Fluorescent and luminescent reporter genes have become popular tools for the real-time monitoring of gene expression in living cells. However, mathematical models are necessary for extracting biologically meaningful quantities from the primary data.Escherichia coli. We show that the mRNA and protein concentration profiles predicted from the models agree quite well with direct measurements obtained by Northern and Western blots, respectively. Moreover, we present computational procedures for taking into account systematic biases like the folding time of the fluorescent reporter protein and differences in the half-lives of reporter and host gene products. The results show that large differences in protein half-lives, more than mRNA half-lives, may be critical for the interpretation of reporter gene data in the analysis of the dynamics of regulatory systems.We present a rigorous method for deriving relative protein synthesis rates (mRNA concentrations) and protein concentrations by means of kinetic models of gene expression. We experimentally and computationally validate this approach in the case of the protein Fis, a global regulator of transcription in The paper contributes to the development of sound methods for the interpretation of reporter gene data, notably in the context of the reconstruction and validation of models of regulatory networks. The results have wide applicability for the analysis of gene expression in bacteria and may be extended to higher organisms. The degradation constants γn of gfp and lux mRNA were determined to be 0.30 ± 0.13 min-1 and 0.33 ± 0.15 min-1, respectively, yielding half-lives of about 2 min. This is almost twice as long as the half-life for fis mRNA, equal to 1.23 min. Notice that the measurements are relatively imprecise, so that the 95% confidence intervals of the host and reporter mRNA half-lives are overlapping . The measured kinetic constants and the errors on the measurements are summarized in Table In order to quantify these systematic biases in our case, we experimentally determined the degradation constants of mRNA and protein of both Fis and its reporters, as described in the Methods section. The GFP and luciferase half-lives were measured to be about 1 h. For the degradation constant of Fis we found the value κpm(t) can be numerically computed from the reporter synthesis rate κpn(t), defined in (11), when the values of γm and γn are known. The only additional assumption needed is that, at the beginning of the experiment, the mRNA concentrations have attained their steady-state value, that is:The measurements of the kinetic constants allow the correction of systematic errors, using the models introduced above. As shown in Section S4 of the the Additional file This assumption is valid since in our experimental conditions the bacteria have been in stationary phase for more than 12 h before dilution into fresh growth medium.γn/γm, which equal 0.54 for gfp mRNA and 0.59 for lux mRNA, the difference in the normalized mRNA concentration profile is seen to be negligible, falling within the confidence band of the original predictions (corresponding to the case γn/γm = 1). This means that the local discrepancies between the Northern blot measurements in Figure fis mRNA and that of gfp and lux mRNA.The results of the correction of the systematic error in the reporter synthesis rate, and thus in the reporter mRNA concentration, are shown in Figure γn/γm varying between 0.25 and 4, the predicted mRNA profile remains the same. Even when γn/γm is varied by 100-fold  can be computed from the GFP or luciferase concentration q(t) when in addition to the values of γm and γn, those of γp and γq are known. As above, we need to make the further assumption that the system is initially at steady state, that is:In a similar way, the protein concentration profile can be corrected for systematic errors due to differences in the degradation constants. The Fis concentration p(t) are derived in Section S4 of the Additional file The formulas required for the computation of γq/γp were measured to be 1.8 (GFP) and 1.7 (luciferase). Figure The ratios of γq/γp. Notice that the expression profiles tend to cluster together around specific values of γq/γp, with minor variations within the clusters caused by differing values of γn/γm. The influence of the difference in mRNA stability is therefore negligible with respect to the difference in protein stability, confirming the results of Figure q(t) = r(t)). This may lead to an underestimation of the amount of GFP in the cell. In order to correct for the effect of this bias, we can rewrite (8) to compute total GFP from active GFP:Figure κr = 0.023 min-1. That is, it takes 25 min to convert half of a given pool of inactive GFP to its active form.The maturation time of GFP was set to 25 min, as determined experimentally for the reporter used in this study (GFPmut3) , thus yiq(t) instead of r(t) for computing the normalized reporter synthesis rate, and thus the normalized mRNA concentration, yields the same conclusion . In this case, there are considerable differences between the expression profiles obtained with luciferase (data not shown). The corrections and the corresponding confidence bands also become much larger. We conclude that a fast-folding reporter protein is essential for reliable real-time monitoring of gene expression.Research in biology has moved from a descriptive science to considering biological processes as dynamical systems . This syi.e., uses no freely adjustable parameters. It implies that when the half-lives of the host-gene products are unknown, we can still obtain a result that preserves the qualitative shape of the expression profile. As long as the systematic biases in the reporter systems remain limited, that is, a rapid folding time of the fluorescent reporter and similar degradation constants of host and reporter gene products, the expression profiles obtained are accurate. This is illustrated by the results for the gene fis coding for a global regulator in E. coli.A first conclusion from our study is that the expression profiles computed from the fluorescence and luminescence data are generally in good agreement with the Northern and Western blots Figure . This isIf the systematic biases are too large to be ignored, corrections for the resulting errors need to be carried out. Our results show that a difference in mRNA half-life does not significantly contribute to these deviations. As a consequence, knowing the order of magnitude of the mRNA half-life of the host gene is already sufficient for reliably calculating the expression profile. The insensitivity of the expression profile to changes in mRNA half-life does not hold for protein half-life. Variations in this parameter maintain the overall shape of the expression profile, but affect the normalized concentration levels and the timing of the peak Figure . In partfis increases at the transition to stationary phase. If this were the case, the actual mRNA and protein concentrations would be higher than the ones computed from the reporter gene measurements. This effect could indeed explain the remaining discrepancies between prediction and measurement in Figure All computations have been carried out under the assumption that the mRNA half-life does not change in the course of the experiment. This assumption is certainly valid during exponential growth, but may fail during growth transitions or in situations where the mRNA half-life is regulated. Indeed, our data show a systematic deviation between the calculated and measured quantities of mRNA and protein at the entry into stationary phase that is partly unaccounted for, even after applying the above corrections. The mRNA and protein half-lives have been measured during exponential phase. Due to technical difficulties, we were unable to measure these parameters in stationary phase. It is conceivable that the mRNA half-life of κp needs to be measured. The techniques for doing this are time-consuming and error-prone, although novel approaches developed in the context of single-cell measurements may improve the absolute quantification of gene products  needs to be determined by relating the fluorescence and luminescence intensity units to the number of (active) molecules, and the absorbance units to the number of (viable) cells. In addition, for an absolute quantification of mRNA concentrations the synthesis constant s e.g., -60). The. Theκp nResearch in biology has made the transition from a more or less intuitive understanding of the system to a quantitative, formal description. This systems biology approach crucially depends on the availability of reliable, quantitative data. Data acquisition techniques have enormously progressed in the past decade, but require sound and general methods for analyzing these data. The current manuscript contributes to the development of such methods and forms the basis for future analyses of the dynamics of regulatory systems. The present formalism is geared towards bacterial expression. However, small modifications of the method will allow to include additional reaction steps inherent in eukaryotic gene expression, such as splicing and nuclear export.All authors made substantial contributions to the work presented in the manuscript. CR, DR and CP constructed the reporter strains. CR and CP carried out most of the experiments reported in the manuscript. DR also contributed to the analysis of the data and the development of the model. HdJ and JG conceived the study, developed the models, carried out some of the experiments, analyzed the data, and wrote the publication. All authors have read and approved the final manuscript.Supplemental Material of Experimental and Computational Validation of Models of Fluorescent and Luminescent Reporter Genes in Bacteria.Click here for file


Multiple discrete regions at 8q24 were recently shown to contain alleles that predispose to many cancers including prostate, breast, and colon. These regions are far from any annotated gene and their biological activities have been unknown. Here we profiled a 5-megabase chromatin segment encompassing all the risk regions for RNA expression, histone modifications, and locations occupied by RNA polymerase II and androgen receptor (AR). This led to the identification of several transcriptional enhancers, which were verified using reporter assays. Two enhancers in one risk region were occupied by AR and responded to androgen treatment; one contained a single nucleotide polymorphism (rs11986220) that resides within a FoxA1 binding site, with the prostate cancer risk allele facilitating both stronger FoxA1 binding and stronger androgen responsiveness. The study reported here exemplifies an approach that may be applied to any risk-associated allele in non-protein coding regions as it emerges from genome-wide association studies to better understand the genetic predisposition of complex diseases. Genome-wide scans of inherited genetic variation in the normal population have recently identified many sites (loci) associated with the predisposition to complex diseases such as cancer. Some of these cancer-associated loci, however, are devoid of genes  and the mechanism(s) of the association are not readily apparent. In the work reported here, we show that loci associated with several cancers in a gene desert found at chromosomal area 8q24 have embedded regulatory sequences affecting gene expression as enhancers, and in one case this activity is modulated by genetic variation. The results provide insight into the mechanism(s) governing genetic cancer risk. Chromosome 8q24 is an established risk locus for many common epithelial cancers. The region was originally discovered by fine-mapping of a prostate cancer linkage peak from a family-based study by deCODE genetics in cis. The ENCODE project and the recent reports on long noncoding RNAs Since the consequences of sequence changes in non protein-coding regions of the genome are more difficult to predict than changes in coding regions, defining the mechanisms by which the 8q24 alleles confer risk has so far been challenging. Another complication is that genetic variants discovered through association studies are rarely the actual causal variant, since they may be associated with disease risk simply due to linkage disequilibrium, which sometimes extends over relatively large distances in the human genome. Because of these factors, understanding the mechanisms that increase cancer risk requires an integrated and systematic approach. One hypothesis is that the 8q24 alleles affect the sequence of unannotated transcripts (e.g. noncoding RNAs or unknown protein-coding genes) or change the regulation of such transcripts Post-translational modifications of histones  have proven useful in annotating sites of regulatory activity in the human genome. Histone 3 acetylation (AcH3) is typically associated with chromatin accessibility and transcriptional activity, and widely used for the prediction of functional elements such as promoters and enhancers The overall objective of this study was to systematically evaluate the possible role(s) of regions within the 8q24 genomic risk interval, overcoming the aforementioned difficulties using a combination of epigenetic, bioinformatic, and molecular biological analyses on multiple cell lines and tissue samples. We report here two main findings: (i) evidence is presented that certain 8q24 risk regions exhibit minimal RNA transcriptional output but bear the markers of regulatory elements that are functionally active as enhancers. (ii) More specifically, we demonstrate that a new androgen-dependent enhancer in one of the prostate cancer risk regions is functionally influenced by a risk-associated single nucleotide polymorphism (SNP) via differential FoxA1 binding.A high resolution tiling array comprising 5-megabases (Mb) of 8q24 was designed and probed with cDNA from cancer cell lines and normal tissue, as well as with DNA obtained by chromatin immunoprecipitation (ChIP) using antibodies for histone modifications and transcription factors. We reasoned that the combination of a focused transcriptional map and a high-resolution epigenomic profile To study the transcriptional landscape at the 8q24 region, we generated double-stranded cDNA from 20 normal prostate tissue samples, the prostate cancer cell lines LNCaP and PC3, the colon cancer line HCT116 and the breast cancer line MCF7. We hybridized each of these cDNA samples to our custom tiling array and normalized the probe intensity values against their genomic DNA background. As shown in In parallel, we generated high-resolution epigenomic profiles for the entire 5-Mb interval using ChIP-chip. For this purpose we hybridized ChIP material to the same tiling array used earlier for transcriptional profiling. Initially, we analyzed AcH3 in three cell lines representing prostate (LNCaP), breast (MCF7) and colorectal (HCT116) cancer. Because three regions independently impose prostate cancer risk, we also interrogated two prostate cancer cell lines (PC3 & LNCaP) more extensively for other key epigenetic marks at high resolution. Additional histone modifications chosen were the activation marks H3K4me1 & H3K4me3 Spatial clustering of the 5-Mb region surrounding and including the 8q24 risk loci is shown in in cis or remote active transcriptional units in trans. We note that we could not rule out the possibility of small non-coding RNAs being transcribed from the region, since RNA species shorter than 200-bp were excluded from our preparation. In order to investigate the regulatory potential of the loci exhibiting active chromatin marks, we next performed a systematic series of heterologous enhancer assays, focusing initially on defined acetylation peaks contained within the cancer risk intervals  minimal promoter. Enhancer activities of the fragments were determined by transient transfection and luciferase assays in LNCaP & PC3 (prostate cancer cells), HCT116 & COLO 205  and MCF7 (breast cancer cells)  . AcP6 . Through this effort we identified two SNPs in AROR15 that were strongly correlated with the risk variant rs10090154 (reported in What are the mechanism(s) that govern the SNP effect on the DHT-mediated enhancer activity described above? Interestingly, the SNP at rs11986220 resides within a putative binding site for forkhead transcription factors, with the A allele better matching the consensus sequence . An intein vivo enhancer activity from this region.Since the histone acetyl transferase and transcriptional coactivator p300 accurately predicts enhancer activity at many loci To follow up on our functional assays, we next genotyped rs11986220 in prostate cancer cases and controls from five ethnic populations in the Multiethnic Cohort . The frequency of the A allele and the magnitude of the association was the same as those of the T allele of rs10090154  in European Americans, Latinos, Native Hawaiians and Japanese , but notA main question remaining is what are the gene targets of our identified enhancers? We suspect that they loop to their target(s) at some distance. Such looping in the three dimensional space of the nucleus may represent the underlying mechanism of transcriptional regulation Since transcript abundance is a heritable trait, associations between risk allele status and mRNA transcript levels can serve as a powerful way to evaluate potential candidate genes. Recently, our group studied the association between 6 prostate cancer risk alleles at the 8q24 locus and MYC mRNA expression in prostate tissue. A large number of specimens (280) were evaluated  and no association was observed With the advent of genome-wide associations of alleles with major diseases, the challenge of characterizing the biological function that is associated with the genomic region of interest is becoming more acute than ever. This challenge is particularly difficult when risk alleles are not located near annotated genes. We need to establish methodologies that can comprehensively and rapidly characterize the main genomic features in a region of interest, which can then be used to lay the foundation for follow-up studies that may lead to the uncovering of disease mechanisms. Here we have shown how the combination of high-density tiling arrays, transcript and epigenetic profiling, and computational analysis can facilitate functional characterizations, which may be tested directly with molecular biology techniques. Accordingly, we used the above-mentioned approach to identify how prostate cancer risk SNPs may affect enhancer activity at the gene-poor 8q24 region. Our chromatin analyses narrowed the location of putative functional domains to regions less than 1.5-kb in size, containing gene enhancers that may influence cancer risk via regulation of gene expression at a distance. We verified that gene regulation is involved by using reporter assays and further showed that the androgen-responsive activity of a strong enhancer in region 1 is affected by a SNP (rs11986220) associated with prostate cancer risk.LNCaP and PC3 cells were maintained in RPMI 1640 supplemented with 5% (v/v) fetal bovine serum (FBS). HCT 116 and COLO 205 cells were cultured in McCoy's 5A with 10% FBS, and MCF7 cells were cultured in DMEM with 10% FBS. All cell lines were obtained from the American Type Culture Collection , except PC3 cells, originally from ATCC, which were derived by us as strongly AR-transcriptionally competent, although not expressing functional AR ChIP analyses were performed as described previously About 100 µg total RNA was extracted from each cell line  using Aurum Total RNA Kit (Bio-Rad). Ribosomal RNA (rRNA) was depleted using RiboMinus Transcriptome Isolation Kit (Invitrogen) according to manufacturer's protocol. About 5 µg double strand cDNA was made from recovered RNA after rRNA depletion using Superscript Double-Stranded cDNA Synthesis Kit (Invitrogen), and then submitted to NimbleGen, Inc. along with 5 µg sonicated genomic DNA (size between 500–2000 bp) from each cell line as a reference. The DNA samples were labeled and hybridized to the same custom tiling array used in ChIP-chip.Twenty fresh frozen radical prostatectomy (RP) samples were derived from an institutional review board-approved study cohort at Dana-Farber Cancer Institute (DFCI) Array data were normalized by estimating the curve of log channels intensity ratio [log(cy5/cy3)] as a function of the control channel [usually log(cy3)]. This curve was subtracted from the observed log intensity ratio and used in subsequent analysis. We also computed the effect of variable probes' G+C content on the array binding ratios, but decided not to use this for further normalization due to the relatively small region covered by the array and the danger of systematic biases. We note that the RNA hybridization readouts were distributed in a highly non-normal fashion, with a significant fraction of the probes covering intronic regions and having higher than average readouts, and a smaller fraction of the probes covering exonic regions that are highly enriched. Since the study focused on a very high resolution mapping of a region that include only a few genes, we decided not to explicitly model RNA data as the product of some putative exonic structure, but focused on an unsupervised analysis of a combined dataset that included both RNA and ChIP data.The spatial clustering algorithm is an unsupervised hidden markov model (HMM)-based method that identifies a set of common pattern in multi-dimensional data that is defined over contiguous genomic segments. The method uses a probabilistic model describing a set of states (clusters in this case) and the probability of transitioning to a particular state Y given that one is presently at state X . Each cluster defines a distribution of values for the measured data tracks and an algorithm assigns each data instance (the measurements for each track at a given locus in the genome) to the cluster that describes it best. The algorithm iteratively updates the distributions defined by the clusters, the data points assigned to the clusters, and the transition probabilities from one cluster to another, until all data points are assigned to clusters that describe them well and which are highly likely to self-transition. This last property ensures that data points representing adjacent regions in the genome are likely to belong to the same cluster, maintaining the biological tendency of contiguous genomic regions to behave similarly. To dissect the 8q24 into regions with distinct epigenomic behaviors we used our recently described implementation of the algorithm firefly-luciferase vector in both directions. All clones were confirmed by sequencing. The primers for subcloning are listed in Renilla luciferase plasmid (Promega) using Lipofectamine LTX Reagent (Invitrogen) according to the manufacturer's protocol. Dual luciferase activities were measured as previously described p-values between alleles were calculated using the student t-test.Fifteen enhancer candidates (∼1500-bp sequence surrounding the AcH3 peak center) and three ARORs (∼500 bp sequence surrounding the AROR peak center) were amplified from LNCaP genomic DNA using High Fidelity Platinum Tag DNA polymerase (Invitrogen). The amplified sequences were then subcloned in either the KpnI or Sac II restriction sites upstream of a thymidine kinase (TK) minimal promoter-Whole cell extracts were prepared from LNCaP cells, cultured in 5% FBS RPMI 1640, and EMSA was performed all as previously described Figure S1Shown are the cumulative distributions of RNA array signal (LNCaP data) for intergenic, intronic and exonic probes. The data show that our data is sensitive to the difference between strongly expressed spliced RNAs, pre-spliced unprocessed transcripts and untranscribed, intergenic sequence.(0.04 MB PDF)Click here for additional data file.Figure S2Possible transcription around the POU5F1 gene fragment. Shown are RNA array readouts in region 3 and the Breast cancer associated locus. Probes at the POU5F1 are enriched in all cell types and in normal prostate tissue, possibly reflecting cross hybridization from the original POU5F1 gene. In LNCaP and in the prostate tissues we also observe RNA signal from strictly unique probes (see the self-chain track in the lower part of the figure) around the POU5F1 fragment and in other proximal probes that were also associated before with spliced ESTs. This suggests that some transcription may be originating from a long region involving risk region 3 and the breast cancer linked region.(0.17 MB PDF)Click here for additional data file.Figure S3Significance of H3K27me3 domains. Shown are aggregate statistics from Kolmogorov-Smirnov tests performed on the H3K27me3 distributions in LNCaP and PC3. For each probe, the distribution of log(IP/input) values centered on that probe and within a window of given size was compared to the distribution of all the values outside the window. The color-coded p-values indicate the significance of the dissimilarity between that window and the rest of the 5 Mb region. Each row within a cell-line corresponds to a different window size . High p-values indicate the presence of significant H3K27me3 domains, with the right-most domain appearing only in LNCaP.(0.13 MB PDF)Click here for additional data file.Figure S4http://www.cbil.upenn.edu/cgi-bin/tess/tess). A potential FoxA1/HNF3α binding site coincided with rs11986220, with the A allele forming a more perfect FoxA1/HNF3α binding site than the T allele.Fifty-bp DNA sequences centered on rs11986220 were scanned for transcription factor binding motifs using Transcription Element Search System (TESS) website ((0.06 MB PDF)Click here for additional data file.Figure S5P300 occupancies AROR15. LNCaP cells were cultured in 5% FBS RPMI 1640 media for 3 days. ChIP analyses were performed using antibody against p300 . DNA samples from ChIP preparation were quantified by qPCR using TaqMan PCR Master Mix (Applied Biosystems). Data were average of triplicate qPCR determinations. The relative enrichment of p300 at PSA enhancer (positive control) and AROR 15 was normalized against neighboring 8q24 control region (negative control defined as 1).(0.03 MB PDF)Click here for additional data file.Table S1Oligonucleotide sequences.(0.02 MB XLS)Click here for additional data file.


However, only a few of these factors have been identified. Here, we analyze the regulation of the cell death gene reaper (rpr) by the Hox protein Deformed (Dfd) and suggest that local activation of rpr expression in the anterior part of the maxillary segment is achieved through a combinatorial interaction of Dfd with at least eight functionally diverse transcriptional regulators on a minimal enhancer. It follows that context-dependent combinations of Hox proteins and other transcription factors on small, modular Hox response elements (HREs) could be responsible for the proper spatio-temporal expression of Hox targets. Thus, a large number of transcription factors are likely to be directly involved in Hox target gene regulation in vivo.Hox proteins play fundamental roles in controlling morphogenetic diversity along the anterior–posterior body axis of animals by regulating distinct sets of target genes. Within their rather broad expression domains, individual Hox proteins control cell diversification and pattern formation and consequently target gene expression in a highly localized manner, sometimes even only in a single cell. To achieve this high-regulatory specificity, it has been postulated that Hox proteins co-operate with other transcription factors to activate or repress their target genes in a highly context-specific manner  reaper, we identified a set of eight transcription factors to be important for the precise spatio-temporal regulation of this gene. Based on our findings, we suggest that Hox proteins functionally interact with a plethora of unrelated transcription factors on small, yet complex enhancer elements to execute their specific functions throughout development of diverse organisms.Bilateral animals share a common genetic mechanism to control development along the anterior–posterior body axis, and transcription factors of the Hox class are key regulators of this conserved process. It is thought that Hox proteins drive morphological diversification of body segments by differentially controlling the expression of downstream genes. However, due to their highly conserved DNA binding domain, the homeodomain, Hox proteins alone bind to very similar and frequently occurring sequences in the genome. This implies that Hox proteins alone are likely insufficient to activate or repress their target genes in a proper spatio-temporal fashion. In contrast to this observation, Hox proteins have very specific effects during development and execute their function with high precision. A solution to this paradox could lie in the context-specific interaction of Hox proteins with other transcriptional regulators; however, only a few examples are known. By analysing the mechanism underlying the regulation of the Hox target gene  Hox genes represent the major regulators for patterning of this body axis in organisms as diverse as fruit flies, fish and humans Hox genes code for transcription factors, Hox-dependent morphogenesis is driven by the differential regulation of downstream genes Distinct morphological structures exist along the anterior-posterior (A/P) axes of animals, and the Hox genes are expressed in broad and partially overlapping domains along the A/P axis Drosophilawingless (wg) expression in the visceral mesoderm of Drosophila embryos had been shown to be dependent on the Hox protein Abdominal-A (Abd-A) and the Dpp/TGF-β signalling pathway, with both activities functioning on a small wg enhancer wg enhancer, and were thus assumed to work in concert with the Hox protein Abd-A spalt major  in the haltere by independently interacting with adjacent Mad/Med and Ubx binding sites in a small sal enhancer Drosophila segmentation cascade, Engrailed (En) and Sloppy paired 1 (Slp1), were shown to assist the Hox proteins Ubx and Abd-A in repressing Distal-less (Dll) expression in the abdomen of Drosophila embryos by occupying their identified binding sites in a minimal Dll enhancer Only a few Hox regulated enhancers have been analyzed in some detail in Drosophila is the apoptosis gene rpr, which is activated by the Hox protein Dfd in the anterior part of the maxillary segment through four binding sites located in the rpr-4S3 regulatory fragment rpr RNA is found only locally rpr expression. Here, we find that eight transcriptional regulators, with diverse roles in patterning or differentiation processes, co-operate with Dfd in the regulation of rpr. Within their spatially restricted expression domains, these regulators are recruited to a minimal rpr enhancer through specific cis-regulatory DNA sequences and act together with Dfd to regulate rpr expression in the appropriate spatio-temporal pattern. Thus, our data support the idea that the combinatorial activity of Hox proteins and diverse transcriptional regulators on small regulatory elements is responsible for the spatially and temporarily restricted expression of Hox target genes in vivo. In addition, our data show that even small Hox-dependent enhancers are complex and integrate diverse regulatory inputs, which result in precise spatio-temporal expression of Hox target genes.Another well-studied direct Hox target gene in rpr regulatory element able to recapitulate endogenous expression in the maxillary segment, we divided the known 674 bp long rpr-4S3 enhancer element lacZ expression driven by the resulting sub-fragments   . While t/3′ line . Taken trpr-4S3/3′ enhancer fragment, we ubiquitously expressed Dfd in the rpr-4S3/3′ reporter strain using the armadillo (arm)-GAL4 driver rpr-4S3 reporter line (data not shown). lacZ RNA never extended into the anterior-dorsal or anterior-ventral zone  enhanced the ability of Dfd to activate reporter gene expression  and Disconnected (Disco) pression . We ubiqpression . Seven tpression : Apt andpression , whereaspression . Two of pression , simultapression . Similar embryos , suggestpression , althougpression . Since opression , we concrpr transcription in the maxillary segment, we analyzed rpr transcripts in embryos mutant for the individual transcription factor genes  gro mutant embryos should behave similarly to a triple knock-out of the repressor genes with regards to rpr regulation. Consistently, the number of cells ectopically expressing rpr was further increased in gro mutants when compared to the single mutants. The effect was most pronounced in the posterior part of the maxillary segment  of the maxillary segment in Dfd mutants . We found that all eight transcription factors interact with conserved regions in the rpr-4S3/3′ enhancer in vitro  for all factors for which functional antibodies were available. A significant enrichment of the rpr-4S3/3′ locus was observed using Dfd, Gcm and En antibodies en, UAS-disco, UAS-gcm strains were obtained from the Bloomington Stock Center; B48gro line, P. Heitzler 9Gems flies, W. McGinnis M68brk, UAS-brk lines, S. Roth Df(1)XR14 flies, H. Saumweber Df(3L)DocA, UAS-Doc1 strains, M. Frasch Df(2L)slp2-Δd66C strain, W. Gehring en flies, I. Guerrero ems line, H. Jäckle slp1 flies, M. Leptin apt strain, R. Schuh ci flies, T. Kornberg w21Dfd, r11Dfd, arm-GAL4, prd-GAL4, UAS-Dfd, UAS-lacZ. The following green balancer lines were used: MioDr/TM3Sb[twi::2xEGFP], In(2LR)Gla wg-Gla/Cyo[twi::2xEGFP] , N/FM7c[twi::2xEGFP].disco, Drosophila Genomics Resource Center (GH27656), gcm cDNA, G. Technau brk cDNA, C. Rushlow Doc1 cDNA, M. Frasch apt cDNA, R. Schuh rpr-4S3/3′ fragment were created by site-directed mutagenesis via two-step PCR or the QuickChange Multi Site-directed Mutagenesis Kit (Stratagene). Primer sequences are available upon request. All products were cloned, sequenced, and shuttled into pH-Pelican plasmid Drosophila Embryo Injection Service. At least three independent lines were analyzed for expression levels. rpr coding regions from different Drosophila species were PCR amplified with specific primers, cloned and sequenced.cDNAs were obtained from: In situ hybridization and immunochemistry were performed as described in situ hybridizations were done as described previously 4 was included in the binding reaction. For the mapping of binding sites, the ability of all eight transcription factors to interact with conserved boxes 1 to 3 was tested, to define the interaction domains on the rpr-4S3/3′ fragment. Subsequently, if known binding sites were present within the conserved boxes, competition experiments were performed to test if these sites are necessary for binding. For all factors with unknown binding sites, systematic competition experiments using overlapping and mutated oligonucleotides covering the binding region were performed. All oligonucleotide sequences used for these experiments can be obtained upon request.EMSA was performed as described previously . For the zinc finger transcription factor Disco, 1 mM ZnSOwww.flychip.org. Four independently staged wild-type embryo populations were collected, chromatin samples were prepared from 5 to 9.5 hr embryo collections. Antibodies used were the following: guinea pig anti-Dfd, guinea pig anti-IgGs , mouse anti-En, mouse anti-LacZ (Invitrogen), rat anti-Gcm  and rat anti-GFP (Invitrogen). A dilution of 1∶500 was used for the anti-Dfd, anti-En and anti-Gcm antibodies, the mock antibodies were used at equivalent protein concentrations. Amplification of the rpr-4S3/3′ and an unrelated, non-coding control locus were analyzed by quantitative real-time PCR in technical triplicates using at least two biological replicates. Precipitates were normalized to input DNA  and compared to the non-coding negative control region. A PCR efficiency of 1.8-fold amplifications per cycle was assumed. PCR primer sequences can be obtained upon request.ChIP experiments were performed as described previously at Figure S1ems (B), en (C) and slp1 (E). Due to the unavailability or inactivity of antibodies, in situ hybridizations for the following genes are shown: gcm (A), apt (D), Doc1 (F), brk (G) and disco (H). Boxes in (A to H) highlight the maxillary segment.Expression patterns of the identified transcription factors in stage 11 wild-type embryos. For the following genes, antibody stainings are shown: (2.53 MB TIF)Click here for additional data file.Figure S2rpr expression when mis-expressed. rpr RNA in situ hybridizations in stage 11 embryos with the following genotypes are shown: (A) wild type, (B) prd::Dfd, (C) prd::ems, (D) prd::Dfd;ems, (E) prd::apt, (F) prd::Dfd;apt, (G) prd::gcm, (H), prd::Dfd;gcm. Co-expression of Ems, Apt and Gcm with Dfd enhances Dfd-dependent ectopic rpr induction , whereas Gcm is able to ectopically induce rpr expression alone. To select identical stages, three characteristic spots of rpr expression in the thoracic segments normally seen in stage 11 wild-type embryos (marked by three asterisks) were used. In (B to H) one spot of ectopic rpr expression at the very posterior end in the prd-GAL4 over-expression embryos is marked by a red arrow, the blue box outlines an additional stripe of rpr RNA expression in the T3 primordium.Identified transcription factors modulate (6.46 MB TIF)Click here for additional data file.Figure S3rpr-4S3/3′ enhancer by phylogenetic footprint analysis. Upper: rpr RNA expression in stage 11 embryos of different Drosophila species used for the phylogenetic footprint analysis. In situ hybridization experiments with species-specific probes show that rpr is expressed in the anterior part of all five Drosophila species . The red boxes highlight the maxillary segment. Bottom: Alignment of the rpr-4S3/3′ enhancer from seven different Drosophila species revealed three highly conserved boxes (I to III). Identified and verified binding sites for all eight co-regulatory transcription factors are highlighted in different colours.Identification of conserved regulatory elements in the (3.37 MB TIF)Click here for additional data file.Figure S4rpr-4S3/3′ enhancer. (A) EMSA for mapping of Slp1 binding site in the rpr-4S3/3′ enhancer using box 3  EMSA using box 3 (as shown in wt) or mutant (cmt) sequence versions. The purple arrowheads indicate specific DNA-protein complexes containing Slp1 protein. Note that in the competitor oligonucleotides only the binding site sequence for the Slp1 protein is mutated, but not for the Dfd binding site sequence. (B) EMSA for mapping of Disco binding site 1 in the rpr-4S3/3′ enhancer using box 1  EMSA using box 1 (as shown in wt) or mutant (cmt) sequence versions. The red and turquoise arrowheads indicate the specific DNA-protein complexes containing either Dfd or Disco protein, respectively. The asterisk indicates a complex with lysate protein seen also in the control. Note that in the competitor oligonucleotides only the binding site sequence for the Disco protein is mutated, but not for the Dfd binding site sequence. (C) EMSA for mapping of Brk binding site in the rpr-4S3/3′ enhancer using box 2  EMSA using box 2 (as shown in wt) or mutant (cmt) sequence versions. The red and blue arrowheads indicate the specific DNA-protein complexes containing either Dfd or Brk protein, respectively. The asterisks indicate complexes with lysate protein seen also in the control. Note that in the competitor oligonucleotides only the binding site sequence for the Brk protein is mutated, but not for the Dfd binding site sequence. (D) EMSA using box 2 (as shown in wt) or mutant (cmt) sequence versions. The red and light green arrowheads indicate the specific DNA-protein complexes containing either Dfd or En protein, respectively. Note that in the competitor oligonucleotides only the binding site sequence for the En protein is mutated, but not for the Dfd binding site sequence. (E) EMSA using box 2 (as shown in wt) or mutant (cmt) sequence versions. The red and light purple arrowheads indicate the specific DNA-protein complexes containing either Dfd or Apt protein, respectively. Note that in the competitor oligonucleotides only the binding site sequence for the Apt protein is mutated, but not for the Dfd binding site sequence.Identification and verification of binding sites for co-regulatory transcription factors in the (3.72 MB TIF)Click here for additional data file.


Since the dawn of genetics, additive and dominant gene action in diploids have been defined by comparison of heterozygote and homozygote phenotypes. However, these definitions provide little insight into the underlying intralocus allelic functional dependency and thus cannot serve directly as a mediator between genetics theory and regulatory biology, a link that is sorely needed.We provide such a link by distinguishing between positive, negative and zero allele interaction at the genotype level. First, these distinctions disclose that a biallelic locus can display 18 qualitatively different allele interaction sign motifs . Second, we show that for a single locus, Mendelian dominance is not related to heterozygote allele interaction alone, but is actually a function of the degrees of allele interaction in all the three genotypes. Third, we demonstrate how the allele interaction in each genotype is directly quantifiable in gene regulatory models, and that there is a unique, one-to-one correspondence between the sign of autoregulatory feedback loops and the sign of the allele interactions.The concept of allele interaction refines single locus genetics substantially, and it provides a direct link between classical models of gene action and gene regulatory biology. Together with available empirical data, our results indicate that allele interaction can be exploited experimentally to identify and explain intricate intra- and inter-locus feedback relationships in eukaryotes. All three concepts have played a key role in the development of population genetics and quantitative genetics theory in evolutionary biology, production biology and biomedicine. Their presence or absence has substantial effects on key genetic features of populations like genetic variance, heritability, fixation rates of alleles, and long-term selection response.Gregor Mendel's finding that hereditary units could be associated with particular observable traits and that in diploid organisms the apparent contribution from the two parents to a trait could be highly asymmetrical biallelic single locus genetics, the basic gene action concepts used to construct the theory only depend on genotypes and their relative positions to each other. The emergence of molecular genetics did not change this situation. Notwithstanding the complex evolution of the gene concept in the molecular biology community The gene action associated with a biallelic locus is defined by the position of the genotypic value  of the heterozygote relative to the genotypic values of the two homozygotes Even though the classical gene action concepts still serve several purposes well, they cannot serve directly as mediators between genetics theory and regulatory biology. The establishment of such a link is sorely needed if we aim for a better understanding of the biological mechanisms underlying intra- and inter-locus additivity and nonadditivity. It would also be an important contribution to the development of a more systems oriented quantitative genetics theory based on how genes actually act and interact allele interaction, which characterizes the degree of functional dependency between the two alleles composing each genotype of a given locus. We then demonstrate that the concept enriches single locus genetics with a number of new characterizing features, and show that it enforces the conclusion that genetic dominance is in fact given by a specific relationship between the allele interactions of all the three genotypes. Finally, we illustrate how the allele interaction concept succeeds in making the sorely needed link between current single locus gene action concepts and regulatory biology by use of gene regulatory models involving positive and negative feedback.Here we show that by considering the additivity and nonadditivity features of each genotype explicitly, regulatory biology can be linked to the basic concepts of single locus genetic theory in a straightforward way. We start out by introducing the concept of Our results suggest that the allele interaction concept can be used experimentally in a very direct way to identify and elucidate intricate intra- and inter-locus feedback relationships manifested at the mRNA and protein expression levels in eukaryotes. Even though this paper is conceptual in character, we emphasize already at this point that all premises underlying this work are either already well supported by empirical data or (together with the reported predictions) well within reach to be tested experimentally, and an experimental test program is indeed suggested.X with homozygote genotypes allele interaction value homoallelic nonadditivity when heteroallelic nonadditivity when additive allele action, and ijx is simply the sum of the two hemizygotic genotypic values. As a combined effect is in general termed “additive” when it is the sum of the individual effects of its underlying components ijX shows positive allele interaction if negative allele interaction if The locus shows a and d of a biallelic locus are given byd = 0, the locus is said to show additive gene action. The degree of dominance is often described by the scaled dominance value The classical additive and dominance genotypic values d can be expressed solely in terms of the By combining eqns. (1) and (2) it follows that the value of d does not follow from whether the two alleles in the heterozygote act in an additive way or not, but is given by a specific relationship between the allele interaction values of all three biallelic genotypes. For example, Thus, in contrast to a common conception among geneticists, allele interaction sign motifs (or sign motifs for brevity) d) of the locus is uniquely determined. Among these 11 we observe that in the 6 cases where d depends not only on the signs of The allele interaction concept enriches single locus genetics considerably as a biallelic locus may in principle display 18  distinct gnitudes . This shi and j in that particular genotype, and we will show in the next section that it indeed carries information about regulatory anatomy. Even though the above definitions can be consistently applied to any phenotypic feature in any diploid organism, the expression (mRNA or protein) phenotype defines a natural starting point from the point of view of data availability and regulatory complexity, and in the rest of the paper we will focus on the mRNA phenotype.Because the allele interaction value et al.Several studies on yeast, mice and humans show that additive allele action  the gene regulation is modeled If the two eqns. (4) have identical parameters and rate functions, the system describes a homozygous locus, otherwise it describes a heterozygous locus. We will also consider the hemizygous locus case where either To compute the allele interaction values if and only if the dose-response functions i. Thus according to this model framework, additive allele action for the expression level phenotype is the rule for genes where regulation of expression of one allele is independent of the expression level of the other allele , their impact could be fully described by varying the parameters We represented the monotonic dose-response functions by the common Hill function Negative autoregulation generated the sign motifs [0 0 –], [0 – –], and [– – –], where the first two appeared at low frequency . The sigominance .Positive autoregulation generated the allele interaction sign motifs [0 0 0], [0 0 +], [0 + +], and [+ + +] . The moscis-regulatory region   . These 7[+ + +])  that neg[+ + +])  that posCoding variation did not introduce any new sign motifs, but the sign motifs belonging to negative autoregulation generated negative as well as positive overdominance (see below for further discussion of overdominance).vice versa. Such functions together with polymorphisms influencing production rates, decay rates or the weights The seven sign motifs characterized by containing at least one positive and one negative sign   define an motifs . The twoHigher-order feedback systems bring up some additional aspects. We studied two cases of positive feedback and two cases of negative feedback between two loci among which only one was polymorphic, using the model presented in cis-variation creates the same allele interaction sign motifs as negative autoregulation except for the additional appearance of the [0 0 0] sign motif at a low frequency. The allele interaction sign motifs of the nonpolymorphic locus are the same as for the polymorphic one . Higher-order feedback thus seems to generate the same allele action sign motifs in the polymorphic locus as autoregulation. This suggests that the allele interaction sign motifs characteristic of feedback may be generic.We analyzed the single locus case in more detail to identify which of the realized allele interaction sign motifs were most prone to generate overdominance compared to partial or complete dominance. Negative autoregulation with monotonic dose-response and without coding variation generated no overdominance, in accordance with our analytical result  reduce transcription of each X by about one half, and Drosophila male flies increase transcription from their single X chromosome about twofold Several known biological mechanisms involving no regulatory feedback do indeed cause nonadditive allele interaction. These are for example transvection and sex-linked dosage compensation. Transvection-like phenomena have been described in a number of different organisms ectively . Furthercis-acting QTLs Our analysis suggests that any cis-acting eQTL showing genetic dominance points towards a possible feedback regulation or some other mechanism causing the nonadditivity. Since homoallelic or heteroallelic nonadditivity is a prerequisite for genetic dominance (eqn. (3)), another prediction is that homozygous loci showing homoallelic nonadditivity are hotspots for the creation of genetic dominant eQTLs in the genome.Quantitative trait locus (QTL) studies addressing the relationship between genetic variation and genome-wide mRNA expression levels in mouse and yeast, have shown that at least 25% of the so-called expression QTLs (eQTLs) are Although we included structural variation in allele products when investigating the relation between feedback and allele interaction sign motifs, our main focus was to elucidate the effects of regulatory variation. In general, regulatory variation is far less understood and studied than coding variation, but its importance for quantitative phenotypic variation and evolution through differential expression is highly significant n-enzyme metabolic pathways when studied within the framework of metabolic control analysis. As this explanation has a very strong standing in the genetics community, we find it appropriate to make a few comments on how it relates to the current paper. We have previously shown that the K & B framework is quite restricted when it comes to explaining why recessive mutants are so common, the appearance of dominant mutations, the existence of functional recessive homozygotes, the phenomenon of overdominance, and how genetic dominance may arise from intralocus interaction This implies that it per definition cannot relate the nonadditivity between alleles in each genotype to the dominance value for RNA and protein phenotypes.Kacser and Burns proposed in 1981 an explanation for genetic dominance based on properties of metabolic systems S. cerevisiae is very well suited as a model organism in this context since a collection of hemizygotes (i.e. heterozygous knockouts) for all nonessential genes is already available Our results open up for an extensive experimental research program on allele interaction in diploid organisms. Allele interaction across various environments can be surveyed in any diploid organism where gene knockouts can be produced. In particular, The validity of this hypothesis can be established by measuring allele interaction for genes known to be downstream regulated or expressed from constitutive promoters. Use of heterologous constructions where one could adjust the promoter strength would provide the most conclusive data. A possible strategy would be to build upon a recently published protocol This can be done by inserting heterologous feedback loops with and without coding variation and with monotonic and nonmonotonic dose-response curves in diploid eukaryotic cells.Given that the default sign motif is [0 0 0], this is likely to provide new information about gene regulation in eukaryotes. Moreover, allele interaction may be caused by quite complex feedback mechanisms involving much more than the direct interactions associated with binding of transcription factors to promoters or protein-protein interactions. This suggests that the allele interaction concept can be used to search for subtle feedback loops (including for instance metabolite levels) not easily detected by network analyses and bioinformatics methods.The clear correspondence between feedback and allele interaction sign motifs is likely to become much more blurred when phenotyping at the metabolic, morphological, physiological or whole organism level. However, insights into the sign and strength of allele interaction arising at various phenotypic levels obtained by causally cohesive genotype-phenotype models Our theoretical analysis clearly demonstrates that single locus genetics is more complex than currently envisaged. The classical concepts of dominant or additive gene actions do not follow from whether the monoallelic contributions of the two different alleles in the heterozygote act in an additive way or not, but are given by a specific relationship between the degree of allele interaction for all the three genotypes. The concepts homo- and heteroallelic allele interactions open for a systematic investigation of the relation between allele interaction sign motifs and gene regulatory mechanisms. The elucidation of one of the oldest concepts in genetics appears to lead to a new experimental approach on how to identify intricate intra- and inter-locus feedback relationships in eukaryotes as well as to provide a most needed directly operational conceptual link between genetics theory and regulatory biology.d was set equal to zero if the heterozygote differed less than 5% from the mean of the homozygotic values. In order to avoid numerical artifacts and to obtain robust predictions, all allele interaction sign motifs with a frequency <1% were discarded.In all simulations the We modeled coding variation by introducing weights To study a nonmonotonic dose-response situation we used the response functions Appendix S1This appendix contains an analytic treatment of a generalization of the gene regulatory model given in eqs. (4)–(6). We present results on stability, allele interaction and genetic dominance.(0.07 MB PDF)Click here for additional data file.


Recent genome wide transcription factor binding site or chromatin modification mapping analysis techniques, such as chromatin immunoprecipitation (ChIP) linked to DNA microarray analysis (ChIP on chip) or ChIP coupled to high throughput sequencing (ChIP-seq), generate tremendous amounts of genomic location data in the form of one-dimensional series of signals. After pre-analysis of these data , biologists need to search for the biological relevance of the detected genomic positions representing transcription regulation or chromatin modification events.Genomic Position Annotation Tool (GPAT) with a simple web interface that allows the rapid and systematic labelling of thousands of genomic positions with several types of annotations. GPAT automatically extracts gene annotation information around the submitted positions from different public databases (Refseq or ENSEMBL). In addition, GPAT provides access to the expression status of the corresponding genes from either existing transcriptomic databases or from user generated expression data sets. Furthermore, GPAT allows the localisation of the genomic coordinates relative to the chromosome bands and the well characterised ENCODE regions. We successfully used GPAT to analyse ChIP on chip data and to identify genes functionally regulated by the TATA binding protein (TBP).To address this problem, we have developed a GPAT provides a quick, convenient and flexible way to annotate large sets of genomic positions obtained after pre-analysis of ChIP-chip, ChIP-seq or other high throughput sequencing-based techniques. Through the different annotation data displayed, GPAT facilitates the interpretation of genome wide datasets for molecular biologists. One of the major issues in genomics is the genome wide mapping of transcription factor binding sites in order to study their function at the scale of the genome. The chromatin immunoprecipitation (ChIP) technique uses antibodies that are specific for a transcription factor or a chromatin modification, to isolate the DNA to which this factor or modified histone is bound in a cell at a given time. The recent appearance of several genome wide analysis techniques, where ChIP is either followed by DNA microarray analysis (ChIP on chip) or coupled to high throughput sequencing (ChIP-seq), made the genome wide mapping of DNA bound factors technically possible. However, these analyses generate tremendous amounts of genomic location data in the form of one-dimensional series of signals.Recently, efforts have been made to develop academic software to pre-analyse these datasets, (e.g. Mpeak ), in ordThe web interface of GPAT is programmed using Python v2.5.1) [.5.1 [2] Existing on-line tools, such as the UCSC genome browser , allow tGalaxy  is a fraCEAS  is a ChICisgenome  is a powGPAT allows users to analyse large batches of genomic positions and to retrieve genomic annotations around these positions. Briefly, the user submits a flat file containing the genomic positions and then selects the annotation search options and the display options Figure . The annThe input format is based on the GFF standard file format . Mouse aFor each submitted genomic position, GPAT calculates a mean value from the two boundary positions and compares this value to the gene annotation positions . GPAT has three different gene annotation search modes corresponding to different biological questions Figure .1. The "direct search" mode determines whether the submitted positions are detected inside a transcription unit. It was developed to allow searches for a binding site inside the transcription units (e.g. retrovirus insertion events).2. In the "window search" mode, a user-defined window is calculated around the submitted positions. Then GPAT searches gene annotations located within this window. It was developed for datasets where no particular binding profile is expected or known (e.g. insulator elements or proteins of unknown function).3. The "promoter search" mode uses the same window as the "window search" mode, but tests whether the transcription start site (TSS) of a transcription unit is found within this window. It was developed for datasets, where a binding in the neighbourhood of the TSS is expected (e.g. transcription factors).Regardless of the search mode chosen by the user, GPAT provides a complete localisation report relative to the matched gene annotation . Furthermore, when multiple gene matches are found, the user can choose to retrieve either the closest annotation only or all the matched annotations.Biologists are often interested in the expression status of the genes neighbouring the submitted genomic locations. Therefore, the GPAT software also gives access to the corresponding gene expression levels in several commonly used model cell lines as provided by the GNF Symatlas  and StemSeveral other genomic features, such as those extracted from the cytogenetic mapping  or the pGPAT displays the results as a table containing the annotated positions hyperlinked to the data source Figure . A direcFinally, an additional analysis step allows the user to create a report file, containing summary information, in addition to the matched gene list. This option calculates the global distribution profile of the matched genomic positions as compared to the TSSs of the corresponding genes using parameters set by the user  is a component of a number of complexes, including the TFIID complex involved in the RNA Polymerase II (Pol II) general transcription machinery. Surprisingly, it has been shown that Pol II transcription can occur in the absence of TBP ,17. HoweWe tested GPAT using a promoter DNA tiling array dataset generated by hybridization of DNA (prepared from Hela cells), which was ChIPed using specific antibodies against Pol II, TBP and GST (as a negative control). For each experiment, three slides  representing approximately one-third of the most characterised promoters of the gene were hybridized (about 5600 genes covered from -8 kb upstream to +2.5 kb downstream with a ~350 bp resolution). After intra-array lowess normalization, peaks corresponding to the factor binding sites were detected using Agilent ChIP analytics (using the neighbourhood model).A flat file containing the genomic positions generated by this pre-analysis was input to GPAT. The promoter search mode using a half window size of 5000 bp successfully retrieved the genes in the neighbourhood of the factor binding sites in less than a minute. This allowed us to build a list of genes potentially regulated by TBP and to obtain information about the presence of the Pol II at these sites Figure . FurtherFinally, the output of GPAT allowed us to attribute an expression level for each of the differently regulated gene categories showing that the genes for which a co-occupancy of TBP and Pol II was detected have the strongest expression levels Figure . This coIn conclusion, these results demonstrate the efficiency of GPAT for the extraction of biological meaning from large genomic position datasets.The current version of GPAT improves the bioinformatics analysis of ChIP on chip or ChIP-seq data. It considerably speeds up and facilitates the steps between data pre-analysis and the biological interpretation of the data. Notably, the amplitude in the stringency of the annotation search provided by the three different research modes should meet the requirements of most of the biological questions addressed. Furthermore, the full localisation report (including distance to the TSS) will help the biologist to easily understand the binding pattern of his studied factors. Finally, the connection of the binding sites and the gene expression data is an essential resource at different steps of the analysis. Firstly, it provides a supplementary filtering step to distinguish between relevant and background signals. Secondly, it adds another layer of complexity to the data interpretation, providing insight into the regulation processes taking place at particular genomic locations.Currently, the investigation process of large scale ChIP data is complicated and involves multiple analysis steps. In order to make this technology available for laboratories without access to bioinformatics expertise, considerable efforts are needed to facilitate the data analysis . To address this issue, GPAT was designed to improve the steps following data pre-analysis . However, several improvements remain to be implemented. GPAT currently supports the mouse and human genomes, but adding other model organisms for which genome wide ChIP data are available (e.g. Drosophila) would make the impact of GPAT wider and could be easily implemented in the future.Furthermore, GPAT provides expression data for several cell lines. One of the recent improvements of the ChIP method allows the use of tissues as a starting material. Since the GNF dataset was also generated for mouse tissues, these datasets could be easily integrated into the GPAT interface.GPAT provides a quick, convenient and flexible way to annotate large sets of genomic positions obtained after pre-analysis of ChIP on chip, ChIP-seq or other high throughput sequencing based techniques. Thanks to the different annotation and experimental data provided (including the expression status of the identified genes), GPAT facilitates the interpretation of genome wide datasets. We hope that GPAT will be of great help to molecular biologists who wish to analyse large-scale genomic data.Project home page Source codeThe GPAT source code can be freely downloaded from: Operating systemsFor use: Standard WWW browserProgramming languagePython, SQL, JavascriptLicenceGNU General Public License v3 (GPL 3)ChIP chip: Chromatin Immunoprecipitation followed by hybridation on DNA microarray; ChIP-seq: Chromatin Immunoprecipitation coupled to high throughput Sequencing; ChIP: Chromatin Immunoprecipitation; GPAT: Genomic Position Annotation Tool; RNA Pol II: RNA polymerase II; TBP: TATA Binding Protein; TSS: Transcription Start SiteAK conceived and implemented GPAT. MF generated the ChIP on chip data. AK did the bioinformatics analysis of the ChIP on chip data. AK and LT wrote the manuscript.GPAT class diagram. Class diagram describing the python object oriented architecture of the GPAT software.Click here for file


It is of particular interest because of its pleiotropic functions: it plays a crucial role in the expression of key genes involved in adipogenesis, lipid and glucid metabolism, atherosclerosis, inflammation, and cancer. Its protein isoforms, the wide number of PPARγ target genes, ligands, and coregulators contribute to determine the complexity of its function. In addition, the presence of genetic variants is likely to affect expression levels of target genes although the impact of PPARG gene variations on the expression of target genes is not fully understood. The introduction of massively parallel sequencing platforms—in the Next Generation Sequencing (NGS) era—has revolutionized the way of investigating the genetic causes of inherited diseases. In this context, DNA-Seq for identifying—within both coding and regulatory regions of PPARG gene—novel nucleotide variations and haplotypes associated to human diseases, ChIP-Seq for defining a PPARγ binding map, and RNA-Seq for unraveling the wide and intricate gene pathways regulated by PPARG, represent incredible steps toward the understanding of PPARγ in health and disease.Peroxisome proliferator-activated receptor gamma (PPAR Gene transcription requires an elaborate network of intra- and extracellular signals, such as hormones, xenobiotics, micro- and macronutrients  and drugs, that converge to the nucleus following different pathways, resulting in the expression of each gene in each tissue. It is a current assumption that transcription is mostly shaped by environmental factors, acting both via direct and indirect mechanisms. Translating exogenous and endogenous signals which affect gene transcription, into a cellular physiological response requires the coordinated action and the fine tuning of transcription factors (TFs) acting at DNA level, including those belonging to the nuclear receptor (NR) superfamily , 2. stimuli and are able to modulate their transcriptional activity through conformational changes fs.185[stop186], resulting in a truncated protein in the DBD [γ interaction with PPRE in target gene promoters, resulting in a failed transactivation [Another  the DBD . Within  the DBD , 76, 82.vitro, that four rare mutations in the LBD of PPARγ result in a reduced PPARγ trans-activating ability in the presence of a synthetic ligand, affecting its ability to recruit cofactors, ligands, and RXRα: Pro495Leu , Val318Met , Phe388Leu, and Arg425Cys  may greatly affect the contribution of a specific SNP to the resulting phenotype.Although some studies have demonstrated the functional impact of  effects , 88, 89 PPARG nucleotide variations obtained in different populations [Taken together, these considerations contribute to explain the conflicting results about ulations , 112–115For instance, Pro12Ala has been often associated with several diseases and phenotype effects , 71, 72,In contrast, two recent conflicting studies in the Indian population have shown that Pro12Ala contributes to T2DM development  and do nin vivo, that Ala12 allele is associated with a reduced LPL activity in postheparin plasma [This SNP has been also associated to altered plasma levels of LPL, leptin, adiponectin, and resistin. Indeed, it was shown, n plasma ; higher n plasma . The effn plasma .Also the association between Pro12Ala and adiponectin plasma levels seems controversial: in the Japanese population Ala12 allele is associated with reduced serum adiponectin levels , 96 wherRETN encoding another adipose tissue-related molecule, the resistin, in the Chinese population; both heterozygous and homozygous Ala12 carriers showed lower plasma resistin levels compared to homozygous Pro12 carriers [In a study by Wang et al. (2004) in , it has carriers . On the carriers .PPARG variant, C1431T; this silent SNP, occurring in the exon 6, is also known as His477His and C161T of exon 6 [Pro12Ala has been described in linkage disequilibrium (LD) with another common f exon 6 . It has f exon 6 , while tf exon 6 . The lack of functional findings within the above-described association studies and possible influence of ethnicity, environmental and genetic factors are likely to explain the controversial results so far reported. Moreover, due to LD between polymorphisms, determining the relative contribution of each SNP on the resulting phenotype is quite difficult.For instance, different studies report that 1431T allele is associated with an increased BMI in obese Finns , a reducγ-dependent transcriptional regulation [About its effects on plasma proteins levels, C1431T has been associated with increased leptin levels . Also Vagulation .Moreover, also resistin levels were significantly increased in individuals carrying C1431T whereas the Pro-C haplotype was more frequent in groups with lower resistin levels. In contrast, Pro-T and Ala-T haplotypes showed increased frequency in groups with higher resistin levels although statistically not significant . PPARG transcript abundance influencing in turn the expression levels of some PPARγ targets involved in the eye physiology [Moreover, nucleotide variations in putative regulatory regions of PPARG have been associated, with different extents, to human diseases. Indeed, we recently identified A-2819G SNP in PPARG promoter and observed a significant association with T2DM and proliferative retinopathy in diabetic females whereas no linkage disequilibrium with Pro12Ala nor association with obesity was observed . It has ysiology . γ molecular targets, since C-681G and C-689T were associated with increased plasma LDL levels and A-14G with a decreased activity of PPARγ4 promoter [Other three variants in PPARG putative promoter have been identified: A-14G, C-681G, and C-689T , even thpromoter , 95, 99.PPARG gene, possibly giving a causal link to human diseases. The introduction of massively parallel sequencing platforms, which have offered to researchers the possibility to identify, in a single experiment, point mutations and/or gross genomic rearrangements, within coding and yet unexplored regulatory regions of disease-causing genes, will surely represent a powerful tool to systematically discover variations in cis-acting proteins, able to specifically bind cis-regulatory elements, such as promoters and enhancers, is needed [core promoter, is usually low, different site-specific TFs participate to the recruitment and/or the stabilization of general TFs' complexes, increasing the cell transcriptional rate. Moreover, histone-modifying enzymes may be recruited by other factors—binding to distal enhancer regions—and determine a favourable chromatin environment and a subsequent transcriptional enhancement. On the other hand, the transcription can be negatively modulated through the binding of repressive factors to distal silencer regions or the competition with TFs themselves.Any genetic information is conveyed from DNA to proteins via mRNA, through a complex and finely regulated process. Unraveling how these genomic information are then translated into gene regulation has been for many decades an intriguing field, fulfilled by many advances, speculations, and scientific debate. To achieve this tuned regulation, the concerted action of multiple s needed , 117. Moγ-mediated phenomena in a specific cell/tissue/organ one cannot ignore the consideration that PPARγ is a transcription factor. Its mechanistic understanding represents a prerequisite for fine-tuning the therapeutic activities of PPARG. To understand PPARMore generally, several human diseases have been directly linked to alterations in the gene expression caused by defects in the structure and/or function of a key transcriptional regulator  althoughγ is not an exception, rather it is likely to be one of the most representative candidate genes in “TF-disease” association studies, for its involvement in many physiological and also pathological processes [In this context, the specific case of PPARrocesses .To this aim, the introduction of massively parallel sequencing platforms in the 2004, coupled with the recent advances in chromatin immune-precipitation (ChIP) followed by sequencing (ChIP-seq), has clearly revolutionized the way we approach to—and also study—different biological phenomena –121. AltThese platforms have been quickly applied to many scientific contexts, giving rise to many “Seq” protocols, specifically developed and suited for a particular research branch, from transcriptomics (RNA-Seq) to the targeted resequencing for the identification of disease-causing nucleotide variations (CNV-Seq and DNA-Seq), including DNA-protein interaction studies (ChIP-Seq) and genome-wide profiling of epigenetic marks (Methyl-Seq). Although it is beyond any doubt that NGS platforms have changed the way we think about many scientific issues, one of the broadest and useful applications of this technology is towards the identification of the genetic causes of inherited diseases, both mendelian and multifactorial. PPARG—from DNA variations to gene expression and its regulation—will surely enhance our understanding about its involvement in health and disease. Identifying novel nucleotide variations, both point mutations and gross genomic rearrangements, within coding regions and yet unexplored intronic and regulatory regions of PPARG by targeted resequencing (on NGS platforms) will be the first brick towards building a more complete and detailed view of PPARG function and activity  within disease-causing genes. Nonetheless, it is clear that such genome-wide approach cannot be used for a routine mutational screening in wide number of affected individuals, due to the high computational and economic effort required, particularly considering that there are few research groups in big companies and/or large corporations, as well as big public and private world leading research institutions, able to sustain these costs. loci appears to be the only reliable way to obtain the high accuracy of NGS data at the accessible costs of a standard array analysis. On the other hand, it appears crucial to have efficient and cost-effective capture methods to enrich the sample with “high-value” genomic regions to sequence in order to avoid off-target sequencing. Thus, targeted resequencing of a small number of candidate genes or disease in-situ synthesized oligonucleotide microarrays) have been successfully used to enhance the sequencing template enrichment [In light of this, different techniques have been recently developed allowing researchers to enrich their sample of target genomic regions to be further sequenced. Multiplex PCR amplification of specific target regions was first used for candidate gene approaches, to enrich the samples with regions of interest, further processed to prepare libraries prior to sequencing –128. Anorichment –133. Comrichment . Severalrichment .RPS19 gene sequence variants contribute to the high clinical variability observed in red cell aplasia. They hypothesized that specific alleles in these non-coding regions may alter the binding of regulatory proteins and/or TFs, possibly altering or removing an important stimulus for hematopoiesis [Since most of human genetics studies have so far mainly focused on protein-coding exons, these regions usually represent high-value targets for targeted resequencing, even though this approach can be—and we believe in most cases it must be—extended to gene regulatory regions (upstream the translation start sites and introns). Indeed, the identification of nucleotide variations in putative or already known regulatory sequences, within non-coding genomic regions, is therefore of great relevance for future research. This approach appears to be very promising above all for the study of TFs binding sites, since their involvement in human disease, both mendelian and multifactorial . Barrio opoiesis .PPARG, coupled to the targeted resequencing on NGS platforms, will represent a very powerful approach for researchers. Indeed, it is likely to allow the identification of all the potential risk-conferring variations, within its coding regions, of putative novel single nucleotide variations (mutations and SNPs) and insertions/deletions or other genomic rearrangements, possibly associated to human diseases. It will also allow gaining further insights into the genomic architecture of its regulatory regions, offering the possibility to rapidly and accurately identify potential sources of variation responsible for the alteration of its mRNA levels. Moreover, the specific enrichment of target regions, followed by targeted resequencing, could also be performed on well-known PPARγ-regulated genes in specific pathways. In this context, the possibility to have high enrichment for the both coding and unexplored regulatory regions of γ target genes have not unequivocally shown a clear correlation between SNPs and the related human diseases, by using these approaches it will be easier to identify specific alleles in non-coding regions of target genes and verify whether these nucleotide variations are responsible for the alteration of known PPRE—and in turn of PPARγ binding to these elements—finding a direct and functional link to the disease.Indeed, since several studies performed on PPARThanks to the introduction of NGS platforms, widely used approaches of chromatin immuno-precipitation followed by microarray (ChIP-chip) have been flanked—and in many cases substituted—by ChIP-seq protocols. Indeed, in ChIP-seq, the DNA fragments of interest  are directly sequenced instead of being hybridized on a chip-array. Thanks to the high resolution, coverage, the wider dynamic range, and the absence of hybridization-based artifacts, ChIP-Seq allows now researchers to improve both quantity and quality of produced data. Moreover, fundamental advances toward a more accurate definition of the consensus sequences for the binding of TFs have been done .To date, this novel approach, which couples in a single experiment a standard ChIP assay to the large-scale massive sequencing of target genome regions, allows researchers to obtain a more complete map of TFs-DNA interactions . Drawingγ acts in combination with RXR as heterodimer and requires the cooperation with many different tissue-selective factors, understanding the differential spatial and temporal recruitment of PPARγ:RXR complex to target genes is likely to improve our knowledge about PPARγ biology.In particular, since PPARγ- and RXR-binding sites map during the adipocytes differentiation of 3T3-L1 cells [γ ChIP assay and about 50 million derived from RXR ChIP. They demonstrated that spatial and temporal recruitment of PPARγ and RXR to target genes varied during adipogenesis  of PPARγ:RXR-binding sites was observed at day 6. This genome-wide ChIP-Seq analysis allowed to confirm the binding of PPARγ:RXR heterodimer to well-established PPREs in already known target genes. In addition, novel target sites in introns of different genes were also identified.In a recent study, Nielsen et al.  by ChIP-L1 cells . In partogenesis . More inpression –30. Theyγ function, the specificity of action, that is, how PPARγ modulates target genes in some cell types but not others. By using this innovative approach, the authors determined which cell-type-specific cofactors are recruited by PPARγ in mouse macrophages and adipocytes [γ transactivation ability on target genes, with characteristic cell-type specific patterns of gene modulation, but the molecular basis of such a specificity has not yet been fully understood. ChIP-Seq was also recently used by Lefterova and colleagues (2010)  to addreipocytes . Indeed,γ binding, by massively sequencing—overall the mouse genome—the regions directly bound by PPARγ. This analysis revealed that PPARγ cooperates with some cell-type-specific factors, PU.1 and C/EBPβ, in the defining the specificity of action for PPARγ in each cell type   . PPARγ ia priori information about the genomic position of TFs binding sites and allow to generate high-resolution binding maps in response to a specific stimulus [Above described works have clearly shown the great potential of sequencing-based ChIP assays, which do not require stimulus , 124. Hostimulus , couplinstimulus .γ among them), will surely provide a complete, accurate, and reliable source of useful of data, enabling to complete, piece by piece, the intricate puzzle of PPARγ functions.We firmly believe that combining a sequencing-based ChIP assay to high-throughput transcriptome analysis by RNA-Seq on NGS platforms, above all for inducible transcription factors  and tag-based sequencing , RNA-Seq does not require prior knowledge of any gene sequence (as occurs for microarrays) or laborious and time-consuming steps for the cloning and sequencing (as occurs for existing tag-based approaches)  expressed within a specific cell and/or tissue—at a particular development stage or after an endogenous or exogenous iewed in ). Several recent studies have clearly demonstrated the advantages of using RNA-Seq in the interrogation of transcriptomes under multiple conditions, such as cell proliferation, differentiation, and various environmental stress –148.γ as TF involved in many cellular pathways, investigating the PPARγ-dependent regulation of target genes expression via RNA-Seq in a single experiment represents a great challenge. In this context, due to the crucial role of PPARγ to PPRE, activating or repressing target gene expression, directly identifying (by RNA-Seq) the gene expression response to PPARγ-modulating drugs (agonists such as TZD), or in particular development conditions (during adipogenesis), will provide researchers the opportunity to directly measure its ability to modulate the transcription of specific genes in a cell/tissue specific manner. Whereas previously described ChIP-Seq allows to draw a binding map of PPARγ in different cell types or tissue, in both physiological and pathological conditions. Several unsolved questions about the “real” impact of PPARγ on the regulation of target gene expression—in several conditions—can now be fruitfully addressed by the use of NGS.Since high-throughput sequencing has definitely proved to be a powerful and quantitative method to sample the transcriptomes at single nucleotide resolution , the useγ, have emerged as crucial transcription factors modulating the expression of genes involved in several important pathways and biological processes and, noteworthy, in human diseases. Despite the huge knowledge in the field, future research efforts will undoubtedly reveal novel mechanisms through which PPARγ integrates these complex physiological and pathological pathways. Particular attention should be given to the question of how the selective effects of PPARγ are achieved in different cell types. It will also be of great importance to understand the subtle mechanisms dictating this selectivity of action through the study of its different isoforms, genetic variations, and their recruited cofactors able to remodel the chromatin structure. Knowing all the PPARγ targets is a prerequisite for a full understanding of the metabolic defects that occur in people with PPARG mutations and/or variation and will help in the interpretation of effects—and also side effects—that can occur with PPARγ agonists already in clinical use. Thus, to have a complete picture of PPARγ functions and implications, studying altogether these aspects, through the use of massively parallel sequencing platforms, will provide a way to better characterize the actions of PPARG products and agonists.Over the past years, PPARs, and especially PPAR


Recent genomic scale survey of epigenetic states in the mammalian genomes has shown that promoters and enhancers are correlated with distinct chromatin signatures, providing a pragmatic way for systematic mapping of these regulatory elements in the genome. With rapid accumulation of chromatin modification profiles in the genome of various organisms and cell types, this chromatin based approach promises to uncover many new regulatory elements, but computational methods to effectively extract information from these datasets are still limited.We present here a supervised learning method to predict promoters and enhancers based on their unique chromatin modification signatures. We trained Hidden Markov models (HMMs) on the histone modification data for known promoters and enhancers, and then used the trained HMMs to identify promoter or enhancer like sequences in the human genome. Using a simulated annealing (SA) procedure, we searched for the most informative combination and the optimal window size of histone marks..Compared with the previous methods, the HMM method can capture the complex patterns of histone modifications particularly from the weak signals. Cross validation and scanning the ENCODE regions showed that our method outperforms the previous profile-based method in mapping promoters and enhancers. We also showed that including more histone marks can further boost the performance of our method. This observation suggests that the HMM is robust and is capable of integrating information from multiple histone marks. To further demonstrate the usefulness of our method, we applied it to analyzing genome wide ChIP-Seq data in three mouse cell lines and correctly predicted active and inactive promoters with positive predictive values of more than 80%. The software is available at  Transcriptional regulation in eukaryotic cells requires highly orchestrated interactions between transcription factors (TFs), their co-factors, RNA polymerase and the chromatin ,2. Severcis-regulatory sequences, but the characteristic chromatin signatures of regulatory elements provide a pragmatic way to systematically identify these elements in the genome without prior knowledge of the underlying sequences. Compared with the other methods, there are several advantages of this chromatin-based approach. First, it requires no prior knowledge of the sequence features of the promoters or enhancers; Second, the chromatin modification profiles could be obtained for most organisms as the existing antibodies can specifically recognize the characteristic histone modifications in different species. Third, this approach does not make the assumption that promoters or enhancers are evolutionarily conserved, thereby can identify fast evolving regulatory elements in the genome.Recently, a chromatin based regulatory element mapping approach has been proposed. This apet al represents the Gaussian function with mean vector μjm and covariance matrix Ujm. The forward and backward algorithm[Q = (number of bins)/k to change the number of states depending on the length of data (we set k = 8) and the minimum Q was set to 3. The background HMM was designed to have the minimum number of states (Q = 3). Each state is composed of 3 mixtures of Gaussian components (M = 3) to capture the complex histone modification patterns. Models with larger m did not improve the prediction performance (data not shown).where algorithm was usedFor a given genomic region, a log likelihood score was calculated using the three HMMs for promoter, enhancer and background. Two log-odd scores were calculated as the following.c1, c2), it is regarded as a background signal. The number of prediction depends on these cut-off values. We plotted Figure The log-odd score reflects how strong a signal is compared to the background. If the log-odd is below a cutoff  and c1 = 2.1 (treated). We used c2 = 0.25 (untreated) and c2 = 0.0 (treated) to compare the prediction result of the enhancer  to find the optimal combination. SA  is a genEcurrent is greater than the previous value (E), the move is always accepted; otherwise, the move is accepted with a probability of T.That is, if Ecurrent. Ecurrent is defined as:To adapt the SA method to our model, we hybridized HMMs with SA. Initially, SA randomly selected a candidate combination of histone modifications. Also, a window size was randomly selected among 1, 2, 4, 6, 8, 10, 12 Kbp. An HMM was trained with the candidate combination and evaluated by Ecurrent is always accepted if Ecurrent>E; otherwise, it is accepted with a probability that generally decreases as the temperature (T) decreases. The next move is made by randomly adding or removing one or two histone patterns and increasing or decreasing one 2 Kb of the window size. This procedure is repeated while decreasing the temperature T. In the simulation we usedThe combination is accepted with the probability given in equation (3). Ecurrent. The training set (52 promoters and 36 enhancers) and the test set are fixed for each run. We set the maximum number of iterations to be 200 to give SA enough burning period. In fact, most simulations were converged in less than 100 iterations. We recorded the results for 30 independent simulations.In the HMM-SA procedure, the 105 promoter and 73 enhancers in the training dataset was divided into training and evaluation sets, half of them were used to train the HMMs and the other half to calculate D = 2.5 kb to the closest annotated TSS of a gene. When multiple predicted sites are supported by the same TSS or any enhancer evidence, we merge these predictions. However, when multiple predicted sites are not within the distance, we counted all of them as FPs. The total number of the predicted promoters in Heintzman et al[We validated the prediction results in the ENCODE regions by calculating how many predicted promoters are supported by annotated TSSs in RefSeq. The adjacent 3 log-odds  are averaged. If multiple peaks of promoters or enhancers are found within 1.5 kb, only the highest log-odd is selected. A prediction was considered as correct if the predicted center is within man et al was 208.To compare the performance of the two methods, we plotted ROC curves for promoter predictions in both untreated and treated cells Figure . We defiMikkelsen et al. generated the genome-wide mapping of chromatin modifications in three mouse cell lines: embryonic stem (ES) cells, neural progenitor cells (NPCs) and embryonic fibroblasts (MEFs) . Four chWe evaluated the classification performance of our method using gene expression and RefSeq annotation on predictions that could be unambiguously assigned to a gene, namely located 2.5 Kb within an annotated TSS. Mikkelsen et. al conducted replicate measurements of gene expression in the same cell lines (GEO accession number is GSE8024). There were 13482 unique genes in their experiments. The numbers of active and inactive genes in each cell line were counted using the majority rule in the replicate experiments and the genes with marginal expression levels or conflicting calls were excluded Table .HMM: Hidden Markov Model; TSS: Transcription Start Site; SA: Simulated Annealing; PPV: Positive Predictive Value; TP: True Positive; FP: False Positive; FN: False Negative; TN: True Negative; DHS: DNaseI hypersensitive Site; ROC: Receiver Operator Characteristics; TF: Transcription FactorThe authors declare that they have no competing interests.KJW implemented the algorithms, performed all tests, and made all images. IC implemented HMM algorithm. BR and WW conceived of the algorithm and participated in its design and coordination. KJW, IC, BR, and WW wrote the manuscript. All authors read and approved the final manuscript.Figure S1. Probability density of Gaussian mixtures for the three HMM states trained on promoter and enhancer for each chromatin marker. The x-axis is the log ratio of ChIP-chip intensity. The black curve is the mixture of 3 Gaussian  H4ac. Analysis on the trained HMM for H4ac.Click here for fileFigure S1 (B). H3ac. Analysis on the trained HMM for H3ac.Click here for fileFigure S1 (C). H3K4me1. Analysis on the trained HMM for H3K4me1.Click here for fileFigure S1 (D). H3K4me2. Analysis on the trained HMM for H3K4me2.Click here for fileFigure S1 (E). H3K4me3. Analysis on the trained HMM for H3K4me3.Click here for fileFigure S1 (F). H3. Analysis on the trained HMM for H3.Click here for fileFigure S2 Active and inactive profile of the ChIP-Seq data. Tag counts at TSS are clustered considering the expression ratio. Histone profiles for active and inactive TSS.Click here for file