcription by binding to box 1. Eliminating box 1 can switch the effect of the ∆mucA background from a significant reduction to a large increase in transcription, suggesting that the previously observed inhibition of class II RNR transcription in the mucoid phenotype happens through AlgR binding in the AlgR box 1. The effect of mutating box 2 is not detectable in a wild-type background, but its mutation switches the effect of the AlgR-overexpression strain from an increase (even higher with phosphorylatable AlgR) to a dramatic decrease in PnrdJ transcription. Box 2 is therefore proposed to be implicated in PnrdJ activation in response to AlgR phosphorylation, in competition with the action of box 1, which would be involved in PnrdJ inhibition in the mucoid phenotype. The implications of this dual mechanism on stress conditions and the mucoid phenotype are further discussed below (see Discussion).To explore the independent action of the AlgR boxes, we performed several colony formation experiments with single-box mutants Fig. . Both bonrdJab) during P. aeruginosa biofilm formation and its transcriptional activation by anaerobic regulators under this condition19. However, the regulators involved in modulating RNR transcription in the biofilm are still unknown. The AlgZR system has been extensively associated with different aspects of biofilm formation, and here we have demonstrated that it controls the nrd genes, which are also differentially regulated in the mucoid phenotype. Therefore, we decided to explore the modulation of class I and II RNR expression by AlgZR in mucoid and non-mucoid biofilms. In Fig. nrdA and nrdJ expression, together with the algD expression as a control, during biofilm formation. Measurements were taken at different time intervals during growth (from 3 h to 72 h). The un-complemented PW9855  strain could not be used, as it presents severely impaired biofilm formation capabilities.Our group recently demonstrated the importance of class II RNR  strain . The AlgZR regulation appears to be responsible for the effect in the mucoid biofilm, as mutating AlgR box 1 eliminates this induction, as well as in the non-mucoid variant.Class I RNR transcription Fig.  is inducnrdJ expression, which can be restored with the mutation of AlgR box 1. Mutating this box causes a general increase in class II transcription while mutating box 2 causes a reduction. The double mutation causes an opposite effect in both the regular mature biofilm and in the mucoid biofilm. The differential action of AlgR box 1 and AlgR box 2 is therefore demonstrated, and it is related to both AlgR-mediated induction of class II RNR in non-mucoid biofilms and AlgR-mediated repression of class II RNR in the mucoid biofilm.Finally, the complex regulation of class II RNR is also reproduced in biofilm formation conditions Fig. . The chanrd gene transcription through an unknown molecular mechanism29. Here, we explore the ability of AlgR to sense oxidative stress and accordingly regulate RNR gene expression.There are several reports which describe that RNR activity is strongly activated under oxidative stress conditions by increasing nrdA) and II (nrdJ) RNR respond to oxidative stress (induced by hydrogen peroxide treatment) by significantly increasing their transcription . Introducing mutations in the identified AlgR binding regions of the nrdA and nrdJ promoter regions mimics the effect of the algR isogenic mutant, rendering them unable to respond to oxidative stress.As hypothesized, class I , class II is known to be especially important in biofilms and positively regulated by the anaerobic system Anr/Dnr, although we proposed the action of other biofilm-related factors19. Class III is highly activated under strictly anaerobic conditions by still unknown regulators. Moreover, the global regulator NrdR, which negatively regulates all RNR expression in almost all bacterial species15, is also present in the P. aeruginosa network30. However, despite all known information, there are yet many missing pieces. RNR activity is modulated under oxidative stress conditions29 and specific environmental conditions through unknown factors. The data linking the AlgZR two-component system to the RNR network12 could reveal another piece of this complex regulation.Several pieces of this regulatory puzzle are already known. In 12, but we realized that these data accounted only for high-affinity binders. Therefore, we also used several published sequences14, including weak binders, to produce a more relaxed search pattern 31, one unique AlgR-box was identified in the PnrdA promoter, and two were identified in the PnrdJ promoter. Although members of the AgrA family such as AlgR usually bind to direct repeats of their binding sequence14, it is known that other genes regulated by AlgR contain different numbers of boxes in their promoters . The distance from these boxes to their predicted transcription start sites . The PalgD promoter was used as a control, demonstrating that all methods are suitable for studying the effects of the AlgZR system; in all models, it acts as an activator of PalgD, whose transcription is activated by non-phosphorylated AlgR. As expected, under some circumstances its basal expression is not sufficient to observe an effect when mutating the gene, and regulation becomes apparent only when over-activating it. Using these methods, we identified a clear control by the AlgZR system of class I and class II RNR, while class III RNR demonstrated no evidence of regulation. This was also assayed under anaerobic conditions  but no differences worth highlighting appear (data not shown). The bioinformatics search identified binding sites only where regulation was later demonstrated.In studying the AlgR res Figs  and 3B, res Figs , and a mres Figs . Wild-tynrdA transcription .Concerning class II RNR regulation, initial experiments suggested that it was also activated by AlgZR Fig. ; howeverels Figs  and 6C; els Figs  and 6C,Dels Figs  and 6C. els Figs . We, theels Figs  and 6D. 29. Surprisingly, we demonstrated that despite being in the exponential phase, where AlgR regulation is not normally very prominent, eliminating the AlgR system caused the RNR network to be insensitive to stress  and ZT2 (constitutive) as fimS-algR33, a combination that favors phosphorylation. AlgR will induce the nrdAB and nrdJ genes, the operon of fimUpilVWXY1Y2E (Type IV pili) for the adhesion to surfaces34, and the rhlAB for quorum sensing cell communication and microcolony formation24. In the absence of these genes, a biofilm is not formed, and cells are more sensitive to stress conditions or antibiotic treatment25. When P. aeruginosa is attached to the surface, cells grow as microcolonies, which can slowly evolve to a biofilm phenotype. In biofilm conditions, a further induction of the RT1 and RT2 promoters in the algR operon starts to appear, controlled by AlgU and RpoS, and algR is therefore also expressed as algR-hemCD (avoiding the fimS gene and so being non-phosphorylated). Then, AlgR favors the induction of genes such as the algD and algC operons for the synthesis of alginate33 and hemC and hemD for the synthesis of the heme group . NrdJ is also induced by the Dnr transcriptional factor19. When biofilm becomes mature, fully anaerobic conditions appear37, inducing both nrdJab and nrdDG19. It has been reported that many genes regulated by AlgR are also controlled by anaerobic transcriptional factors such as Anr or Dnr 42. We observed a significant decrease when inhibiting AlgR and Dnr binding and a gradual reduction of PnrdJ expression when mutating both systems  it activates AlgRalgR gene has a different effect than mutating both AlgR-boxes on PnrdJ shows us that there could be more regulation events that have not yet been described. Besides, the differences observed between surface colonization and biofilm formation conditions suggest interactions with other factors. However, we believe that these new results bring us closer to understanding the regulation of complex RNR networks such as that of P. aeruginosa, as well as how it adapts to environmental conditions and evolves throughout the biofilm life cycle. The link between oxidative stress, the AlgZR system, and RNR regulation provides, for the first time, a molecular explanation for this effect. In most bacterial species, there are no described analogs of the AlgZR system, but there are AgrA family two-component systems which could be candidates; further experiments will be conducted to evaluate whether these results extend to other bacterial species.Several pieces of information remain unknown. The fact that mutating the Pseudomonas aeruginosa and Escherichia coli strains were used, as listed in Supplementary Table −1 gentamicin, 40 µg ml−1 tetracycline, or 500 µg ml−1 carbenicillin for P. aeruginosa and 30 µg ml−1 chloramphenicol, 10 µg ml−1 gentamicin, 50 µg ml−1 ampicillin and 50 µg ml−1 kanamycin for E. coli. Anaerobic growth was performed in LB medium containing 10 g/l KNO3 in screw-cap tubes (Hungate Tubes) that were filled to the top with N2.Different 43. DNA was transferred into P. aeruginosa cells via electroporation using a Gene Pulser XCell™ electroporator (Bio-Rad) as previously described30. The absence of mutations introduced during cloning was verified via DNA sequencing.Molecular biology enzymes and kits were purchased from Fermentas (ThermoFisher) and used according to the manufacturer’s instructions. DNA amplification was performed by PCR using DreamTaq MasterMix (2X) or High-Fidelity PCR Enzyme Mix  following the manufacturer’s instructions, with the primers listed in Supplementary Table algR gene (PA5261) into the pET28a overexpression system (Novagen) downstream of the T7 RNA polymerase promoter. The algR gene was amplified from P. aeruginosa PAO1 by PCR using the primers AlgR-up and AlgR-low and High-Fidelity PCR Enzyme Mix. The fragment amplified (747 bp) was cloned into the pGEM-T easy vector and transformed into E. coli DH5α. After plasmid isolation using GeneJET Plasmid Miniprep Kit, the plasmid was digested with NdeI and NotI restriction enzymes and ligated with T4 DNA ligase into the pET28a vector, obtaining plasmid pETS201. Finally, pETS201 was transformed into the Rosetta(DE3) strain for AlgR overproduction and purification.An AlgR transcriptional factor overproducer was built by cloning the algR gene was specifically mutated by PCR-based site-directed mutagenesis as previously described19 using primer pair 1  strain for AlgRD54N overproduction and purification.To produce the AlgRD54N mutant overproducer, the algR gene, the neighboring gene fimS, and their promoter region was amplified (2286 bp) using primer pair 2 and cloned into pUCP20T, generating pETS203. The algR was site-specifically mutagenized as previously described19 to produce algRD54N using mutagenic primer pair 1 and outer primer pair 2 and cloned into pUCP20T to generate pETS204.Complementation vectors for providing extra copies of AlgR and AlgRD54N were constructed by cloning the corresponding genes under the control of their own promoter regions into the pUCP20T vector. First, a band containing the algD, algR and PA1157 transcriptional GFP fusions, 900 bp, 483 bp and 769 bp long fragments encompassing the algD, algR, and PA1157 promoter regions were amplified by PCR using primer pairs 3–5; the obtained DNA fragments were cloned into pGEM-T easy and transformed into E. coli DH5α cells. BamHI and SmaI restriction enzymes were used for fragment digestion and for cloning into pETS130-GFP, to generate pETS205, pETS206 and pETS207 plasmids for algD, PA1157 and algR promoter regions, respectively.To construct the 19, using outer primer pairs 6 and 7 for the nrdAB and nrdJab promoter regions, respectively; mutagenic internal primer pairs 8–10 were used. Two regions of the PnrdJ promoter mistakenly identified as AlgR-boxes as artifacts in the bioinformatic search were also mutated using mutagenic primer pairs 11 and 12. For all the positively identified AlgR boxes, the mutant DNA fragments were later cloned into the pGEM-T easy vector and transformed into E. coli DH5α cells. BamHI and SmaI restriction enzymes were used for fragment digestion and cloning into pETS130-GFP, to generate pETS208 (for PnrdA box 1), pETS209 (for PnrdJ box 1) and pETS210 (for PnrdJ box 2). For the exact sequence of the mutations introduced, see Fig. For AlgR-box mutagenesis in the studied promoters, PCR-based site-directed mutagenesis was used as previously describedP. aeruginosa strains were grown in separate Erlenmeyer flasks containing 20 ml LB broth and the specified antibiotic. Flasks were incubated at 37 °C and agitated at 200 rpm. Bacterial growth was monitored by measuring optical density at 550 nm (OD550). Upon reaching the desired OD550, three independent 1 ml samples were taken from each analyzed strain and centrifuged for 10 minutes at 13000 rpm; the supernatant was removed, and the pellet was washed with PBS 1x containing 2% formaldehyde. Suspensions were left on ice for ten minutes before being centrifuged again, the supernatant removed and PBS 1x added. The fluorescence was then measured after diluting the sample 8 times in PBS 1x, using 96-well plates  on an Infinite 200 Pro Fluorescence Microplate Reader (Tecan).The different To determine gene expression during biofilm formation, an aerobic static biofilm was grown on 96-well plates  in LB containing 0.2% glucose. At the desired time, the planktonic cells on the supernatant were removed, and the biofilm was washed three times with PBS 1x and then fixed with 2% formaldehyde. GFP was measured using Infinite 200 Pro Fluorescence Microplate Reader (Tecan). Fluorescence obtained at each time point was compared with fluorescence at 3 h of biofilm formation to calculate the induction factor of the gene expression.550 of 0.05 of the corresponding P. aeruginosa strains were grown on 6-well plates  containing LB with 1.5% agar and the corresponding antibiotics. Plates were incubated at 37 °C, and GFP expression was measured at different phases of colony formation; fluorescence measurements were performed by using an Infinite 200 Pro Fluorescence Microplate Reader (Tecan).For gene expression measurement during colony formation, 5 μl inocula at an OD45), to generate count matrices characteristic of the AlgR binding box. As the binding sequence is small and somehow flexible14, different sources were considered to obtain the AlgR box motif. Three sets of sequences were therefore used: set 1, to obtain a motif characteristic of strong binders, formed by the 50 most enriched sequences in ChIP-seq after AlgR precipitation12; set 2, to form a more flexible motif sequence, 25 randomly selected sequences from the enriched group in the same ChIP-seq experiments; and set 3, to capture the variation observed in some experimentally demonstrated boxes, confirmed by a previously published cluster of representative binding sites14. Assuming one occurrence of the AlgR box on every sequence, a single 11-bp long motif was obtained from each set , to search for AlgR binding sites. We calibrated the search from each count matrix by using a negative control  and a positive control . For the final search, DNA probes used were 900 bp long for PnrdJ and PnrdD  and 1050 bp long for PnrdA, given the predicted long 5′ UTR present .Using the generated count matrices, we used FIMO . When cultures reached an OD550 ≈ 0.5, protein overexpression was induced by adding IPTG to a concentration of 1.0 mM ; cells were cultured at 37 °C for 6 hours and later pelleted by centrifugation.AlgR and AlgRD54N proteins were overexpressed in a Rosetta (DE3) For preparing the protein extract, the pellet was suspended in 15 ml of AlgR lysis buffer per liter of original culture , supplemented with 1 mM PMSF as a protease inhibitor. The resulting suspension was sonicated on ice using a 6 mm conical microtip, until clear, to generate the crude extract (CE). It was centrifuged at 15000 g for 30 minutes at 4 °C, keeping the supernatant as the soluble fraction (SF), which was frozen at −80 °C for long term storage.TM HP column  in an FPLC system . First, the column was equilibrated with 5 column volumes (CV) of Buffer A . Protein samples were diluted with buffer A to a concentration of less than 1 mg/ml of total protein content and then injected into the column. A washing step was then carried out with 5 CV of Buffer A, and contaminant proteins were removed with a non-specific elution step using 5 CV of Buffer A with 50 mM imidazole. Finally, the protein was recovered in a specific elution step using 5 CV of Buffer A with 400 mM imidazole. The resulting fractions were analyzed by SDS-PAGE protein electrophoresis and dialyzed against AlgR Binding buffer  and stored at −80 °C  using a 5 ml His-TrapnrdAB and nrdJab operons (PnrdA long and PnrdJ long bands) or fragments of these promoters  and the negative control (inner region of non-related anr gene) and positive control, using the algD operon promoter region . All probes were generated by amplifying the corresponding region in a first PCR reaction that uses the reverse primer to also add the arbitrary sequence 5′-CTGGGCGTCGTTTTAC-3′ at the 3′ end of every probe  and later applying a second PCR reaction using primer WellRed-M13 to label the probes; WellRed-M13 is a WellRED dye-labeled oligo (Sigma-ALDRICH) coupled to the near-infrared fluorophore D3-phosphoramidite (D3-PA). Resultant probes are hence double-stranded DNA fragments labeled with a single molecule of D3-PA. Primer pairs 13–21 were used for EMSA band generation (see Supplementary Table P. aeruginosa PAO1 genomic DNA. All probes harboring mutations in putative AlgR-boxes were copied from the corresponding plasmids including mutant promoters (see the DNA manipulation and plasmid construction section).DNA probes for EMSA were produced for analyzing full promoter regions of the 2, 2 mM dithiothreitol, and 10% glycerol. Water was added to every reaction for a final volume of 20 µl. Reactions were incubated at room temperature for 20 minutes before gel electrophoresis.Purified AlgR or AlgRD54N proteins were used in binding reactions for a total amount of 0, 1, 4 or 10 pmol per reaction. A fixed amount of 100 fmol of DNA was used for all bands. Binding reactions also contained BSA (0.25 µg/reaction) and salmon sperm DNA (1 µg/reaction), as well as 2x-AlgR-binding buffer, added to a final 1x concentration of 20 mM Tris-HCl (pH 7.8 at 25 °C), 100 mM KCl, 2 mM MgClElectrophoresis was performed in 5% acrylamide gels, prepared with a 37.5:1 proportion of acrylamide:bis-acrylamide and using 5% triethylene glycol as an additive for increased DNA-protein complex stability. Final images were obtained by scanning the gels using the Odyssey Imaging System (LI-COR Biosciences) working in the 700-nm channel.P. aeruginosa PAO1 genomic DNA and designed so that binding sites were closer to one of the ends, to easily distinguish binding events. Primer pairs 22–25 were used for generating the DNA probes . Purified DNA probes were diluted to 2–4 nM with DNA AFM buffer . Ten microliters of DNA solution were pipetted onto a freshly cleaved mica and allowed to deposit for 1 min. The mica surface was then rinsed with 200 µl of MilliQ water and dried under a nitrogen stream. For the DNA-protein complex images, protein (AlgR/LexA) was previously mixed with the DNA fragments to a molar ratio of 3:1; the complex was incubated for 20 minutes at room temperature, and 10 µl of the solution was deposited on freshly cleaved mica and allowed to deposit for 4 minutes before rinsing and drying. Topographic images were obtained with a commercial AFM system  in conventional dynamic mode. A PPP-CONTR (Nanosensors) tip was used, with a nominal spring constant of ~ 0.3 N/m and tip radius of ~7 nm, scanning in ambient conditions using small oscillation amplitudes (~20 nm). Image resolution was not lower than 6 nm/pixel since this is close to the tip radius curvature. AFM image processing and determination of DNA length were carried out using WSxM 5.0 develop 7.0 (WSxM solutions).DNA probes for AFM studies were generated by PCR from All data generated or analyzed during this study are included in this manuscript and it supplementary information files, or is available upon request.Supplementary Information


Corynebacterium glutamicum is an important industrial producer of various amino acids and other metabolites. The C. glutamicum genome encodes seven sigma subunits (factors) of RNA polymerase: the primary sigma factor SigA (σA), the primary-like σB and five alternative sigma factors . We have developed in vitro and in vivo methods to assign particular sigma factors to individual promoters of different classes. In vitro transcription assays and measurements of promoter activity using the overexpression of a single sigma factor gene and the transcriptional fusion of the promoter to the gfpuv reporter gene enabled us to reliably define the sigma factor dependency of promoters. To document the strengths of these methods, we tested examples of respective promoters for each C. glutamicum sigma factor. Promoters of the rshA (anti-sigma for σH) and trxB1 (thioredoxin) genes were found to be σH-dependent, whereas the promoter of the sigB gene (sigma factor σB) was σE- and σH-dependent. It was confirmed that the promoter of the cg2556 gene (iron-regulated membrane protein) is σC-dependent as suggested recently by other authors. The promoter of cmt1  was found to be clearly σD-dependent. No σM-dependent promoter was identified. The typical housekeeping promoter P2sigA (sigma factor σA) was proven to be σA-dependent but also recognized by σB. Similarly, the promoter of fba  was confirmed to be σB-dependent but also functional with σA. The study provided demonstrations of the broad applicability of the developed methods and produced original data on the analyzed promoters.The online version of this article (doi:10.1186/s13568-017-0436-8) contains supplementary material, which is available to authorized users. The multisubunit RNA polymerase (RNAP) holoenzyme in bacteria consists of core enzyme  and a dissociable σ subunit (σ factor) that recognizes specific promoter sequences. Sigma factors are thus key regulatory elements that control different classes of promoters and activate expression of the respective groups of genes (regulons or sigmulons). Bacterial cells adapt in this way to changes in nutritional and environmental conditions. Bacteria typically possess a primary σ factor that is responsible for the transcription of housekeeping genes and a variable number of alternative σ factors that enable the cell to cope with various environmental stimuli. Since the activities of the different holo-RNAPs and the respective promoters orchestrate the cell metabolism in complex responses to various nutrition, growth and stress conditions, engineering σ factors has recently become a promising field in biotechnology and synthetic biology, particularly for the development of synthetic transcriptional control    if their −35 and −10 promoter sequences match the generally accepted consensus of housekeeping promoters. The consensus sequence of σA-dependent promoters (−35 ttgnca and −10 TAnnnT) was deduced from a large number of defined promoters  to reliably determine which sigma factors control transcription driven by individual tested promoters in C. glutamicum. We analyzed both housekeeping σ factors and ECF σ factors involved in stress responses. Overexpression of the sig genes encoding ECF σ factors usually results in the stronger expression of the σ factor-specific genes, even in the absence of the respective stress signal. This is advantageous particularly when the conditions under which the respective sigma factor is active are not known. The consistency of results achieved by the in vivo and in vitro techniques provided reliable promoter classification as well as new data on the analyzed promoters.In this study, we used an in vitro transcription system and in vivo methods  C. glutamicum ATCC 13032 and its deletion derivatives C. glutamicum ΔsigB, ∆sigE, ΔsigH and ΔsigM were used as hosts for testing the activities of promoters cloned in the promoter-test vector pEPR1. E. coli was cultivated aerobically in 500-ml flasks containing 80 ml of 2xYT medium  were cloned in the promoter-test vector pEPR1 containing the promoterless gfpuv reporter gene  in the expression vector pEC-XT99A . C. glutamicum strains harboring both the pEPR1 + promoter and pEC-XT99A (or pEKEx3) + sig gene were obtained by successive transformation. The presence of correct plasmids in two-plasmid strains was checked by restriction enzyme analysis and PCR.We constructed a system for the in vivo identification of sig gene overexpression on the activity of a particular promoter, the assay was performed as follows: The two-plasmid strain carrying the sig gene in the expression vector and a promoter in the promoter test vector pEPR1 was cultivated aerobically in 80 ml 2xYT medium at 30 °C. IPTG (1 mM) was added to the culture when OD600 reached 1 to overexpress a particular sig gene. Samples of the culture were then taken at various time points . The cells were washed with phosphate-buffered saline pH 8.0 (PBS)  (3 × 60 s at speed 6 m/s with Lysing Matrix B in 2-ml tubes). After centrifugation  the fluorescence of the cell-free extract was measured with a Saphire2 microplate spectrophotometer . Protein concentration in the extract was determined by Bradford assay and promoter activity was expressed in arbitrary units/mg protein. Cells harboring the pEPR1 construct and the expression vector without a sig gene were used as a control.To evaluate the effect of C. glutamicum were isolated as described previously, using the C. glutamicum rpoC-H8 strain producing the RNAP core and E. coli BL21 (DE3) with pET-22b(+) constructs producing individual C. glutamicum σ factors  were cloned in the vector pRLG770 (using EcoRI and HindIII sites) in such a way that a 150-nt transcript (terminated at the rrnB terminator) was produced by the in vitro transcription  overnight in a cleavage buffer containing 25 mM 4-ethylmorpholine acetate, and the resulting peptides were subjected to an LC–MS/MS analysis using collision-induced fragmentation in a Synapt G2Si mass spectrometer (Waters) coupled to an ACQUITY UPLC M-class system . For protein identification, the tandem mass spectra were searched against the NCBI bacterial database using an in-house Mascot search engine.The proteins extracted from the H is the most studied ECF sigma factor, which forms a transcriptional regulatory network enabling the C. glutamicum cell to respond to temperature, oxidative and growth-phase induced stresses  in addition to the PsigH promoters found upstream of the operon .σtrxB1 gene was first described as σM-dependent . To detect whether some other stress sigma factor at least weakly contributes to PtrxB1 activity in vivo, the effects of deletions in the genes sigH, sigM and sigE on the PtrxB1 activity during growth were tested. As shown in Fig. trxB1 remained at the level of the control (WT) in the ∆sigM and ∆sigE strains, whereas it was approximately 14-fold lower in the ∆sigH strain and essentially at the same level as that obtained with the strain only carrying the empty vector pEPR1. We therefore concluded that the PtrxB1 promoter is σH-specific.In vivo analysis using the two-plasmid system in the ity Fig. c. Very ssigE gene deletion on the transcriptional activity of the cg1266 gene. This gene was suggested to be σE-dependent . Based on the similarity of the promoter region of the C. glutamicum sigB gene to the Mycobacterium tuberculosis sigB promoter (controlled by σE and σH) it was suggested that the C. glutamicum sigB may be under the control of σE . To test whether RNAP + σE can initiate transcription from PsigB in vivo more efficiently when σH does not compete with σE, we used the two-plasmid assay in the ∆sigH strain with all seven sigma factors. As shown in Fig. H and σE increased expression of the gfpuv reporter gene from PsigB. These results clearly demonstrated that RNAP + σE can drive transcription from PsigB but σH competes with σE for RNAP or binding to the promoter.Next, we tested the effect of the C has recently been described . We selected the promoter of cmt1  to test its σD dependency. In vitro transcription assays only provided a specific band when RNAP + σD was used  were localized by determination of the respective transcriptional start sites (TSSs). Three of the genes  were found to be σH-dependent by Ehira et al. (H-dependent transcription was detected (data not shown). We were thus unable to confirm the σM dependency of any of these promoters. The failure to detect an in vitro transcript with RNAP + σM might be due to the limitations of the technique used. In contrast to all other σ factors purified for the in vitro assays, most of the σM protein was detected in the insoluble fraction after its isolation from E. coli extracts using affinity chromatography (data not shown). Various modifications of the protocol did not improve the ratio of the soluble/insoluble fraction. The σM protein was therefore denatured and renatured. Since no σM-specific promoter was detected by the in vitro assay, there is a possibility that the renatured σM was not functional in vitro. Another reason for failing to prove σM-dependent transcription using the in vitro transcription may be missing activators. To test whether σM is present in the C. glutamicum ∆sigM cells carrying the pEC-XT99A vector with cloned sigM after IPTG induction in the in vivo assay, we analyzed the proteins of the cell extract by LC–MS/MS. Among other proteins the presence of the σM protein was confirmed by the identification of four peptides which covered 22% of the σM protein sequence  as well. We also observed this phenomenon for other σA- and σB-dependent promoters (data not shown). To see the effect of σ factor gene overexpression more clearly, the fluorescence intensity values were expressed as differences between the fluorescence at the sampling time  and the fluorescence at time 0  resulted in an increase in P2sigA promoter activity in both the exponential and stationary phase, whereas the overexpression of other sigma factors did not change its activity, or even decreased it (σH)  to the promoter. We conclude that P2sigA is predominantly σA-dependent and may probably also be active with σB in vivo under specific conditions (e.g. stress and the stationary phase).It is difficult to reliably prove that a promoter is σAAT Fig. a, are clate Fig. b. The va time 3, , 24 h an time 3, , 24 h anfba gene  was found to be downregulated in the sigB deletion strain both under conditions of oxygen deprivation and during aerobic cultivation . However, an alternative system utilizing the expression vector pEKEx3 with cloned sig genes and pEPR1-Pfba proved that Pfba is recognized by σB under the conditions used  or other type of physiological control for their natural activity.To compensate for the drawbacks of each of these approaches, both in vivo and in vitro methods should be applied for the analysis of sigma factor–promoter interactions. By combining the results of in vitro and in vivo experiments, one can achieve an unambiguous assignment of sigma factors to promoters.M. The achieved results show that the system can produce data that is almost free of secondary effects. We were also able to convincingly document the recognition overlap of two σ factors at a single promoter .The main aim of this work was to integrate the results of our newly developed in vitro and in vivo techniques so that we can reliably classify individual promoters according to their σ factor dependency. This approach proved to be useful, and we were able to find a representative example of a promoter for every sigma factor with the exception of σrshA gene, which is located immediately downstream of sigH, encodes the anti-σH factor. The gene is transcribed together with the sigH from σA-dependent promoters and separately as a monocistronic rshA transcript from PrshA which was proposed to be σH-dependent . The in vivo two-plasmid system also showed that PtrxB, PsufR and PtrxC are σH-specific, although the presence of σM in the C. glutamicum cells after the induction of sigM expression was proved by mass spectrometry.The ter Fig. . Similarter Fig. . Neitherity Fig. d. The acclgR, P2dnaK and P2dnaJ2 are recognized by both σE and σH .The σA- and σB-dependent promoters exhibited relatively high expression in all growth phases even without the overexpression of a sig gene. The cell levels of σA and σB are apparently high enough to drive expression from the tested promoters. Moreover, as was shown for the σB-dependent promoters Ppqo  for overexpressing


Escherichia coli during growth on lactose, L-arabinose, and D-xylose. The metabolism of these sugars is regulated in a hierarchical manner, where lactose is the preferred sugar, followed by L-arabinose, and then D-xylose. Previously, the preferential utilization of L-arabinose over D-xylose was found to result from transcriptional crosstalk. However, others have proposed that cAMP governs the hierarchical regulation of many non-glucose sugars. We investigated whether lactose-induced repression of L-arabinose and D-xylose gene expression is due to transcriptional crosstalk or cAMP. Our results demonstrate that it is due to cAMP and not transcriptional crosstalk. In addition, we found that repression is reciprocal, where both L-arabinose and D-xylose also repress the lactose gene expression, albeit to a lesser extent and also through a mechanism involving cAMP. Collectively, the results further our understanding of metabolism during growth on multiple sugars.Catabolite repression refers to the process where the metabolism of one sugar represses the genes involved in metabolizing another sugar. While glucose provides the canonical example, many other sugars are also known to induce catabolite repression. However, less is known about the mechanism for catabolite repression by these non-glucose sugars. In this work, we investigated the mechanism of catabolite repression in the bacterium  Usually, the cells first consume the sugar yielding the highest growth rate, followed by the sugar yielding the next highest growth rate, and so on3. They do this by repressing the expression of the genes involved in metabolizing the less preferred sugars2. This mechanism maximizes the growth rate by ensuring that cells devote their limited metabolic resources towards the preferred sugars2. The classic example involves the growth of the bacterium Escherichia coli on glucose and lactose, where E. coli will consume glucose before lactose5. The sequential utilization of these two sugars results in diauxic growth, where the cells first grow on glucose and then, following a short period of no growth, proceed to grow on lactose. This two-phase pattern of growth is also observed when E. coli is grown on mixtures of glucose and some other sugars2.During growth on a mixture of sugars, bacteria will often consume the sugars sequentially through a process known as catabolite repressionE. coli, glucose catabolite repression is regulated by the phosphotransferase system, a multi-protein phosphorylation cascade that couples glucose uptake and metabolism7. One protein in this cascade, EIIAGlc, plays a central role in glucose catabolite repression. When EIIAGlc is phosphorylated, it activates adenylate cyclase, which increases the concentration of cAMP in the cells. cAMP activates the expression of many genes involved in the metabolism of non-glucose sugars through the action of the cAMP receptor protein CRP. In addition, the expression of these genes is also induced by their cognate sugars. Because EIIAGlc phosphorylation increases when the cells are not metabolizing glucose, these CRP-regulated metabolic genes are expressed only in the absence of glucose and presence of their cognate sugar. In addition, unphosphorylated EIIAGlc can bind and inhibit the ability of some sugar transporters to uptake their cognate sugars through a process known as inducer exclusion10. Thus, when the cells are metabolizing glucose and EIIAGlc is predominantly in the unphosphorylated form, these sugars are not transported into the cell and, therefore, are unable to induce the expression of their cognate metabolic genes. These two mechanisms are believed to be the main factors regulating glucose catabolite repression in E. coli.In E. coli on mixtures of L-arabinose and D-xylose (hereafter referred to simply as arabinose and xylose)11. In this case, the cells will consume arabinose before xylose, though the extent of repression is less pronounced than observed with glucose and arabinose or glucose and xylose. Arabinose catabolite repression results from regulatory crosstalk between these two sugar utilization systems12. The arabinose transport and metabolic genes are regulated by the transcription factors AraC and CRP. When AraC is bound with arabinose and CRP with cAMP, they activate the expression of the arabinose genes. Similarly, the xylose transport and metabolic genes are regulated by the transcription factors XylR and CRP. When XylR is bound with xylose and CRP with cAMP, they activate the expression of the xylose transport and metabolic genes. Arabinose catabolite repression results from arabinose-bound AraC binding to the promoters of the xylose genes and inhibiting their expression through a competitive mechanism. In addition, xylose weakly represses the expression of the arabinose genes using an analogous mechanism involving xylose-bound XylR. This reciprocal mechanism generates a small subpopulation of the cells that selectively consume xylose even in the presence of arabinose13.Catabolite repression has also been observed with sugar mixtures not involving glucose. One prominent example is the growth of E. coli14. They observed hierarchical expression of the metabolic genes for these sugars. In particular, they found that sugars higher up in the hierarchy repress the expression of the genes involved in the metabolism of the sugars lower down in the hierarchy. These results suggest that the cells selectively utilize these six sugars in a similar hierarchical manner. In addition, they proposed that the hierarchical expression of these genes is regulated by CRP through the differential production of cAMP. A number of studies have shown that cAMP levels are inversely proportional to the growth rate of the cells, irrespective of the sugar, due to inhibition of adenylate cyclase by the downstream metabolites17. In support of this hypothesis, Aidelberg and coworkers found that the expression hierarchy matches the growth rates associated with these sugars. Furthermore, they found that the promoters for genes associated with sugars at the top of the hierarchy are more sensitive to cAMP than those at the bottom of the hierarchy. In other words, when the cell is growing on a higher-growth yielding sugar such as lactose, insufficient cAMP is produced to induce the expression of genes for lower-growth yielding sugars such as rhamnose. This mechanism is appealing as it readily explains how cells selectively utilize sugars in accords with their growth yield. These results also suggest that multiple mechanisms are involved in the selective utilization of non-glucose sugars, where some involve transcriptional crosstalk, as is the case with arabinose and xylose, and others do not.A recent study by Aidelberg and coworkers investigated the utilization of six non-glucose sugars  in E. coli is grown on a mixture of lactose and arabinose or lactose and xylose, it will consume the lactose first. Motivated by the study of Aidelberg and coworkers, we sought to determine whether the preferential utilization of lactose is due to cAMP or regulatory crosstalk. Using a number of different approaches, we conclude that the selective utilization of lactose is mainly due to cAMP. These results indicate that E. coli employs multiple mechanisms to regulate the utilization of non-glucose sugars.In this work, we investigated the selective utilization of lactose, arabinose, and xylose. When araB and xylA promoters, which respectively control the expression of the arabinose and xylose metabolic genes. These results are consistent with the results reported by Aidelberg and coworkers14. In addition, we found that lactose represses the xylA promoter more strongly than the araB promoter.As a first step towards investigating catabolite repression in sugar mixtures involving lactose and arabinose or lactose and xylose, we measured how these sugars individually affect the expression of each other’s metabolic genes. To measure gene expression, we employed single-copy transcriptional fusions to the fluorescent protein Venus. Briefly, we grew the cells in TB7 medium containing 1 mM of the cognate sugar, which is sufficient to fully activate the expression of the corresponding metabolic genes, and increasing concentrations of a second sugar. As shown in Fig. lacZ promoter, which controls the expression of the lactose metabolic genes. As shown in Fig. lacZ promoter. These results indicate that the repression is reciprocal. In other words, lactose represses arabinose metabolism and arabinose represses lactose metabolism. However, repression by lactose is somewhat greater than repression by arabinose. In the case of xylose . As shown in Fig.  mM. As sWe also measured the sugar utilization in M9 minimal medium. As shown in Figure 12. In particular, arabinose-bound AraC binds the xylA promoter and represses transcription. Similarly, xylose-bound XylR binds the araB promoter and represses transcription, albeit to a lesser extent. One possibility is that lactose represses arabinose and xylose gene expression also using competitive mechanism. Briefly, the transcriptional repressor LacI regulates the expression of the lactose genes. When LacI is bound with allolactose, a by-product of lactose metabolism, it no longer binds the lacZ promoter and represses transcription. We therefore hypothesized that allolactose-bound LacI binds to the araB and xylA promoters and represses transcription of the respective metabolic genes. To test this hypothesis, we measured expression from the araB and xylA promoters in a ΔlacI mutant. As shown in Fig. araB and xylA promoters in the absence of LacI. These results demonstrate that lactose catabolite repression does not involve transcriptional crosstalk by LacI.Previous work demonstrated that arabinose represses xylose metabolism and vice versa through a mechanism involving transcriptional crosstalkWe next tested whether lactose is able to repress arabinose and xylose gene expression in the absence of arabinose and xylose metabolism. The goal here was to see whether lactose was affecting some downstream metabolic process that somewhat would affect arabinose and xylose gene expression. As shown in Figs We next tested whether the repression of the lactose genes by arabinose and xylose was respectively dependent on arabinose and xylose metabolism. As shown in Figs lacZ mutant. Not surprisingly, we observed no repression (data not shown). However, these experiments were uninformative, because they provide no insight regarding the mechanism of lactose-mediated repression. The reason is that LacZ, the enzyme that cleaves lactose into glucose and galactose, also produces allolactose, the inducer for LacI19. Thus, we cannot determine whether loss of repression in a ΔlacZ mutant results from the loss of the LacI inducer or the inability of the cells to utilize lactose.We also performed the reciprocal experiment, where we tested whether lactose is able to repress arabinose and xylose gene expression in the absence of lactose metabolism using Δ14. To further test this hypothesis, we measured whether the addition of cAMP would relieve lactose inhibition of the arabinose and xylose promoters. As shown in Fig. araB and xylA promoter activities in presence of lactose. In the case of the araB promoter, repression was relieved by the addition of 5 or 10 mM cAMP. However, in the case of the xylA promoter, repression was only slightly relieved, even though similar concentrations of cAMP were employed. We were unable to test higher concentrations of cAMP (>10 mM), because they hindered cell growth. Nonetheless, these results are consistent with a mechanism whereby lactose inhibits arabinose and xylose metabolism through CRP.Aidelberg and coworkers previously proposed that the hierarchy among lactose, arabinose and xylose is due to differential promoter activation by the CRP global regulatorlacZ promoter by arabinose. As shown in Figure lacZ promoter. These results demonstrate that arabinose-mediated repression of the lacZ promoter is due in part to cAMP. Whether it represents the sole mechanism is unknown.We also tested whether the addition of cAMP would relieve the repression of the lacZ promoter has a higher affinity for cAMP-CRP than the CRP binding site within the xylA promoter14. If catabolite repression is due to differential activation by CRP, then replacing the weak CRP binding site within the xylA promoter with a stronger one from the lacZ promoter should relieve repression. The xylA promoter has a single CRP binding site20. The lacZ promoter has two CRP binding sites22. The distal site is considered to be the main CRP binding site for the lacZ promoter. Therefore, we replaced the native CRP binding site within the xylA promoter with the distal CRP binding site from the lacZ promoter. As shown in Fig. xylA promoter containing the distal lacZ CRP binding site than with the one containing the native CRP binding site. These results further support a mechanism where lactose-mediated repression of xylose metabolism is due to cAMP.Previous results suggest that the CRP binding site within the E. coli is capable of growing on a number of different sugars. While much is known about how E. coli regulates its metabolism when grown on a single sugar, far less is known about how this bacterium regulates its metabolism when grown on mixtures of sugars, particularly when the mixture does not involve glucose. A number of studies have shown that E. coli will selectively utilize non-glucose sugars, first consuming the sugar that yields the highest growth rate and then consuming the sugar that yields the next highest growth and so on14. Still many questions remain about the mechanisms governing the selective utilization of these sugars. Two mechanisms have recently been proposed. One involves transcriptional crosstalk between the regulatory systems governing the metabolism of individual sugars. This mechanism was shown to govern the preferential utilization of arabinose over xylose12. The other mechanism involves cAMP, a second messenger involved in glucose catabolite repression6. This mechanism was proposed to govern the preferential utilization of multiple non-glucose sugars14. However, this study did not consider whether transcriptional crosstalk was also involved. Indeed, cAMP does not appear to govern the preferential utilization of arabinose over xylose12. Furthermore, repression is reciprocal in the case of arabinose and xylose, where arabinose strongly represses the expression of the xylose metabolic genes and xylose weakly represses the expression of the arabinose metabolic genes12. These results indicate that the hierarchy among sugars is not necessarily strict but rather depends on the relative concentrations of the two sugars.E. coli: lactose, arabinose, and xylose. Our goal was to determine whether transcriptional crosstalk or cAMP/CRP governed the selective utilization of these sugars. In rich medium, we found that both lactose and arabinose inhibited the utilization of the other  pathway. Arabinose and xylose metabolism involves many more steps, as they both enter the glycolytic pathway through the non-oxidative branch of the pentose phosphate pathway. This likely explains why their metabolism is more inefficient, in addition to their lower energetic yield. Therefore, one would expect that lactose inhibits the metabolism of arabinose and xylose, as this would reflect a more efficient allocation of limited metabolic resources.This hierarchy matches the growth rates associated with these sugars25, was deleted , a non-metabolizable lactose analog, competitively inhibits AraC28. We tested whether it competitively inhibits XylR. However, we found that it had no effect on xylose gene expression (data not shown). We also tested whether thiomethyl galactoside (TMG), another non-metabolizable lactose analog, inhibits arabinose and xylose gene expression  or M9 minimal medium . All experiments were performed at 37 °C. Antibiotics were added as needed at the following concentrations: ampicillin, 100 μg/mL; chloramphenicol, 20 μg/mL; kanamycin, 40 μg/mL.Luria-Bertani (LB) medium  was used for strain construction. All experiments were performed in either buffered tryptone broth  was PCR amplified using the primers SK435F and XW435R, and then the PCR fragment was cloned into the integrative plasmid pVenus using SalI and EcoRI restriction sites. The plasmid pSK460 contains a transcriptional fusion of the lacZ promoter to the fluorescent protein Venus. To construct this plasmid, the lacZ promoter (genomic region: 366222–366545) was PCR amplified using the primers SK418F and XW418R, and then the PCR fragment was cloned into the integrative plasmid pVenus using SalI and EcoRI restriction sites. The plasmid pXW238 contains a transcriptional fusion of the xylA promoter to the fluorescent protein Venus, where the native CRP binding site of the xylA promoter (TTTTGCGAGCGAGCGCACACTT) (genomic region: 3730899–3730878)31 was replaced with the CRP binding site of the lacZ promoter (TAATGTGAGTTAGCTCACTCAT) (genomic region: 366394–366415)32. To construct this plasmid, the plasmid pXW211 was PCR amplified using the primers EA061F and EA062R, which contain the CRP binding site from the lacZ promoter as 5′ overhangs. The resulting PCR fragment was then phosphorylated using T4 polynucleotide kinase and ligated back into a circular plasmid using T4 DNA ligase. The plasmid was then integrated into λ phage attachment sites using the CRIM integration system33. After confirming by PCR that a single integration took place, the integrated plasmid was moved into a clean background using P1 transduction35.Table 600 of ~0.2. The cultures were then subcultured into 1 mL of fresh TB7 medium containing the specified sugar to an initial OD600 of 0.01 in 96 deep-well plates. The cultures were then incubated on a microplate shaker at 600 rpm for 5 h. In the experiments involving M9 as the base media, the cells were grown overnight in M9 minimal media. The overnight culture was diluted 1:50 into fresh media and grown for 5 h to an OD600 of ~0.2. One mL aliquots were then distributed to individual wells in a 96 deep-well plate. The specified sugars were then added to each well, and then the plate was incubated on the microplate shaker at 600 rpm for 4 h. At the end of every experiment, 100 µL samples were taken from each well and transferred to a black, clear-bottom Costar 96-well microplate. Fluorescence  and absorbance (OD600) were measured using Tecan Safire2 microplate reader. The fluorescence readings were normalized with the OD600 to account for cell density. All experiments were performed on three separate days, and the average values with the standard deviations are reported.In the experiments involving TB7 as the base medium, the cultures were first grown overnight in TB7. The overnight culture was then diluted 1:100 into fresh media and grown for 2 h to an OD600 of 0.05. The flasks were then incubated on a standard orbital shaker at 250 rpm and 37 °C. Samples (1 mL) were taken at specified time intervals and filtered through 0.22 μm filters (Millipore) for subsequent analysis. Arabinose, lactose and xylose concentrations were measured using a Shimadzu HPLC system equipped with Shimadzu RID-10A refractive index detector , and an Aminex HPX-87H column (300 mm × 7.8 mm) . The HPLC oven was adjusted to 65 °C and a mobile phase of 0.5 mM H2SO4 was run at 0.6 mL/min for 15 min per sample. Kinetic experiments were run in duplicates with the averages and standard deviations reported.Cells were first grown overnight in the specified media. The overnight culture was then sub-cultured into 60 mL of fresh media containing the specified sugars in a 125 mL shake flask with an initial ODAny other datasets generated during and/or analyzed during the current study are available from the corresponding author upon request.Supplementary Information


Drosophila embryos, but mutations result in a pair-rule phenotype. This was explained by the interaction of Ftz-F1 with the homeodomain protein Ftz that is expressed in stripes in the primordia of segments missing in either ftz-f1 or ftz mutants. Ftz-F1 and Ftz were shown to physically interact and coordinately activate the expression of ftz itself and engrailed by synergistic binding to composite Ftz-F1/Ftz binding sites. However, attempts to identify additional target genes on the basis of Ftz-F1/ Ftz binding alone has met with only limited success. To discern rules for Ftz-F1 target site selection in vivo and to identify additional target genes, a microarray analysis was performed comparing wildtype and ftz-f1 mutant embryos. Ftz-F1-responsive genes most highly regulated included engrailed and nine additional genes expressed in patterns dependent on both ftz and ftz-f1. Candidate enhancers for these genes were identified by combining BDTNP Ftz ChIP-chip data with a computational search for Ftz-F1 binding sites. Of eight enhancer reporter genes tested in transgenic embryos, six generated expression patterns similar to the corresponding endogenous gene and expression was lost in ftz mutants. These studies identified a new set of Ftz-F1 targets, all of which are co-regulated by Ftz. Comparative analysis of enhancers containing Ftz/Ftz-F1 binding sites that were or were not bona fide targets in vivo suggested that GAF negatively regulates enhancers that contain Ftz/Ftz-F1 binding sites but are not actually utilized. These targets include other regulatory factors as well as genes involved directly in morphogenesis, providing insight into how pair-rule genes establish the body pattern.The orphan nuclear receptor Ftz-F1 is expressed in all somatic nuclei in  Highly conserved cascades of regulatory genes control embryonic development of diverse animal species. These regulatory genes are often members of large families, encoding DNA binding transcription factors (TFs) that activate or repress expression of larger sets of downstream or target genes that are directly involved in cell growth and differentiation. Understanding mechanisms used by embryonic TFs to select genomic binding sites is key to understanding their function. Protein-protein interactions play a major role in target site selection, particularly for transcription factors with weak monomeric DNA binding specificity. Additionally, the activity of ubiquitously expressed TFs can be limited by restricted expression of a key partner protein, thus relieving constraints on specific expression domains for the remaining members of a regulatory complex.Drosophila, has acquired a unique partner, the orphan nuclear receptor Ftz-F1.The Hox family of TFs regulates segmental identity and controls diverse processes in all metazoans. Hox proteins share a highly conserved sequence-specific DNA binding domain, the homeodomain . Despiteftz arose as a homeotic gene as a result of an ancient duplication of a ftz/Antennapedia ancestral gene but has taken on exclusive pair-rule function in Drosophila. Ftz lost the ability to functionally interact with Exd in Drosophila and gained the ability to specifically interact with Ftz-F1 /P[enhancer-lacZ]; Dr/Tm3Sb males. From this cross, w; P[enhancer-lacZ]/+; ftz9H34/Tm3Sb males and females were crossed to generate w; P[enhancer-lacZ]/P[enhancer-lacZ]; ftz9H34/Tm3Sb. Offspring were self-crossed to analyze expression in a ftz background. Embryos derived from ftz-f1 germline clones (referred to as ftz-f1mutants) were generated with the autosomal FLP-DFS technique [ftz-f119 [yw hsFLP;FRT2Aftz-f119/TM3Sb virgin females were crossed with w; FRT2AovoD/TM3Sb males. Females were allowed to lay eggs for 1 day in vials and their progeny were heat-shocked for 2 hours at 37°C in a circulating water bath on the third and fourth days after egg laying. Subsequently, embryos derived from the females of genotype yw hsFLP;FRT2Aftz-f119/ FRT2AovoD  were analyzed. All of the FRT2AovoD recombinant chromosomes were associated with a fully penetrant DFS phenotype such that all eggs laid by these females are derived from germline recombination events. For the control for the microarray experiment, yw hsFLP;FRT2A/ FRT2A virgins were crossed to w; FRT2AovoD/TM3Sb males and subjected to the same heat shock and selection protocol in parallel.Flies were maintained at 25°C on a standard diet. The echnique –29 usingftz-f119 , 31. BriFor in situ hybridization, standard protocols were followed , 33 withftz-f1 or control females (see above) were mated to w1118 males in collection cages at 25°C and allowed to lay eggs for up to 2 hours. Eggs were dechorionated in 3% sodium hypochlorite for 3 minutes and then covered with halocarbon oil and aged. Embryos were visualized under phase contrast optics at 100-200x magnification. Individual embryos were pooled into groups of roughly equivalent developmental stages and kept out of the light path of the microscope as much as possible. To generate pools of embryos at specific stages, each pool was monitored closely and selected by visual inspection at late cellularization, stage 5, at the onset of gastrulation, stage 6, or at mid germband extension, stage 8. Embryos were manipulated using a small needle. As each embryo reached the desired stage of development, it was transferred to 100 μl of TRIZOL on ice. Approximately 100 embryos were collected per time point. Individual collections were stored at -80°C. Each experiment was done in triplicate. Total RNA was extracted using the Qiagen RNEasy kit according to standard protocols. Samples were processed using the Affymetrix one-cycle cDNA synthesis protocol prior to hybridization to Affymetrix Drosophila 2.0 expression arrays.ftz-f1 mutant condition  and batch number (processing and hybridization batch). Using 3 replicates of 3 stages and 2 states of ftz-f1 gave a total of 18 genechip arrays. All arrays were normalized by the expresso function using quantiles normalization, only perfect match, and median polish summary method. This generated the normalized expression set used for all further data analysis. The normalization procedure produced log2 expression results, and the fold change between the average of any two data sets s1 and s2 was calculated as Affymetrix drosophila2 genechip CEL files were imported into BioConductor/R  using th maanova , was run maanova . ProbeseDrosophila melanogaster genome. This dataset was processed with the meme application fasta-get-markov, to generate a 5th order hidden markov model of the available background enhancers found in the Drosophila blastoderm [Drosophila melanogaster embryos, along with the location of maximal binding within each interval. Genomic sequence for each of these regions was extracted from the repeat masked genome. For both MEME and DREME, a q-value threshold of 0.05 was used with the previously computed background file searching both forward and reverse strands. MEME was restricted to a maximum motif width of 10, while DREME’s default of 8 was used [Drosophila JASPAR database [Drosophila genome to determine the background likelihood of the occurrence of any given sequence. Enhancer sequences were scanned using pwms from the Jaspar database and compared to this background to determine the chance of a motif occurring randomly at each position. Motifs with a probability score of 0.9 or higher were considered candidates.401bp windows surrounding the peak Ftz binding position for all intervals  were merwas used . To seardatabase . To searftz-f1 germline clones embryos (referred to throughout as ftz-f1 mutants) at three time points during development: stage 5, the blastoderm forms, Ftz 7 stripes are established; stage 6, gastrulation, 7 strong Ftz stripes; and stage 8, germband extension, Ftz stripes begin to fade  of the probesets showed a 1.1 fold increase in expression level between stage 5 and stage 6. The intersection of these two data sets produced a list of 379 potential targets for validation, fewer than 2% of all genes examined on the microarray. 314 (83%) of these potential targets showed upregulation in response to absence of Ftz-F1 but, since Ftz-F1 has been shown to activate transcription  showed detectable alterations in response to absence of  protein . This, aftz expression (ken and Barbie (ken), engrailed (en), astray (aay), midline (mid), tarsal-less , 5-hydroxytryptamine (serotonin) receptor 2A (5-HT2A), tartan (trn), hedgehog (hh), Antennapedia (Antp), and bloated tubules (blot). tektin-C was not included because it does not show a detectable level of expression in early embryos by in situ hybridization. For 9 of the 10 remaining candidate target genes, expression of the target gene overlapped with ftz expression. ken is expressed in two stripes, which overlap with ftz stripes 1 and 7 . For blot, Ftz and Ftz-F1 appear to have a quantitative effect on blot expression levels but are not responsible for establishing its striped expression. Finally, the loss of tal expression in ftz-f1 mutants verifies its identification in the microarray experiment. The fact that tal does not overlap ftz expression  as the most over-represented sequence . mid-lacZ was expressed in seven stripes . This weak expression did not correspond to wildtype expression of 5-HT2A, and the expression was spotty. trn-lacZ was expressed in seven of the eight stripes in which endogenous trn is expressed , demonstrating that these are functional CREs  suggests that there could be low levels of Ftz genomic binding not detected even at 25% FDR. Together, these results suggest that the presence of Ftz binding and the ability to bind Ftz-F1 are highly predictive of enhancer activity in vivo.In sum, six of the eight enhancers tested directed strong reporter gene expression in patterns virtually identical to the corresponding endogenous genes  to detect enhancer expression and en (blue) to identify the ftz mutants, which express only seven en stripes. Enhancer-lacZ reporter gene expression pattern was examined during germband extension when expression was strongest  that responds indirectly. Note that this indirect regulation cannot be via en, as Ftz regulates only the seven alternate en stripes that overlap with Ftz. Antp-lacZ expression was much weaker in the ftz background  were analyzed along with four previously confirmed Ftz/Ftz-F1-responsive CREs , drm1 and drm5 [de novo algorithms used by the MelinaII program -Consensus, Meme, MDScanner, and Gibbs—identified the binding site for Ftz-F1 as the most common motif in the confirmed enhancers  binding sites, using the JASPAR database . Bindingken), but binding was weak  was not functional in directing reporter gene expression. Ftz binding was found in the vicinity of all of the Ftz-F1-responsive genes identified in this study and also near previously identified Ftz-responsive genes not identified in the microarray –drm2 and drm34 [drm2 was bound by Ftz in the ChIP-chip analysis—drm34 was not identified at either 1% or 25% FDR. This is the only known CRE missed in the ChIP-chip analysis, suggesting that a small number of bona fide Ftz target genes were not found with the current data. Within the 403 Ftz binding peaks, the most over-represented sequence was the Ftz-F1 binding site , compared to sequences containing potential Ftz and Ftz-F1 binding sites but not functioning as enhancers , was carried out to identify candidate co-regulators that may limit the binding and/or activity of Ftz and/or Ftz-F1 to specific genes. Binding sites for Deaf-1 and Zeste were present in all of the Ftz-dependent enhancers while false enhancers were enriched in binding sites for Dichaete and GAGA Factor. All of these proteins are likely to contribute to the regulation of many early embryonic genes, consistent with their complex phenotypes.ken-lacZ . Of the genes showing the largest difference in the microarray experiment, ten of the top eleven are co-regulated by Ftz and Ftz-F1, and identification of enhancers suggests that at least seven of these ten are directly regulated by them. Ftz/Ftz-F1 are PRGs responsible for the formation of even-numbered parasegments and are thought to be among the more downstream PRGs, that, for example, directly regulate en expression. However, this study and others suggest that they have many other direct targets.This study identified more than 50 genes that are expressed at higher levels in wildtype than en and hh play multiple roles, the most relevant to this study being their well-known roles as segment polarity genes [mid encodes a T-box transcription factor, classified as a segment polarity gene, but its most well-studied roles are in development in neuroblast specification, axon guidance, and heart development [Antp is a central Hox gene and was one of the first proposed candidate targets of pair-rule genes, including Ftz [ken encodes a zinc finger transcription factor implicated in genitalia development and as a regulator of the JAK/STAT pathway [ken [ken 2-stripe pattern. aay was identified in screens for regulators of peripheral nervous system development [trn encodes a cell surface protein that has been implicated in cell movement and migration in various cellular contexts including roles in imaginal discs, tracheal branch outgrowth, retinal epithelial integrity [Drosophila embryo (unpublished observation). Blot is a putative neurotransmitter with a role in morphogenesis of epithelium [5HT2 encodes a serotonin receptor with specific ligand binding with a documented role in convergent extension in Drosophila [The new Ftz/Ftz-F1 target genes identified in this study all have known roles in embryonic development. ty genes . While Eelopment –70. Antpding Ftz , 72. Thi pathway –75. Inteway [ken , suggestelopment . It encoelopment  and Fig elopment . trn encntegrity –83. Trn,ithelium . Similarosophila –88.ftz itself, for which there is strong evidence for direct Ftz/Ftz-F1 regulation, seven encode nucleic acid binding proteins that function as TFs themselves  and one is a signaling molecule (hh). Five fall into a number of different classes that could implicate them in playing more direct roles in morphogenesis: protein phosphatase (aay), serotonin receptor (5-HT2A), cell surface protein (trn), neurotransmitter transport (blot), and sulfatase (Sulf1). This suggests that the hierarchy remains regulatory to a large extent at the level of pair-rule genes but also identifies a group of direct targets that may play direct roles in segment formation. However, we suggest that the list of known targets is biased by their strong phenotypes. All of the top microarray targets investigated here were known genes, but the next set  Stage 4 , stage 5 , stage 6 , and stage 7  embryos showing Ftz-F1(green) is maternally expressed and localizes to the nuclei. Ftz (red) reaches its peak level during late cellularization (stage 5), when it is expressed in seven stripes. At the onset of gastrulation (stage 6), the most anterior stripe of Ftz is immediately posterior to the cephalic furrow. The Ftz stripes weaken throughout germband extension (stage 8). Both proteins are co-expressed (yellow) in nuclei of the primordia of even numbered parasegments. (Lower panel) Embryos were collected and hand staged at the times and stages indicated. Samples from each timepoint were immunostained with anti-Ftz antibody to verify stage. Phase contrast microscopy of live, dechorionated embryos in halocarbon oil was used to monitor progress of embryonic development, as shown in bottom panel.(TIF)Click here for additional data file.S2 Fig Schematic representations of each enhancer across the top of each section, as indicated. Melina II software was used to search for common motifs using 4 algorithms—CONSENSUS, MEME, MDScan and Gibbs, as indicated. The colored boxes represent motifs found by each algorithm common to the group of enhancers queried for (A) the 10 confirmed enhancers and (C) the 3 false enhancers.  All 4 algorithms identified the most common motif (B) in confirmed enhancers to be the binding site for Ftz-F1 and (D) in false enhancers to be the binding site for Fkh.(TIF)Click here for additional data file.S3 Figken enhancer, suggesting it may inhibit activation by Ftz/Ftz-F1.ChIP-chip data published by Schuettengruber et al., 2009 shows binding by fold change (y-axis) of transcription factors GAF, PC, and Pho along the DNA (x-axis). No discernable difference between Pho and PC binding at confirmed versus false enhancers was evident. GAF bound at all three of the false enhancers but not near nine of the confirmed enhancers, and only weakly near the (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Epstein–Barr Virus (EBV) BamHI-A rightward frame 1 (BARF1) protein is considered a viral oncogene in epithelial cells and has immune-modulating properties. During viral lytic replication BARF1 is expressed as an early gene, regulated by the immediate early EBV protein R. However, in viral latency BARF1 is exclusively expressed in epithelial tumors such as nasopharyngeal (NPC) and gastric carcinoma (GC) but not in lymphomas, indicating that activation of the BARF1 promoter is cell type specific. Undifferentiated NPC is characterized by high expression of ΔNp63 isoforms of the epithelial differentiation marker p63, a member of the p53 family of transcription factors. Transcription factor binding site analysis indicated potential p53 family binding sites within the BARF1 promoter region. This study investigated ability of various p53 family members to transactivate the BARF1 promoter. Using BARF1 promoter luciferase reporter constructs we demonstrate that only p63 isoform ΔNp63α is capable of transactivating the BARF1 promoter, but not the TAp63 isoforms, p53 or p73. Direct promoter binding of ΔNp63α was confirmed by Chromatin Immune Precipitation (ChIP) analysis. Deletion mutants of the BARF1 promoter revealed multiple ΔNp63 response elements to be responsible for BARF1 promoter transactivation. However, ΔNp63α alone was not sufficient to induce BARF1 in tumor cells harboring full EBV genomes, indicating that additional cofactors might be required for full BARF1 regulation. In conclusion, in EBV positive NPC and GC, BARF1 expression might be induced by the epithelial differentiation marker ΔNp63α, explaining BARF1 expression in the absence of lytic reactivation. Epstein–Barr virus (EBV) is associated with several human malignancies of B-cell origin, such as Burkitt’s lymphoma, Hodgkin’s disease and lymphoproliferative disorders in immune- compromised individuals, or epithelial origin such as nasopharyngeal carcinoma (NPC) and gastric carcinoma (GC) ,2. In EBThe BARF1 gene encodes a 220 amino acid (aa) protein, of which the membrane associated leader peptide comprising the first 20 aa is cleaved off, leading to efficient secretion of hexameric BARF1 ,8,9. BAREBV positive carcinomas are predominantly undifferentiated and characterized by a high expression of p63 ,19,20,21Several cancers, including NPC, show an overexpression of ΔNp63. The p63 gene is rarely mutated in cancer, and several studies indicate that the (in) balance between isoforms rather than the reduction of one particular isoform might be important . EBV migThus far, there have been no suggestions in regard to why BARF1 is exclusively expressed in EBV positive epithelial malignancies and not in lymphomas, but the cellular background may point towards a role of dissimilar transcription factors. In this study the potential role of p53 family members in the epithelial restricted expression of EBV protein BARF1 was evaluated. Multiple potential p53 binding sites were found in the BARF1 promoter region. Specifically the ΔNp63α isoform was capable of inducing the BARF1 promoter. These findings suggest that BARF1 expression in undifferentiated carcinomas is mediated by the epithelial differentiation marker ΔNp63α.SNU-719, a naturally derived EBV-infected gastric carcinoma cell line was maintained in RPMI1640 medium . AGS celpSG5 and pcDNA3.1 were obtained from Stratagene  and Invitrogen, respectively. The SG5-R expression vectors  were previously described ,40. ExprThe use of a CpG-free reporter construct enables to study the effect of promoter methylation without non-specific silencing due to methylation of the vector backbone. In vitro DNA methylation of the luciferase constructs was accomplished by CpG methylase , as recommended by the manufacturer. Completion of DNA methylation was confirmed by digestion with HpaII .5 cells plated in 1 mL medium in a 12-wells plate. Luciferase assays were performed 48 h after transfection by using extracts prepared by freeze-thawing the cell pellet in reporter lysis buffer according to the instructions of the manufacturer . Luciferase activity was assayed using the luciferase reporter assay system (Promega).Cells were seeded the day prior to transfection. Transfections were performed using Lipofectamine 2000 (Invitrogen) according to the manufacturer’s instructions, except that for the reporter assays the reagent: DNA ratio was 1.5 µL:0.5 µg in 100 µL Opti-MEM for 2 × 10CNE-2 Akata and AGS B95.8 cells were transfected with expression vectors for TAp63α, ΔNp63α or an empty control expression vector using Lipofectamine 2000 (Invitrogen). Cells were first cross-linked in 1.5 mM EGS (ethylene glycolbis succinimidylsuccinate]) for 30 min at room temperature followed by cross-linking in fresh 1% paraformaldehyde for 10 min at RT. The cross-linking reaction was quenched using 125 mM glycine. Following IGEPAL (CA-630) mediated cell lysis and DNA fragmentation by sonication, DNA-protein complexes were immunoprecipitated with anti-p63 , and control anti-IgG  antibodies. Protein-DNA cross-linking was reversed at 65 °C overnight, and DNA was purified using the Qiagen gel extraction kit. The presence of BARF1 promoter DNA fragments in each precipitate was detected using PCR using forward primer: GGCCCTGAACATGAGGTAGC and reverse: GCCAACAGGAGGAGCTGAGC, and those for E-cadherin were forward: CATGGCTCACACCTGAAATCC and reverse: AGTACAGGTGCACACCACCA and JAG-1 forward: ACCTTTCACCATTCCCCTAC and reverse: GCCCAAGGACAAAATAGCCA as previously used by Testoni et al. . Primers® 480 system (Roche) After quantification to known concentration of the corresponding gene constructs, values were normalized to U1A.Cells were plated in 6 well plates and expression vectors for TAp63α, ΔNp63α or an empty control expression vector using Lipofectamine 2000 (Invitrogen). After 48 hours cells were harvested in 1 mL Trizol (Invitrogen). Guanidinium isothiocyanate-phenol-chloroform extraction was performed to isolate total cellular RNA, followed by DNase (Promega) treatment and ethanol-precipitation. cDNA was synthesized using AMV Reverse Transcriptase (Promega) and sequence specific primers: BARF1 forward: GCCTCTAACGCTGTCTGTCC and reverse: GAGAGGCTCCCATCCTTTTC, U1A forward: CAGTATGCCAAGACCGACTCAGA and reverse: GGCCCGGCATGTGGTGCATAA. RT-PCR was performed with SybrGreen (Roche) and afore mentioned primers using the LightCyclerLysates from luciferase assay and ChIP were diluted in 2× loading buffer  with β-mercaptoethanol, denatured for 5 min at 95 °C and separated on a 10% SDS-page gel. After transferring to Hybond ECL nitrocellulose membrane , the membrane was blocked in PBS with 0.05% Tween-20 (PBST) containing 3% non-fat dried milk for 1 h at RT, after which anti-p63 (Santa Cruz Biotechnology) was incubated for 2 hours at RT in PBST containing 5% BSA. After incubation with peroxidase labeled secondary antibody rabbit anti-mouse  bands were visualized with ECL+ . Positive control lysates for endogenous p63 were obtained by pBABE-LIC-ΔNp63α transfected 293T cells. Briefly, ΔNp63α was PCR amplified from pcDNA3.1-ΔNp63α and cloned into pBABE-LIC vector  as described .EBV protein BARF1 is selectively transcribed in undifferentiated NPC and GC associating with viral latency, but not in EBV associated B cell malignancies unless the lytic cycle is activated. To analyze which cell type specific transcription factor could potentially transactivate the BARF1 promoter, a transcription factor search was performed on the BARF1 promoter region from −680 bp through the promoter area of the BARF1 gene to the ATG start site, as defined recently . Using MThe epithelial differentiation marker ΔNp63α, a member of the p53 family, is upregulated by EBV and shows elevated levels in NPC and GC ,19,20,21However, among the p63 family isoforms, particularly the ΔNp63α isoform showed specific transactivation of the BARF1 promoter in both AGS and SNU-719 cells A,B. NextThe EBV episome is highly methylated in latent carcinoma  and prevThe ΔNp63α-mediated activation of the BARF1 promoter could either involve direct binding via the DNA-binding domain, or via the previously defined interaction of ΔNp63α with a CCAAT binding factor ,47. EvalTo identify the transcription factor binding site responsible for BARF1 promoter activation by ΔNp63α, deletion mutants of the reporter construct were made, shortening the BARF1 promoter sequence starting from the original 679 nucleic acids upstream of the ATG start site (ATG-679) to ATG-63 in small steps A. After To obtain evidence that ΔNp63α is capable of transactivating BARF1 expression in the context of EBV infection, two different EBV positive epithelial cells were transfected with ΔNp63α, TAp63α or empty control vectors. As a positive control, an R expression was used to induce BARF1 mRNA as described before . QuantitIn vivo, the EBV-encoded BARF1 gene is exclusively transcribed in EBV-positive undifferentiated epithelial malignancies, like NPC and GC. In these tumors EBV remains in a latent state and the regulation of BARF1 expression remains undefined . We receEBV associated nasopharyngeal and gastric cancers are characterized by high levels of the p53 family member ΔNp63α ,19,20,21The protein p63 is essential for epithelial cell development and differentiation. Previous studies have shown that ΔNp63 is responsible for maintaining the proliferative potential of basal epithelial cells, while the interplay of TAp63 with ΔNp63α would facilitate epithelial differentiation . EBV posA number of viruses utilize the epithelial differentiation process for productive viral replication and a tight control of p63 expression is essential for the survival of these viruses. Kaposi’s sarcoma-associated herpesvirus (KSHV) , human pLikewise in the EBV life cycle, the differentiation status of the host cell is essential in determining whether new viral progeny will be made, or whether the virus remains in a latent state and thereby hiding from the immune system. A differentiation responsive element can be found in the promoter of the lytic switch protein BZLF1  , turningThe differentiation status of EBV positive cells is also influenced by cyclin D1 , whose eOverexpression of ΔNp63α was not sufficient to activate BARF1 expression in the context of the intact viral genome, although ΔNp63α was capable of transactivating the BARF1 promoter reporter construct. The inability of solely ΔNp63α to activate BARF1 expression indicates that specific chromatin modeling as well as other cofactors might be required for full BARF1 regulation. Previous research by our group indicated that the BARF1 promoter is almost completely methylated both in NPC and Burkitt’s lymphoma cells ,46,71. MThe inability of ΔNp63α to upregulate BARF1 expression can also be explained if the maximum BARF1 promoter transactivating capacity of ΔNp63α was already expressed. The cell lines tested are all undifferentiated  or poorly differentiated (CNE-2) expressing at base line already p63 [In conclusion, this study provides the first indication that expression of the BARF1 protein during viral latency in epithelial cells is potentially regulated by the epithelial differentiation factor ΔNp63α.