Saccharomyces. Within the assumption inherent in the system our modeling suggests that TBP occupancy at promoters is rather transient compared to other general factors, despite the importance of TBP in nucleating assembly of the preinitiation complex. PathCom is suitable for modeling any assembly/disassembly pathway, given that all the proteins (or species) come together to form a complex.Apparent occupancy levels of proteins bound to DNA in vivo can now be routinely measured on a genomic scale. A challenge in relating these occupancy levels to assembly mechanisms that are defined with biochemically isolated components lies in the veracity of assumptions made regarding the in vivo system. Assumptions regarding behavior of molecules in vivo can neither be proven true nor false, and thus is necessarily subjective. Nevertheless, within those confines, connecting in vivo protein-DNA interaction observations with defined biochemical mechanisms is an important step towards fully defining and understanding assembly/disassembly mechanisms in vivo. To this end, we have developed a computational program PathCom that models in vivo protein-DNA occupancy data as biochemical mechanisms under the assumption that occupancy levels can be related to binding duration and explicitly defined assembly/disassembly reactions. We exemplify the process with the assembly of the general transcription factors  at the genes of the budding yeast  For proper cell function, cells need to precisely coordinate the expression of their genes on their DNA at precise times. In order to better understand how the cell works, it is important to understand how, when, and why a cell needs to turn on or off certain genes at certain times. In order to assist the cell to properly express its genes, there are hundreds of proteins that can bind and access DNA. Each protein has a unique function and these proteins assemble together into a very large complex to turn on genes. The assembly of these proteins has defined to some extent, however the whole process of assembly and disassembly of this complex in the cell is still poorly understood. In our modeling analysis, we have attempted to utilize genome-wide binding data to better understand how the transcription machinery that “reads” genes might disassemble, in light of what is known about the assembly process. This knowledge helps us better understand how cells coordinate their on/off-switching of their genes. Eukaryotic genes are thought to be regulated by hundreds of proteins that assemble into pre-initiation complexes (PIC's) at promoters using an ordered pathway. One aspect of the PIC assembly pathway involves the recruitment of the general transcription factors (GTF's), such as TBP and TFIIB, by sequence-specific activators. TBP and TFIIB then contribute to the recruitment of RNA polymerase II (pol II) and other GTF's, which eventually start transcription.A fundamental question concerning our understanding of gene regulation is the extent to which each assembly and disassembly step is distinct at every gene in a genome. Is the traditional biochemical view that TBP “locks in” or commits to a promoter, and in a recurring manner nucleates PIC formation valid in vivo? And is the PIC disassembly process in vivo, simply the reverse of the assembly process? Parts of the assembly/disassembly pathway have been rigorously defined in vitro with a few purified proteins and DNA, and this has provided us with our current parsimonious view of PIC regulation The goal here is to evaluate in vivo occupancy data in light of biochemical mechanisms that are intended to reflect the corresponding in vivo reaction. The extent of biological insight is predicated on rather subjective assessments of the assumptions associated with interpretation of in vivo data. Within the context of declared constraints and assumptions, we propose a means to model in vivo protein-DNA occupancy data, so as to better integrate and conceptualize massive genomic datasets. This study is focused on the means of such modeling and the assumptions inherent in the data, using specific examples of PIC assembly.Currently, perhaps the most widely used assay to measure the occupancy of proteins at genes in vivo is the chromatin immunoprecipitation assay (ChIP). In ChIP, proteins are crosslinked to DNA, the protein is then purified, and the bound DNA identified either through directed PCR or through genome-wide detection platforms (ChIP-chip and ChIP-seq). In this way, for example, the relative occupancy level of TBP, TFIIB, pol II, and many other proteins at every promoter in the genome in a population of cells can be assayed.Recent studies using differential ChIP and photobleaching experiments have provided compelling evidence for a dynamic state of PIC components in living cells The existence and origins of distinct occupancy levels of PIC components on genes has not been systematically explored, and thus is the impetus for conducting the modeling studies described here. Differential occupancy patterns for the GTFs have been described Here, we develop a ChIP modeling program, termed PathCom, in the context of a fixed PIC assembly pathway to infer allowable dissociation mechanisms. We validate the simulation using an existing chemical kinetics simulator COPASI The overall goal here is to inter-relate ChIP in vivo occupancy data with biochemical assembly/disassembly mechanisms, in a way that attempts to support or dispute such mechanisms. Such inter-relationships can be complex when one considers that hundreds of proteins are involved in transcriptional regulation. Therefore, we start by modeling only two factors (the GTF's TBP and TFIIB), and increase complexity by adding more GTFs one at a time up to six factors. While we focus on PIC assembly/disassembly mechanisms on a genomic scale, any number of factors and combination of assembly/disassembly steps in gene regulation may be considered, given that all proteins (or species) come together to form a complex.TBP (T) binds to DNA (D) to form a protein-DNA (TD) complex, and in the presence of TFIIB (B) form a TDB ternary complex : A TBP bThe constant availability of energy to drive directional processes allows the pairing of any association and dissociation mechanism. Consequently, there are four paths by which an in vivo occupancy level is achieved for a two-component reaction. The availability of only two experimental constraints (TBP and TFIIB occupancy levels on DNA) is insufficient to specify the predominant association and dissociation pathways. In the absence of a necessary additional experimental constraint, we created a hypothetical constraint for the purposes of modeling, in which we eliminated all but one association pathway. That allowed us to evaluate the two possible dissociation pathways. The reciprocal modeling could also be done, by eliminating all but one dissociation mechanism. Since the purpose of this study is to demonstrate how the modeling works and to discuss its assumptions, caveats, and utility, we illustrate the process using a single association pathway that has good experimental support and model all possible dissociation pathways.Biochemical Figure S1). These occupancy levels were reproducible and verified by a second data source (Affymetrix high density tiling arrays) also present in the previous study  Using published genomic datasets of TBP and TFIIB occupancy To compare occupancy levels between proteins, it was necessary to place them on the same scale. We achieved this by scaling ChIP occupancy values (fold over background) for each factor from 0% to 100%. Our rationale, assumptions, and method for doing this are described in the From this analysis, several insights were obtained: 1) Some occupancy levels simply do not distinguish among mechanisms. 2) In contrast to the simplified in vitro derived biochemical mechanism, TFIIB might remain at most promoters after TBP has dissociated . How TFIIB does so is a matter of speculation that the data do not address.Based upon known TBP/TFIIB/DNA biochemical interactions, the notion that TFIIB might dissociate after TBP would seem untenable. However, the additional complexity that exists in vivo might accommodate such a mechanism if other proteins not explicitly defined in this model retained TFIIB at the promoter, after TBP had dissociated. TFIIB engages pol II at promoters via specific interactions Towards our goal of modeling the assemblage of many proteins, we next consider a three-factor assemblage. The interaction of TFIIB with pol II (P) and TBP is structurally and biochemically well defined Rule 1: Does the mechanism make it unconditional that one protein's occupancy level must be greater than another? For example, in the two factor mechanism, if TFIIB enters last and leaves first . When iterated over all factors in a mechanism, this rule determines the allowable orders of dissociation. For example, consider a fixed assembly order with TBP first, then TFIIB, then pol II , which was used to generate the compatibility chart in We sought to validate the approach taken by PathCom, to ensure that it reflected enzymological concepts for which this modeling attempts to emulate. Our validation employed COPASI, a freely available program that simulates biochemical kinetics To maximize the parameter search space and avoid local minima, COPASI imposes some randomness in moving through the decision-making process. Since the system is under-constrained and randomness is involved, each repeated modeling run converges on a different solution for each mechanism . The values of the underlying rate constants generated by the Parameter Estimator in COPASI are not meaningful; rather the resulting E-value provides a quantitative measure of the suitability of a mechanism to fit the data. Re-running COPASI on the same dataset returns essentially the same E value (not shown). Thus, COPASI provides a robust means of evaluating alternative mechanisms and validating PathCom.10 E-value assessments using COPASI. In all cases, the COPASI-reported E-values matched the Boolean decisions made by PathCom . In Importantly, the analysis indicates that given a fixed association mechanism, there are a limited number of dissociation mechanisms  set, which has high TBP and TFIIB occupancy but relatively low occupancy of pol II  We further examined the plausibility that TBP might not be fully bound at “high” occupancy promoters by looking at experimentally determined “digital footprints” of TBP bound at those promoters having the highest TBP occupancy  and ), or had very low occupancy of all tested factors ) are expected to have higher variation, and thus less reliably interpreted. Therefore, these groups were not examined further. For the remaining groups, one to two mechanisms were found to be compatible. A common theme was that TBP dissociated first, then pol II, and then TFIIB, which was consistent with the conclusions drawn from the two-factor assembly analysis described above.n, where is the number of modeled proteins). Consequently, membership in each group diminished, some to negligible levels. Those with negligible membership did not represent predominant patterns and may have arisen by chance as a consequence of noisy occupancy levels. Therefore, we combined groups of genes that lacked a viable membership level  to the mechanism: TBP → TFIIB → pol II→ TFIIH. This mechanism is applicable even if pol II and TFIIH were entering together. As shown in In the four-factor mechanism, groups having a relatively large gene membership typically were limited to being compatible with only one or two of the 24 theoretically possible dissociation mechanisms (We next added TFIIF (F) Figure 4The occupancy levels in the five-factor modeling were compatible with mechanisms that had TBP and pol II dissociate early and TFIIB and TFIIF dissociating late . In addition, we determined whether genes with <10% occupancy or ≥10% occupancy had a tendency toward having TATA versus TATA-less promoters, using data from In regards to the genome-wide distribution of the GTF's, we did not see a random partitioning of genes into high vs low occupancy states for each factor. Principal component analysis (PCA) indicates the presence of a single major component (not shown), and several minor ones. This would be consistent with the strong tendency of the GTF's to work together. What is interesting about the PCA is that TFIIB, pol II, TFIIF, and TFIIH were the main drivers in the first principal component, despite pol II having relatively low occupancy at the promoter region. TBP contributed the least to the principal components  all GTF's, 2) all GTF's except TBP, and 3) all GTF's except TBP and pol II. The group having high levels of all GTF's was by far the most highly transcribed, which is not surprising. This group included the ribosomal protein genes. However, for the major groups, low levels of TBP were more closely linked to low levels of transcription than the occupancy level of any of the other factors including pol II. This confirms on a genomic scale the earlier notion established on a few genes that TBP recruitment or retention is rate-limiting in transcription While the number of dissociation mechanisms scale factorially (n!) with the number (n) of proteins involved, we did not see an equal distribution of genes into each type of mechanism, and we did not see a corresponding increase in the number of compatible dissociation mechanisms. Instead, the number of compatible mechanisms remained rather fixed at one to two, for a given association mechanism. The general pattern observed for most genes, was that if TBP, TFIIB, pol II, and the other GTFs assembled in the listed order, then the dissociation order was generally TBP, then pol II, then the other GTFs, with the latter being less resolved.http://www.ebi.ac.uk/arrayexpress/) using the accession numbers E-MEXP-1676 and E-MEXP-1677 (low-density tiling microarray probes). High-density tiling microarray data was obtained from material in Factor occupancy data was obtained from ArrayExpress (th percentile rank (top 200 probes) as 100% . All remaining data were scaled between 0 and 100% occupancy by subtracting background (1.0) from all data, and dividing through by the value at the 99th percentile rank.This scaling was necessary to compare occupancy levels across different factor datasets. In principle such scaling eliminates differences in crosslinking efficiencies and ChIP yields between factors. Fold-over-background values equal to or less than 1 represent background and thus were re-coded as 0% occupancy. Several limitations of the ChIP assay precluded accurate assessment of 100% occupancy. First, ChIP hybridization signals generally correlated with actual occupancy levels but were not tightly linked (see below), and so the maximum detected fold enrichment over background could not simply be set to 100%, inasmuch as the variance might be quite substantial. Second, ChIP assays do not measure absolute binding, and so even if the variance were eliminated, we could not be certain that the maximum detected level of binding represented 100% occupancy. Nonetheless, if all factors are held to the same standard, and data from groups of similarly behaving genes are aggregated, then approximations can be made. Therefore, we coded any value above the 99Dataset S2, a chi-square test demonstrated that TBP occupancy levels were independent of DNA sequence . Next we tested TFIIB, which binds both TBP and DNA, and also found it to be independent of the sequence of the TATA box . Nevertheless, to minimize the influence of crosslinking efficiency on measured occupancy levels, similarly behaving genes were grouped, and their median occupancy level was used in the modeling. In addition, we focused on those groups having high gene membership, which should further alleviate fluctuations associated with individual genes.It is generally assumed that ChIP signals scale linearly with actual occupancy level. However, it is possible that a factor bound to one type of DNA sequence may crosslink more readily than when bound to a different sequence. To test the effect of underlying DNA sequence on crosslinking efficiency, we examined the distribution of TBP occupancy levels at each of the eight TATA box subtypes n theoretically possible groups, where “n” is the number of GTFs being modeled. Parsing the data at a 15% cutoff, or into three groups  did not substantially alter the outcomes, and its main conclusions.To increase the robustness of the occupancy values, as well as focus the modeling on predominant patterns, we grouped genes in accordance with their occupancy level for each factor. Genes (probes) having a GTF occupancy below 10% were parsed into low (L) occupancy groups. All others were parsed into high (H) occupancy groups, resulting in 2Dataset S3. Because of consolidation, the number of actual clusters is less than 2n. Note that consolidation was not performed when we were analyzing the two- and three-factor models in order to make the modeling explanations more clear.Groups having low membership do not represent predominant patterns and so were consolidated as follows: Groups having >100 genes were exempt from consolidation because they have substantial membership, and groups having <10 genes were required to be consolidated for lack of viable membership. Otherwise, if the membership of an existing group was split by more than a 4∶1 ratio when an additional factor was added to the model (e.g. from 2-factor models to 3-factor models), then the two resulting clusters were consolidated . The final occupancy median calculations can be found in Text S1 for information how PathCom was designed and how it was intended to be used). Below each protein in the header, the user enters the percent occupancies calculated along with the name of each cluster (or gene). After execution, the program then reads each cluster's occupancies on each line. Given the fixed order of association of proteins specified by the user in the header, the program generates all possible dissociation sequences. Note that if the user changes the association order, the pool of dissociation reactions will remain the same, but the numbering of each dissociation reaction will be different, because PathCom uses the specific association to generate the dissociation sequences. The program processes each dissociation sequence, pairing it with the fixed association sequence, and given the rules of compatibility (discussed in the paper), computes whether the input protein occupancies are compatible with the mechanism (association + dissociation) it is testing. PathCom processes all possible dissociation sequences for all groups entered. PathCom writes the results to a tab-delimited text file. In this file, the horizontal axis is labeled with every mechanism identification number and the vertical axis is labeled with every cluster name. Also, PathCom writes a file that matches each dissociation sequence with its dissociation sequence identification number. Every time a set of occupancies and a mechanism are compatible, the program reports “−1”, and when they are not, the program reports “0.” Results can be clustered through Cluster then visualized graphically in Treeview S2 for users of Windows and Mac OS, respectively.PathCom requires the user to enter occupancies of proteins in a tab-delimited text file followed by the name of the cluster line by line. In a header, before the occupancies are entered, users enter one-letter codes to denote protein identities (of the user's choice) followed by a number to indicate the order in which the proteins assemble . Since the observed occupancy levels for a factor represent the sum of all intermediate species having that factor, it was necessary to employ the Parameter Estimation function to optimize this sum, using the free protein concentration equal to (1 – Occ/100)×10, where “Occ” is the measured percent occupancy level, and had a practical lower limit of 0.1% . The Parameter Estimator may converge on a local minimum, which may not represent the optimal solution. Running the estimator multiple times alleviated the local minimum, since it employs a random search component. COPASI creates an objective value (E) used to measure goodness of fit between simulated and measured values:10 E) reflect better congruence between modeled and measured data.COPASI conducts chemical kinetic and stochastic simulations Since each modeling run has a manual component and becomes computationally draining with a large number of factors, it became impractical to run COPASI to fully generate the compatibility charts for four or more factors. Nonetheless, we employed COPASI to spot check these charts, and found 100% agreement with PathCom.Figure S1Scatter plot showing the distribution of percent of maximally measured occupancy of TBP and TFIIB.(0.16 MB TIF)Click here for additional data file.Figure S2Scatter plots showing the occupancy level of each replicate. Also shown are two plots comparing the median percent occupancies of TBP and TFIIB in the four two-factor clusters using both the low and high density tiling array data.(0.26 MB TIF)Click here for additional data file.Figure S3All six possible three-factor assembly pathways are shown and their corresponding PathCom compatibility cluster plots are shown, detailing which possible disassembly pathways arise under each possible assembly pathway. See (0.23 MB TIF)Click here for additional data file.Figure S4Shown are the experimentally determined digital footprints of genes having the highest occupancy of TBP (with TATA-boxes). The bases boxed in red highlight the TATA-boxes. The lack of discernable footprints suggests that TBP does not fully occupy its most occupied sites.(0.28 MB TIF)Click here for additional data file.Figure S5The two strongest principal components in a Principal Components Analysis (PCA) done on the six general transcription factors. They are plotted to show each factor's relative contribution to the principal components.(0.11 MB TIF)Click here for additional data file.Figure S6Compatibility chart for three factor modeling using COPASI, in which the DNA concentration was reduced from 10 to 1.(0.20 MB TIF)Click here for additional data file.Protocol S1PathCom code for Windows users.(0.01 MB TXT)Click here for additional data file.Protocol S2PathCom code for Mac OSX users.(0.01 MB TXT)Click here for additional data file.Text S1Instruction on how to use PathCom.(0.52 MB DOC)Click here for additional data file.Dataset S1Principal Component Analysis (PCA) of the six GTF's(0.01 MB XLS)Click here for additional data file.Dataset S2The results of chi-square testes on whether underlying TATA-sequence variation might have had any effect on the cross-linking efficiencies of TBP and TFIIB.(0.04 MB XLS)Click here for additional data file.Dataset S3Median occupancy levels for gene groups(0.03 MB XLS)Click here for additional data file.


Streptococcus mutans, the global response regulator CovR plays an important role in biofilm formation, stress-tolerance response, and caries production. We have previously shown that CovR acts as a transcriptional repressor by binding to the upstream promoter regions of its target genes. Here, we report that in vivo, CovR activates the transcription of SMU.1882, which encodes a small peptide containing a double-glycine motif. We also show that SMU.1882 is transcriptionally linked to comA that encodes a putative ABC transporter protein. Several genes from man gene clusters that encode mannose phosphotranferase system flank SMU.1882 -comA genes. Genomic comparison with other streptococci indicates that SMU.1882 is uniquely present in S. mutans, while the man operon is conserved among all streptococci, suggesting that a genetic rearrangement might have taken place at this locus. With the use of a transcriptional reporter system and semi-quantitative RT-PCR, we demonstrated the transcriptional regulation of SMU.1882 by CovR. In vitro gel shift and DNase I foot-printing analyses with purified CovR suggest that CovR binds to a large region surrounding the -10 region of the P1882. Using this information and comparing with other CovR regulated promoters, we have developed a putative consensus binding sequence for CovR. Although CovR binds to P1882, in vitro experiments using purified S. mutans RpoD, E. coli RNA polymerase, and CovR did not activate transcription from this promoter. Thus, we speculate that in vivo, CovR may interfere with the binding of a repressor or requires a cofactor.In  Streptococcus mutans, which is a part of the normal flora of the human oral cavity, is considered to be the primary etiological agent in the formation of dental caries S. mutans is also a leading cause of bacterial endocarditis, with greater than 14% of viridians streptococcus-induced endocarditis triggered by infections of S. mutansS. mutans metabolizes the dietary carbohydrates ingested by its host to produce glucan and to anchor itself to the tooth surface, forming densely populated microbial biofilms known as dental plaque that includes hundreds of species of other oral bacteria S. mutans to persist and to maintain a dominant presence in the oral cavity is due to its ability to adapt and respond to a variety of adverse growth-limiting conditions and production of antimicrobial peptides known as mutacins Bacteria have evolved several strategies to adapt to unexpected changes in their extracellular environment, but the predominant mechanism is the so-called two-component signal transductions system (TCS) The TCS CovRS has been extensively studied in streptococcal spp., particularly in group-A-streptococcus (GAS) S. mutans encodes 14 putative TCS in its genome S. mutans does not encode covS, but does express CovR, also known as GcrR or TarC S. mutans. As with other streptococcal spp., CovR is an important global response regulator in S. mutans, as it regulates the expression of various virulence genes that are essential for biofilm formation, such as the glucosyltransferases (gtfB/C), glucan-binding proteins C (gbpC), and also of its own expression S. mutans, while CovR negatively regulates the expression of the afore-mentioned genes, our data indicates that CovR can also function as an activator of gene expression  one such example is an open reading frame encoded by SMU.1882. In order to gain further insight into the importance of CovR in the physiology of S. mutans, and the mechanism of gene regulation, we begin by having a look at the regulation of SMU.1882 by CovR. In this communication, we report that CovR, under in vivo conditions, activates the expression of SMU.1882. CovR directly binds to the putative promoter region of SMU.1882 and protects a region containing putative promoter elements against DNase I digestion. However, binding of CovR to the promoter of SMU.1882 did not stimulate transcription under in vitro conditions, suggesting other factors may be involved under in vivo conditions.In GAS, CovR typically functions as a transcriptional repressor in silico analysis suggests that the SMU.1882 peptide is a secretory molecule S. mutans along with other streptococcal spp., including S. agalactiae, S. gordonii, S. mitis, S. pneumoniae, and S. pyogenes, S. thermophilus, using software at the Comprehensive Microbial Resource website (http://cmr.jcvi.org/tigr-scripts/CMR/CmrHomePage.cgi), revealed that SMU.1882 is unique to S. mutans, as it is apparently not found in these streptococcal spp. (data not shown); however, several small GG containing peptides and homologs of the upstream SMU.1883 gene are present in some streptococci. While SMU.1882 and the adjacent comA homolog, SMU.1881, are unique to the S. mutans strains, the flanking genes, which encode mannose-phosphotransferase system (man-PTS) are very well conserved among streptococci and other FirmicutesBioinformatics analysis of SMU.1882 gene (354-bp) reveals that it encodes a hypothetical protein of 117 amino acid residues in length containing three double-glycine (GG) motifs, one of which is at position 23. A recent http://linux1.softberry.com), indicates the presence of putative -35 (GTGAGT) and -10 (TATAAT) box motifs 151- and 127-bp, respectively, upstream of the putative start codon with a putative ribosomal binding site (AGAGA) 11-bp upstream of the start codon. A putative rho-independent terminator sequence (GCCTGACTACATAGATGTCAGGC) was identified at position 402-bp downstream of SMU.1882 by TransTerm (transterm.dev.java.net) program.A schematic diagram of the SMU.1882 locus and the genes in its vicinity are shown in comA (SMU.1881) was transcriptionally linked to SMU.1882, PCR analysis was performed using cDNA, with chromosomal DNA and RNA as the positive and negative controls, respectively. As shown in comA, which would account for the presence of the 3-kb transcript seen in the northern blot. This product could be generated due to read-through transcription from the SMU.1882 promoter.Since the transcripts of many CovR regulated genes have relatively shorter half-life, we therefore evaluated the stability of SMU.1882 transcript. The results of the mRNA stability assay indicated that the chemical half-life of the SMU.1882 transcript was approximately 2–3 minutes . NortherS. mutans UA159, and its isogenic ΔcovR derivative, IBS603, were grown to mid-exponential phase (KU70), followed by harvesting and extraction of RNA. The gyrA gene was included as control to ensure that equivalent amounts of RNA were being used for each reaction. covR mutant strain, with approximately three-fold difference in expression of SMU.1882; complementation of the ΔcovR mutant strain with plasmid pIB30 (IBS603/pIB30), which carries the full length, wild-type covR gene in trans, restores expression of SMU.1882 to the wild type level. Since SMU.1883 gene appears to be divergently transcribed from the promoter upstream of the SMU.1882, we also measured the level of SMU.1883 transcript by sqRT-PCR. We found no significant difference between the wild type and the covR mutant strains. Thus, although SMU.1882 and SMU.1883 share the same intergenic region, CovR appears to only activate transcription of the SMU1882 gene. The results of the sqRT-PCR were further supported by real-time RT-PCR analysis. As shown in covR mutant strain was approximately 2.5-fold lower compared to the wild type strain.To gain insight into the link between CovR and expression of SMU.1882, sqRT-PCR was used to quantify the level of CovR-dependent expression of SMU.1882. Wild-type cis-effect that may alter the gene expression. Transcriptional fusion reporter strains were constructed by cloning the putative promoter region of SMU.1882 into plasmid pIB107, which contains a promoterless gusA gene covR-deletion mutant (IBS606), and complemented covR-deletion mutant (IBS606/pIB30) strains, containing the P1882-gusA transcriptional fusion reporter, were assayed for Gus activity. As shown in 1882. However, GusA activity was restored to the parental level when the ΔcovR strain was complemented with covR in trans (IBS606/pIB30). Taken together, the results of the RT-PCR analyses and the reporter fusion assays indicate that expression of SMU.1882 is up regulated in the presence of CovR.To confirm that CovR can regulate SMU.1882 promoter at a non-native locus, we used a transcriptional reporter fusion for analysis to avoid any 1882. As shown in 1882 DNA fragment with increasing amounts of CovR leads to retardation in the mobility of the DNA fragment. Incubation of the 32P-labeled P1882 fragment with a 25-fold higher amount of non-labeled P1882 DNA fragment resulted in no shifting of the labeled band, while the addition of a 25-fold higher amount of a DNA fragment containing the promoter region for the nlmA gene (PnlmA), which encodes a peptide component of mutacin IV and is not regulated by CovR, did not inhibit shifting of the CovR-bound P1882 fragment (Electrophoretic mobility shift assays (EMSA) and DNase I protection assays were used to demonstrate the specific binding of CovR to the putative promoter region of SMU.1882, Pfragment . This de1882 that binds to CovR, using the same P1882-CovR mixtures prepared for EMSA, as described above. Analysis of the gel indicates that CovR binds to the DNA fragment from position -200 to -1, relative to the start codon, and protects this region against digestion by DNase I  of S. mutans. A 32P-labeled DNA fragment containing a 267-bp internal SMU.1882 sequence derived from the genome of UA159 was used as a probe against the XmnI-digested chromosomal DNA from a number of S. mutans isolates. The results of the Southern hybridization analysis are shown in XmnI-digestion UA159 genomic DNA. As shown in S. mutans.Bioinformatics analysis at the CMR website indicates that gene SMU.1882 is not found in streptococcal S. mutans strains that have been characterized to some degree. To determine whether CovR-activation of SMU.1882 is unique to strain UA159, sQRT-PCR analysis was performed on S. mutans strain GS-5 and its isogenic covR mutant derivative, IBS07; the results indicate that CovR-dependent regulation of SMU.1882 is not limited to strain UA159. covR mutant with full-length covR (IBS07/pIB30), in trans, restores the expression of SMU.1882 slightly higher than the wild-type level. sQ-RT results were further confirmed by real-time RT-PCR that showed a similar reduction of SMU.1882 transcript in IBS07 as compared with GS-5 . However, the addition of CovR did not lead to any observable increase in transcription from P1882. The level of transcription in the reaction mixes containing purified CovR (lanes 4–6) did not appear to be any different than the lanes that did not contain CovR (Lane 3).S. mutans predominantly functions as a repressor S. mutans, one such gene that appears to be activated by CovR is SMU.1882. The goal of this study was to elucidate the nature of the regulatory interaction between CovR and SMU.1882.Many bacterial TCS activate gene expression, but CovR in GAS and GBS typically acts to repress gene expression; of the 271 genes regulated by CovR in GAS, only 26 are upregulated by CovR  has, covR, and dppA, under in vitro conditions, requires the presence of a consensus binding sequence (CB) ATTARA, or the near-CB ATTAAC S. mutans genes for CovR-binding is unclear, since the ATTARA sequence is not found in the intragenic sequences of some CovR-regulated genes of S. mutansS. mutans, it is surprising that the ATTARA sequence is not more prevalent in the promoter regions of S. mutans genes. A single ATTAAA sequence is found 67-bp upstream of the start codon; whether this sequence is a CB for P1882 remains to be studied. However, mutation of the ATTARA sites of PdppA in GAS did not inhibit transcription in vivo, although the ATTARA sequences appeared to be essential under in vitro conditions 1882 may not function as a CB under in vivo conditions.In GAS, binding of CovR to the promoter sequences of the S. mutans, CovR has been shown to bind directly to five promoters, including P1882, to modulate gene expression gtfC, this CBS was present twice, one on the sense and another on the nonsense strands. However, the significance of this newly derived CBS in CovR binding needs to be verified experimentally.In 1882 under in vivo conditions, while in vitro gel shift and DNase I protection assays show that CovR directly binds to a portion of the intragenic region upstream of SMU.1882. Taken together, the results suggested to us that CovR might play a direct role in the activation of expression of SMU.1882. However, the results of the in vitro transcription assays appear to indicate otherwise, since activation of SMU.1882 expression was not observed with the addition of purified CovR. Our results are similar to the observations made with the in vitro transcription reactions with PdppA of GAS, where CovR appeared to be required for in vivo activation of transcription of dppA, but could not activate transcription under in vitro conditions in vitro transcription reaction conditions were much different than the in vivo conditions or the role of CovR may not be to directly activate transcription of SMU.1882, but to displace other proteins that may be inhibiting or silencing transcription of SMU.1882. It is possible that the RpoD (sigma factor) does not functionally interact with E. coli RNA core polymerase and thus CovR's effect is not seen in vitro. However, we believe this scenario is unlikely since several heterologous sigma factors are shown to functionally interact both in vivo and in vitro with E. coli RNA polymerase The results of the sQRT-PCR and reporter expression analyses demonstrate that CovR is required for the increased expression of PSalmonella, it has been shown that response regulator such as PhoP S. mutans and the role of CovR is to overcome the silencing effect of HLP by replacing it from the promoter. We believe this scenario is very unlikely since the binding of HLP to P1882 is non-specific as opposed to the silenced promoters where HLP binding is specific (data not shown). Furthermore, if a repressor protein, such as HLP, binds to the intergenic region between SMU.1882 and SMU.1883, then the expression of SMU.1883 would also be affected in the covR deleted strain, which was not the case  strongly suggest that SMU.1882 might encode a bacteriocin related peptide. Inactivation of SMU.1882 did not produce any noticeable phenotypes when tested against five streptococcal and one lactococcal species (data not shown). Analysis of the S. mutans UA159 genome by BAGEL S. mutans resides in the dental plaque that contains over 700 different bacterial species S. mutans may need such a high number of bacteriocins. Although the target species remain to be identified, we believe that SMU.1882 is one of such bacteriocin that may provide a fitness advantage to S. mutans by controlling the growth of other competing species in the dental plaque.At the present time, the exact physiological role of SMU.1882 is unclear. This gene appears to be found only in Escherichia coli strain DH5α was grown in Luria-Bertani (LB) medium supplemented (when necessary) with ampicillin (50 or 100 µg/ml) or kanamycin (50 µg/ml). S. mutans strains UA159 and GS-5, and other mutans isolates, were typically grown in Todd-Hewitt medium  supplemented with 0.2% yeast extract (THY). Growth of the S. mutans cultures was monitored using a Klett-Summerson colorimeter with a red filter.S. mutans strains UA159 (and its derivatives) and GS-5 (and its derivatives), grown to mid-exponential phase (Klett unit (KU) 70), under the growth conditions specified above. The cultures were harvested via centrifugation, re-suspended and incubated in RNA protect reagent (Qiagen) at room temperature for 30 min, and then re-centrifuged to form a pellet, which was either stored overnight at −20°C or processed immediately for RNA extraction. RNA extraction was performed as described by Biswas et al. RNA, which was used for various experiments, was isolated from cultures of et al. de novo synthesis of RNA. RNA was then collected from aliquots of S. mutans at 8 time points, during a total incubation period of 30 minutes at 37°C. At each time point, sodium azide  was added to the culture to terminate cellular processes, and then immediately placed in dry ice. RNA (4 µg) from each time point was denatured, loaded onto a 1% agarose gel, and separated via electrophoresis for northern blot analysis. DNA probes were synthesized via PCR amplification using the primer pairs Smu1882 F3 and Smu1882 R3 , and added to a hybridization tube containing the transfer membrane and ULTRAhyb buffer (Ambion), followed by overnight incubation. Following hybridization, the membrane was washed according to manufacturer's instructions, dried, and exposed to a phosphorimager plate and analyzed using a Typhoon phosphorimager (Molecular Dynamics).The stability of SMU.1882 mRNA was determined using a protocol described by Biswas covR mutant strains. The S. mutans cultures were grown to mid-exponential phase (KU70), followed by RNA extraction. sqRT-PCR was performed as previously described gyrA gene was included as control to ensure that equivalent amounts of RNA were being used for each reaction. For real-time RT-PCR, cDNA was prepared from samples as described above. cDNA levels were quantified in 7500 Fast real-time PCR machine (Applied Biosystems). The reactions were performed in 20 µl final volume, consisting of 1X Fast SYBR Green Master Mix (Applied Biosystems) and 0.5 µM of each sense and antisense primer pair. For every primer pair, four different cDNA concentrations were analysed in duplicate. Cycling conditions were as follows: 95°C for 1 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min with fluorescent measurement at the end of each cycle and one dissociation step of 95°C for 15 s, 60°C for 30 s and 95°C for 15 s. gyrA housekeeping gene served as an internal control to normalize the expression levels between samples. No template controls were included in all real-time PCR reactions. Standard curves were plotted (GraphPad Prism) for all three samples using log input dilution series of K-DNA templates against cycle threshold values (Ct) of the performed qPCR. Slopes and R2 values were obtained and were used to calculate real time PCR reaction efficiencies [E = 10̂(−1/slope)]. The relative expression levels were calculated according to the method developed by Pfaffl Semi-quantitative (sq) RT-PCR was used to quantify the level of expression of various genes in UA159 and GS-5, along with the respective covR-deleted strain, we used pIB10 covR gene with flanking regions. A Km resistant cassette with flanking modified loxP sites, was amplified from pUC4ΩKm2 BseRI -NruI digested and T4-DNA polymerase blunted sites to generate pIB-601. Plasmid pIB-601 was linearized with EcoRI and transformed into S. mutans UA159 as previously described loxP-Kmr cassette from the chromosome, IBS-601 was transformed with pCrePA cre recombinase gene from a temperature-sensitive replicon (pWV01). Subsequently, Emr and Kms colonies were selected; those colonies contain pCrePA and lost the Km resistance gene from the chromosome. Subsequently, pCrePA plasmid was cured as described previously to generate IBS-603, which contains a markerless covR deletion.To create a covR deletion construct in GS-5 and NG-8, plasmid pIB26 covR gene. This plasmid was linearized with NotI and used for S. mutans transformation.To generate 1882 from S. mutans UA159, a reporter strain was constructed by cloning the putative promoter region of SMU.1882 into plasmid pIB107 gusA gene. The putative P1882 region was amplified from S. mutans chromosomal DNA using primers Smu1882 F1 and Smu1882 R1, which contain BamHI and XhoI sites, respectively , IBS603 (ΔcovR), and IBS603/pIB30  to generate the reporter strains IBS602, IBS606, and IBS606/pIB30, respectively. The reporter strains were grown to mid-exponential phase (KU70) in THY broth containing Em to maintain the complementing plasmid. The cells were harvested via centrifugation and lysed for β-glucuronidase (GusA) assays, as described by Biswas et al. To quantify transcription from Pectively . This fr1882. Primer Smu1882 F1 was labeled with γ32-ATP and, along with Smu1882 R1, was used to PCR amplify a DNA fragment containing P1882, with a radiolabel at the 5′-end of the fragment. Each fragment was incubated with various concentrations of 6XHis-tagged CovR protein in binding buffer  as described by Biswas et al. EMSA and DPA were used to demonstrate binding of CovR to P1882 fragment with or without CovR. Reactions were prepared similarly to EMSA and after 40 min incubation, the mixtures were treated with a dilute DNase I solution (Epicentre) for 5 minutes, as described previously DPA was performed using the same PrpoD gene without its stop codon, was PCR amplified from chromosomal DNA of S. mutans UA159, using the primer pair Smu-Nco-RpoD-F and Smu-Xho-RpoD-R. The DNA fragment was digested with XhoI and NcoI, and cloned into XhoI/NcoI-digested pET23D (Novagen), generating pIB611. Plasmid pIB611 was then transformed into E. coli BL21 DE3 pLysS. His-RopD was purified on Ni NTA column (Qiagen) following manufacturer's protocol and the purified protein was dialyzed against buffer B.To synthesize His-RpoD, an 1127-bp DNA fragment, containing the entire 1882 was amplified from UA159 genomic DNA by PCR using the primer pair: smu1882-F2 and smu1882- R5. Similarly, the promoter Pami, which was originally isolated from a Streptococcus pneumoniae gene that encodes amidase, was amplified from pIB163 In vitro transcription assay was carried out with the desired templates in reaction buffer containing 50 mM Tris-Cl (pH 7.5), 150 mM NaCl, 2 mM MgCl2, 0.1 mM EDTA, and 0.1 mM DTT in 20 µl volume. CovR (1–10 pmol) was preincubated in the reaction buffer along with the template DNAs for 20 min at room temperature. After incubation, 500 µM each  of ATP, CTP, GTP, and 50 µM UTP with 1.0 µCi of [α-32P] UTP [∼9000 Ci/mmol] were added to the reaction mixture. To initiate transcription, 0.25 U E. coli core RNA polymerase (Epicentre), RpoD (1.0 µg), and RNase inhibitor . After the incubation at 37°C for 15 min, samples were resolved by electrophoresis in a 4% urea gel. Transcripts were quantitated by densitometry with the ImageQuant software  by exposing the dried gel on a phosphorimager plate.A DNA fragment containing P


IGF2-H19 locus, CTCF plays an important role in organizing allele-specific higher-order chromatin conformation and functions as an enhancer blocking transcriptional insulator. Here we have used chromosome conformation capture (3C) assays and RNAi–mediated depletion of cohesin to address whether cohesin affects higher order chromatin conformation at the IGF2-H19 locus in human cells. Our data show that cohesin has a critical role in maintaining CTCF–mediated chromatin conformation at the locus and that disruption of this conformation coincides with changes in IGF2 expression. We show that the cohesin-dependent, higher-order chromatin conformation of the locus exists in both G1 and G2 phases of the cell cycle and is therefore independent of cohesin's function in sister chromatid cohesion. We propose that cohesin can mediate interactions between DNA molecules in cis to insulate genes through the formation of chromatin loops, analogous to the cohesin mediated interaction with sister chromatids in trans to establish cohesion.Cohesin is a chromatin-associated protein complex that mediates sister chromatid cohesion by connecting replicated DNA molecules. Cohesin also has important roles in gene regulation, but the mechanistic basis of this function is poorly understood. In mammalian genomes, cohesin co-localizes with CCCTC binding factor (CTCF), a zinc finger protein implicated in multiple gene regulatory events. At the imprinted  IGF2-H19 gene locus. Imprinted genes are widely studied due to their roles in fetal growth and cancer and have the unusual property of expressing only one parental copy of the gene. CTCF is thought to regulate imprinting of IGF2 and H19 by enabling DNA to form loops that separate the genes into silent or active domains. In this paper we describe, for the first time, the looping structure of the human IGF2-H19 locus and show that cohesin stabilises CTCF–mediated DNA loops. Depletion of cohesin leads to disruption of long-range chromatin interactions and changes expression levels of the IGF2 gene. This work adds a new level of understanding of how cohesin can play a role in gene expression.Recent work has shown that cohesin, a protein best known for its role in holding sister chromatids together, and CTCF, a protein implicated in the formation of chromatin loops, localize to the same regions of DNA in mammalian genomes. This observation raised the intriguing possibility that cohesin might facilitate the role of CTCF in structuring chromatin. CTCF is well known for its role in regulating genomic imprinting at the  Cohesin is an evolutionarily conserved protein complex composed of the core subunits, SMC1, SMC3, SCC1/RAD21 and SCC3/SA (reviewed in Cohesin mediates gene regulation at least in part by interaction with insulator elements IGF2-H19 locus plays a role in the aetiology of embryonic growth disorders and in various cancers (reviewed in IGF2 and H19 genes H19 gene and is known as the imprinting control region (ICR). It acquires methylation on the paternal allele during male germ cell development and is therefore also called the H19 differentially methylated region (DMR) or domain (DMD). CTCF binding at the insulator prevents the IGF2 gene from accessing enhancers downstream of the H19 gene. This physical separation is thought to maintain the silence of the maternal IGF2 allele. On the methylated paternal allele CTCF is excluded from binding and the IGF2 promoters can interact with the enhancers IGF2 there is a differentially methylated region, DMR0, which in human has variable methylation in somatic tissues and is hypomethylated in cancers IGF2 and H19 there is a Centrally Conserved DNase I hypersensitive domain (CCD), which in mice has tissue specific enhancer functions The IGF2-H19 locus  analysis 19 locus  and examIGF2-H19 locus in human cells because we are interested in the effects of chromatin looping in human cells. We have previously developed protocols for depletion of cohesin subunits by RNAi for human cells IGF2-H19 locus. H19 expression in these cells is monoallelic and as with most normal adult tissues IGF2 is expressed only at basal levels H19 ICR  flanking the locus as anchors. BamHI cuts the locus frequently, but there are no restriction sites within the CTCF AD. The nearest site to the CTCF AD is on the edge of the DMR0 region (restriction site a). For this reason we have regarded CTCF AD/DMR0 as a single 3C element in our analysis. The resulting data represent an average of association frequencies on both parental alleles across the whole locus. Random ligations are expected to decrease exponentially the further a restriction site is away from the anchor, while specific associations occur as “spikes” above the random ligation curve We determined the chromatin conformation at the human IGF2 expression in these cells. However, when an anchor primer was placed within the CTCF AD/DMR0 region (primer b1), we found strong association frequencies with the CCD (restriction site h) and the CTCF DS (restriction site q), but not with the ICR or the enhancer , weak but specific associations were detected with the CTCF AD/DMR0 region  were not significantly reduced  and the CTCF AD/DMR0 regions (restriction sites a- c1) were also reduced . TogetheH19 which remained mono-allelic . Previous studies in mice have identified allele specific interactions of the ICR and the enhancers, but CTCF sites other than the ICR have not yet been analysed at the mouse locus. By extending our analysis to a wider number of CTCF sites we found a previously unknown association of the ICR with a CTCF DS site on the presumed maternal allele in the human cell line, as well as biallelic CTCF mediated interactions involving the CCD site. The CCD region in mice has tissue specific silencer or enhancer activities which are independent of imprinting Our first systematic q3C analysis of the human Our data are consistent with the possibility that multiple CTCF-cohesin mediated loops come together in a chromatin “hub” as depicted in IGF2 and H19 is developmentally down regulated in most adult tissues but reactivated in various cancers . Quantitation was done by extrapolation to standard curves for the pimers. Wilcoxon signed rank tests were done to compare paired RNAi and control samples when n≥3. P≤0.05 was considered significant.Methylation analysis was by bisulphite- and pyrosequencing with primers as described previously IGF2 exon 9). ChIP primers are shown in Chromatin Immunoprecipitation (ChIP) was done as described previously 6 of cells were cross-linked in 1% formaldehyde at 37°C for 10 minutes. After washing in PBS, the cells were lysed on ice in lysis buffer (Tris-HCl (pH = 8) 50 mM, SDS 1%, EDTA 10 mM) for 10 minutes. Nuclei were recovered by centrifugation and resuspended in BamHI digestion buffer (New England Biolabs (NEB)), supplemented with Triton-X100 to a final concentration of 1.8% and incubated 1 h at 37°C. 1.5×106 nuclei were digested overnight with 1000 U of BamHI (NEB) in a 300 µl reaction volume.Quantitative 3C was described previously BamHI restriction site within the locus was assessed by qPCR across each restriction site. The percentage of digestion was determined by comparing template amplification of digested and undigested fractions (not religated) after normalising to copy number as previously described Digestion efficiency at each EcoRI (which cuts outside the hybrid religated products) was incorporated prior to reversal of cross links, phenol chloroform purification and ethanol precipitation. This step is necessary to remove possible qPCR bias caused by size differences in the religated products.Ligation was carried out on 2.5 ng/µl digested chromatin in a 1.5 ml reaction volume of T4 ligase buffer containing 3200 U of T4 ligase (NEB). A further overnight digestion step with 1000 U of IGF2-H19 locus on commercially obtained genomic DNA (Becton Dickinson (BD)). These amplicons were column purified and quantified using Nanodrop UV spectroscopy. Equimolar amounts of amplicons were mixed, BamHI digested, re-ligated, phenol-chloroform extracted, ethanol precipitated, dissolved in H2O and stored at −20°C. Q-PCR was done with Sybr-green (ABI Power SYBR) on a 384 well real time machine . Quantitative determination of association frequencies was essentially done as described IGF2 and H19 which did not have BamHI restriction sites . Template copy number was used to ensure that the amount of 3C template was within the range of the standard curve for any given product.3C PCR primers flanking restriction sites were designed to have similar melting temperatures, and the PCR efficiency of each primer combination was assessed on a PCR standard template. A stock of PCR standard template was prepared similar to that described previously BamHI restriction sites and compared the outcome of the 3C association frequencies across the locus when normalised to adjacent BamHI sites (2–3 B-actin), alternative BamHI sites (1–3 B-actin), or alternative internal sites within the IGF2-H19 locus (data not shown). 3C primers and combinations are in All interaction frequencies were normalised to the circularisation frequency of the i-fragment as internal digestion-ligation control. We verified our normalisation method by including a β-actin gene region  that contained 3 Biological replicates for siRNA 3C experiments were done by splitting test and control cells each into 3 equal aliquots prior to synchronisation. After harvesting the cells and digesting the chromatin for 3C experiments, the replicate templates were evaluated for digestion efficiency and normalised for equal amounts before ligation. Prior to PCR amplification, the replicate templates' copy numbers were determined by qPCR as described above, and equal amounts (copy number) of DNA recovered after 3C for control and RNAi template was used for the 3C qPCR. Association frequencies were normalised to the circularisation frequency of the i-fragment as described above. Normalisation data for 3 biological replicates are shown in 6 cells were first fixed in formaldehyde and nuclei were prepared as for 3C. One aliquot was used for 3C and the remaining aliquots were briefly sonicated to produce chromatin fragments of 500 bp and then digested overnight with BamH1 as for 3C protocol. After digestion the nuclei were pre-cleared on agarose beads (UPSTATE) and immunoprecipitated with antibodies to cohesin  were annealed according to manufacturer's instruction and used at a final concentration of 75 nM. The siRNA transfection was performed using lipofectamine RNAiMAX (Invitrogen).To obtain cells enriched in G1 and G2 phase and depleted of the cohesin subunit SCC1 the siRNA transfection was performed either 6 hours before starting the first thymidine block (for G2 phase) or 2 hours after releasing the cells from the first thymidine block (for G1 phase). The siRNA oligos , the P value has to be less than 0.05/n, for any particular row in order to be considered significant with P<0.05. This correction ensures that the 5% probability applies to the entire set of comparisons, and not separately to each individual comparison.Two-way ANOVA with Bonferroni's post-test was performed using GraphPad Prism version 5.01 for Windows, GraphPad Software, San Diego California USA, IGF2 gene, mixed and labeled with dig-11-dUTP using the Biotin High Prime Kit (Roche). For the DNA probe the human BAC RP11-650021 spanning the IGF2 gene (chr.11: pos. 2057305–2245714) was directly labeled with Alexa 594 by random priming. Cells on coverslips were fixed for 15 min with 4% formaldehyde, 5% acetic acid in PBS and stored after another PBS wash in 70% ethanol at 4°C.To obtain a probe for RNA FISH two PCR products of 2000 bp and 600 bp (primer sequences available upon request) were generated from the last exon of the human Denaturation and hybridisation of the slides and probes was done as described in Figure S1BglII restriction sites at the IGF2-H19 locus. (A) INS, IGF2 and the H19 gene are displayed together with the cohesin/CTCF binding regions. Blue boxes represent genes and the green oval represents the downstream enhancer (Enh). Red rectangles indicate the positions of the CTCF/cohesin binding regions CTCF AD, CCD, ICR, and CTCF DS. Vertical blue lines in a row show BglII restriction sites across the whole locus. Sites that were analysed for ligation (3C RS) are labelled with numbers. The third line shows the position of specific regions analysed for 3C and these are enlarged in the panels below. Note that CTCF AD has a restriction site 3 Kb far from DMR0 and can be analysed separately from other sites in the IGF2 locus. In panels B-E BglII restriction sites are depicted as red vertical lines relative to BamHI restriction sites shown as black lines and labelled with letters as in IGF2 gene showing exons as boxes (blue for coding- and white for non-coding exons). Promoters are shown as arrows above the exons. The DMR0 region is shown as a yellow bar. (C) Enlargement of the intervening region between IGF2 and H19 showing the position of the CCD (red box) relative to the BglII and BamHI restriction sites. (D) Enlargement of ICR and H19 region. The CTCF/cohesin binding region is shown as red box and the position of the ICR is marked with a yellow box. (E) Enlargement of the enhancer and the CTCF DS region .Schematic representation of (0.66 MB TIF)Click here for additional data file.Figure S2IGF2-H19 locus using BglII restriction. (A) Schematic representation of a 350 Kb genomic region including INS and the IGF2-H19 locus. Blue boxes represent the position of genes and the green oval depicts the localisation of the enhancer (Enh). Red boxes below represent the CTCF/cohesin binding regions CTCF AD, CCD, ICR, and CTCF DS. Positions of BglII restriction sites are marked with vertical blue lines and sites analysed by 3C are showed with numbers. Vertical pink lines in panel (B–D) indicate the position of the two different BglII restriction sites that were used as anchors. In each panel the respective anchor is marked with a thicker pink line. The X-axis is labelled according to the genomic position and position 0 was arbitrarily fixed 42 Kb upstream of INS. In  unsynchronised cells were analysed for the 3C interactions of the locus. (B) Associations detected with the anchor site in the ICR (restriction site 22 as anchor). The ICR associates with the CTCF AD and also with P2. (C) Associations with the CTCF AD (restriction site 4 as anchor). Interactions can be detected with the CCD and CTCF DS. (D) CTCF AD association frequencies (restriction site 4 as anchor) in cohesin depleted (SCC1 RNAi) and control (G2 control) cells synchronised in G2 phase. Interactions can be detected with the CCD and CTCF DS. Associations with the CCD were significantly decreased after SCC1 RNAi while interactions with the CTCF DS are only mildly affected. These results are similar to that obtained in BamHI enzyme.Chromatin conformation of the (0.64 MB TIF)Click here for additional data file.Figure S3IGF2-H19 locus using an alternative anchor site in the ICR. (A) Schematic representation of a 350 Kb genomic region including INS and the IGF2/H19 locus. Blue boxes represent the position of genes, the green oval depicts the localisation of the enhancer (Enh)  in the graphs and histograms shown in the other panels. The blue vertical line across each graph indicates the position of the anchor restriction site (j) in the ICR.  Association frequencies of the ICR (anchor site j) throughout the locus are analysed in unsynchronised cells as well as in cells synchronised in G1 and G2 phases. Association frequencies are displayed on a log scale to include all data points (B) but also on a linear scale (C) to better visualise differences. For all three cell populations, associations of the ICR can be detected with the CTCF AD/DMR0 region as well as with the CTCF DS. The CCD is too close to the ICR to distinguish an interaction. ICR association frequencies with intervening regions between the CCD and the IGF2 gene  vary between G1 and G2 phase. Since these are random ligation interactions we don't know what this means. We do not see this with an anchor near the k restriction site Click here for additional data file.Figure S4IGF2/H19 locus in G1 phase cells. (A) Schematic representation of a 350 Kb genomic region including INS and the IGF2-H19 locus. Blue boxes represent the position of genes, the green oval depicts the localisation of the enhancer (Enh) . Anchor primers are shown as black arrows below the restriction site map. (B) Enlarged view of the region comprising the CTCF AD/DMR0 and the IGF2 gene showing exons as boxes (blue for coding and empty for non-coding exons). Promoters are shown as arrows above the exons and the DMR0 region is shown as a yellow bar. Reciprocal 3C primers are shown as grey arrowheads. (C) Association frequencies of the ICR (j-primer as anchor) with restriction sites in the CTCF AD/DMR0 (a and b1) and IGF2 gene promoter (c1) in cohesin depleted (SCC1 RNAi) and control (G1 control) cells synchronised in G1 phase. Association frequencies with restriction sites in IGF2 upstream region drop significantly in cohesin depleted cells compared to a random ligation with a restriction site in the intervening region (restriction site e) that is not reduced. (D) The effect of cohesin depletion on associations between the enhancer (m primer) and restriction sites close to promoters P2 (b1 restriction site), and P3 (d restriction site), was analysed. Upon cohesin depletion no significant change in the association frequencies was observed, confirming that these interactions occur independently of CTCF/cohesin binding Click here for additional data file.Figure S5BamHI restriction of i and j sites followed by random ligation results in circularisation of this fragment (i-fragment). Circularisation of i-fragment can be detected by using primers amplifying 180 bp spanning the re-ligated i and j sites. Click here for additional data file.Table S1Statistical analysis of difference in locus-wide association frequencies with ICR, enhancer, CTCF AD/DMR0, and CTCF DS anchors.(0.31 MB DOC)Click here for additional data file.Table S2ChIP primers.(0.04 MB DOC)Click here for additional data file.Table S3BamH1 template.3C primers for (0.12 MB DOC)Click here for additional data file.Table S4BglII template.3C primers for (0.06 MB DOC)Click here for additional data file.


The transcriptional regulatory machinery of a gene can be viewed as a computational device, with transcription factor concentrations as inputs and expression level as the output. This view begs the question: what kinds of computations are possible? We show that different parameterizations of a simple chemical kinetic model of transcriptional regulation are able to approximate all four standard arithmetic operations: addition, subtraction, multiplication, and division, as well as various equality and inequality operations. This contrasts with other studies that emphasize logical or digital notions of computation in biological networks. We analyze the accuracy and precision of these approximations, showing that they depend on different sets of parameters, and are thus independently tunable. We demonstrate that networks of these “arithmetic” genes can be combined to accomplish yet more complicated computations by designing and simulating a network that detects statistically significant elevations in a time-varying signal. We also consider the much more general problem of approximating analytic functions, showing that this can be achieved by allowing multiple transcription factor binding sites on the promoter. These observations are important for the interpretation of naturally occurring networks and imply new possibilities for the design of synthetic networks. lac operon or Endo-16. We analyze chemical kinetic models of transcriptional gene regulation and show that even simple models are capable of nearly arbitrary analog computations, ranging from elementary arithmetic operations to general analytic functions. Understanding the computational capacities of genes, and of biochemical systems more generally, tells us what to look for when studying natural systems and tells us what we can hope to build by biological engineering.The biochemistry of the cell is daunting in its complexity. In order to understand this complexity, we are often forced to use metaphors or construct analogies to systems that we understand better. One long-standing analogy is to digital computers, with their large networks of interacting components that manage to act in coherent and useful ways. Indeed, we know from both theoretical models and empirical observations that biological entities such as genes can sometimes be described accurately by digital, or logical, expressions—turning on or off in response to regulatory signals. However, far more sophisticated computations can also take place, as has been documented in the responses of genes such as the  The lacet al.'s model of the sea urchin developmental gene Endo-16 contains logical as well as additive and multiplicative operations lac transcription rate as a function of the concentrations of cAMP and an analogue of allolactose show four plateaus of different rates connected by smooth boundaries However, detailed analysis of transcriptional regulatory networks reveals a behavior richer than logical responses. Yuh Besides logical or digital computation, several other notions of computation have been explored. Analog computations made by artificial neural networks can in principle be implemented by chemical systems A different line of reasoning has focussed on the robustness of biochemical networks to variability in inputs and parameters—intuitively important features for real systems The present work focuses on the general steady-state analog computational capacities of genes. Most of the paper considers a simple chemical kinetic model of transcriptional regulation for a single gene with two transcription factors. We assume that concentrations of proteins represent non-negative real numbers, with the transcription factor concentrations acting as inputs to the gene and the steady state expression of the gene acting as the output. Different choices for the kinetic rates allow the gene to approximate different binary arithmetic operations: addition, subtraction, multiplication, division, and testing for equality and various inequalities. Variations on the model allow alternative implementations of the same functions as well as other functions, such as the square root. In the model, these operations can be reproduced with arbitrary accuracy, although in reality, biological limits on the parameters would limit accuracy. We analyze the accuracy of these approximations in terms of the deviation between the steady state output and the desired output. We also analyze the precision of the approximations, in terms of the variability of the output over time in a stochastic interpretation of the model. Through theoretical analysis and simulations we show that such “arithmetic genes” can be combined in networks to compute more sophisticated functions. As an example, we describe an eight gene network that tracks the mean and standard deviation of a time-varying signal and detects times at which the signal is statistically significantly elevated. We also consider a model of a gene regulated by a single type of transcription factor but having multiple binding sites on the promoter. With such a gene, arbitrary analytic functions can be approximated up to a fixed order based on power series expansions. We demonstrate by designing a gene that approximates the cosine function.A and B. These factors may bind irreversibly to form an inertdimer, or they may bind to the DNA, where they affect the rate of transcription. Transcripts are translated into proteins at a fixed rate, and both transcripts and proteins decay at fixed rates. The reaction equations below formalize the model. (Please note that Koa = oaf/oab). We use [X] to denote the steady state concentration of molecular species X. None of the reactions create or destroy the factors A or B. We assume that the binding of single molecules of these factors to the DNA does not significantly affect the concentration of free A or B, so that [A]+[C] = [totA] and [B]+[C] = [totB], where [totA] and [totB] denote the total concentration of molecules A and B respectively, either bound or unbound.o, τa, τb, and τab to denote the fractions of time that the promoter spends unbound or bound by different combinations of transcription factors. We assume that the reactions for transcription factors binding to the promoter are at equilibrium.oaKabK = obKbaK. However, the system is degenerate and does not lead to a solution for the occupancy times until we recognize that the occupancy times must sum to one.Z then follows.oaKabK = obKbaK, the term oaKabK can be replaced by obKbaK in any of the equations above. These equations are also true for more restricted binding scenarios, including: A and B cannot be bound to the DNA simultaneously (signified by abK = baK = 0); A must bind before B binds, and B must unbind before A can unbind (obK = baK = 0); and B cannot bind the DNA at all (obK = baK = abK = 0). In these alternative scenarios, however, it no longer holds that oaKabK = obKbaK.Most of our analysis concerns the steady state behavior of this system. We use τtotA] and [totB] treated as the inputs.  abK = baK = 0, (iii) or = 0, (iv) tzraKoar = tzrbKobr = tdzd, (v) oaK[A]≪1, and (vi) obK[B]≪1. These conditions can be interpreted as: (i) A and B do not dimerize, (ii) A and B cannot both be bound to the DNA at the same time, perhaps because their binding sites overlap, (iii) there is not transcription if neither A nor B are bound to the DNA, (iv) there is a certain balance between the production and decay rates of mRNA and protein, and (v),(vi) either A and B bind to the DNA comparatively weakly or else we are considering only comparatively small concentrations of A and B. Then Equation 24 tells us that [Z]≈[totA]+[totB], so that the gene approximates the addition operation. . Thus, [A] = [totA]−[C] = [totA]−min = [totA]⊖[totB]. Dimerization itself, then, solves the zero-truncated subtraction problem. If we desire [Z]≈[totA]⊖[totB], we need only assume that undimerized A binds to the promoter and activates transcription. For an accurate approximation, binding should be weak, so that oaK[A]≪1.As [Z] = 0 if the comparison is false and [Z] = onZ if the comparison is true, for some chosen onZ>0. The operation [totA]>[totB] can be implemented with the same set of reactions as subtraction, but A should bind the promoter strongly so that [Z]≈onZ in the presence of any amount of undimerized A. For [totA] = [totB] we assume that A and B dimerize, that A and B can individually bind the promoter, acting as repressors. If [totA] = [totB], then there will be no undimerized A or B, and [Z] = onZ. Otherwise, there will be some undimerized A or B, and, if it binds the promoter strongly, will turn off transcription so that [Z]≈0.For the comparison operations, the goal is ≪1, then at steady state These arithmetic operations can be implemented in other ways, even restricting attention to the model in totA] and [totB]. We analyze accuracy in terms of relative error. For the arithmetic operations of addition, multiplication, division, and zero-truncated subtraction, we define relative error as f is the desired operation. This is not well defined when f is zero. However, for the models in Z] = 0 whenever f is zero, so we can take the relative error to be zero. For the comparison operations we define relative error as Z] in The accuracy with which the models in totA] and [totB] as well as the equilibrium association constants for the binding of A and B to the DNA  or repression (in the case of  = ). For either gene, the relative error is either zero or τo, depending on whether the comparison is true or false.For the comparison operations, accuracy is highest when totA] and [totB] are fixed. Let X(t) denote the concentration of molecular species X at time t. Noise in Z(t), defined as the standard deviation of Z(t) over time divided by the mean of Z(t) over time, can be attributed to three sources: (i) inherent fluctuations due to the stochastic birth-death process for Z, (ii) variability in T(t) due to its own inherently stochastic birth-death process, which in turn creates variability in the production rate of Z, and (iii) variability in the promoter state, which affects the production rate of T, and by extension, of Z. If one assumes that the transcription factor binding reactions are at steady state, then the third source of noise is absent, and the noise in Z(t) is equivalent to that in the simpler systemtr is the net rate of transcript production.Z can be calculated exactly from the chemical master equation, and the noise is Z(t) bears no necessary relationship with the accuracy with which [Z] approximates a desired function, because accuracy and noise depend on different sets of parameters. For the addition gene, for example, accuracy is determined by oaK and obK. These could be large or small regardless of the noise level, which is determined by tzr, td and zd. This is true even considering the production-decay balance constraint for the gene, tzraKoar = tzrbKobr = tdzd. The parameters ar and br do not occur in the formulas for either accuracy or noise and can be used to ensure that the constraints are satisfied. This is essentially the same as the observation by Thattai and van Oudenaarden that the mean and variance of gene expression levels are controlled by independent sets of parameters In a stochastic kinetics interpretation of the model in Z(t) returns. Intuitively, however, the faster the binding and unbinding reactions are compared to the rate of transcription, the more this noise is filtered out by the slower transcription process. For example, consider the addition gene with inputs [totA] = 30 molecules and [totB] = 70 molecules. We used the Gillespie algorithm oaf, obf, oab, obb) by scaled versions . By varying ρ, we could change the rates of binding and unbinding, while leaving oaK and obK constant. (See Z(t) for three different choices of ρ, in which the noise in Z(t) can be seen to decrease for increasing ρ. Z(t) for a wider range of ρ. For sufficiently large ρ, the steady state approximation for the promoter is good, and the noise is seen to converge to the value predicted by Equation 33, indicated by the dashed line. Z] is at nearly the correctly value of 100, and is independent of ρ. Independence of accuracy from promoter state fluctuations has been shown analytically via Master equation analysis for some binding scenarios If the transcription factor binding reactions are not at steady state, then the third source of noise in I varies with time. Suppose it is important for the cell to detect times at which I(t) is significantly elevated. For example, the cell might be a bacterium and I(t) might be correlated to the concentration of an extracellular sugar. If the bacterium encounters a high-sugar environment, it may want to begin expressing the genes needed to transport and metabolize the sugar. Or, I may be a toxin, and high toxin levels might trigger a defensive or developmental decision, such as sporulation, to protect the bacterium. In a synthetic biology context, I might be a signal sent by the experimenter to trigger a response or a marker for a diseased cell that should be destroyed.Arithmetic genes can be combined into networks to perform more sophisticated computations. As an example, consider a cell in which the concentration of a molecule A single threshold-activated gene, for example the “greater than” gene of a priori or because the signal itself is difficult to measure experimentally. Whether or not the signal is elevated at a given moment thus depends on the the signal's mean value and typical fluctuations—properties which may themselves change over time. More formally, if the statistics of I(t) are relatively constant for some period, then a natural definition of elevated is I(t)>μI+cσI, where μI is the mean of I(t) over time, σI is the standard deviation, and c is a constant specifying how large an elevation is of interest. To solve the problem, the cell must identify the signal statistics μI and σI, and then compute whether the signal is elevated at any particular time.We consider a more challenging version of the problem in which the statistics of the signal are not known ahead of time. This may be because the operating environment of the cell is not known I(t). The two μ genes are simply activated proportional to their input and are assumed to reach steady state at a slower time scale than variations in the input signal. Thus, the μ genes effectively compute a recency-weighted time-average of their inputs, the exact nature of which depends on the details of the kinetic parameters. Gene G1 multiplies I(t) by itself, and because G1 operates at a faster time scale than I, we can approximate the expression of its protein as Z1(t) = I(t)2. Gene G2 averages Z1(t) over time, so we can approximate its expression as Z2(t) = tE(Z1(t)) = tE(I(t)2), where tE denotes averaging over time. By similar reasoning, the expression of genes G3 and G4 is Z3(t) = tE(I(t)) and Z4(t) = (Z3(t))2 = (tE(I(t)))2. Genes G5 and G6 compute Z7(t) = μI+cσI, for constant c, and G8 compares this result to I(t), turning on when the latter is greater than the former.c = 3, so that G8(t) should turn on, to an expression level of 100 nM, whenever I(t) exceeded its mean plus three standard deviations. Details of the simulation, including the exact equations and kinetic parameters, can be found in the To test this network, we simulated a differential equation model of transcript and protein dynamics subject to a time-varying input see . The simThe elementary arithmetic operations provide a basis for analog computation, in that these operations can be combined to compute more complex functions. However, from the standpoint of the cell, it seems desirable to perform computations with as little biomolecular machinery as possible. From the form of Equation 24, it is not clear how other elementary operations, such as exponentiation, logarithms, or trigonometric functions, might be implemented. Whether these particular functions are of useto cells can be debated, but certainly cells need to compute more complicated functions than elementary arithmetic. One way to approximate more complicated operations is by power series expansions. The focus of this section is on analytic functions—functions with Taylor series expansions that converge pointwise to the correct values, at least for some range of inputs. For simplicity, we consider single-input functions. Generalization to multiple-input functions is straightforward.A. We assume N binding sites for A on the gene's promoter, each acting independently with equilibrium association binding constant K. We assume that transcription rate depends only on the number of binding sites occupied by A. This model is formalized by the reaction equations below.iP denotes the promoter bound by i copies of factor A. T denotes transcript, and Z denotes protein. The steady state expression of protein Z isi binding sites for A are occupied. Now, consider an analytic function f(x) with power series expansionic are nonnegative. Then the gene can approximate the function up to order N by assuming K[A]≪1, so that the term (1+K[A])N is approximately one, and matching the coefficients in Equations (39) and (40).ic are negative, then the series can be divided into positive and negative components.ic+ = max and ic− = −min. Two genes of the type in f.c0 = 1, c1 = 0, c2 = −1, c3 = 0, c4 = 1, … The blue and green curves in ir. The red curve in Z] = [Z+]−[Z−], where [Z+] is the steady state protein concentration for the gene approximating the positive portion of the series, and [Z−] is the steady state protein concentration for the gene approximating the negative portion of the series. The Taylor series approximation and [Z] match each other closely, though neither is a good approximation to cos([A]) above a concentration of about [A] = 2 nM. If the cell places importance on approximating the function at higher concentrations, then different parameters are need. The cyan curve in Z] for the same two-gene system when parameters are optimized to minimize the mean square error between [Z] and cos([A]) over the range [A]∈ Nm. , various inequality comparisons, and fractional powers. Unary, ternary, quaternary, etc. operations can be similarly implemented at a biochemical level. These models are not the only way, and may not be the best way, that these operations can be implemented. For example, we have not considered models with cooperativity between binding sites, allosteric features for transcription factors, regulation of translation or degradation, or chromatin-related mechanisms. One way to explore the utility of these mechanisms would be to perform an explicit computational search through some appropriate space of allowed reaction systems  in the output depends on the transcription factor binding and unbinding rates, while the accuracy of the output depends only on the ratio of the rates. This implies that there is no trade-off between precision and accuracy. Each can be selected independent of the other, by evolution or by synthetic biologists, subject to biological limitations on the parameters. We have not analyzed the speed with which these networks reach equilibrium, which could also be an important factor in some settings. Indeed, in many processes, such as development, expressing genes at the correct time is just as important as expressing them at the right level. Further, some genetic networks, such as circadian networks We expect that notions of computation more sophisticated than simple logical operations, already being documented experimentally A and B cannot bind the promoter simultaneously, we lumped the two bound states into one.P1 denotes the promoter bound by either A or B, f is the forward binding rate of A and B to the promoter, and b is the unbinding rate. Transcription then follows the reactiontotA] = [A] = 30 nM, [totB] = [B] = 70 nM, −1·sec−1, b = ρ sec−1, tr = 1 nM·sec−1, −1, −1, −1, where ρ is the dimensionless parameter we used to control the binding and unbinding rates of A and B to the promoter, while leaving the equilibrium association constant unchanged. For different values of ρ, the system was simulated from the initial condition P0 = 1, P1 = T = Z = 0, for one million reactions. Our Gillespie simulations for the analysis of noise for the Addition gene used a slightly simplified set of equations. Because factors I(t), and a transcript concentration iT(t) and protein concentration iZ(t) for each gene i. All variables have units nanomolar (nM). The input signal was primarily a sinusoidal oscillation with a period of four hours. Oscillations had mean 10 and amplitude 2 for the first eight of the 20 days simulated, mean 20 nM and amplitude 2 nM for the next three days, mean 10 nM and amplitude 2 nM for another 3 days, and mean 10 nM and amplitude 6 nM for the final six days. An independent Gaussian disturbance with mean zero and variance one was added to the signal during every 10 minute period. At the starts of days 5, 6, 7, 17, 18 and 19, there were 10 minute spikes in the signal at levels 16, 18, 20, 20, 22 and 24 nM respectively. We assumed the transcription factor binding equations were at steady state in order to speed the calculations. Transcription was driven at the rate expected based on the transcription factor concentrations. The exact differential equations simulated were:Our simulations of the network for detecting elevated signals involved 17 variables: the exogenous input Z2 and Z4 called for by gene 5, which subtracts one concentration from the other. Rather, we drive gene 5 transcription at a rate corresponding to what would be observed if any available Z2 and Z4 dimerized instantly and irreversibly. Similarly, we do not model production and subsequent decay of the homodimer of Z6 suggested by our design for the square root operation. Rather, we simply assume that Z6 dimerizes at rate zd6 and instantly decays.The system of differential equations was simulated using the ode45 function of MATLAB. Equations 46 through 61 employ several simplifications compared to the equations one would obtain from a direct translation of the kinetic equations in The kinetic parameters are summarized in −1, −1, −1, and K = 0.01 nM−1. For the direct Taylor series approximation, Equation 41 implied transcription rates r0+ = 5.78×10−5 nM·sec−1, r2− = 2.89×10−2 nM·sec−1, and r4+ = 48.1 nM·sec−1. For the optimized curve, we allowed all the parameters to vary, including the transcription rates of the positive and negative genes, and their rates of translation and transcript and protein decay. We use the matlab function “fminsearch” to optimize the parameters, minimizing the mean squared error between steady state [Z] and cos([A]). Because all parameters should be non-negative, they were reformulated as ip = exp(ib) where ip is the thi parameter and ib is the unconstrained parameter that we actually optimized. This resulted in the constrained parameters shown in For our study of the cosine approximation, we used parameters