Yersinia species employ the Ysc-Yop type III secretion system (T3SS) encoded by a highly conserved pYV virulence plasmid to export the virulence effectors into host cells. The Ysc-Yop T3SS is tightly regulated by multiple contributing proteins that function at different levels. However, systematic transcriptional regulation analysis of Ysc-Yop T3SS is lacking and the detailed mechanism under this regulation process is still elusive. Aimed at systematically characterizing transcriptional regulations of all T3SS genes in Y. pseudotuberculosis, we amplified 97 non-coding fragments from the pYV plasmid and analyzed transcriptional responses of the T3SS genes under different growth conditions. Transcriptions of T3SS genes were induced at 37°C and genes encoding T3SS effectors were highly induced by further depletion of Ca2+. The temperature induced gene transcription process is mediated by modules encoded on the chromosome, while the Ca2+ depletion-induced process is controlled by the positive regulatory protein LcrF as well as the negative regulatory protein LcrQ. In this process, LcrQ shares the same targets with LcrF and the effect of LcrQ is dependent on the presence of LcrF. Furthermore, over-expression of LcrF showed the same phenotype as that of the lcrQ mutant strain and intracellular amount balance of LcrQ and LcrF is important in T3SS regulation. When the expression level of LcrF exceeds LcrQ, expression of the Ysc-Yop T3SS genes is activated and vice versa. Together, these data support a model in which LcrQ blocks the activation role of LcrF in regulating the transcription of T3SS genes in Yersinia.Pathogenic  Yersinia includes three human pathogenic bacterial species, Y. pestis, Y. enterocolitica and Y. pseudotuberculosis. These pathogens harbor a conserved virulence plasmid called pYV (or pCD) which encodes the Ysc-Yop type III secretion system (T3SS) Yersinia can efficiently inject effectors termed Yops (Yersinia outer proteins) into eukaryotic host cells with this secretion system. These translocated Yops enable bacteria to evade host immune responses by modulating host cell signaling pathways and facilitate the following infection The genus Yersinia is both positively and negatively regulated and is induced by low Ca2+ conditions at 37°C in vitro or by direct contact with host cells in vivoY. enterocolitica) is an anti-activator which causes the feedback inhibition for Ysc-Yop T3SS gene expression. This protein is secreted outside of cells by the secretory machinery under low Ca2+ conditions at 37°C with the help from a chaperone protein named SycH lcrGVH-yopBD operon were also demonstrated to play important roles in regulating T3SS gene expression The Ysc-Yop T3SS in Y. enterocolitica) is the only protein from the pYV plasmid that positively regulates the transcription of T3SS genes yop genes and also binds to RNA polymerase as supposed by the AraC family proteins ylpA, yadA, yopE, yopH, virC and lcrGVHyopBD operons, are dependent on LcrF yopE, yopH, virC and lcrGVHyopBD have been confirmed and the ∼40 bp AT-rich region in each of these promoters is characterized to be recognized by LcrF The AraC family activator LcrF  and E. coli strains were grown in Luria-Bertani (LB) medium. When appropriate, antibiotics were used at the following concentrations: nalidixic acid  15 μg ml−1, chloramphenicol 30 μg ml−1, ampicillin 100 μg ml−1, kanamycin 100 μg ml−1. All oligos used in this study are listed in Table S1.Bacterial strains and plasmids used in this study are summarized in Y. pseudotuberculosis YPIII strain is not available. We collected and analyzed available sequences of the virulence plasmids of different Y. pseudotuberculosis strains from the NCBI database, e.g., pYV from IP 32953 (NC_006153.2) and IP 31758 (NC_009704.1), pYPTS01 from PB1+ strain (NC_010635.1). There are 99 annotated genes on the pYV plasmid from IP32953, 66 genes from IP31758, and 87 genes on pYPTS01 from PB1+ strain. To cover all possible genes, we chose the sequence of the pYV plasmid from IP32953 as the template to design primers (Table S1) for cloning of the non-coding fragments from the pYV plasmid in YPIII. All these amplified non-coding fragments were cloned into the promoter cloning vector pZT100 2+ depletion.The sequence of the pYV virulence plasmid in Yersinia strains cultured at 26°C, or 37°C or 37°C with Ca2+ depletion was extracted with TRIzol reagent (Invitrogen) according to the manufacturer’s instruction. After treatment with RNase-free DNase I (Promerga), cDNA was obtained by reverse transcription  using 2 μg of each RNA sample. Real-time PCR was performed in reactions containing 1×iTaq™ Universal SYBR Green Supermix Reagent (Bio-Rad), cDNA templates and the gene specific primers using the CFX Connect Bio-Rad system. All these assays were tested in duplicates. The mRNA level in each sample was normalized to the level of 16S rRNA gene transcript and amplification specificity was assessed using melting curve analysis. The degrees of expression change were calculated using the 2−ΔΔCt method.Total RNA of E. coli BL21(DE3) by pET28a plasmid carrying the lcrF gene from YPIII (named pET28a-LcrF), and purified by Ni-NTA resin. About 400 ng of each DNA fragment, which was amplified from the YPIII genomic DNA, was incubated with various amounts of purified LcrF protein in 20 μl of binding buffer . A ∼200 bp fragment amplified from the non-coding region upstream of the pYV0053 gene was used as a negative control. Probe binding were performed at 37°C for 1 h, and were then loaded onto a 6.5% native polyacrylamide gel. Electrophoresis was performed in 0.5×TBE buffer on ice. The gel was stained with ethidium bromide for 20 min and scanned using a Syngene GeneGenius gel documentation system.The gel-retardation was performed as described previously lcrF construction, two DNA fragments flanking the lcrF gene were amplified by PCR from the genomic DNA of YPIII using two pairs of primers: named LcrF-in-upF/R, and LcrF-in-downF/R (Table S1). These two fragments were mixed and then used as the template DNA in a second round of PCR with primers LcrF-in-upF and LcrF-in-downR. The PCR product was digested with SalI and BglII and inserted into the suicide plasmid pDM4 E. coli S17-1 and transconjugation was performed as described lcrF strain. To complement ΔlcrF, a plasmid named pOVR200 was constructed, which contains the pMB1 replicon, the bla gene and the lacI gene from pET21a (Novagen) and a lac promoter upstream of the multiple cloning site. The lcrF gene was PCR amplified from YPIII and was cloned into pOVR200 to obtain pOVR-LcrF, which was transformed into ΔlcrF. The pOVR200 plasmid was also transformed into ΔlcrF as a control.For ΔlcrQ gene was amplified from YPIII and cloned into pOVR200 to obtain pOVR-LcrQ, which was transformed into YPIII. For co-expression of LcrF and LcrQ (or GST-LcrQ), the low copy plasmid named pKT100 The pOVR-LcrF plasmid obtained above was transformed into YPIII to over-express LcrF protein. To over-express LcrQ protein, Yersinia strains grown in Ca2+ rich medium (YLB with 2.5 mM Ca2+) were diluted (1:20) into fresh Ca2+-depleted medium (YLB containing 5 mM EGTA and 20 mM MgCl2) Overnight cultures of 2, and 0.05% Congo Red Dye at 37°C. White colonies were picked up and cultured overnight in YLB medium and then spread on Congo Red plate. After overnight culture at 37°C, a single white colony  was chosen for PCR to further confirm that the pYV plasmid was cured using five pairs of primers, which paired to different regions on the pYV plasmid.The pYV plasmid in YPIII was cured as described by others lcrF and sycH genes to obtain T25-LcrF and T25-SycH. The pUT18 carrying T18 fragment was applied to clone lcrF and lcrQ genes to obtain T18-LcrF and T18-LcrQ. These two pairs of plasmids were co-transformed into E. coli BTH101 and were cultured in LB medium with 1 mM IPTG at 30°C for 2 days. The β-galactosidase activities were determined as described The adenylate cyclase based bacterial two-hybrid system All data for the β-galactosidase activity assays were shown as mean ± standard deviation (SD) of the results of multiple independent experiments. Student’s T test was used to compare the data between two relevant groups.Y. pseudotuberculosis IP 32953 strain, we successfully amplified 96 non-coding regions from the pYV plasmid in YPIII. Only three regions upstream of pYV0042, pYV0043 and pYV0044 could not be obtained. We next sequenced the region from pYV0041 to pYV0045, a deletion at the C-terminal of the coding region of pYV0041 and a full deletion of the pYV0042 and pYV0043 genes were found in YPIII. Using this new sequence, we designed another pair of primers to amplify the non-coding region of pYV0044. In total, 97 non-coding fragments were amplified and cloned into a lacZ reporter vector to obtain non-coding fragment-lacZ fusions. The promoter activities of these non-coding fragments under T3SS non-inducible (26°C and 37°C) and inducible (37°C with Ca2+ depletion) conditions were subsequently analyzed. As shown in in vitro. As shown in To systematically characterize the transcriptional responses of Ysc-Yop T3SS genes in YPIII under T3SS inducible conditions, we compared the promoter activities of all genes from the pYV plasmid. Using primers designed from 2+ depletion. As shown in 2+ was depleted. On the contrary to temperature changes, this activation is dependent on factors encoded on the pYV plasmid, since all these activations were strongly decreased in the ΔpYV strain .To test if these factors are encoded by genes on the pYV plasmid, we next compared this activation in YPIII parent strain with the pYV plasmid cured strain (ΔpYV). Our results showed that induced folds at 37°C for all these promoters are similar between YPIII parent and ΔpYV strains Fig. 1B,2+ depleted conditions do not overlap, suggesting the Ysc-Yop T3SS activation process induced by 37°C and low Ca2+ is different. As summarized in  the genes induced at 37°C could be divided into four categories according to their functions: I) pYV plasmid replication; II) secretion machinery components; III) effector proteins; and IV) hypothetical proteins. On the contrary, the genes induced by Ca2+-depletion mainly encode effectors for Ysc-Yop T3SS (Table S3). Based on these analyses, we conclude that the Ysc-Yop T3SS inducing process first involves genes encoded from the YPIII chromosome, which activate the transcription of most of the secretion machinery components when the culture temperature is shifted from 26°C to 37°C, and full activation requires other key factors encoded on the pYV plasmid to maximally express the effector proteins.Comparing 2+-depletion induced gene expression. To systematically analyze the regulatory targets of LcrF, We compared the promoter activities from the pYV plasmid under T3SS inducible conditions in YPIII parent and the lcrF mutant (ΔlcrF) strains. Interestingly, activities of those nine Ca2+-depletion induced promoters, including promoters upstream of sycO (pYV0002), yadA (pYV0013), yopE (pYV0025), yopK (pYV0040), lcrG (pYV0058), yopN (pYV0065), yscN (pYV0067) and yopH (pYV0094), were all dramatically decreased in ΔlcrF . These data are inconsistent with the results from Y. enterocolitica in which the role of LcrF is not affected by Ca2+2+ at 37°C, which suggests other co-factor(s) may sense the change of Ca2+ concentration and coordinate with LcrF in regulating the Ysc-Yop T3SS.Since transcription of all the targets of LcrF is induced by Ca2+ concentration changes, but the mechanism under this regulatory role has not been illustrated. To test whether LcrQ is involved in controlling the regulatory effect of LcrF, we next screened the targets of LcrQ by comparing the promoter activities in YPIII parent and LcrQ over-expressed strains using our promoter library. Interestingly, all and only the targets of LcrF showed decreased promoter activities in the LcrQ over-expressed strain compared with those in YPIII parent strain , suggesting that LcrQ represses the promoter activities activated by pYV-encoded protein. To test whether this factor is LcrF, we tested the repressive effect of LcrQ in ΔlcrF strain and found no repressive role to these promoters , which encodes a component of secretion machinery, accumulates the intracellular LcrQ protein and therefore represses the expression of Yops yscV618–644 carrying the control plasmid pOVR200 , which suggests these two operons are also the direct targets of LcrF. The regulation of LcrF to its targets is not affected by Ca2+, but the transcription of all these genes in YPIII are induced by low Ca2+ signal. These data suggest that some other factors are also involved in activating the role of LcrF under low Ca2+ conditions. The role of LcrQ in T3SS regulation has not been documented. LcrQ has been proposed to form a complex with YopD-LcrH proteins Y. enterocolitica) binds to ribosomes together with YopD-LcrH and subsequently inhibits translation of the yop genes Pseudomonas aeruginosa is triggered by low Ca2+ at 37°C and is also globally regulated by two proteins with opposing effects: the AraC family positive regulator ExsA and the secreted negative regulator ExsE Yersinia and find the binding proteins of LcrF and LcrQ, which will provide information to detail the mechanism of transcriptional regulation to the Ysc-Yop T3SS.LcrF and LcrQ are two characterized regulators for Ysc-Yop T3SS in lcrF gene to inhibit its transcription. The ATP-dependent ClpXP and Lon proteases degrade YmoA when the environment temperature achieved at 37°C, which in turn releases the transcription of lcrF gene and subsequently activates the T3SS in YersiniayopE and yopH genes in regulating T3SS In addition to regulators encoded by the pYV plasmid, several chromosome encoded proteins are also involved in the regulation of the Ysc-Yop T3SS. For example, YmoA, a chromosome-encoded histone-like protein, directly binds to the promoter region of the lcrQ gene would facilitate the role of LcrF to activate the Yops at 37°C in the presence of Ca2+, as demonstrated by the fact that blockage to the role of LcrF by LcrQ is diminished in the ΔlcrQ strain. However, as shown in lcrQ strain under Ca2+ rich conditions is different from the normal secretion pattern, which suggests some other factors may also participate in controlling the secretion pathway of Ysc-Yop T3SS. In addition, the promoter activities of yopE and yopH remains high in ΔlcrF background  yopE at the post-transcriptional level, e.g., YopD protein Fig. S5) but its promoter activity was not activated by LcrF protein. Transcription is only the first step for gene expression. Regulation at other levels should be considered together to fully clarify the complicated regulatory process of Ysc-Yop T3SS.As stated in the introduction, the Ysc-Yop T3SS is regulated at both transcriptional and post-transcriptional levels. In this study, our proposed model is only for the transcriptional regulation of the Ysc-Yop T3SS, which could not explain all the phenotypes we have observed in our study. For example, as suggested in our model, deletion of the ckground Fig. 3E,Fig. S6), and several of these non-coding fragments located inside an operon, e.g., the non-coding fragment upstream of yopD gene (pYV0054), which is located in the lcrGVH-yopBD operon, were detected with promoter activity, suggesting the transcriptional regulation of pYV-encoded T3SS is complicated. On the other hand, we did not observe promoter activities of some genes located as the first gene in an operon. This may due to the fact that some fragments we chose do not cover the full promoter regions, since open reading frame annotation is only based on bioinformatics analysis from the NCBI, which may introduce mis-annotation. Nevertheless, the promoter bank we have constructed in this study will facilitate further transcriptional regulation analysis in Yersinia and results obtained in this study would provide information for further transcriptional regulation analyses related to the Ysc-Yop T3SS.Among the 97 cloned non-coding fragments, 68 have promoter activities Click here for additional data file.Figure S22+ at 37°C.Regulatory role of LcrF to its targets in the ΔpYV strain in the absence (A) or presence (B) of Ca(TIF)Click here for additional data file.Figure S3Repressive effect of LcrQ over-expression on promoter activities of yopEp, lcrGp and yopHp.(TIF)Click here for additional data file.Figure S4The binding of LcrF protein to lcrGp (A), pYV0053p (B), yopNp (C) and sycNp (D). LcrF protein was added at final concentrations of 0, 0.025, 0.05, 0.1, 0.25, 0.5 and 1 μM respectively.(TIF)Click here for additional data file.Figure S5Relative level of LcrQ protein in LcrF over-expressed strains (quantified from ).(TIF)Click here for additional data file.Figure S6Location of promoters on the pYV plasmid. The non-coding fragments with promoter activities detected in this study were labeled with black “P”, and those located upstream of the first gene in an operon but showed no promoter activity in our test were labelled with grey “P”.(TIF)Click here for additional data file.Table S1Primers used in this study.(XLS)Click here for additional data file.Table S2Genes induced at 37°C on the pYV plasmid.(DOC)Click here for additional data file.Table S32+ depletion at 37°C on the pYV plasmid.Genes induced by Ca(DOC)Click here for additional data file.


We sought to improve the accuracy of such models by quantifying the occupancy of TFs on synthetic promoters in vivo and incorporating these data into statistical thermodynamic models of cis-regulation. Using chromatin immunoprecipitation-seq, we measured the occupancy of Gcn4 and Cbf1 in synthetic promoter libraries composed of binding sites for Gcn4, Cbf1, Met31/Met32 and Nrg1. We measured the occupancy of these two TFs and the expression levels of all promoters in two growth conditions. Models parameterized using only expression data predicted expression but failed to identify several interactions between TFs. In contrast, models parameterized with occupancy and expression data predicted expression data, and also revealed Gcn4 self-cooperativity and a negative interaction between Gcn4 and Nrg1. Occupancy data also allowed us to distinguish between competing regulatory mechanisms for the factor Gcn4. Our framework for combining occupancy and expression data produces predictive models that better reflect the mechanisms underlying combinatorial cis-regulation of gene expression.Many studies have identified binding preferences for transcription factors (TFs), but few have yielded predictive models of how combinations of transcription factor binding sites generate specific levels of gene expression. Synthetic promoters have emerged as powerful tools for generating quantitative data to parameterize models of combinatorial  Regulated gene expression lies at the heart of many biological processes including development ,2, diffein vivo by the TF using chromatin immunoprecipitation assays    and thro studies . These scis-regulation.Recently, some investigators attempted to correlate whole-genome expression profiles and experimentally determined  or predicis-regulation.The main difficulty with the thermodynamic approach is parameterizing the models given the available genomic data. Even for a promoter with a relatively small number of TFBS, the number of possible promoter states is immense compared with the number of gene expression observations that can be made, even with high-throughput techniques. Moreover, we usually do not know the identities of all TFs that influence any particular gene promoter. An alternative approach is the use of synthetic promoters composed of defined TFBS. In this approach, promoters with combinations of known TFBS are synthesized and then used to drive the expression of fluorescent reporter genes or sequence barcodes . The synPrevious synthetic promoter approaches used only expression data to infer relationships between the TFBS content of gene promoters and the gene expression levels they drive. For example, the model from  performsWe sought to extend the synthetic promoter approach by developing a ChIP-based metric of TF occupancy on synthetic promoters. We applied this approach to libraries of binding sites for TFs responsive to the standard 2% glucose condition  and condlys2 locus via PCORE (URA3 (PCORE) was inserted into the lys2 locus using primers RZ131 and RZ132 (Supplementary Table S2) and standard transformation protocols (lys2 region by polymerase chain reaction (PCR) amplification using the primers RZ133 and RZ134 (Supplementary Table S2) and plasmid prs313-BirA-NLS  and by Sanger sequencing.Strain BC905  was created by integrating BirA into the genome of strain BY4742  at the rotocols  with selBirA-NLS  as a temCBF1, GCN4, MET31 and NRG1 were C-terminally tagged with the myc-C-avi tag by amplifying myc-C-avi with a KAN cassette from plasmid PUG6-myc-C-avi (Supplementary Table S2: RZ129 and RZ130 (CBF1), RZ137 and RZ138 (GCN4), RZ135 and RZ136 (MET31) and RZ127 and RZ128 (NRG1) and transforming the resulting PCR product into BC905 using G418 selection to create strains BC906 (BC905 + CBF1::myc-C-Avitag), BC907 (BC905 + GCN4::myc-C-Avitag), BC908 (BC905 + MET31::myc-C-Avitag) and BC909 (BC905 + NRG1::myc-C-Avitag). Insertion was verified by PCR  and by Sanger sequencing. The resulting strains were backcrossed to BY4741, sporulated and offspring-selected, which matched the appropriate genotype . Retention of the tag and BirA was verified by PCR post-mating.yc-C-avi  using prAll strain growth was done in YPD; synthetic complete medium with 2% glucose (SC); synthetic complete medium lacking uracil with 2% glucose (SC-Ura); synthetic complete medium lacking Trp with 2% glucose (SC-Trp); minimal medium + 2% glucose with 300 µM his, 1 mM lys, 2 mM leu, 400 µM Trp (Min); minimal medium + 2% glucose with 300 µM his, 1 mM lys, 2 mM leu, 200 µM Ura (Min + Ura-Trp); or in these same media supplemented with 0.9 µM biotin .Supplementary Table S2, RZ84–RZ91) were annealed, then mixed in ratios inversely proportional to the melting temperatures of the annealed products and ligated together. The ligation products were size selected with YM100 Microcon columns and cloned into plasmid pJG102  conditions as described previously  with theStrains were grown as described, and then fixed by adding 4% paraformaldehyde solution  to a final concentration of 1%. Yellow Fluorescent Protein (YFP) intensities were measured by flow cytometry on a Beckman Coulter Cell Lab Quanta SC. The final expression measurement was the median of at least 10 000 observations of the ratio of raw fluorescence to volume of the cell, as reported as the ‘electronic volume’ by the instrument, normalized to the mean expression of three to four no-insert control promoters on the same plate. The expression for promoters with <80% of counts with fluorescence intensity between 10 and 900 raw fluorescence units was treated as missing data in downstream analyses.Supplemental Methods for a complete description of the ChIP and qPCR protocols.Synthetic promoter-containing strains were pooled and fixed with 1% final concentration of formaldehyde, quenched with glycine, washed and the resulting cell pellet was frozen at least overnight at −80C. Pellets were thawed and resuspended in lysis buffer  with protease inhibitor (Roche #11836170001), then bead beaten and sonicated. The supernatant was clarified by centrifugation, then applied to phosphate-buffered saline-washed Dynal M280 streptavidin-coated magnetic beads  and incubated for 1 h. The supernatant was removed and set aside as input. The beads were washed twice for 5 min per wash in each of lysis buffer, high salt lysis buffer , LiCl wash buffer , SDS wash buffer  and TE . The beads were resuspended in 250 µl TE + 0.5% SDS + 10 µl of 20 mg/ml Proteinase-K (NEB P8102S) and distributed into three 250 µl PCR tubes. Then 72.5 µl of input material was combined with 72.5 μl of TE + 1% SDS to which 10 µl of 20 mg/ml proteinase-K was added and distributed into two or three 250 μl PCR tubes per replicate. The tubes were incubated for 4 h at 42°C, 2 h at 72°C and 6 h at 65°C. The material from each replicate was recombined and purified via ChIP cleanup columns (Zymo D5205), eluting in 40 µl of elution buffer. ChIP success was validated by quantitative PCR (qPCR) analysis of known targets for each TF. See Supplementary Table S1) and three different starting concentrations of input material. PCR products between 150- and 600-bp long were gel-purified on a 1.5% TAE agarose gel. Input samples were retained on the basis of similar gel intensities to the corresponding IP sample as an approximate concentration measure. The resulting samples were combined, ethanol precipitated and reconstituted in 30 µl of water. The forward sequencing adapter was added by digestion/ligation exactly as for library sequencing. The final concentration of sequenceable fragments was determined by qPCR using SYBR Green QPCR master mix, primers RZ259 and RZ260 and eight synthetic promoter standards, diluted across five orders of magnitude. The material was sequenced on the Illumina HiSeq 2000 platform using one lane of a paired-end 101-bp run.ChIPed synthetic promoters were sequenced by adding adapter sequences to synthetic promoters in the input and IP samples via PCR amplification using 23 µl of IP material with 1 µl each of 10 µM primers that were barcoded in the forward read based on sample identity and in the reverse read based on the identity of the tagged TF . The ratio of normalized IP counts to normalized input counts for a particular promoter was divided by the median normalized IP/input ratio of all promoters lacking a binding site for the ChIPed factor to give the normalized relative occupancy. Scaling to the median background occupancy effectively scales the occupancy values relative to the non-specific binding of the factor. This places all occupancy values from all factors and conditions on the same relative scale, assuming that the non-specific binding distribution is the same for all factors. For demonstrating technical replicate variance, the occupancy was calculated separately for each replicate. For modeling purposes, the replicates were generally combined by summing the promoter coverage across replicates and computing occupancy from the summed values. The exception was Gcn4 in AAS where a single ChIP replicate was used due to substantial depletion of promoters with four or more binding sites in the input of two of the replicates. Promoters with <50 reads in the inputs were excluded from the analysis.k TFs. The weight of any particular state is the sum of ΔGs of protein–DNA and protein–protein interactions that occur in that state. The statistical weight of a given state divided by the sum of the statistical weights of all possible states is the probability of that state occurring. The occupancy of any particular TF is the sum of the probabilities of all states in which that TF is bound. Likewise, the probability of polymerase binding to a given promoter is the sum of the probabilities of all states in which polymerase is bound. As implemented, the model assumes that the probability of polymerase binding is related to the observed expression by a constant scaling factor. See Supplementary Methods for a complete description of the model.To model gene expression and TF occupancy, we used a thermodynamic model of transcription described previously ,35,36. IR statistical package) to find values for the ΔGs in the model that maximize the correspondence between expression (and/or TF occupancy) predicted by the model and the experimentally measured expression (and/or TF occupancy). This problem is tractable because we assume that the ΔG of any particular protein–DNA or protein–protein interaction is constant across all synthetic promoters. For example, in this work, we were fitting models that contained between 6 and 15 different ΔGs (Supplementary Table S5) using experimental measurements from between 114 and 291 synthetic promoters , indicating that the C-myc-Avi-tag does not alter protein function. The exception was the Cbf1-tagged strain, which showed a Cbf1-site-dependent effect on expression . In all thermodynamic modeling, we corrected for the effect of tagged Cbf1 by introducing a polymerase-Cbf1 interaction term specific to the tagged Cbf1, but enforcing the TF–DNA interaction term to be the same between the tagged and untagged versions of the protein.These strain libraries were grown for either ChIP or expression analysis in both glucose and AAS conditions. In general, the libraries showed similar expression distributions to each other in both growth conditions . Samples from the Met31- and Nrg1-tagged libraries did not show target-specific enrichment  and were excluded from all further analyses. Samples from the Cbf1- and Gcn4-tagged libraries were analyzed by high-throughput sequencing of the synthetic promoters in both the input and precipitated fractions (‘Methods’ section), which produces a measure of occupancy of each tagged TF on each member of the promoter libraries. The occupancy of Cbf1-containing promoters by Cbf1 increases almost linearly with the number of Cbf1 binding sites, with tight distributions around the median occupancy scores for a given number of Cbf1 sites  of TFs with DNA, TFs with other TFs and TFs with RNA polymerase . In the cis-regulatory interactions that govern the promoters in our libraries.We fit the model to data from synthetic promoter libraries by finding values for the ΔGs that minimize the difference between the predicted and experimentally measured expression values, across the entire library. During fitting, we always compare models that include different numbers and types of interactions. The resulting collection of ΔGs that comprise the best-fit model provides a quantitative description of the 2R = 0.53) and comparable with the fit previously obtained by (2R = 0.60), despite using two fewer parameters to model the data, having two different tagged TFs and having a greater diversity of promoters (212 unique promoters in glucose versus 131 unique promoters published previously). We performed 5-fold cross-validation on the final model and observed no loss of predictive power, suggesting that the model is not over-fit (Supplementary Table S4). The final parameter values for this model are listed in Supplementary Table S5. Notably, when fitting solely with expression data, no TF–TF interactions were found to significantly improve the fit.We first fit the model using expression data collected in both glucose and AAS, ignoring the occupancy data A. The moained by  (R2 = 0.2R = 0.54), suggesting that a large amount of TF occupancy can be accurately predicted by only the presence and configuration of TFBS. Cbf1 had no significant interaction terms with any other factors, which agrees with the linear increase of Cbf1 occupancy we observed with increasing numbers of Cbf1 sites. However, the ΔGGcn4–Gcn4 and the ΔGGcn4–Nrg1 interactions both made significant improvements to the fit of the model . Neither of these interactions was significant in the model that used only expression data, which suggests that the occupancy data contain extra information that reveals interactions that are undetectable in the expression data. Adding the ΔGGcn4-Gcn4 interaction to a model that includes the ΔGGcn4-Nrg1 interaction also led to a significant improvement in the performance of the model . The final model, which includes the TF–DNA binding energies, along with the ΔGGcn4-Nrg1 and ΔGGcn4-Gcn4 interactions, predicts virtually no change in the DNA binding energy of Cbf1 between the two conditions (ΔΔG: −0.08), versus a large change in the DNA binding energy of Gcn4 when moving from glucose to AAS (ΔΔG: −2.74). This is consistent with the known regulation of Gcn4; amino acid starvation increases translation and transcription of Gcn4 mRNA, and stabilization of Gcn4p through dephosphorylation (2R = 0.57 for occupancy versus 2R = 0.53 for expression), suggesting that the model describes the variation in both types of data equally well.We next attempted to fit the thermodynamic model using the normalized relative occupancy data we obtained from ChIP, ignoring the expression data. We used occupancy data collected from the Cbf1 and Gcn4-tagged strains grown in both the glucose and AAS conditions. We used these data to fit a thermodynamic model relating promoter sequence to the occupancy of the TFs on the synthetic promoters B. For thrylation , all of Supplementary Table S5). In general, the model converged on reasonable predictions of both expression and occupancy .Finally, we attempted to fit a thermodynamic model with both expression and occupancy data simultaneously. We chose parameters to include based on which parameters were significant in the expression-only and occupancy-only fits (ccupancy C. In parSupplementary Table S5). Forcing the model to use the same polymerase interaction term for the Gcn4 site in both conditions resulted in a significantly worse fit . Attempting to fit the model by constraining the ΔGGcn4–RNAP term while allowing the ΔGGcn4−DNA in glucose to vary, as was done previously (2R = 0.50), but the resulting change in binding energy equates to 8.12 × 10−14-fold lower apparent Ka. This change in binding energy is not biologically reasonable and is likely an artifact of fitting the data to an inappropriate model. Taken together, the results suggest that the switching behavior of Gcn4 is not a result of differential affinity of Gcn4 for its binding site between the glucose and AAS conditions.The Gcn4 binding site showed different behavior between the glucose and AAS conditions. eviously , resulteThere are two possibilities for why the Gcn4 site switches behavior between conditions. The first is that post-translational modifications, or interactions with other proteins, cause Gcn4 to convert between two forms, an activating form that predominates in AAS and a repressing form that predominates in glucose. Alternatively, Gcn4 may always be an activator, but in glucose competes with a repressor for binding to the same site. The occupancy data can distinguish between these two hypotheses. If expression is negatively correlated with Gcn4 occupancy in glucose, it would suggest that Gcn4 is switching from an activator to a repressor in glucose. However, if expression is positively correlated with Gcn4 occupancy in glucose, it would suggest that Gcn4 is still an activator in glucose and that a repressive factor competes with Gcn4 for binding in glucose. To distinguish between these two hypotheses, we plotted Gcn4 occupancy versus expression for both the glucose and AAS conditions B. As expWe extended the thermodynamic model to incorporate competitive binding at the Gcn4 site between Gcn4 and an unknown repressor. We did this by adding promoter states where the competing protein is bound to the site instead of Gcn4. To fit the model, we assumed that the effect on polymerase of the two competitors was consistent between conditions, but allowed the relative affinity of the two factors for the Gcn4 site to vary between conditions. When this model was fit with only expression data, it performed exactly the same as the model that allows Gcn4 to switch from an activator to a repressor in different conditions. Thus, the switching behavior of the Gcn4 binding site can be modeled equally well by either having a repressive factor compete with Gcn4 for binding (competitive model) or by having the Gcn4 protein switch from an activator to a repressor between conditions (non-competitive model). With only expression data, these two models cannot be distinguished.R2: 0.425, 0.556 and 0.431, 0.554, non-competitive and competitive models, respectively; see also Supplementary Table S5), although the competitive model is marginally better at predicting expression. However, the non-competitive model consistently set the glucose Gcn4-RNAP term as highly unfavorable (i.e. made Gcn4 a strong repressor in glucose) and resulted in fits that rarely converged to similar parameter values (<20% of the time) and usually resulted in singularities in the parameter Jacobian matrix (>90%). In contrast, the competitive model resulted in the same fit 50% of the time, with a non-singular Jacobian matrix. In this best fit, the difference in Gcn4–DNA binding energies between the two conditions equates to fold change in the apparent Ka of ∼25-fold, which is somewhat larger than previous estimates  . Funding for open access charge: NIH [RGM092910A].Conflict of interest statement. None declared.


SHP.Farnesoid X receptor/retinoid X receptor-alpha (FXR/RXRα) is the master transcriptional regulator of bile salt synthesis and transport in liver and intestine. FXR is activated by bile acids, RXRα by the vitamin A–derivative 9-cis retinoic acid (9cRA). Remarkably, 9cRA inhibits binding of FXR/RXRα to its response element, an inverted repeat-1 (IR-1). Still, most FXR/RXRα target genes are maximally expressed in the presence of both ligands, including the small heterodimer partner (SHP). Here, we revisited the FXR/RXRα-mediated regulation of human hSHP promoter element was analyzed to locate FXR/chenodeoxycholic acid (CDCA)- and RXRα/9cRA-responsive elements. hSHP promoter constructs were analyzed in FXR/RXRα-transfected DLD-1, HEK293 and HepG2 cells exposed to CDCA, GW4064 (synthetic FXR ligand) and/or 9cRA. FXR-DNA interactions were analyzed by in vitro pull down assays.A 579-bp hSHP promoter elements lacking the previously identified IR-1 (−291/−279) largely maintained their activation by FXR/CDCA, but were unresponsive to 9cRA. FXR-mediated activation of the hSHP promoter was primarily dependent on the −122/−69 region. Pull down assays revealed a direct binding of FXR to the −122/−69 sequence, which was abrogated by site-specific mutations in a binding site for the liver receptor homolog-1 (LRH-1) at −78/−70. These mutations strongly impaired the FXR/CDCA-mediated activation, even in the context of a hSHP promoter containing the IR-1. LRH-1 did not increase FXR/RXRα-mediated activation of hSHP promoter activity.FXR/CDCA-activated expression of SHP is primarily mediated through direct binding to an LRH-1 binding site, which is not modulated by LRH-1 and unresponsive to 9cRA. 9cRA-induced expression of SHP requires the IR-1 that overlaps with a direct repeat-2 (DR-2) and DR-4. This establishes for the first time a co-stimulatory, but independent, action of FXR and RXRα agonists. NR1H4) and the liver receptor homolog-1 (LRH-1/NR5A2) are central factors in the control of bile salt homeostasis. In the liver, LRH-1 regulates expression of cholesterol 7 alpha-hydroxylase (CYP7A1), the rate-limiting enzyme in bile salt synthesis, as well as the bile salt export pump (BSEP/ABCB11) the major hepatobiliary bile salt exporter NR2B1) and upon activation by bile salts induces the expression of BSEP NR0B2) +-taurocholate cotransporting polypeptide; NTCP/SLC10A1) The farnesoid X receptor (FXR/ShpBSEPSHPOSTα/β) FAS) Both LRH-1 and FXR belong to the superfamily of nuclear receptors. FXR/RXRα binds to an inverted repeat sequence spaced by 1 nucleotide (IR-1) conforming to the consensus G/AGGTCAnTGACCT BSEP promoter BSEP and SHP promoters. In contrast to the effect on BSEP expression, however, CDCA and 9cRA synergistically activate SHP transcription, in both in vivo and in vitro experiments BSEP and SHP may be fundamentally different, while they are both considered to be “typical” FXR/RXRα target genes. Opposite effects of RXRα ligands on CDCA-induced expression of FXR/RXRα target genes have been described by others also FXR/RXRα-mediated transcriptional control is primarily regulated by their ligands, bile acids (in particular chenodeoxycholic acid (CDCA)) and 9-cis retinoic acid (9cRA), respectively. Earlier, we and others have shown that these ligands have opposite effects on binding of FXR/RXRα to the IR-1 and the resulting transcriptional activity of the human Over the last decade it has become evident that the function of FXR and SHP is not restricted to bile acid synthesis, but that these factors also play a role in liver regeneration, viral replication, tumor suppression, fibrogenesis, glucose and lipid metabolism DLD-1 (ATCC® CCL221™) and HepG2.rNtcp 2 well. If needed, total amount of DNA was adjusted with pCMV5 plasmid to 1.3 µg per well.HepG2.rNtcp and HEK293 cells were transfected as described previously hSHP promoter construct in pGL3 basic was a kind gift from Dr. S.M. Houten . pcDNA5-mLrh-1 was a kind gift from Dr. J. Hageman  The 579-bp (569/+10) hSHP promoter were made by PCR from the pGL3 hSHP −569/+10. pGL3 SHP −569/+10 FXRE KO KpnI/BglII-ligated into pGL3 basic . Oligo's  used to generate these SHP promoter mutants are shown in Table S1. Endotoxin-free plasmids were isolated  from E. coli Top10 cells . All promoter constructs were sequenced  to assure that the correct mutations were introduced.5′-truncated mutants of the mRNA was isolated from DLD-1, HepG2.rNtcp and HEK293 cells. RT-QPCR was performed as was described before Cells were lysed in 500 µl passive lysis buffer . After centrifugation, 20 µl of the supernatant was used to determine luciferase activity in a MPL1 Microplate Luminometer (Berthold Detection Systems). Using 50 µl luciferase substrate , delay time set to 2.05 seconds and measuring time set to 10 seconds.TCAAGGTTGTTGGGCCATTCCCCGGGGCAATGTCTGTGTGTTTTTTTCAATGAACATGACTTCTGGAG; the putative LRH-1 binding site is indicated in “italics”) and “mutated” -122/−69  region were used to precipitate FXR. Biotin-labeled DNA probes containing a fragment of the BSEP promoter including the IR-1  or a fragment of the LacI promoter (GTAGTGGCGAAATTGTGAGCGCTCACAATTCGTTTGGCCG) were used as positive and negative controls, respectively. Nuclear extracts were pre-incubated (1 h at 4°C) with 3-fold excess of unlabeled “wild type” −122/−54, “mutated” −122/−54, BSEP-IR-1  or LacI DNA probes in competition experiments. FXR binding was analyzed by western blotting, using anti-FXR , exposed in a ChemiDoc XRS system and quantified using the Quantity One software package .A FXR pull down assay was performed as described before Data are presented as means ± sd. Differences between conditions were determined in SPSS by Kruskal-Wallis followed by pair-wise comparison of groups by Mann-Whitney U with p≤0.05.SHP, OSTβ, ileal bile acid binding protein (IBABP), FGF19; see Figure S1) is super-induced. Here, we performed a detailed analysis of the human SHP promoter to obtain insight in the molecular mechanisms by which bile acids and 9cRA synergistically induce transcription of FXR/RXRα target genes. The -569/+10 SHP promoter element described earlier SHP . CDCA treatment resulted in a 12-fold increase of the SHP promoter activity and this was super-induced by 9cRA (+45% to 17-fold induction compared to untreated cells). An FXRE/IR-1 was previously identified at position −291/−279 in the SHP promoter SHP promoter element up to position −303 (−303/+10) retained the CDCA-induction (9.8-fold) and 9cRA super-induction (+43% to 14-fold) characteristics. Further 5′ shortening of the SHP promoter element to −278/+10 (deleting the IR-1) led to the loss of 9cRA super-induction. Remarkably, the CDCA-induction of the −278/+10 SHP promoter element remained intact . These data indicate that the 9cRA-mediated activation of the human SHP promoter depends on the IR-1 sequence at position 291/−279. However, this sequence is (largely) dispensable for the CDCA-induced activity. This latter finding was highly surprising and prompted us to study this in further detail.The RXRα ligand 9cRA lowers BSEP expression by inhibiting FXR/RXRα binding to the IR-1 SHP promoter element was comparable to the −303/+10 SHP element  and was fully dependent on the presence of FXR  were deleted from the −303/+10 and the −278/+10 93 cells Figure 4SHP promoter was screened for putative nuclear receptor binding sites. An IR-1-like sequence is detected at −118/−106 (AtGTCtgTGtgtT) with 7 out of 12 IR-1 consensus nucleotides. Alternatively, FXR-regulation may act through a previously identified DR-1/PPARγ binding site at position −90/−78 SHP promoter activity, it was almost completely abrogated when the LRH-1 site was mutated . In addition, CDCA treatment of FXR/RXRα-transfected DLD-1 cells did not induce LRH-1 expression . Collectively, these data indicate that the FXR-mediated induction of human SHP is independent of LRH-1, but requires the LRH-1 binding site.The −122/−69 region from the  mutated Figure 5SHP promoter by applying an FXR-pull down assay SHP promoter. This SHP promoter fragment efficiently precipitated FXR from nuclear extracts of CDCA-treated DLD-1 cells that stably overexpress hFXR (DLD-1.hFXR), similar as the positive control (BSEP-IR-1) did . The second most enriched FXR-binding sequence in the genome-wide chromatin immunoprecipitation experiments appeared to confirm to an LRH-1 binding site that is in close proximity to the IR-1 Shp promoter.Sequencing of DNA fragments that bind FXR in the mouse genome has recently revealed that indeed the IR-1 is the most prominent binding site of this transcription factor SHP promoter lacking an IR-1 is similar in cells without endogenous LRH-1 (HEK293) and in cells that contain intermediate (DLD-1) or high levels of LRH-1 (HepG2.rNtcp). This suggest that FXR-mediated regulation does not depend on the presence of LRH-1, which is in line with the observation that GW4064-induced Shp mRNA expression in mouse liver was hardly affected by the absence of Lrh-1 Our data show that 1) FXR-mediated expression of human SHP is largely independent of the IR-1, 2) LRH-1 does not enhance FXR/RXRα-induced SHP promoter activity, 3) FXR is precipitated with a DNA fragment containing the LRH-1 site, which is abrogated when this site is mutated; 4) FXR-mediated induction of a hSHP promoter, but required a downstream sequence than harbors a LRH-1 binding site. The minimal LRH-1 binding site was, however, not sufficient to bind significant amounts of FXR in pull-down assays , suggesting that sequences flanking the LRH-1 binding site are required for efficient FXR binding. This is corroborated by the observation that mutations in the −122/−69 region outside the LRH-1 consensus sequence also reduced the FXR-induced SHP promoter activity, though this was less pronounced than the mutations in the LRH-1 site. Taken together, we conclude that the LRH-1 site in the human SHP promoter is most important site for FXR-mediated expression. Importantly, this novel FXR binding site is fully conserved in the human, mouse and rat SHP promoters .Most surprising was the fact that the IR-1 was extraneous for FXR-mediated induction of the Figure S7). RXRα homodimers and RXRα/RAR heterodimers have been shown to bind DR-2 elements SHP promoter activity was indeed induced by 9cRA-activated RXRα, which was not affected by co-transfection with RAR (data not shown). So, alternatively, 9cRA may act via RXRα homodimers by binding the DR-2 in the −291/−279 region in the SHP promoter.Another remarkable finding was that the previously identified IR-1 was actually required for 9cRA-induced expression of SHP. Previously, it has been shown that the IR-1 overlaps with an LXRα/RXRα DR-4 response element at −284/−269 OSTβ, IBABP, FGF19 and others observed this for phospholipid transfer protein (PLTP) SULT2A1: sulfotransferase 2A1 (SULT2A1/STD)OSTα showed a BSEP-like pattern (9cRA blocks CDCA-induced expression). Since 9cRA was found to reduce binding of FXR/RXRα to the IR-1 sequence (as present in the human BSEP and SHP promoter), the IR-1 containing promoters of OSTβ, IBABP and FGF19 need to harbor compensatory mechanism that ultimately lead to maximal transcription with both ligands.At present it is unknown how common this alternative pathway of FXR-mediated transcription through LRH-1(-like) sequences is. The FXR/CDCA-induced activity via the LRH-1 site is insensitive to 9cRA. Together with the fact that the “IR-1” is required for the 9cRA-mediated induction of SHP provides the first molecular mechanism explaining how these ligands (9cRA and CDCA) lead to maximum induction of transcription, albeit via two independent sites. Maximum expression after exposure to both ligands was also observed for SHP promoter. Still, we show that the IR-1 was also dispensable for FXR-mediated induction of the SHP promoter in HEK293 and HepG2 cells, which were used in the earlier studies. A putative explanation for these, seemingly opposite, observations may be that both the IR-1 and the LRH-1 site can bind FXR and activate SHP expression, but that their relative contribution may depend on the cellular and/or nuclear levels of FXR, RXRα and their ligands. These factors may vary between cell types, passage numbers and the experimental conditions in these 3 studies. The RXRα ligand (9cRA) may reduce the binding of the FXR/RXRα heterodimer to the IR-1 sequence and stimulate LXRα/RXRα or RXRα homodimer binding in the −291/−269 region. In contrast, 9cRA does not affect the FXR-mediated regulation of SHP via the −122/−69 element. Physiologically, this maintains SHP-mediated regulation of bile salt synthesis (CYP7A) and bile salt import  independent of the vitamin A/9cRA levels. In line with this, bile salt-mediated induction of Shp was maintained in vitamin A-deficient mice Our data contrast to those previously reported by Lu et al. In conclusion, our study reveals for the first time a molecular mechanism of FXR-activated transcription that is not inhibited by the RXRα ligand 9cRA, which is the most frequent mode of regulation observed for FXR-target genes. Surprisingly, this is mediated through a non-IR-1 FXR response element that shows typical characteristics of an LRH-1 DNA binding consensus.Figure S1Gene- and cell type-specific regulation of FXR/RXRα target genes by 9cRA. HepG2.rNtcp (A) and DLD-1 (B) cells were transfected with expression plasmids for hFXR and hRXRα and treated with or without 100 µmol/L CDCA and/or 1 µmol/L 9cRA. mRNA levels of FXR target genes were determined by Q-PCR. Data are corrected for 18S and displayed as means ± SD; n≥3. CDCA-treated conditions are set to 100. Significant differences (P≤0.05) are indicated when compared to untreated condition (a), 9cRA-treated condition (b), CDCA-treated condition (c) or CDCA/9cRA-treated condition (d) in a pair-wise comparison by Mann-Whitney U test.(TIF)Click here for additional data file.Figure S2Inactivation of the IR-1 at position −291/−279 does not abolish FXR/CDCA-mediated induction of the −569/+10 hSHP promoter. DLD-1 cells were transfected with hFXR and hRXRα expression plasmids and the −569/+10 SHP promoter construct (B). Cells were treated with or without 100 µmol/L CDCA and/or 1 µmol/L 9cRA. Luciferase activity was measured to determine the SHP promoter activity (A). Data presented as means ± SD; n≥3. Vehicle-treated conditions are set to 1. P≤0.05 for *) in a pair-wise comparison by Mann-Whitney U test.(TIF)Click here for additional data file.Figure S3No synergy between FXR and LRH-1 in human SHP regulation. LRH-1 dose dependently induced activation of the −569/+10 hSHP promoter fragment, confirming the presence of a functional LRH-1. In the presence of FXR a similar dose response curve is observed. However, in the presence of CDCA, FXR and LRH-1 do not synergistically activate the SHP promoter. LRH-1 rather limits the FXR/CDCA-dependent activation at a higher dose.DLD-1 cells were transfected with hFXR and hRXRα expression plasmids, the −569/+10 SHP promoter construct and/or increasing amounts of the mLrh-1 expression plasmid as indicated. Cells were treated with or without 100 µmol/L CDCA. Luciferase activity was measured to determine the SHP promoter activity. Data presented as means ± SD.(TIF)Click here for additional data file.Figure S4FXR does not induce LRH-1 expression in DLD-1 cells. DLD-1 cells were transfected with expression plasmids for hFXR and hRXRα and treated with or without 100 µmol/L CDCA. mRNA levels of LRH-1 were determined by Q-PCR. Data are corrected for 18S and displayed as means ± SD; n≥3.(TIF)Click here for additional data file.Figure S5FXR does not interact with the minimal LRH-1 responsive element in the hSHP promoter. FXR was precipitated from nuclear extracts of hFXR-overexpressing DLD-1 cells using a DNA probe containing the −89/−65 region of the human SHP promoter (ACTTCTGGAGTCAAGGTTGTTGGGC) including the LRH1-RE (underlined), an 53-bp fragment of the BSEP promoter containing the IR-1 or a LacI probe. Competition experiments were performed with 3-fold excess IR-1 or LacI probe lacking a biotin label.(TIF)Click here for additional data file.Figure S6Comparison of human, mouse and rat SHP promoter sequences. The IR-1  is not fully conserved, whereas the LRH-1 binding site (underlined) is fully conserved in the mouse, rat and human SHP promoter.(TIF)Click here for additional data file.Figure S7The 9cRA responsive element accommodates an IR-1, an DR-4 and a putative DR-2. The IR-1 (−291/−279) overlaps with a previously identified DR-4  and a putative DR-2 (binds RXRα/RXRα) and RXRα/RAR).(TIF)Click here for additional data file.Table S1Oligo's used to create mutant constructs of the hSHP −569/+10 promoter.(TIF)Click here for additional data file.Table S2Taqman primer-probe sets used for QPCR. Probes were FAM TAMRA labeled.(TIF)Click here for additional data file.


Experimentally characterized enhancer regions have previously been shown to display specific patterns of enrichment for several different histone modifications. We modelled these enhancer chromatin profiles in the human genome and used them to guide the search for novel enhancers derived from transposable element (TE) sequences. To do this, a computational approach was taken to analyze the genome-wide histone modification landscape characterized by the ENCODE project in two human hematopoietic cell types, GM12878 and K562. We predicted the locations of 2,107 and 1,448 TE-derived enhancers in the GM12878 and K562 cell lines respectively. A vast majority of these putative enhancers are unique to each cell line; only 3.5% of the TE-derived enhancers are shared between the two. We evaluated the functional effect of TE-derived enhancers by associating them with the cell-type specific expression of nearby genes, and found that the number of TE-derived enhancers is strongly positively correlated with the expression of nearby genes in each cell line. Furthermore, genes that are differentially expressed between the two cell lines also possess a divergent number of TE-derived enhancers in their vicinity. As such, genes that are up-regulated in the GM12878 cell line and down-regulated in K562 have significantly more TE-derived enhancers in their vicinity in the GM12878 cell line and vice versa. These data indicate that human TE-derived sequences are likely to be involved in regulating cell-type specific gene expression on a broad scale and suggest that the enhancer activity of TE-derived sequences is mediated by epigenetic regulatory mechanisms. Transposable elements (TEs) are repetitive genetic sequences that can move from one location in the genome to another. TE-derived sequences are abundant in eukaryotes and make up substantial fractions of their genomic DNA. TEs have long been dismissed as selfish DNA elements that make little or no contribution to the function of their host genomes et al. which shows that an Alu element serves as part of an enhancer that up-regulates the CD8 alpha gene in accordance with its role in differentiation along the hematopoietic lymphoid lineage in-vivo mouse enhancer assay Enhancers are distal regulatory sequences, found outside of proximal promoter regions, which can increase the expression of genes by interacting with transcription factors. There are a handful of studies that provide experimental evidence for the exaptation of TE sequences as functional enhancers in the human genome. The first example comes from a study in 1993 by Hambor + T-cells In addition to the experimental evidence showing that individual TE sequences provide functional enhancers to host genomes, we previously found evidence to suggest that human TEs may provide numerous enhancer sequences genome-wide. Our prior analysis showed that TE sequences reside in a substantial fraction of DNaseI hypersensitive (DHS) sites Experimentally characterized active enhancers display a distinct pattern of chromatin modifications that is significantly different from other regulatory regions as well as the genomic background Specific chromatin modification profiles have been shown to mark functionally active enhancer regions in the human genome Functionally active enhancers are marked by an enrichment of the transcription co-activator protein p300 We employed two controls to validate the discriminatory power of the chromatin modification profile captured by our enhancer training set. As a first control, we compared the genomic location profiles for mapped ChiP-Seq tags of the five enhancer enriched histone modifications with the three remaining modifications around the 137 p300 binding sites. The five enhancer enriched histone modifications present in our training set  display unique patterns of enrichment, with tag count peaks centered around the p300 binding sites, whereas the other three histone modifications  do not show any specific pattern of enrichment over enhancer regions . As a seWe attempted to further ascertain the discriminating power of our enhancer training set by performing cross-validation together with receiver operating characteristic (ROC) analysis on the genomic loci that constitute our enhancer training set see . The resTo plot the ROC curve, for each of 10 cross-fold validations, we computed Spearman's rank correlations (ρ) between ChIP-seg tag counts that represent the chromatin profile of the enhancer training sets as an ensemble against tag counts for the p300 bound regions that make up the validation sets (true positives). Then, as a control, we performed similar correlations between the chromatin profile of the training sets and a sets of random genomic loci the same size of the validation sets . Across a range of ρ-values, the fraction of true positives represented by the validation set loci was plotted against the fraction false positives from random genomic loci to yield the ROC curve in n = 98, P = 1E−7).Having established the validity of our enhancer training set, we used it to search for regions that display similar chromatin profiles in order to identify potential TE-derived enhancers, which may or may not be bound by p300, genome-wide. To that end, we built a test set made up of the DHS sites in the GM12878 and K562 cell lines. Using a 10 kb window and a step size of 100 bp, we computed Spearman's rank correlations between the enhancer training set histone modification profile and test set profiles at each step. For each DHS site, the genomic site that yields the highest correlation value was recorded, and the results were filtered using a correlation cut-off of 0.5 or higher , suggesting that TE-derived enhancers are more cell type specific.In order to identify functionally active TE-derived enhancers, we intersected the genomic loci of our predicted enhancers in each cell line with TE annotations produced by the RepeatMasker program 2 test, GM12878: P = 9E−272, K562: P = 4E−217 –t test, GM12878: t = 5.6, P = 1E−3, K562: t  = 4.6, P = 3E−3). In absolute terms, LTR elements donate the highest number of enhancers (387) in the K562 and the second highest number of enhancers (383) in the GM12878 cell lines. A number of previous studies have demonstrated that LTRs provide transcription start sites and protein coding sequences to the human genome P = 3E−19, K562: ρ = 0.89, P = 2E−17). The observation that older TE families donate relatively more enhancers than younger ones suggests that older elements may possess a stronger ability to recruit epigenetic marks, making them more likely to be exapted by the host genome. We have also previously shown that older TEs are bear more histone modifications than younger ones and therefore demonstrate a higher potential to be exapted by the human genome To evaluate the contribution of various families of TEs in donating enhancers, we divided TE-derived enhancers into 6 major families, based on the Repbase classification system To evaluate the functional effect of TE-derived enhancers, we investigated their role in the regulation of gene expression. Enhancers can influence the expression of genes that lie as many as tens-of-thousands of bases away from the transcriptional start site of genes. We determined the functional effect of our predicted TE-derived enhancers by relating them to cell type specific gene expression. To do this, we mapped enhancers to genes by finding enhancers in 100 kb windows surrounding transcriptional start sites.t test, GM12878: t = 31.2, P = 3E−208; K562: t = 31.4, P = 4E−211). Furthermore, the expression level of genes is strongly positively correlated with the number of TE-derived enhancers it has in its vicinity . These findings suggest that TE-derived enhancers make a contribution to the up-regulation of the expression of nearby genes. As a control, we did the same analysis using non-TE derived enhancer sequences predicted in the same way; the results are qualitatively identical underscoring the potential functional significance of TE-derived enhancers . In addition, the trend is not entirely linear; the most extreme bins on either end show the greatest relationship between gene expression divergence and TE-enhancer frequency divergence. This suggests that the strongest influence of TE-derived enhancers is observed for the most differentially expressed genes. Non-TE derived enhancers show a similar, if more linear and stronger, positive correlation between gene expression divergence and enhancer divergence  with 1,970 genes that are up-regulated in GM12878 and down-regulated in K562 and 2,148 genes that are down-regulated in GM12878 and up-regulated in K562 cell lines . Similarly, the 2,148 genes that are up-regulated in K562 and down-regulated in GM12878 have 0.37 TE-derived enhancers per gene in K562 and 0.30 TE-derived enhancers per gene in GM12878 cell line . These analyses demonstrates that there are more TE-derived enhancers present near genes that are differentially up-regulated in one cell line versus the other, highlighting their contribution to the regulation of differential expression between cell types.In order to further investigate this phenomena, we used ANOVA on 20 and 21 samples of normalized exon array data from the GM12878 and K562 cell lines respectively to determine the maximally differentially expressed genes. We found 4,118 genes that are significantly differentially expressed . These results are consistent with a positive regulatory role, i.e. activation of expression, for the cell-type specific TE-derived enhancers identified here.Since we are comparing two cell lines here, it is formally possible that the differences in expression between cell lines are not related to up-regulation of genes associated with cell-type specific TE-derived enhancers in one cell line. Rather, it may be that the corresponding enhancer sequences, which bear distinct chromatin profiles in the alternate cell line, are actually exerting some negative regulatory effect therein. To control for this possibility, we compared the levels of expression for genes associated with cell-type specific TE-derived enhancers against the expression levels of all genes within cell types. We found that the genes associated with cell-type specific TE-derived enhancers are expressed at significantly higher levels than other genes within the same cell type  or higher were taken for further evaluation as potential enhancers.The test set vectors were fashioned in a similar way except in this case individual DHS sites were used instead of p300 binding sites as in the training set. Individual enhancer test set profiles were centered at the start point of the DHS sites and Spearman's rank correlations were computed individually between the five vectors of the training and the test set profiles and the resulting correlations were averaged. We used a sliding window with a step of 100 bp from the start of the DHS sites, computed correlations at every step and took the highest average correlation computed from all the steps within a DHS site. Average Spearman's correlation values of , taking 9 of the subsamples as the histone modification training set and a single subsample as the validation set for testing the model. This procedure was iterated 10 times with each of the subsamples used one time as the validation set for testing the model. Then for each cross-validation, Spearman rank correlation coefficients (SCCs) between the histone modification profile of the enhancer training set versus the histone modification profiles of the validation set were computed along with SCCs for the histone modification profile of the training set versus modification profiles of a set of random genomic sequence the same size as the validation set. The resulting ROC curve is based on the relative distributions of the SCC for all ten of the enhancer training versus validation sets (true positives) along with the SCC all ten of the enhancer training versus random genomic sequence sets . The rates of true positives and false positives were calculated by taking the normalized frequency of correlation values i = 0.02, N = 137 , and FoC = frequency of correlation values in range. The rate of true positives (TRP) was plotted against the rate of false positives (FPR) to yield the ROC curve in where interval We downloaded Affymetrix exon array signal intensity data from the GEO database under accession number GSE12760. This dataset contains 20 samples of GM12878 and 21 samples of K562 cell lines collected from the different laboratories that are part of the ENCODE project P = 1E−7 obtained after using Bonferroni correction for multiple tests, to calculate ANOVA.Differentially expressed genes were identified using one way ANOVA  implemented in the Genesis software package We used five sequence annotation datasets from the March 2006 build  of the human genome. Three of these datasets were obtained from the ENCODE section of the UCSC Genome Browser t distribution with d.f. = n−2 with the formula We used the statistical software R for calculating the Spearman's rank correlation coefficients ρ for all correlation analyses. The statistical significance of Spearman's rank correlation coefficients ρ was determined using the Student's 2 test with d.f. = 5 and Student's t test with d.f. = 1 to determine the statistical significance of the over- and under-represented TE-families that donate predicted enhancers in each cell line , whereas other modification that show no specific pattern of enrichment over the p300 binding sites and were thus excluded from further analysis .(PPT)Click here for additional data file.Figure S2Control 2: Histone modification enrichment patterns at p300 binding sites versus random genomic loci. Epigenetic histone modification levels at 137 p300 binding sites as well as 137 random genomic loci in the K562 cell line are shown. Random genomic loci do not show any discernable pattern of histone modification enrichment compared to p300 binding sites.(PPT)Click here for additional data file.Figure S3Over and under-represented TE families in contributing enhancers. Number of TE-derived enhancers observed for different TE families in the GM12878 (blue) and K562 (brown) cell lines normalized by the relative genomic abundances of TE families.(PPT)Click here for additional data file.Figure S4Functional role of non TE-derived enhancers in regulating gene expression. Average expression levels (y-axis) of genes that are co-located with different numbers of non TE-derived enhancers (x-axis) shown for GM12878 (blue) and K562 (brown) cell lines.(PPT)Click here for additional data file.Figure S5Functional role of non TE-derived enhancers in regulating differential gene expression. Gene expression divergence between GM212878 and K562 (y-axis) is plotted against normalized differenced in the numbers of cell type specific non TE-derived enhancers (x-axis) co-located with the genes. Expression divergence and enhancer frequency divergence between the GM12878 and K562 cell lines is calculated by subtracting the values of K562 from those of GM12878 cell line.(PPT)Click here for additional data file.Table S11,986 TE-derived enhancers in the GM12878 cell line.(TXT)Click here for additional data file.Table S21,127 TE-derived enhancers in the K562 cell line.(TXT)Click here for additional data file.Table S3121 TE-derived enhancers shared between the GM12878 and K562 cell lines.(TXT)Click here for additional data file.Table S42 statistics for over and under represented TE families in contributing enhancers.χ(PPTX)Click here for additional data file.


Enhancers play a pivotal role in regulating the transcription of distal genes. Although certain chromatin features, such as the histone acetyltransferase P300 and the histone modification H3K4me1, indicate the presence of enhancers, only a fraction of enhancers are functionally active. Individual chromatin marks, such as H3K27ac and H3K27me3, have been identified to distinguish active from inactive enhancers. However, the systematic identification of the most informative single modification, or combination thereof, is still lacking. Furthermore, the discovery of enhancer RNAs (eRNAs) provides an alternative approach to directly predicting enhancer activity. However, it remains challenging to link chromatin modifications to eRNA transcription. Herein, we develop a logistic regression model to unravel the relationship between chromatin modifications and eRNA synthesis. We perform a systematic assessment of 24 chromatin modifications in fetal lung fibroblast and demonstrate that a combination of four modifications is sufficient to accurately predict eRNA transcription. Furthermore, we compare the ability of eRNAs and H3K27ac to discriminate enhancer activity. We demonstrate that eRNA is more indicative of enhancer activity. Finally, we apply our fibroblast trained model to six other cell-types and successfully predict eRNA synthesis. Thus, we demonstrate the learned relationships are general and independent of cell-type. We provided a powerful tool to identify active enhancers and reveal the relationship between chromatin modifications, eRNA production and enhancer activity. Distal-acting enhancers are key elements in the regulatory processes that establish cell-type-specific patterns of gene expression. Genome-wide identification of functionally active enhancers is necessary to understand the expression of genes, as well as developmental, and disease-related, processes. Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) has allowed the genome-wide mapping enhancers by virtue of their specific chromatin features ,2. EnhanAlthough a large portion of the genome possess enhancer-related chromatin features, only a fraction of enhancers are functionally active. H3K27ac, in combination with H3K4me1, is an important indicator of enhancer activity ,14. OtheA more direct indicator of enhancer activity has emerged from a recent genome-wide study that identified many short (<2 kb) non-coding RNAs, which are bi-directionally transcribed from enhancers, and are termed enhancer RNAs (eRNAs) ,20. The Both eRNAs and chromatin marks modifications have been used to identify active enhancers ,17,20,22Herein, we systematically assessed the relationship between chromatin modification, eRNA transcription and enhancer activity. Three questions are specifically addressed: (i) What is the relationship between chromatin modifications and eRNA transcription? (ii) How does this relationship contribute to the identification of active enhancers? (iii) Is this relationship cell-type-specific?To this end, we integrated ChIP-seq for 24 chromatin modifications and global run-on sequencing (GRO-seq), which provide the eRNA expression levels, in fetal lung fibroblast (IMR90) cells. We developed a logistic regression model that uses chromatin modifications as predictors and GRO-seq levels as response variable. We show that the combination of four modifications can accurately predict eRNA transcription. We further applied this relationship to identify active enhancers. We demonstrated that no single chromatin modification is associated with enhancer activity; instead, eRNA transcription and a combination of chromatin modification can best predict enhancer activity. Finally, we ascertained that this relationship is general, as demonstrated by successfully using a model trained on IMR90 to predict enhancer activity in six different cell-types.http://www.roadmapepigenomics.org, see Supplementary Table S1 for the complete list of 24 marks) (http://genome.ucsc.edu/cgi-bin/hgTables?command=start), as was done by Wang et al. (http://genome.ucsc.edu/cgi-bin/hgTables?command= start). The average mappability for each intergenic enhancer was calculated, and the enhancers with an average mappability of <0.85 were filtered out. For the remaining intergenic enhancers, the read densities within each enhancer region were averaged and transformed to a logarithmic scale, i-th chromatin mark at the j-th enhancer and j-th enhancer, respectively. The histogram of Supplementary Figure S1). K-means  ,24. The 4 marks) . The dow4 marks) . To avoig et al. . The ‘Du+ and −. The logistic regression model isLet Supplementary Figure S4). Specifically, we first split the data into K folds (K = 10), using K-1 folds as training data and the remaining fold as test data. This approach is the outer cross-validation. Within each loop of outer cross-validation, we performed K-fold inner cross-validation to select top models. The selected top models were then evaluated using the test data in the outer cross-validation. We repeated K loops of outer cross-validation and selected the final top models. As the models selected in the inner loop were not exposed to the test data in the outer loop, nested cross-validation avoids over-fitting when the number of candidate models is large.Nested cross-validation includes two nested loops of cross-validations, namely, inner and outer cross-validations  and Matthew’s correlation coefficient (MCC). The input to the trained classifier is a set of chromatin modifications for an enhancer, whereas the output is the probability that this enhancer produces eRNA. Setting a threshold for this probability produced a particular rate of true and false positives with respect to this threshold. As such, MCC was defined ashttp://genome.ucsc.edu/cgi-bin/hgTables?command=start). As was done for P300-bound enhancers, K-means clustering was performed on both the sense and anti-sense strands, and 6506 eRNA+ and 4950 eRNA− enhancers were identified. We applied the model trained on P300-bound enhancers to this set of H3K4me1+me3− enhancers.Peaks of H3K4me1 were called using MACS . As we hhttp://www.roadmapepigenomics.org) (http://genome.ucsc.edu/cgi-bin/hgTables?command=start). Peaks of H3K27ac ChIP-seq data were called using MACS. Enhancers located within 2 kb of regions enriched for H3K27ac were defined as H3K27ac+ enhancers. Other enhancers were defined as H3K27ac− enhancers, as previously reported . Predicted eRNA-positive (P-eRNA+) and eRNA-negative (P-eRNA−) enhancers were derived from the logistic regression model. Each enhancer was assigned to its closest gene based on the distance to a TSS and considering a maximum distance of 100 kb, as described by Rada-Iglesias et al. (IMR90 RNA-seq data were obtained from the NIH Roadmap Epigenomics Mapping Consortium (ics.org) ,24. We uics.org)  to calcureported . eRNA+ as et al. .Supplementary Table S1 for a list of the six marks) and the histone acetyltransferase P300 in the mouse ESC (mESC) cells were obtained from (http://genome.ucsc.edu/cgi-bin/hgTables?command=start). The mESC GRO-seq data are publicly available in BED format Supplemenned from . Peaks wned from . As withken from .http://www.roadmapepigenomics.org) (Supplementary Table S1 for a complete list of the chromatin marks in the five cell-types). We used P300 ChIP-seq data to identify putative enhancers in the H1 cells. Because the P300 ChIP-seq data are not available for the four H1-derived cells, the enhancers in these four cell lines were defined as the peaks of the H3K4me1-enriched regions and were required to be located at least 3 kb away from the H3K4me3-enriched regions or a known TSS. We again filtered out enhancers whose mappability values were <0.8 and identified a total of 14 724 enhancers in H1 cells, 37 215 enhancers in ME cells, 31 803 enhancers in TBL cells, 25 555 enhancers in MSC cells and 25 473 enhancers in NPC cells. We then applied our logistic regression model trained on IMR90 data to the five cell-types to predict eRNA+ and eRNA− enhancers. As with the IMR90 cells, we used MACS to call H3K27ac peaks in these five cell-types.The genome-wide map of chromatin modifications in H1-ESC and the four H1-derived cells  was downloaded from the website of the NIH Roadmap Epigenomics Mapping Consortium (ics.org) ,24,32. B+K27ac+, eRNA+K27ac−, eRNA−K27ac+ and eRNA− K27ac−) were amplified from the genomic DNA and cloned into the enhancer site of pGL3-promoter vectors, and the inserted sequences were verified by DNA sequencing. IMR90 cells were maintained following the protocol of the American Type Culture Collection. Briefly, cells were grown in MEM supplemented with 10% fetal bovine serum (FBS), 1% pen/strep, 1% NEAA and 1% sodium pyruvate and split 1:3 every 3 days. Cells were seeded into 24-well plates 1 day before transfections. The cells were transfected with 500 ng of a pGL3 plasmid and 50 ng pRL-CMV, washed with PBS 24 h after transfection and then lysed for 20 min. Luciferase activity was measured using the dual luciferase assay (Promega) using a Veritas microplate luminometer (Turner BioSystems), normalized to Renilla luciferase activity and then divided by the values for a pGL3 minimal promoter empty vector control. All assays were performed in triplicate.Fourteen enhancers comprising four subgroups . Binding of the transcriptional coactivator P300 often marks distal enhancers . As one increases m, the AUCs and MCCs of the best-performing m-modification model increase . However, the improvement is insignificant when more than four modifications are used . Taken together, these findings suggest that the incorporation of multiple chromatin marks performs better than using only H3K27ac but that four modifications are sufficient.We started with the full model, which includes all 24 chromatin marks. The full model has an AUC of 0.95 and MCC of 0.75. It clearly demonstrates that chromatin modifications can discriminate enhancers according to eRNA expression levels a. We nexSupplementary Table S2 for a complete list). The best-performing model is H3K27ac+H3K27me3+H3K79me1+H3K9ac, including two acetylations and two methylations. Of the selected 432 models, 99.1% have at least one histone acetylation , indicating their positive contribution to eRNA expression. These findings imply that H3K79me1, H3K8ac and H3K9ac are novel chromatin signatures that are associated with active enhancers (see later in the text).In addition to H3K27ac and H3K27me3, our analysis identified a set of novel chromatin marks that are highly predictive of eRNA production. We found that 41% of the selected models included H3K79me1 . For instance, H3K27me3 is the most frequently selected of the 24 chromatin modifications, although its individual predictive power is not the highest. When H3K27me3 is combined with other modifications, the combined model achieves greater accuracy than any single modification in isolation, which indicates that integration of multiple chromatin marks improves prediction accuracy.Notably, the most frequently observed chromatin marks do not necessarily have the largest individual predictive power . Moreover, only 62% of the predicted eRNA+ enhancers were bound by P300. These findings indicate that the chromatin features associated with eRNA production are common and not limited to P300-bound enhancers.To test whether the derived model and chromatin features are general to a broader set of enhancers, not necessarily only those bound by P300, we applied the best-performing four-modification model to a broader set of enhancers that are enriched for H3K4me1 and depleted in H3K4me3   and eRNA-negative (P-eRNA−) elements. The P-eRNA+ enhancers have significantly higher levels of associated gene expression than the P-eRNA− enhancers. More importantly, the significance level of gene expression is comparable with the significance using measured eRNA levels (P = We next applied the best-performing four-modification model (H3K27me3+H3K27ac+H3K79me1+H3K9ac) to classify enhancers into predicted eRNA-positive  and investigated their activity . Both eRNA production and H3K27ac enrichment are positively correlated with luciferase activity. However, the difference in luciferase activity is much more significant between the p-eRNA+ and p-eRNA− enhancers , GRO-seq and P300 ChIP-seq in mESC .The six chromatin modifications in mESCs were also available in IMR90. Thus, we trained the model using these six modifications in IMR90 cells and evaluated it in mESC. This model has an AUC of 0.93 and MCC of 0.69, which are comparable with the AUC and MCC values (AUC = 0.93 and MCC = 0.70) obtained when the model is trained and tested using the same six chromatin modifications in IMR90 cells a and b. +K27ac+ and p-eRNA+K27ac− enhancers have high-level bi-modal GRO-seq signals . In all five cell-types, p-eRNA+K27ac− enhancers were associated with significantly higher levels of gene expression than the p-eRNA-K27ac+ enhancers  Acetylations. In corroboration of previous reports that histone acetylations are redundant  and is eThird, we compared eRNA production and H3K27ac ability to predict enhancer activity and demonstrated that eRNA transcription is more robust. Although the mechanism of eRNA transcription is unclear, this process may involve multiple chromatin modifications. Although H3K27ac might be one of these modifications, it is not the only chromatin mark involved. Thus, the inclusion of multiple chromatin modifications as a ‘histone code’ improves the accuracy of prediction of eRNA transcription. A previous study showed that eRNA transcription depends on the presence of an intact target promoter . Thus, i+me3− enhancers. Our model predicted a set of active enhancers, only 62% of which overlapped with P300 binding sites. Furthermore, we applied the top four-modification model to H3K4me1+me3− enhancers and found that the expression levels of P-eRNA+K27ac− enhancer-associated genes are significantly higher than those of P-eRNA-K27ac+ enhancer-associated genes. This observation is consistent with the results observed for P300-bound enhancers and thus serves as additional evidence that eRNA expression is a more robust indicator of enhancer activity compared with H3K27ac. Taken together, these findings indicate that the model trained on P300-bound enhancers is not limited to P300-bound enhancers.We trained our model on P300-bound enhancers because this approach allowed us to perform an unbiased analysis on model/chromatin feature selection. However, our analysis is not limited to P300-bound enhancers. The selected chromatin features are general and common to a broader set of enhancers. As described, we applied our model to H3K4me1Lin Sun and Zhao Chen contributed equally to this paper.Supplementary Data are available at NAR Online.National Institutes of Health [R01GM096194 to W.W. and U01ES017166 to W.W.] (in part).Funding for open access: Conflict of interest statement. None declared.


Escherichia coli is a paradigm system for quantitative understanding of gene regulation in prokaryotes. Yet, none of the many mathematical models built so far to study the dynamics of this system considered the fact that the Lac repressor regulates its own transcription by forming a transcriptional roadblock at the O3 operator site. Here we study the effect of autoregulation on intracellular LacI levels and also show that cAMP-CRP binding does not affect the efficiency of autoregulation. We built a mathematical model to study the role of LacI autoregulation in the lactose utilization system. Previously, it has been argued that negative autoregulation can significantly reduce noise as well as increase the speed of response. We show that the particular molecular mechanism, a transcriptional roadblock, used to achieve self-repression in the lac system does neither. Instead, LacI autoregulation balances two opposing states, one that allows quicker response to smaller pulses of external lactose, and the other that minimizes production costs in the absence of lactose.The lactose operon of  Escherichia coli are directly regulated by a small molecule  and the enzyme for lactose degradation (LacZ), therefore the lactose repressor (LacI) regulates the transport and metabolism pathways simultaneously . A large class of cellular response systems regulates the flux and concentration of small molecules by controlling transport and metabolism pathways via two feedback loops connected by a common transcription regulatory protein that senses the intracellular concentration of the small molecule ,2. In famolecule . The proaneously . The laccription . The Laccription . The strperators ,8. When revented . In thisproduced . While tproduced ,10, thisproduced . Previouproduced  and incrproduced . In thisin vitro transcription, was created by inserting the lacZP promoter region  from E. coli MG1655 (GeneBank: NC_000913.2) between the EcoRI and PstI sites of plasmid pSEM2008. The lacZP promoter region was amplified using the primers ATATATCTCGAGCAACTCTCTCAGGGCCAGGCGGTGAAGGGC and ATATATCTGCAG AATAATTCGCGTCTGGCCTTCCTGTAGCCAGC. The EcoRI and XhoI, and the lacZP PCR fragment was cut with PstI and XhoI. The two fragments were inserted between the EcoRI and PstI sites of plasmid pSEM2008 by a three-piece ligation. The pSEM2008 plasmid was obtained by inserting the DNA fragment containing the rrnBT1T2 terminators (nt 4559 → 4141) from pKK223-3  between the KpnI and EcoRI sites of pSA850 , the lacI gene was PCR amplified using the primers AAAAGCTAGCAAAACCTTTCGCGGTATGGCTGAT and AAAAGAATTCAACGGAA GCACGTCGATCGGCCAAC, the amplified DNA fragment was digested with NheI and EcoRI, and inserted into the pSEM1026 vector  cells, extracts of 2 × 108 MG1655 cells that were grown in the presence and absence of isopropyl β-d-thiogalactopyranoside , respectively, and extracts obtained from 2 × 108 E. coli MC4100 cells.Protein samples were loaded on a 10% Bis-Tris gel as follows: 10E. coli MC4100 extract, was used as an internal control.After separation, the proteins were transferred to an Immobilon-P PVDF Membrane of 0.45 µm pore size (Millipore), then were blocked overnight with 5% nonfat dry milk in PBST . The blot was then incubated with primary antibody overnight at 4°C. It was then incubated with peroxidase-conjugated antibody and developed with Supersignal West Pico kit (Thermo Scientific). The dilutions of the antibodies were anti-LacI antibody , peroxidase-conjugated anti-mouse antibody . Band intensities were quantified and background corrected. The bottom signal, which is also present in the L), internal allolactose (A), LacI mRNA (mI), LacI tetramers (I), LacY permeases (Y) and the LacZ enzymes (Z). The deterministic differential equations that model the dynamics of these variables are described below:extL) by LacY, for which we have chosen a Michaelis–Menten form where yv is the maximum rate of import per LacY molecule, and the constant extK is the extL concentration at which the import rate per permease is half of its maximum value. The second term is similar and represents the export of internal lactose by LacY. The export has a different Michaelis constant, which is larger (extλK). The final term models both hydrolysis of internal lactose as well as its conversion to allolactose, catalyzed by LacZ. Again a Michaelis–Menten form was chosen for these reactions, with each having a maximum rate of zv per LacZ. We further assume that both reactions have the same Michaelis constant K  all operators are unbound, (ii) O1 is bound, (iii) a DNA loop is formed between O1 and O2 and (iv) a DNA loop is formed between O1 and O3. This is because the other bound states have significantly lower energies, and hence lower probability of occurrence, than the three bound states we allow (lac operon and the lacI gene as a function of the active LacI concentration (I*):εs are related to the binding energies of the LacI-operator complexes, lac operon is controlled by the cyclic-AMP-CRP level, C:The dynamical variables we keep track of in our model are the concentrations of internal lactose  (lac operon mRNA is produced at a high enough rate to have little effect on the fluctuations of LacY and LacZ levels.Using eration) ,26 to prL and A concentrations are always in quasi-equilibrium. Setting dL/dt = dA/dt = 0 makes the lactose and allolactose concentrations both equal to the physically sensible  solution of the following:For these deterministic differential equations, there is no need to have a separate equation for dZ/dt. Instead Z can simply be calculated from Y because In the stochastic simulations, we keep track of the actual number of molecules of LacI tetramers, LacI mRNA, LacY and LacZ tetramers. The net production and degradation rates, from the equations of the deterministic model, expressed in appropriate units, can be treated as probabilities per unit time for the production and degradation of each species. We use the Gillespie algorithm  to deterextL) is varied in the simulations, the remaining parameters are always kept fixed.The number of parameters used in the model could be reduced by non-dimensionalizing the equations but this is not necessary here because we can fix most of the parameter values. Only the external lactose concentration (γ = 0.0087 min−1 = ln(2)/(80 min) was chosen assuming a doubling time of 80 min (extK) and the active transport turnover number (yv) were 0.27 mM and 48/s, respectively  ≈ 1300/30 nM−1. Further, the experimental results presented in this article set the ratio between the autoregulated and the fully expressed levels of LacI to be 1/3. Therefore ε1 + ε2 = ε3/2, i.e. ε2 = 415.33/30 nM−1 and ε3 = 866.66/30 nM−1. This choice results in 1300-fold repression in the deterministic simulations but a lower mean repression is observed in the stochastic simulations because of the noise in LacI levels. The repression level can be increased by increasing the LacI-operator binding energies or by increasing the number of lacI mRNAs produced per cell generation. We have tested these possibilities and the conclusions reported here were not affected.The  18-fold . Assuminα + εcC)/(1 + εcC) = 0.9.We study our model only in conditions where the cAMP-CRP level is fixed and close to saturation, so we take the factor (AK = 1 µM (K = 1.4 µM (lacI mRNA (ck) is set to 1/80 nM/min because the lacI gene is transcribed approximately once per cell generation on average  and lacZ (zk) were chosen to be 90 and 100 nM/min, respectively, to obtain ∼10 µM LacY and slightly higher LacZ tetramer concentration when LacI is inactivated  is 3600/min , therefolacI mRNA (lk) was chosen to be 90 γγm/ck per min to obtain 90 nM LacI tetramers/cell at saturating intracellular allolactose concentration.The maximal translation rate of the This leaves one remaining parameter, λ, which sets the rate of LacY-mediated lactose export. To fix this, we need one additional constraint. We use the approximation that ∼3 billion glucose molecules are needed to generate a new cell . We founIn vivo studies of the distribution of different lacI mRNA species in rifampicin-treated cells showed that in the presence of LacI, transcription is blocked at the O3 and O1 operators with similar efficiencies (lacIP transcription is controlled in vitro. The plasmid constructed (pSEMJ1) contained an enhanced lacIP promoter and the WT lac control region including both the O3 and O2  cells as a lower bound on the actual number of proteins in vivo  interval over which the average level of LacY transporter (output) changes significantly. Previously, systems controlled by negatively autoregulated regulators were found to have a larger input dynamic range and a more linear dose-response compared to similar systems regulated by constitutively expressed regulators . Similar results were obtained in a second set of simulations where the LacI levels were in the range of 10–30 nM (The neration ,26. Due ic cells . Negativic cells . We perfic cells . Interesic cells  inset, w10–30 nM .Figure Escherichia coli cells need to optimize their gene expression pattern in environments where the quality and amount of carbon sources fluctuate, most likely in an unpredictable fashion ( fashion . To test fashion . We find fashion . FurtherE. coli (E. coli transcription elongation is inhibited (lac system is intrinsically noisy because of the low probability of lacI transcription (lacI gene and the lacZYA operon by a single LacI tetramer bound to O1 and O3. The intrinsic noise can generate heterogeneous expression of LacY and LacZ in the cells of E. coli populations in the absence of lactose (lac system was also found to exhibit bistable behavior in the presence of low levels of non-metabolized inducers such as thiomethyl β-d-galactoside and IPTG but no bistability was observed when lactose was used as an inducer (Autoregulation is a common feature of sugar-specific transcription regulatory proteins in E. coli . NegativE. coli , howevernhibited . The laccription  and beca lactose . The lac inducer ,51.lac operon at a high level but can use lactose immediately when it becomes available. Negative autoregulation of LacI decreases the probability of having zero LacI in the cell, from which we can speculate that in natural habitats the cost associated with higher LacY expression and slower turn off of the constitutive low system is higher than the potential benefit of fast lactose utilization in a fraction of the population. Unlike the constitutive low system, the constitutive high system is at least as economical as the WT system in the absence of lactose—although it expresses LacI at a higher level, expression of the lac operon is much lower in this system. However, cells with constitutive high LacI levels would perform worse than constitutive low or WT cells in an environment where these systems compete for small pools of lactose that appear rarely and intermittently. This is because a fraction of the populations having the constitutive low or WT systems can start to utilize the lactose source much earlier than the average turn-on time, while the constitutive high system lacks this opportunity.The capability of negative transcriptional autoregulation to reduce gene expression noise has been demonstrated both experimentally and theoretically ,52. Howelac system.Because the LacI level can be changed in the WT system, this system balances the two opposing states, one that allows quicker response to smaller pulses of external lactose, and the other that minimizes production costs in the absence of lactose. The resulting increased dynamic response range therefore enhances the overall performance of the autoregulated Danish Council for Independent Research|Natural Sciences; the Intramural Research Program of the National Institutes of Health; National Cancer Institute; Center for Cancer Research and the Danish National Research Foundation. Funding for open access charge: Danish National Research Foundation.Conflict of interest statement. None declared.