To understand the regulatory function of SHP with regard to miR-206 gene expression, we determined the putative transcriptional initiation site of miR-206 and also its full length primary transcript using a database mining approach and RACE. We identified the transcription factor AP1 binding sites on the miR-206 promoter and further showed that AP1 (c-Jun and c-Fos) induced miR-206 promoter transactivity and expression which was repressed by YY1. ChIP analysis confirmed the physical association of AP1 (c-Jun) and YY1 with the endogenous miR-206 promoter. In addition, we also identified nuclear receptor ERRγ (NR3B3) binding site on the YY1 promoter and showed that YY1 promoter was transactivated by ERRγ, which was inhibited by SHP (NROB2). ChIP analysis confirmed the ERRγ binding to the YY1 promoter. Forced expression of SHP and AP1 induced miR-206 expression while overexpression of ERRγ and YY1 reduced its expression. The effects of AP1, ERRγ, and YY1 on miR-206 expression were reversed by siRNA knockdown of each gene, respectively. Thus, we propose a novel cascade “dual inhibitory” mechanism governing miR-206 gene transcription by SHP: SHP inhibition of ERRγ led to decreased YY1 expression and the de-repression of YY1 on AP1 activity, ultimately leading to the activation of miR-206. This is the first report to elucidate a cascade regulatory mechanism governing miRNAs gene transcription.MicroRNAs play a critical role in many essential cellular functions in the mammalian species. However, limited information is available regarding the regulation of miRNAs gene transcription. Microarray profiling and real-time PCR analysis revealed a marked down-regulation of miR-206 in nuclear receptor  SHP, NROB2) is a well established nuclear transcriptional co-repressor. SHP interacts with a broad range of nuclear receptors and transcription factors and inhibits their transactivation −/−SHP mice Small heterodimer partner  are highly conserved small RNA molecules of 22 nucleotides in length which regulate the gene expression by binding to the 3′-untranslated regions (3′-UTR) of specific mRNAs In this study, we cloned the full length primary transcript of miR-206 and elucidated a regulatory cascade activating miR-206 expression by SHP which involved AP1 (transcription factor activator protein 1), YY1 (Ying Yang 1), and ERRγ (estrogen related receptor gamma). This is the first report to elucidate a cascade regulatory mechanism governing miRNAs gene transcription.−/−SHP mice, which exhibited a 2-fold or greater decrease in expression (−/−SHP mice than in wild-type (WT) controls , which was followed by the consensus polyadenylation signal. This suggested that the 3′-end of this EST was complete. The 5′-end of this EST ended close to the 3′-end of the miR-206 hairpin sequences and did not contain miR-206, based on the genomic location of pre-miR-206 (−/−SHP mice as compared to the wild-type (WT) mice  mice , consistWT) mice . Because−/−SHP mice indicated a secondary effect due to the loss of SHP repression. We hypothesized a “dual inhibitory mechanism”, by which SHP repressed an intermediate gene that inhibited the miR-206 promoter resulting in a final outcome of SHP activating pri-miR-206 expression.A number of studies have established SHP as a transcriptional repressor. Thus, the decreased miR-206 expression in The transcription factor activator protein 1 (AP1) is a heterodimer nuclear protein composed of the proto-oncogene products c-Jun and c-Fos, and is involved in regulation of cell proliferation and tumor promotion However, this positive regulation was abrogated by YY1 co-expression  in the miR-206 promoter decreased AP1 activity, but did not abolish it , suggestChromatin immunoprecipitation (ChIP) analysis with a primer set covering the AP1 sites 1 and 2 confirmed the physical association of AP1 and YY1 with the endogenous miR-206 promoter in mouse hepatoma Hepa-1 cells by using specific c-Jun and YY1 antibodies . In contWe hypothesized that YY1 might be the intermediate gene through which SHP regulated miR-206 expression. Although the YY1 promoter had more than 90% GC content and was initially difficult to amplify, we successfully cloned it into a luciferase reporter . ERRγ is−/−SHP mice . The RNA quality control was performed using Bioanalyzer 2100. SHP knockouts (−/−SHP) and wild type mice on a pure C57/BL6 background were analyzed. The mice were given normal chow diet under feeding conditions. MiRNA microarray including labeling, hybridization, image scanning and initial data analysis was carried out by LC sciences . All protocols were deposited at ArrayExpress. LC-miRHumanMouseRat-9.1-070207-MRA-1030 array was used which was deposited in MIAMExpress. In brief, arrays were made based on μParaflo microfluidic technology (Atactic Technologies). On the μParaflo microfluidic chip, each detection probe consisted of a chemically modified nucleotide coding segment complementary to target microRNA  or other RNA (control or customer defined sequences) and a spacer segment of polyethylene glycol to extend the coding segment away from the substrate. The detection probes were made by in situ synthesis using photogenerated reagent (PGR) chemistry (Array Protocol: LC Mir-Array-Prtl-060518). Small RNAs (<300 nt) were 3′-extended with a poly(A) tail using poly(A) polymerase. An oligonucleotide tag was then ligated to the poly(A) tail for later fluorescent dye staining; two different tags were used for the two RNA samples in dual-sample experiments (Labeling Protocol: LC Mir-Label Prtl-060518). Hybridization was performed using a micro-circulation pump (Atactic Technologies). The hybridization conditions were 100 µL 6xSSPE buffer  containing 25% formamide, 34°C, and overnight (Hybridization Protocol: LC Mir-Hyb Prtl-060518). Hybridization images were collected using a laser scanner . Scan resolution was set at 10 µ and PTM is set between 350 to 700 V (Scanning Protocol: LC Mir-Scan Prtl-060518). Data were analyzed by first subtracting the background and then normalization. The background was determined using a regression-based background mapping method. The regression was performed on 5% to 25% of the lowest intensity data points excluding blank spots. Raw data matrix was then subtracted by the background matrix. Normalization was carried out using a LOWESS  method on the background-subtracted data . The data was deposited to the ArrayExpress database and the accession number is E-MEXP-1721 Protocols for animal use were approved by the Institutional Animal Care and Use Committee at the University of Utah. Total RNA with miRNA was isolated from the liver of two month old male mice (n = 3) using http://microrna.sanger.ac.uk/ sequences). The BLASTN search of the mouse genome was done online. The mouse EST and ncRNA database were downloaded from GenBank. A 3 kb genomic sequence centered in the miRNA precursor was extracted manually and used as query to search the EST and ncRNA database with the command “Megablast -e 1e-100 -F “m L” -D 3”. The Blast Packages (v2.2.10) were downloaded from the NCBI website (ftp://ftp.ncbi.nlm.nih.gov.blast/executables). The ncRNA and ESTs with the opposite transcription direction compared to query were removed from the Blast hits. The hits with an aligned length less than 95 of their original length were filtered out. After filtering, the ESTs matched to the −2 kb to 2 kb flanking regions were selected for further analysis MiRNAs precursor sequences were downloaded from the Sanger Institute  quantification of miRNA expression was carried out using TaqMan® MicroRNA Assays Kit  according to manufacturer's protocol. snoRNA202 was used as an internal control to normalize RNA input in the real-time RT-PCR assay. The detailed method was described in our recent publication an RNeasy Mini Kit  and mRNA was isolated using an Oligotex Direct mRNA Mini Kit . The GeneRacer Kit  was used to map the transcriptional initiation site of primary transcript. The first strand cDNA was synthesized at 65°C with Thermo-X™ reverse transcriptase using 2 µg of mRNA, followed by a polymerase chain reaction with 95°C denaturation step and then 45 cycles of touchdown annealing temperature. Primer sequences are indicated in Total liver RNA was isolated using 4 cells were plated per well in a 24-well plate. 30 ng of miR-206 or YY1 luciferase reporter construct, different concentrations of expression plasmid, and 30 ng of beta-gal plasmid pSV-β-Galactosidase Control Vector  were transfected using FuGENE HD . Different amounts of expression vector pcDNA3 were added to keep the final amount of DNA constant for all transfections. Thirty six hours after transfection, luciferase and β-galactosidase assays were performed using the Luciferase Assay System system and Beta-Glo® Assay System . Luciferase activities were normalized to galactosidase activities for each transfected well. For each experimental trial, wells were transfected in triplicate and each well was assayed in triplicate.Expression plasmids of SHP, ERRγ, YY1, c-Jun, and c-Fos were cloned into the pcDNA3 vector. Luciferase reporters of miR-206 and YY1 were cloned into the pGL3 reporter construct . Twenty four hours before transfection, 5×10°C for 10 min. Cells were washed with cold PBS twice and disrupted in SDS Lysis Buffer containing the protein inhibitor cocktail. Chromatin was sonicated to shear DNA to an average length between 200 bp and 1000 bp as verified by agarose gel. The sonicated cell supernatants were diluted 10 fold in ChIP Dilution Buffer containing the protein inhibitor cocktail and an aliquot of the solution was reserved for input control. Ten micrograms of YY1 , ERRγ , or c-Jun  antibodies were added and the chromatin solution was gently rotated overnight on ice. The protein A agarose slurry was added to the antibody bound chromatin solution and incubated at 4°C for 1 hr with constant rotation. The agarose beads were collected by centrifugation, washed and the antibody bound chromatin was released from the agarose beads. Finally, the DNA was purified by phenol/chloroform extraction and ethanol precipitation. The binding region was detected using primers in PCR reactions. A 10 kb region downstream from the binding site was used as negative control (n.s.). All ChIP primers used for miR-206 promoter and YY1 promoter are indicated in : 5′-CTACTTATGCAGCTAGAGATACAAG-3′ and R: 5′-ACTTCCAATAAGTCTTTGACCCATG-3′.ChIP Assays were performed using the ChIP Assay Kit . Hepa-1 cells were cultured until 70%–80% confluence. Chromatin was cross-linked with 1% formaldehyde at 37Mus musculus ERRγ, SHP, YY1 and c-Jun were purchased from Origene Company . Nmuli cells were cultured until 70%∼80% confluence. ShRNA constructs were transfected into the cells using transfection reagent Fugene HD (Roche) according to the manufacturer's instructions. The level of miR-206 expression was determined using real-time PCR.ShRNA constructs against All the experiments were repeated at least three times and the error bars represent the standard error of the mean (SEM). Statistical analyses were carried out using Student's unpaired t test; p<0.01 was considered statistically significant.Table S1MiRNAs with the largest magnitude of down-regulation in SHP−/− mice. miR-206 and miR-133b were clustered on chromosome 1, whereas miR-1 and miR-133a were clustered on chromosome 2. The expression level of miR-206 was markedly higher than miR-133b, and the expression level of miR-1 was markedly higher than miR-133b.(1.30 MB TIF)Click here for additional data file.Table S2Down-regulated miRNAs in SHP−/− mice on chromosome 1.(1.30 MB TIF)Click here for additional data file.Figure S1(a) Determining the putative transcriptional initiation site (TIS) by 5′-RACE for pri-miR-206. Total liver RNA was isolated using RNeasy Mini Kit  and mRNA was isolated using a Oligotex Direct mRNA Mini Kit . A GeneRacer Kit  was used to map the transcriptional initiation site of the primary transcript. (b) Chromatogram of 5′ RACE sequences of the pri-miR-206 primary transcript. The putative transcriptional initiation site of pri-miR-206 (G) is indicated by a pink arrow.(3.89 MB TIF)Click here for additional data file.Figure S2Mutagenesis assays. Two upstream putative AP1 sites (sites 1&2) (see (3.89 MB TIF)Click here for additional data file.Figure S3(a) Cloning of the high GC content mouse YY1 promoter. The PCR product was cloned into a pGL3-basic vector and used for transfection assays. The construct was verified by sequencing. (b) YY1 promoter sequences. TSS, transcriptional start site; ERRE, putative ERR binding site; YY1 pro. F and R, forward and reverse primers used to clone the YY1 promoter (pro.) and for ChIP assays.(3.89 MB TIF)Click here for additional data file.Figure S4Real-time PCR analysis of miR-206 expression in skeletal muscle of SHP−/− and SHP+/+ mice. Data is represented as mean±SEM. *Significantly different (p<0.01).(3.89 MB TIF)Click here for additional data file.


Saccharomyces cerevisiae, and its results were compared with available data, yielding reasonably good correlations. The calculated coefficients were used to perform a global analysis of translation in yeast, revealing some interesting aspects of the process. We have shown that two commonly used measures of translation efficiency – ribosome density and number of protein molecules produced – are affected by two distinct factors. High values of both measures are caused, i.a., by very short times of translation initiation, however, the origins of initiation time reduction are completely different in both cases. The model is universal and can be applied to any organism, if the necessary input data are available. The model allows us to better integrate transcriptomic and proteomic data. A few other possibilities of the model utilisation are discussed concerning the example of the yeast system.Translation is still poorly characterised at the level of individual proteins and its role in regulation of gene expression has been constantly underestimated. To better understand the process of protein synthesis we developed a comprehensive and quantitative model of translation, characterising protein synthesis separately for individual genes. The main advantage of the model is that basing it on only a few datasets and general assumptions allows the calculation of many important translational parameters, which are extremely difficult to measure experimentally. In the model, each gene is attributed with a set of translational parameters, namely the absolute number of transcripts, ribosome density, mean codon translation time, total transcript translation time, total time required for translation initiation and elongation, translation initiation rate, mean mRNA lifetime, and absolute number of proteins produced by gene transcripts. Most parameters were calculated based on only one experimental dataset of genome-wide ribosome profiling. The model was implemented in  Translation is the production of proteins by decoding mRNA produced in transcription, and is a part of the overall process of gene expression. Although the general theoretical background of translation is known, the process is still poorly characterised at the level of individual proteins. In particular, the quantitative parameters of translation, such as time required to complete it or the number of protein molecules produced from a transcript during its lifetime, are extremely difficult to measure experimentally. To overcome this problem, we developed a computational model that, on the basis of only few datasets and general assumptions, measures quantitatively the translational activity at the level of individual genes. We discussed it concerning the example of the yeast system; however, it can be applied to any organism of known genome. We used the obtained results to study the general characteristics of the yeast translational system, revealing the diversity of strategies of gene expression regulation. We exemplified and discussed other possible ways of model utilisation, as it may help in examining protein-protein interactions, metabolic pathways, gene annotation, ribosome queueing, protein folding, and translation initiation. It also may be crucial for better integration of cell-wide, high-throughput experiments. The rate of translation differs for individual proteins, reflecting both the intrinsic capability of an mRNA molecule to be translated and the environmental factors affecting the efficiency of the translation process. The first is well characterised in other studies Although the general theoretical background of translation is known, the process of protein synthesis is still poorly characterised at the level of individual proteins. Experimental determination of absolute translation rates  is a tremendous task and we are not aware of any such research. Even though the factors specified above have been studied separately for some proteins, little is known about the extent to which they affect the process and how they cooperate to keep the synthesis rate at the required level. Another strategy to examine translation activity is to integrate genome-wide expression datasets from different sources In practice, upon the development of techniques to examine transcriptome data experimentally , the mRNA concentration has become a broadly used measure of protein abundance. Nevertheless, recent research indicates that there is only a partial correlation between mRNA and protein abundances Saccharomyces cerevisiae, however, it can be used to study translation in any other organism of known genome, but only if the following data are available: (i) a dataset of mRNA relative abundance and ribosome footprints; (ii) tRNAs decoding specificities; (iii) average cell volume; (iv) average number of active ribosomes in a cell; (v) average number of mRNA transcripts in a cell; and (vi) a dataset of mRNA half-lives .In this study, we developed a model to measure the absolute, translational activity at the level of individual genes. The model was implemented in In our calculations for the yeast system the first dataset came from one genome-wide experiment provided by Ingolia et al. in vivo. In addition, other calculated parameters of translation, such as the overall rate of protein synthesis, were in agreement with earlier reports.We compared our results with direct experimental studies measuring the mRNA and protein levels of chosen genes. Good correlation with most of the experimental data was observed, and calculated mRNA and protein abundances did not differ significantly from those reported The calculated translational parameters were also used to study the general characteristics of the yeast translational system, revealing the diversity of strategies of gene expression regulation. For instance, we showed that two commonly used measures of translation efficiency – ribosome density and number of protein molecules produced – are affected by two distinct factors. We observed strong negative correlations between values of both measures and translation initiation time, however, the origins of initiation time reduction for most efficient transcripts are completely different. In case of elevated ribosome density, short initiation is caused mostly by mRNA instristic capability of being translated discussed at the beginning of this section. Contrary, in case of high number of protein molecules produced, short initiation is caused primarily by elevated mRNA concetrations.Finally, we exemplified and discussed other possible ways of model utilisation, as the model may be of considerable help in examining gene expression regulation, protein-protein interactions, metabolic pathways, gene annotation, ribosome queuing, protein folding, and translation initiation. Additionally, the model provides an overall and quantitative picture of the translation process, crucial for better integration of transcriptomic and proteomic data from high-throughput experiments.The following translational parameters were attributed to the yeast genes . This iIn Supplementary Interacting proteins are often precisely co-expressed, presumably to maintain proper stoichiometry among interacting components As a model, we chose the 20S proteasome complex, built of 28 proteins. There are 14 genes in the yeast genome coding for proteasome subunits The analysis of the translational parameters  argues that the number should be at least doubled Two reports provide an independent, yet coherent, estimation of the total number of ribosomes: 187,000Assuming Escherichia coliIn the previous section, we calculated the values of E. coli aa-tRNA molecules S. cerevisiae, we decided to use E. coliE.coli tRNA molecules is negligible. In Eq.16, The transport mechanism in the cytoplasm is diffusion, thus the aa-tRNAs act as a random walker, and the ribosomes on mRNAs with vacant A sites are the targets. We assume a yeast cytoplasm volume All 61 codons that code for the 20 amino acids have one or more aa-tRNAs and varying numbers of near-cognates. Near-cognates are defined as having a single mismatch in the codon-anticodon loop in either the 2nd or 3rd position. Since some cognate tRNAs have a mismatch in the 3rd position, these tRNAs are excluded from the set of near-cognates based on  and calcS. cerevisiae at 20, 24, 30, and 37According to It has been found that subsequent ribosomes are loaded onto the transcript sufficiently fast to make them interfere with each other, leading to ribosome queuing t0 measured at exponential growth in YPD medium for 5,718 ORFs. It was determined experimentally by independent studies that the absolute mRNA half-life of the yeast gene YOR202W (HIS3) ranges from 7 Click here for additional data file.Figure S2The comparison of translational parameters between genes of high and low protein abundance, as well as between genes of high and low ribosome density.(0.23 MB PDF)Click here for additional data file.Table S1A separate csv file containing calculated quantitative measures of translation for 4,621 yeast genes. There are 151 genes for which ribosome queuing was reported ; the values of translational parameters of these genes may be irrelevant. Column descriptions: (gene) the systematic name of the yeast gene taken from Saccharomyces Genome Database; (L) length of the transcript CDS in codons; (x) absolute number of gene transcripts in a yeast cell; (b) absolute number of proteins produced from one molecule of a transcript during its lifespan; (B) total amount of protein molecules produced from transcripts of a particular type (B = b * x); (g) ribosome density in number of ribosomes attached to a transcript per 100 codons (g< = 10); (w) absolute number of ribosomes attached to one transcript; (P) translation initiation frequency (the inverse of I); (Pz) relative rate of binding of free ribosomes to the 5′ end of a transcript; (Ps) relative rate of successful accomplishment of initiation once the ribosome-mRNA complex is formed; for clarity, normalised by the maximal observed value of Ps (65.88365), reported for gene YLL040C; (T) total time of translation of one protein molecule from a given transcript in ms (T = I + E); (I) total time (in ms) required for translation initiation, defined as a temporal interval from the point when the free 5′ end of a transcript becomes available for ribosomes to the moment when a ribosome finds the initiation AUG codon and the entire complex starts the phase of elongation; (E) total time required for translation elongation of a transcript in ms; (mean_E) mean time required for elongation of one codon of a transcript in ms; (h) estimated half-life of a transcript in ms; (m) estimated mean lifetime of a transcript in ms; and (queue) ribosome queuing index estimated at 30 Celcius degree: value “0” - no ribosome queuing was observed for a transcript, value “1” - ribosome queuing was observed for a transcript, and value “2” the translation at the 5′ end of a transcript is slow enough to delay the attachment of the successive ribosomes to the mRNA molecule.(0.58 MB CSV)Click here for additional data file.Table S2The list of codons and their properties.(0.02 MB PDF)Click here for additional data file.Table S3The translational parameters calculated for 14 genes coding proteins of the 20S yeast proteasome.(0.02 MB PDF)Click here for additional data file.Table S4Decoding specificities of yeast tRNAs and calculated values of the model parameters for particular codons.(0.02 MB PDF)Click here for additional data file.


A surprising level of evolutionary plasticity is revealed by analysis of differences between related yeasts in the mechanisms regulating the essential cellular process of ribosomal gene expression. S. cerevisiae the transcription factors Rap1, Fhl1, Ifh1, and Hmo1 form a multi-subunit complex that controls ribosomal gene expression, while in C. albicans this regulation is under the control of Tbf1 and Cbf1. Here, we analyzed, using full-genome transcription factor mapping, the roles, in both S. cerevisiae and C. albicans, of each orthologous component of this complete set of regulators. We observe dramatic changes in the binding profiles of the generalist regulators Cbf1, Hmo1, Rap1, and Tbf1, while the Fhl1-Ifh1 dimer is the only component involved in ribosomal regulation in both fungi: it activates ribosomal protein genes and rDNA expression in a Tbf1-dependent manner in C. albicans and a Rap1-dependent manner in S. cerevisiae. We show that the transcriptional regulatory network governing the ribosomal expression program of two related yeast species has been massively reshaped in cis and trans. Changes occurred in transcription factor wiring with cellular functions, movements in transcription factor hierarchies, DNA-binding specificity, and regulatory complexes assembly to promote global changes in the architecture of the fungal transcriptional regulatory network.Gene expression variation between species is a major contributor to phenotypic diversity, yet the underlying flexibility of transcriptional regulatory networks remains largely unexplored. Transcription of the ribosomal regulon is a critical task for all cells; in  S. cerevisiae and C. albicans to generate global changes in transcriptional network structures and new ribosomal regulatory complexes.Conserved metabolic machineries direct energy production and investment in most life forms. However, variation in the transcriptional regulation of the genes that encode this machinery has been observed and shown to contribute to phenotypic differences between species. Here, we show that the regulatory circuits governing the expression of central metabolic components (in this case the ribosomes) in different yeast species have an unexpected level of evolutionary plasticity. Most transcription factors involved in the regulation of expression of ribosomal genes have in fact been reused in new ways during the evolutionary time separating  Recently, several groups have linked phenotypic traits to changes in the expression of conserved gene in diverse metazoans like Darwin finches, sticklebacks, and flies S. cerevisiae, co-ordinate expression of RP subunit genes is brought about by a protein complex made of the essential factors Rap1, Hmo1, Fhl1, and Ifh1. Rap1 and Hmo1 recruit the nutrient-dependent Fhl1-Ifh1 complex exclusively to RP genes S. cerevisiae, the regulation of RP genes depends on intricate interactions among four regulatory proteins, specific DNA elements, and signaling pathways.Ribosomal proteins (RPs) and rRNAs are among the most conserved components of the cell, and the transcriptional regulation required to produce their stoichiometric and condition-dependent expression is a central cellular process. In C. albicans TF Tbf1 (assisted by Cbf1 at some loci) is the key DNA-binding regulator of RP genes and the rDNA locus in most fungal species C. albicans, while Rap1 governs the transcription of RP genes in S. cerevisiae. But the means by which the C. albicans Tbf1-dominated regulatory network performs the task of connecting ribosomal transcription with cellular signaling and the fate of the other S. cerevisiae regulators remains unknown. Here, we have used chromatin immunoprecipitation followed by microarray analysis (ChIP-CHIP) with full-genome coverage to show that conserved orthologous TFs can be profoundly repositioned within the regulatory network during evolution. Specifically, their regulons, their connections with cellular functions, their hierarchical position within the regulatory network, their DNA-binding specificity, and their assembly into higher order complexes are shaped during evolution.Previous studies have proposed that RP regulation has a high level of flexibility during evolution S. cerevisiae and C. albicans phylogenic branches and found that Cbf1, Hmo1, Rap1, Ifh1, Fhl1, and Tbf1 have a readily assignable ortholog in both groups and at least one region in their protein sequence is highly conserved  and the specialist (Ifh1 and Fhl1) factors. Generalist TFs have connections with multiple functional categories in at least one of the two species, while the specialists are highly targeted to RP gene regulation in both  network  and S4.C. albicans Cbf1 specifically binds upstream of RP genes, at the rDNA locus, and upstream of glycolytic genes  but moderately recruited to genes involved in functions such as mono- and polysaccharide metabolism  , ergosterol metabolism , and cell cycle regulation  (Our 0051726) .C. albicans Tbf1 is exclusively located at RP gene loci as previously described, with all Tbf1 peaks with log2 enrichment ratios above 5-fold located in RP intergenic regions (72/72) S. cerevisiae reveal that while it binds at only a few RP intergenic regions (5 RP genes/371 target genes), it is concentrated at sub-telomeric and telomeric regions  and RNA polII TF activity   , togetheS. cerevisiae Rap1 binds RP gene promoters , glycolytic enzyme promoters , the silent mating type locus, and the telomeres as reported  RP gene. Instead, C. albicans Rap1 binds to telomeres (p = 1.34×10−03). Altogether, apart from the connections of Cbf1 with sulfur starvation and respiration and of Rap1 with telomeric repeats, all edges in the functional network of these generalist TFs appear to have been reorganized between S. cerevisiae and C. albicans.reported  [49],,. Ining data  [58],. Th spacing , and thealbicans .C. albicans and S. cerevisiae; functional analysis of their genes with tetracycline repressible alleles and of their gene products by yeast-two-hybrid and co-immunoprecipitation supports that they are essential RP regulators forming a conditional FHA-FHB heterodimer  containing (pRPL11-wt-lacZ) or devoid (pRPL11-Δtbf1-lacZ) of the tbf1 element tbf1 palindrome caused dissociation of both Tbf1 and Fhl1 from the lacZ chimera (primer: lacZ), while binding was normal on the remaining wild-type RPL11 locus (primer: RPL11)  . This coS. cerevisiae and Tbf1-dependent in C. albicans raises the question of the evolution of the ribosomal regulatory complex. To better characterize the properties of these distinct TF assemblies, we interpolated the signal ratios of our tiling array data to each individual base pair and evaluated the co-variance of TF occupancies by calculating the pairwise scalar product of signal intensities along RP promoter regions for each pair of TFs. This detailed, multivariate analysis shows that not only the players within the RP regulatory complex have changed but that their positioning with respect to each other has also evolved. The most striking difference is the strict co-variance of Fhl1 and Tbf1 occupancies in C. albicans compared with the more loose association of Fhl1 and Rap1 signals in S. cerevisiae  before and after treatment with rapamycin  with downstream regulatory events, such as histone modifications, is conserved between species despite the substitution of the main DNA-binding module of the RP regulatory complex. DNA-binding TFs and the regions they associate with might thus be the most interchangeable parts of a machine conveying external stimuli to the transcriptional apparatus. Tinkering with components of the transcriptional regulation of metabolic machines must therefore obey to both internal and external demands and is likely subjected to some selective pressure.cis and trans. The changes observed include transitions from the control of general to highly specialized regulons, rewiring to distinct cellular functions and to chromosomal structural elements (centromeres and telomeres), modifications of the hierarchical position of TFs within the regulatory network, modifications in TF DNA-binding specificities, and the remodeling of TF assemblies. This reutilization of conserved TFs at RP promoters was thus accompanied by large-scale changes in the architecture of the fungal TRN. This reorganization of the ribosomal gene expression circuitry thus constitutes a striking example of evolutionary tinkering.Extensive as well as more limited transcriptional rewirings have been predicted for prokaryotic regulatory circuits CBF1, HMO1, IFH1, FHL1, and TBF1 were generated in the CAI4 background Cell growth, transformation, and DNA preparation were carried out using standard procedures URA3 PCR product containing 100 bp homology up and downstream of each ORF and transformed in the C. albicans BWP17 background HIS1 and Myc-ARG4 PCR products pRPL11-wt-lacZ and pRPL11-Δtbf1-lacZ chimeras were reported elsewhere S. cerevisiae Cbf1, Tbf1, Ifh1, and Hmo1 were obtained from Open Biosystems The Tbf1-TAP and Cbf1-TAP constructs were previously reported Protein interaction assays were done with fragments of Fhl1 and Ifh1 cloned by PCR in the Yeast-two hybrid plasmids pGADT7 and pGBKT7 between SfiI and XmaI (Clontech Laboratories Inc.). pGADT7 and pGBKT7 plasmids were then transformed in the haploid yeast strains pJ69-4a or pJ69-4α, respectively http://www.ircm.qc.ca/microsites/francoisrobert/en/317.html with the following exceptions: chromatin was sonicated to an average 300 bp and 700 ul of whole-cell extract (WCE) were incubated with IgG-sepharose , anti-HA (12CA5), or anti-H3K9 antibody  adsorbed to protein G sepharose  C. albicans whole genome microarrays C. albicans tiling arrays, or S. cerevisiae tiling arrays (Agilent Technologies). Microarray hybridization, washing, scanning, and normalization were performed as described 2 fold enrichment ratios, (2) interpolating oligonucleotide probe data to each base pair by a natural spline function, and (3) defining discrete peaks by sequentially extracting the most intense 1 bp peaks and masking a neighboring window of 600 bp (10 probes). Significant peaks were defined as having an enrichment value superior by at least two standard deviations (Z score = 2.0) to the mean of raw probe fold enrichment distributions defined individually for each experiment. The justification for this threshold can be found in ChIP experiments were performed as described previously with some modifications C. albicans Fhl1-HA/Ifh1-Myc or S. cerevisiae DH36 600nm of 0.7–0.8) and exposed to various treatments: 1 ng/ml rapamycin for 30 min, heat shock at 46°C for 1 h, osmotic shock (OS) with 0.5 M of sodium chloride in YPD for 30 min, and hypoxia in oxygen-depleted YPD from 0.1 OD600nm to mid-log phase in sealed flasks. Cells at a final OD600nm of 1.0–1.5 were harvested by centrifugation and lysed by bead beating in IP150 buffer . The lysates were then cleared by centrifugation and protein concentration was estimated using the Bradford assay. One mg of total protein was added to 40–50 ul of monoclonal mouse anti-Myc (9E10) or anti-HA (12CA5) conjugated beads (Roche) and incubated at 4°C with end over end movement overnight. The next morning, beads were spun down at 4,000 rpm at 4°C, washed 3 times with IP150 buffer, boiled with SDS-PAGE loading buffer, and resolved on a 4%–20% gradient SDS-PAGE. Proteins were transferred onto a nitrocellulose membrane, blocked in 10% milk in PBST for 1 h at room temperature, and exposed to rabbit Anti-Myc (1∶1000) (Santa Cruz) or Anti-HA (1∶2500) (Roche) antibody overnight at 4°C. The membranes were finally hybridized with a goat-anti-rabbit HRP (1∶5000) (Thermo Scientific) and revealed with the Lumi-light Western Blotting Substrate (Roche).C. albicans ACT1 ORF as a reference.Quantitative real-time PCR was performed using the Corbett Rotor-Gene RG-3000A  with SYBR Green fluorescence (Qiagen). Real-time PCR was performed using 1 ng of ChIPed DNA or total genomic DNA extracted from WCE. Cycling was for 15 min at 95°C, followed by 45 cycles of 95°C, 10 s, 56°C, 15 s, and 72°C, 15 s. All samples were tested in triplicate and means were used for further calculations. Fold enrichments of tested promoter sequences were estimated by using the coding sequence of the Electropherograms of RNA species distributions were obtained by capillary electrophoresis with fluorescence detection on an Agilent Bioanalyzer 2100 (Agilent Technologies). Bioanalyzer RNA 6000 Nano Chips (Agilent Technologies) were loaded with 250 ng of total RNA before and after treatment with tetracycline by following the manufacturer's protocol.http://www.embl.de/~chenna/clustal/darwin/) http://pbil.univ-lyon1.fr/software/seaview.html) http://rana.lbl.gov/EisenSoftware.htm).Multiple sequence alignments were performed with clustalX (http://meme.sdsc.edu/meme4/cgi-bin/meme.cgi) cis-regulatory elements, every mini-motif composed of two nucleotide triplets separated by less than 16 bp (XXXn(0–15)XXX) was tested for its over-representation in peak regions compared to its occurrence in a randomized sequence space of equal size. Sequences possessing the mini-motif were defined as those with at least one instance of the motif or its reverse complement in their upstream region. The enrichments were calculated using a hypergeometric distribution.For motif detection, a DNA sequence corresponding to a 300 bp window centered on each tiling array peak was extracted. The sequences corresponding to the highest quarter of signal ratios for each TF were submitted to the MEME online server  and the Saccharomyces genome database  as well as http://www.broad.mit.edu/regev/orthogroups/. GO annotations of C. albicans and S. cerevisiae were obtained from CGD and SGD. Gene's GO annotations were systematically expanded to include the GO terms hierarchical relationships obtained from http://www.geneontology.org/. These annotations were used as queries for the sets of targets of each TF. GO enrichments in each set of TF-bound target genes were calculated with the hypergeometric distribution p<10–4 with a randomized set of GO categories of equal size, and we used a conservative threshold of p<10–2 in the analysis of the TF-GO network.Orthology tables relating http://www.yeastract.com/) http://kinase.com/scerevisiae/yeastkinase.htm), respectively. These lists were limited to the set of genes that share orthologs in S. cerevisiae and C. albicans and then intersected with lists of TF target genes from our ChIP-CHIP data using Microsoft Access.The lists of TFs and kinases used to generate the TF-TF and TF-kinase networks presented in http://pajek.imfm.si/doku.php) and the raw flatfiles were converted to Pajek format with Excel2Pajek (http://vlado.fmf.uni-lj.si/pub/networks/pajek/howto/excel2Pajek.htm).The TF-GO, TF-TF, and TF-kinase networks represented in Dataset S1Targets of C. albicans Cbf1, Hmo1, Rap1, Fhl1, Ifh1, and Tbf1 transcription factors determined by full-genome tiling arrays.(1.09 MB XLS)Click here for additional data file.Dataset S2Targets of S. cerevisiae Cbf1, Hmo1, Rap1, Fhl1, Ifh1, and Tbf1 transcription factors determined by full-genome tiling arrays.(1.04 MB XLS)Click here for additional data file.Figure S1Variation in the primary sequence and domain organization of orthologous TFs. Alignment similarity maps of orthologs of the transcription factors (TFs) Cbf1, Hmo1, Rap1, Tbf1, Fhl1, and Ifh1 involved in the ribosomal protein (RP) transcriptional regulatory network of S. cerevisiae or C. albicans. C.a. and S.c. stand for C. albicans and S. cerevisiae and were used in all figures. Shading of the alignments reflects the percentage of conservation within the C. albicans  or the S. cerevisiae  branches or between the two branches. Histograms reflect the average phylogenetic distance derived from the PHYLIP distance matrix within (intra-S.c. and intra-C.a.) or between (C.a.-S.c.) branches. Distances showing a significant difference (p<0.01) compared to the reference tree are highlighted with arrowheads.(0.67 MB TIF)Click here for additional data file.Figure S2Validation of generalist TF target promoters by ChIP-qPCR in C. albicans (A) and S. cerevisiae (B). (C) Validation of the occupancy of Tbf1-TAP, Ifh1-TAP, and Fhl1-TAP at RP gene promoters and the rDNA control regions (NTS1 and NTS2) in C. albicans by ChIP-qPCR. Error bars reflect one standard deviation from the mean of three independent biological replicates.(0.21 MB TIF)Click here for additional data file.Figure S3Validation of ChIP-CHIP thresholds. (A) Distribution of signal intensities for each transcription factor and in each species. The threshold for each experiment (Z score of 2.0) is shown as a black bar. (B) The p value of overlap of orthologous TF regulons across species is threshold insensitive. Randomization with (C) and without (D) correction for promoter length shows that long promoters are an inherent source of experimental noise at Z score values below 1.5. The threshold used (2.0) is displayed as a dashed line.(0.47 MB TIF)Click here for additional data file.Figure S4Enrichment of TF target gene sets for ribosome, carbon utilization, respiration, and sulfur/amino acid biosynthesis GO categories is robust to threshold. The heatmap depicts the strength  of TF-GO interactions.(0.33 MB TIF)Click here for additional data file.Figure S5Binding of Cbf1 to all S. cerevisiae centromeric regions. No significant binding was observed for Hmo1, Rap1, Ifh1, Fhl1, and Tbf1.(0.67 MB TIF)Click here for additional data file.Figure S6Results of ChIP-CHIP experiments showing binding to transcription factors (A and B) and central cell cycle regulators (C and D) gene promoters by generalist transcription factors in S. cerevisiae (B and D) and C. albicans (A and C).(0.88 MB TIF)Click here for additional data file.Figure S7Evolution of TF interactions with promoters of genes involved in cell signaling and the cell cycle. A map of all significant regulatory relationships between each pleiotropic TF and kinases or cyclins listed in the kinase database  was drawn.(0.20 MB TIF)Click here for additional data file.Figure S8Evolution of the co-occurrence of generalist TFs in promoter regions within species. (A) Heatmap reflecting the p value of the overlaps of target genes of generalist TFs within and between species in the subset of orthologous genes conserved between S. cerevisiae and C. albicans. Within species overlaps between the sets of targets of Cbf1 and Rap1 (A), Cbf1 and Tbf1 (B), and Rap1 and Tbf1 (C) in C. albicans and S. cerevisiae. The p values of each overlap were calculated using a hypergeometric distribution and are shown beneath each Venn diagram. NS stands for nonsignificant overlap.(0.23 MB TIF)Click here for additional data file.Figure S9Validation of the Rap1 motifs obtained in C. albicans  and S. cerevisiae (B). The C. albicans Rap1 motif (A) is highly enriched at Rap1-bound regions (33/40 regions above a Z score of 2.0). The S. cerevisiae motif derived from our data is consistent across various randomized sets of 40 Rap1 target promoters  (C). The C. albicans consensus is clearly partitioned in two classes (C), one of which (class I) includes the C. albicans telomeric repeat .(0.63 MB TIF)Click here for additional data file.Figure S10Phenotypic characterization of transcription-factor-conditional-mutants in C. albicans. (A) The ifh1/tetO-IFH1 and fhl1/tetO-FHL1 conditional mutants are growth defective in rich medium. Ten-fold dilutions of the indicated strains were spotted on YPD with or without 100 µg/ml of tetracycline. (B) Effect of tetracycline shutoff of CBF1, HMO1, IFH1, FHL1, TBF1, and TOR2 expression on rRNA abundance as observed on a total RNA electropherogram (RFU: Relative Fluorescence Units). (C) Expression profiling of ribosomal genes in conditional mutants shows that Ifh1, Fhl1, Tbf1, and Tor2 shutoff specifically down-regulate RP genes. RP genes are also down-regulated after rapamycin treatment and in a cdc35Δ/cdc35Δ mutant. Time of tetracycline or rapamycin treatment in hours is shown.(1.82 MB TIF)Click here for additional data file.Figure S11Results of ChIP-CHIP experiments showing the enrichment profiles of various TFs at 10 randomly chosen RP genes of C. albicans (A) and S. cerevisiae (B).(1.03 MB TIF)Click here for additional data file.Figure S12Distribution of the pairwise distances (in bp) between peaks of enrichments of transcription factors occupying ribosomal protein promoters in S. cerevisiae (A) and C. albicans (B).(0.21 MB TIF)Click here for additional data file.Figure S13Ifh1 and Fhl1 interact in a nutrient- and stress-dependent fashion in C. albicans. The yeast two-hybrid assay (Y2H) confirms that the Fhl1-Ifh1 heterotypic interaction occurs within and between species through their FHA and FHB domains, respectively. The FHA and FHB domains were expressed from pGADT7 and pGBKT7 Y2H vectors and monitored by growth on selective media (A) or beta-galactosidase assays (B). (C) Co-immunoprecipitation of full-length in vivo tagged Fhl1-HA and Ifh1-Myc after rapamycin treatment and various stresses . (D) Model of the ribosomal protein regulatory complex of C. albicans and S. cerevisiae.(0.51 MB TIF)Click here for additional data file.Table S1Strains used in this study.(0.17 MB DOC)Click here for additional data file.Text S1Supporting results.(0.11 MB DOC)Click here for additional data file.


Escherichia coli the transcriptional regulation of gene expression involves both dedicated regulators binding specific DNA sites with high affinity and also global regulators – abundant DNA architectural proteins of the bacterial nucleoid binding multiple sites with a wide range of affinities and thus modulating the superhelical density of DNA. The first form of transcriptional regulation is predominantly pairwise and specific, representing digitial control, while the second form is (in strength and distribution) continuous, representing analog control.In the bacterium Here we look at the properties of effective networks derived from significant gene expression changes under variation of the two forms of control and find that upon limitations of one type of control  the other type can compensate for compromised regulation. Mutations of global regulators significantly enhance the digital control, whereas in the presence of global DNA architectural proteins regulation is mostly of the analog type, coupling spatially neighboring genomic loci. Taken together our data suggest that two logically distinct – digital and analog – types of control are balancing each other.By revealing two distinct logical types of control, our approach provides basic insights into both the organizational principles of transcriptional regulation and the mechanisms buffering genetic flexibility. We anticipate that the general concept of distinguishing logical types of control will apply to many complex biological networks. Escherichia coli populations can demonstrate remarkable flexibility in genetic adaptation [E. coli the largest electronically accessible network integrating the data on the transcriptional regulation of genes is available [One important objection to Lamarckian evolution by inheritance of acquired characteristics emphasized by Bateson over forty years ago is the reduction of adaptational flexibility upon progressive specialization, necessitating the occurrence of genotypic changes compensating for this limitation . In unicaptation . The mecvailable . The intvailable , e.g. a vailable ,7 indicavailable -10 . Indeedvailable ,11 and tvailable ,13. FromIn the following, we will translate the patterns in gene expression changes observed under systematic variation of the two types of control into effective networks and study their connectivity. The effective networks are derived as subnetworks of two larger (static) networks: (1) the transcriptional regulatory network based upon the action of dedicated transcription factors; (2) spatial proximity of two genes on the circular chromosome.We will statistically compare the properties of these effective networks with those obtained by random sampling of the static networks with a certain number of expression changes. The core quantity derived from these comparisons is the ratio of connected to isolated nodes (control ratio) and, furthermore, its z-score with respect to the random networks. This z-score we denote the confidence level of the particular control type .E. coli cells. The rationale is to investigate transcript profiles obtained under conditions where we either modulate the analog component of regulation under constant digital control, or modulate the digital component keeping the analog control constant. We modulate the analog component by experimentally varying the negative superhelical density (-σ) of chromosomal DNA within the same genetic background . Such variation of -σ is carried out within three genetic backgrounds – the wild type E. coli and two mutant strains lacking one of the two abundant DNA architectural proteins, either FIS or H-NS. These comparisons produce the so-called intra-strain transcript profiles [fis or hns, both representing hubs in the TRN) and comparing the wild type and mutant transcript profiles at a single constant superhelical density – either DNA relaxation (-σ < 0.033) or high negative supercoiling (-σ > 0.08). These comparisons produce the so-called inter-strain transcript profiles [fis, hns for wild type, fis mutant and hns mutant backgrounds respectively), and four inter-strain profiles (wt-fis and wt-hns both at relaxation (↓σ) and high negative supercoiling (↑σ) reflecting analog-type control).In this study we aim at understanding the relationships between the digital and analog types of control in transcriptional regulation by using the model system of exponentially growing profiles   of E. coli is the basis of many recent studies on network architecture ,15, as ws Figure . A conves Figure , indicats Figure , as expefis and hns mutants, respectively. The GPNs of the hns mutant primarily reflect the spatial connectivity between de-repressed genetic loci, especially since H-NS represses whole regulatory systems rather than selectively targeted individual gene components [fis experiments (wt-fis ↑σ and ↓σ) and the genes with negative log ratio in both hns experiments (wt-hns ↓σ and ↑σ). A consistent difference of calculated CTCs is observed over the whole sensible range of GPN thresholds  in Figure hns mutant (wt-hns ↓σ and ↑σ) in Figure The schematics used in Figure mponents ,19. We ts Figure . In bothA unifying approach enabling to combine the data derived by different methodologies is essential for understanding the basic organizational principles of transcriptional regulation, especially since recently transcriptional sub-networks with organizationally distinct architectures have been described . In thisE. coli strains lacking global DNA architectural proteins. In turn, alterations of the digital component (changing TRN by deleting hubs) expose a substantial contribution of analog-type control (approximated by the GPN) to transcriptional regulation in wild type cells. Since the digital and analog types of control are constituents of a single transcriptional regulatory system of the cell, our data suggest that these two logically distinct types of control are balancing each other, such that upon limitations of one type of control  the other type can compensate for compromised regulation  effectively exposes the contribution of digital-type control (represented by the TRN) to transcriptional regulation, which is significantly increased in n Figure . What iscription .coli, spatial organisation of transcription is observed in both, prokaryotes and eukaryotes [E. coli this phenomenon can be readily rationalized on the basis of topological domains of variable size underlying the organization of bacterial chromosome [fis and hns genes are also consistent with long-range effects of these proteins on the three-dimensional structure of DNA [While this network is intimately involved in spatial organization of transcription in E.karyotes ,22. In Eromosome -25. Inderomosome . Thus thromosome ,18. Furte of DNA ,18,25. Fe of DNA . We noteE. coli will affect the determinants of global DNA architecture. Indeed, a recent study of long-term experimental evolution in E. coli unmasking DNA topology as a key target for selection identified fitness-enhancing mutations in topoisomerase and fis genes [fis is a relatively late acquisition in bacterial evolution [One prediction from the observed interdependence between digital and analog types of transcriptional control is that adaptive mutations in is genes . Furtheris genes . We propis genes . In thisvolution , whereasvolution ,29.E. coli TRN database RegulonDB with respect to both, their digital and analog control characteristics – for example, consideration of the supercoiling sensitivity of the genes – might be a worthwhile extension of this database.We believe that the general concept of distinguishing logical types of control developed in this study will apply to many complex biological networks. We shall also emphasize that based on our data, reinterpretation of the interactions contained in the fis and hns LZ strains was carried out using E. coli K12 V2 OciChip™ DNA microarray. The genetically engineered E. coli LZ41 and LZ54 strains contain drug-resistant topoisomerase gene alleles enabling to selectively inhibit either DNA gyrase or topoisomerase IV activity and respectively induce either relaxation or high negative supercoiling [fis and hns mutants of the LZ41 and LZ54 strains were obtained by phage P1 transduction. Introduction of the fis and hns mutations in the LZ41 and LZ54 strains does not substantially alter the global supercoiling response to drug (norfloxacin) addition [Transcript profiling for wild type, rcoiling . The fisaddition . Each exaddition . A one-caddition .The latest version of the RegulonDB 5.6 data sets  "gene prflhC, flhD; gatR_1, gatR_2; hupA, hupB; ihfA, ihfB; rcsA, rcsB) were replaced by unique Regulon DB identifiers . The effective TRN subnet of a DNA microarray transcript profile is the set of affected genes in the TRN and their regulatory interactions contained in RegulonDB . All affected genes with spatial distance (here distance is relating to ORF start and stop position) below the selected proximity threshold t were considered as connected. GPN subnets were generated for a meaningful range of 1b <t < 10 kb, resulting in connected genes within an operon scale at t ≈10b, up to completely conntected GPNs for t > 10 kb. Connected and unconnected subnet components were further analysed [see Additional file Preceding GPN subnet construction, the inter-strain transcript profile data was split up into genes with positive and negative log ratios, respectively. Genes with positive log ratios refer to high transcript levels in wild type background, genes with negative log ratios refer to high transcript levels in R was calculated as the number of connected nodes Nconnected (i.e. the size of the connected subnet component) over the number of isolated nodes Nisolated (i.e. the size of the unconnected subnet component), R = Nconnected/Nisolated. The control type confidence, CTC, is the z-score of R, calculated from the mean R and its standard deviation obtained from 10000 runs of the corresponding null model. In the case of the digital null model, the same number of affected nodes was mapped randomly on the TRN (see Figure For each subnet, the control ratio The robustness of calculated ratios and CTCs was verified by 10% random data replacement with data of all affected genes from the remaining DNA microarray sets (see Figure CTC, control type confidence, GPN, gene proximity network, TRN, transcriptional regulatory network.CM, MG, MTH, and GM conceived the study. CM and MG analyzed the data. CM, MG, MTH, and GM wrote the paper. All authors read and approved the final manuscript.Dataset S1. Gene identifier and corresponding edge lists of the seven directed effective TRNs emerging from the analysis of the seven transcript profiles.Click here for fileDataset S2. Gene identifier and corresponding edge lists of the eight undirected GPNs emerging from the seperated analysis of the four inter-strain transcript profiles with positive and negative log ratio, respectively.Click here for file


The eukaryotic genome is packaged as chromatin with nucleosomes comprising its basic structural unit, but the detailed structure of chromatin and its dynamic remodeling in terms of individual nucleosome positions has not been completely defined experimentally for any genome. We used ultra-high–throughput sequencing to map the remodeling of individual nucleosomes throughout the yeast genome before and after a physiological perturbation that causes genome-wide transcriptional changes. Nearly 80% of the genome is covered by positioned nucleosomes occurring in a limited number of stereotypical patterns in relation to transcribed regions and transcription factor binding sites. Chromatin remodeling in response to physiological perturbation was typically associated with the eviction, appearance, or repositioning of one or two nucleosomes in the promoter, rather than broader region-wide changes. Dynamic nucleosome remodeling tends to increase the accessibility of binding sites for transcription factors that mediate transcriptional changes. However, specific nucleosomal rearrangements were also evident at promoters even when there was no apparent transcriptional change, indicating that there is no simple, globally applicable relationship between chromatin remodeling and transcriptional activity. Our study provides a detailed, high-resolution, dynamic map of single-nucleosome remodeling across the yeast genome and its relation to global transcriptional changes. Saccharomyces cerevisiae. We isolated nucleosomal DNA before and after subjecting cells to heat shock, then identified the ends of these DNA fragments and, thereby, the location of nucleosomes along the genome, using ultra-high–throughput sequencing. We identified localized patterns of nucleosome displacement at gene promoters in response to heat shock, and found that nucleosome eviction was generally associated with activation and their appearance with gene repression. Nucleosome remodeling generally improved the accessibility of DNA to transcriptional regulators mediating the response to stresses like heat shock. However, not all nucleosomal remodeling was associated with transcriptional changes, indicating that the relationship between nucleosome repositioning and transcriptional activity is not merely a reflection of competing access to DNA.The eukaryotic genome is packed in a systematic hierarchy to accommodate it within the confines of the cell's nucleus. This packing, however, presents an impediment to the transcription machinery when it must access genomic DNA to regulate gene expression. A fundamental aspect of genome packing is the spooling of DNA around nucleosomes—structures formed from histone proteins—which must be dislodged during transcription. In this study, we identified all the nucleosome displacements associated with a physiological perturbation causing genome-wide transcriptional changes in the eukaryote  Ultra-high-throughput sequencing is used to show that distinct, localized patterns of nucleosome repositioning at promoters underlie the genome-wide transcriptional response to a physiological stimulus. The eukaryotic genome is compacted into nucleosomal arrays composed of 146-bp DNA wrapped around a core histone octamer complex . ComputaRecently, chromatin immunoprecipitation (ChIP)-sequencing technology was used to map the positions of nucleosomes containing the variant H2A.Z histone across the yeast genome . H2A.Z nTo map the location of individual nucleosomes on a genomic scale and at high resolution, we used ultra-high–throughput sequencing methodology (Solexa/Illumina) to sequence the ends of nucleosome-associated DNA. Our approach enabled us to map individual nucleosomes nominally at single-nucleotide resolution. Nucleosome density and stability at promoters and over coding regions were correlated specifically with transcription rate rather than absolute transcript levels. Two different modes of chromatin remodeling were associated with transcriptional regulation. Gene activation was mainly accompanied by the eviction of one to two nucleosomes from the promoter, and gene repression was mainly accompanied by the appearance of nucleosomes with varying stability over the promoter. Our work constitutes the first study of dynamic single-nucleosome remodeling in response to transcriptional perturbation across an entire eukaryotic genome.We used micrococcal nuclease to isolate mononucleosome-associated DNA from yeast cells before and after a physiological perturbation (heat shock for 15 min) that causes genome-wide transcriptional changes, and sequenced the ends of the fragments. Only uniquely aligning reads were used to define the ends of nucleosomal DNA. After aligning sequence reads to the genome, we defined nucleosome peaks by first using a Parzen window probability estimation of read densities, then defining a peak of width 146 bp around the centers of appropriately spaced maxima in the density function . Our approach yielded nucleosome positions at single-nucleotide resolution. We calculated a score for the position and stability of each nucleosome, which were normalized to account for differences in sequencing depth. Scores in the range of 0.2 to 0.25 and higher indicated nucleosomes whose positions often matched in the two independent biological samples and, hence, indicated bona fide nucleosomes; nucleosomes below this threshold were defined by too few reads to be discernable above background. At a score cutoff of 0.25, we defined the locations of 49,043 nucleosomes in normally growing cells and 52,817 nucleosomes in heat-shocked cells. Assuming that two adjacent nucleosomes cannot be closer than 200 bp, altogether about 73% of the yeast genome is covered by a positioned nucleosome. Since only uniquely aligning reads were used in our analysis, and the yeast genome contains an appreciable fraction of repeated sequence elements, we estimate that about 78% of the genome is covered by positioned nucleosomes.PHO5 promoter. The yeast PHO5 promoter is repressed during growth in rich media by specifically positioned nucleosomes flanking a short, hypersensitive region containing a binding site for the transcription factor Pho4 [PHO5 promoter . We plotted the average nucleosome profile over all yeast genes to get an idea of how individual nucleosomes were distributed in relation to promoters and coding regions. Several features of chromatin organization were evident from this plot . A positive value in the remodeling profile at a given promoter position indicated that there was a nucleosome covering the position during normal growth, but was depleted or evicted upon heat shock. A negative value indicated the opposite, namely, the appearance of a more strongly positioned nucleosome following heat shock. We grouped the remodeling profiles by p = 0.02) [p < 0.02). Group 3 showed enrichment for the remodeler Swi5 (p = 0.002).We first analyzed remodeling profiles for promoters that were activated at least 2-fold and promoters that were repressed at least 2-fold by heat shock A and 4B. = 0.02)  PromoterThe difference in nucleosome profiles between Group 1 and Group 2 genes and the differential enrichment of the two major stress transcription factor targets points to two distinct modes of action by these activators. Hsf1 is constitutively bound to many heat shock gene promoters . The nucGenes repressed more than 2-fold after heat shock could also be clustered into four major groups based on their nucleosome remodeling profiles B. Group p = 2.6 × 10−5). In addition, Group 1 was significantly depleted for targets of all the above-mentioned transcription factors, and was also significantly depleted for ribosomal protein genes (p = 6.5 × 10−4).The enrichment of transcription factor targets in these four groups based on data from the yeast functional regulatory network  and tranIn order to quantitate whether distinct modes of nucleosome remodeling were generally used for gene activation and repression, we calculated a nucleosome remodeling score for both nucleosome eviction and nucleosome appearance . Activated genes showed significantly higher nucleosome eviction than nucleosome appearance, whereas repressed genes showed significantly higher nucleosome appearance than eviction C. AlthouSince ribosomal protein genes form one of the most prominent classes of genes that are transcriptionally repressed by heat shock, we analyzed nucleosome changes at their promoters separately. Ribosomal protein genes were clustered into three classes based on the presence or absence of a well-positioned nucleosome between −50 and +100 bp in normally grown cells, and the nucleosome score. Upon heat shock, we observed the appearance of medium- to high-scoring nucleosomes between −200 and +100 bp of almost all of these ribosomal protein genes in the three groups D.This analysis of nucleosomal changes at the promoters of the most strongly regulated genes indicates that chromatin remodeling events accompanying transcriptional regulation are restricted to a small number of discrete patterns involving one or two nucleosomes, rather than encompassing a larger domain around the promoter. We also clustered the nucleosome remodeling profiles for genes whose expression did not change appreciably by the physiological perturbation (less than 1.2-fold change). Surprisingly, we still observed similar specific patterns of single-nucleosome remodeling events at many of these promoters, indicating that specific nucleosome events are not universally associated with transcriptional changes E.UBC4 promoter, which is activated by heat shock, three moderately positioned nucleosomes covering two distinct Hsf1 binding sites as well as the TSS were evicted, whereas a single, well-positioned nucleosome appeared between the two Hsf1 binding sites . CaCl2 was added to a final concentration of 3 mM, and micrococcal nuclease digestions were carried out at concentrations ranging from 100 U/ml to 600 U/ml for 10 min at 37 °C. The reactions were stopped by adding 100 μl of 5% SDS and 50 mM EDTA. A total of 3 μl of 20 mg/ml proteinase K was added to each tube, and incubated at 65 °C overnight. The DNA was purified by phenol-chloroform-isoamyl alcohol (25:24:1) extraction, and precipitated using ethanol. The DNA was treated with DNase-free RNase, re-extracted with phenol-chloroform-isoamyl alcohol, precipitated with ethanol, and resolved on a 1.25% agarose gel alongside a 100-bp ladder. The mononucleosome size band (approximately 150–200 bp) was excised and purified using the Invitrogen Pure-Link quick gel extraction kit. The purified DNA was sequenced using Solexa sequencing technology.Yeast S288C cultures were grown in rich medium and subjected to 15-min heat shock as described previously ,33. At tS288C cells from 50-ml cultures before and after heat shock at 39 °C for 15 min were resuspended in 8 ml of AE buffer . RNA extraction, cDNA labeling, and microarray manufacture and hybridizations were done as described previously ,20,33. FPHO5 just upstream of the known Pho4 binding and DNaseI hypersensitive site [YCR023C and YCR024C. qPCR was performed using SYBR green chemistry on an ABI 7900 instrument. Enrichment of target loci in the ChIP sample relative to sonicated genomic DNA was calculated for both unstressed cells and cells subjected to heat shock.Primer pairs used in ive site . ControlSaccharomyces Genome Database (SGD) (http://www.yeastgenome.org/) and only reads that mapped uniquely to the genome were considered in the majority of our analysis. We generated 514,803 and 1,036,704 uniquely aligning reads for the normal and heat-shock growth conditions, respectively. Reads mapping to the plus and minus strands were processed separately. Reads were clustered using a Parzen window–based approach. Essentially, a Gaussian kernel was centered on each base pair in the genome, and a weighted score was calculated at that position. The mean of the Gaussian was taken as the position under consideration, with the standard deviation (smoothing bandwidth) set at 20 bp. Each read contributed to the mean position based on its kernelized distance from the mean. The weighted score indicated the likelihood of finding an edge of the nucleosome at the position. Thus, the entire genome was converted into a likelihood landscape that was further processed to find local maxima . For each nucleosome in unstressed cells, the distance to the nearest nucleosome after heat shock was calculated. These data are reported in Reads equal in number to those we obtained from normal and heat-shocked cells were selected at random from the yeast genome assembly Oct 2003, and peak finding was done as described. This process was iterated 20 times. The average maximum score obtained in the simulations was used as a scaling factor to normalize nucleosome peak scores for cells grown at 30 °C. Normalization was done by dividing nucleosome peak scores by the scaling factor. We then calculated a scaling factor for the heat-shock data by multiplying the scaling factor for the 30 °C data by the ratio of the median peak scores for 39 °C to the peak scores for 30 °C. This was done to correct for differences in sequencing depth for the two samples, thus enabling quantitative comparison of nucleosome profiles across the two conditions.The upstream −600 bp to downstream +1,000 bp of each uncharacterized and verified ORF in SGD was binned at 10 bp, and nucleosomes were mapped to each bin. The zero point was the TSS. A nucleosome was said to map to a given bin if it completely overlapped with the 10-bp bin. Each bin was assigned the score of the overlapping nucleosome. In the cases where our algorithm detected overlapping positions for a nucleosome, and more than one nucleosome mapped to a single bin, the bin was assigned the highest score. Genes were separated into TATA-containing or TATA-less , and theThe NPP score was generated by calculating the similarity of the experimentally derived nucleosome profile over the coding region of every gene to an artificially generated profile where six nucleosomes of score 1.0 were regularly placed with 30-bp linker lengths. In general, genes with well-positioned nucleosome had profiles that were most similar to the synthetic profile and hence, had a high NPP score. The first (+1) nucleosome downstream of the TSS is adjacent to a gap and is likely to be more strongly sequence dependent for positioning than a nucleosome that is flanked by other nucleosomes. We therefore derived AA/TT profiles from the sequence underlying the first nucleosome. To derive high-confidence sequence profiles, we aligned all genes to the first nucleosome as shown in Genes that did not a have 200-bp–long promoter region were excluded for this analysis. For all of the genes that passed this filter, the difference between the nucleosome scores in normally grown cells and cells after heat shock was calculated bin-wise from −400 bp upstream to +200 bp downstream of the start codon. For the plots and clusters shown in p-value cutoff of 0.01. These were considered the putative binding sites while the functional (“true”) binding sites were derived from published ChIP-chip data [p-values. This analysis was done with data from both normal and heat-shock conditions. To calculate the significance of the change in binding site occupancy upon heat shock, the p-values for the heat-shock nucleosome data were divided by the p-values derived from the normal condition data.Transcription factor motifs were mapped across the entire genome using position-weight matrices derived from  using Pahip data ,18,36. AFigure S1(A) and (B) show different regions of the genome. In each track, the raw sequencing data is on top and consists of uniquely aligning reads extended by the average fragment length selected for sequencing. Below this are the nucleosome positions calculated by our analysis algorithm, with their scores shown next to their positions see . Previou(1.7 MB EPS)Click here for additional data file.Figure S2(A) Nucleosome profile of all genes in the yeast genome from −600 bp to +1,000 bp with respect to the TSS. Nucleosome positions are shown as gray ovals below the profile. The intensity of the filled oval reflects the average probability score of the nucleosome, and the dotted oval around the filled oval marks the spread of that nucleosome across all genes.(B) The 3′ end of genes is marked by a strongly positioned nucleosome, followed by a relatively nucleosome-free region. The inset shows the 3′ end of convergently transcribed genes in which the 3′ end is not followed by another promoter.(C) Average nucleosome profiles for TATA-containing (973) and TATA-less  promoters, aligned with respect to the TSS.(950 KB EPS)Click here for additional data file.Figure S3Linker lengths were binned into 10-bp (top) or 5-bp bins (bottom), and their frequency distribution was plotted. The most frequent inter-nucleosomal distance, or linker length, was 25–30 bp. The small peak of linker length at 180 bp in the top graph likely reflects the nucleosome-free region at promoters.(1.5 MB EPS)Click here for additional data file.Figure S42 ratio in a DNA+RNA hybridization on genomic microarrays.(A) Box plots showing TBP occupancy using data derived from , and (B)(1.8 MB EPS)Click here for additional data file.Figure S5p < 0.04). Although cluster 3 shows nucleosome appearance, this set could include promoters where a nucleosome was evicted from a downstream region and repositioned upstream Click here for additional data file.Figure S6(A) Reads mapping to the plus strand (red) and minus strand (blue) were processed separately.(B) Each base position was assigned a score that was derived from the sum of the relative contributions of all reads in its neighborhood as defined by a Gaussian kernel positioned at that coordinate. A local maximum on the plus strand (red) followed by a corresponding maximum on the minus strand (blue) within a distance of 100 to 200 bp defines a nucleosome. Peaks that were assigned higher Parzen scores defined higher confidence nucleosomes as shown by the grey shading.(1.4 MB EPS)Click here for additional data file.Table S1(A) Overlap between nucleosomes mapped in this study with previous studies. Percentages were calculated with reference to the lower of the two numbers considered in the overlap. The threshold for displacement was ≤50 bp.(B) Overlap between nucleosome positions before and after transcriptional perturbation in this study.(62 KB DOC)Click here for additional data file.Table S2(66 KB DOC)Click here for additional data file.


The analysis results in transcriptional programs which generalise both transcriptional modules and TF-target gene incidence matrices and provide a higher-level summary of these structures. The method is independent of prior specification of training sets of genes, for example, via gene expression data. The analysis is based on known binding motifs.Transcriptional regulation is an important part of regulatory control in eukaryotes. Even if binding motifs for transcription factors are known, the task of finding binding sites by scanning sequences is plagued by false positives. One way to improve the detection of binding sites from motifs is by taking cooperativity of transcription factor binding into account. We propose a non-parametric probabilistic model, similar to a document topic model, for detecting Mus musculus genes. We discovered just 68 transcriptional programs that effectively summarised the action of 149 transcription factors on these genes. Several of these programs were significantly enriched for known biological processes and signalling pathways. One transcriptional program has a significant overlap with a reference set of cell cycle specific transcription factors.We applied our method to putative regulatory regions of 18,445 Our method is able to pick out higher order structure from noisy sequence analyses. The transcriptional programs it identifies potentially represent common mechanisms of regulatory control across the genome. It simultaneously predicts which genes are co-regulated and which sets of transcription factors cooperate to achieve this co-regulation. The programs we discovered enable biologists to choose new genes and transcription factors to study in specific transcriptional regulatory systems. Organisms ranging in complexity from bacteria to higher eukaryotes are able to react and adapt to environmental and cellular signals. These responses are often encoded as complex gene regulatory networks. In these networks the expression of a gene's products is regulated by the activity of other genes. Although regulation can occur at many levels, we focus on transcriptional regulation, one of the most important and pervasive methods of regulation in eukaryotes. Transcriptional regulation occurs when certain gene products, transcription factors (TFs), bind to the DNA at binding sites (TFBSs) and affect the transcription of the regulated gene by modulation of the RNA polymerase complex. TFBSs often appear in clusters or cis-regulatory modules (CRMs), presumably to enable interactions between TFs binding there.Drosophila melanogaster that govern spatial patterns of development in its embryo is described in [TFs do not work in isolation from each other. Particularly in higher organisms, combinatorial operations are often necessary for the response of a cell to external stimuli or developmental programs. Such a response is frequently implemented as a transcriptional switch where a combination of presence or absence of certain TFs regulates the expression of a certain gene. Several well characterised examples of the coordination of TFs are known. For instance, a set of well studied TFs in ribed in ; higher ribed in ; the devribed in  which deConversely, cellular processes often involve the coordinated expression of sets of genes. Hence there is reason to suppose that not only do particular sets of transcription factors regulate particular genes but that these sets are also reused across the genome: that is, co-regulated genes are often targets of the same TFs. Genomic data commonly available today, such as sequence data, expression data or TF localisation data, do not permit direct inference of the higher order structure in transcriptional regulation. Most analyses of these data operate at the individual TF level. When the data permit it and the biologist is interested in this level of detail, it is certainly appropriate. However, genomic data is often noisy or incomplete. In this case a summary or view of higher order structure in transcriptional regulation is easier to interpret.The databases TRANSFAC  and JASPOur model aims to discover cooperative effects between transcription factors in noisy sequence analysis data. We use a model that has had success in the field of document modelling where the task is to infer the latent topics that best summarise a corpus of documents. Each document is modelled as a mixture of several topics drawn from a shared pool of unknown topics and each topic is modelled as a collection of words. Only the documents are given as input to the model.transcriptional program (TP). A TP captures the notion of a set of transcription factors that act in a coordinated manner across a set of target genes. So in the same way that a document's topics define its context, a gene's transcriptional programs summarise its transcriptional regulation. Figure To explain the use of this model in the context of transcriptional regulation we draw an analogy: in our model a document is analogous to a gene; a word is analogous to a transcription factor and the occurrence of a word in a document is analogous to a binding site in a gene's CRM. To complete the picture, a topic is analogous to what we term a Hierarchical Dirichlet processes (HDPs) are a natural framework to use in document-topic modelling and hence for our work in transcriptional regulation. In our framework, transcriptional programs are modelled as distributions over transcription factors. Each gene's transcriptional regulation is modelled as a mixture of these programs. Dirichlet process mixtures (DPMs) are a non-parametric technique for modelling mixtures where the number of components is unknown. We use DPMs to model the mixture associated with each gene's transcriptional regulation. In order to share transcriptional programs between genes we use a common base distribution for the DPMs which is itself a DPM. This step makes our model hierarchical. An extensive review of HDPs is given in .ad hoc rules in these combinatorial approaches to reduce the number of highly similar TMs. Alternatively, an incidence matrix (or bipartite graph) is calculated linking each TF to the genes it regulates [Quite a few approaches have been suggested in the literature to identify groups of TFs that co-regulate genes, often called transcriptional modules (TM). They all differ from our approach in several respects. One major difference is that our concept of transcriptional programs (TP) is slightly more abstract than TMs. A TM is often defined as a set of TFs that physically bind next to each other in the vicinity of the regulated gene. Many approaches either enumerate all possible combinations of TFs up to a certain number  as potential TMs and search for over-representation of TMs in various groups of genes -10. Theregulates -13  set. Our method differs in that it fits a model of the entire set of genes at once.de novo or PSSM-based) and expression data are commonly used. Heuristics or probabilistic models are used to search for consistent structure amongst these data sources. Almost all this work has been carried out in Saccharomyces cerevisiae. ReMoDiscovery [Kreiman  looks foiscovery  builds oiscovery  combinesiscovery  is a biciscovery  use GibbSaccharomyces cerevisiae. Their model partitions the genes into a fixed number of mutually exclusive sets which have the same expression pattern across experiments. Each gene is the target of exactly one motif profile, hence their model does not allow so much structure in the latent profiles/programs. Also, the number of partitions must be fixed somewhat arbitrarily in advance by the user. They focus on Saccharomyces cerevisiae which has a simpler transcriptional code than Mus musculus, the focus of our study.Segal et al.  have intVarious other probabilistic models that require specification of the number of modules by the user have been implemented. Xu et al.  build onOther algorithms also use non-parametric probabilistic models to obviate the need to specify the number of modules. Gerber et al.  use hierMus musculus genes using PSSMs from TRANSFAC. This generated 78,085 putative TFBSs of 149 TFs which scored above a stringent threshold (see Methods). We ran our model on these putative TFBSs and it discovered 68 latent transcriptional programs.We analysed the promoter regions of 18,445 The number of TFBSs explained by each of the 68 programs varied considerably. Most of the TFBSs were explained by the largest 10 programs Figure . As demoAs the model associates each TFBS with a program, even those TFBSs for which co-operative effects cannot be found must be associated with a program. The model uses the largest two programs (programs 0 and 1) for these TFBSs: their distribution over factors is vague and they target many genes. To some extent, the programs that explain more binding sites are less likely to represent true cooperative effects. We looked at the number of target genes of the programs in this context. That is, we analysed the total number of target genes of all programs smaller than a given size Figure . IncludiIn general, we found a good separation between the programs, in that any given TF or gene is unlikely to be associated with many programs and conversely that most programs were associated with a small number of TFs and genes Figure . This waIn order to test whether the transcriptional programs capture real biological structure we validated the TPs using an analysis of enrichment for GO terms , signallEach program is associated with a set of transcription factors and a set of target genes. We tested the genes and the factors in each program for enrichment of terms in the biological process GO ontology. We used a standard hypergeometric test in conjunction with the weight method implemented in the top GO R-package  as a sigWe tested the genes and the factors in each program for enrichment in signalling pathways defined in the KEGG database. After Bonferroni correction for multiple testing, we found no significant results. However, we did find a significant result in conjunction with our analysis of known interacting TFs from the literature.We tested the target genes in each program for enrichment in tissue-specific co-expressed genes from the SymAtlas dataset. Genes over-expressed in embryonic tissues were significantly enriched in the targets of transcriptional program 53. Program 53 accounts for fewer than 100 binding sites out of the 78,085 sites, yet was strongly predictive of membership of the group of over-expressed genes. This demonstrates the ability of our method to find small signals in large datasets.We took well known sets of interacting transcription factors from the literature and looked for programs that contained them. We looked for sets of TFs associated with the liver, muscle development, and the cell cycle. The three factors in transcriptional program 12  contain two of the three transcription factors in our analysis that are known to regulate the cell cycle .When we tested the targets of program 12 for enrichment in the KEGG cell cycle pathway (without correcting for multiple testing) we obtained a p-value of 9e-4. The extra TF in program 12 that is not in our literature derived set, MTF1, has been implicated in the cell cycle  and as aSeveral of the discovered programs have well defined biological meanings. Not many of the factors of the transcriptional programs were significantly enriched for GO terms. However, program 28 did contain 5 of 7 TFs that are annotated with the term "liver development" in its nine factors.Several of the target sets of the programs were strongly associated with different GO terms. In particular, program 12 was particularly enriched for genes with nuclear products and those that are involved in nucleosome assembly. Program 18 appears to be associated with the sense of smell as it has strong enrichment for "olfactory receptor activity" and "sensory perception of smell".Discovering structure in sequence analyses is a difficult task. We are limited by the set of PSSMs available, our inability to predict regulatory genomic regions and the high false positive rate of PSSM scanning. Out of the three sets of interacting TFs that are most cited in the literature, we only recovered one of them. However, our method is looking for structure in a much larger dataset than other methods and does not have a positive set and a negative set of genes with which to discriminate.Our model does find significant structure in these analyses and it is reasonable to suppose that this structure underlies some mechanisms of transcriptional regulation. This is to be expected given our understanding of the underlying biology. A valuable property of our method is that it finds structure at both large and small scales.We are working on expanding our model to include other data sources. We anticipate using ChIP-seq and ChIP-chip data when it is available for enough TFs, either in conjunction with sequence analyses or other data sources related to regulation such as expression data.We have shown that non-parametric probabilistic models are useful tools for unsupervised learning in this context. Techniques for genomic data integration are just starting to be applied with success to higher eukaryotes and we believe HDPM models are useful non-parametric tools for this task.We extracted 1,000 repeat-masked base pairs upstream of the mouse transcriptional start sites (assembly July 2007) as defined in the UCSC Genome browser . After rWe extracted a set of PSSMs from TRANSFAC version 11.4 for which we could map the factors they represent onto Ensembl gene identifiers . From eaK induces a distribution over K-mers that models binding sites for the transcription factor(s) it represents. Each position is modelled independently in this distribution. We can represent the PSSM as a matrix, P, where Pk, brepresents the probability of seeing base b at position k in the PSSM.A PSSM of length K-mer, W = w1...wK, and using a simple uniform background model we can calculate the log odds ratio L(W) between the binding site model (the PSSM) and the background model, V is a prior on how likely we believe binding to be.Given a V of -4.7 . Our experience working with biologists has shown us that this is a reasonable threshold to use. Our model does allow for noisy data and should accommodate false positives in the large vague transcriptional programs that do not model cooperative effects. We were constrained by our computational resources from lowering this threshold significantly.In this work we used a threshold of -1.3 and a Up to this point we have been dealing with each PSSM independently. Unfortunately they are not independent as, for instance, there are many factors for which TRANSFAC has more than one PSSM. Two PSSMs for the same factor are very likely to represent TFBSs at the same location in a promoter. We do not wish our model to learn this strong correlation instead of true transcriptional programs. We therefore reduce our set of TFBSs by taking the highest scoring set of non-overlapping TFBSs.In the field of information retrieval HDPs  are ofteg is linked to a distribution over transcriptional programs, which is represented by a (possibly infinite) vector θg = , where θgk is the contribution of program k to gene g. All θgk sum up to one for each g. A program k in turn is linked to a similar distribution over transcription factors, that is, program k is represented by a vector ϕk = (ϕk1...ϕkJ), where ϕkj is the contribution of transcription factor j to program k assuming there are J transcription factors in total. All ϕkj sum up to one for each k. To sample a random transcription factor for binding site i upstream of gene g, we first sample a multinomial random variable variable zig ~ Mult(θg) which indicates the transcriptional program the factor is drawn from. Next, we sample a second multinomial random variable zig into account. Sample xig specifies which transcription factor binds at binding site i upstream of gene g.Our model is best described generatively, that is, we describe how to sample a suitable transcription factor from our model given a target gene. We follow the description in . A gene θg and ϕk. In order to do this, we place conjugate Dirichlet priors on the parameter vectors and use a variational approach to approximate their posterior distribution (for details see [θg ~ Dir(απ) and ϕk ~ Dir(βτ). α and β are scalar strength parameters that control the variances of the θg and ϕk respectively. π and τ are vectors and represent their respective means.When calibrating the model using data, the task is to infer posterior distributions for parameters ails see ). More sθg and π are infinite dimensional vectors. πg is modelled using an explicit stick-breaking construction [γ controls how many transcriptional programs are used. Formally, the stick-breaking model is defined byWe do not wish to constrain our model to use a fixed number of transcriptional programs. Instead, we use a non-parametric approach where we allow a countably infinite number of transcriptional programs. Now truction  where γ πk are obtained by starting with a stick of length 1, and continuing to break pieces off it, their lengths representing the probabilities πk. Even if continued indefinitely the pieces all sum up to one, the total length of the stick, forming a proper probability distribution over the natural numbers. The size of piece k is determined as a fraction Intuitively, probabilities Our model is presented graphically in Figure We implemented the collapsed variational inference technique described in  completeϕk, and each gene can be summarised as a distribution over programs, θg. In order to examine the programs we have learnt, we thresholded these distributions to discover which programs are over-represented in which genes and which factors are over-represented in which programs. However, due to the collapsed nature of the inference algorithm we do not directly obtain a posterior over them as they have been integrated out. The inference algorithm does infer which factors have binding sites in which genes due to which transcriptional programs. These inferences are summarised as the expectations of various counts and these allow us to estimate the θg and ϕk and hence associate transcriptional programs with genes and with transcription factors.In our model each transcriptional program is represented as a distribution over factors, ngkf as the number of binding sites for factor f drawn from transcriptional program k in the promoter of gene g. A'.' in the subscript indicates summation over that index. For example nkf .is the number of binding sites of factor f drawn across all genes from program k. Now we make point estimates:More formally, in an analogous notation to , we defik all those factors, f, for which g, with transcriptional programs, k, for which We define p-value found in an GO term enrichment analysis.Correcting for multiple testing in a GO ontology is difficult due to its hierarchical nature. To validate the strength of our results we generated random samples from the same populations of genes and transcription factors to test for enrichment. We choose sample sizes to cover the range of sizes in the discovered transcriptional programs. For each size we sampled 100 independent sets and calculated the exponent of the best p-value and refer to its base 10 logarithm as the p-score. In Figure p-scores for the randomly sampled factors and target genes. We sampled 100 times at each of 50 different sample sizes for the factors and the targets. The sizes were chosen to reflect the range of sizes of the actual transcriptional programs. Hence each diagram represents 50 * 100 = 5000 independent samples. The sample size does not appear to affect the extreme value distribution of the best p-scores' exponents. From 10,000 independent samples, the lowest p-score is around -6. Plotting the sorted p-scores against the base 10 logarithm of the proportion that are equal or better gave us a good linear fit. This fit has an intercept very close to -2 which together with the linear relationship suggest adding 2 to the p-score to obtain a multiple testing corrected p-value exponent. That is a p-score of -6 would be equivalent to a p-value of 10 -4.To assess the significance of the results from the GO enrichment analysis, we generated random samples of factors and genes. Each sample was analysed for GO enrichment using the same procedure as for the transcriptional programs that our model predicts. For each sample we take the best uncorrected JR designed the PSSM scanning algorithm. He implemented the code for PSSM scanning, the topic-document HDPM and the analysis. He analysed the results and wrote the main body of this paper. SO helped develop the PSSM scanning algorithm. LW participated in the design of the model, the statistical analysis of the programs and wrote parts of the paper. All authors read and approved the final manuscript.Transcriptional programs – programs.tar.gz. A gzipped archive containing the transcriptional programs output by our method.Click here for file