HTT) gene. The clinical features of HD are characterized by cognitive, psychological, and motor deficits. Molecular instability, a core component in neurological disease progression, can be comprehensively evaluated through longitudinal transcriptomic profiling. Development of animal models amenable to longitudinal examination enables distinct disease-associated mechanisms to be identified.Huntington’s Disease (HD) is a progressive neurodegenerative disorder caused by an expansion in the polyglutamine (polyQ) region of the Huntingtin (HTT gene and a range of polyQ repeats. With this unique group of transgenic HD nonhuman primates (HD monkeys), we profiled over 47,000 transcripts from peripheral blood collected over a 2 year timespan from HD monkeys and age-matched wild-type control monkeys.Here we report the first longitudinal study of transgenic monkeys with genomic integration of various lengths of the human Messenger RNAs with expression patterns which diverged with disease progression in the HD monkeys considerably facilitated our search for transcripts with diagnostic or therapeutic potential in the blood of human HD patients, opening up a new avenue for clinical investigation. HTT. The hallmark of HD is neurodegeneration and formation of mutant HTT protein aggregates, predominantly in the striatum and cerebral cortex [HD is a dominant genetic disorder caused by pathologic expansion of a polyglutamine (polyQ) repeat in exon one of the IT15 gene encoding l cortex . Howeverl cortex , thus thl cortex . Approxil cortex .The average age of onset for HD is 37–38 years [There are reports of RNA analysis from a range of biological tissues types as well as from various model systems with HD, including humans and rodents -12 TransHTT gene with a range of polyQ repeats greater than the 10–11 polyQ repeats that are normally present in wild-type rhesus macaque. As rhesus macaque endogenously express 10-11Q, the pathogenic threshold of polyQ repeats in monkeys is expected to be lower compared to 35–37 polyQ repeats in healthy human populations [Here, we propose to elucidate the longitudinal transcriptomic patterns in peripheral blood of transgenic HD monkeys, an animal model which has a long lifespan of many years and are amenable to routine blood collection . Previouulations . BiologiHTT gene , under the control of the human polyubiquitin-C (UBC) or human HTT promoter respectively, with different number of polyQ repeats [vs 1; p < 0.05, respectively) and 37 months , the HD monkeys rated significantly above controls   or a larransgene , whereasanscript ,22. Indeal blood ,13, para (HDPMRS), which i (HDPMRS) that is To systematically map the transcript expression patterns with HD progression, we interrogated over 47,000 mRNAs in peripheral blood samples collected from the HD monkeys and age-matched controls at 5, 11, 17 and 23 months of age with rhesus macaque Affymetrix arrays. Spiking controls present on the arrays showed very minimal differences across all hybridizations (not shown). We selected 32,617 out of 52,024 Rhesus Affymetrix probe sets on the microarray for further analysis based on a “present” detection call from the Affymetrix MAS5 algorithm in 10% or more of the samples , runt-related transcription factor 2 (RUNX2), NADH dehydrogenase 1 alpha subcomplex 5 (NDUFA5), cell division cycle 25 homolog A (CDC25A), arachidonate 12-lipoxygenase (ALOX12), lamin A/C (LMNA), latexin (LXN), AXL receptor tyrosine kinase (AXL), epsin 2 (EPN2), and tumor protein p63 (TP63) . Those same candidates were also chosen based on their corresponding dysregulation in studies from human control, presymptomatic and symptomatic HD patients (GEO Database numbers 1331 and 1332) [ showed longitudinal statistical dysregulation by two-way ANOVA from 5–39 months .To further examine transcriptome changes in the HD monkeys, we measured all 11 mRNAs at extended timepoints of 29, 32, and 39 months to validate the continuous longitudinal changes of the candidates Figure . Indeed,s Figure F. AdditiNDUFA5, are part of the canonical HD signaling pathway (KEGG pathway for Huntington’s disease) [HTT-mediated pathogenesis can be evaluated in peripheral blood. Moreover, novel HD-associated mRNAs, such as LXN or FZD8, were also identified through the correlation of HD monkey and human profiles. Interestingly, LXN was previously shown to be an important regulator of hematopoiesis [mHTT would be altered in both humans and monkeys, in spite of other physiological or genetic factors which might be present and non-specific to HD. Overall, our data suggests the HD monkey model may help to uncover primate-associated transcriptomic changes due to mHTT.Here we report the first longitudinal transcriptomic profiling study from an HD monkey model. HD is a highly progressive disorder , therefodisease) , suggestopoiesis -33, whilopoiesis -36. Furtopoiesis -39. TranFZD8, LMNA, LXN, NDUFA5, and ALOX12) were dysregulated at a very young age, presumbly in the earlier stages of life based on HDPMRS, while other ongoing clinical evaluations including cognitive behavior and imaging studies will further define their phenotypes. Genes with disrupted expression prior to onset of cognitive and behavioral deficits could potentially be well suited as therapeutic targets aimed to prevent development of disease symptoms. Furthermore, NDUFA5 appeared to also show a longitudinal escalation in transcript dysregulation with time and disease, suggesting it may also serve as a blood biomarker for HD progression. Interestingly, very recent reports indicate the mitochondrial respiratory chain, which NDUFA5 is part of, was functionally impaired in platelets from pre-symptomatic and symptomatic HD patients [All five candidates we validated with disease associated trends up to 39 months of age in the HD monkeys  cells of rHD1 fertilized embryo [As the HD monkeys further develop, future studies will focus on extensive molecular mapping encompassing early to late stages of HD progression and define which candidates may be optimal as biomarkers or as therapeutic targets. Upcoming studies will also be able to correlate transcriptomic changes with primate-specific HD behavioral phenotypes, as well as other clinical measurements . Additiod embryo , settingWe anticipate that on-going longitudinal studies in the current group of HD monkeys as well as future generations will lead to a defined list of transcripts strongly correlated with the progressive pathogenic stages in HD. Future generations of HD monkeys can be engaged in testing of novel pharmacological treatments, offering new approaches to investigate HD.HTT gene (NCBI Reference Sequence: NM_002111.6) containing 84 CAGs under the control of ubiquitin promoter (LV-uHtt-84Q) and lentiviruses carrying Exon 1–10 of the human HTT gene containing 73Q under the control of human HTT promoter (LV-hHtt-73Q) were used to infect metaphase II arrested rhesus macaque oocytes followed by fertilization and embryo transfer into surrogate females. The number of pathogenic polyQ repeats, copy numbers of transgene per cell and general genomic structure of transgene for each monkey are outlined in Figure We developed four transgenic HD monkeys, born and raised at the Yerkes National Primate Research Center of Emory University strictly following the IACUC protocols for animal care. The control group was composed of two females and two males, and the HD transgenic group consisted of 4 males generated through a lentiviral-mediated method as described by Yang and colleagues . In brieHuntington’s disease primate model rating scale (HDPMRS) is modified from the Unified Huntington’s Disease Rating Scale (UHDRS)  to evaluBlood was collected and processed from all HD and control monkeys at 6 month intervals of 5, 11, 17, and 23 months of age. In brief, the isolation of WBC utilized gradient separation with lymphocyte separation media (Lonza). The WBC containing layer from whole blood collected in heparinized tubes was diluted with phosphate buffered saline (PBS) and pipetted on top of the lymphocyte separation medium followed by centrifugation for 15 minutes at 20°C. After centrifugation, the interphase containing the mononuclear WBC are removed and washed twice with PBS prior to storage of pellet at −80°C.Genomic DNA (gDNA) was isolated from blood collected from all HD monkeys for genotyping and sequence analysis. gDNA was extracted using the Promega Wizard kit and quality was assessed by gel electrophoresis. Primers for PCR amplification extended from the promoter to beyond the polyQ region for all HD monkey transgene analysis. Primer sequences for HD monkey rHD1, generated with pLV-uHtt-84Q, were Ubiquitin-F1 5′- GAG GCG TCA GTT TCT TTG GTC -3′ and HDintron1-R 5′- GCT GGG TCA CTC TGT CTC TG -3′. Primer sequences for HD monkeys  generated with pLV-hHtt-73Q, were: HTTproF6.5 5′- GTT CTG CTT TTA CCT GCG GC -3′ and HDproexon4/5R 5′- CCG AGG GGC ACC ATT CTT TTT -3′. All reactions were carried out with the annealing temperature of 62°C for 35 cycles in the BioRad iCycler. Amplified PCR products were cloned into the pGEMT-easy vector (Promega) and sequenced by Genewiz.HTT transgene in HD monkeys, gDNA was analyzed by qPCR using two independent means of normalization for result confirmation. First, using the HTTexon1 Taqman primers listed in Additional file HTT, all reactions were run in a final 1× volume of Fast Advanced Master Mix (Applied Biosystems) and normalized to the geometric mean of GAPDH and β-actin. The normalized Ct values of each HD monkey were then compared to the average of the normalized Ct values for the control monkeys. In an independent experiment, we also normalized Ct values obtained with the HTTexon1 Taqman primers with Ct values using a Taqman assay designed to only recognize the endogenous rhesus HTT gene (HTTexon26). The exon1 Ct values of each HD monkey were normalized to their exon26 Ct values and compared with the average of the control monkeys.To obtain the relative copy number of mutant HTT mRNA expression from blood, 750 ng of total RNA was reverse transcribed to cDNA with the High Capacity Reverse Transcription Kit (Applied Biosystems). Quantitation of HTT transcript levels were evaluated by qPCR using a custom-designed gene-specific Taqman assay (Applied Biosystems) and reactions run on the BioRad CFX96 cycler . A phenol-chloroform extraction of the RNA was done by addition of 100 μl of chloroform to the Trizol homogenates followed by centrifugation at 12,000 × g for 10 minutes at 4°C. The aqueous layer was removed for RNA precipitation overnight with isopropanol and 40 μg of glycogen (Invitrogen) at −20°C. The precipitated RNA was pelleted at 12,000 × g for 30 minutes at 4°C. All RNA pellets were washed twice with 75% ethanol and then subsequently dissolved in water (RNase/DNase free).In a longitudinal gene expression study, RNA from WBCs of HD and control monkeys collected at four age-points  were analyzed by using Affymetrix Rhesus Macaque Genome arrays. All microarrays and 2-way ANOVA statistical analysis was done at Ocean Ridge Biosciences.Biotin-labeled cRNA samples prepared from total RNA using Affymetrix 3′ IVT Express Kit (PN 901229) were hybridized onto the Rhesus Macaque Genome arrays (PN 900655) for 16–18 hours. Scanned images of the arrays were analyzed using RMA algorithm to export the data CHP files using the Affymetrix Expression Console software, version 1.1. Statistical analysis (2-way ANOVA) was performed by dividing the samples into two treatment groups  and four age-points  using BRB-ArrayTools, version 4.1. Detailed descriptions for the Affymetrix array methods and analysis are further outlined in Additional file To compare the mRNA profile from the HD monkeys with those from human HD patients, we analyzed genome array results from the blood of human subjects deposited into the NCBI GEO database. The studies consisted of WBC isolated from 14 controls and 12 symptomatic HD patients and analyzed by both Affymetrix and Codelink arrays (Geo files GDS1331 and GDS1332 respectively). We calculated the fold change and P values for each probeset from both the Affymetrix and Codelink gene expression profiling. For added stringency, we identified mRNA candidates with similar dysregulation patterns  between the two sets of array data. The genes with similar disruption between the two human studies were then compared with the significant probesets  identified in our array profiling results from the HD monkeys.For validation of selected mRNA candidates, 200 ng of total RNA for each sample was reverse-transcribed to cDNA using the High Capacity RNA to cDNA kit (Life Technologies), following the manufacturer’s protocol. For qPCR, 1 μl of cDNA was primed with custom-designed Taqman assays from Applied Biosystems for the target mRNA in a final reaction volume of 1× Taqman Universal PCR Master Mix (Life Technologies). Primer sequences for all Taqman assay primers and probes are listed in Additional file The authors have no commercial association that might create a conflict of interest in connection with the submitted manuscript.JK-experimental design, sample preparation, data-analysis, wrote the manuscript; YL-data analysis; KC, JB, DW-array processing and data analysis; YX-HDPMRS scoring; KL, TC, HE, SM, KL, AN, HB-animal care, sample collection and processing; SHY-construct design and generation of HD monkeys; SHL and XJL-construct design; JJY-sample preparation and processing; AWSC-construct design, generation of HD monkeys, experimental design, wrote and approved the manuscript. All authors read and approved the final manuscript.Wild-type rhesus macaque polyQ sequence. The polyQ sequence from the UCSC genome database was further confirmed and aligned with polyQ sequence from a wild-type rhesus housed at the Yerkes National Primate Center.Click here for fileHD rating scores. HD rating scores by HDPMRS at between 12 and 37 months for all HD and control monkeys.Click here for fileMicroarray data. Raw fluorescence values for each sample at each timepoint from the Affymetrix Rhesus Macaque array analysis.Click here for fileIngenuity analysis of HD monkey profiling results. The Ingenuity pathway program was used to identify signaling pathways  associated with HD mRNA transcripts dysregulated with a P value < 0.001 in the Affymetrix profiling.Click here for fileHuman HD candidate graphs. Eleven selected transcripts from human HD blood array data (obtained from the GEO database) were graphed from control, pre-symptomatic, and post-symptomatic patient samples.Click here for fileHuman HD array probeset information. The human array probeset information for the selected HD-associated candidates.Click here for fileqPCR expression analysis of selected HD mRNA candidates at extended timepoints. The mRNA expression of eleven candidates with HD association in monkey and human blood were analyzed at additional timepoints of 29, 32, and 39 months.Click here for fileHD Rating Scale. Parameters measured in the HD primate rating scale.Click here for fileRhesus Taqman assay information. All forward, reverse, and probe sequences for rhesus Taqman assays engaged for this manuscript.Click here for fileMicroarray methods. Additional information regarding the Rhesus Macaque Affymetrix profiling are outlined.Click here for file


Cheap and massively parallel methods to assess the DNA-binding specificity of transcription factors are actively sought, given their prominent regulatory role in cellular processes and diseases. Here we evaluated the use of protein-binding microarrays (PBM) to probe the association of the tumor suppressor AP2α with 6000 human genomic DNA regulatory sequences. We show that the PBM provides accurate relative binding affinities when compared to quantitative surface plasmon resonance assays. A PBM-based study of human healthy and breast tumor tissue extracts allowed the identification of previously unknown AP2α target genes and it revealed genes whose direct or indirect interactions with AP2α are affected in the diseased tissues. AP2α binding and regulation was confirmed experimentally in human carcinoma cells for novel target genes involved in tumor progression and resistance to chemotherapeutics, providing a molecular interpretation of AP2α role in cancer chemoresistance. Overall, we conclude that this approach provides quantitative and accurate assays of the specificity and activity of tumor suppressor and oncogenic proteins in clinical samples, interfacing genomic and proteomic assays. Since the complete sequencing of the human genome in 2001 in vitro or using cellular assays in silico predictions.Several laboratory techniques have been devised for large scale identification of transcription factor target sites, either cis-regulatory sequences and gene promoters 3–4GGC-3′5′-GCCN consensus sequence Activating protein 2 alpha (AP2α) is a transcription factor whose binding sites were first discovered in cellular and viral AP2α biological function stretches from the regulation of neural crest formation during mice development to a proposed role in the mitochondrial pathways leading to apoptosis ab initio assignment of the target gene specificity of the AP2α tumor suppressor, as a purified protein as well as from healthy and cancer breast tissues from patients. Several target genes were validated in cell-based assays. The PBM-based approach may thus allow the identification of previously unknown target genes of tumor suppressors in cancer cells, and it provides novel markers of cancer progression at the interface of proteomics and genomics.PBMs have so far been used mostly to assess interactions to short synthetic DNA sequences, for the modeling of the DNA sequence specificity of transcription factors. Here we show that PBMs can be used to perform large-scale assays of the interaction of regulatory proteins from crude cellular extracts with long genomic fragments such as promoters and enhancers. Assay of approximately 6000 human genomic sequences allowed an We first aimed at identifying potential target genes of the AP2α oncogene/tumor suppressor protein by assessing its binding to double-stranded DNA sequences immobilized on a microarray. We used a collection of human genomic DNA sequences that was originally devised for ChIP-chip assays As PBMs rely on immobilized DNA molecules that reach high local concentrations at the surface of microarrays, slow or undetectable off-rates may occur in vitro, we assessed globally the functional significance of potential AP2α target genes. A statistical analysis yielded 282 DNA sequences that were significantly bound by AP2α . 49 genes represented on the hu6k microarray were also listed in the TRANSFAC dataset. Among these 49 sequences, 6 were directly bound by the recombinant AP2α protein on the PBM  gene, an activator of p53-mediated apoptosis Several promoters were selected for experimental validation from the PBM and in silico analysis allowed the identification of functionally relevant target genes.Next, we assessed whether these AP2α interactions may take place in the cell in the context of chromatin. AP2α binding sites having the highest score computed from the weight matrix prediction algorithm were localized on the DNA sequences imobilized on the PBM, and the corresponding genomic sequences were assayed for AP2α–interaction by chromatin immunoprecipitation-quantitative PCR (ChIP-qPCR) using a colon cancer cell line that constitutively expresses this transcription factor It has been proposed that AP2α DNA-binding specificity is modulated by synergistic or antagonistic interactions with other DNA binding proteins present in human tumor cells, and that changes in these interactions may lead to tumor progression Among the 998 target sequences obtained from both the healthy breast tissue and the breast cancer protein extracts using the log fold change (protein/DNA) as endpoint, 53 also associated with the purified protein . These lUsing a position weight matrix (PWM) prediction algorithm for AP2α binding sites Comparison of AP2 binding specificity from normal and tumor tissues yielded generally correlated results, as many genes that were bound by AP2α in the healthy tissue extract were also bound using tumor extracts . The higin silico were found to mediate AP2α-regulated expression in transfection assays. Occupancy of the AP2α binding sites within the native chromatin structure of tumor cell lines was confirmed experimentally. This also indicated that valid target genes may be identified by combining these PBM and modeling approaches. Thus, the increase in non-specific background binding to the long DNA molecules, as resulting from the use of relatively long promoter and enhancer sequences, did not mask sequence-specific functional interactions.In this study, we evaluated whether double-stranded DNA fragments bearing whole promoters and regulatory sequences and immobilized on a PBM may be used to identify the target genes of an oncogenic transcription factor. We found that binding values obtained from the PBM correlated well with the affinities determined by surface plasmon resonance or computed with a weight matrix. This indicated that PBMs provide reliable estimations of the binding strength. Genomic fragments that bind AP2α on the PBM and/or In vivo, an additional level of complexity arises from the chromatin structure, which may shield the DNA from protein binding. Thus, a high binding potential in vitro or in silico cannot provide definitive evidence that a putative binding site will be occupied in any given cell type. Furthermore, binding may be occluded by other transcription factors that interact with overlapping sites on the promoter, or conversely, protein association may allow interactions to non-canonical sites. The relative contributions of chromatin and of other transcription factors to the actual binding site occupancy in vivo remains difficult to assess for eukaryotic transcription factors, as large-scale assays of promoter binding occupancy with chromatin alone, or with competing or synergizing transcription factors but without chromatin, have not been available.The finding that the most prominent functional feature of AP2α target genes relates to cancer was validated experimentally for two newly identified AP2α target genes. Kallikrein 5 is a member of the kallikrein family of extracellular proteases that includes the prostate-specific antigen, and it is currently emerging as some of the most prominent biomarkers of tumor progression for various types of cancers Interestingly, we found that promoter-binding by AP2α from total cell nucleus extracts differs significantly from that observed with the purified protein. Because nuclear extracts contain regulatory proteins such as transcription factors but do not mediate nucleosome deposition, differences in the binding patterns obtained from healthy and cancer tissues imply that AP2α binding is regulated by other regulatory proteins present in the extracts. This finding correlates well with previous observations of the synergistic or antagonistic interactions of AP2α with other transcription factors and oncogenes such as p53, Rb and c-Myc in vitro or from in silico predictions. PBMs can thus complement other microarray-based assays of disease related proteins, as may be needed to achieve more efficient integrated molecular diagnostics of cancer.Overall, we conclude that the use of genomic DNA-bearing PBMs and unfractionated cell nuclear extracts can provide so far unavailable data allowing (i) to distinguish transcription factor cooperative or antagonistic effects from those elicited by chromatin, and (ii) to interpret some of the discrepancies noted between cellular assays such as ChIP and assays of purified components Written informed consent obtained from patients undergoing tumor biopsies and the protocol for use of human tissue samples were in accordance to the guidelines of -and approved by- the ethical committee of the University Hospital of Lausanne (Switzerland).Five breast cancer frozen tissues were obtained from the Tissue Bank of the University Hospital of Lausanne. Five normal breast tissues from reduction surgeries were obtained from Dr. Brisken C. Laboratory . All tissue samples were histologically examined by a pathologist (H.A.L.).SW480, humancolorectal adenocarcinoma hybridoma (ATTC line number CCL-228), was cultured according to the supplier's instructions. This cell line is documented to express little or no significant AP2 levels Echerichia coli W3110 utilizing the previously described expression vector pGEX-AP2 following the manufacturer's instructions Full-length human TFAP2α protein was overexpressed as a GST-fusion protein in CGA CGT TGT AAA ACG ACG GC-3′ and 5′-TGA CCA TGA TTA CGC CAA GC-3′ primers, as described previously Plasmids bearing standard DNA sequences for AP2α binding served as template for PCR-mediated DNA amplification 2, 10 mM KCl) and incubation allowed on ice for 15 min. Cells were lysed by addition of 0.5 µL NP-40 [0.02% w/v] and mixed by vortexing for 5 s. Nuclei were harvested by centrifugation for 5 min at 3500 rpm, 4°C and the pellet washed with 300 µL Low Salt Lysis Buffer. After an additional centrifugation step (3500 rpm at 4°C for 5 min) the supernatant was taken off and the pelleted nuclei resuspended in 25 µL of High Salt Lysis Buffer . Samples were mixed by vortexing for 5 s and incubated on ice for 30 min with vortexing every 5–10 min in order to allow extraction of nuclear proteins. The supernatant containing the nuclear proteins was transferred into new coded tubes and diluted by adding 25 µL of Low Salt Buffer to bring the final NaCl concentration to 200 mM and immediately frozen in liquid nitrogen.Tissue sample sections of 30 µm were thawed on ice in a solution of 1× PBS  containing a cocktail of protease inhibitor  followed by centrifugation of the tissue fragments at 4°C for 5 min at 3500 rpm. Pellets were recovered into 250 µL Low Salt Lysis Buffer . After incubation for 1.5 h at 37°C in the absence of light, slides were washed twice for 2 min in 1×TEN and spun dry.Custom-made PBMs, or the hu6k microarrays containing approximately 6000 human genomic sequences of similar length  was applied onto each array slide and incubated at room temperature (RT) in the absence of light for 1 h, as described previously AP2-binding reaction mix .Functional classification of AP2 target genes was performed on the average of data from 3 independent PBM experiments using the Ingenuity Pathways Analysis (IPA) software  by TFAP2A, of which 45 were also represented on the PBM. Potential AP2 binding sites on the immobilized sequences were predicted using a previously described PWM (2) at a flow rate of 5 µl/min unless otherwise stated. Streptavidin was covalently coupled to the surface of a sensor chip CM5 using standard amine coupling procedure by injecting the reagents in the following order: 35 µl 0.05 M NHS/0.2 M EDC mixture, 50 µl streptavidin (300 µg/ml) in 10 mM acetic acid (pH 4.5) and 35 µl 0.1 M ethanolamine. Finally 100 µl SDS 0.5% at 100 µl/min was injected to remove unbound proteins. The amount of immobilized streptavidin was approximately 3000 RU. Biotinylated DNA fragments were diluted in HNM buffer (1–2 ng/µl) and injected over the streptavidin-coated surface until desired amount was immobilized . Unbound DNA was removed by injecting 100 µl SDS 0.5% at 100 µl/min. Different concentrations of AP2α ranging from 0 to 400 nM were applied in a random order to the surface for 10 minutes in order to reach equilibrium. Surface regeneration was achieved with 100 µl SDS 0.5% at 100 µl/min to dissociate bound proteins. One channel without DNA probes was used as reference in all experiments to correct the binding response for bulk refractive index changes and unspecific binding. Apparent affinity constants were obtained by plotting the responses at equilibrium versus AP2α concentration. BiaEvaluation Software Version 4.1 (Biacore) and IGOR Pro Version 5.05A (Wavemetrics) were used for data processing.Measurements were carried out in a Biacore 3000  machine. Immobilization and binding experiments were performed in HNM buffer  and one sequence assigned as an AP2α target by weight-matrix analysis (KLK-5) were inserted upstream on the minimal CMV promoter of pJK6-CMVmin-eGFP, resulting in three reporter plasmids AP2 or the empty pSP72(RSV)NN plasmid as control Chromatin immunoprecipitation (ChIP) and quantitative PCR (qPCR) were essentially performed as previously described Figure S1Analysis of AP2α binding to PBM DNA sequences.(TIF)Click here for additional data file.Figure S2AP2α weight matrix as computed from SELEX data.(TIF)Click here for additional data file.Figure S3Correlation of AP-2α binding scores as predicted in silico (A) or determined using a PBM (B).(TIF)Click here for additional data file.Figure S4Additional network diagrams illustrating prominent molecular relationships of the products of AP2α-bound genes.(TIF)Click here for additional data file.Figure S5Functional validation in HEPG2 cells of AP-2α target genes, as selected from PBM and weight matrix-based predictions.(TIF)Click here for additional data file.Table S1Potential AP2α target sequences identified by PBM using the AP2α purified protein. Sequences bound by recombinant AP2α protein on hu6k microarray (P<0.05).(PDF)Click here for additional data file.Table S2Network associated functions generated by IPA using sequences ID from (PDF)Click here for additional data file.Table S3Human genomic sequences cloned into the eGFP reporter plasmid for experimental validation. Highest AP2α weight matrix score are indicated in the table and corresponding sequences are underlined.(PDF)Click here for additional data file.Table S4Potential AP2α target sequences identified by PBM using the nuclear protein extract from normal breast tissues. Sequences bound by AP2α protein in nuclear cell extract from healthy breast tissues on hu6k microarray (P<0.05).(XLS)Click here for additional data file.Table S5Potential AP2α target sequences identified by PBM using the nuclear protein extract from breast tumor tissues. Sequences bound by AP2α protein in nuclear cell extract from cancer breast tissues on hu6k microarray (P<0.05).(PDF)Click here for additional data file.Table S6Analysis of the first 50 sequences found to be bound by AP2α in recombinant AP2 extract and in normal and cancer tissues extracts. Scores were defined by Weight Matrix Prediction Algorithms for AP2α and p53. For AP2α, scores <30 were defined as low binding sites. For p53, scores <50 were defined as low binding sites.(PDF)Click here for additional data file.Table S7Network associated with disease generated by IPA using the 149 genes differentially regulated in normal versus cancer tissues nuclear extracts.(XLS)Click here for additional data file.


Induction and promotion of liver cancer by exposure to non-genotoxic carcinogens coincides with epigenetic perturbations, including specific changes in DNA methylation. Here we investigate the genome-wide dynamics of 5-hydroxymethylcytosine (5hmC) as a likely intermediate of 5-methylcytosine (5mC) demethylation in a DNA methylation reprogramming pathway. We use a rodent model of non-genotoxic carcinogen exposure using the drug phenobarbital.Exposure to phenobarbital results in dynamic and reciprocal changes to the 5mC/5hmC patterns over the promoter regions of a cohort of genes that are transcriptionally upregulated. This reprogramming of 5mC/5hmC coincides with characteristic changes in the histone marks H3K4me2, H3K27me3 and H3K36me3. Quantitative analysis of phenobarbital-induced genes that are involved in xenobiotic metabolism reveals that both DNA modifications are lost at the transcription start site, while there is a reciprocal relationship between increasing levels of 5hmC and loss of 5mC at regions immediately adjacent to core promoters.Collectively, these experiments support the hypothesis that 5hmC is a potential intermediate in a demethylation pathway and reveal precise perturbations of the mouse liver DNA methylome and hydroxymethylome upon exposure to a rodent hepatocarcinogen. Methylation of the fifth carbon of a cytosine base (5-methylcytosine (5mC)) in the dinucleotide sequence CpG is a well-established epigenetic modification of vertebrate DNA thought to have important roles in the preservation of genomic integrity, allele-specific expression of imprinted genes, maintenance of X-chromosome inactivation in females, tissue-specific gene regulation and long-term silencing of genes and retrotransposable elements ,2. UntilThe genome-wide patterns of 5hmC have been described for both cultured cells -25 and tCAR; also known as nuclear receptor subfamily 1 group I member 3 (Nr1i3)) and peroxisome proliferator activated receptor alpha (Ppara), which mediate the transcriptional regulation of enzymes involved in response to drug exposure ).5hmC: 5-hydroxymethylcytosine; 5mC: 5-methylcytosine; bp: base pair; CAR: constitutive androstane receptor (Nr1i3); CGI: CpG island; ChIP: chromatin immunoprecipitation; GEO: Gene Expression Omnibus; H3K27me3: histone H3 lysine 27 tri-methylation; H3K36me3: histone H3 lysine 36 tri-methylation; H3K4me2: histone H3 lysine 4 di-methylation; HmeDIP: 5hmC DNA immunoprecipitation assay; IAP: intracisternal A-particle; ICP: intermediate CpG content; IP: immunoprecipitation; LINE: long interspersed nuclear element; MeDIP: methylated DNA immunoprecipitation; NGC: non-genotoxic carcinogen; PB: phenobarbital; PPR: promoter proximal region; qPCR: quantitative PCR; RMA: robust multichip average; TDG: thymine DNA glycosylase; TSS: transcription start site.The authors declare that no competing interests exist. Authors who work for Novartis Institutes for Biomedical Research (NIBR) confirm that this does not alter their adherence to all the Genome Biology policies on sharing data and materials.Conceived and designed the experiments: JM, HL, RT, JT & RM. Performed the experiments: JH, JT, DR, HL, AM, FB and RT. Analyzed the data: JT, AM and CN. Contributed reagents/materials/analysis tools: DS. Wrote the paper: JT and RM . All authors have read and approved the manuscript for publication.Figure S1. (a) Dot blot comparing the specificities and sensitivities of two commercially available 5hmC antibodies. Antibodies were tested over a dilution gradient of genomic liver DNA (200 ng/μl to 2.5 ng/μl) as well as for their relative affinities against PCR products synthesized in the presence of either dCTP, dmCTP or d5hmCTP [(b) The HmeDIP technique is highly specific for 5hmC-modified DNA relative to unmodified or methylated DNA. qPCR was run on immunoprecipitated material that had been spiked with a 5hmC-rich DNA sequence. Enrichment of the 5hmC rich control DNA was only observed when immunoprecipitated with the 5hmC antibody and not with an antibodies against 5mC, non-modified C or IgG. d5hmCTP . AlthougClick here for fileFigure S2. qPCR analysis on pre- and post-whole genome amplification (WGA) material introduced no bias upon amplification. Confirmatory qPCR showing hmC enrichment at Tex19.1 and H19 promoter regions (known hmC-enriched loci) alongside a 5hmC-negative control region (Gapdh promoter) both prior to and after WGA. 5mC enrichment was tested in a similar way using positive control regions of the H19 ICR and Cyp2b10 promoter regions and a negative ICR.Click here for fileFigure S3.  Validation of microarray profiling by qPCR. Correlation between HmeDIP and MeDIP qPCR enrichment values (% IP/input) to log2 values seen on the promoter microarrays is presented for 5hmC (a) and 5mC (b). Trend line (red) and R2 values are also shown. Pearson correlation values ('cor') shown alongside represent the closeness of fit between qPCR enrichment values (Y-axis) and log2 scores taken from microarray data (X-axis). Loci used for validation of HmeDIP were as follows: upstream region, 5' and 3' promoter regions of Cyp2b10, promoter regions of Tex19.1, Actb, Gapdh and H19. Loci used for the validation of MeDIP are as follows: ICRs of H19 and Gnas, intron 1 of Cyp2b10 and promoter regions of CSA, Pou3f4, Ccdc34, Tyms, Ccr1, Cbx2 and Tacr3  Overall there are no large changes in the distribution of 5hmC (a) and 5mC (b) enriched peaks upon normalization with the exception of an increase in upstream 5hmC and 5mC peaks and reduction of inter-genic 5hmC peaks.Click here for fileFigure S5. Validation of 'EpiMark' quantitative analysis techniques reveals highly similar levels of the DNA modifications between individuals. (a-c) The values of 5hmCpG (a), 5mCpG (b) and CpG (c)were plotted between two biological replicates to test the reproducibility of the result. Loci tested were as follows: TSS regions of Gapdh, H19 and Tspan10, an intra-genic region of Gstm3, a region upstream of the Cyp2b10 promoter and an inter-genic region spanning chr7: 149709621-149709863  and 5mC (red) levels at repeat elements  in liver DNA. Enrichment values are plotted as percentage IP relative to the input.Click here for fileFigure S8. Sliding window analysis of 5hmC and 5mC over full-length genes (n = 775) covered on the array. Levels of 5hmC increase throughout the body of a gene in a transcription-dependant manner. Expression levels were calculated within the 775 'small' gene set. Top 25% = highly transcribed (green), bottom 25% = lowly transcribed (red). In contrast to 5hmC, levels of 5mC are higher over the TSS of lowly expressed genes and show less of a correlation with transcriptional levels throughout the gene body.Click here for fileFigure S9. Examples of 5hmC-enriched TSS regions along with more typical 5hmC-depleted TSS regions. Two examples of 5hmC-enriched genes are shown on the left (Rnas10 and Tmem139). In comparison, a more typical (5hmC depleted) TSS region is shown on the right for Bckdha and Exosc5. All scales on the Y-axis are from log2 1.5 to -log2 1.5. Individual X-axis scale bars are represented by a black bar. The 5hmC profile is in purple and 5mC in red; the transcription start site (black arrow) and gene structure are also illustrated. Exons are shown as perpendicular lines.Click here for fileTable S1. List of genes with a 5hmC-enriched 'TSS region' (n = 508).Click here for fileTable S2. Average promoter 5hmC and 5mC log2 scores across all the genes on the array.Click here for fileFigure S10. Genes with 5hmC enrichment over their TSS show some enrichment towards those that are expressed in a tissue-specific manner. The relative levels of tissue specificity were plotted  against groups of genes with high tissue specificity as well as housekeeping genes. In comparison to all of the genes on the array there is a small but significant  increase in the level of tissue specificity for genes with 5hmC-enriched regions spanning the TSS. The red lines represent the median score of the group of genes. The upper circles represent outliers of the majority of genes.Click here for fileFigure S11. Examples of CGIs with high CpG density (HCP) that are enriched for 5hmC. Although the majority of probes covering CGIs do not contain peaks of 5hmC, those that do show a striking enrichment over certain loci. (a-c) The majority of the 5hmC-enriched probes mapping to CGIs are inter-genic  or intra-genic (c). Intragenic CGIs in (a) are found at chr9:101,153,320-101,154,414 (left panel) and chr11:90,279,237-90,279,648 (right panel). Intergenic CGIs shown in the example in (c) are found within the gene Zip4.  Promoters with CGIs (Actb is the example (d)) are also found to be largely depleted in the 5hmC modification in contrast to those that lack CGIs (Gstm6 is the example (e)). All scales on the Y-axis are from log2 +2 to -log2 2. Transcription start site (black arrow) and gene structure are illustrated below each example. Exons are shown as perpendicular lines. Scale bars are represented at the bottom right of each figure.Click here for fileFigure S12. 5hmC and 5mC levels are not enriched over 'CGI shores'. The total number of probes found over regions 1 kb upstream of annotated CGIs were selected (upper panel and lower panel grey bar) and the number of these probes also containing peaks in 5hmC or 5mC analyzed . From this analysis a small number of the probes were seen to contain peaks of 5hmC (approximately 10%) or 5mC (approximately 2%).Click here for fileFigure S13. Heat map analysis of average 5hmC levels over PPRs in control ('WT') and PB-exposed ('PB') mouse livers. Average 5hmC levels are represented by a color scale ranging from strongly enriched  to depleted . PPRs are clustered by both Euclidian and Ward methods. This analysis reveals that although the majority of PPRs do not reveal strong changes in their 5hmC levels upon PB exposure, certain regions do reveal moderate levels of perturbation.Click here for fileFigure S14.  Scatter plot comparing average promoter changes for 5hmC (a) or 5mC (b) against changes in gene expression levels following 28-day PB exposure. Only a subset of genes show >log2 1.5-fold induction (red rectangle) and no correlation with strongly down-regulated genes are noted.Click here for fileFigure S15. Log2 fold changes of PB-treated over vehicle (control) samples averages for histone methylation marks (n = 2) and 5mC or hmC (n = 5) for all 20,717 genes. The fold changes for H3K4me2 are calculated in a window of +2/-1 kb of TSS, and gene body for H3K27me3 and H3k36me3 and +1/-1 kb of TSS for 5mC and hmC. The density of genes/promoters is indicated by the grade of blue; data points at the periphery of the main data density are indicated by black dots. The red dot indicates the gene Cyp2b10.Click here for fileFigure S16. Log10 histone scores of vehicle (control) versus PB treated per mark  in 20,717 promoter regions (+2/-1kb of TSS) for H3K4me2 and gene bodies for H3K27me3 and H3K36me3. The dotted green line has intercept 0 and slope 1, the red line is a regression line (R2). The further off the diagonal a gene is, the stronger the treatment effect. The density of genes/promoters is indicated by the grade of blue; data points at the periphery of the main data density are indicated by black dots. The red dot indicates the gene Cyp2b10.Click here for fileFigure S17. PB-mediated changes to both 5hmC and 5mC levels are not observed over the enhancer elements present on the array. Box plot of 5hmC (purple) and 5mC (red) log2 levels over 1 kb enhancer elements in control or 28-day PB-exposed mouse livers reveal no global changes at these sites.Click here for fileFigure S18. Genes that are unchanged in their expression following PB exposure do not correlate to changes in both DNA and histone modifications. Scatter graph plots display the average changes in epigenetic marks  against fold change in expression for 30 genes unchanged in their expression state following 28-day PB treatment. Trend lines are displayed in red with associated Pearson correlation ('cor') and P-values. These findings contrast those observed over 30 PB-induced genes  and DNA modification changes (MeDIP/HmeDIP-chip) across select Cyp2b and Gst genes. Patterns of log2 changes in the signals of 5mC (red), 5hmC (purple), H3K36me3 (green), H3K27me3 (orange) and H3K4me2 (blue) are plotted over these regions. The Y-axis of ChIP-Seq samples were plotted on a scale of +70 to -70+ reads whilst promoter arrays (5hmC and 5mC) plotted from +1.5 log2 to -1.5 log2 values.Click here for fileFigure S20. Plots of average changes in epigenetic marks over Gst gene family in comparison to genes unaffected in their gene expression states following 28-day PB exposure. Average changes in the log2 scores (DNA modifications) or fold change in the number of reads (histone modifications) of the marks are plotted against regions outlined earlier in Figure P-value < 0.005) are denoted by the red asterisk. Red dotted line represents 0 change in epigenetic mark upon PB exposure.Click here for fileTable S5. List of DNA primers used in this study.Click here for file


AAUAAA and AUUAAA, and is regulated by conservation of cis-elements flanking the mapped pA sites. Moreover, we found two or more alternative pA sites downstream of ORF54, K2 (vIL6), K9 (vIRF1), K10.5 (vIRF3), K11 (vIRF2), K12 (Kaposin A), T1.5, and PAN genes and experimentally validated the alternative polyadenylation for the expression of KSHV ORF54, K11, and T1.5 transcripts. Together, our data provide not only a comprehensive pA site landscape for understanding KSHV genome structure and gene expression, but also the first evidence of alternative polyadenylation as another layer of posttranscriptional regulation in viral gene expression.RNA polyadenylation (pA) is one of the major steps in regulation of gene expression at the posttranscriptional level. In this report, a genome landscape of pA sites of viral transcripts in B lymphocytes with Kaposi sarcoma-associated herpesvirus (KSHV) infection was constructed using a modified PA-seq strategy. We identified 67 unique pA sites, of which 55 could be assigned for expression of annotated ∼90 KSHV genes. Among the assigned pA sites, twenty are for expression of individual single genes and the rest for multiple genes (average 2.7 genes per pA site) in cluster-gene loci of the genome. A few novel viral pA sites that could not be assigned to any known KSHV genes are often positioned in the antisense strand to ORF8, ORF21, ORF34, K8 and ORF50, and their associated antisense mRNAs to ORF21, ORF34 and K8 could be verified by 3′RACE. The usage of each mapped pA site correlates to its peak size, the larger (broad and wide) peak size, the more usage and thus, the higher expression of the pA site-associated gene(s). Similar to mammalian transcripts, KSHV RNA polyadenylation employs two major poly(A) signals,  cis-elements surrounding KSHV pA site determines the expression level of viral genes. Lastly, we identified and experimentally validated alternative polyadenylation of KSHV ORF54, T1.5, and K11 during viral lytic infection. To our knowledge, this is the first report on alternative polyadenylation events in KSHV infection.A genome-wide polyadenylation landscape in the expression of human herpesviruses has not been reported. In this study, we provide the first genome landscape of viral RNA polyadenylation sites in B cells from KSHV latent to lytic infection by using a modified PA-seq protocol and selectively validated by 3′ RACE. We found that KSHV genome contains 67 active pA sites for the expression of its ∼90 genes and a few antisense transcripts. Among the mapped pA sites, a large fraction of them are for the expression of cluster genes and the production of bicistronic or polycistronic transcripts from KSHV genome and only one-third are used for the expression of single genes. We found that the size of individual PA peaks is positively correlated with the usage of corresponding pA site, which is determined by the number of reads within the PA peak from latent to lytic KSHV infection, and the strength of  Kaposi sarcoma-associated herpesvirus (KSHV), also referred as human herpesvirus 8 (HHV-8), is a member of gammaherpervirus subfamily cis-elements may be also involved in pA site definition RNA polyadenylation (pA) of nascent transcripts is a critical posttranscriptional step in maturation of eukaryotic transcripts cis-elements in regulation of KSHV gene expression.A polyadenylation landscape of human herpesviruses has not been reported at the genome-wide level. In this study, we performed a genome-wide analysis on RNA polyadenylation of KSHV transcripts from B cells with latent or lytic viral infection by using a modified polyadenylation- sequencing (PA-seq) technology + RNA fraction from each sample was used for preparing 3′-end cDNA libraries with a modified PA-seq method followed by Illumina paired-end sequencing To elucidate a role of RNA polyadenylation in KHSV gene regulation, we performed a genome-wide analysis of viral pA sites to monitor their usage during KSHV infection. Three KSHV-positive B-cell lines , which support both latent and lytic virus infection, were chosen in this study. For each cell line, polyadenylation events were compared between latent and lytic infection . The celFor KSHV pA site analysis we focused only on the sequence reads uniquely mapped to KSHV genome and further clustered to identify PA-peaks. We pooled KSHV sequence reads from all samples and performed a peak calling analysis using F-seq algorithm with significant enrichment over a background model Subsequently, we determined the nucleotide (nt) position with the highest number of reads within individual peaks in the peak calling analysis as a PA mode  and desiA higher prevalence of pA sites shows strand bias, with 43 pA sites in the minus strand and 24 in the plus strand of the KSHV genome. The majority of the mapped pA sites are positioned in the intergenic regions of KSHV genome, outside of annotated ORFs, with exception of the pA sites in the coding regions of ORF7 at nt 7032 and ORF61 at nt 98274 and of K12 at nt 118012, 118032 and 118087.vnct internal repeats . We assigned each pA site to a known viral gene or gene cluster region based on the following criteria: (1) both of the gene(s) and the corresponding pA site must be on the same strand of viral genome, (2) the pA site must be positioned outside of the coding region of the viral gene(s), and (3) the gene(s) assigned to a mapped pA site must be positioned upstream of the pA site. These criteria assume that viral transcripts originated from a promoter(s) upstream of the gene will be polyadenylated from the first available pA site downstream. Accordingly, we assigned 55 pA sites to all known KSHV genes . The rem repeats . From pr repeats . Thus, oWe next aimed to determine the length and composition of 3′ UTR for each KSHV protein-coding gene. The unassigned pA sites and the pA site for viral non-coding RNA genes were excluded from this analysis. A total of 50 pA sites were used to calculate the 3′UTR length from a pA site to the adjacent termination codon of the closest upstream ORF. We found that the calculated 3′ UTR length of KSHV genes varies greatly in size from 2 nts (ORF38) to 1925 nts (ORF62) . The dissr = 0.86  of the pA site-associated transcripts. The limitation of this approach is cluster genes utilizing a single pA site, in which the number of sequence reads reflects a combined level of all gene transcripts. The pA site usage was compared from latent to lytic infection. First, we determined each pA site usage in individual samples to obtain a sample-specific pA site usage and then normalized the number of sequence reads within each viral pA peak to the total sequence reads mapped to KSHV and host genome in each sample . Combinas = 0.86 . Becausecis-elements around viral pA sites is in agreement with what has been found in human transcripts cis-elements in regulation of KSHV polyadenylation, we performed similar analyses separately for three groups of pA sites with a narrow, broad, or wide peak  of all 67 pA sites identified. Prevalence of each nucleotide at individual position was calculated and followed by motif analysis using WebLogo software . A high ide peak . The proAAUAAA) and non-canonical (NNAUNA) PAS were identified within 50 nts upstream of the mapped 59 pA sites (AAUAAA (69%) followed by AUUAAA (9%). The usage of other non-canonical PAS for viral RNA polyadenylation ranges from 1% to 3%  signal (PAS) strength of KSHV pA sites further reaffirmed this conclusion. The canonical   Given that the pA sites obtained by PA-seq, in general, showed a high correlation with previously mapped KSHV pA sites, we carried out a series of experiments to reconfirm several novel pA sites discovered in this study by 3′ RACE. These include the pA site downstream of ORF27, the pA site mapped within the coding region of ORF61, the alternative pA sites downstream of ORF54 and T1.5, and a cluster of 5 unassigned pA sites downstream of the  size(s) . The altWe further verified the PA-seq-identified pA sites from the mRNAs antisense to ORF21, ORF34, and ORF K8 by 3′ RACE and confirmed the production of the antisense RNAs in B cells during viral lytic infection . The reaLori). The exporiL). In addioriL), leadingoriL). BecauseoriL).T1.5 RNA contains a few short ORFs and has potential to encode small peptides The usefulness of PA-seq was further extended to examine the expression of a few host genes for its possible application to unveil a pA site landscape of the host genome before and after KSHV lytic infection. Human IL6 (hIL6) and GAPDH were initially chosen because B cells with lytic KSHV infection exhibit increased expression of human IL6 In this report we present the first viral genome landscape of polyadenylation sites from three PEL cell lines with KSHV latent or lytic infection. The comprehensive pA site landscape for the entire KSHV genome was revealed by using a modified PA-seq strategy which conveys single nucleotide resolution and strand specificity KSHV has been evolved to use one pA site for the expression of multiple genes in many regions of the genome. Supporting this notion, our PA-seq analysis identified numerous regions of the KSHV genome with several viral genes (up to 5 genes) sharing a common pA site . As a coThe usage of each mapped pA site in this study was determined by counting the sequence reads associated with each pA site to approximate the steady-state expression level of the associated gene(s). When the sequence-reads of a given pA site in viral lytic infection were compared with that in viral latent infection, we could distinguish pA site usage from viral lytic genes to viral latent genes. The pA site for lytic gene expression could be used 100-fold more in lytic infection than in latent infection, whereas the pA site usage for latent gene expression displays only little increase (less than 10-folds) in lytic infection. Two pA sites downstream of K12, a classical viral latent gene, could be an exception because both showed an increased usage in viral lytic infection. The increased usage of two K12 pA sites is consistent with the finding that a lytic inducible promoter could be activated for K12 expression per se. Previous reports showed that the PAS strength directly affects the overall level of mature transcripts AAUAAA. For example, the presence of a weaker early SV40 PAS leads to lower expression of a reporter gene than the construct containing a stronger SV40 late PAS when both were driven by the same promoter Moreover, the efficient expression of viral RNA transcripts was found being related to the peak size of a pA site in this study. It should note that each transcript could give rise to only one read in PA-seq. Thus, the read count in a PA peak simply reflects the abundance of the corresponding transcript. In fact, more sequence reads are not expected to inflate the size of a PA peak, especially when the pA cleavage events are precise. Therefore, the positive correlation we observed between the sizes of PA peaks and the expression levels of corresponding transcripts may suggest some degree of “slippage” in polyadenylation of viral transcripts to ensure high-level expression at the lytic stage. When compared to the pA sites falling into a broad or wide peak, an RNA transcript carrying a pA site with a narrow peak was less expressed, with fewer PA-seq sequence reads. Although this difference in the pA sites with a narrow peak might be attributable partially to their frequent usage of non-canonical PAS, there must be other unknown mechanisms governing the utilization of a pA site with a narrow peak, other than canonical vs non-canonical PAS Recent studies unveiled highly prevalent alternative RNA polyadenylation in various organisms and its profound role in regulation of gene expression vnct” 13-bp repeats and composed of 5 individual pA sites within a ∼250-bp region from nt 29376 (-) to 29615 (-). The second cluster of three pA sites from 118012 (-) to 118087 (-) is also located in the minus strand downstream of “zppa” repeat region containing two 23-bp repeats  16dUTTTVN-3′, ‘bio’ denotes duo biotin group, ‘dU’ stands for deoxyuridine, ‘V’ represents any nucleotide except T and ‘N’ denotes any nucleotide). After second strand synthesis, resulted dsDNA was pulled down by Dynabeads MyOne C1 (Invitrogen) and dephosphorylated with APex Heat-Labile Alkaline Phosphatase (Epicentre) enabling PCR strand specificity for selective adaptor ligation. Dephosphorylated dsDNA was released from beads by USER enzyme digestion (NEB) and end-repaired, followed by an “A” base addition at the ends. Notably, only the first-stand cDNA contains a 5′ phosphate, and thus can be ligated to bar-coded Illumina paired-end Y linker without a nick. The usage of a dUTP in the oligo(dT) primer and the de-phosphorylation step reinforce strand-specificity, and allow precisely mapping of pA cleavage site at singe-base resolution. Ligation products between 250 bp and 450 bp were gel purified and PA-seq libraries were generated by 16-cycle PCR with Phusion Hot Start High-Fidelity DNA Polymerase (Finnzymes). The obtained libraries were subjected to two technical replicate sequencing by an Illumina HiSeq2000 sequencer.The 3′end library for each sample was constructed using a modified PA-seq strategy www.broadinstitute.org/igv/) to assure their suitability for pA site analysis. Individual KSHV pA sites were then designated by peak calling using F-Seq program Obtained raw reads were first aligned to KSHV genome (GenBank acc no U75698.1), EBV B95-8 strain genome (GenBank acc no V01555.2) and human genome (UCSC version hg19) by Burrows-Wheeler Alignment tool (BWA) http://weblogo.berkeley.edu/) The sequence surrounding the mapped pA sites was covered from 50 nts upstream and 50 nts downstream of each identified pA site for the motif analysis. The percentage of occurrence for each nucleotide was calculated, plotted and smoothed with the loess function in R software (R version 2.12.1). Polyadenylation signals (PAS) occurred within 50 nts upstream of pA site were assigned manually. Graphical representation of sequence conservation was generated by Weblogo v3 (Transcript 3′ end was identified by SMARTer RACE cDNA Amplification Kit (Clontech). The primer sequences used in 3′RACE are listed in 32P labeled oligo probes: oVM 208 (5′-CGTGGCTGTGCTTCTCATCAT-3′) for T1.5 lncRNA, oJM7 (5′-GTTACACAACGCTTTCACCTACA-3′) for PAN lncRNA, oZMZ270 (5′-TGAGTCCTTCCACGATACCAAA-3′) for GAPDH and oST197 (5′-AAAATATGGAACGCTTCACGA-3′) for U6 snRNA.Total RNA was isolated using TRIzol reagent. The cytoplasmic and nuclear fractions of RNA were isolated as described in vitro transcription using DNA fragment of KSHV genome (nt 24906–25375 for T1.5 and nt 29018–29481 for PAN lncRNAs) as templates. The hybridization was performed as previously described The single stranded sense and antisense RNA probes were prepared by FISH Tag RNA Multicolour Kit (Invitrogen) by tΔC method Total cell RNA isolated by TRIzol (Invitrogen) was treated with Turbo DNA-free DNase to remove DNA. Five micrograms of total cell RNA was used to synthesize cDNA using SuperScript First-Stand Synthesis System (Invitrogen). The GAPDH and human IL6 (hIL6) transcript levels were determined by RT-qPCR using Figure S1PA-seq analysis of KSHV transcripts. (A) Three KSHV-infected PEL (primary effusion lymphoma)-derived B-cell lines were used in PA-seq analysis during virus latent infection (left column) or lytic infection (right column). (B) Total numbers of sequence reads from each sample mapped to KSHV genome (Genbank acc no U75698.1) or human genome (UCSC version hg19). The other unassigned reads including those mapped to EBV genome (Genbank acc no V01555.2) (3233 reads or 0.02% in latent and 1998744 reads or 13.11% in lytic infection of JSC-1 cells) are shown as others. (C) A bar graph depicting % distribution of the sequence reads from each sample assigned to KSHV or human genome or others unassigned reads.(TIF)Click here for additional data file.Figure S2Determination of KSHV pA sites by F-seq analysis. Diagram shows PA peak (red line) identified by F-seq analysis of viral sequence reads (blue bars) aligned to the KSHV genome. The PA mode, a nucleotide position with the highest number of reads within the peak, was designated as a pA site. The peak size is a distance from nucleotide position of the beginning to the end of the peak within which a pA site is assigned. The total number of all reads within the peak represents usage of the pA site.(TIF)Click here for additional data file.Figure S3Visual distribution of KSHV-specific sequence reads obtained by PA-seq across viral genome. (A) Positions and frequency  of the sequence reads derived from B cells with latent (blue bars) or lytic (red bars) infection were visualized on KSHV genome by IGV software (http://www.broadinstitute.org/igv/). Green lines in the middle represent positions of reported KSHV genes. (B) A zoom-in to the locus containing ORF50 (RTA)-K8-K8.1 gene cluster where the sequence reads distribute in a plus (+) strand of the KSHV genome. Below is a diagram of previously reported gene structure and primary transcripts associated with this gene locus. Boxes represent an ORF with positions of mapped promoters (arrows) and a pA cleavage site (CS). The reads in latent infection represent spontaneous reactivation of this locus in a very small fraction of BCBL-1 cells and BCBL-1-derived TREx cells.(TIF)Click here for additional data file.Figure S4Illustration of pA site mapped to the KSHV genome in individual B cell lines with latent (blue) or lytic (red) KSHV infection. Scaled bars for each pA site represent normalized PA-seq reads per million.(TIF)Click here for additional data file.Figure S5Localization of KSHV T1.5 and PAN lncRNAs in PEL cells. Specificity of each probe described in (TIF)Click here for additional data file.Table S1Positions and strand specificity of all KSHV pA sites determined by F-seq analysis of combined six PA-seq libraries.(PDF)Click here for additional data file.Table S2The pA sites mapped by PA-seq in selected KSHV viral transcripts are comparable to the pA sites previously mapped by traditional methods.(PDF)Click here for additional data file.Table S3Utilization of identified pA site with individual or cluster of KSHV genes. Adjacent 3′UTR length calculated as a distance between mapped KSHV sites to an immediately upstream KSHV ORF. N/A-not applicable.(PDF)Click here for additional data file.Table S4KSHV genes contain alternative pA sites which can be used during virus infection. Individual pA site usage (%) was calculated from total number of sequence reads for all pA sites in a given gene transcript.(PDF)Click here for additional data file.Table S5Normalized pA site reads mapped to the KSHV genome in individual B cell lines with latent or lytic KSHV infection.(PDF)Click here for additional data file.Table S6The usage of individual KSHV pA sites during latent and lytic infection from combined datasets of three PEL cell lines . The fold increase for each pA site was calculated by dividing the sequence reads from lytic samples by number of the reads from latency. N/A-not applicable.(PDF)Click here for additional data file.Table S7Classification of KSHV pA sites based on the PA peak size. The size of each peak was calculated as a distance between start and end of the peak and it was correlated with pA site usage (te usage . Based o(PDF)Click here for additional data file.Table S8Frequency of pA site usage in correlation to PA peak size. The Pearson (r) and Spearman (sr) correlation coefficients expressing correlation between peak size and number of reads were calculated for each group of pA site.(PDF)Click here for additional data file.Table S9Canonical and non-canonical PAS (red) detected in a region covering 50 nts immediately upstream of the mapped pA site. N/D-not detectable.(PDF)Click here for additional data file.Table S10Prevalence of canonical and non-canonical PAS 50 nts upstream of the mapped pA sites in all, narrow, broad, wide, top 10, and bottom 10 pA sites.(PDF)Click here for additional data file.Table S11Primers used in 3′RACE analyses.(PDF)Click here for additional data file.


Myxococcus xanthus, a prokaryote, encodes many proteins involved in copper homeostasis that are differentially regulated by this metal. A σ factor of the ECF (extracytoplasmic function) family, CorE, has been found to regulate the expression of the multicopper oxidase cuoB, the P1B-type ATPases copA and copB, and a gene encoding a protein with a heavy-metal-associated domain. Characterization of CorE has revealed that it requires copper to bind DNA in vitro. Genes regulated by CorE exhibit a characteristic expression profile, with a peak at 2 h after copper addition. Expression rapidly decreases thereafter to basal levels, although the metal is still present in the medium, indicating that the activity of CorE is modulated by a process of activation and inactivation. The use of monovalent and divalent metals to mimic Cu(I) and Cu(II), respectively, and of additives that favor the formation of the two redox states of this metal, has revealed that CorE is activated by Cu(II) and inactivated by Cu(I). The activation/inactivation properties of CorE reside in a Cys-rich domain located at the C terminus of the protein. Point mutations at these residues have allowed the identification of several Cys involved in the activation and inactivation of CorE. Based on these data, along with comparative genomic studies, a new group of ECF σ factors is proposed, which not only clearly differs mechanistically from the other σ factors so far characterized, but also from other metal regulators.The dual toxicity/essentiality of copper forces cells to maintain a tightly regulated homeostasis for this metal in all living organisms, from bacteria to humans. Consequently, many genes have previously been reported to participate in copper detoxification in bacteria.  Myxococcus xanthus, a soil-dwelling bacterium, also has to cope with changes in copper concentration in its environment. The large genome of this myxobacterium encodes many genes involved in copper homeostasis, all of which are differentially regulated, indicating that many regulators participate in copper homeostasis in this prokaryote. Here, we identify one of these regulators (CorE), which belongs to the family of the extracytoplasmic function (ECF) σ factors. We demonstrate that CorE represents a novel group of ECF σ factors and of metal regulators, because its activity is modulated by the redox state of copper. This ability resides in a Cys-rich domain, which has also been found in other σ factors of different bacterial phyla. Therefore, we propose that CorE is the first member of a mechanistically new group of ECF σ factors.Copper exerts a dual effect on living organisms. It is essential for life, but an excess provokes cell damage, forcing cells to maintain a regulated homeostasis for this metal. These two antagonistic biological effects of copper are clearly illustrated by two human genetic disorders, Menkes syndrome and Wilson disease, caused by deficiency or accumulation of this metal, respectively.  Myxococcus xanthus is a soil-dwelling δ-proteobacterium of the group of myxobacteria used as a model to study multicellular behavior and differentiation, because it exhibits a complex developmental cycle triggered by starvation M. xanthus cells not only have to adapt their metabolism and behavior to changing nutritional concentrations, but also to other parameters, such as metals.Copper is a transition metal that functions as an ideal biological cofactor due to its ability to alternate between the redox states Cu(I) and Cu(II). However, copper also generates reactive oxygen species that cause cell damage Escherichia coli, one of each of these elements is encoded in the genome Synechocystis PCC6803, Enterococcus hirae, and Lactococcus lactis), or one ATPase and one chaperone (Bacillus subtilis) M. xanthus genome encodes a large number of paralogous genes to confer copper tolerance: three MCOs, at least two Cus systems, and three P1B-type ATPases, as well as the genes required for the biosynthesis of carotenoids Copper is required by prokaryotes in trace amounts because it is used as a cofactor by a few proteins. Hence, most bacterial homeostatic mechanisms are devoted to conferring resistance to this metal. The most common mechanisms are copper-transporting P1B-type ATPases, copper chaperones, multicopper oxidases (MCOs), and Cus systems extracytoplasmic function) family, which corresponds to group 4 of the σ70 proteins 2 and σ4) required for interaction with the RNA polymerase core enzyme and recognition of the promoter One of the signal transduction mechanisms used by bacteria to direct gene expression at the transcriptional level in response to stress signals is represented by alternative σ factors M. xanthus named CorE (for copper-regulated ECF σ factor). We demonstrate that CorE requires copper in order to bind to DNA and that its activity is modulated by the redox state of this metal. According to these data, we propose a new group of ECF σ factors, defined by a Cys-rich domain (CRD) located at the C terminus of the protein, which is essential for activation and inactivation of the protein.In this report, we identify a novel metal sensor involved in copper homeostasis in M. xanthus genes involved in copper homeostasis are located in the genome in two clusters cuoB, a gene encoding a protein with high similarity to ECF σ factors was found (MXAN_3426), suggesting that it could regulate the expression of genes involved in conferring copper resistance. This σ factor has been designated as CorE. The analysis of the CorE sequence has revealed a domain architecture with the conserved regions σ2  and σ4  typical of this type of σ factors Most corE-lacZ fusion was constructed, and the analysis of this strain revealed that corE was up-regulated by copper (corE) was also generated, and the phenotypic analysis of this strain confirmed that this regulator conferred copper tolerance  strain, it was observed that only the MCO cuoB and the P1B-type ATPase copB remained undetectable in the ΔcorE background in the presence of copper . A manual search for homologous sequences to this putative CorE-binding site in the M. xanthus copper regions 1 and 2 lacZ were introduced into the WT and ΔcorE backgrounds and β–gal specific activity was determined in these strains. The results obtained revealed that the gene for the heavy-metal-associated protein exhibits an expression profile in the WT strain very similar to those of cuoB and copB after copper supplementation  mimetic divalent metals Cd2+, Ni2+, and Zn2+. Similarly, Ag+ was used to mimic Cu(I). The results obtained revealed that only Cd2+ and Zn2+ could induce cuoB expression . Quantitative analyses of cuoB expression in both strains reported no expression of this gene in the absence of copper . Western blot analyses using antibodies against the His tag confirmed that corE was indeed expressed at very high levels in the absence as well as in the presence of copper  and Cu(II). As shown in cuoB up-regulation could only be observed when copper was added to the medium. However, the maximum expression levels were diminished when the reducing agent ascorbate was also included in the medium to favor the formation of Cu(I) , also yielded expression levels lower than those obtained with only copper , while the reduced state of the metal leads to an inactive conformation. This notion agrees well with the lack of a peak when up-regulation of cuoB is achieved by Zn2+ and Cd2+, which are metals with only one redox state  and that TTM chelates Cu(II). All of the data presented in this section demonstrate that CorE activity is modulated by the redox state of copper.To confirm the results obtained cuoB do not increase when corE is over-expressed (This mechanism of action implies that Cu(II) must be available in the cytosol during the next 2 h after copper supplementation. Although it is assumed that all the copper in the reducing environment of the cytoplasm is present as Cu(I) under normal circumstances xpressed . HoweverM. xanthus cells will obtain protection against the metal by means of at least two other mechanisms , which are sequentially induced after copper addition One paradox is the fact that CorE is activated by Cu(II) and inactivated by Cu(I), while genes under its control encode proteins, such as CopA, CopB, and CuoB, that utilize Cu(I) as a substrate Although many bacterial transcriptional regulators need metal to bind DNA 4 domain consisting of 38 residues named CRD. Six of these residues are Cys. As different arrangements of Cys have been proved to be key elements in several metal-binding proteins M. xanthus in-frame deletion mutant was constructed in which most of the CRD region was deleted. This strain, designated as ΔcorECRD, encoded a protein containing the two domains σ2 and σ4 of CorE, but none of the six Cys of CRD. To analyze the activity of CorECRD, the two fusions cuoB-lacZ and copB-lacZ were introduced in this mutant and β-gal activity was assayed in the absence and in the presence of copper. The data obtained revealed that neither cuoB or copB were up-regulated by copper (data not shown), a result identical to that shown in corE gene was deleted. These data demonstrate that CRD is essential for the copper-dependent transcription of the genes controlled by CorE.CorE contains a short C-terminal extension after the σcorE strain harboring the fusion cuoB-lacZ. The effect of the mutations was evaluated by analyzing the expression of cuoB in the absence and in the presence of copper. The results obtained showed different patterns. Mutations C181A and C206A exhibited transcription profiles very similar to those of the WT  of CRD might also be required for the proper functioning of the protein (see below).The effect of each point mutation in r silver . Mutatior silver , also exilver , and inactivating the σ factor in those that favor the formation of Cu(I). However, how exactly CorE distinguishes between Cu(I) and Cu(II) is not easy to predict, because all of the residues identified so far that modulate the activity of CorE are Cys. Although Cys are able to coordinate Cu(I) and Cu(II), they require the presence of other amino acids, such as His, Asp, Glu, or Met to exert this function 2+ binding to sequester their cognate σ factor. However, the anti-σ domain and CRD differ in many aspects: i) CRD is an extra portion of the σ factor; ii) elimination of CRD does not activate the σ factor; and iii) CRD senses the redox state of copper to activate or inactivate the σ factor.The role of CRD in CorE resembles the function of the anti-σ domain present in many anti-σ factors Proteobacteria (9 α and 5 δ), four to Acidobacteria, two to Verrucomicrobia, and one to Nitrospira  are absolutely conserved among these σ factors . SurprisDue to the diversity of the ECF σ factors, Staroń et al. So far, seven families of metal de-repressors, metal co-repressors, and metal activators are known M. xanthus was grown in CTT medium at 30°C, supplemented with the additives indicated in each figure, as previously described E. coli was grown on Luria-Bertani (LB) medium at 37°C Genotypes of the bacterial strains and plasmids used in this study are listed in lacZ fusion strains used in this study are the same as previously reported lacZ fusions, respectively. Plasmids were always introduced into M. xanthus strains by electroporation to obtain integration into the chromosome by homologous recombination. Southern blot analyses were carried out to confirm the proper recombination events. β-gal specific activity in cell extracts obtained by sonication of the strains harboring lacZ fusions was determined as previously described o-nitrophenol produced per min and mg of protein. Measurements shown are the averages of data from triplicate experiments.The methodologies used for obtaining the in-frame deletion mutants and the transcriptional oar gene (MXAN_1450) using M. xanthus chromosomal DNA as a template corE was also amplified by PCR. A BamHI site was introduced at the start codon of oar in frame with another BamHI site introduced at the start codon of corE. Both PCR products were cloned in a vector derived from pUC19 in which the ampicillin-resistance gene was substituted by one that encodes resistance to tetracycline (Tetr). The resulting plasmid, pNG06, was introduced by electroporation into an M. xanthus strain with the genotype ΔcorE cuoB-lacZ, and several kanamycin-resistant (Kmr) and Tetr colonies were analyzed by Southern blot to confirm the proper recombination event. β-gal specific activity was determined to quantify cuoB expression. As a control, the plasmid pNG00 was constructed, in which corE was cloned under control of its own promoter. This plasmid was also electroporated into the ΔcorE cuoB-lacZ to restore corE at its original genomic location . The transformed cells were grown in LB medium containing 25 µg/ml kanamycin at 37°C until the optical density at 600 nm (OD600) reached 0.7 to 0.8. Induction was performed by the addition of 1 mM of isopropyl-β-D-thiogalactopyranoside. Induced cultures were incubated with shaking at 37°C for 6 h. One liter of the cell culture was collected by centrifugation and resuspended in 20 mM Tris-HCl (pH 7.5) containing the protease inhibitors leupeptine and antipain (2 µg/ml each), 10 µg/ml DNAse I, and 5 mM MgCl2. Cells were disrupted in a French pressure cell (at 9000 psi), followed by centrifugation (13000×g for 30 min at 4°C) to remove cell debris. The resulting soluble extract was loaded onto a HisTrapHP column  equilibrated with 20 mM Tris-HCl (pH 7.5) containing 0.5 M NaCl and 30 mM imidazole. Elution was carried out with a linear imidazole gradient (30–250 mM) in the same buffer. Protein fractions were analyzed by using SDS-PAGE. Those fractions containing hCorE were pooled, concentrated by ultrafiltration (cutoff of 10 kDa), and equilibrated to 20 mM Tris-HCl (pH 7.5). The purified hCorE protein content was determined by the Bio-Rad protein assay kit as specified by the manufacturers, using bovine serum albumin as standard. The purity of the samples was higher than 90%.The resulting plasmid was used to transform the strain M. xanthus or E. coli extracts. Cells were disrupted by sonication and centrifuged to remove cellular debris. Proteins were separated by SDS-PAGE and transferred onto a membrane of Immobilon-P at 0.8 mA/cm2 for 1.5 hours. hCorE was detected with an anti-His G-AP antibody (Invitrogen), which is conjugated with alkaline phosphatase, using nitro-blue tetrazolium chloride and 5-bromo-4-chloro-3′-indolyphosphate p-toluidine as substrates, following the instructions specified by the manufacturer.This methodology was used to detect hCorE either in copB was amplified from the M. xanthus genome using primers 3422EMSA265F and 3422EMSA265R . Binding reactions contained 20 mM Tris-HCl (pH 7.5), 2 mM MgCl2, 0.25 mg/ml of bovine serum albumin, 0.5 mM dithiothreitol, 15% glycerol (v/v), 40 mM KCl, 0.5 nM of labeled DNA (13000 cpm), and a 500-fold molar excess of competitor DNA (polydIdC). When indicated, 500 nM of hCorE protein, 0.1 mM CuSO4, 0.05 or 0.1 mM BCS, 0.1 mM AgNO3, or 0.1 mM TTM were also added to the reaction mixtures. After incubation for 10 min at 30°C, the mixtures were loaded onto a pre-run 5% polyacrilamide gel and run at 100 V for 1 h. The gel was dried under vacuum and exposed to an autoradiography film at −80°C.A DNA fragment containing an upstream sequence of the predicted ribosome-binding site of EMSA265R . After pcorE sequence) was used as a template. The primers were designed using the QuikChange Primer Design Program (http://www.genomics.agilent.com/). Oligonucleotides used to generate the six point mutations are listed in M. xanthus JM51EBZY (ΔcorE cuoB-lacZ) to obtain strains SDM181EBZY to SDM206EBZY (Single amino acid substitutions in the CRD of CorE were generated using the QuikChange II site-directed mutagenesis kit (Stratagene). Plasmid pNG00 (containing the WT M206EBZY .http://www.ncbi.nlm.nih.gov/genomes/lproks.cgi) and the DOE Joint Genome Institute (http://www.jgi.doe.gov/). All the sequences obtained with an E-value<2e-10 that conserved at least 4 Cys in the C terminus were back-searched against Pfam (http://pfam.sanger.ac.uk/) 2 (PF07638) and σ4 (PF08281) regions conserved in all the ECF σ factors http://espript.ibcp.fr/ESPript/cgi-bin/ESPript.cgi). For synteny determinations, two upstream and two downstream predicted proteins from CorE were initially aligned with the other 20 predicted proteomes using the BLASTP program . We then manually analyzed the gene organization of positive matches. In the case of conservation, we extended the BLASTP searches to other genes within the same region as already described by Pérez et al. Genes encoding ECF σ factors with a CRD in the prokaryotes were identified by BLASTP analysis of all the genome sequences deposited in the database of the National Center for Biotechnology Information  First, the upstream regions of Figure S1M. xanthus copper and other metal homeostasis in the WT strain (blue line) and the ΔcorE mutant (red line). The systems or genes analyzed are indicated in each panel. Cells were incubated on CTT agar plates containing the metal that yields highest induction for each system 2+ (panel C), and 0.1 mM Cd2+ (panel D). In the case of panel H, cells were incubated on CF medium, because czc3 is induced by starvation Expression of the systems involved in (TIF)Click here for additional data file.Figure S2M. xanthus strains harboring cuoB-lacZ fusions used in this report. (A) cuoB-lacZ fusion in the WT background. (B) cuoB-lacZ fusion in the ΔcorE in-frame mutant. (C) cuoB-lacZ fusion in the ΔcorECRD in-frame mutant. (D) Genotype of the strain harboring the cuoB-lacZ fusion, and corE cloned under the strong constitutive oar promoter. (E) Genotype of the strain harboring the cuoB-lacZ fusion, and corE cloned under its own promoter. Promoters (P) and genes are represented as colored blocks and arrows, respectively. Gene identifiers: oar: MXAN_1450, corE: MXAN_3426, cuoB: MXAN_3425, lacZ: lacZ gene from E. coli. Deletions of the entire corE or only CRD are expressed as red segments with a vertical dotted line. For the sake of simplicity, the gene located between the PcuoB and cuoB has not been depicted.Genotype of the (TIF)Click here for additional data file.Figure S3In silico identification of the CorE-binding site and determination of the genes of the CorE regulon. (A) Comparison of the upstream regions of the four genes regulated by CorE. (B) Sequence logo constructed at WebLogo (http://weblogo.berkeley.edu/) (TIF)Click here for additional data file.Figure S4cuoB up-regulation by different metals and oxidants. The WT strain harboring the cuoB-lacZ fusion was spotted onto CTT agar plates containing metals or oxidants at the concentrations indicated above each picture. Plates also contained 5-bromo-4-chloro-3-indolyl-β-D-galacto-pyranoside to monitor β-gal activity (blue color development). Pictures were taken after 48 h of incubation.Qualitative analysis of (TIF)Click here for additional data file.Figure S5cuoB in the presence (continuous lines) and the absence (dashed lines) of copper when hcorE was cloned under control of its own promoter (blue lines) or of oar promoter (red lines). Error bars indicate standard deviations.Expression of (TIF)Click here for additional data file.Figure S6cuoB expression. The expression was qualitatively analyzed on CTT media containing 5-bromo-4-chloro-3-indolyl-β-D-galacto-pyranoside  and the indicated concentrations of BCS, BCA, or TTM (controls contain no chelator). The culture media contained either no other additives (cell spots inside the orange rectangle), copper (green rectangle), or zinc (blue rectangle).Effect of different chelators on (TIF)Click here for additional data file.Figure S7cuoB in strains harboring point mutations in the CRD region of CorE in media supplemented with only copper, copper plus Ag+, or copper plus BCS. The mutated Cys is indicated in each panel. Cells were incubated on CTT agar plates containing only 0.15 mM copper (blue lines), 0.15 mM copper plus 0.05 mM BCS (red lines), or 0.15 mM copper plus 0.05 mM silver (green lines). In panel C the concentrations of copper and silver used were doubled to increase the up-regulation by copper and highlight the inhibitory effect of Ag+ in the mutant C181A, which is not observed in panel B. In all the cases, samples were harvested at different times to determine β-gal specific activity. Note that the scales in panels C, D, and H are different. Error bars indicate standard deviations.Expression of (TIF)Click here for additional data file.Table S1Bacterial strains used in this study.(DOC)Click here for additional data file.Table S2Plasmids used in this study.(DOC)Click here for additional data file.Table S3Oligonucleotides used in this study.(DOC)Click here for additional data file.