We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales. The basis of this risk assessment paradigm would be perturbation of cellular responses using a carefully selected suite of in vitro assays. Central to this vision is the idea of “toxicity pathways” – innate cellular signaling pathways that are perturbed by chemicals and pharmaceuticals, and the determination of chemical concentration ranges where those perturbations are likely to be excessive, thereby leading to adverse health effects if present for a prolonged duration in an organism. A key element of the proposed approach is the use of computational systems biology models as a tool to generate hypotheses about cellular level dose-response based on existing data sets, and to identify data and knowledge gaps that can help guide the design of in vitro assays, focused animal studies, and improved in vitro – in vivo extrapolation (IVIVE) methods.The 2007 report by the National Research Council (NRC) of the U.S. National Academies of Science, titled “Toxicity testing in the 21st Century: A Vision and A Strategy” -α nuclear receptor (NR) pathway in primary human hepatocytes is used as an example for computational reconstruction of a toxicity pathway network from genomic data. We then use the example of aryl hydrocarbon receptor (AhR) activation in the liver to outline the process of developing a multi-scale spatial model of the liver lobule and interactions among multiple hepatic cell types consequent to exposure to toxic agents. Finally, we outline a predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI) in response to administration of acetaminophen, which spans multiple scales from the organ/tissue-level to the molecular and cellular levels. These varied modeling approaches, applied across different pathways and tissues, will be pivotal in creating twenty-first century toxicity pathways. Changes in the topology and dynamic behavior of these pathways subsequent to recognition of the external ligand account for the particular shape of the dose-response curve for specific phenotypic end points. A finite number of core “stress response pathways” mediate the response of cells to various chemical stimuli to maintain homeostasis, or to make specific cell-fate decisions such as proliferation, differentiation, or apoptosis  in individual cells are mediated by cell-membrane or cytosolic “receptor” molecules and downstream signaling and transcriptional networks, which together comprise intracellular regulatory network motifs that underlie cellular homeostasis and fate decisions including phenotypic transitions negative feedback, which enables homeostasis and acceleration of response time in gene circuits positive feedback, which generates switching behavior between multiple phenotypic states coherent feed-forward loop, which can introduce a time delay in activation as well as detect persistence in the activating signal incoherent feed-forward loop, which can function as a pulse generator and response accelerator  models can play a key role in this process, allowing mechanistic prediction of the dose-response based on pathway dynamics  are a common approach to modeling dynamical systems like intracellular signaling circuits. An assumption with deterministic ODE models is that the molecular components of the network of interest exist in a well-mixed volume such as the cytosol or nucleus, and that the amounts or concentrations of all molecular species in the network can be approximated by continuous variables. A typical deterministic model consists of a set of coupled ODEs, each describing the rate of change in the concentration or abundance of a molecular component, and incorporating terms that account for the known biochemical interactions among the various molecular species  is assumed to take either of two values, 0 (off) and 1 (on), is often a good approximation of the gene expression patterns in these processes. The state of each gene is updated according to its current state and that of other regulatory genes it is connected to in the network, as governed by a preset Boolean logical rule table. This binary-state assumption reduces the dependence on various kinetic parameters in the model, instead making full use of the large available database of qualitative protein–protein and protein–gene interactions. As with deterministic and stochastic models, a simulation of a Boolean network model should converge to an attractor state representing the binary gene expression pattern of a particular phenotypic state , also referred to as “individual-based modeling”  use an integrated visualization/programing interface , glucocorticoid receptor (GR), and progesterone receptor (PR), as well as the PPARs, liver X receptors (LXRs), retinoic acid receptors (RARs), and retinoid X receptors (RXRs). NRs act on chromatin combinatorially and dynamically throughout the genome to regulate transcriptional responses to physiological and environmental stimuli phosphorylation of PPARα in the cytosol;(ii)translocation of PPARα to the nucleus;(iii)heterodimerization with its binding partner retinoid X receptor alpha (RXRα);(iv)binding of the heterodimer at DNA-response-elements (PPREs) in the promoters of target genes; and(v)alterations in binding of co-activators and co-repressors.In the canonical picture, activation of PPARα in liver parenchymal cells causes downstream alterations in gene expression through a series of coordinated steps:In recent years, genome-wide profiling of TF-binding activity has provided an unprecedented global picture of gene regulation. Chromatin immunoprecipitation (ChIP) in combination with microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) has been widely used for genome-wide location analysis and characterization of TF-chromatin interactions both for NRs and other TFs genes with the NR directly bound to their promoters;(ii)genes with the NR indirectly bound to their promoters by tethering to other directly bound TFs(iii)non-NR-bound genes regulated by other TFs.Accordingly, we are using a combination of: (i) microarray-based gene expression data; (ii) published ChIP-on-chip analyses of genome-wide NR binding; and (iii) curated lists of directly bound NR target genes to develop a comprehensive picture of NR-mediated transcriptional regulation. Differentially expressed genes responding to specific NR ligands are classified into three groups:The non-NR TFs that regulate genes in groups (ii) and (iii) are identified from the TRANSFAC  database of TF-DNA interactions. These transcriptional interactions may be summarized in a “latent regulatory network”  belongs to a class of toxicants known as halogenated aromatic hydrocarbons (HAHs). The toxic effects of HAHs in mammals are mediated through binding to the AhR Accumulation of TCDD in the target tissue.(ii)Formation of a complex with AhR.(iii)Activation of growth-regulatory genes by the AhR-TCDD complex.(iv)Cellular responses to the altered expression of growth-regulatory gene products.(v)The effect of these cellular events on tumor promotion and progression.The hepatic dose-response of TCDD culminating in liver cancer consists of the following key steps  through (v) are not well understood.The carcinogenic effects of TCDD are believed to be mediated by tumor promotion rather than initiation  and non-parenchymal cells (hepatic stellate cells and Kupffer cells), along with oval cell proliferation, can serve as an ontological tool to assemble diverse (i)cellular heterogeneity across the lobule;(ii)multiple cell types in the liver lobule that participate in liver injury.A realistic spatial model of the liver lobule and drug/chemical-induced toxic effects needs to account for:The virtual tissue formalism can be used to develop a spatial agent-based model of the human liver lobule (the “lobule ABM”), with individual hepatocytes represented by single agents. There have been some initial efforts toward development of agent-based representations of the liver Output from a standard TCDD physiologically based pharmacokinetic (PBPK) model This TCDD input dose acts upon the individual agents (hepatocytes) in the model, which occupy heterogeneous states by virtue of differential gene expression: e.g., graded expression of the Ah receptor along the lobule The agents (hepatocytes) are implemented as intracellular signaling cascades: i.e., key molecular events associated with the AhR toxicity pathway Figures A and 5C.Subsequent steps incorporate additional events into the signaling cascade shown in Figure Drug-induced liver injury is the most common adverse drug event leading to termination of clinical development programs and regulatory actions on drugs, as well as the most common cause of acute liver failure in the United States  and methapyrilene (MP).N-acetyl-p-benzoquinone imine (NAPQI). As GSH concentration is depleted, NAPQI forms protein adducts and induces mitochondrial oxidant stress, leading to cell death. The DILIsym™ model includes the basic biochemistry to describe these processes, and model simulations are consistent with molecular data, e.g., changes in liver GSH following APAP administration  yields the toxic reactive metabolite, n Figure , and then Figure . Formalin Figure . Howevern Figure ,b, suggen Figure ,b. In adn Figure . Togethen Figure . Using tn Figure A, and inn Figure B. This eN-acetyl-cysteine (NAC) is the standard therapy for APAP overdose  and in species-specificity . The model for MP was constructed using in vitro data, including the log P, pKa, metabolic partitioning in rat and mouse hepatocytes, and covalent binding in mouse, rat, and human microsomes. Multiple doses of MP were evaluated in simulated mice, rats, and humans. The DILIsym™ model predicted hepatotoxicity in rats between 150 and 200 mg/kg, consistent with the available literature  and related drugs . Further model development is ongoing and focuses on expanding model capabilities to address other mechanisms of hepatotoxicity.in vitro toxicology testing strategies and determination of pathway targets, as well as improved estimation of dose-response characteristics from a network biology perspective.We have presented some concepts relevant to implementation of a new vision for toxicity testing in the twenty-first century for chemical and pharmaceutical molecules NAS/NRC, , centereThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


H19/Igf2 locus. Similarly, a role for CTCF has been established in normal hematopoietic development; however its involvement in leukemia remains elusive. Here, we show that Ctcf binds to the imprinting control region of H19/Igf2 in AML blasts. We also demonstrate that Smarca5, which also associates with the Cohesin complex, facilitates Ctcf binding to its target sites on DNA. Furthermore, Smarca5 supports Ctcf functionally and is needed for enhancer-blocking effect at ICR. We next asked whether CTCF and SMARCA5 control the expression of key hematopoiesis regulators. In normally differentiating myeloid cells both CTCF and SMARCA5 together with members of the Cohesin complex are recruited to the SPI1 gene, a key hematopoiesis regulator and leukemia suppressor. Due to DNA methylation, CTCF binding to the SPI1 gene is blocked in AML blasts. Upon AZA-mediated DNA demethylation of human AML blasts, CTCF and SMARCA5 are recruited to the −14.4 Enhancer of SPI1 gene and block its expression. Our data provide new insight into complex SPI1 gene regulation now involving additional key epigenetic factors, CTCF and SMARCA5 that control PU.1 expression at the −14.4 Enhancer.CCCTC-binding factor (CTCF) can both activate as well as inhibit transcription by forming chromatin loops between regulatory regions and promoters. In this regard, Ctcf binding on non-methylated DNA and its interaction with the Cohesin complex results in differential regulation of the  H19/Igf2 locus by blocking communication between the Igf2 promoter and its enhancer leading to increased transcription of H19 on the maternal allele. Ctcf association within a cohesin complex at the H19/Igf2 locus enables long-range chromatin interactions Control of tumor suppressor gene expression requires cooperation of epigenetic factors and represents crucial yet not fully understood events in leukemogenesis. Ctcf is a key epigenetic regulator and zinc-finger transcription factor, whose binding to DNA can be prevented by DNA methylation www.biogps.org). Moreover, Smarca5 is required for proliferation and/or differentiation of immature hematopoietic progenitors with extensive cytokine-induced proliferative capacity Smarca5 (Snf2h), is an epigenetic chromatin-remodeling factor that assembles and slides nucleosomes along the DNA fiber. Smarca5 together with a bromodomain-containing protein WCRF180 has been demonstrated to load the Cohesin complex onto DNA SPI1, Sfpi1) is a key regulator of hematopoiesis. The expression levels of PU.1 are precisely controlled by several myeloid transcription factors, including PU.1 itself SPI1 promoter have been suggested to regulate long-range interactions at the SPI1 locus SPI1 gene in AML may become epigenetically dysregulated due to defective binding of factors that recognize unmethylated DNA.The transcription factor PU.1 . Bone marrow samples from two AML patients with dysplastic features and two advanced high-risk MDS were utilized (see Written donor’s informed consent (based on 1964 Declaration of Helsinki) was obtained. ized see . Normal TaqMan RT-PCR : cycling 40x 10 s/95°C, 20 s/60°C, 30 s/72°C. Analyses involved standard curve equation and linear CT transformation using δδCT equation primers: .Western blot lysates (RIPA) were gently (3×1 s) sonicated on Branson Sonic Dismembrator (model 500) with a micro-tip. SDS-PAGE was run, dry-blotted and incubated with antibodies: anti-Smarca5 , anti-Ctcf , anti-PU.1 , secondary-HRP-conjugated anti-rabbit , HRP-conjugated anti-actin (I-19), sc-1616; Santa Cruz).Nuclear immunostaining was done with anti-Smarca5 , anti-Ctcf , secondary antibodies . Heterochromatin staining: Vectashield Mounting Medium containing 4′,6-diamidino-2-phenylindole (DAPI). Confocal laser scanning microscopy: Leica TCS SP2 with AOBS system.The reporter vectors pIHLE, pIHLME and pIHLIE are based on the pGL3 luciferase reporter plasmid (Promega). The vectors were generated and kindly provided by Prof. Mitsuyoshi Nakao from Kumamoto, Japan. The reporter assay has been described previously Genomic DNA was treated using bisulfite, amplified (primers in 6 MEL or K562 cells were lysed (RIPA) and gently sonicated. 100 µg of precleared protein extract was incubated 3 hrs/4°C with anti-Ctcf (cat#07–729) or anti-Smarca5  and next with proteinA/proteinG overnight. Control antibody: IgG, cat.# NI01, Calbiochem, 5∶100). Immunoprecipitates (IP) were washed with set of buffers. IPbuffer (0.02% SDS/2%Trion X-100/4 mM EDTA/40 mM Tris-HCl (pH = 8)/300 mM NaCl), WashI (0.1%SDS/1%Triton X-10/2 mM EDTA (pH = 8)/20 mM Tris-HCl (pH = 8)/50 mM NaCl), WashII (0.1% SDS/1% Triton X-10/1% EDTA (pH = 8)/20 mM Tris-HCl (pH = 8)/500 mM NaCl). IPs were resolved on SDS/PAGE, blotted, and immune-detected.For co-IP, 6×10Chromatin immunoprecipitation (ChIP) H19 and Igf2 genes, we first used mouse erythroleukemia (MEL) cells. MEL cells express the H19 mRNA and the Igf2 mRNA, albeit at approximately 1000-fold lower level  in MEL cells  on mRNA  and −9.7  with or without previous AZA treatment. Indeed, lack of CTCF in OCI-M2 resulted in negligible occupancy of both RAD21 and SMC1 at the at −15.6 . Moreoveat −15.6 . ImportaTo associate the DNA methylation status at the −14.4 Enhancer with CTCF occupancy, we used a sequencing approach of bisulfite-treated DNA. First, we mapped the CTCF binding site  and deteSPI1 gene in mixed myeloid cells as well as in AZA-treated OCI-M2 blasts indicated that CTCF and SMARCA5 might regulate the transcriptional outcome of SPI1. In order to address this possibility we decided to manipulate the levels of CTCF and SMARCA5 in human AML OCI-M2 blasts. Plasmid encoding the CTCF cDNA (pCTCF) or a SMARCA5 siRNA were transfected together with an additional plasmid encoding a green fluorescent protein (GFP) and immunoblots were performed on GFP positive cells. Perturbation experiments were done in OCI-M2 in absence of AZA as well as upon AZA that is known to stimulate expression PU.1 and its targets  can be detected at the URE while not at other regions. Upon DNA demethylation of AML blasts, the CTCF/SMARCA5 can newly interact with the −14.4 kb enhancer in the absence of cohesin  with the promoter.Our work further demonstrates an important role for CTCF/SMARCA5 in the regulation of PI1 gene  & 5 duri cohesin  & 5, CTC cohesin  & 5. It SPI1 gene used for? At the moment we do not have clear view but speculate that inhibition of PU.1 level is as important as its activation and may be used in blocking early differentiation decisions upon preceding DNA demethylation SPI1 gene activation were previously described What may be the mechanism of enhancer-blocking at H19 and SPI1 gene regions, these factors co-occupy and co-regulate these targets in a cooperative fashion. While DNA hypermethylation in AML prevents binding of CTCF (and its partners) leading to structural properties characterized by repression of the SPI1 gene, according to our ChIP data CTCF become recruited upon AZA in the −14.4 Enhancer while other regions were negative . Amplification plot (right) is defined by X-axis with number of cycles and Y-axis with fluorescence signal (linear delta Rn).(EPS)Click here for additional data file.Figure S2Downregulation of Smarca5 expression in induced MEL-shSmarca5 cells. MEL- Smarca5 cells were treated with doxycycline (1 µM/ml) for 96 hours. Total mRNA was analyzed in 24 hr intervals (X-axis). Level of mRNA was determined by RT-PCR. mRNA level was normalized to Hprt1 and is shown as fold change relative to untreated cells (Y-axis), standard error (SE).(EPS)Click here for additional data file.Figure S3Co-IP of SMARCA5 and CTCF in K562 cells. Antibody for IP: Ctcf (upper panel) or Smarca5 (lower panel), Western blot for Smarca5 (upper panel) or Ctcf (lower panel). Arrows indicate specific signals.(EPS)Click here for additional data file.Figure S4Distribution of Ctcf and Smarca5 immunostaining in MEL cells . MEL cells were fixed with 3,5% paraformaldehyde and immunostained with antibodies against Ctcf (green), Smarca5 (red), and DAPI (blue). Merge image as well as intensity profile graphs correspond to the section depicted by the line (see Merge). Fluorescence intensity profiles acquisition and determination of heterochromatin content was performed using ImageJ 1,38x software. Y-axis in intensity profile graphs represents fluorescence of respective fluorophor and X-axis represents distance.(EPS)Click here for additional data file.Figure S5mRNA expression of PU.1 and its target genes in OCI-M2 and normally differentiating mixed myeloid cells (heat map). mRNA from mixed myeloid cells, untreated OCI-M2 and OCI-M2 treated for 72 hrs with GCSF-AZA (see MM) were analyzed by RT-PCR. Y-axis: specific mRNA relative to average of HPRT1 and GAPDH was normalized to untreated OCI-M2.(EPS)Click here for additional data file.Figure S6A AZA-mediated DNA demethylation in untreated OCI-M2 and AZA-treated OCI-M2. Sequencing of bisulphite-treated DNA was performed over the regions −15.6 kb, −14.4 kb and −11.0 kb of SPI1 locus. The primer sequences are shown in B AZA-associated DNA demethylation was performed in CD34+ cells isolated from MDS patient without AZA therapy  and control donor (N = 1). Y-axis: % of unmethylated CpGs; x-axis: number of CpG. Arrows indicate demethylated CpGs.(EPS)Click here for additional data file.Table S1Summary of patients.(DOCX)Click here for additional data file.Table S2Primer sequences.(DOCX)Click here for additional data file.


Heterochromatin at the pericentromeric repeats in fission yeast is assembled and spread by an RNAi-dependent mechanism, which is coupled with the transcription of non-coding RNA from the repeats by RNA polymerase II. In addition, Rrp6, a component of the nuclear exosome, also contributes to heterochromatin assembly and is coupled with non-coding RNA transcription. The multi-subunit complex Mediator, which directs initiation of RNA polymerase II-dependent transcription, has recently been suggested to function after initiation in processes such as elongation of transcription and splicing. However, the role of Mediator in the regulation of chromatin structure is not well understood. We investigated the role of Mediator in pericentromeric heterochromatin formation and found that deletion of specific subunits of the head domain of Mediator compromised heterochromatin structure. The Mediator head domain was required for Rrp6-dependent heterochromatin nucleation at the pericentromere and for RNAi-dependent spreading of heterochromatin into the neighboring region. In the latter process, Mediator appeared to contribute to efficient processing of siRNA from transcribed non-coding RNA, which was required for efficient spreading of heterochromatin. Furthermore, the head domain directed efficient transcription in heterochromatin. These results reveal a pivotal role for Mediator in multiple steps of transcription-coupled formation of pericentromeric heterochromatin. This observation further extends the role of Mediator to co-transcriptional chromatin regulation. DNA is packaged into chromatin structure, which is important for various genome functions such as gene expression and maintenance of genetic information. Heterochromatin is a condensed chromatin structure and involved in epigenetic regulation of gene expression through repression of transcription. Heterochromatin at the pericentromeric repeats in fission yeast is assembled by two distinct mechanisms, RNAi-dependent and Rrp6, a component of the nuclear exosome, -dependent mechanisms. In addition, heterochromatin spreads into neighboring regions in an RNAi-dependent manner. Both mechanisms are coupled with the transcription from the target loci by RNA polymerase II, but the molecular nature of the coupling is not understood. Here we showed that the multi-subunit complex Mediator, which directs initiation of RNA polymerase II-dependent transcription, functions in the coupling between transcription and heterochromatin assembly. Mediator is required for Rrp6-dependent heterochromatin assembly and contributes to the RNAi-dependent spreading of heterochromatin via enhancement of production of siRNA by RNAi machinery. These observations highlight the multi-functions of Mediator in the transcription-coupled processes. Schizosaccharomyces pombe provides a good model system for investigating heterochromatin formation. In fission yeast, heterochromatin is preferentially enriched across large chromosomal domains at the pericentromeres, subtelomeres and the mating-type locus. These regions are rich in methylation of histone H3K9 (H3K9me), which is catalyzed by the histone methyltransferase Clr4, a homolog of mammalian SUV39h Heterochromatin is a silent higher-order chromatin structure that is associated with various genome functions such as transcriptional regulation, chromosomal segregation, suppression of recombination and repression of selfish elements. The fission yeast otr) region (containing of dg and dh repeats), and the outer portion of the innermost repeats (imr), which surround the central core (cnt) domain, the site of kinetochore assembly Several distinct pathways promote heterochromatin assembly in fission yeast. At the pericentromere, RNAi machinery plays essential roles in heterochromatin formation Heterochromatin, once established, spreads into neighboring region, which is typically shown by the heterochromatin formation and silencing of the genes inserted into heterochromatin. This process depends on RNAi system and probably couples with transcription ago1 and rrp6Nuclear RNA is monitored by a nuclear RNA surveillance system involving exosomes with 3′-5′ exonuclease activity, and a portion of the ncRNA at the pericentromere has been shown to be degraded by the nuclear exosome cenH sequence, which shows 96% homology to centromeric dg and dh repeats, is present at the silent mating-type (mat2/3) locus and serves as an RNAi-dependent heterochromatin nucleation center The in vivoArabidopsis thaliana, Mediator directs the transcription of ncRNA genes by recruiting RNAPII to their promoters Mediator, which is a well-conserved protein complex consisting of at least 20 subunits, was first identified as a factor that mediates DNA transcription factors binding at regulatory sequneces and RNAPII at promoters for the efficient start of transcription Since both Rrp6-dependent heterochromatin formation and RNAi-dependent heterochromatin formation are coupled with transcription, we assumed that the factor(s) that interacts with RNAPII directs the coupling. Therefore, we assessed the role of several RNAPII-interacting factors in pericentromeric heterochromatin assembly. We found that the disruption of Med18 and Med20, non-essential subunits of the Mediator head domain (MHD), compromised both RNAi-dependent and Rrp6-dependent heterochromatin assembly at the pericentromere. In addition, the head domain is required for transcriptional activation in heterochromatin. Therefore, we propose that Mediator links transcription of ncRNA and its processing by RNAi and exosomes for the formation of centromeric heterochromatin.+otr1R::ade6 and +imr1L::ura4) to monitor heterochromatic silencing  and was resistant to 5-fluoroorotic acid (5-FOA), a counter-selective drug for ura4+ expression. By contrast, heterochromatin mutants, such as clr4Δ, formed white or pink colonies on the Low Ade plate and showed sensitivity to 5-FOA , only disruption of med18  and med20 resulted in the formation of pink colonies and increased sensitivity to 5-FOA  and med31 (med31-H1), which were isolated by the screening of heterochromatic mutants  and pink colonies (med18Δ-p med20Δ-p and med8-K9-p) produced predominantly white colonies and pink colonies, respectively  and inserted marker genes. Strand-specific northern analysis showed a large increase in those transcripts in both med18Δ- and med20Δ-w cells (med18Δ-p and med20Δ-p cells. Both point mutants of the other Mediator subunits (med8-K9 and med31-H1) also showed accumulation of the transcripts  assay. Since heterochromatic ncRNA is mainly transcribed during G1/S-phase cdc25 temperature-sensitive mutation. The results show that RNAPII accumulated during G1 to early S-phase, followed by the accumulation of transcripts . Med20-5Flag showed a similar oscillating pattern, but the peak disappeared slightly earlier than the Pol2 peak , the levels of histone H3K9me and Swi6 were decreased and RNAPII occupancy was increased in the white epiclones of med18Δ and med20Δ  as well as euchromatin (act1 and fbp1)  . Comperedcr1Δ and rrp6Δ cells retained similar levels of H3K9me at centromeric repeats  were detectable. Note that the amount was varied because of the state of variegation. Since the structure of heterochromatin (H3K9me and Swi6) at the repeats was substantially maintained in both white epiclones  in heterochromatic transcription, based on the observation of an increase in the transcription of pericentromeric ncRNA in Mediator mutants cenH sequence (cenH sequence at the mating-type locus and the inserted +ura4 gene (+kint2::ura4) in dcr1Δ and dcr1Δ med18Δ mutants. In med18Δ cells, the level of H3K9me was decreased to half of that of wild-type cells for an unknown reason, but the level of Swi6, which is essential for transcriptional gene silencing in heterochromatin, was maintained, consistent with the observation that silencing at this locus was not affected by med18Δ , or by an increase in heterochromatic transcription. To examine the latter possibility, localization of RNAPII at cenH and +kint2::ura4 was examined by ChIP assay  or decrease (Down) in expression between the epiclones revealed that a common set of genes were affected in both med18Δ cells and med20Δ cells, irrespective of the state of heterochromatic silencing . Comparison of med18Δ-w and med20Δ-w, or med18Δ-p and med20Δ-p, showed that both subunits shared a common set of targets (The effect of ilencing . Therefo targets . This ismed18Δ-w and med20Δ-w) was compared with that in dcr1Δ cells, it was evident that the expression of a common set of genes was upregulated (med18Δ-p and med20Δ-p) and dcr1Δ cells . These results suggest that some euchromatic genes, including stress response genes, are repressed by the RNAi/Mediator system, which may function via a mechanism partly similar to that of RNAi-mediated heterochromatin.When the expression pattern in the white epiclones of the Mediator mutants  as well as euchromatin (act1 and fbp1) and deletion of med18 did not affect the localization, suggesting Mediator acts in a step after the association of Rrp6/exsosome with chromatin. This is analogous to the function of Mediator in the RNAi-dependent pathway; MHD functions after recruitment of RITS complex and RDRC to chromatin. Considering the co-transcriptional nature of RNA-quality control In contrast to the RNAi pathway, little is known about the Rrp6-dependent heterochromatin formation pathway. As deletion of hromatin , indicatmed20Δ cells was much more severe than that in ours. Secondly, Carlsten et al. claimed that siRNA from the dh repeat in med20Δ cells was diminished but that siRNA from the dg repeats was comparable to that in wild-type cells. Thirdly, both papers assert that Mediator negatively regulates heterochromatic transcription. The first two discrepancies could be caused by the variegated phenotype of MHD mutants. If this variegated phenotype was overlooked or disregarded, the results would be affected by which epiclones were used in the experiments. In addition, since the amount of H3K9me/Swi6 varies depending on the position in the repeats, the discrepancies between their results and ours might reflect a difference in the sites used for ChIP analysis. The third discrepancy could be explained by the use of pericentromeric transcription for their analysis. As described in the dcr1  using the hot phenol method 32P]dATP (3000 Ci/mmol) using T4 Polynucleotide Kinase (TOYOBO). After hybridization for 24 h, membranes were washed four times in 2× SSC/0.1% SDS for 10 min at 42°C before exposure to an imaging plate for 1–2 days. For re-probing, probes on the membrane were stripped by boiling in 200 ml of 0.5× SSC/0.1% with shaking. Detection of siRNA was performed as described previously Total RNA was isolated from logarithmically-growing For RT-PCR analysis, total RNA was cleaned up and treated with Recombinant DNase I (RNase-free) (TaKaRa) according to the manufacturer's instructions. RT-PCR was performed using PrimeScript Reverse Transcriptase (TaKaRa) according to the manufacturer's instructions. Primer sequences are shown in qPCR was performed using SYBR premix Ex-Taq (TaKaRa) and the Thermal Cycler Dice Real time system TP800 (TaKaRa). Primer sequences are shown in cdc25-22 cells were grown at 25°C to a concentration of 2×106 cells/ml and then shifted to 36°C for 4 hr and 15 min to stop the cell cycle at the G2/M phase. Samples for ChIP assay were collected every 30 min for 300 min after shifting the cells back to 25°C to release cell cycle block. ChIP assay was performed as described in the Experimental Procedures. To prepare RNA for RT-PCR, the input fractions of ChIP were adjusted to 0.25% SDS and 0.25 mg/ml proteinase K and incubated for 45 min at 45°C and then at 65°C for more than 4 hours to reverse crosslinking. Samples were extracted once with phenol-chloroform. After ethanol precipitation, the samples were resuspended in a suitable volume of DEPC-treated distilled water. RT-PCR was performed as described in the Experimental Procedures.med18Δ and med20Δ were analyzed separately. The sequences of the probes and original data from the microarray experiments were deposited to GEO (http://www.ncbi.nlm.nih.gov/geo) with accession number GSE43543.Microarray analysis for gene expression was performed as described previously Methods used in the supplemental information are described in Figure S1+ura4 and +ade6expression. Note that PMGS (EMMS-NH4Cl (nitrogen), +L-glutamic acid, monosodium, as nitrogen) plates were used as N/S plates. (B) Schematic of Med8-K9 and Med31-H1 proteins. med8-K9 contains a point mutation (C540T) causing C-terminal truncation of the Med8 protein (Q135*) (top). The C-terminal residues 176–200 of Med8 (Med8C) are the predicted interface for interaction with Med18 med31-H1 contains a point mutation (T230A) causing C-terminal truncation of the Med31 protein (L77*) (bottom). Residues 22–77 of Med31 are the predicted interface for interaction with Med7, a core Mediator subunit belonged to the middle domain. (C) Silencing assay at the pericentromere. Shown are the results of serial dilutions of the indicated strains spotted onto non-selective media (N/S), medium with 5-FOA, and medium with a limited amount of adenine (Low Ade) to assay for the presence of +imr1L::ura4 and +otr1R::ade6.Mediator is required for heterochromatin silencing at the pericentromere. (A) Silencing assay at the pericentromere. Shown are the results of serial dilutions of the indicated strains spotted onto non-selective media (N/S), medium without uracil (-Ura), medium with 5-fluoroorotic acid (5-FOA), and medium without adenine (-Ade) to assay (DOC)Click here for additional data file.Figure S2w indicates the white epiclones and p indicates the pink epiclones. Cells were grown for several generations. The rates were measured as described in supplemental experimental procedures.Conversion rates of white/pink epiclones. Stability assay of variegation phenotypes. Conversion rates (percentage of cells that convert the epigenetic state per generation) were measured using indicated strains. (DOC)Click here for additional data file.Figure S3+ura4 and +ade6 reporters inserted within the pericentromeric region are shown (+imr1L::ura4 and +otr1R::ade6). Black bars indicate the location of probes used for northern analysis. (B) Northern analysis of pericentromeric transcripts in wild-type and mutant cells. Analysis was performed using oligonucleotide probes against dh and dg forward (Fw) and reverse (Rv) strands, +imr1L::ura4 and +otr1R::ade6 forward strand transcripts. rRNA and tRNA were used as loading controls.Mediator mutations cause accumulation of centromeric RNA. (A) Schematic of fission yeast centromere 1. Locations of the (DOC)Click here for additional data file.Figure S4cdc25-22 mutation . Enrichment at dh repeats relative to the adh1 promoter region (adh1 pro) is shown. Error bars show the standard error of the mean (n = 3). (C) Septation index (percentage of cells with division septum) was measured to monitor cell cycle progression after release. In a block and release experiment, the peak of septation (90 to 120 min) indicates S phase. (D) RT-PCR of dh transcripts was performed as described in A and B. Transcripts derived from constitutively expressed act1 served as controls. “No RT” indicates that no reverse transcriptase was added in the reaction. Numbers under the panels of dh and act1 indicate the increase in transcript relative to the values at 0 min (dh) and 60 min (act1), respectively.Med20 localizes the pericentromeric heterochromatin with RNAPII in a cell cycle-dependent manner.  ChIP-qPCR analyses for RNAPII and Med20-5FLAG were performed every 30 minutes after release from G2/M block by (TIF)Click here for additional data file.Figure S5med8-K9 and med31-H1. Analyses were performed with RNA isolated from the indicated strains  using oligonucleotide probes specific for dg/dh centromeric repeats. tRNA was used as a loading control.Northern analysis of siRNA in the Mediator mutants (DOC)Click here for additional data file.Figure S6cenH dh repeats or +kint2::ura4 relative to input WCE in the indicated strains. Anti-RNA polymerase II C-terminal domain (CTD) serine 2 phosphorylation was used. Error bars represent the standard error of the mean (n = 3). P values were determined using a two-sided Student's t-test.Mediator is required for transcriptional activation in heterochromatin. ChIP analysis of RNAPII at (TIF)Click here for additional data file.Figure S7dh repeats relative to the gene free region in the indicated strains. Error bars show the standard error of the mean (n = 3).Mediator negatively regulates RNAPII in the compromised heterochromatin. ChIP analysis of RNAPII at (TIF)Click here for additional data file.Figure S8med18Δ-w (left circles) vs. med20Δ-w (right circles) mutants. (B) Transcripts of med18Δ-p (left circles) vs. med20Δ-p (right circles) mutants.Effect of Mediator disruption on euchromatic genes. Venn diagram showing the number of transcripts whose expression levels were increased (up) or decreased (down) >1.5-fold in mutants compared to the wild type. The p-value was calculated using Fisher's exact test. (A) Transcripts of (DOC)Click here for additional data file.Table S1Strains used in this study.(DOCX)Click here for additional data file.Table S2Primers used in this study.(DOCX)Click here for additional data file.Text S1Supplemental methods.(DOCX)Click here for additional data file.


The assimilation of nitrogen in bacteria is achieved through only a few metabolic conversions between alpha-ketoglutarate, glutamate and glutamine. The enzymes that catalyze these conversions are glutamine synthetase, glutaminase, glutamate dehydrogenase and glutamine alpha-ketoglutarate aminotransferase. In low-GC Gram-positive bacteria the transcriptional control over the levels of the related enzymes is mediated by four regulators: GlnR, TnrA, GltC and CodY. We have analyzed the genomes of all species belonging to the taxonomic families Bacillaceae, Listeriaceae, Staphylococcaceae, Lactobacillaceae, Leuconostocaceae and Streptococcaceae to determine the diversity in central nitrogen metabolism and reconstructed the regulation by GlnR.glnRA operon), and the transport of ammonium (amtB-glnK) and glutamine/glutamate . In addition less-conserved associations were found with, for instance, glutamate dehydrogenase in Streptococcaceae, purine catabolism and the reduction of nitrite in Bacillaceae, and aspartate/asparagine deamination in Lactobacillaceae.Although we observed a substantial difference in the extent of central nitrogen metabolism in the various species, the basic GlnR regulon was remarkably constant and appeared not affected by the presence or absence of the other three main regulators. We found a conserved regulatory association of GlnR with glutamine synthetase (Our analyses imply GlnR-mediated regulation in constraining the import of ammonia/amino-containing compounds and the production of intracellular ammonia under conditions of high nitrogen availability. Such a role fits with the intrinsic need for tight control of ammonia levels to limit futile cycling. The assimilation and re-distribution of nitrogen within a cell is essentially controlled within the central metabolic conversions between alpha-ketoglutarate, glutamate and glutamine Figure A. The enBacillus subtilis, the expression of these genes is mediated by four major transcription factors: CodY, GlnR, TnrA [B. subtilis GlnR is activated in the presence of FBI-GS [2+ and alpha-ketoglutarate [Streptococcus mutans cross-linking and pull-down assays demonstrated that GlnR also interacts with GlnK and that the interaction enhances the binding of GlnR to its cognate site upstream of the glnRA operon [Marked differences exist in the transcription control of the genes encoding the enzymes involved in central nitrogen metabolism across the bacterial kingdom. In the Gram-positive model organism nR, TnrA  and GltCnR, TnrA ,6. Of thnR, TnrA . GltC isnR, TnrA . The chaf FBI-GS  and TnrAf FBI-GS ,9. It walutarate . The twoA operon .B. subtilis and many other low-GC Gram-positives the genes encoding GlnR (glnR) and GS (glnA) constitute the operon glnRA[B. subtilis GlnR was reported to repress the transcription of the glnRA operon (negative autoregulation), and of tnrA[ureABC) [glnQHMP  in both organisms and of gdh (encoding glutamate dehydrogenase) in the former, and the amtB-glnK and citBZ-idh operons  . On the [ureABC) , for inst system ), amtB-gein GlnK ), nasA aeduction ), gabP (nsporter ) and pucegulator ), while ymporter ), gltAD synthase ,22) and proteins ). Similarogenase ) in the rogenase ,26.Bacillus species, TnrA has been identified only in a few. It is an intriguing question whether in the absence of one of these main regulators the others take over its role. We therefore decided to extend (i.e. from 16 to 173 genomes) a previous comparative analysis reported by [Comparative genome analyses have shown that GlnR, TnrA and CodY are characteristic for the low-GC Gram-positive species although their distribution is not uniform. For instance, whereas GlnR is found in almost all orted by  to identWe have redefined the binding motifs of GlnR and TnrA on basis of the available experimental and sequence data and used them to identify their respective regulons anew. For that purpose we have applied a footprinting approach formulated earlier by us  and a siWe identified the orthologs of the genes encoding the enzymes of central nitrogen metabolism , the related transport systems and the regulators CodY, GlnR and TnrA, in the sequenced genomes of species related to the class Bacilli on basis of BLAST searches with the sequences of experimentally verified proteins (see methods for details). In Tables Lactobacillus acidophilus and its close relatives lack all three regulators. There are only three other species, Bacillus halodurans, Bacillus clausii and Bacillus selenitrireducens that lack a gene encoding GlnR. The global regulator CodY is present in most species except for those of the families Lactobacillaceae and Leuconostocaceae. TnrA is only present in species of the order Bacillales within the families Bacillaceae, Paenibacillaceae and the genus Exiguobacterium with the exception of the species and strains of the Bacillus cereus group, Alicyclobacillus acidocaldarius, Brevibacillus brevis and Lysinibacillus sphaericus.Remarkably, within the family Lactobacillaceae, Bacillus anthracis strains, in the gut-related Lactobacilli (e. g. L. johnsonii and L. gasseri) and in some meat-related species . It was recently put forward that transport of ammonia (NH4+) should be active and tightly regulated to limit futile cycling [L. lactis); the corresponding genes are indeed found genetically associated to amtB in many of the analyzed species. However, at the same time, it is absent in many others, including all analyzed Lactobacillaceae. Moreover, GlnK was shown to interact with TnrA in B. subtilis[S. mutans[Similarly, we observed a large variation in the presence of the enzymes of central nitrogen metabolism, but much less so in the related transport systems. The set of enzymes is complete within the family of the Bacillaceae and mostly reduced in the other families; in many of the Lactobacillaceae, Leuconostocaceae and Streptococcaceae only glutamine synthetase and one of the other enzymes is present. In the case of transport, at least one ammonium transporter AmtB , also r cycling . This co subtilis,10 and GglnQHMP operon found in Escherichia coli and B. subtilis and ii) a glnPHQ variant found in the Lactobacilli and Streptococci. This latter operon consists of 2 genes, in which glnPH represents a fusion-gene of the permease glnP and the substrate-binding protein encoding glnH. In fact, in the case of the Streptococci the substrate-binding domain was duplicated to generate a glnPHHQ variant, whereas in some Lactobacilli the glnPH gene has been duplicated. The E. coli system has been related to high-affinity glutamine transport [L. lactis system was shown to transport both glutamine and glutamate [E. coli-type system present in Streptococcus mutans was proven also to be involved in the transport of glutamate [gdh) or glutamate synthetase (gltAB). Therefore these species are unable to synthesize glutamate, which makes it essential to have a glutamate transport system.Most of the analyzed species carry one operon encoding a glutamine ABC transporter . Two variants of this system were identified: i) a ransport , whereaslutamate . Recentllutamate . RemarkaL. lactis. These transporter-protein families have been related to the cation symport of dicarboxylates and amino acids. The former family is represented by GltP (glutamate/aspartate [B. subtilis), whereas the latter family is represented by GlnT  and AGCS-family (2.A.25), with the exception of the species within the families Listeriaceae and Leuconostocaceae, some Lactobacillaceae and spartate ), GltT (spartate ), DctA (spartate ), YhcL (spartate ) and Nqtlutamine ), AlsT (lutamine ), YrbD  of the promoter in all analyzed species and a second binding site overlapping the promoter in many of the Bacillus species  procedure described in the methods. The results of this search can be found in B. subtilis suggest they are large enough to bring about some separation between GlnR and TnrA binding sites, in line with the observed variable affinities of these transcription factors for the same sites [The specific GlnR and TnrA motif were used to search the me sites .ilvB[glnRA (glutamine synthesis) and tnrA. Additional shared sites were found upstream of alsTpucHpucJKLM and the amtB-glnK operon. Although these sites have not been attributed to GlnR earlier and were described to be activated by TnrA [amtB-glnK operon (import of ammonia) it was formerly concluded that it is not repressed by GlnR on the basis of a singular observation. It was found that the amtB-glnK operon remained repressed in a GlnR deletion mutant  of experimentally validated sites was indeed captured for GlnR as well as for TnrA. Moreover, most other true binding sites scored just below the cut-off. Only 4 out of 22 reported TnrA binding sites were not recovered in this way. Some of the sites were actually found at a relatively large distance from the translation start (e.g. in the case of ilvB) and man by TnrA ,57, the e. glnR57) in the e. glnR57. However by CodY . In S. mK operon . The samides see , suggesttnrA but not ykzB[pucH but not pucR[gabPoppABCDF and glnQHMP. Other genes/operons that seem to be TnrA-specific in B. subtilis include: yclG, encoding an uronase; ydaB, encoding an acyl-CoA ligase; pel, encoding a pectate lyase; braB, encoding a branced-chain amino acid-Na + symporter; and pucABCDE, encoding a xanthine dehydrogenase operon.It was proposed that GlnR lacks the capability to recruit RNA polymerase and therefore acts solely as a repressor . Given t not ykzB and on p not pucR. MoreoveS. pneumoniae and S. mutans on basis of the Streptococci-specific motif  and an operon containing enzymes of the urea cycle (arcAB). The clear regulatory connection between GlnR and the arcAB operon (encoding arginine deiminase and ornithine carbamoyltransferase) was found in all sequenced S. pneumoniae strains, but was absent in the other species. The absence of a change in arcAB and glnQHMP expression upon inactivation of glnR may be explained by the presence of additional regulatory interactions.GlnR binding-site predictions were performed for the oral Streptococci R mutant ,26. We ftions of . These iS. mutans UA159 the genes and operons found to be mostly affected in the knockout mutant [nrgA-SMU_1657c operon, coding for the ammonium transporter AmtB and its nitrogen regulatory protein GlnK; the citB-citZ-idh operon coding for aconitate hydratase, citrate synthase and isocitrate dehydrogenase; the glnQHMP and glnPHHQ operons encoding glutamine ABC transporters; and Smu.807, coding for a putative membrane protein, which is in a divergon with glnPHHQ. The best hits resulting from our analysis are also located upstream of the same operons. Moreover, we found a clear binding site preceding the genes gdh and thrC. The citB-citZ-idh operon has been shown to be essential for glutamate biosynthesis in S. mutans[In the case of t mutant  were theS. mutans.glnRA operon in all analyzed species and with tnrA in all Bacilli. Only in a few species of the order Bacillales the related GlnR-binding sites deviated from the consensus (e.g. in some Geobacillus species). Another connection that was conserved in almost all of the analyzed species was that with amtB (often amtB-glnK).GlnR binding-site predictions were performed for selected genomes that represented all sequenced species of the class Bacilli. We then collected the function annotation of all proteins encoded by genes/operons downstream of a putative GlnR-binding site that fitted the selection criteria (see methods) to generate an overview of the regulatory connections that are conserved between more than three species  and alste, [alsT in variogdh gene (encoding glutamate dehydrogenase) and the citBZ-icd operon , ansA and ansZ (both encoding asparaginase [aspA (encoding aspartase) and asnA (encoding aspartate-ammonia ligase).Other associations that were conserved included that with the rogenase ) in manyg urease ) and theeductase ) in seveectively ), ywoCD raginase , aspA  and of the ycsFGI-kipIAR-ycsK operon . In the initial description of the ycsFGI-kipIAR-ycsK operon [ycsF was related to the lactam (e.g. 2-pyrrolidinone) utilization gene lamB of Aspergillus nidulans[kipA (orf12) was related to a urea amidolyase of yeast. Later, KipI was identified as a protein inhibitor of auto-phosphorylation of kinase A, the sensor histidine kinase responsible for processing post-exponential phase information and for providing phosphate input to the phosphorelay that activates developmental transcription via phosphorylated Spo0A, and KipA as a protein that counteracts the inhibition [Pseudomonas aeruginosa[ycsFGI-kipIAR-ycsK operon connects GlnR-mediated regulation to the regulation of sporulation in some Bacilli.We also found a clear GlnR-binding site upstream of several genes involved in regulation, for instance of K operon , ycsF wa nidulans, and kiphibition . YcsG sheruginosa. The opeeruginosa, in lineB. subtilis many operons related to the purine degradation pathway are controlled by GlnR and/or TnrA, like pucABCDEpucHpucIpucJKLM and ureABC. The relation between purine catabolism and control by TnrA was established before experimentally. It was observed that a tnrA mutant strain could not use purines or its metabolic intermediates as a nitrogen source during nitrogen limited conditions [Another important finding was that in nditions . NeverthB. subtilis and B.cereus suggesting that GlnR does not take over regulatory roles of TnrA. Similarly, the presence or absence of CodY does not seem to affect the size of the GlnR regulon in the Lactobacillaceae. In L. lactis, a species that has CodY, it was shown experimentally that at least three genes/operons  are repressed by GlnR [L. lactis. In L. plantarum and L. monocytogenes, two species that lack CodY, the same genes/operons appear to be preceded by a GlnR-binding site and only a few additional genes were found connected to GlnR indicating that GlnR does not take over the role of CodY in these species. The predicted GlnR regulon was smallest, consisting of only glnRA, in the meat isolates Macrococcus caseolyticus (a CodY and GlnR containing Staphylococcus) and L. sakei (a GlnR containing Lactobacillus).We observed no clear dependency between the composition of the predicted GlnR regulon and the presence or absence of the other nitrogen-related regulators CodY and TnrA. For instance, there are only a few differences between the predicted GlnR regulon of  by GlnR . We indeWe have analyzed all sequenced Bacilli for the presence of genes encoding central nitrogen metabolism and transport of the related metabolites, and identified their connection to the nitrogen metabolism regulator GlnR. Although there is a considerable variety in the presence of the central enzymes GS, G, GDH and GOGAT, and in the number of available transport systems for the central nitrogen-related metabolites, the composition of the GlnR regulon is relatively invariable between species. Moreover, we hardly found an effect of the absence or presence of the other regulators CodY, TnrA and GltC on the size of the predicted GlnR regulon.B. subtilisS. pneumoniaeS. mutans and L. lactis our predictions complied with the available experimental data. Moreover, within the Bacilli we identified several new potential members of the GlnR regulon, including the ywoCD operon and the ycsFGI-kipIAR-ycsK operon.We made an initial conservative regulon prediction by restricting the regulatory association to those connections that are conserved between at least three species. In general, our findings are also in line with previous comparative in silico analysis performed on a limited number of species . CarefulglnRA operon), and with ammonium (amtB-glnK) and glutamine/glutamate transport . At the same time, the lesser conserved associations point to a somewhat broader role. Many of the conserved associations include genes that are either directly (e.g. ansAarcAaspAgdhnasDEFureABC) or more indirectly  relate to the intracellular production of ammonia or are related to the import of aminated compounds (e.g. gabPopp-dpppucIywoCD). Thus, it appears that the main conserved role of GlnR is to prevent the influx and intracellular production of glutamine and ammonium under conditions of high nitrogen availability. The connection of GlnR-mediated repression with the control of intracellular ammonia concentration is interesting. Such a role fits with the intrinsic need for tight control of ammonia levels as put forward by [4+) should be tightly regulated to limit futile cycling by diffusion of ammonia out of the cell.Our analysis confirmed that for most species the size of the GlnR regulon is relatively small. The main regulatory associations in the species of the class Bacilli are with the incorporation of ammonium into central metabolism (or with the production of ammonium at high glutamine concentrations!) via glutamine synthetase  was used to examine the function information within the genomic context. Frequency representations of aligned sequences were created with Weblogo [glnR gene knockouts in Streptococcus pneumoniae and Streptococcus mutans used in this research were extracted from the Gene Expression Omnibus from NCBI [Complete genomic sequences and initial annotations were obtained from NCBI ; data file 3: GlnR motif search in Streptococci; data file 4: GlnR and TnrA motif search in Bacillus subtilis.The raw data resulting from the various analyses can be found at −5) on all publicly available sequenced Bacilli genomes. Then the list of collected sequences (given as −30) separated from the rest of the sequences with considerably higher e-values (separation < e−15). In the case of the transcription regulators the separation remained clear, however with higher e-values due to a short length of the regulator protein sequence. In the case of the transporters, for the Amt-family (1.A.11), the DAACS-family (2.A.13) and the AGCS-family (2.A.25) an e-value cut-off also sufficed to collect all family members, whereas for the PAAT-family; 3.A.1.3 (ABC transport) the coding sequences of the putative glutamine/glutamate substrate binding domains were aligned, the alignment was inspected by eye, deviant sequences were removed, and a bootstrap neighbour joining tree was generated . The trglnRA operon was retrieved for all species and the promoter region was aligned  were retrieved and the binding site was identified on basis of the published characteristic GlnR/TnrA motif and the short distance upstream of the promoter. The collection of sites was then used to generate a position frequency scoring matrix.The upstream region of the conserved gulation . The coli, the nucleotide frequency fN(j)  at every position j = 1 to i can be used directly to provide all target sequences of size i with a score by just adding up the input-based frequencies that relate to the nucleotide composition of the target. Division of the score by the length of the sequence i results in a ‘similarity’ score that can range from 0 to 1. Dividing this number by the highest attainable score given the input matrix then yields a relative ‘similarity’ score. In case the input sequences are representative for high-affinity sites, the ranking of target sequences according to score should approximately correspond to a ranking based on affinity. The method was tested and appeared at least as good to identify putative regulatory elements on basis of known input motifs as the commonly used tool MAST [The position frequency matrices were used to identify potential binding sites in the analyzed genomes using a similarity search method formulated before by us . The metool MAST , yet proThe authors declare that they have no competing interests.TGK conceived and designed the study, identified the GlnR and TnrA motifs and predicted their regulons, and drafted and revised the manuscript. EK helped designing the study and performed motif searches. YH implemented and validated the motif search algorithm. LO helped designing the study and revising the manuscript. RJS and WMV coordinated the study and helped revising the manuscript. CF coordinated the study and helped designing it, carried out the classification and annotation of protein sequences and helped drafting and revising the manuscript. All authors read and approved the final manuscript.Distribution of genes related to central nitrogen metabolism.Click here for fileBacillus subtilis GlnR and TnrA regulon.Click here for fileamtB upstream regions.GlnR motif search in Click here for fileConserved GlnR-mediated regulation.Click here for file


Streptococcus pneumoniae, links between CcpA and virulence have been established, but its role as a master regulator in different nutritional environments remains to be elucidated. Thus, we performed whole-transcriptome and metabolic analyses of S. pneumoniae D39 and its isogenic ccpA mutant during growth on glucose or galactose, rapidly and slowly metabolized carbohydrates presumably encountered by the bacterium in different host niches. CcpA affected the expression of up to 19% of the genome covering multiple cellular processes, including virulence, regulatory networks and central metabolism. Its prevalent function as a repressor was observed on glucose, but unexpectedly also on galactose. Carbohydrate-dependent CcpA regulation was also observed, as for the tagatose 6-phosphate pathway genes, which were activated by galactose and repressed by glucose. Metabolite analyses revealed that two pathways for galactose catabolism are functionally active, despite repression of the Leloir genes by CcpA. Surprisingly, galactose-induced mixed-acid fermentation apparently required CcpA, since genes involved in this type of metabolism were mostly under CcpA-repression. These findings indicate that the role of CcpA extends beyond transcriptional regulation, which seemingly is overlaid by other regulatory mechanisms. In agreement, CcpA influenced the level of many intracellular metabolites potentially involved in metabolic regulation. Our data strengthen the view that a true understanding of cell physiology demands thorough analyses at different cellular levels. Moreover, integration of transcriptional and metabolic data uncovered a link between CcpA and the association of surface molecules (e.g. capsule) to the cell wall. Hence, CcpA may play a key role in mediating the interaction of S. pneumoniae with its host. Overall, our results support the hypothesis that S. pneumoniae optimizes basic metabolic processes, likely enhancing in vivo fitness, in a CcpA-mediated manner.In Gram-positive bacteria, the transcriptional regulator CcpA is at the core of catabolite control mechanisms. In the human pathogen  The survival of bacterial pathogens in different host environments depends largely on the expression of specific virulence factors Bacillus subtilis and Lactococcus lactis, glucose (Glc) is generally the preferred carbohydrate and the catabolite control protein A (CcpA) is the main transcriptional regulator functioning at the core of carbon catabolite control cre) sites, the location of which, relative to the promoter region, determines whether it acts as a transcriptional inhibitor or activator cre-binding activity of CcpA is enhanced by interaction with the phosphoprotein HPr, a component of the multi-protein phosphoenolpyruvate-dependent phosphotransferase system (PTS), when phosphorylated at serine 46 (HPr(Ser-P)) by a fructose 1,6-bisphosphate (FBP) activated kinase In low-GC Gram-positive organisms, such as Streptococcus mutans (about 9%) and Streptococcus pyogenes (up to 20%), including the production of a number of virulence factors Although the mechanisms by which catabolite control is exerted are among the most studied in model bacteria, a paucity of data hampers our understanding on how human pathogens adapt to different host environments. In the pathogenic Streptococci, increasing evidence supports key roles of CcpA and HPr(Ser-P) Streptococcus pneumoniae is a major cause of life threatening diseases including pneumonia, meningitis and septicaemia, as well as other less severe but highly prevalent infections (e.g. otitis media). Pneumococcal disease is preceded by colonization of the human nasopharynx, S. pneumoniae's natural habitat. It is well established that progression from carriage to pneumococcal invasion is associated with changes in expression of virulence factors, such as capsule and surface proteins cps) locus S. pneumoniaeS. pneumoniae D39 and its isogenic ccpA mutant. Our approach is in line with the current view that true understanding of a microbial metabolism requires quantitative analyses of system components at different cellular levels S. pneumoniae regulates expression of carbohydrate utilization genes and virulence factors in a carbohydrate-dependent, CcpA-mediated manner. Hitherto, the information available has largely been limited to the response of a few specific metabolic genes involved in carbohydrate utilization S. pneumoniae. This choice was based on the following: Glc is the most common preferred carbohydrate, and is also found in considerable amounts in the bloodstream and during infection in the respiratory tract of the host S. pneumoniae as well as virulence factor-encoding genes in a carbohydrate-dependent and independent manner. These results enhance our understanding of how CcpA-mediated regulation can contribute to the metabolic fitness of S. pneumoniae in the host, which further supports the view that virulence and basic microbial physiology are closely intertwined.In this work, the impact of CcpA on pneumococcal physiology was reappraised by combining genomewide transcriptomics with metabolite analysis of S. pneumoniae was missing. We constructed a S. pneumoniae ccpA deletion-insertion mutant  containing Glc without pH control  with expression in trans of ccpA under the control of the nisin promoter (ccpAnisRK closely resembles that of D39ΔccpA (data not shown).Hitherto, a systemic appraisal to the role of CcpA in the physiology of  control . Under tpromoter . It shouS. pneumoniae, growth on galactose was also assessed in batch cultivations as described for Glc  and transition-to-stationary (TS) phases of growth. In the four conditions tested, Glc_M, Gal_M, Glc_TS, Gal_TS, between 14 and 19% of the ORFs in the genome were significantly, differentially expressed in the ccpA deletion mutant, of which more genes upregulated than downregulated (S. pyogenes (up to 20% genes affected) S. mutans, L. lactis, and B. subtilis (less than 9% genes affected) To determine the effect of the The COG category with the highest number of genes affected was carbohydrate transport and metabolism [G], which genes are mainly repressed by CcpA . Also, 2Strikingly, roughly similar numbers of genes were regulated when galactose instead of glucose was used as the carbon source . This melacE, galK, pyk, ldh, pfl, sodA, nanA), were assessed by qRT-PCR. For all genes tested, expression data obtained by qRT-PCR proved to correlate positively with transcript abundance as measured by microarray analysis  codifying the mannose/glucose-PTS, a predominant Glc transporter in Streptococcaceaecre site in its promoter region. Another PTS operon (SPD_0559-0562), which also contains a cre site in its promoter region, was strongly negatively regulated on Glc, but only mildly on Gal. Curiously, the latter PTS system has been previously postulated to transport Gal The first step in the metabolism of any carbohydrate is transport across the cell membrane. Genes in 17 out of 29 operons encoding carbohydrate uptake systems are affected in the A arrays . As for S. pneumoniae indicates that it possesses both the Leloir and tagatose 6-phosphate Gal catabolic pathways. Interestingly, the Leloir genes were repressed by CcpA independently of the carbohydrate, whereas the tagatose 6-phosphate pathway was weakly repressed by CcpA on Glc and strongly activated on Gal  by Gal, regardless of CcpA  and an upstream lacR were upregulated in the ccpA mutant only on Gal.A genome survey of d on Gal . Also of of CcpA . As for t manner . For exain silico cre site prediction was performed in the genome sequence of D39  have a cre site in a position that agrees with the observed regulation in the DNA microarray analysis  and in ldh (lactate dehydrogenase), but activation of pyk (pyruvate kinase) was also considerable, which strongly suggests a predominant role of CcpA in assuring the efficient conversion of carbohydrates to lactate in this bacterium.The role of CcpA in controlling expression of genes involved in central carbon metabolism is well established ic genes . The larpfl , which encodes a key activity that directly competes with lactate dehydrogenase for pyruvate, as well as of pfl-AE (pyruvate formate-lyase activating enzyme)  and ackA,  were upregulated in the mutant. Curiously, adh  was also repressed by CcpA, but only in Glc containing medium. Moreover, in the parent strain D39 the expression of pfl, pfl-AE and adh was induced by Gal, as denoted by the decimal fold change ratio Glc/Gal (ldh and repression of mixed-acid fermentation genes)  . Carbohy Glc/Gal . Overalln genes)  strengthccpA and the resulting expression profiles prompted us to perform an in-depth characterization of the fermentation patterns on Glc and Gal.The dissimilar growth of D39 and D39Δ2O2), a known minor end-product of streptococcal aerobic metabolism, was also detected. In the wild-type, the maximal concentration reached was about 0.04±0.002 mM, while in the mutant this concentration was circa 7-fold enhanced. The low concentrations measured most likely reflect a limitation in oxygen availability due to the nearly anaerobic conditions used for growth. At the time point of growth arrest, the ccpA mutant had consumed 8% less Glc, and the maximal consumption rate was about 30% of the wild-type level  than in the ccpA mutant (0.23±0.01 mM). At the time point of maximal biomass, the ccpA mutant had consumed 31% of the initial Gal, 5% more than that consumed by D39. Loss of CcpA did not affect the maximal Gal consumption rate  was higher in the CcpA-deficient strain. The same trend was observed for cultures growing on Gal: loss of CcpA resulted in higher biomass yields relative to substrate or ATP. The lower biomass yields in D39 indicate that, independently of the carbohydrate, CcpA directs the carbon source for catabolic processes. Assuming that all ATP is formed by substrate level phosphorylation, the higher ATP yields in conditions with higher acetate production were expected  formed per mol of substrate consumed or per mol of ATP  by targeted metabolomics using 31P-NMR . In this31P-NMR , detaileccpA mutant, independently of the phase of growth. Likewise, the lower glycolytic metabolites 3-phosphoglycerate (3-PGA) and phosphoenolpyruvate (PEP) accumulated to higher concentrations in strain D39ΔccpA. The size of the glycolytic pools showed a growth phase dependency: the concentrations of G6P, FBP, 3-PGA and PEP, were smaller in the TS phase. The decrease in the level of the lower glycolytic metabolites from M to TS phases of growth was unexpected and is contrary to the accumulation profile of 3-PGA and PEP in other Streptococcaceae species S. pneumoniae. The concentrations of the glycolytic intermediates were also lower in the TS phase of growth when Gal was used as sole carbon source. In contrast to Glc, loss of CcpA on Gal resulted in slightly lower glycolytic pool levels. Furthermore, D39 and D39ΔccpA cells growing on Gal accumulated tagatose 1,6-bisphosphate (TBP), α-galactose 1-phosphate  and α-glucose 1-phosphate (α-G1P), denoting simultaneous dissimilation of Gal via the tagatose 6-phosphate and Leloir pathways , the concentration of TBP was 10-fold lower in D39ΔccpA. This striking difference might be a direct consequence of the higher lac transcript levels in D39 (ccpA mutant.During growth on Glc the levels of the upper glycolytic metabolites glucose 6-phosphate (G6P) and FBP were higher in the pathways  and 5. Ws in D39  and S4, s in D39 . It shouccpA mutant. Furthermore, the accumulation profile of the soluble cell wall precursor UDP-N-acetylglucosamine was not changed due to inactivation of ccpA, whereas the concentration of UDP-N-acetylmuramoyl-pentapeptide was higher in D39ΔccpA. Curiously, on Gal the soluble cell wall components were below detection limits (<0.5 mM), except for UDP-N-acetylglucosamine, which accumulated in S. pneumoniae D39 in MS growth. Accumulation of CDP-choline, the phosphorylcholine donor in the synthesis of teichoic acids, was higher in the ccpA mutant, decreasing in both strains from M to TS phases during growth on Glc. The growth dependent behaviour was also observed on Gal, but in this carbohydrate the levels of CDP-choline were approximately the same in D39 and D39ΔccpA.NDP-activated intermediates are soluble precursors in the synthesis of several cell surface structures of utmost importance for virulence and pathogenesis +, identical levels of CTP and ADP, and circa 2-fold higher amounts of ATP in MS growth  potentially involved in metabolic regulation. These findings extend beyond transcriptional regulation the role of CcpA in controlling cellular network properties. Furthermore, CcpA-dependent changes in cell wall and capsule UDP-activated precursors indicates a role of CcpA in the modulation of cell surface properties, known to be critical in virulence and pathogenesis.The metabolite profiles herein presented clearly support the operation of two routes for Gal catabolism and suggest a unique mechanism underlying the regulation of glycolysis in ccpA mutant , which accounted for about 94% of the total polysaccharide in D39ΔccpA  only during growth on glucose. Moreover, the increased transcript level of glmS and murF in D39 on glucose might correlate with the high demand for synthesis of biomass as denoted by the high growth rate in this condition.An interesting observation, which might be associated with virulence, was the strong activation by CcpA of two genes involved in cell wall synthesis  and S. pneumoniae (up to 19%), which despite their genetic relatedness apparently have evolved distinct regulatory networks. In S. mutants, catabolite repression can be independent of CcpA and requires specific components of carbohydrate-PTS permeases The CcpA regulon was substantial on Glc, the “preferred” carbohydrate of many Firmicutes, which supports high pneumococcal growth rate , but remS. pneumoniae the routes for Gal catabolism have not been biochemically characterized, but complete Leloir and tagatose 6-phosphate pathways can be deduced from pneumococcal genome sequence data (http://www.ncbi.nlm.nih.gov/genome/lproks.cgi). We propose that both pathways are operative during growth on Gal. The presence of the intermediates α-Gal1P (Leloir) and TBP (tagatose 6-phosphate), as well as the elevated expression levels of the genes galM, galKT-2 (Leloir), and lacABCD (tagatose 6-phosphate), fully support this claim. Interestingly, lacFE are also induced by Gal, suggesting a role of the pneumococcal lactose-PTS in Gal uptake as reported for S. mutans and L. lactispfk and pyk, and ldh indicate that CcpA's role is to enable maximal Gal catabolism. In light of our results, an answer to the question as to why substrate consumption and growth rates on Gal are not stimulated by CcpA is difficult to put forward. Of note in Gal-grown D39 cells, is the elevated concentration of TBP (10-fold higher than in D39ΔccpA), which might be toxic for the cells. Indeed, unrestricted accumulation of phosphorylated metabolites has previously been evoked to explain negative effects on growth rate pfl and pfl-AE allow a more efficient competition with lactate dehydrogenase for the common substrate pyruvate, consequently increasing the flux to mixed acid products, including acetate. Production of this organic acid is accompanied by ATP formation, which is certainly an advantage during the fermentation of Gal, as the glycolytic ATP generation is limited by the substrate consumption rate (three times lower than that of Glc). Shifts to mixed acid fermentation as a means to counteract for ATP production during growth on slowly metabolized carbon sources or limiting carbohydrate concentrations carbohydrates are well documented in StreptococacceaeIn this study, CcpA ensures fast growth on Glc, but not on Gal, as the growth rate on the latter carbohydrate is basically independent of this pleiotropic regulator. In S. pneumoniae, and thus most likely influences the ability to persist and proliferate in the host. Considering our data showing Glc- and Gal-dependent regulation it is tempting to hypothesize that in the specific host microenvironments basic metabolic processes are tuned by CcpA to maximize cellular profit  was observed in a pneumococcal ccpA mutant rfbCBD genes in the transition-to-stationary phase of Glc-grown cells, we have not found significantly differentially expressed cps genes in any other tested conditions. Furthermore, the amount of capsule produced was not influenced by CcpA  is in full agreement with the transcript levels of the biosynthetic genes. The tight control exerted by CcpA on the availability of CDP-choline might provide a means to influence the association of capsule, proteins and other molecules to the cell wall. Indeed, binding of macromolecules to the cell wall is conditioned by the decoration of the wall polysaccharides (teichoic acids and lipoteichoic acids) ccpA mutant circa 42% are presumably subject to direct regulation by CcpA, as denoted by the presence of a cre sequence. This value is in good agreement with the 38% previously determined using the Tn-seq method to evaluate genetic interactions cre site. CcpA influenced the transcript level of 22 other transcriptional regulators, and thereby indirectly affects genes that are under the control of these regulators. Interestingly, we found that CcpA-responsive genes are also part of other regulatory networks. About 15 genes, mainly involved in amino acid and carbohydrate metabolism, but also the virulence gene pcpA, are commonly regulated by the nutritional regulator CodY and CcpA S. pyogenes have highlighted the importance of a combination of independent transcriptional regulators to coordinate gene expression during infection S. pneumoniae virulence is shaped by co-regulation of independent transcriptional regulators.Of the promoters differentially regulated in the in vivo fitness and persistence in adverse host niches. Previously, the role of the transcriptional regulator CcpA in the physiology of the pneumococcus has been underappreciated. In this work we resorted to a combined transcriptome and metabolite profiling approach to demonstrate that CcpA is a master regulator involved in varied cellular processes in S. pneumoniae. We have ascertained that S. pneumoniae optimizes basic metabolic processes, modulates expression of virulence factors and most likely tunes association of macromolecules with the cell wall in a CcpA-mediated manner. The observed carbohydrate-dependency of the CcpA regulatory network indicates a key role in adjusting to specific nutritional cues within host niches. Exploring the unveiled links between CcpA and other regulators will certainly contribute to an improved understanding on the ability of S. pneumoniae to colonize and cause disease. The insights gained thus far from this comprehensive analysis foresee CcpA as a key factor in the interaction between S. pneumoniae and its host.Factors controlling the interaction between pathogens and their host are of utmost importance for S. pneumoniae strains were grown as standing cultures without aeration in M17 broth (Difco™) containing Glc 0.5% (w/v) or on Glc-M17 (0.5% w/v) agar plates with 1% (v/v) defibrinated sheep blood (Johnny Rottier) at 37°C. When appropriate 150 µg ml−1 spectinomycin, 2.5 µg ml−1 chloramphenicol, 0.25 µg ml−1 erythromycin or 15 µg ml−1 trimethoprim were added to the medium. Capsule quantification, transcriptomic and metabolic experiments were performed with cells grown in chemically defined medium (CDM) −1), sodium pyruvate (0.01% w/v), choline (0.001% w/v) and cysteine (0.4 g L−1) in static flasks at 37°C without pH control . Glc (56±1 mM) or Gal (57±1 mM) was used as the carbon source. For complementation studies, nisin was added at time zero. Growth of the cultures was monitored by measuring optical density at 600 nm every hour. Maximum specific growth rates (µmax) were calculated through linear regressions of the plots of ln (OD600) versus time during the exponential growth phase.Strains and plasmids used in this study are shown in Phusion DNA polymerase (Roche), T4 DNA ligase (Roche) and restriction enzymes (Fermentas) were used according to the recommendations described by the suppliers. Purification of the PCR products was performed using the High pure PCR product purification kit from Roche. Purified PCR products or recombinant plasmids were introduced into S. pneumoniae by transformation as described in Kloosterman et al.L. lactis MG1363 was transformed with plasmid DNA by electroporation according to Holo and Nes Chromosomal DNA isolation was performed according to the procedure of Johansen and Kibenich S. pneumoniae D39, ccpA forms a monocistronic operon, flanked upstream by a well defined promoter region and downstream by a strong terminator . The ccpA deletion was accomplished by allelic replacement mutagenesis as follows: the up- (547 bp) and downstream (517 bp) regions of the ccpA gene (1010 bp) were PCR-amplified from D39 chromosomal DNA using the primer pairs ccpA_D39_KO-1/ccpA_D39_KO-2 (spec) and ccpA_D39_KO-3 (spec)/ccpA_D39_KO-4, respectively. The resulting PCR products were, by means of overlap extension PCR R gene  using the primers ccpA_D39_KO-1 and ccpA_D39_KO-4. The ΔccpA::spc PCR product (2096 bp) obtained in this way was transformed to S. pneumoniae D39, giving rise to the ccpA deletion mutant (D39ΔccpA in ccpA gene with the spc gene and correct integration of the insert were confirmed by PCR and sequencing (Service XS). To rule out the possibility of suppressor mutations arising with the deletion of ccpA, the transformation efficiency of the ΔccpA::spc PCR product was compared with that of the SPD_0786::spc (SPD_0786 insertion-deletion) PCR product. SPD_0786 is a non-essential transcription factor, which disruption does not affect growth of S. pneumoniae in CDM . A similar number of transformants per ng of DNA (CFU ng−1) was obtained, strongly suggesting that our ccpA strain contains no suppressor mutations.Oligonucleotide primers used in this study are listed in ccpA::spc region was PCR-amplified from the chromosomal DNA of the ccpA mutant strain using the primer pairs ccpA_D39_KO_1 and ccpA_D39_KO_4. Similarly, a fragment (2725-bp) encompassing the SPD_0786::spc region of a D39 SPD_0786::spc strain was amplified using appropriate primer pairs. An identical concentration of each product was used to transform strain D39 as described above. Spectinomycin resistant colonies were selected and counted for each transformation and the number of colonies was compared to evaluate the transformation frequency.For the transformation frequency assay the 2096-bp fragment comprising the ΔccpA complementation strain was constructed using the NICE system improved for S. pneumoniaenisRK genes, activate the expression of the nisA promoter in the presence of nisin. The procedure to construct the ccpA complementation strain is described as follows. The ccpA mutant strain (D39ΔccpA) was transformed with chromosomal DNA from D39nisRK, a stable derivative of S. pneumoniae D39 that harbours the nisRK genes and a TrimR gene in the bgaA locus. Trimethoprim resistant clones  and transition-to-stationary (OD600 of 1.3±0.1) phases of growth  was <0.01 and the ratio  was >1.5 or <0.66, but >2 or <0.5 in at least one of the four conditions. For the Gal_TS comparison, data were derived from two independent hybridizations and a Bayesian p-value <0.01, FDR <0.08 was applied. To exploit the data obtained in this way, also an in silico comparison was done between Glc and Gal, both for the M and TS phases and also in both D39 wild type as well as ΔccpA. To this end, raw signals (i.e. slide images) of the Glc experiments were compared with the corresponding signals of the Gal experiments and analyzed as described above. The microarray data have been deposited at GEO under accession number GSE31819.f growth . mRNA isL. lactis consensus sequence  prediction was used. The resulting predictions , 90 genes were left that contain a putative cre site in their promoter region, or downstream of the translational start.Using the dictions  were com−1 Tris-HCl (pH 7.5), 2.5 mmol l−1 MgCl2, 0.1 mmol l−1 CaCl2). Samples were purified with the Roche RNA isolation kit. Reverse transcription was performed on 5 µg of total RNA using the same method as used during the microarray procedure. Quantification of specific cDNA of spd_0420 (pfl), spd_0667 (sodA), spd_0790 (pyk), spd_1053 (lacE), spd_1078 (ldh), spd_1504 (nanA) and spd_1634  was performed on an CFX96 Real-Time PCR System  using Maxima SYBR Green qPCR Master Mix  on 5 ng cDNA template in a 20 µl reaction volume. Primers used are listed in metG (spd_0689) cDNA using the 2−ΔΔCT method Cell samples obtained as described above for transcriptome analyses using DNA microarrays were treated with RNase-free DNase I  for 60 min at 37°C in DNaseI buffer , filtered  and the supernatant solutions were stored at −20°C until analysis by high performance liquid chromatography (HPLC). Prior to analysis, samples were allowed to thaw at room temperature. Glc or Gal and end-products were quantified in an HPLC apparatus equipped with a refractive index detector  using an HPX-87H anion exchange column (Bio-Rad Laboratories Inc.) at 60°C, with 5 mM H2SO4 as the elution fluid and a flow rate of 0.5 ml min−1. Alternatively, quantification of metabolites in the supernatant solutions was performed by 1H-NMR in a Bruker Avance II 500 MHz spectrometer (Bruker BioSpin GmbH). Formic acid  was added to the samples and used as an internal concentration standard. The ATP yield was calculated as the ratio of ATP produced to Glc or Gal consumed. The global yields of ATP were calculated from the fermentation products determined at the time-point of growth arrest assuming that all ATP was synthesized by substrate-level phosphorylation. A factor of 0.39, determined from a DW (mg ml−1) versus OD600 curve, was used to convert OD600 into dry weight (mg biomass ml−1).Hydrogen peroxide was quantified in fresh supernatant solutions using the Amplex® Red hydrogen Peroxide/Peroxidase assay kit from Invitrogen.Strains were grown as described above. Culture samples (2 ml) were taken in mid-exponential and transition-to-stationary phases of growth, centrifuged  and filtered . The Amplex® Red hydrogen Peroxide/Peroxidase assay kit from Invitrogen was used to quantify the hydrogen peroxide contents in the fresh supernatant solutions.Strains were grown in chemically defined medium (CDM) as described above. Culture samples of 1-ml were harvested in mid-exponential and transition-to-stationary phases of growth, centrifuged  and transition-to-stationary (OD600 of 1.3±0.1) cultures corresponded to approximately 14 mg of protein and the centrifugation was performed at 4°C, 6,000×g, during 3 min. The dried extract was dissolved in 1 ml of deuterated water (pH 6.5 or pH 5.5) containing 5 mM EDTA and stored at −20°C for further analysis by 31P-NMR. This methodology was successfully used to measure phosphorylated metabolites in L. lactisS. pneumoniae ethanol extracts the following phosphorylated metabolites were detected: G6P, FBP, 3-PGA, PEP, α-Gal1P, α-G1P, TBP, UDP-Glc, UDP-N-acetylmuramoyl-pentapeptide, UDP-N-acetylglucosamine, UDP-glucosamine, CDP-choline, NAD+, CTP, ATP and ADP. A small peak due to fructose 6-phosphate was also detected, but its quantification hampered by strong overlapping with a resonance due to the medium component β-glycerophosphate. A weak resonance due to dihydroxyacetone phosphate was present, but reliable integration was not possible. The concentration limit for detection/quantification of metabolites in ethanol extracts by 31P-NMR was approximately 0.4–0.5 mM. Resonances were assigned by addition of pure compounds to the extracts or on the basis of comparison with a previous study 31P-spectra by comparison with the area of a resonance due to methylphosphonic acid (Aldrich), added as an internal standard, and after application of an appropriate factor for correcting saturation of resonances. The reported concentration values for intracellular phosphorylated compounds are averages of three independent growth experiments.The ethanol extracts were prepared as described previously by Ramos 31P-NMR spectra were recorded using a selective probe head (31P-SEX) at 37°C on a Bruker AVANCE II 500 MHz spectrometer (Bruker BioSpin GmbH) by using standard Bruker pulse programs. Spectra were referenced to the resonance of external 85% H3PO4, designated at 0 ppm.600 of 0.35±0.02) and transition-to-stationary (OD600 of 1.3±0.1) phases of growth were centrifuged , resuspended in PBS and pelleted at 3000×g, 4°C, for 20 min. The pellet was resuspended in 500 µL of 150 mM Tris-HCl (pH 7.0) and 1 mM MgSO4 and treated as described elsewhere et al.et al.Samples for the determination of the capsular glucuronic acid amounts were prepared as follows: cells grown in CDM and harvested in mid-exponential  Schematic overview of the ccpA gene and its flanking genes and the genetic replacement of the ccpA gene with a spectinomycin marker in strain D39. Hooked arrow, putative promoter; lollipop, putative terminator; black area, ccpA gene replaced with a spectinomycin cassette; numbers inside the genes, D39 SPD numbers; arrows and numbers above the figure indicate primers used to construct the D39ΔccpA mutant; zoomed area (inset), putative cre box, −35 and −10 promoter regions; the number of bp spacing these regions are subscripted after {n}. (B and C) Growth of strains D39 (▪), D39ΔccpA (▴) and D39ΔccpAnisRKpNZ[ccpA] (○) in CDM containing 1% Glc (B) or 1% Gal (C) at 37°C in rubber-stoppered static bottles without pH control ; growth curves as in 600) were measured hourly. Each point of the growth curves is an average of at least three independent experiments and the error was in all cases below 15%. The growth rates for each strain are also indicated and the values shown are averages ± SD. The arrows indicate the mid-exponential and transition-to-stationary time-points at which D39 and D39ΔccpA samples were withdrawn for transcriptomic and metabolic profiling analysis.(TIF)Click here for additional data file.Table S1Overview of all significantly up- or downregulated genes as determined by the DNA microarray analysis of D39ΔccpA compared to D39 wild-type (yellow columns). Only genes were included that have a ratio of (D39ΔccpA/D39 wt) >1.5 or <0.66, and >2.0 or <0.5 in at least one of the four conditions tested . In addition, putative cre sites are given in columns AG to AJ. For more details, see column headers. Glc = glucose, Gal = galactose, wt = D39 wild-type, ccpA = D39ΔccpA, M = mid-exponential growth phase, TS = transition-to-stationary growth phase, bayes p. = bayesian p-value, FDR = false discovery rate, COG = cluster of orthologous groups.See also . In case(XLS)Click here for additional data file.Table S2Expression ratio of selected genes linked to metabolic and virulence pathways (spd_0420 (pfl), spd_0667 (sodA), spd_0790 (pyk), spd_1053 (lacE), spd_1078 (ldh), spd_1504 (nanA) and spd_1634  as determined by qRT-PCR in exponentially and transition to stationary phases of growth. Samples were obtained as described for the microarray experiments and the experiments performed as in metG (spd_0689) cDNA and the data are averages of four repeats.(XLS)Click here for additional data file.Table S3Subtable abstracted from  Putative substrates for carbohydrate transporters regulated by CcpA are indicated as well. ABC, ATP binding cassette transporter; PTS, phosphoenolpyruvate:carbohydrate phosphotransferase system.(XLS)Click here for additional data file.Table S4Overview of all significantly . In case genes were also significantly up- or downregulated (according to the same criteria) in the D39ΔccpA versus D39 wild-type comparison, these data were also given (yellow columns). As in cre sites are given as well. For more details, see column headers and legend (XLS)Click here for additional data file.Table S5List of genes with putative cre sites, as predicted and compiled in this study as described in detail in the (XLS)Click here for additional data file.Table S6List of genes with putative cre sites that were not significantly affected in the DNA microarray analyses of D39ΔccpA compared to D39 wild-type.(XLS)Click here for additional data file.Table S7Subtable abstracted from (XLS)Click here for additional data file.