The complexity of tissue- and day time-specific regulation of thousands of clock-controlled genes (CCGs) suggests that many regulatory mechanisms contribute to the transcriptional output of the circadian clock. We aim to predict these mechanisms using a large scale promoter analysis of CCGs.Epo) gene was analysed, which exhibits many binding sites for circadian regulators. We provide experimental evidence for its circadian regulated expression in the adult murine kidney. Basing on a comprehensive literature search we integrate our predictions into a regulatory network of core clock and clock-controlled genes. Our large scale analysis of the CCG promoters reveals the complexity and extensiveness of the circadian regulation in mammals. Results of this study point to connections of the circadian clock to other functional systems including metabolism, endocrine regulation and pharmacokinetics.Our study is based on a meta-analysis of DNA-array data from rodent tissues. We searched in the promoter regions of 2065 CCGs for highly overrepresented transcription factor binding sites. In order to compensate the relatively high GC-content of CCG promoters, a novel background model to avoid a bias towards GC-rich motifs was employed. We found that many of the transcription factors with overrepresented binding sites in CCG promoters exhibit themselves circadian rhythms. Among the predicted factors are known regulators such as CLOCK∶BMAL1, DBP, HLF, E4BP4, CREB, RORα and the recently described regulators HSF1, STAT3, SP1 and HNF-4α. As additional promising candidates of circadian transcriptional regulators PAX-4, C/EBP, EVI-1, IRF, E2F, AP-1, HIF-1 and NF-Y were identified. Moreover, GC-rich motifs  and AT-rich motifs  are significantly overrepresented in promoter regions of CCGs. Putative tissue-specific binding sites such as HNF-3 for liver, NKX2.5 for heart or Myogenin for skeletal muscle were found. The regulation of the erythropoietin ( Organisms throughout evolution have developed biological clocks to better adapt to the twenty-four hour period of the solar day. Endogenous circadian oscillations have been observed in a variety of species including cyanobacteria There is a hierarchical organisation of the circadian rhythm with the master clock in the suprachiasmatic nucleus (SCN) of the hypothalamus controlling peripheral oscillators in most other tissues. Light signals detected by the eyes can synchronize through the retinohypothalamic tract the phase of the SCN, but not that of the peripheral clocks. The SCN sends synchronization signals to other cells of the body putatively by hormone secretion, sympathetic enervation and indirect cues such as body temperature, feeding time and activity rhythms.Period genes , Cryptochrome genes (Cry1 and Cry2) and nuclear receptors  by binding to E-box elements in their promoters. PER and CRY proteins form complexes and repress their own expression by interacting with the CLOCK∶BMAL1 dimer. REV-ERBα and RORα regulate the transcription of Bmal1 in a separate feedback loop through ROR regulatory elements. Light input to the SCN and intercellular coupling between SCN neurons is mediated by CREB binding motifs in the promoters of clock genes such as Per1. Furthermore, it is known that clock output genes  regulate clock-controlled genes (CCGs) through D-boxes The cell-autonomous oscillations in both central and peripheral organs are generated by similar molecular components. In single cells the self-sustained oscillations are driven by interlocked transcriptional-translational feedback loops. The transcription factor heterodimer CLOCK∶BMAL1 activates the expression of The complexity of tissue- and day time-specific regulation of thousands of CCGs suggests that additional regulatory mechanisms contribute to the circadian clockwork in the central and peripheral tissues. Indeed, recent experiments show that e.g. heat-shock factor HSF1 in silico approach to the question of regulatory mechanisms of the clock output pathways. We based our study on a meta-analysis of DNA-array data from rodent tissues. As illustrated in Our promoter analysis of CCGs is a large scale As described in cis-elements several hundred base pairs upstream of the transcription start site (TSS) and also in the first intron. Accordingly, we extracted promoter regions ranging from 3 kbp upstream to 2 kbp downstream of the TSS using the EnsEmbl 43 mouse genome. Transcription factor binding sites (TFBSs) were predicted using the algorithm by Rahmann et al. Epo gene promoter (for details see supplementary Previous promoter studies CACGTG) or SP1 binding sites. Consequently, we use GC-matched controls as a background model: first we determine the GC-content of the gene group of interest . The complete results of our bioinformatic analysis are available in the supplementary The TRANSFAC database (version 10.4) contains over 500 position weight matrices of vertebrate transcription factors. Even after clustering similar matrices Z-score of the matrix higher than 2. This threshold allows us to focus on the significantly overrepresented motifs. The lowest z-score among the known circadian-related motifs is the score of the ROR motif - 2.07. We do not observe known circadian regulatory motifs below this threshold.Only the top 5% of the matrices are considered . This criterium shortens exceedingly long lists .We list only TRANSFAC matrices that have a direct link to our list of 2065 CCGs. This is fulfilled if a transcription factor associated with the matrix is itself a CCG or if target genes annotated in TRANSFAC are on our list of 2065 CCGs.These criteria do not include any subjective evaluation, i.e. the compilation of the tables has been achieved automatically. The resulting tables are the major result of our study and form the basis for the further detailed analysis.mPer2 gene , HLF, TEF and E4BP4 bind to D-boxes in a phase-specific manner gene see .The RORα matrix is overrepresented in the promoters of 167 selected CCGs but with a lower z-score . Other nTGACGT) are overrepresented in the list of all CCGs (with the z-score of 5.65) and appear as overrepresented in all tissue-specific motif lists (CREB elements (consensus sequence: if lists . Hits ofRedundancy among transcription factor binding sites has been extensively discussed TTAYRTAA (where Y is a pyrimidine and R a purine). Cyp7a, Otc, Ttr, Adh1, Slc2a2, Sst, Orm1, Cdkn1a, C/Ebpα, Icam1, Top1) and of CRE-BP1  have been also reported as clock-controlled The D-box binding bZIP transcription factors, DBP (VBP), HLF, TEF and E4BP4, have the consensus sequence mBmal1 promoter Since hormone response elements resemble each other, competing regulation of clock-controlled genes is likely. A well documented example is the antagonistic binding of REV-ERBα and RORα to the In So far, we analyzed clock-related transcription factors with overrepresented binding sites in the list of all CCGs. These factors appear again in many lists of tissue-specific genes  and ERRα. We find several regulators of the immune response as well , in particular in the liver. Motifs of the myocyte enhancer factor MEF-2 can be found with high z-scores . In the CRE-binding factors are overrepresented in the SCN, reflecting the light input pathways and coupling via neurotransmitters Epo promoter region for transcription factors that are regulated in circadian rhythms . We found that promoters of CCGs reported in several DNA-array studies exhibit relatively high GC-content. Consequently, our analysis is based on GC-matched control promoters chosen randomly from the mouse genome. This way we obtained z-scores quantifying the overrepresentation of transcription factor binding sites (TFBSs) in promoters of CCGs.Among our predictions we found known regulatory sites such as E-boxes, D-boxes, CREB elements and hormone response elements. D-boxes obtain particularly high scores pointing to a central role of the transcription factors DBP, HLF and E4BP4. Nuclear receptor binding sites of HNF-4, RORα, AR, LXR, PPARγ, GR, T3R and ERRα were found predominantly in tissue-specific analysis, which is in agreement with the nuclear expression atlas For a detailed discussion we exploit the list of 2065 CCGs. Many of the novel predicted regulators appear on the list of 2065 CCGs others have annotated target genes included in the list . These transcription factors are compiled in How reliable are these predictions? Due to short length and low information content of binding site motifs, prediction of individual TFBSs is often error-prone, leading to many false positives In the following we show that many of the predicted transcription factors play indeed essential roles in hormonal, metabolic and detoxification regulatory networks.It is well established that hormones such as glucocorticoids Even though hormone receptor recognition motifs do not display the highest z-scores, the combined action with other overrepresented transcription factor motifs might play a role in the regulation of many clock-controlled genes. Tronche and others mention the competition of the GR with AP-1, NF-κB, CREB, GATA-1 and OCT-1 as well as its interaction with C/EBPβ or STAT5 in transcriptional regulation Epo gene expression in the kidney  have been previously reported in humans Our experimental data provides evidence of circadian oscillation of e kidney , which iEpo promoter regions contain several potential binding sites for transcription factors , input pathways (CREB elements), direct output (D-boxes) and secondary regulation . Most of the DNA-arrays used in our meta-analysis measured expression profiles in constant darkness, therefore, we do not expect major regulation effects from light input. The described set of overrepresented transcription factors forms a large network with strong links to the endocrine system, metabolism and the immune system . Some ofOur approach cannot directly address the important role of co-factors such as PGC1-α From published microarray analyses detecting genes with circadian expression we selected 6 studies on mammalian tissues and containing information on gene expression phases and amplitudes Per2 expression phase and grouped into 4 time intervals each of the length of 6 hours.From the complete list of the CCGs we selected a subset of 167 promoters of top-scoring genes that have been reported in at least three published experiments. The fact that these genes have been detected by independent experiments indicates their robust circadian expression. Using the gathered expression information we performed the promoter analysis on groups of CCGs organized in a hierarchical manner. The initial complete list was first subdivided into tissue-specific gene sets that where then regrouped into sets of genes having the same expression phase within each tissue. The phases were calculated relative to the cis-elements within several hundred base pairs upstream of a gene TSS as well as on its first intron. The sequences have been downloaded from EnsEMBL 43 mouse genome. We used the method of Rahmann We extracted sequences ranging over 3 kbp upstream and 2 kbp downstream of the transcription start site (TSS) of each gene. The choice of the region was motivated by previous promoter studies The promoter regions were scanned for overrepresentation of putative cis-elements as compared with a background set of genes not known to be oscillating. All 815 position specific count matrices from the TRANSFAC version 10.4 were used and the search of the binding sites was performed on the 5 kbp promoter regions of each analyzed CCG subset. The number of predicted sites of each motif was calculated and compared to the corresponding average number of predicted sites in sampled background promoter sets. The background promoter set is sampled 100 times randomly from promoters of all EnsEMBL mouse genes excluding those reported as clock controlled. Additionally, during the sampling procedure the GC-content of the analysed sequence group is considered. We define GC-content intervals of the width of 1% and calculate the number of genes in the analysed set falling into each of such GC-content bins. The proper number of background sequences of each of the CG-content bins is sampled. This background model was motivated by the observation that the CCG promoters tend to have a higher GC-content as compared to other mouse gene promoters . Using sFor further analysis we selected only vertebrate motifs having the number of predicted binding sites at least 2 standard deviations above the mean number of its predicted binding sites in the sampled reference promoter set. Assuming a Gaussian distribution of the number of predictions compare , a z-scoFirstly, this overrepresentation search procedure was performed on the promoters of both all CCGs and of the selected subset of robustly oscillating genes. Next, we subdivided the full list into tissue-specific gene lists. The liver gene list is strikingly larger – over 3 times than other tissues lists, presumably due to the prevalence of liver experiments or to the fact that circadian regulation in liver is particularly strong and widespread. Finally, each tissue-specific list has been subdivided into 4 lists of genes with the peak of expression belonging to a proper phase interval.Epo mRNA expression levels were analyzed by real-time PCR. Adult murine kidney specimens (C57BL/6 mice) were obtained at defined time points  after release of entrained mice in constant darkness. Total RNA was prepared by using a commercial kit , and used as a substrate for cDNA synthesis and quantitative real-time PCR using the reagents and instructions of the PCR machine manufacturer . The following primer and probe sequences were used for PCR amplification: GAPDH: FW 5′- TGT GTC CGT CGT GGA TCT GA -3′; RW 5′- CCT GCT TCA CCA CCT TCT TGA -3′; probe: FAM- CCG CCT GGA GAA ACC TGC CAA GTA TG - TAMRA; Per2: FW 5′- ATG CTC GCC ATT CAC AAG A -3′; RW 5′- GCG GAA TCG AAT GGG AGA AT -3′; probe: FAM- ATC CTA CAG GCC GGT GGA CAG CC -TAMRA. For Epo expression analysis a commercial Taqman Gene Expression Assay  was used. Transcript levels were compared on the basis of differences in the threshold cycles (Ct) values. Only samples with equal levels of GAPDH mRNA (±0.5 Ct) were taken into account. Transcript levels of the genes of interest are normalized to those of GAPDH.Epo gene regulation in neuronal cells with a fetal phenotype, were grown in DMEM, 10% fetal calf serum, and 1∶100 P/S to a confluence of approximately 60 to 70%. After 24 hours, cells were transfected with the following plasmids using Lipofectamine™\ 2000 transfection reagent  at a 4∶1 (Lipofectamine™∶DNA) volume ratio : pGL2 basic vector (control) or pGL2p117Le126 (kindly provided by Kerry L. Blanchard) that harbors the 5′ 117-bp Epo promoter sequence, the first intron, and the 3′ 126 bp Epo enhancer fragment with the hypoxia response element (NCBI accession no. M11319.1. Nucleotide (nt) 270 to 661 plus nt 3449 to 3575). Effects of CLOCK and BMAL1 were analyzed by co-transfection of vector plasmids: pDEST26-clock and pDEST26-bmal1 300 ng each. The HIF-1α expression plasmid  served as positive control for the induction of the pGL2-p117e126-L-Epo reporter. The luciferase reporter vector pGL3_E6S containing 6 E-boxes of the mPer1 promoter served as positive control for the efficacy of CLOCK and BMAL1 transfection Human neuroblastoma-derived cells , an established cell line for analyzing Figure S1Murine erythropoietin (Epo) promoter. The figure shows a set of selected transcription factor binding sites predicted with a false discovery rate of 5% (6.96 MB TIF)Click here for additional data file.Figure S2Competition of E-box binding sites. Sequence logos of binding sites showing strong similarity to E-box. E-box are known to regulate clock genes and clock output pathways. Motifs highly similar to E-box (consensus sequence: CACGTG) are found to be overrepresented in the promoters of CCGs.(5.90 MB TIF)Click here for additional data file.Figure S3Functional classification of overrepresented transcription factors. The predicted factors are assigned to organ-specific or functional systems.(0.72 MB TIF)Click here for additional data file.Text S1Regulation of Epo gene.(0.04 MB DOC)Click here for additional data file.Text S2Functional classification of overrepresented transcription factors.(0.07 MB DOC)Click here for additional data file.Text S3Additional references to the regulatory network presented in (0.08 MB DOC)Click here for additional data file.Table S1Overrepresented motifs in all analysed gene lists.(0.09 MB XLS)Click here for additional data file.


The 250 kDa P2P-R protein  was cloned over a decade ago and was found to bind both the p53 and Rb1 tumor suppressor proteins. In addition, P2P-R has been associated with multiple biological functions, such as mitosis, mRNA processing, translation and ubiquitination. In the current studies, the online GeneNetwork system was employed to further probe P2P-R biological functions. Molecular studies were then performed to confirm the GeneNetwork evaluations.GeneNetwork and associated gene ontology links were used to investigate the coexpression of P2P-R with distinct functional sets of genes in an adipocyte genetic reference panel of HXB/BXH recombinant strains of rats and an eye genetic reference panel of BXD recombinant inbred strains of mice. The results establish that biological networks of 75 and 135 transcription-associated gene products that include P2P-R are co-expressed in a genetically-defined manner in rat adipocytes and in the mouse eye, respectively. Of this large set of transcription-associated genes, >10% are associated with hormone-mediated transcription. Since it has been previously reported that P2P-R can bind the SRC-1 transcription co-regulatory factor , the possible effects of P2P-R on estrogen-induced transcription were evaluated. Estrogen-induced transcription was repressed 50-70% by the transient transfection of P2P-R plasmid constructs into four different cell types. In addition, knockdown of P2P-R expression using an antisense oligonucleotide increased estrogen-mediated transcription. Co-immunoprecipitation assays confirmed that P2P-R interacts with SRC-1 and also demonstrated that P2P-R interacts with estrogen receptor α.The findings presented in this study provide strong support for the value of systems genetics, especially GeneNetwork, in discovering new functions of genes that can be confirmed by molecular analysis. More specifically, these data provide evidence that the expression of P2P-R co-varies in a genetically-defined manner with large transcription networks and that P2P-R can function as a co-repressor of estrogen-dependent transcription. It is based on the analysis of transcripts whose expression co-vary within genetic populations, such as the BXD strains of mice and the HXB strains of rats or other genetic reference panels. GeneNetwork The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports ,2. Such In 2005, we published the first report documenting the ability of the systems genetics tool GeneNetwork to predict interactions between molecules that could be then confirmed by molecular analysis . The P2PFrom the time when P2P-R was cloned it has been implicated in multiple cellular processes such as mitosis ,9, mRNA The outcome of these systems genetics studies implies that P2P-R is an important member of large genetically-defined transcription networks in adipose and eye tissues. That prediction was then confirmed by data showing that P2P-R can function as a co-repressor of estrogen-induced transcription. Immunoprecipitation experiments strengthen such functional studies by confirming that P2P-R interacts with SRC-1 and by demonstrating that P2P-R also interacts with estrogen receptor α (ERα). These data suggest that P2P-R may be a component of large macromolecular complexes that impact steroid-dependent transcription.Since most studies concerning the cloning and characterization of P2P-R were performed using the 3T3T proadipocyte system , we initWhen the top 1000 gene transcripts that are co-expressed with P2P-R and have correlation coefficients ≥ 0.67 were exported for gene ontology analysis to WebGestalt, 67 gene products were found to be associated with transcription with p = 2.81e-4  , Ep300 [+0.8056], Pparbp [+0.7709], Glcci1 [+0.7690], Esrrg [+0.7574], Srebf2 [-0.7568], Ncoa6 [+0.7423], Ncor1 [+0.7115], Ncoa1 (Src-1) [+0.6987] and Ncoa2 [+0.6787] and Nr2f6 [-0.6653] (the parenthetic numbers indicate positive or negative expression correlation coefficients with P2P-R expression).The findings from studies on fat cell-adipose and eye tissues therefore strongly suggest that P2P-R has the potential to function as a member of a genetically-defined transcription complex. Two important questions arise from these findings. Does a P2P-R-associated transcription network exist in all tissues? Can the role of P2P-R in transcription be confirmed by molecular studies? To address the first question, studies were performed on several other tissues in the GeneNetwork database to search for additional P2P-R transcription networks. There was no evidence of a P2P-R-associated transcription network in liver or whole brain databases, however, in a cerebellum database a smaller P2P-R-associated transcription set could be identified (data not shown). These data suggest that the role of P2P-R in transcription may be tissue specific. The studies in the following sections address the second question and show that P2P-R can indeed function as a transcriptional co-repressor.Nuclear Receptor Signaling Atlas online database http://www.nursa.org that P2P-R interacts with Ncoa1 (SRC-1), a well-known steroid receptor co-regulator . In this regard, of special note is the April 1, 2005 report in the see ref. . This daU83913), while HeLa and 293T cells were additionally co-transfected with ERα. Co-transfection experiments were performed in the presence of either vehicle (DMSO) or 100 picomolar 17-beta estradiol (E2) to determine if the transfection of P2P-R influences basal and/or estrogen-dependent transcription. The results presented in Figure Based on these observations, we sought to investigate whether P2P-R impacts estrogen-dependent transcription in cell lines that either contain estrogen receptor α, such as MCF-7 and Ishikawa cells or lack native estrogen receptors, such as HeLa and 293T cells. To this end MCF-7 and Ishikawa cells were transiently co-transfected with an ERα-reporter construct with an empty vector or with a plasmid construct that encodes the near full length P2P-R  The data reported in this paper establish that the function of gene products can be confidently predicted by determining their association with specific genetically-defined biological networks, using systems genetics approaches and tools, such as GeneNetwork. GeneNetwork has been previously used to predict a molecular interaction between P2P-R and Pum2, the gene transcript which is most consistently co-expressed with P2PR in three tissues of the BXD mouse genetic reference panel. That prediction was confirmed by molecular studies showing that Pum2 binds to a specific sequence in the 5'UTR of P2PR mRNA to modulate P2PR expression .The current studies expand the use of GeneNetwork databases in an attempt to identify biological networks in which P2P-R is a member, leading to the prediction of new function(s). Data derived using these systems genetics approaches suggest that P2P-R is an important member of large genetically-defined transcription networks in adipose and eye tissues, thereby having the potential to impact the expression of many hundreds of genes. That prediction was then confirmed by molecular studies showing that P2P-R can indeed function as a transcriptional co-repressor.The results specifically demonstrate that P2P-R acts as a co-repressor of estrogen-dependent transcription in four different cell systems including MCF-7, Ishakawa, HeLa and 293T cells. The co-repressor activity of P2P-R was further confirmed by evidence that knocking down P2P-R protein expression, using an antisense oligonucleotide, facilitates estrogen-induced transcription. Immunoprecipitation experiments support those functional studies by showing that the P2P-R protein is a component of the estrogen-transcription complex that includes ERα and SRC-1. Preliminary data also suggest that P2P-R interacts with the Mad2 mitotic protein , which is of interest in this context, since it has previously been reported that Mad2 associates with estrogen receptor beta .Scaffold/Matrix Attachment Regions) elements of the nuclear matrix, was shown to interact with the C-terminal domain of RNA polymerase II, and with ERα and to suppress ERα-mediated transcription , anti-SRC-1  and anti-ERα . Immunoprecipitations were performed as previously described [4, #SC-72PP and MEB carried out transcriptional assays and co-immunoprecipitation experiments. RAW participated in GeneNetwork analysis. TG and RES designed the study, supervised data collection and analysis and drafted the manuscript. All authors read and approved the final manuscript.List of fat cell transcription network components. Seventy five (75) transcription-associated gene products including P2P-R are genetically co-expressed in fat cells derived from the HXB/BXH rat recombinant inbred genetic reference panel.Click here for fileList of eye tissue transcription network components. One hundred thirty five (135) transcription-associate gene products including P2P-R are genetically co-expressed in the eye tissue derived from the BXD mouse recombinant inbred genetic reference panel.Click here for file


Few genome-scale models of organisms focus on the regulatory networks and none of them integrates all known levels of regulation. In particular, the regulations involving metabolite pools are often neglected. However, metabolite pools link the metabolic to the genetic network through genetic regulations, including those involving effectors of transcription factors or riboswitches. Consequently, they play pivotal roles in the global organization of the genetic and metabolic regulatory networks.Bacillus subtilis . We provide a systematic graphic representation of regulations of each metabolic pathway based on the central role of metabolites in regulation. We show that the complex regulatory network of B. subtilis can be decomposed as sets of locally regulated modules, which are coordinated by global regulators.We report the manually curated reconstruction of the genetic and metabolic regulatory networks of the central metabolism of B. subtilis.This work reveals the strong involvement of metabolite pools in the general regulation of the metabolic network. Breaking the metabolic network down into modules based on the control of metabolite pools reveals the functional organization of the genetic and metabolic regulatory networks of  Helicobacter pylori  clusters, and is strongly inhibited by oxaloacetate (OAA), and weakly by fumarate (FUM), malate (MAL) and ATP. This regulation is written asIf a gene is transcribed from several promoters, several conditions of transcription are included:A complex, encoded by several genes, is modelled as a sum of genessdhA, sdhB and sdhC is designated sdhA+sdhB+sdhC in the gene column. The sdh genes belong to the same operon, transcribed from a single promoter. Only one condition of transcription is included when genes that encode a complex are in operon. By contrast, trpE and trpG that encode the two subunits of the anthranilate synthase do not belong to the same operon. trpE belongs to the trp operon (Op92), and trpG belongs to an operon involved in folate synthesis (Op98). The trpG gene is also transcribed from a second constitutive promoter leading to the production of a monocistronic trpG transcript (indicated as trpG). The operons are also presented as a sum of operons in this case. Each part of the sum corresponds to one gene. For the anthranilate synthase:For example, the succinate dehydrogenase, encoded by trpG, are described as a sum of conditionsThe conditions of transcription for Op92, Op98, and the constitutive promoter upstream from trpE) and Op98 (trpG), and translation from the trpG constitutive promoter always occurs (TRUE condition).When post-transcriptional regulations occur, they are indicated and represented as a sum of conditions. The TRAP protein prevents the translation of both Op92  "modulation" in genes transcription; and (ii) activation of genetic regulators (switch from "off" to "on") according to the presence of external metabolite pools.We used the constraints-based framework with Flux Balance Analysis  to predposed in  which eni) A gene whose transcription is only modulated by a transcription factor is considered present for the FBA analysis. For example, during growth on glucose, both CcpA and CcpN transcription factors are active. The sucC-sucD genes are kept for the FBA analysis since CcpA only modulates their transcription, while the pckA gene is not considered since CcpN strongly inhibits its transcription  The concentration of intracellular metabolite pools cannot be quantitatively predicted since the model is only qualitative. A simplified and qualitative description for the evolution of metabolite effectors has been used, according to their hybrid/internal/external status [ and keep or eliminate genes in consequence. If some genes are eliminated, go back to step 2, else test the lethality of the phenotype. Only one iteration was sufficient in the case of our reconstructed metabolic network.3. Evaluate the transcriptional rule for genes whose transcription condition is given by Boolean conditions (B. subtilis (Bacillus subtilis), CDS (coding sequence), CIT (citrate), C14-C18 (fatty acids with 14 to 18 carbons), C14-C20 (fatty acids with 14 to 20 carbons), CTR (citrulline), CYS (cysteine), CYT (cytidine), DAHP (3-deoxy-D-arabino-heptolosonate-7-phosphate), DNA (deoxyribonucleic acid), dADO (deoxyadenosine), dCYT (deoxycytidine), dGUO (deoxy-guanosine), dINO (deoxyinosine), dR5P (deoxyribose-5-phosphate), dURI (deoxyuridine), E. coli (Escherichia coli), FBP , FUM (fumarate), GDP (guanosine diphosphate), GLN (glutamine), GLT (glutamate), GLY (glycine), GMP (guanosine monophosphate), GTP (guanosine triphosphate), ppGpp (guanosine teraphosphate), pppGpp (guanosine pentaphosphate), GR (genetic regulator), GUA (guanine), GUO (guanosine), HIS (histidine), HXTH (hypoxanthine), ILE (isoleucine), IMP (inosine monophosphate), INO (inosine), LEU (leucine), LB medium (Luria-Bertani medium), LYS (lysine), MACOA , MAL , MET (methionine), MQH2/MQ (menaquinone/menaquinol), mRNA (messenger RNA), NADH/NAD (nicotinamide adenine dinucleotide reduced/oxidized), NIT (nitrate), OAA , OAS (O-acetyl-L-serine), ORN (ornithine), O2 (dioxygen), PCS (primary carbon source), PHE , PHO (phosphate), Plead (peptide leader), PRO (proline), PRPP (5-phosphoribosyl-1-pyrophosphate), PTS (phosphotransferase system), RBS (ribosome binding site), RNA (ribonucleic acid), RNAP (RNA polymerase), SAM (S-adenosyl-L-methionine), SER (serine), SCS (secondary carbon source), SNS (secondary nitrogen source), SUCC (succinate), TCA cycle (tricarboxylic acid cycle), TF (transcription factor), THR (threonine), THY (thymidine), TRP (tryptophan), TYR (tyrosine), UMP (uridine monophosphate), URA (urate), URI (uridine), UTP (uridine triphosphate), VAL .AA (amino acids), ADE (adenine), ADO (adenosine), ADP (adenosine diphosphate), ALA , ASN (asparagine), ASP (aspartate), ALTA , ALTO , AMP (adenosine monophosphate), ARG (arginine), ATP (adenosine triphosphate), BCAA (branched-chain amino acids), B. subtilis, designed the graphic representations, participated in the local/global definition, identified the modules and drafted the manuscript. FBB developed the mathematical concepts upon which the module definition is based. IMV, PB, PN, and SA validated the reconstruction of B. subtilis and contributed to the writing of this manuscript. VF conceived of the study, participated in its design and coordination, and defined this manuscript. All authors read and approved the final manuscript.AG carried out the reconstruction of Bacillus sub-tilis. This file contains the metabolic pathways description and their regulation, according to our formalism defined in the sections Results and Methods of this paper.Model of the central metabolism of Click here for fileBacillus subtilis. This file details the level of knowledge available for each metabolic pathway. The pathways which have been less characterized are explicitely mentioned.Review of the reconstruction of Click here for fileBacillus subtilis. This file details the level of knowledge available for each amino acid: genes and kinetic reactions, genetic and enzymatic regulation.Available information on the amino acid metabolism of Click here for fileBacillus subtilis. The file details the phenotype analysis of B. subtilis and presents the potential utilization of amino acids as carbon or nitrogen sources.Predictions based on the reconstruction of Click here for fileBacillus subtilis. This file provides for each metabolic pathway: (i) a detailed representation of the kinetic reactions; (ii) the organization of genes in operon; (iii) a functional representation with the formalism detailed in the section Results of this paper.Functional representation of the central metabolism of Click here for fileBacillus subtilis and their genetic regulation.Isoenzymes in metabolic pathways and their genetic regulation. This file details all known isoenzymes of the central metabolism of Click here for fileMathematical definition of module. The file presents the study of the steady-state behaviour of the main control structures identified in metabolic pathways.Click here for filei) the "local/global" status of each genetic regulation; (ii) if the regulation is a modulation.Local versus global regulation of metabolic pathways. This file details the known genetic regulation of each metabolic pathway and precises (Click here for file


RPTOR gene is a key component of this pathway. A previous survey of candidate genes found that RPTOR contains multiple SNPs with strong correlations between allele frequencies and climate variables, consistent with the action of selective pressures that vary across environments. Using data from a recent genome scan for selection signals, we honed in on a SNP (rs11868112) 26 kb upstream to the transcription start site of RPTOR that exhibits the strongest association with temperature variables. Transcription factor motif scanning and mining of recently mapped transcription factor binding sites identified a binding site for POU class 2 homeobox 1 (POU2F1) spanning the SNP and an adjacent retinoid acid receptor (RAR) binding site. Using expression quantification, chromatin immunoprecipitation (ChIP), and reporter gene assays, we demonstrate that POU2F1 and RARA do bind upstream of the RPTOR gene to regulate its expression in response to retinoids; this regulation is affected by the allele status at rs11868112 with the derived allele resulting in lower expression levels. We propose a model in which the derived allele influences thermogenesis or immune response by altering MTOR pathway activity and thereby increasing fitness in colder climates. Our results show that signatures of genetic adaptations can identify variants with functional effects, consistent with the idea that selection signals may be used for SNP annotation.The mechanistic target of rapamycin (MTOR) pathway regulates cell growth, energy homeostasis, apoptosis, and immune response. The regulatory associated protein of MTOR encoded by the  RPTOR is a key component of the mechanistic target of rapamycin pathway, which regulates cell growth, metabolism, and immune response; and its deregulation is associated with human diseases, including cancer and diabetes. Previous studies showed that variation in RPTOR carry strong signatures of adaptations to different climates. Here, we used evolutionary genetics approaches coupled with transcription factor motif data mining to refine the location of the selection target. We then used functional assays to show that the selected polymorphism resides in a sequence element that regulates gene expression levels in response to retinoids. The derived allele at this SNP, which results in lower expression levels, increases in frequency with decreasing temperatures, consistent with the notion that it confers a selective advantage in colder climates possibly through its effects on energy metabolism or immune response. These results suggest a novel regulatory role for retinoids in MTOR signaling. Moreover, they support the proposal that evolutionary approaches can be informative for SNP functional annotation.Climate has exerted strong selective pressures in human populations during their dispersal, and signatures of these adaptations are still detectable in the geographic distribution of polymorphisms.  A major goal of human genetics is to identify functional genomic regions, especially those containing variants that influence common disease susceptibility or disease-related phenotypes. However, due to the complexity of the genome, it is not easy to distinguish functional from non-functional regions, especially for regulatory elements, which can lie far from the target gene. Because adaptive variation must necessarily have functional, in addition to fitness effects, signals of positive natural selection have been proposed as an informative approach to the functional annotation of the genome. Many genome-wide selection scans have been performed to date based on different approaches . This approach may be particularly informative for human populations who originated in Sub-Saharan Africa and migrated out of Africa 60–100k years ago to occupy most of the earth landmass RPTOR gene as a target of spatially-varying selective pressures because many variants within the gene exhibited particularly strong correlations between allele frequency and latitude RPTOR gene codes for a protein involved in the target of rapamycin (MTOR) pathway, which in turn is important in cell growth, proliferation, apoptosis RPTOR variants correlated with latitude, or one in strong linkage disequilibrium (LD) with these SNPs, conferred adaptations to selective pressures that vary across environments. However, the mechanism through which this variation affects the function of the RPTOR gene remains unknown.One of these studies identified the in vitro functional assays to localize the most likely target of selection and to propose a mechanism underlying its effect on RPTOR gene function. More specifically, we used the results of a genome-wide selection scan to identify the variant with the strongest evidence as a target of climate adaptations . This SNP lies within a predicted POU class 2 homeobox 1 (POU2F1) binding site and near a retinoid acid receptor (RAR) binding site identified by Chromatin immunoprecipitation (ChIP)-chip RPTOR expression in response to retinoid acid (RA). Consistent with this prediction, we observed a significant increase of RPTOR expression in both MCF-7 and HepG2 cell lines after treatment with RA. We further showed that both POU2F1 and RAR bind to the region spanning the SNP of interest in both cell lines. Finally, we determined that the two alleles at this SNP influence RA-mediated transcriptional response by means of reporter gene assays using enhancer constructs containing, respectively, the ancestral and the derived allele. Based on these results, we propose that the SNP that is strongly correlated with climate variables affects fitness by influencing RPTOR gene expression.In the present study, we used population genetics analyses and RPTOR gene and 100 kb upstream and downstream of the gene  was also calculated to determine whether the BF value of a SNP of interest is unusual relative to those of other SNPs matched by allele frequency; as with formal p-values, a low rank indicates strong evidence for a correlation (i.e. a large BF) To refine the location of the polymorphism targeted by climate-related selective pressures, we mined the results of a recent genome-wide scan for signals of allele frequency correlation with climate variables . Allele frequencies for a total of 156 SNPs were obtained in the genomic region spanning the RPTOR cDNA from a small, but diverse group of HapMap lymphoblastoid cell lines. As shown in r2<0.62, 0.34, and 0.69 in YRI, CEU, and ASN populations, respectively). These results indicate that the selection signal is not due to a non-synonymous SNP and suggest that the selection target is not likely to be within the coding region. Because the true target of selection could be a regulatory SNP, we re-sequenced also 22.6kb of conserved non-coding elements near the RPTOR gene, the RPTOR promoter, and a 2.6 kb region spanning rs11868112. As shown in r2<0.23, 0.48, and 0.54 in YRI, CEU, and ASN populations, respectively, result not shown). Therefore, our targeted re-sequencing survey did not identify SNPs with likely functional effects and that could drive the signal seen at rs11868112.To determine whether an unknown coding SNP could account for the above correlations with climate variables, we re-sequenced the We used the re-sequencing data to perform neutrality tests based on the allele frequency spectrum, but no significant departure was detected . This maRPTOR gene, which is ubiquitously expressed and is a strong biological candidate for adaptations to different local environments, and 41.3 kb 3′ to the NPTX1 gene, which is transcribed in the opposite orientation relative to RPTOR. NPTX1 codes for neuronal pentraxin 1 that is expressed only in the central neurons of the nervous system where it plays a role in synaptic plasticity NPTX1 gene is a less likely target of adaptations to different climates. We hypothesized that the SNP rs11868112 is located within a long-distance regulatory element and that this SNP influences the activity of this regulatory element. This hypothesis was bolstered by the fact that this SNP lies less than 1 kb away from a retinoic acid receptor α (RARA) binding site detected by ChIP-chip in the breast cancer cell line MCF-7 http://www.gene-regulation.com). Since POU2F1 is known to cooperate with RARA to regulate gene expression RPTOR gene expression is regulated by retinoids via activation of the RARA and that rs11868112 modulates the activation of RPTOR expression by modifying POU2F1 binding affinity to the DNA.SNP rs11868112 lies 26.2 kb 5′ to the RPTOR expression, we treated HepG2 and MCF-7 cell lines with the selective RARA agonist AM580, which has greater specificity for RARA compared to all-trans RA RPTOR mRNA levels by quantitative real time PCR. The MCF-7 cell line was included because the RARA binding site was originally identified in these cells while the human hepatocellular carcinoma cell line HepG2 was included because the liver plays a prominent role in biological processes relevant to energy metabolism (e.g. carbohydrate and lipid metabolism). The genotype of rs11868112 is TT and CT for MCF-7 and HepG2, respectively. As shown in RPTOR expression in HepG2 varied substantially across time points for vehicle (DMSO) control treatment. A 33.7% higher expression level was observed for 12 hrs treatment with AM580 versus DMSO (P = 0.01). For all other time points, no significant difference (P>0.05) was observed for AM580 and DMSO treatment. In MCF-7 cells, where we observed considerably less variation in RPTOR expression for DMSO treatment across time points, we found a significant increase of RPTOR mRNA levels upon AM580 treatment at 12, 24 and 48 hrs  of the putative RARA binding region for the chromatin immunoprecipitated DNA with the RARA antibody  of the putative POU2F1 binding region in the chromatin immunoprecipitated with the POU2F1 antibody in both HepG2 and MCF-7 cells  and to study the retinoic acid dependency of POU2F1 recruitment, we performed ChIP with antibodies against POU2F1 in the same cell lines. Upon AM580 treatment, we observed a significant enrichment  exhibited a 19.2% higher luciferase activity than the construct for the derived allele (T) allele (P = 0.011) 12 hrs after AM580 treatment (P>0.09). This observation is consistent with the maximal RA-dependent induction of RPTOR expression at 12 hrs after AM580 treatment. Similar results were obtained with the MCF-7 cell line, where the reporter construct for the ancestral allele showed a 24.1% higher luciferase activity than those for the derived allele at 12 hrs after AM580 treatment  associated with a strong signal of selection identified in a genome-wide study. The allele frequencies at this SNP are strongly correlated with latitude and winter temperature variables. A re-sequencing survey did not identify additional SNPs that are in strong LD with rs11868112 and that are likely to have functional effects. Because SNP rs11868112 lies within a predicted POU2F1 binding site located close to a RARA binding site identified by ChIP-chip, we hypothesized that this SNP influences the transcriptional response to RA. Consistent with this hypothesis, we showed that POU2F1 and RARA do bind to the genomic region spanning and near SNP rs11868112, respectively. Furthermore, reporter gene assays suggest that this region functions as a RA-dependent enhancer and that the allele status at rs11868112 affects enhancer activity. Although we cannot conclusively identify the target gene of this enhancer, RPTOR appears to be a strong candidate because it is induced by the selective RARA agonist AM580 in two different cell lines. The fact that the time of differential RPTOR expression in response to RA treatment coincides with the time of allele-specific down-regulation in reporter gene assays further supports this proposal. Overall, these results provide an example of how a selection signal can identify a functional SNP and suggest a role for the regulation of RPTOR expression in human adaptations to different environments.In this study, we combined population genetics, bioinformatics, and experimental approaches to identify a Despite the clear signal of selection given by the correlation between allele frequency and climate variables, standard neutrality tests did not detect a significant departure from expectations. This included tests of the frequency spectrum as well as haplotype homozygosity  RPTOR gene, a distal enhancer upstream to the proximal UCP1 promoter was found to contain a RAR response element, which mediates its transcriptional induction RPTOR gene is induced by RA treatment in liver and breast epithelial cells is consistent with the known function of RAR in regulating the expression of another gene with a clear role in thermogenesis. More recently, experiments in mice identified a role for retinoids in adipose tissue remodeling and, more specifically, in the acquisition of BAT-like properties in white adipose tissue RPTOR is induced by retinoids provides a possible mechanistic link connecting the action of retinoids in adipose tissue remodeling and the finding that the adipose-specific raptor KO exhibits increased mitochondrial uncoupling  in white adipose tissue.Although these geographic patterns of human phenotypes are well established, the genetic factors contributing to these adaptations are only partially understood. Genetic variants that can increase mitochondrial uncoupling efficiency might be advantageous in cold climates and thus may have been selected during human migrations northward. The derived T allele rs11868112, which is associated with lower RA-dependent transcription levels, may result in increased mitochondrial uncoupling in adipose tissue and, hence, higher thermogenesis. Consistent with the hypothesis that this allele confers resistance to cold stress, it increases in frequency with decreasing winter temperatures in worldwide population samples, and it is relatively rare in the equatorial populations of sub-Saharan Africa and South East Asia see . AlthougRPTOR expression associated with the T allele at high latitudes could reflect a shift to maintain the appropriate balance between pathogen pressures and immune response, with an exaggerated immune response possibly resulting in increased risk for autoimmune diseases.Another possible explanation for the signal of selection observed at SNP rs11868112 may be related to the role of the MTOR pathway in the regulation of the immune response. Indeed, the MTOR pathway plays multiple roles in immunity, especially in the activation and proliferation of T cells RPTOR expression associated with the T allele is not sufficient to generate significant phenotypic and fitness differences between arctic and tropical populations. One possibility is that RPTOR and the MTOR pathway are located at the top of the signaling cascade It might be argued that the modest (∼20%) decrease of RPTOR gene for HGDP individuals was obtained from published Illumina HumanMap 650Y data (http://hagsc.org/hgdp/files.html). The genotype data for the same SNPs from four HapMap Phase III populations  (http://www.hapmap.org) and five additional populations  were also incorporated . In total, 61 human populations were included in the current study. This study uses the Bayesian geographic analyses method of Coop et al. (2010) Genotype data in the http://ecrbrowser.dcode.org/) and by choosing regions conserved between human and at least two additional species . Population genetics indices, including segregating sites (S), nucleotide diversity (π) w) http://genapps.uchicago.edu/labweb/index.html). The expected distribution of nucleotide diversity and Tajima's D was generated by coalescent simulations using the software ms http://www.pharmgkb.org).Twenty-four unrelated Hapmap samples  were chosen for re-sequencing the coding regions and 48 unrelated HapMap samples  for re-sequenced in the non-coding regions. cDNA was synthesized from RNA extracted from the lymphoblastoid cell lines of the HapMap samples using the Super Transcript III First-Strand Synthesis System for RT-PCR  and utilized as template. Conserved non-coding regions were identified by using the ECR genome browser  supplemented with 10% fetal bovine serum . The human breast cancer cell line MCF-7 was maintained in Dulbecco's Modified Eagle's Medium  with 10% FBS and 0.1% insulin . Before any AM580 or DMSO treatment, cells were grown for 48 hrs in medium with 10% charcoal-stripped FBS (Invitrogen).RPTOR mRNA levels were determined by real time PCR using the power SYBR green (Applied Biosystems) with primers 5′-CGGGGAGGTCTGGGTCTTCAA-3′ and 5′-CTCCTGCTCCCGCTGTAGTGC-3′β-actin was used as a calibrator gene in real time PCR with the primers 5′-ACGTGGACATCCGCAAAGAC-3′ and 5′-CAAGAAAGGGTGTAACGCAACTA-3′Cells were treated with 100 nM AM580 (Sigma) or DMSO (Sigma) for 2hrs, 4hrs, 8hrs, 12hrs, 24hrs, and 48hrs, and then harvested. RNA was extracted using the RNeasy Mini Kit  and cDNAs were synthesized with the High Capacity Reverse Transcription Kit (Applied Biosystems). 7 cells grown for 48 hours in medium with charcoal-stripped FBS and then treated with 100nM AM580 or DMSO for 1 hr, were incubated for 10 minutes with 1% formaldehyde at room temperature. The fixed cells were treated with 1.25 M glycine for 5 minutes, washed twice with ice-cold phosphate buffered saline (Invitrogen) containing protease inhibitor cocktail  and phenylmethylsulfonyl fluoride , scraped, lysed and sonicated to obtain 200–800 bp fragments with the Sonicator 4000 . The solubilized chromatin was diluted 10-fold with dilution buffer, and pre-cleared with protein A beads. After centrifuging and transferring the supernatant, 1% sample was stored as input and the remaining chromatin was incubated with rabbit polyclonal anti-POU2F1 (sc-232X) or anti-RARA  and normal rabbit IgG (Santa Cruz Biotechnology) and immunoprecipitated with protein A beads. After washing with low salt, high salt, LiCl and TE buffer twice, the immunoprecipitated chromatin was eluted and de-crosslinked. Upon proteinase K treatment (Qiagen) DNA was recovered by QIAquick PCR purification kit (Qiagen). The obtained DNA was quantified by real time PCR with iQ SYBR green  and primer pairs 5′- AGGTCTGCAACACAGCACAT -3′ and 5′- CTGGGAGCTATGCCTGGTC -3′, and 5′-CTAAGTGCTGGGTCGTAAGTTGT-3′ and 5′-GAATGCAGGCTATAAATCAGGAG-3′ to quantify the enrichment for POU2F1 and RARA binding site, respectively. For each ChIP assay, three technical replicates were performed for three biological replicates on a StepOne Plus Realtime PCR System (Applied Biosystems).ChIP was carried out using the ChIP Assay Kit  according to the manufacturer's protocol. Briefly, 105′-TTGCGAAAGTAAATGCTAT-3′ and 5′-CAGAGGGGCCTTGAGATGACCA-3′ were used. In the second round of PCR the primers 5′-CAGTC-GCTAGC-TTCCCTCACTCTGTCCCCCAATG-3′ and 5′-CAGTC-CTCGAG-TTCCTGACCTGCCAAATCTGTG-3′ were used to append the PCR fragment with NheI and XhoI restriction sites, respectively. For both PCR reactions, iProof High-Fidelity DNA Polymerase (Bio-Rad) was used to avoid the introduction of mutations. After digestion with NheI and XhoI , the DNA fragment was cloned into the pGL3-promoter vector . The plasmid with the ancestral allele (C) was generated with the QuikChange Site-Directed Mutagenesis Kit  using primer pair CACAAACTTGTAGGAGGG-3′5′-GCCCTTGACAAGCTGAGCTTGTCAAGGGC-3′5′-CCCTCCTACAAGTTTGT(target in bold) according to the manufacturer's recommendations. All plasmids were verified by sequencing prior to following experiments.and A 3.7 kb segment containing the derived T allele of rs11868112 and the putative RAR binding site see  was ampl6 MCF-7 cells were seeded into 10-cm plates 24 hours before transfection. Plasmid DNA (9.5 µg) was transfected using FuGene HD  according to the manufacturer's recommendations. Plasmid pRL-TK (Promega) DNA (0.5 µg) was co-transfected as internal control. Twenty-four hours after transfection, cells were split, cultured for 12h, and starved for 48 hours in medium with charcoal-stripped FBS. After treatment with 100nM AM580 or DMSO for 2hrs, 4hrs, 8hrs, 12hrs, 24hrs, and 48hrs, cells were harvested and luciferase activity was determined using the Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer's protocol. The enhancer activity was determined as the ratio between Firefly and Renilla luciferase activity. Four independent replicates were performed for each experiment.Two million HepG2 cells or 2.5*10t-tests in SPSS 15.0  and the null hypothesis was rejected when P<0.05.For all analyses of expression, POU2F1 and RARA binding, and reporter gene data, we used independent two-tailed Figure S1RPTOR coding region resequencing. Each column indicates one SNP while each array denotes one individual. Blue, red, and yellow represent homozygous of common allele, heterozygous, and homozygous of rare allele, respectively. Af, Eu, and As indicate YRI, CEU, and ASN HapMap populations, respectively. All positions refer to RPTOR mRNA sequence (Genbank ID NM_020761).Visual genotype for (0.05 MB TIF)Click here for additional data file.Figure S2RPTOR non-coding region resequencing. Each column indicates one SNP while each array denotes one individual. Blue, red, yellow, and grey represent homozygous of common allele, heterozygous, homozygous of rare allele, and missing data, respectively. Af, Eu, and As indicate YRI, CEU, and ASN HapMap populations, respectively. All positions refer to the genome sequence (build 36) for chromosome 17.Visual genotype for (2.18 MB TIF)Click here for additional data file.Figure S3RPTOR non-coding resequencing in this study. The human RPTOR gene cluster is aligned to frog, fish, chicken, opossum, mouse, dog, and macaque (from top to bottom) genomes by ECR browser (http://ecrbrowser.dcode.org/). The vertical bar on top represents surveyed regions while red, blue, and green indicate conserved non-coding region, promoter, and rs11868112 nearby region, respectively. The sequence identity ranges from 50% to 100% and is displayed by the height of the peak while green, blue, salmon, red, and yellow denote repeat, coding, intronic, intergenic, and untranslated region, respectively.Seleted region for (0.13 MB TIF)Click here for additional data file.Figure S4RPTOR. The black bars from left to right indicate POU2F1, RAR binding sites and RPTOR gene, respectively. The distances between them are displayed below. The primer position for ChIP-real time PCR, the SNP in predicted POU2F1 binding site is also provided.Schematic figure for the enhancer ∼25kb upstream of (0.03 MB TIF)Click here for additional data file.Table S1RPTOR SNPs in the different human populations.Derived allele frequencies of the (0.15 MB XLS)Click here for additional data file.Table S2RPTOR SNPs in the HGDP panel.Bayes Factors for the (0.55 MB DOC)Click here for additional data file.Table S3RPTOR SNPs in the HGDP panel.Bayes Factors empirical p values for the (0.55 MB DOC)Click here for additional data file.Table S4Population index for the resequenced regions in YRI (A), CEU (B), and the ASN (C) populations.(0.08 MB DOC)Click here for additional data file.Table S5RPTOR PCR and resequencing in this study.Primers for (0.05 MB DOC)Click here for additional data file.


The zinc-responsive transcription factor Zap1 has a striking role in fungal biofilm formation and is reported to regulate matrix formation. Candida albicans, the major fungal pathogen of humans. We report here that the C. albicans zinc-response transcription factor Zap1 is a negative regulator of a major matrix component, soluble β-1,3 glucan, in both in vitro and in vivo biofilm models. To understand the mechanistic relationship between Zap1 and matrix, we identified Zap1 target genes through expression profiling and full genome chromatin immunoprecipitation. On the basis of these results, we designed additional experiments showing that two glucoamylases, Gca1 and Gca2, have positive roles in matrix production and may function through hydrolysis of insoluble β-1,3 glucan chains. We also show that a group of alcohol dehydrogenases Adh5, Csh1, and Ifd6 have roles in matrix production: Adh5 acts positively, and Csh1 and Ifd6, negatively. We propose that these alcohol dehydrogenases generate quorum-sensing aryl and acyl alcohols that in turn govern multiple events in biofilm maturation. Our findings define a novel regulatory circuit and its mechanism of control of a process central to infection.A biofilm is a surface-associated population of microorganisms embedded in a matrix of extracellular polymeric substances. Biofilms are a major natural growth form of microorganisms and the cause of pervasive device-associated infection. This report focuses on the biofilm matrix of  Candida albicans. We found that the zinc-responsive regulatory protein Zap1 controls the expression of several genes important for matrix formation in C. albicans. These target genes encode glucoamylases and alcohol dehydrogenases, enzymes that probably govern the synthesis of distinct matrix constituents. The findings here offer insight into the metabolic processes that contribute to biofilm formation and indicate that Zap1 functions broadly as a negative regulator of biofilm maturation.A biofilm is a surface-associated population of microbes that is embedded in a cement of extracellular compounds. This cement is known as matrix. The two main functions of matrix are to protect cells from their surrounding environment, preventing drugs and other stresses from penetrating the biofilm, and to maintain the architectural stability of the biofilm, acting as a glue to hold the cells together. The presence of matrix is a contributing factor to the high degree of resistance to antimicrobial drugs observed in biofilms. Because biofilms have a major impact on human health, and because matrix is such a pivotal component of biofilms, it is important to understand how the production of matrix is regulated. We have begun to address this question in the major human fungal pathogen  A biofilm is a community of surface-associated microorganisms embedded in a matrix of extracellular polymeric substances. Biofilms are common microbial growth forms in nature and are a leading cause of human infection Biofilm matrix is broadly defined as an extracellular polymeric material that is maintained within a biofilm C. albicans, the major fungal pathogen of humans. The C. albicans matrix is composed primarily of carbohydrate and includes protein, hexosamine, phosphorus, and uronic acid C. albicans cells C. albicans biofilms grown in an in vivo catheter infection model, where it can be used in diagnosis of catheter-based infection Our focus is the biofilm matrix of C. albicans transcription factor, Zap1/Csr1 (orf19.3794), that governs matrix production. This transcription factor is closely related to the Saccharomyces cerevisiae zinc-response regulator Zap1, and we show that expression of three zinc transporter genes depends upon C. albicans Zap1/Csr1. This observation supports a recent report S. cerevisiae and C. albicans Zap1 both regulate zinc-responsive gene expression. However, we also show that Zap1/Csr1 controls genes that influence overall matrix levels. Our results provide a foundation for a mechanistic understanding of matrix production and its regulation.Matrix production is closely tied to biofilm formation, yet little is known about its regulation or production mechanisms. We describe here a C. albicans transcription factor gene insertion mutants for defects in biofilm formation ZAP1/CSR1 (orf19.3794). This phenotype was observed for several additional zap1/zap1 insertion mutants as well as a newly created zap1Δ/zap1Δ deletion mutant. This unusual phenotype was complemented by introduction of a wild-type ZAP1 construct into the zap1Δ/zap1Δ mutant, but not by the vector lacking the ZAP1 insert. Therefore, loss of ZAP1 function causes an unusual glistening appearance of in vitro-grown C. albicans biofilms.We have described screens of zap1Δ/zap1Δ mutant and the zap1Δ/zap1Δ+pZAP1 complemented strain or the reference wild-type strain  promoter region, as expected if C. albicans Zap1 activates its own expression  analysis of biofilm cells . We founand ZRT3 , thus arand PRA1 . We alsopression . We noteand IFD6 , whose czap1Δ/zap1Δ mutant could, in principle, be inhibitors of matrix production; genes that are upregulated in the zap1Δ/zap1Δ mutant could be activators of matrix production. We reasoned that overexpression of matrix inhibitors in the zap1Δ/zap1Δ mutant may cause reduced levels of soluble β-1,3 glucan. To test this idea, we introduced highly expressed TDH3 promoter sequences to replace promoter regions of the following target genes: ZRT2, ZRT1, PRA1, CSH1, and IFD6. We confirmed their overexpression through qPCR assays in the zap1Δ/zap1Δ transformants  background. Once again, we used the TDH3 promoter to replace promoter regions of target genes YWP1, orf19.3499, HXT5, GCA1, GCA2, HGT2, and ADH5, and used qPCR to confirm overexpression , which is induced under low zinc conditions Interestingly, the conserved Zap1 circuit encompasses many additional genes, as indicated by comparison of Zap1-responsive genes in our dataset with their any more . The extACCTTGGTGGTTA and TAGTGGTTAT  for Ura+ strains or in YPD+uri  for Ura− strains. Transformants were selected for on synthetic medium  or on YPD+clonNAT400  for nourseothricin-resistant isolates. Growth on low-zinc medium was assayed with synthetic medium lacking added zinc . To obtain nourseothricin-sensitive isolates having flipped out the SAT1 marker, nourseothricin-resistant transformants were grown for 8–12 h in YPD liquid medium, plated at a low cell density of 200 cells/plate on YPD+clonNat25 , and allowed to grow for 24 h at 30°C as previously described C. albicans strains used in this study are listed in C. albicans strains were derived from BWP17 zap1/zap1) was made by PCR product-directed gene deletion ZAP1 from 1,000 bp upstream of the start codon to 500 bp downstream of the stop codon. This fragment was inserted into pGEMT-Easy (Promega), digested with NgoMIV and AlwNI, and subsequently inserted by in vivo recombination in S. cerevisiae into NotI- and EcoRI-digested HIS1 vector pDDB78 HIS1 locus. The zap1/zap1 mutant strain was made His+ by transforming CJN1091 with NruI-digested pDDB78 to yield strain CJN1201.All NAT1-TDH3 promoter plasmid pCJN542 TDH3-IFD4 overexpression strain CJN1680 was constructed by transforming CJN1201, the zap1/zap1 mutant, using PCR products from template plasmid pCJN542 and primers IFD4-F-OE-Ag-NAT-Ag-p-CJN and IFD4-R-OE-Ag-NAT-Ag-TDH3p-CJN. These primers amplify the entire Ashbya gossypii TEF1 promoter, the C. albicans NAT1 open reading frame, the A. gossypii TEF1 terminator, and the C. albicans TDH3 promoter with 100 bp of hanging homology to 500 bp upstream into the promoter of IFD4 for the forward primer and 100 bp of hanging homology from exactly the start codon of IFD4. The homology in these primers allows for homologous recombination of the entire cassette directly upstream of the natural locus of IFD4 so that its expression is driven by the TDH3 promoter instead of its natural promoter. By the same method, primers IFD6-F-OE-Ag-NAT-Ag-p-CJN and IFD6-R-OE-Ag-NAT-Ag-TDH3p-CJN were used for overexpression of IFD6 to produce strain CJN1631; ZRT2-F-OE-Ag-NAT-Ag-TEF1p and ZRT2-R-OE-Ag-NAT-Ag-TDH3p-CJN for overexpression of ZRT2 to produce strain CJN1655; ZRT1-F-OE-Ag-NAT-Ag-TEF1p-CJN and ZRT1-R-OE-Ag-NAT-Ag-TDH3p-CJN for overexpression of ZRT1 to produce strain CJN1651; and PRA1-F-OE-Ag-NAT-Ag-p-CJN and PRA1-R-OE-Ag-NAT-Ag-TDH3p-CJN for overexpression of PRA1 to produce strain CJN1623. The TDH3-19.4899 overexpression strain CJN1638 was constructed by transforming DAY185, the wild-type reference strain, using PCR products from template plasmid pCJN542 and primers 4899-F-OE-Ag-NAT-Ag-p-CJN and 4899-R-OE-Ag-NAT-Ag-TDH3p-CJN. By the same method, primers 999-F-OE-Ag-NAT-Ag-p-CJN and 999-R-OE-Ag-NAT-Ag-TDH3p-CJN were used for overexpression of ORF19.999 to produce strain CJN1675; ADH5-F-OE-Ag-NAT-Ag-p-CJN and ADH5-R-OE-Ag-NAT-Ag-TDH3p-CJN for overexpression of ADH5 to produce strain CJN1642; YWP1-F-OE-Ag-NAT-Ag-p-CJN and YWP1-R-OE-Ag-NAT-Ag-TDH3p-CJN for overexpression of YWP1 to produce strain CJN1659; 3499-F-OE-Ag-NAT-Ag-p-CJN and 3499-R-OE-Ag-NAT-Ag-TDH3p-CJN for overexpression of ORF19.3499 to produce strain CJN1633; 4384-F-OE-Ag-NAT-Ag-p-CJN and 4384-R-OE-Ag-NAT-Ag-TDH3p-CJN for overexpression of HXT5 to produce strain CJN1663; and HGT2-F-OE-Ag-NAT-Ag-p-CJN and HGT2-R-OE-Ag-NAT-Ag-TDH3p-CJN for overexpression of HGT2 to produce strain CJN1667. Transformation into C. albicans strains and selection on YPD+clonNAT400 plates has been described IFD4 gene), annealing to a sequence within the promoter of each gene and the reverse primer Nat-OE-R-det2-CJN annealing to a sequence found in the NAT gene.The SAT1-flipper cassette , was constructed as follows. PCR was done using template pFA6a-13myc-kanMX6 SAT1-flipper cassette plasmid, pSFS2A SAT1 flipper cassette with 65 bp of hanging homology to the ZAP1 ORF minus its stop codon for the forward primer and 65 bp of hanging homology to the ZAP1 UTR precisely downstream of the stop codon for the reverse primer. The homology in these primers allows recombination of the entire 13myc epitope tag and complete SAT1 flipper cassette directly downstream of the ZAP1 ORF, lacking its natural stop codon, so that the ZAP1 ORF contains a C-terminal 13myc epitope tag translational fusion. Correct integration of the C-terminal 13myc epitope tag and SAT1 flipper was verified by colony PCR using detection primers 3794detFUpMyctag-CJN and AHO300 to check the upstream integration and 3794detRDownMyctag-CJN and AHO301 to check the downstream integration. The C-terminal tagged nourseothricin-sensitive Zap1-myc strains, CJN1688 and CJN1694, were constructed by flipping out the SAT1 cassette from strains CJN1684 and CJN1685, respectively, as described previously SAT1-flipper cassette: 3794detFUpMyctag-CJN and AHO300, and 3794detRDownMyctag-CJN and AHO302. The 13myc epitope tag and the region of homology to the 3′ end of ZAP1 used for integration of the SAT1-flipper cassette was confirmed by sequencing the colony PCR product generated using primers 3794detFUpMyctag-CJN and AHO283.The C-terminal myc-tagging plasmid pADH34 , containIn vitro biofilm growth assays were carried out in Spider medium and visualized by CSLM as described previously C. albicans infection to determine biofilm development on the internal surface of the intravascular devices. The distal 2 cm of the catheter was cut from the entire catheter length, and biofilms were imaged by SEM at 50× and 1,000× magnification, as described previously A rat central-venous-catheter infection model, as described previously C. albicans in vitro biofilm and planktonic cells were collected at 12 h for glucan measurements. Viable cell burdens were determined using plate counts to ensure the cultures contained similar number of cells. Supernatants were centrifuged at 3,000g for 10 min, and were stored at −20°C until glucan analysis. Glucan concentrations were determined using the commercially available Glucatell -β-D-Glucan Detection Reagent kit (Associates of Cape Cod) according to manufacturer's directions. Four in vitro glucan assay replicates were performed for each sample. Statistical significance  was determined with a Student's t-test.Cultures were grown on silicone disks or in suspension in RPMI medium, as described above. Culture supernatants from p-values) was determined with a Student's t-test. Viable cell burdens were measured by harvesting kidneys at the end of the experiment as an estimation of total-body organ burden.After 12 h of growth in the in vivo biofilm model, serum was collected from the venous catheter. Serum samples were frozen at −20°C until glucan analysis. β-1,3 glucan was measured in the serum using the Fungitell -β-D-Glucan Detection Reagent kit (Associates of Cape Cod) according to manufacturer's directions. Three in vivo glucan assay replicates were performed for each rat catheter. Statistical significance  according to the manufacturer's instruction. We find that this kit yields the cleanest, most stable, and highest quality and quantity of RNA compared with the hot phenol method for extraction of RNA from a C. albicans biofilm.Biofilms for expression microarray analysis were grown in Spider medium at 37°C without silicone squares. Instead, the bottom of a six-well polystyrene plate was used as a substrate for biofilm growth in order to maximize the efficiency of harvesting cells for RNA extraction. We find that one six-well plate containing biofilms for one strain yields sufficient RNA for expression microarray analysis. Similar to the silicone square method ZAP1, ZRT2, and ZRT1 using the primers ZAP1-FNor and ZAP1-RNor for ZAP1, ZRT2-FNor and ZRT2-RNor for ZRT2, and ZRT1-FNor and ZRT1-RNor for ZRT1. For quantitative real-time reverse transcription-PCR (qPCR) analysis, 10 µg of total RNA was DNase-treated at 37°C for 1 h using the DNA-free kit (Ambion), cDNA was synthesized using the AffinityScript multiple temperature cDNA synthesis kit (Stratagene), and qPCR was done using the iQ SYBR Green Supermix (Bio-Rad) as previously described ZRT2, ZRT1-FqRTPCR and ZRT1-RqRTPCR for ZRT1, PRA1-FqRTPCR and PRA1-RqRTPCR for PRA1, IFD4-FqRTPCR and IFD4-RqRTPCR for IFD4, IFD6-FqRTPCR and IFD6-RqRTPCR for IFD6, ZAP1-FqRTPCR and ZAP1-RqRTPCR for ZAP1, YWP1-FqRTPCR and YWP1-RqRTPCR for YWP1, 3499-FqRTPCR and 3499-RqRTPCR for ORF19.3499, HXT5-FqRTPCR and HXT5-RqRTPCR for HXT5, 4899-FqRTPCR and 4899-RqRTPCR for ORF19.4899, 999-FqRTPCR and 999-RqRTPCR for ORF19.999, HGT2-FqRTPCR and HGT2-RqRTPCR for HGT2, and ADH5-FqRTPCR and ADH5-RqRTPCR for ADH5. The iCycler iQ detection system (Bio-Rad) was used with the following program: initial denaturation at 95°C for 5 min, followed by 40 cycles of 95°C for 45 s, 58°C for 30 s, and 72°C for 30 s. Amplification specificity was determined by melting curve analysis. Bio-Rad iQ5 software was used to calculate normalized gene expression values using the ΔΔCt method, using TDH3 as a reference gene. For ease of interpretation, the reference strain expression level values were set to 1.0 for each gene set, and the normalized expression of each gene relative to TDH3 expression is shown. Results are the means of three determinations.Northern analysis was performed as described previously zap1/zap1 mutant strain versus CJN1193, the zap1/zap1+pZAP1 strain. LOWESS normalization and statistical analysis of the data were conducted in GeneSpring GX version 7.3 (Agilent Technologies). Data are reported in p<0.05) with a change in transcript abundance of at least 1.5-fold. The results of this analysis with adjusted p<0.05 are listed in Transcription expression profiling using long-oligonucleotide microarrays was performed as previously described C. albicans Assembly 20 genome (http://www.candidagenome.org/), as previously described g, washed twice in 10 ml ice cold TBS , and the pellets frozen in liquid nitrogen prior to cell lysis. Cell lysis and shearing of DNA were done by resuspending the pellets in 700 µl lysis buffer  supplemented with complete protease inhibitor cocktail tablets (Roche). The cell suspension was vortexed at 4°C for 4 h in the presence of 0.5-mm acid-washed glass beads, and the lysate was collected. Chromatin was sheared by sonication in a Bioruptor water bath sonicator  at 4°C, the sheared lysate was centrifuged at 12,000g for 10 min at 4°C, and the supernatant was collected. 50 µl of extract was added to 200 µl TE/1% SDS, and stored at −20°C as the ChIP input material. For chromatin IPs, 300 µl of the crude lysate was added to 200 µl lysis buffer, and 10 µl of mouse monoclonal antihuman c-myc antibody  was added to the mixture. Extract plus antibody was incubated overnight at 4°C, with agitation. The following day, 50 µl of a 50% suspension of protein G-Sepharose Fast-Flow beads (Sigma) in lysis buffer was added and incubated 2 h at 4°C, with agitation. The beads were pelleted for 1 min at 1,000g, the supernatant removed, and the beads washed 5 min at room temperature with ice-cold buffers as follows: twice in lysis buffer, twice in high salt lysis buffer , twice in wash buffer , and once in TE . After the last wash, 110 µl of elution buffer  was added to each sample, and the beads were incubated at 65°C for 10 min with periodic agitation. The beads were spun for 30 s at 10,000g at room temperature, and 100 µl of the supernatant was stored. A second elution was carried out with 150 µl elution buffer 2 , and eluates from the two elution steps were pooled . Both the ChIP and input samples were incubated overnight at 65°C, and cooled at room temperature. For cleaning the IPed DNA, 250 µl proteinase K solution  was added to each sample, and samples were incubated at 37°C for 2 h. 55 µl 4 M LiCl was added to each, and the samples were extracted once with 450 µl phenol/chloroform/isoamyl alcohol solution (25∶24∶1). 1 ml ice cold 100% ethanol was added and the DNA was precipitated overnight at −20°C. The DNA was pelleted by centrifugation at 12,000g for 30 min at 4°C, washed once with ice cold 70% ethanol, and the pellets air dried. IP samples were resuspended in 25 µl TE, and input samples were resuspended in 100 µl TE+100 µg/ml RNaseA and incubated 1 h at 37°C. ChIP-enriched DNA was amplified, fluorescently labeled, hybridized, and washed as described in detail in http://johnsonlab.ucsf.edu/). 250 bp centered on the midpoint of the peaks in the promoter regions bound by Zap1 were submitted to MEME v3.5.7 (http://meme.nbcr.net) for motif analysis http://rsat.scmbb.ulb.ac.be/rsat/, using 1,500 bp of 5′ region sequence and a search for 8 bp motifs The ChIP–chip tiling arrays were designed by tiling 181,900 probes of 60-bp length across 14.3 Mb included in the Dataset S1C. albicans strains used in this study. This file gives the genotypes and sources for all C. albicans strains.(0.06 MB DOC)Click here for additional data file.Dataset S2Oligonucleotide sequences. This file gives the specific nucleotide sequence for each oligonucleotide.(0.04 MB XLS)Click here for additional data file.Dataset S3pADH34 sequence. This file gives the nucleotide sequence of vector pADH34, which was used for epitope tagging.(0.01 MB TXT)Click here for additional data file.Dataset S4Microarray data. This file gives complete microarray results for the comparison of the zap1Δ/zap1Δ mutant and zap1Δ/zap1Δ+pZAP1 complemented strain (worksheet 1), a separate list of significantly regulated genes from this dataset (worksheet 2), and a comparison of Zap1-responsive genes in C. albicans and in S. cerevisiae, aligned as orthologs or best hits. Expression data for S. cerevisiae are from Lyons et al. (3.44 MB XLS)Click here for additional data file.Dataset S5Verification of Zap1-responsive gene expression. This file provides data that support microarray results to indicate that Zap1-responsive genes are expressed at altered levels in the zap1Δ/zap1Δ strain and that the TDH3 promoter fusion strains do indeed overexpress the relevant gene.(18.31 MB DOC)Click here for additional data file.Dataset S6ChIP mapping of genomic Zap1 binding sites. This file gives comprehensive mapping information for genomic Zap1 binding sites.(2.98 MB XLS)Click here for additional data file.Dataset S7Detailed protocol for ChIP. This file describes the protocol for ChIP used in this report.(0.04 MB DOC)Click here for additional data file.


EGFR transcripts. This shows that in-depth analysis of the transcriptome using RNA-seq is a valuable tool for understanding complex transcriptional events. Furthermore, the development of new algorithms for estimation of gene expression from RNA-seq data is necessary to minimize length bias.Several recent studies have indicated that transcription is pervasive in regions outside of protein coding genes and that short antisense transcripts can originate from the promoter and terminator regions of genes. Here we investigate transcription of fragments longer than 200 nucleotides, focusing on antisense transcription for known protein coding genes and intergenic transcription. We find that roughly 12% to 16% of all reads that originate from promoter and terminator regions, respectively, map antisense to the gene in question. Furthermore, we detect a high number of novel transcriptionally active regions (TARs) that are generally expressed at a lower level than protein coding genes. We find that the correlation between RNA-seq data and microarray data is dependent on the gene length, with longer genes showing a better correlation. We detect high antisense transcriptional activity from promoter, terminator and intron regions of protein-coding genes and identify a vast number of previously unidentified TARs, including putative novel  Less than 2% of the human genome encodes for proteins, yet a large fraction, recently estimated to 60% to 90% of the genome can be transcribed Several studies In parallel to experimental discovery of regulatory RNAs, computational methods are being developed to identify conserved structural RNA elements likely to be involved in transcriptional and translational control In this study, we use massive DNA sequencing to investigate RNA longer than 200 nucleotides from three human cancer cell lines. We show that approximately 20% of all protein-coding genes have antisense transcription coupled to them and that antisense transcription is prevalent in introns.In this study we investigate the transcriptome of three cell lines, A431, U-2 OS and U251, by applying the massive SOLiD DNA sequencing technology facilitating sense/antisense identification of reads. The cell lines were chosen to represent three different lineages; epithelial, mesenchymal and glia cells. A total of 10 to 15 million high quality 50-basepair reads were obtained for each cell line. The reads were mapped onto the human reference genome (hg18), after which reads were aggregated for each gene. An expression value was calculated based on the number of reads per kilobase gene and million reads in each sample (RPKM) To validate the results obtained from RNA-seq, we compared the data to gene expression data from the A431 and U251 cell lines obtained using microarrays (no data was available for U-2 OS). Since the microarray platform only generates relative expression values, the correlation between the RNA-seq data and the microarray data was calculated using the log2 value of the ratio between A431 and U251, which in the RNA-seq case yields one value per Ensembl-gene. Since one gene can be represented by several microarray probes, we used three different methods to convert these to a single value that could be compared to the RNA-seq data  is shown, along with two small clusters several tens of kilobases away. Whether or not the two small clusters are in fact novels exons remains to be investigated. In PPARGC1A) on chromosome 4. Expression from this regions is detected at high levels in A-431 cells, but is almost completely shut off in U-2 OS and U-251 cells. This provides an intriguing example of complex transcription, and could indicate gene regulation through antisense transcript expression. This gene has been implicated in diabetes where lower expression has been linked to insulin resistance and DNA damage Even though most novel TARs seem to be lowly expressed, we find a few interesting instances among these. Several clusters are detected downstream of a tRNA pseudogene on chromosome 3. We believe that this is the result of transcription which has been initiated upstream of the pseudogene and continues downstream . The pseIn the current study we have investigated the transcriptional levels of three human cancer cell lines using RNA-seq. We show that the correlation between DNA microarray data and RNA-seq data depends on gene length, and that the reason for this is the increased precision in expression level measurements for longer genes due to the fact that a higher number of reads will map to longer genes than short, as described earlier We show that for approximately 20% of all human protein-coding genes, there is at least weak antisense transcription to exonic sequences. We also show that many of the antisense signatures overlap between the investigated cell lines . During http:www.ncbi.nlm.nih.gov/geo), more regions like these will likely be detected and characterized. It will be of great importance to functionally characterize these novel non-protein-coding transcripts and their potential role in gene regulation.The ENCODE project showed that transcription was frequent even outside of protein-coding genes A431, U-2 OS and U251 cells were grown as described earlier et al., and was pre-processed as described elsewhere http:www.ensembl.org) as described elsewhere Two-color DNA microarray data for the cell lines A431 and U251 was provided by Gry To investigate the sense and antisense expression in different genomic regions, we calculated the number of reads that map to each region of interest. Some regions Click here for additional data file.Figure S2Correlation to microarray data, binned per gene length in intervals of 2000 bps. See main text for discussion.(11.70 MB TIF)Click here for additional data file.Figure S3Information on read mappings for additional cell lines.  Fraction of reads mapping to different regions.  Relative tag density in different regions.  Fraction reads mapping to the sense and antisense strand for different regions. See main text for discussion.(0.20 MB PDF)Click here for additional data file.Figure S4Smooth scatterplots of log10(rpkm) between samples along with Spearman's rho correlation. The correlation is .87 to .88 between all samples. This indicates that most genes have similar levels across all samples.(1.41 MB PDF)Click here for additional data file.Figure S5Smooth scatterplots of log10(antisense-rpkm) between samples. Spearman's rho correlation coefficient is here slightly lower than that in the sense-case (supplementary (1.41 MB PDF)Click here for additional data file.


On the other hand, there is clear evidence for intrinsic sequence preferences of nucleosomes, and it is unclear to what extent these sequence preferences affect the observed pattern. To test the barrier nucleosome model, we quantitatively analyze yeast nucleosome positioning data both up- and downstream from NFRs. Our analysis is based on the Tonks model of statistical physics which quantifies the interplay between the excluded-volume interaction of nucleosomes and their positional entropy. We find that although the typical patterns on the two sides of the NFR are different, they are both quantitatively described by the same physical model with the same parameters, but different boundary conditions. The inferred boundary conditions suggest that the first nucleosome downstream from the NFR (the +1 nucleosome) is typically directly positioned while the first nucleosome upstream is statistically positioned via a nucleosome-repelling DNA region. These boundary conditions, which can be locally encoded into the genome sequence, significantly shape the statistical distribution of nucleosomes over a range of up to ∼1,000 bp to each side.The positions of nucleosomes in eukaryotic genomes determine which parts of the DNA sequence are readily accessible for regulatory proteins and which are not. Genome-wide maps of nucleosome positions have revealed a salient pattern around transcription start sites, involving a nucleosome-free region (NFR) flanked by a pronounced periodic pattern in the average nucleosome density. While the periodic pattern clearly reflects well-positioned nucleosomes, the positioning mechanism is less clear. A recent experimental study by Mavrich  Within the last five years, knowledge about nucleosome organization on the genome has grown dramatically. To a large extent, this has been achieved by an increasing number of experimental studies determining nucleosome positions at high resolution over entire genomes. Particular attention has been paid to promoter regions, where a canonical pattern has been established: a nucleosome-free region with pronounced adjacent oscillations in the nucleosome density. Here we tested to what extent this pattern may be quantitatively described by a minimal physical model, a one-dimensional gas of impenetrable particles, commonly referred to as the “Tonks gas.” In this model, density oscillations occur close to a boundary at dense packing. Our systematic quantitative analysis reveals that, in an average over many promoters, a Tonks gas model can indeed account for the nucleosome organization to both sides of the nucleosome-free region, if one allows for different boundary conditions at the two edges. On the downstream side, a single nucleosome is typically directly positioned such that it forms an obstacle for the neighboring nucleosomes, while such a barrier nucleosome is typically missing on the upstream side. The long DNA molecules of eukaryotic genomes are packaged into a compact structure with the help of histone proteins Recent experiments measuring the distribution of nucleosomes across the genomes of several model organisms have robustly identified three salient features Saccharomyces cerevisiaeIndeed, bioinformatic and biophysical approaches that parameterize sequence-encoded effects on nucleosome positioning have been remarkably successful in modeling and predicting the large-scale genomic nucleosome occupancy et al.However, while the observed oscillatory pattern downstream of the TSS is reminiscent of the pattern calculated by Kornberg and Stryer Kornberg noted early on in vivo. This analysis must be based on a quantitative description of statistical positioning. In statistical physics, the interplay between interaction and entropy of particles in a one-dimensional configuration space has long been quantified in simple models for gas/liquid systems Here, we treat the average nucleosome density As Eq. (1) describes a nontrivial effect that arises only from properties which the “nucleosome gas” shares with any other one-dimensional gas of impenetrable particles, it can be regarded as a ‘null model’, i.e., a quantitative reference that helps to identify relevant effects beyond the universal features for systems of this class. With this goal in mind, we wanted to compare Eq. (1) to patterns extracted from experiments.To extract the consensus distribution of nucleosomes around the NFR at the 5′ end of genes, previous studies aligned the genes at their TSS and averaged the nucleosome distributions over all genes In addition to the appropriate choice of reference point for the alignment, it was important to process the experimental data in a way such that it became directly comparable to the physical density et al.et al.We performed our analysis on the data of Mavrich Our analysis leading to To interpret the extracted pattern within the physical model described above, we performed a nonlinear least-squares fit to Eq. (1), as described in ‘Before discussing the obtained parameter values and the robustness of the fitting procedure, we address the immediate question that emerges from the above results: On the one hand, the agreement between model and data is consistent with the hypothesis that most of the nucleosomes downstream of the +1 nucleosome are statistically positioned. On the other hand, the statistical positioning mechanism has no intrinsic bias to a particular direction, i.e., the pattern upstream of the NFR should be described as well by a viable physical model. However, the upstream consensus pattern reported in previous studies displays much less pronounced oscillations than on the downstream side in vivo.The difference in the up- and downstream pattern might be an indication of positioning mechanisms beyond statistical positioning. Alternatively, this difference might be due to an intrinsic asymmetry of the NFRs, caused by different molecular determinants for the up- and downstream NFR boundary. Such an asymmetry would lead to a different boundary condition for the nucleosome gas on the two sides of the NFR. To illustrate the possible effect of the boundary condition on the pattern in the nucleosome gas, In particular, it is instructive to contrast the case of a perfectly repulsive barrier  at their promoters, and that the NFRs are flanked by salient oscillatory patterns in the nucleosome density in vivoThat statistical positioning in the vicinity of barriers is a mechanism capable of producing a non-random nucleosome arrangement has long been established theoretically Two additional studies on statistical positioning in genic regions appeared after our work was completed Taken together, our and the existing studies of statistical positioning support the view that long-range correlations in nucleosome positions produced by localized features in the effective free energy landscape for nucleosome binding are an important determinant of the genome-wide nucleosome organization. Indeed, for yeast, where TSSs are typically spaced bp apart , statistThe approach taken in the present study may be classified as a “reverse approach”, which starts from the observed distribution of nucleosomes along the genome and ultimately seeks to determine from it the underlying free energy landscape for nucleosome binding  and the end sites of open reading frames  with same ‘feature ID’ to one ‘gene’ with a total of 4792 genes. See Our list of start and end sites of genes is based on the list of transcribed regions and open reading frames as reported in “Supplementary Research Data” of Ref. We used alignments of the read density map to the positions of nucleosomes surrounding the nucleosome free region (NFR) at the TSS for a quantitative test of statistical positioning. Since the read density map we used for our analysis does not allow direct annotation of individual nucleosomes, we had to employ the list of identified nucleosomes from the “Supplementary Research Data” of Ref.  the TSS  such thaaveraged .To test the influence of gene starts and ends close to the  2000 bp  and usin 2000 bp .et al.: Based on the reads aligned to the yeast genome, these authors identified individual nucleosomes using a peak detection algorithm after correcting for MNase bias . To test for the importance of such a correction, we performed an alternative analysis towards a nucleosome density around the et al.. This explanation appears likely, since not all of the yeast nucleosomes are well positioned, i.e., a significant portion of nucleosomes will not lead to a clear peak in the distribution, given the average over many cells that is taken in the experiment.As a side-remark note that the proxy Our one-dimensional gas description of statistical nucleosome positioning uses a continuous genome coordinate Physically, the oscillations of Eq. (1) provides an analytic expression for the particle density close to a perfect boundary. This expression can in fact be interpreted and utilized in two different ways: (i) The origin, a posteriori our assumption appears reasonable, due to the quantitative agreement of model and data, which suggests that the decaying oscillations genuinely reflect the many-body physics of the Tonks gas – such an agreement is not expected if the variation were merely experimental error. Another assumption, shared with basically all models for nucleosome organization, is the equilibrium assumption made by associating the nucleosome distribution with a static free energy landscape. In vivo, transcription, DNA replication, and active remodeling processes regularly translocate and evict nucleosomes, and it is questionable to what extent these processes can be captured by a static free energy landscape. Though little is known about the kinetics of chromatin reorganization, we can consider some simple scenarios to illustrate that this assumption may not be as bad as it seems: For instance, remodeling enzymes that merely increase the mobility of nucleosomes, without preference for a certain direction or position, would only speed up the equilibration in a free energy landscape, but not affect its shape. If the remodelers do have any sort of bias, but work rapidly, their effect can be included into a modified free energy landscape. Other passive (competitive binding) and active (repositioning) processes can similarly be included in an effective free energy landscape, as long as their kinetics is rapid on the timescale of interest. Remodelers may also modify the interaction potential between the nucleosomes, beyond the simple hard-core repulsion of the Tonks model. Other effects, including transient unwrapping of the nucleosomal DNA As stated in the main text, our application of the Tonks gas model to the nucleosome data is based on a number of simplifying assumptions. For instance, we assumed that the variation in nucleosome position indicated by the distribution of reads is a true reflection of the cell-to-cell variability. In practice, the nucleosome positions inferred from the reads have some (unknown) experimental error. However, To systematically compare the quantitative model to the The parameter estimates from fits to the +1 nucleosome alignment are robust against variations in details of the fitting procedure: (i) Fitting to the average over all genes yields almost the same parameter estimates as a fit to an average where only genes larger than 2000 bp are considered. For the latter, nucleosome density is estimated slightly higher due to the slightly further ranging oscillations , but it  compare  suggesti compare  indicatiFor the fit to just the −1 nucleosome alignment, we used the parameter estimates for nucleosome density  offset  , which wIn our systematic fitting procedure described above, we assumed the same average nucleosome density up- and downstream from the NFR. This must be justified by comparing the average density in intergenic regions to that in genic regions. To estimate their ratio, we used the proxies for nucleosome density described above, i.e., the read density  Two-particle distribution function (0.32 MB PDF)Click here for additional data file.Figure S2et al.et al. (Comparison between two proxies for the nucleosome density. (A) Section of the read density map ((0.46 MB PDF)Click here for additional data file.Figure S3Distribution of gene start and end sites and effects on alignments. (A) Probability distribution (black) and cumulative distribution (red) for the length of genes. Typical sizes of genes are about 1000 bp, but about nearly a third is larger than 2000 bp. (B) Same distributions for the distance between neighboring TSSs. Distances are in general comparable to the size of genes, but a number of TSSs are very close to each other. (C) Alignment of read density to +1 nucleosome and average over all genes (red dots) and those 1269 genes being larger than 2000 bp only (gray dots). The averages are very similar, but close inspection shows that amplitudes are slightly larger and oscillations range further when considering large genes only. (D) Alignment of read density to −1 nucleosome and average over all genes (blue dots) and those 952 genes where no gene starts or ends were found within 1000 bp upstream of the TSS (gray dots). The averages are very similar, but amplitudes are slightly smaller when considering those genes without other gene starts or ends upstream only.(0.52 MB PDF)Click here for additional data file.Figure S4Best fit of Tonks gas model (gray line) to +1 nucleosome alignment of read density including genes larger than 2,000 bp only (red dots). Visual inspection yields good agreement between model and data, comparable to the analogous fit to the data including all genes . For est(0.41 MB PDF)Click here for additional data file.Figure S5Best simultaneous fits (gray lines) to −1 and +1 nucleosome alignments of read density  given the four possible boundary conditions (both the +1 and −1 nucleosome may be directly or indirectly positioned) with nucleosome density and normalization being equal for both alignments see ‘.(1.00 MB PDF)Click here for additional data file.Figure S6Nucleosome organization around the 3′ end of genes. (A) Sketch of a typical nucleosome organization around both the 5′ and 3′ ends of genes. Throughout this study, the focus is primarily on the 5′ NFR with its flanking −1 and +1 nucleosomes. The nucleosomes flanking the 3′ NFR are here referred to as the 3′ end −1 nucleosome and the 3′ end +1 nucleosome. We determined the positions of 3′ end (0.42 MB PDF)Click here for additional data file.Table S1Parameter estimates from independent fits of Tonks gas model to (0.03 MB PDF)Click here for additional data file.Table S2Parameter estimates (density (0.03 MB PDF)Click here for additional data file.Table S3Parameter estimates for simultaneous fits of Tonks gas model to sitioned . See ‘Ma(0.04 MB PDF)Click here for additional data file.