A reciprocal translocation involving chromosomes 8 and 21 generates the AML1/ETO oncogenic transcription factor that initiates acute myeloid leukemia by recruiting co-repressor complexes to DNA. AML1/ETO interferes with the function of its wild-type counterpart, AML1, by directly targeting AML1 binding sites. However, transcriptional regulation determined by AML1/ETO probably relies on a more complex network, since the fusion protein has been shown to interact with a number of other transcription factors, in particular E-proteins, and may therefore target other sites on DNA. Genome-wide chromatin immunoprecipitation and expression profiling were exploited to identify AML1/ETO-dependent transcriptional regulation. AML1/ETO was found to co-localize with AML1, demonstrating that the fusion protein follows the binding pattern of the wild-type protein but does not function primarily by displacing it. The DNA binding profile of the E-protein HEB was grossly rearranged upon expression of AML1/ETO, and the fusion protein was found to co-localize with both AML1 and HEB on many of its regulated targets. Furthermore, the level of HEB protein was increased in both primary cells and cell lines expressing AML1/ETO. Our results suggest a major role for the functional interaction of AML1/ETO with AML1 and HEB in transcriptional regulation determined by the fusion protein. Acute myeloid leukemias (AML) are a group of hematologic malignancies initiated by chromosomal abnormalities that often give origin to oncogenic proteins with transcriptional regulatory functions. These aberrant transcription factors bind to specific sequences on DNA and influence the activity of adjacent genes. The result is that leukemic blasts display abnormalities in their gene expression programs, which are ultimately responsible for the malignant phenotype. In this study, genome-wide approaches were exploited not only to identify target genes, but also to discover interactions among different transcription factors, with the aim of defining disease-linked regulatory networks. We performed a detailed analysis of the DNA binding pattern of an oncogenic transcription factor, AML1/ETO, which is responsible for approximately 10–15% of AML. We identified a specific signature, which is characterized by the presence of binding regions for AML1/ETO and for other transcription factors, AML1 and HEB, and found that the DNA binding pattern of AML1 and HEB is significantly affected in cells expressing AML1/ETO. Our results, therefore, describe genes regulated by AML1/ETO and demonstrate that this oncogenic protein can significantly interfere with the function of other transcriptional regulators. AML1 and ETO genes, and express the resulting AML1/ETO fusion protein. AML1 is a DNA-binding transcription factor required for hematopoiesis Chromosomal translocations generating fusion genes are the genetic hallmark of Acute Myeloid Leukemia (AML) AML1/ETO9a includes ETO exon 9a, which leads to a frameshift of the original coding sequence and consequently to a C-terminally truncated protein. Co-expression of AML1/ETO and AML1/ETO9a results in earlier onset of AML and blocks myeloid cell differentiation at a more immature stage, suggesting the two isoforms could cooperate in patients to induce leukemia.Full length AML1/ETO is not sufficient to induce AML in mice, and requires treatment with mutagens to induce leukemic transformation in vitro, the conserved core sequence TGT/cGGT AML1 and AML1/ETO were originally characterized as DNA binding proteins that recognize, M-CSFR and BCL-2 promoters AML1/ETO functions as a transcriptional repressor by recruiting NCoR/SMRT/HDAC complexes to DNA through its ETO moiety AML1/ETO was recently hypothesized to target DNA through E-box motifs as the result of physical interactions with transcription factors of the E-protein family, in particular HEB/TCF12 The large-scale determinants of DNA binding by AML1/ETO and the correlation to global transcriptional effects remain to be elucidated. In this study, we present a comprehensive analysis of the DNA binding pattern of AML1/ETO, and its correlation to AML1 and HEB binding sites. The fusion protein preferentially binds to regions that are occupied by the wild-type AML1 transcription factor, without necessarily displacing it. The DNA binding pattern of HEB is reorganized following AML1/ETO expression, and the E-protein re-localizes to AML1/ETO binding regions. Our study provides an accurate description of the genomic distribution of AML1/ETO, and identifies AML1 and HEB as crucial elements for its transcriptional regulatory function.A U937 cell line that conditionally expresses HA-tagged AML1/ETO under the control of the mouse metallothionein (Mt) promoter U937-AE,  was usedDifferent microarray platforms  were expThe binding profile of AML1/ETO in human promoters was investigated through ChIP experiments with an anti-HA antibody on lysates of U937-AE and U937-Mt cells. ChIP products were PCR amplified, labeled with Cy3/Cy5 fluorescent dyes and hybridized to the NimbleGen Systems Human HG17 Promoter Array set, which explores 4 kb upstream and 1 kb downstream the transcription start site (TSS) of 24,434 annotated genes. Two biological replicates were prepared and hybridized to independent array sets. A proprietary software . The binding profile of AML1/ETO9a was similar to that of the long isoform, although relative enrichment levels were not comparable in all cases . This reAffymetrix GeneChip U133 2.0 arrays were used to identify genes differentially expressed in U937-AE cells compared to U937-Mt. Samples were processed and data analyzed as previously described . ExpressCross-comparison between ChIP-chip and expression data led to the identification of 358 genes with AML1/ETO peaks in their promoter regions whose transcriptional levels change >1.5-fold , of whichttp://www.genome.ad.jp/kegg/) Functional classification of the 2,513 AML1/ETO target genes according to KEGG molecular interaction networks  AML1/ETO peaks mapped to 254 known genes, and can be further sub-localized as follows: 36% are within promoters (defined as −4 kb to +1 kb from the TSS), 4% within exons, 37% inside introns and 7% in 3′ sequences in proximity of the gene . Of the The distribution of AML1/ETO peaks in U937-AE cells was compared with that of histone mark H3K4me3, which consistently associates with active promoters. ChIP products obtained with an anti-H3K4me3 antibody on lysates of U937-AE cells were hybridized to the Chr. 19 Array. H3K4me3 peaks clustered in the proximity of TSS , while ABCL3, VAV1 and UHRF1) and lower in one gene (GNA15)  were, therefore, used to titrate fusion protein levels, and qChIP was performed on both promoter and downstream binding sites in four of genes that present peaks in both locations . AML1/ET (GNA15) ; howeverWe next investigated if the binding profile of AML1/ETO reflects that of the native AML1 transcription factor. A ChIP-chip experiment was performed on the same cell lines using an anti-AML1 antibody that does not cross-react with AML1/ETO, since it recognizes the AML1 C-terminal portion, which is lost in the fusion protein. ChIP products were hybridized to Chr.19 Arrays: 883 AML1 peaks were identified in U937-AE cells and 919 peaks in U937-Mt cells . 420 of in vivo.The extent of overlap between AML1/ETO and AML1 binding regions was quantified by comparing peak coordinates . Of the in vivoDetailed sequence analysis using both supervised and unsupervised methods revealed a specific sequence signature associated with genomic occupancy of AML1/ETO, which includes a significant enrichment for AML1 and HEB binding sites . The finMany HEB peaks in U937-AE cells coincide with AML1/ETO binding regions: of the 408 AML1/ETO peaks, 285 (70%) are also recognized by HEB . To valiIn the absence of AML1/ETO, HEB and AML1 localize to the same genomic regions in approximately 25% of their global binding sites, suggesting they may be involved in co-regulation of common target genes . InteresThe correlation between gene expression and DNA binding pattern of AML1/ETO, AML1 and HEB was investigated by expression tiling. The Chr. 19 array was hybridized with RNA derived from U937-Mt and U937-AE cells and expression levels of the 1305 genes on chromosome 19 were calculated as described in HEB mRNA levels were, instead, identical in U937-AE and U937-Mt cells prior to and after ZnSO4 induction . On the other hand, 30% of target genes are induced, including HOX genes  and the cell cycle regulators CDKN1A and CDKN1B. CDKN1A is of particular interest since it is know to regulate maintenance of quiescent hematopoietic stem cells In U937 cells, AML1/ETO binds to the promoter regions of 2,513 non-redundant genes, and causes transcriptional changes in 358 of them. Of these, 70% are repressed, including many genes involved in neutrophilic/myeloid differentiation  myeloid leukemogenesis.The U937 AML1/ETO-HA#9 clone (U937-AE) was generated by stable transfection of AML1/ETO-HA cDNA cloned in the inducible pSG-MtNEO plasmid vector as already described U937-AML1/ETO9a cells (U937T-AE9a) were generated by stable transfection of U937T cells U937 cells were grown in RPMI-1640 supplemented with 10% FCS and 2 mM glutamine at 37°C in a humidified atmosphere containing 5% CO2. HeLa cells were grown in DMEM medium supplemented with 10% FCS and 2 mM glutamine. Myeloid cell lines used for Western Blotting were grown according to standard procedures.Immunopurified anti-HA from clone 12CA5 was used against AML1/ETO. Commercially available antibodies against ETO (Santa Cruz sc-9737), AML1/RUNX1 (Abcam ab23980), HEB (Santa Cruz sc-357), H3K4me3 (Abcam ab8580) were used in ChIP assay and Western blot. The anti-AML1/RHD (Oncogene PG285) antibody, recognizing both wild-type AML1 and AML1/ETO, anti-alpha-tubulin (SIGMA T9026), anti-vinculin (SIGMA V9131) and anti-actin (SIGMA A4700) were used for Western blotting.6 cells were used for each IP and incubated at 4°C overnight with the antibody of interest previously coated on Dynabeads Protein A magnetic beads . For anti-ETO ChIP, Protein G Sepharose beads were used .Cells were cross-linked with 1% formaldehyde for 10 minutes at room temperature, harvested and washed twice with 1× PBS. Pellet was resuspended in ChIP lysis buffer  and sonicated to obtain an average chromatin length of 500 bp. 5×10Beads were then washed twice with each of the following buffers: Mixed Micelle Buffer , LiCl/detergent wash , Buffer 500  an a final wash was performed with 1× TE. Finally, beads were resuspended in 1× TE containing 1% SDS and incubated at 65°C for 10 minutes to elute immunocomplexes. Elution was repeated twice, and the samples were further incubated overnight at 65°C to reverse cross-linking, along with the untreated input . After treatment with 0,5 mg/ml proteinase K for 3 hours, DNA was purified with Wizard SV Gel and PCR Clean-up system  according to manufacturer's protocol and eluted in nuclease-free water. ChIP products and input DNA were used for quantitative PCR or further treated for ChIP-chip.The NimbleGen Systems Human HG17 Promoter Array set from NimbleGen catalogue, which contains >700,000 probes exploring 4 kb upstream and 1 kb downstream of 24,434 annotated genes, were used for genome-wide identification of AML1/ETO binding sites. In addition, custom designed high-density oligonucleotide arrays, containing ∼360,000 isothermal probes (from 50 to 60 bp long) contiguously tiled along chromosome 19, were produced by NimbleGen Systems . Non-repetitive sequences of chromosome 19 available from UCSC Hg.17 were used for the probe design.GCGGTGACCCGGGAGATCTGAATTC and JW103: GAATTCAGATC. DNA was amplified in a 1st LM-PCR reaction using oligo JW102 and Phusion DNA Polymerase , and PCR products were purified with Wizard SV Gel and PCR Clean-up system. 200 ng were used as template in a 2nd LM-PCR. 4 µg of purified DNA were labelled by NimbleGen Services using random priming and Cy3/Cy5 fluorescent dyes. Input DNA and ChIP DNA were labeled with different fluorophores and co-hybridized on the same array. For every probe, results were rated as log2 (ratio ChIP/input). Labeling and hybridization were performed by NimbleGen Services, Reykjavik, Iceland. Data were visualized and images extracted using SginalMap software (NimbleGen Systems).ChIP products were obtained as described above. 80% of a single ChIP product and 40 ng of the input were further treated for array hybridization as described by Kim et al. th percentile, window of 1000 bp, |log|>2. For the analysis of Chr.19 Array data, stringent parameters were applied: 98th percentile, window of 500 bp, |log|>7.A perl script called PeakPicker  was developed as a tool to perform peak analysis on different types of ChIP-chip platforms. Briefly, PeakPicker identifies binding regions (peaks) by centering a sliding window of user-defined size around every probe of the array, then picking out the number of probes within the window that are above a given percentile see . For thePeakPicker generates a tab file representing a list of binding peaks sorted by their first and last nucleotide mapped on UCSC Hg.17. Comparison between two lists of peaks is obtained by measuring the percentage of physical overlap. Peaks with at least 20% physical overlap are considered co-occurrent. To generate the high stringency list of 408 AML1/ETO binding peaks on Chr.19, we compared anti-ETO and anti-HA lists using a threshold of 60% peak overlap.All reactions were performed with the following reagents: 0.4 µM of each primer, 12.5 µl of SYBR Green PCR Master MIX , and a fixed volume of template DNA in a final volume of 25 µl. Thermal cycling parameters were: 2 minutes at 50°C, followed by 10 minutes at 95°C, followed by 40 cycles of 15 seconds at 95°C, and 1 minute at 60°C. We used 1/40 of the eluted DNA from both ChIP samples and input. Oligonucleotides were designed to validate regions occupied by AML1/ETO according to bioinformatics analysis of NimbleGen Promoter Array . For each of the U937 cell lines (AE and Mt), three independent RNA extractions were performed, and an equal quantity of each of the three RNA preparations was then mixed to generate an RNA pool for each sample.RNA pools were labeled and hybrydized to the Affymetrix HG-U133 Plus 2.0 array . Results derived from U937-AE cells (sample) were compared to results from U937-Mt (baseline) cells by “comparative analysis” with GCOS software. Data were then analyzed using GenePicker software 50 regulated genes were chosen for validation by qRT-PCR on an independent set of RNAs. qPCR was performed as already described for ChIP samples, using 15 ng of cDNA reverse transcribed with Super Script III  according to manufacturer's protocol (see http://david.abcc.ncifcrf.gov) Functional classification was performed starting from the list of AML1/ETO target genes  and the U937-AE and U937-Mt were treated with 100 µM ZnSO4, collected and resuspended in CoIP buffer . After brief sonication, lysates were cleared by centrifugation at 13,000 rpm and incubated with the indicated antibody and Protein A sepharose beads . Immunoprecipitates were washed 4 times in the CoIP buffer, eluted and separated by electrophoresis on denaturing SDS-PAGE. Immunoblotting was performed according to standard procedures.Bone marrow cells were harvested from 8- to 10-week-old 129SvEv mice and treated for purification of undifferentiated cells (Lin−). After centrifugation through a Ficoll gradient, mononucleated cells were enriched for progenitors by affinity depletion of cells presenting myeloid, erythroid, and lymphoid differentiation markers using commercially available reagents . Depleted cell were also collected as the differentiated subpopulation (Lin+).5 Lin−/mouse). After 4 weeks, animals transduced with AML1/ETO expressing cells were treated with N-ethyl-N-nitrosourea (50 mg/kg), as described Leukemic mice were generated by retroviral transduction of PINCO-AML1-ETO or PINCO-PML/RAR into Lin− cells, as described ATGGATCCTAATCCCCAGCAACAACGC  and r- ATCTCGAGTTACATATGACCCATAGG . PCR reactions were run on an agarose gel, extracted, purified and inserted into BamHI-Xho digested pCDNA3.1 (Invitrogen), which contains an in-frame FLAG tag sequence at the 5′.The full-length HEB isoform (RefSeq accession number: NM_207036) was isolated from U937 cells by PCR amplification of cDNA, using the following primers: f-ATGGTACCAGAAGAATCTCACTTC and r-ATGAATTCCTAAGAGCTGGCCAGG primers. FLAG-pCDNA3.1 vectors containing SMYD2 and EPN3 were a gift from Bruno Amati and Pier Paolo Di Fiore, respectively. PINCO and PINCO-AML1/ETO vectors used in the co-transfection studies were already published A FLAG-pCDNA3.1 vector containing full length ZNF384 was also generated by PCR-cloning using f-Transfection in HeLa cells was performed using Lipofectamine (Invitrogen) and 500 ng of each plasmid (1 well in a 6-well plate), diluted in serum-free DMEM culture medium. Cells were collected and lysed for Western Blotting 24 hours after transfection.http://www.ncbi.nlm.nih.gov/geo/), and can be retrieved as Data Series with accession number GSE10537.Microarray data included in this manuscript have been deposited in GEO, (Table S1AML1/ETO target regions in human promoters identified by ChIP-chip on the NimbleGen Human Promoter Array set.(0.32 MB XLS)Click here for additional data file.Table S2Genes regulated by AML1/ETO in U937-AE cells.(0.23 MB XLS)Click here for additional data file.Table S3Validation of genes regulated by AML1/ETO.(0.03 MB XLS)Click here for additional data file.Table S4Genes that present binding of AML1/ETO and whose expression is regulated in U937 cells.(0.08 MB XLS)Click here for additional data file.Table S5Functional classification of AML1/ETO target genes.(0.13 MB XLS)Click here for additional data file.Table S6408 regions on chromosome 19 bound by AML1/ETO identified on the Chr. 19 Array.(0.12 MB XLS)Click here for additional data file.Table S7H3K4me3 regions on chromosome 19 in U937-AE cells.(0.15 MB XLS)Click here for additional data file.Table S8Binding regions of the endogenous AML1 transcription factor on chromosome 19 in U937-AE cells and U937-Mt cells.(0.36 MB XLS)Click here for additional data file.Table S9Binding regions of the endogenous HEB transcription factor on chromosome 19 in U937-AE and U937-Mt cells.(0.41 MB XLS)Click here for additional data file.Table S10Expression levels of genes on chromosome 19 in U937-Mt and U937-AE cells.(0.36 MB XLS)Click here for additional data file.Figure S1Calculation of baseline values for qChIP experiments.(0.10 MB DOC)Click here for additional data file.Figure S2qChIP analysis of AML1/ETO9a binding on AML1/ETO target regions.(0.18 MB DOC)Click here for additional data file.Figure S3Validation of transcriptional regulation of AML1/ETO target genes identified by gene expression profiling.(0.17 MB DOC)Click here for additional data file.Figure S4AML1/ETO interacts with the E-protein HEB in U937-AE cells.(0.10 MB DOC)Click here for additional data file.Figure S5qChIP analysis of HEB binding on AML1/ETO target regions in SKNO-1 cells.(0.12 MB DOC)Click here for additional data file.Figure S6Rearrangement of HEB and AML1 binding patterns in AML1/ETO expressing cells.(0.88 MB DOC)Click here for additional data file.Figure S7AML1/ETO preferentially binds in the proximity of expressed genes.(0.10 MB DOC)Click here for additional data file.Figure S8Displacement of HEB from its native binding sites is associated to transcriptional regulation.(0.20 MB DOC)Click here for additional data file.Text S1Supplementary data and methods.(1.58 MB DOC)Click here for additional data file.


A generic feature in all intracellular biochemical processes is the time required to complete the whole sequence of reactions to yield any observable quantity-from gene expression to circadian rhythms. This widespread phenomenon points towards the importance of time delay in biological functions. Theoretically time delay is known to be the source of instability, and has been attributed to lead to oscillations or transient dynamics in several biological functions. Negative feedback loops, common in biochemical pathways, have been shown to provide stability and withstand considerable variations and random perturbations of biochemical parameters. The interaction of these two opposing factors-of instability and homeostasis-are features that are widespread in intracellular processes. To test the effect of these divergent forces in the dynamics of gene expression, we have designed and constructed simple negatively auto-regulated gene circuits consisting of a basic regulator and transcriptional repressor module, and compared it with one, which has delayed repression. We show, both theoretically and experimentally, that delayed repression induces transient increase and heterogeneity in gene expression before the gain of stability effected by the negative feedback. This design, therefore, seems to be suitable for conferring both stability and variability in cells required for adaptive response to a noisy environment. Networks of genetic and metabolic reactions, underlying intra-cellular processes, are interconnected multi-step chemical reactions having widely different time scales. The complex regulation of these metabolic and transcriptional networks is brought about by the interaction of simpler regulatory structures Much excitement has been generated in recent years to study how stochasticity/perturbation in the different reaction steps affects gene expression kinetics using both theory and experimental ‘forward engineering’ approach, based on the construction and analysis of artificial “gene circuits” for single genes, gene networks, and multi-step regulated pathways auto-regulation), or by any other factor  E. coli being of the negatively auto-regulated type. From previous theoretical studies, negative auto-regulation has also been predicted to shift noise to higher frequencies, which is more easily filtered out by gene networks-a property conjectured to contribute to the prevalence of such auto-regulatory motifs in the regulation of 40% of E. coli genes Among the basic regulatory designs, feedback inhibition of gene expression is the most common motif in gene regulation, where the expression of a gene is down-regulated by either its protein product  and slow reactions , is higher, and hence have been postulated to be a significant source of delay in the subsequent steps of the reaction Even though delay-induced instability is widely discussed in theoretical literature for negative feedback systems In the following sections we discuss the theoretical and experimental results and compare them for the non-delayed (Basic) and the delayed negative feedback (Delay) circuits (described in t in The time course of Green Fluorescent Protein (GFP) in the Basic and Delay circuits for the four variable deterministic model and stochastic model (average of 100 simulations) are shown in c . Thus, with increasing delay time, the system shows damped oscillations, and the steady state is reached with progressively larger excursion in the phase plane. Our theoretical analysis, therefore, predicts that our model gene circuits have the intrinsic property of transient “overshoot” dynamics with increasing delay.Delays can make an otherwise-stable system less stable, i.e., the rate of decay of perturbations can decrease with an increase in the values of delay parameters, and a system, which has been otherwise non-oscillatory, can show oscillatory tendencies before converging to the steady state The fact that both the deterministic and stochastic versions of the models show overshoot for the Delay circuit indicates that this feature is due to delay in repression and not solely due to intrinsic or extrinsic noise in the cell population. Hence from now onwards, we show results only from the stochastic model of the gene circuits.600), respectively. The Basic circuit shows an initial increase in GFP expression, which decays towards a steady level over 9 hours. The Delay circuit, on the other hand, shows a large increase (overshoot) in fluorescence, which also decays towards a steady level, although this level is not the same as that reached by the basic circuit. We have done flow cytometric analysis to show that this is due to increased size of cells in the Delay circuit during growth at later time points, which show high fluorescence. A size correction reduces the large increase to some extent in the Delay circuit compared to the Basic circuit.Both the Basic (TG) and the Delay (C2TG) circuits  were indTo address the role of plasmid size/copy number variation and translational efficiency between the Basic and Delay circuits, we have constructed a Control Delay circuit  with the Basic and Delay circuits having plasmid variation , we determined the distribution of the GFP expression levels in the cells at different time points. The frequency distributions of the GFP fluorescence in the model Basic and Delay circuits are shown in The GFP fluorescence in 10,000 cells in populations of Basic (TG) and Delay (C2TG) circuits were studied at different time points, after induction with 25 ng/ml Doxycycline, using Flow Cytometry ; (ii) InFor studying the bimodal distribution of fluorescence in the cell populations in It may be noted that there is no role of inducer concentration in the models, and hence all cells in both model circuits are induced equally, and their GFP increase with time in A broader distribution of GFP fluorescence is observed in the Delay circuit cell population compared to the Basic circuit. To make a comparative quantification of the intra-population cellular heterogeneity in gene expression in the Basic and Delay circuit at different time points, we plot in Considering the fact that the mean protein levels are higher in the Delay circuit populations , its higThe model Delay and Basic circuits do not predict any significant difference in their CV over time except at an early time point see . We showThe complex regulation of metabolic and transcriptional networks in the cell is brought about by the interaction of simpler regulatory structures. Study of these simpler structures using theoretical modelling and experimental gene circuit construction provide us with important clues regarding the function of larger, more complex networks. The advantage of this approach is the greater freedom available to dissect out and study specific properties of the regulatory structure of interest in isolation. It allows experimental testing of theoretical predictions, made using mathematical models of the regulation, more easily. The negative auto-regulatory motif has been well studied using this approach, since it is one of the simplest and ubiquitous control structure found in nature E. coli, obtained from public databases (Regulondb: http://regulondb.ccg.unam.mx/ and Biocyc: http://biocyc.org/) into “Delay” and “Basic” type, depending on the position of the repressor gene with respect to its promoter, as shown in Delay or Basic type is termed if the repressor is the first gene, or, is not the first gene in the operon. In The design adopted by us in the Basic and Delay circuits is a common motif in negative regulation, predominantly observed in biosynthetic pathways lac, trp or the gal operons have been extensively studied to estimate the time involved in basic biological processes such as transcription and translation The numerous sequences of biochemical reactions that underlie the complexities of expressing a single gene points towards the fact that the characteristics of such delay may be essential in understanding whole-genome regulation. Time delays involved in operons, such as, the is the cause of transient instability, which can be a simple mechanism for generating a pulse of protein through the overshoot mechanism that is observed even in the absence of forced population synchrony Delayed feedback strongly affects the dynamics of genetic networks-it can show transient behaviour, or transition to a new fixed point accompanied by oscillations. Theoretical analysis of three eukaryotic genetic regulatory networks showing oscillations have been attributed to a common design of a negative feedback loop with time delay underlying them The interesting observation in our work is the presence of larger heterogeneity in gene expression in the cells with the Delay circuit compared to the Basic negative auto-regulatory case. In the Basic circuit, we observe a more homogeneous distribution of GFP in the cell populations that gradually moves from a low fluorescence value to higher ones with increasing inducer concentration. In the Delay circuit, on the other hand, at intermediate inducer values, fluorescence distribution is spread wider, and some cells seem not to express GFP at all. A single population is seen only at high inducer concentration representing all induced cells. Similar bimodality has also been observed for gene expression without negative feedback at medium inducer concentration and intermediate repression values also contribute to population variability in gene expression. Our results add to the growing body of evidence regarding the importance of transient dynamics in bringing about variability that in some cases may aid in the choice of alternate cell fates Interestingly, our theoretical model circuits are a minimal representation of the real cells, and they have no consideration of the realistic details such as, cell size or, variation of cell size during growth. It also considers full induction of all cells from the beginning. Our simulation results clearly predict what the experimental circuits would do at high induction levels, viz, the variability in both circuits would reduce, indicating that the heterogeneity in gene expression in the cell population occurs primarily during the unregulated state at lower induction level, which finally returns to the same level as the basic circuit, albeit delayed, due to establishment of repression. This also points to the fact that all heterogeneity, that is attributed to noise alone, need not be so, as delayed negative auto-regulation can The generic origin of delay in biochemical pathways (transcription regulatory pathways or metabolic pathways) implies that there is a high likelihood that the two properties shown in our study-transient overshoot and generation of heterogeneity in gene expression in cell population-play an important role in gene regulation. This motif of regulation can do two things successfully even before the stabilising effect of repression sets in. The overshoot allows for gene products being available in large amount for multi-step pathways to function, and it can also act as a dominant source of large deterministic variability paving way to increase the phenotypic diversity of a population of cells before the negative regulation sets in. It has been documented that phenotypic diversity can be crucial for many processes E.coli DH5α as described in The different circuits  were constructed and cloned in The design of our negative auto-regulatory transcriptional modules with and without delay is given in On induction by Doxycycline (inducer), gene expression ensues, and fluorescence increases. Depending on the promoter strength, the number of plasmid copies, the half-life of the repressor, and the concentration of inducer, a dynamic balance is achieved between the intracellular inducer molecules and the repressor, and repression is re-established. The detailed study of growth of the circuits TG and C2TG show similar behaviour (The fluorescence of a representative population (200 µl) was measured in live cells using a Fluorescence ELISA reader . The settings used were 491 nm absorbance, 535 nm emission, with 530 nm auto cut off to reduce background fluorescence. The instrument sensitivity was kept at maximum and an average of 6 measurements was taken. As controls for auto fluorescence, uninduced samples of each induced circuit were also measured at each time point. The auto fluorescence was subtracted from the induced fluorescence and normalised by the growth (measured as OD at 600 nm) of the circuit to give the final fluorescence value per unit time.Flow cytometry was carried out using a FACS Calibur or a FACS Vantage (Becton-Dickenson) with a 488 nm laser using fixed cells as described in Deterministic and Stochastic models of the gene circuits corresponding to the negatively auto-regulated pathways with and without delay in repression were constructed using coupled differential equations and the modified Gillespie Algorithm It is known that variation in plasmid copy number in populations of cells is an important source of variability Supporting Information S1Construction of circuits; Cell Culture; Fixing protocol; Growth and Induction kinetics of different circuits; Gating of the FACS data.(0.04 MB DOC)Click here for additional data file.Supporting Information S2Models, Methods and Parameters(0.05 MB DOC)Click here for additional data file.Figure S1Growth of the circuits with and without induction. Legend: Uninduced-TG: triangles with solid lines, C2TG: Black circles with dotted lines. Induced-TG: squares with solid lines, C2TG: diamonds with dotted lines.(0.04 MB TIF)Click here for additional data file.Figure S2Growth of the circuits at different inducer concentrations (a) 25 ng/ml, (b) 50 ng/ml and (c) 75 ng/ml of Doxycycline. .(0.05 MB TIF)Click here for additional data file.Figure S3Kinetics of GFP fluorescence of the Basic , Control Delay , and Delay  circuits upon induction in four independent experiments at different inducer concentrations-(a) 25, (b) 50, and (c) 75 ng/ml of Doxycycline, at 1 hr interval.(0.35 MB TIF)Click here for additional data file.Figure S4SDS-PAGE results showing TetR kinetics at different time points after induction-(A) Basic (TG) and (B) Delay (C2TG) circuits. The arrow indicates the position of the TetR band. The lower panel in each figure indicates the control band.(0.10 MB TIF)Click here for additional data file.Figure S5Quantification of intensity of the bands .(0.03 MB TIF)Click here for additional data file.Figure S6Gating of cell populations according to size, showing the greater uniformity of cell size in the gated population as compared to ungated population. (A) and (C) for TG and C2TG populations at 0 hr before gating, and, (B) and (D) for TG and C2TG populations after gating.(0.14 MB TIF)Click here for additional data file.Figure S7The effect of gating on the fluorescence distribution of the cells. TG (A) ungated and (B) gated; C2TG (C) ungated and (D) gated. X- axis = different time points represented in serial numbers, Y-axis = percentage of cells .(0.70 MB TIF)Click here for additional data file.Figure S8Frequency distribution of the gated population at various time points after induction of the circuits (A) TG and (B) C2TG. The X- axis: fluorescence in arbitrary units; Y-axis Time in min; Z-axis: Frequency.(0.53 MB TIF)Click here for additional data file.Figure S9Kinetics of TetR in the Basic (solid circles with dashed lines) and the Delay (squares with solid lines) circuits for (A) deterministic model and (B) stochastic model (average of 100 simulations).(0.13 MB TIF)Click here for additional data file.Table S1Comparison of kc value with k values from Nyquist loci for increasing delay.(0.02 MB DOC)Click here for additional data file.Table S2Parameter values used for deterministic and stochastic simulations(0.02 MB DOC)Click here for additional data file.


Drosophila TFs to assess existing TFBS and cis-regulatory module (CRM) detection methods, with a strong focus on the use of multiple genomes. Particularly, for CRM-modelling we investigate the addition of orthologous sites to a known PWM to construct phyloPWMs and we assess the added value of phylogenentic footprinting to predict contextual motifs around known TFBSs. For CRM-prediction, we compare motif conservation with network-level conservation approaches across multiple genomes. Choosing the optimal training and scoring strategies strongly enhances the performance of TG prediction for more than half of the tested TFs. Finally, we analyse a 35th TF, namely Eyeless, and find a significant overlap between predicted TGs and candidate TGs identified by microarray expression studies. In summary we identify several ways to optimize TF-specific TG predictions, some of which can be applied to all TFs, and others that can be applied only to particular TFs. The ability to model known TF-TG relations, together with the use of multiple genomes, results in a significant step forward in solving the architecture of gene regulatory networks.Networks of regulatory relations between transcription factors (TF) and their target genes (TG)- implemented through TF binding sites (TFBS)- are key features of biology. An idealized approach to solving such networks consists of starting from a consensus TFBS or a position weight matrix (PWM) to generate a high accuracy list of candidate TGs for biological validation. Developing and evaluating such approaches remains a formidable challenge in regulatory bioinformatics. We perform a benchmark study on 34  A CRM is a promoter or enhancer sequence that contains TFBSs for one or more TFs and that controls a specific aspect of the expression pattern of the TG atonal TF in Drosophila melanogaster (Dmel) in different tissues is regulated by discrete CRMs, some of which are also autoregulatory in silico TG discovery methods.The characterization and understanding of gene regulatory interaction networks that rigorously control the execution of genetic programs that make functional cells, tissues, and organisms is a key challenge for post-genome biology. Such regulatory interactions are formed by transcription factors (TFs) and their target genes (TGs) and are implemented via TF DNA-binding sites (TFBS) located in in vivo biological validation. Bioinformatics approaches have been developed to predict TFBSs for TFs that have a known consensus TFBS or position weight matrix (PWM). Unfortunately, this approach results in a 1000-fold excess of false predictions when applied on a genomic scale Drosophila Dorsal Ideally, computational approaches to mine entire genomes for TFBSs and CRMs would generate highly accurate lists of candidate TGs that can be taken directly to per se is not sufficient to identify enhancers because many enhancers are functionally conserved without sharing high levels of sequence identity across a long DNA stretch There are a few reasons why such methods cannot be generalized. First, PWMs for most TFs are built from few instances and the minimal number of instances for a useful PWM is not known. Second, not all TFs regulate their TGs via homotypic TFBS clusters. Third, for most CRMs that contain heterotypic clusters the cooperating TFs, let alone their PWMs, are unknown. Fourth, several independent studies have found that sequence conservation in silico validations on known TGs with identified TFBSs for 34 Drosophila TFs from the FlyReg database Drosophila species at a range of evolutionary distances from D. melanogaster provides the opportunity to study the evolution of genes Dmel PWMs built from the known binding sites versus phyloPWMs built from orthologous binding sites from 10 other Drosophila species. Next, we exploit all Drosophila genomes to improve the prediction of homotypic TFBS clusters, either by applying motif conservation or network-level conservation filters. For TFs that show a low performance in this approach, we investigate the use of heterotypic enhancer models consisting of de novo discovered motifs by phylogenetic footprinting, also using all Drosophila genomes. Finally, we model the known TGs of a 35th TF not included in the initial assessment, namely the eye determination TF Eyeless (Ey). We find a significant overlap between predicted TGs and a list of candidate targets obtained from a recently published and biologically validated microarray experiment.To investigate these issues we perform a benchmark study on genome-scale TG prediction for individual TFs. The benchmark consists of An important conclusion of this study is that no general rule exists that applies to all, or even most, TFs. However, most TFs benefit greatly from the use of multiple genomes in enhancer scoring. Also, we find that by performing cross-validations, the optimal strategy and parameters can be determined for each TF. By training these parameters and through the extensive use of multiple genomes, combined with Gene Ontology filters or microarray data, we estimate that genome-wide discovery of TGs is feasible for about 50% of the TFs tested.f times, each time using a different target sequence as the positive region. The process is also repeated t times, once for each TF. This way, 166 rank positions (Σitif) are obtained. Rank positions are then plotted cumulatively to yield a special type of Receiver Operating Characteristic (ROC) curves. For different analysis methods or parameter settings, different curves are obtained that can be compared, as done previously for related and different problems The first, and most straightforward, strategy for motif-based TG prediction, is to use an existing consensus site or PWM for the TF under study. The genome-wide discovery of TGs through homotypic cluster prediction Next we examined the performance of unbiased homotypic cluster detection in detail. We find that 37 out of 166 test regions rank in the top 2% (10/501 scored sequences) representing 19 of 34 TFs. To investigate the performances of each individual TF in more detail, we plot ROC curves per TF and calculate AUC scores for each TF separately . AmongstDrosophila genomes, an interesting question is whether better PWMs can be built using sequences that are orthologous to the known TFBSs. The issue of phylogenetic conservation of TFBSs is paradoxical because on the one hand, TFBSs are known to have a high evolutionary turn-over rate Drosophila species and Dmel- to the PWM improves the overall performance  would allow too many negative sequences in the phyloPWM, and that a more stringent cut-off  would not introduce enough variability. To our surprise, we do not observe an increased average performance across all 34 TFs with any of these phyloPWMs . When looking at individual TFs, we find that only three TFs benefit from a phyloPWM with more than 10% performance increase, namely HLHm5, Scalloped, and Ventral Veins Lacking. Dmel-PWM and the phyloPWM. PWMs built from fewer than 20 sites are found to be more susceptible to phylogenetic extension than PWMs built from more than 20 sites (Given the availability of multiple ance see . A priorhyloPWMs . This is20 sites . Note thDmel and D. pseudoobscura (Dpse) during enhancer scoring has been suggested as a useful filter Dmel and Dpse regions we find a better performance than using Cluster-Buster on Dmel alone . To ensure that the order statistics themselves do not bias the results, we combined the rankings obtained from scrambled PWMs. This did not result in an increased performance compared to scrambled PWMs from Dmel alone  and others are better suited for motif conservation applications . Again, we could not find any obvious correlation between the methods and the size of the starting data sets, neither in terms of target genes nor in terms of binding sites per TF. The TF-wise performances for each method, both for the Dmel PWM and the phyloPWM are presented in Using multiple genomes resulted in improved performance in homotypic cluster prediction for many TFs. However, it is known that many TFs operate in cooperation with other TFs that bind to different binding sites in the neighborhood, which is often a relatively small sequence window . To take the existence of such heterotypic motif clusters into account in our dataset of 166 enhancers, we added a pattern discovery step to identify a shared context between all regions targeted by the same TF, always excluding the left-out test region . The traDmel training set. The number of motifs found by MotifSampler is a parameter of the method, and was set to 5. Oligo-analysis has the advantage that only significantly over-represented k-mers  are reported, yielding between zero and 18 motifs per regulon, with an average of 4.3 and a standard deviation of 4.17. Oligo-analysis gives slightly better results than MotifSampler in the high-specificity range in which we are interested. The reason why the performance for oligo-analysis decreases in the low-specificity range is our conservative approach that instructs Cluster-Buster to rank the left-out region last when oligo-analysis does not find any common motifs in the training set. Note that oligo-analysis yields over-represented DNA words, not PWMs, while Cluster-Buster requires PWMs as input for the scoring step. To solve this problem we transform each over-represented word into a pseudo-PWM  increases from 5 to 19 out of 34 .Drosophila genomes we chose to attribute a CRM to a gene when it either lies in an intron of that gene or within 5 kb upstream of that gene. Each sequence region of a gene  receives the maximal CRM score found within that region. For the network-level conservation approach, each region in the Dmel genome is associated with its orthologous region in another genome using UCSC's genome alignments. The top 100 scoring genes for each of the 34 TFs , together with the locations of the predicted motif clusters, are presented on our website (http://med.kuleuven.be/cme-mg/lng/cisTarget).The ultimate goal of computational target gene prediction is to obtain a high quality set of candidate targets by scanning one or more genomes. The cross-validations described above show promising results for a number of transcription factors. However, in practice several obstacles arise. The first obstacle in genome scanning is the definition of the search space and the association of a predicted regulatory region with a target gene. For the bona fide target genes, because about 10% of the targets (18 out of 166 for the phyloPWM with motif conservation) were found in the top 1%  in the LOOCV. We attempted to enrich for true targets within the top 100 genes by comparing over-represented Gene Ontology (GO) terms with the GO annotation of the TF itself. When the optimal model – based on the LOOCV results - is chosen for each TF, 15 TFs show a top 100 TG prediction with a significant enrichment of at least one biological process of the TF .The second, more problematic obstacle is the size of the test set that now consists of 93330 regions . Sensitivities of 50% will no longer be feasible because the top 10% regions represent more than 1000 genes, which is too large for biological validation and even for further filtering by functional annotations. On the other hand, we know from the LOOCV tests that even the top 1% (136 genes) could contain a few sine oculis (so). However, recent studies have identified four more targets of Ey: eya, shf, Optix, and atonalin vitro binding studies and in vivo reporter assays. Ostrin et al. conducted a microarray experiment and identified 188 potential TGs of Ey whose expression is independent of the function of the retinal differentiation TF atonal. The total of five target regions can now be subjected to cross-validation. The assessment suggests that the homotypic approach with multiple genomes performs best for Ey, although the differences between methods are small in the high specificity range in which we are interested (data not shown). Although Ostrin et al. used a phyloPWM in their study Dmel PWM. The homotypic model using the Dmel PWM and network-level conservation was applied to the full genome, ranking all 5 kb upstream regions and all introns. The top 100 scoring genes have “photoreceptor cell differentiation” over-represented with a p-value of 6.45e-06. The 9 photoreceptor differentiation genes in the top 100 are bun, eya, fz, hth, lilli, mbl, Optix, pnt, and sdk. It is important to note that neither GO:0048748, nor any other eye development GO term, is over-represented in the top100 candidate genes when the scoring is performed on Dmel alone.Filtering based on functional annotation is however limited to the detection of genes with GO IDs. For genes without GO IDs computational predictions can be complemented with data from other sources, such as phenotypic data, protein-protein interaction or expression data. As an example we analyzed a regulon of the eye determination transcription factor Ey. Ey was not included in our earlier dataset because the FlyReg database only contains one target gene of Ey, namely et al. To determine the significance of this result, the same procedure was repeated for 100 randomizations of the Ey PWM. The top 100 scoring genes from randomized PWMs contain 3.70±2.84 genes  in common with the Ostrin set of 188 genes. The maximal difference between the true ranking and the randomizations is obtained at a threshold of 171 genes  of CRMs is not clear. Do most TFs regulate their TGs via multiple or single TFBSs? How conserved do such TFBSs need to be at the sequence level to be detectable? Can the contextual information around a given TFBS in a CRM be harnessed to improve TG detection? Does the availability of multiple related genomes foster or complicate CRM detection? And finally, do different TFs follow different logics? Developing and assessing methods that can address these issues is clearly a major goal of regulatory bioinformatics.Gene regulatory networks of TFs and their TGs play key roles in development, homeostasis and behavior. The relationship between a TF and its TGs is achieved through “DNA words”, usually 4–12 bp long, acting as binding sites for the TF in question. A collection of such motifs regulating a specific aspect of the expression of a gene defines a CRM. Therefore, to understand network organization, we need to understand the organizational logic of CRMs. Since most networks mediating a specific biological function consist of multiple TFs and their- sometimes overlapping- TGs, the capacity to detect TGs genome-wide and Drosophila genomes and the Drosophila database of TF-TG relations (FlyReg) to perform a benchmark study on genome-wide in silico TG prediction. Drosophila is ideally suited for such an approach not only because multiple genomes are now available, but also because a few regulatory networks, such as the segmentation network, have been well-established in vivo resulting in several deeply understood TF-TG relations. One challenging problem of target gene prediction is the mapping of an in silico predicted regulatory locus to the gene it potentially regulates. Indeed, several known enhancers are located one or several genes upstream of the actual target gene, for example the cluster of dorsal binding sites that regulates zen is located directly upstream of CG1162. To circumvent this problem in the benchmark analysis and to make direct comparisons at the sequence level possible, we added isolated regulatory regions to sets of negative sequences. This setup has the additional advantage that it requires less computational resources so that more parameters can be varied. We chose the same region size for each enhancer and each transcription factor to exclude confounding effects of the size when comparing across factors (Cluster-Buster can generate higher scores for larger regions). For the cross-validations we arbitrarily set this size to 1 kb. Such a choice is justified because the cross-validations are intended to compare relative performances rather than absolute performances. When a cross-validation procedure is applied on a single factor under study, the region size could be considered as an additional parameter. For the genome-wide scoring, the size of the motif clusters is determined by Cluster-Buster.To begin to address these issues we took advantage of the availability of 12 Dmel genome Cross-validation tests and subsequent genome-wide TG predictions result in both higher average performances across a data set of more than 30 TFs  already possess enough variation and do not benefit from the addition of more sites. A small amount of PWMs with very few, but highly conserved sites  do benefit from a phylogenetic extension.We attempted to address some of the questions facing regulatory bioinformatics. The major conclusions from our work are as follows. First, there is unlikely to be a single unifying CRM logic, at least at current levels of genome annotation resolution. We find that whereas some TFs perform optimally with single TFBS parameters (data not shown), others use clusters of homotypic TFBSs and still others use heterotypic TFBS clusters. Thus, a methodology that can determine the optimal approach per TF de novo discovered motifs could contribute to a better enhancer model for the TF. Second, it improves the scoring of a CRM model by taking advantage of functional enhancer conservation or TFBS conservation. We found no obvious explanation  why some TFs perform better with network-level conservation and others with motif conservation. When more cis-regulatory data becomes available as validation sets, for example through open community based annotation The availability of multiple genomes becomes especially useful in two other ways. First, it improves the training of a CRM model from a set of known target regions, to discover sites that are both conserved and shared across this set. We have assessed whether such bona fide TGs, false positive predictions, or- most likely- a mix of both. Several ways can be envisioned to further improve the performance. For example, enhancer predictions can be combined with other data types to filter the ranked enhancers, such as GO terms, as we have shown for the TFs in de novo motif discovery, enhancer prediction, or target gene prioritization.Although several of the tested variables, most importantly the integration of multiple genomes, can result in significantly enhanced TG prediction accuracy, more work is needed to improve on this performance because only a portion of the true target enhancers could be detected. Again, it can be expected that performances will increase further, when more knowledge about regulatory regions emerges. For example, King et al. found recently that in vertebrates some regulatory regions correlate well with phastCons conservation scores (used for our motif conservation), while other regulatory regions correlate better with alignment-based scores that are corrected for background neutral substitution ratesIn summary, we have tested several strategies and parameters for the computational prediction of TF-TG relations through TFBS and CRM detection. The selection of the best strategy for each individual TF, combined with the extensive use of multiple genomes during both the training and scoring of enhancer models results in a significant step forward in the bioinformatics to solving the architecture of gene regulatory networks.http://rana.lbl.gov/dan/matrices.html). The scoring was done using Patser Dmel sequences are assembled using the liftOver utility of the UCSC Genome Browser Dmel TFBSs, used to build phyloPWMs, are also obtained through the UCSC liftOver utility. PhastCons conservation scores were downloaded from the UCSC download pages. Species and UCSC assemblies used throughout the analyses are D. melanogaster (dm2), D. simulans (DroSim1), D. sechellia (DroSec1), D. yakuba (DroYak1), D. erecta (DroEre2), D. ananassae (DroAna2), D. pseudoobscura (dp3), D. persimilis (DroPer1), D. virilis (DroVir2), D. mojavensis (DroMoj2), and D. grimshawi (DroGri1).Our dataset consists of 166 TF-target relations for 34 transcription factors, generated by selecting all known TFs from FlyReg N targets, an enhancer model is trained on N-1 positive regions of 1000 bp. The model is used to score the Nth (left-out) target region and a set of negative sequences. All sequences are then ordered by descending score and the rank of the left-out region is recorded. For one TF, this process  is repeated N times, each time leaving out another target region. The process is also repeated for each TF. This way, a total of 166 rank positions are obtained. By ordering these, and plotting them cumulatively, a kind of Receiver Operating Characteristic (ROC) is obtained. For different analysis methods, different curves are obtained that can be compared. Also, the area under this curve (AUC) is a measure for the overall detection performance integrating sensitivity and specificity values.Methods for motif discovery, for CRM prediction, and for the use of multiple genomes are compared through leave-one-out cross-validation (LOOCV) see . For a rhttp://go.princeton.edu/cgi-bin/GOTermFinderThe program Cluster-Buster th order backgroundmodel from the whole 1 kb dataset. Resulting motifs were clustered and ranked according to information content as done in For each LOOCV run, the Gibbs sampling program MotifSampler http://www.repeatmasker.org). Oligonucleotide occurrences of all sizes between 6 and 8 were counted on both strands, only considering the renewing occurrences (self-overlapping occurrences of a same word were discarded). The threshold of significance was set to 1, corresponding to an E-value of 1 false positive oligonucleotide per 10 training sets. Each run of oligo-analysis returns a set of over-represented oligonucleotides, which were used to construct a pseudo-PWM for each over-represented oligo with a value of 10 for the letters forming the word, and 0 for the other letters  within a set of negative sequences  is plotted cumulatively. As negative sequences were used 500 randomly selected proximal promoter sequences, upstream of the annotated transcription start site (black curve) or 308 REDfly enhancers of maximally 1 kb length (blue curve), then all genomically extended to 1 kb, or 250 flanking sequences around the positive region (green curve), or 500 randomly generated sequences of 1 kb using a 5th order Markov model trained on all Dmel upstream sequences.(0.15 MB PDF)Click here for additional data file.Figure S2Cmparison of PhyloGibbs PWMs and real PWMs. All 166×5 motifs resulting from PhyloGibbs were compared to all 34 real PWMs, using the progam MotifComparison that implements the Kullback-Leiber distance between matrices (0.70 MB PDF)Click here for additional data file.Figure S3Overlap between Ey candidate targets obtained from microarray data and from genome-wide binding site prediction. At different cut-offs N (x-axis), the N top scoring Ey candidate targets, based on motif detection, are compared with the 188 Ey candidate targets obtained from gene expression studies (0.08 MB PDF)Click here for additional data file.Table S1Dataset used in the study. 166 TF-target relations extracted from the FlyReg database (0.11 MB PDF)Click here for additional data file.Note S1Linking oligo-analysis output with Cluster-Buster input.(0.07 MB PDF)Click here for additional data file.


Combinatorial regulation of gene expression is ubiquitous in eukaryotes with multiple inputs converging on regulatory control elements. The dynamic properties of these elements determine the functionality of genetic networks regulating differentiation and development. Here we propose a method to quantitatively characterize the regulatory output of distant enhancers with a biophysical approach that recursively determines free energies of protein-protein and protein-DNA interactions from experimental analysis of transcriptional reporter libraries. We apply this method to model the Scl-Gata2-Fli1 triad—a network module important for cell fate specification of hematopoietic stem cells. We show that this triad module is inherently bistable with irreversible transitions in response to physiologically relevant signals such as Notch, Bmp4 and Gata1 and we use the model to predict the sensitivity of the network to mutations. We also show that the triad acts as a low-pass filter by switching between steady states only in response to signals that persist for longer than a minimum duration threshold. We have found that the auto-regulation loops connecting the slow-degrading Scl to Gata2 and Fli1 are crucial for this low-pass filtering property. Taken together our analysis not only reveals new insights into hematopoietic stem cell regulatory network functionality but also provides a novel and widely applicable strategy to incorporate experimental measurements into dynamical network models. Hematopoiesis—blood cell development—has long served as a model for study of cellular differentiation and its control by underlying gene regulatory networks. The Scl-Gata2-Fli1 triad is a network module essential for the development of hematopoietic stem cells but its mechanistic role is not well understood. The transcription factors Scl, Gata2 and Fli1 act in combination to upregulate transcription of each other via distal enhancer site binding. Similar network architectures are essential in other multipotent cell lines. We propose a method that uses experimental results to circumvent the difficulties of mathematically modeling the combinatorial regulation of this triad module. Using this dynamical model we show that the triad exhibits robust bistable behavior. Environmental signals can irreversibly switch the triad between stable states in a manner that reflects the unidirectional switching in the formation and subsequent differentiation of hematopoietic stem cells. We also show that the triad makes reliable decisions in noisy environments by only switching in response to transient signals that persist longer than the threshold duration. These results suggest that the Scl-Gata2-Fli1 module possibly functions as a control switch for hematopoietic stem cell development. The proposed method can be extended for quantitative characterization of other combinatorial gene regulatory modules. Appropriate spatiotemporal control of gene expression is central to metazoan development. Unraveling the dynamical properties of GRNs will be the key to understanding their functionality. Throughout development, cells progress through a succession of differentiation steps from stem cells via immature progenitors to fully differentiated mature cells, and each of these subtypes is associated with a unique regulatory state of the GRN Various computational approaches have been used for regulatory network analysis in the past. Boolean models provide qualitative information about network behavior such as the existence of steady states and network robustness and are most useful for large networks or when experimental information is scarce Gata2-3 and Fli1+12 enhancers contain multiple Gata2 (GATA), Fli1 (ETS) and Scl (E-BOX) binding motifs. The Scl+19 enhancer contains ETS and GATA binding motifs. Scl, Gata2 and Fli1 are all essential for normal hematopoiesis in mice The hematopoietic system has long served as a powerful model to study the specification and subsequent differentiation of stem cells lacZ reporter vector and tested using stable transfection of hematopoietic progenitor cell lines The triad architecture  is very It has been suggested that the dense connectivity and positive feedback loops within stem cell GRN modules play important roles in stabilizing the stem cell phenotype Scl+19 enhancer; the full model is derived in the Full quantitative characterization of the combinatorial nature of transcriptional regulation requires measurements of binding affinities between the DNA and TRs as well as interaction strengths among TRs. Moreover, the contribution of each individual TR and each possible combination to the transcriptional rate must be assessed. This information is extremely tedious to measure due to the combinatorial multiplicity of TR configurations and does not exist for the majority of experimental systems. Experimental data for synthetic libraries of transcriptional reporters that contain the gene regulatory elements is more readily available. We develop thermodynamic methods to characterize the combinatorial transcriptional regulation by distal enhancers based on this type of data and apply it to model the Scl-Gata2-Fli1 triad - a core module of the GRN of hematopoietic stem cells. Recently this system has been experimentally characterized Scl+19 enhancer is only revealed upon integration of the enhancer-promoter construct into chromatin and that the activity of the enhancer is independent of its position (upstream or downstream) relative to the reporter gene Scl+19 enhancer.We assume that the distant enhancers increase the transcriptional rate via modulation of chromatin remodeling rather than through direct interaction with transcriptional machinery. This assumption is motivated by the observations that activation of the te ; see . This waScl+19 enhancer contains binding sites for Gata2 and a Fli1 dimer and therefore can exist in closed and four different open states .The cumulative probability of all open state configurations is then given by s denotes the the Scl+19 enhancer: e.g. for the Gata2-bound state Scl+19 enhancer and the superscript to specify the binding configuration (cf. The Direct measurements of the binding free energies in this expression may be tedious but these can be straightforwardly computed from the ratios of the transcription rates from synthetic reporter libraries with full or mutated enhancer sites. Ratios of the reporter expression levels of cell lines with wild-type (wt) and mutated (mut) enhancers can be used as constraints on the values of the binding free energies.Scl, Gata2 and Fli1 form an interconnected triad of positive interactions and play an important role in hematopoietic differentiation Assuming first-order degradation kinetics, deterministic rate equations for the change in TR concentrations take the formGata2-3 enhancer):Scl+19 and Fli1+12 enhancers respectively). These equilibrium constants are dimensionless quantities characterizing the maximum possible fold enhancement of gene expression by the respective enhancer. The partition functions are used to compute Gata2 synthesis rate In addition to distant enhancers, Notch and Bmp4 are known to serve as activators of the promoters of Gata2 and Fli1, Gata2 respectively Conversion to dimensionless form can greatly simplify the model allowing easy interpretation of simulation results. We normalize the species concentrations of Scl, Gata2 and Fli1 as OFF state and a high expression ON state (ON state. This irreversible progression switch behavior is expected from the triad module which has been reported to play a significant role in the specification of HSCs in the hemogenic endothelium. We use the above-described approaches to estimate the parameters for our model. Equations (25)–(27) relate the gene expression results from the Scl+19 enhancer to the chromatin equilibrium constant Gata2-3 and Fli1+12 enhancers are estimated based on the experimental results and We use the model developed in the preceding sections to analyze the steady state response of the triad to Notch and Bmp4. By varying ates see . For cerON state . This swIn addition qualitative information about system behavior, namely its switchability as a response to physiologically relevant Notch and Bmp4 signals, places an upper bound on chromatin equilibrium constants values. For a different set of y states . As a reOFF to ON (data not shown). The calculated free energies are shown in We expect the triad to be switchable in response to both Notch and Bmp4. Therefore we choose the chromatin equilibrium constants from within the narrow ranges shown above and calculate the TR-enhancer binding free energies using these chosen values. For this chosen set of parameter values the model shows an irreversible bistable response to Notch and Bmp4 . Bmp4 coGata2-3 enhancer. Through competition for binding sites and subsequent chromatin remodeling Gata1 can switch the triad from high expression back to the low expression state. We represent the chromatin remodeling effect of Gata1 by including a factor 0<ON to OFF states. The switching is irreversible – the system will remain OFF even after Gata1 signal is gone . This threshold is a few fold larger than Scl-lifetime, the longest timescale for the system. Our simulations therefore indicate that the triad module is capable of filtering transient signals that are shorter than the threshold simulation. We refer to this property as low-pass filtering – a term accepted for similar phenomena in engineering literature To further investigate the dynamics of triad switching in response to transient stimuli we have computed the minimal pulse duration that can cause irreversible switching as a function of signal amplitude  of the reduced module and the full triad are identical. The triad and reduced module are equivalent low pass filters for deactivation signals such as Gata1.For a controlled comparison of the response of the two modules to Gata1 we fix the Scl concentration of the reduced module such that the threshold level of Gata1 is identical . This fiCombinatorial gene regulation is ubiquitous in eukaryotes with complex DNA regulatory regions acting as integration points for multiple signals and pathways involved in gene regulation. The characterization of these regulatory regions through mathematical models is an important step towards understanding the functionality of gene regulatory networks. In order to fully characterize each regulatory element, one needs to determine dynamical functions that describe the rate of transcription as a function of TR concentrations. The most biochemically and biophysically realistic method of characterizing transcriptional regulation is rooted in statistical thermodynamics where each state of the regulatory region is assigned a free-energy so that the probability of each state can be computed from Boltzmann distribution Scl+19 enhancer-reporter constructs  state to high expression state (ON). The model predicts this switching to be irreversible – the triad will remain ON even after the signals are gone .Studies in heterogeneous cell populations derived from differentiating ES cells or mouse fetal liver had suggested low level binding of Scl itself to the Gata2-3 enhancer and is reported to actively promote chromatin modification Scl+19 deleted) that ensures the same switching threshold. The results indeed show essentially identical low-pass filtering properties of the two modules because Scl dynamics are not rate limiting in this case.Gata1 acts at the Experiments have shown that the knockout of any one of the genes Scl, Gata2 or Fli1 affects the development of HSCs and leads to severely impaired hematopoiesis. Thus the expression of these TRs is critical for hematopoiesis. More recent studies have shown that these three genes regulate each other by acting at distant enhancers as activators. Results from our model provide insight into the function of this module of TRs and suggest that the triad is a central regulator that controls the specification of HSCs during early hematopoiesis and the generation of progenitors committed to differentiation from these cells.The bistable switch properties of the triad are hallmarks of a decision module. The triad switches irreversibly from the low to high expression state in response to external cues such as Notch and Bmp4 that are important for establishing definitive HSCs in the hemogenic endothelium. The bistable response predicted by the model is robust to fluctuations in parameter values. Experimental results also support this prediction ON to OFF switch for the triad is much faster than the OFF to ON switch. Deactivation by Gata1 is also more sensitive to stochastic fluctuations in triad protein concentrations. The cells can be switched to the OFF state to produce progenitor cells committed to differentiation by fluctuations in triad TR concentrations. Thus asymmetric partitioning of these proteins during cell division can allow sub-threshold Gata1 concentrations to silence Gata2 expression in one of the daughter cells by chromatin modification. The probability of this stochastic exit from the pluripotent HSC state of the cell is governed by the Gata1 concentration in the cell. This observation is consistent with experimental analysis of a multipotent hematopoietic progenitor cell line which demonstrated that these cells exist in two distinct subpopulations when cultured under self-renewal conditions with the more differentiation prone subpopulation expressing higher levels of Gata1 The response to deactivation by Gata1 is not affected by Scl dynamics. As a result the OFF to ON and ON to OFF switches to fulfill different functional requirements. The activation response is slow, irreversible and robust to fluctuations in external signals to allow the development of HSCs in a noisy intercellular signaling environment. Simulation results for the dynamics of deactivation suggest that it may be faster than the OFF to ON switch and may exploit stochastic intracellular fluctuations during the cell cycle to maintain the HSC population and guarantee a continuous supply of lineage committed progenitors at the same time.Our model of the triad module shows that it responds differently to activation and deactivation signals. This allows the From a model based on the quantitative experimental characterization of the triad enhancers we have predicted several qualitative features of the steady state and transient response of the triad as well as its sensitivity to mutations and over-expression. We favored a deterministic model for our analysis of the triad function because of the reliability and robustness of the predictions that we have been able to extract from this approach. Even so, a stochastic model can potentially offer additional information about noise properties of the system and we intend to use results presented here to guide the construction of a full stochastic model in the future. Taken together the results presented here are consistent with prior experimental data and provide new mechanistic insights into potentially critical features of the regulatory networks that govern the specification and subsequent differentiation of hematopoietic stem cells. Moreover, our strategy of exploiting experimental data to infer biophysical properties should be widely applicable to aid regulatory network reconstruction in a wide range of cellular and developmental systems.We extend the Shea-Ackers The TR-DNA binding and unbinding processes are fast compared to transcription and translation and can be assumed to be at equilibrium. We note that the equilibrium assumption may only be applicable for the population-average deterministic model we construct here and may fail to accurately describe single-cell data.The rate of gene transcription is linearly related to the probability of RNA polymerase  of the DNA regulatory element Closed chromatin configuration for the enhancers: The DNA of the regulatory enhancer is tightly wrapped around histones. No DNA binding proteins (including RNA polymerase) can access binding sites when the DNA is in this configuration. No gene transcription occurs while the gene is in closed chromatin state.Open chromatin configurations: Spontaneous unwrapping of DNA from the histones produces a configuration where none of the TRs are bound to the enhancer but binding of RNA polymerase to the promoter is allowed. Gene transcription can happen in this state.Occupied enhancer configurations: DNA is unwrapped from the histones and enhancers are occupied by TRs. This set of configurations includes all possible configurations of TRs at the enhancer. RNA polymerase can bind to the promoter in this state leading to gene transcription.We use these definitions to formulate the probabilities Scl+19, Gata2-3 and Fli1+12 enhancers upstream of a SV promoter controlling a lacZ reporter gene and integrated this construct into the genome of wild-type HSCs that show high expression of Scl, Gata2 and Fli1 The Gottgens group cloned the Scl+19 enhancer. The The expression of Scl is under the control of two TRs Gata2 and Fli1 with different binding sites in the We define Gata2-3 enhancer includes binding sites for Gata2, Scl and the 2 Fli1 TRs. Many TR binding sites can be deleted without affecting the reporter gene expression enhancement Gata2-3 enhancer. Of all possible configurations of Gata2-3 enhancer occupation only the Gata2 bound, Fli1 bound, Gata2-Fli1 bound and Scl-Gata2-Fli1 bound configurations are therefore included in the partition function Gata2-3.Fli1+12 enhancer binding sites. This enhancer includes two Gata2 biding sites (primary site at 5′ end). The Scl binding site and the secondary Gata2 site (3′ end) cannot enhance gene expression by themselves. The primary Gata2 site and the Fli1 dimer sites have some effect on gene expression and together they raise gene expression ∼20 fold. Single mutation of either the Scl or secondary Gata2 binding sites has a negligible effect on the gene expression. Deletion of both sites together reduces the gene expression enhancement from ∼60 fold to ∼20 fold. Thus the Gata2 bound, Fli1 bound, Gata2-Fli1 bound and Gata2-Scl-Fli1-Gata2 bound configurations have a significant effect on the gene expression. Incorporating these experimental results simplifies the partition functions The Gata2-3 and Fli1+12 enhancers are not separable  enhancer and three mutant enhancers: Mutant enhancer 1 (mut1)-Fli1 binding site deleted, Mutant enhancer 2 (mut2)-Gata2 binding site deleted, Mutant enhancer 3 (mut3)- all binding sites deleted. The transcription rates lacZ reporter transcription rates are proportional to the fluorescence intensities measured in these experiments because all experiments were performed in the presence of excess fluorescent substrate and wild-type and mutant constructs were assayed at the same time using the same reagents. Moreover the experimental conditions were controlled to ensure that the proportionality constants that relate various transcription rates Deletion of binding sites from the enhancer Scl+19, Gata2-3 and Fli1+12 enhancers being in open state in the absence of Notch and Bmp4.Note that the experimental results were obtained in HSCs which show high expression levels of Scl, Gata2 and Fli1 Gata2-3 and Fli1+12 enhancers. The full equations for all free energies are presented in The introduction of the mutant enhancer reporter construct is not expected to affect the growth rate or availability of RNA polymerases in a significant manner. Thus Since mRNA is labile relative to stable cellular proteins, we assume that the mRNA concentration for the triad proteins is at steady state. We can thus directly relate the rate of transcription in vivo. We make our equations dimensionless to avoid the measurement of actual Scl, Gata2 and Fli1 concentrations. We normalize these TR concentrations by their wildtype concentrations. In wildtype HSCs the Scl, Gata2 and Fli1 concentrations are at steady state. Let these steady state wildtype concentrations be The major hurdle in the analysis of this ODE model is the determination of all TR-enhancer interaction free energies from the equations described above (25)–(27) and in the supplement Click here for additional data file.Figure S2Scl+19 enhancer as an example.Schematic diagram of ratchet model of distal enhancer action using (0.29 MB PDF)Click here for additional data file.Figure S3Switchable bistability in triad response to Notch, Bmp4 and Gata1 is robust to variation in chromatin equilibrium constants.(0.41 MB PDF)Click here for additional data file.Figure S4Scl, Gata2 and Fli1 make the high expression state of the triad sensitive to fluctuations in TR levels.Heterozygous deletions of (0.67 MB PDF)Click here for additional data file.Text S1All or none regulation of gene expression from distant enhancers(0.07 MB PDF)Click here for additional data file.Text S2Modeling the effects of heterozygous deletions of triad genes(0.06 MB PDF)Click here for additional data file.Text S3Estimation of free energies from enhancer-reporter library expression results(0.05 MB PDF)Click here for additional data file.Table S1Enhancer-reporter library expression results(0.04 MB PDF)Click here for additional data file.Table S2Notation used in the main text and supplements(0.03 MB PDF)Click here for additional data file.Table S3Free energies for the triad enhancer-TR configurations(0.04 MB PDF)Click here for additional data file.


The NF-κB family of transcription factors is crucial for the expression of multiple genes involved in cell survival, proliferation, differentiation, and inflammation. The molecular basis by which NF-κB activates endogenous promoters is largely unknown, but it seems likely that it should include the means to tailor transcriptional output to match the wide functional range of its target genes. To dissect NF-κB–driven transcription at native promoters, we disrupted the interaction between NF-κB p65 and the Mediator complex. We found that expression of many endogenous NF-κB target genes depends on direct contact between p65 and Mediator, and that this occurs through the Trap-80 subunit and the TA1 and TA2 regions of p65. Unexpectedly, however, a subset of p65-dependent genes are transcribed normally even when the interaction of p65 with Mediator is abolished. Moreover, a mutant form of p65 lacking all transcription activation domains previously identified in vitro can still activate such promoters in vivo. We found that without p65, native NF-κB target promoters cannot be bound by secondary transcription factors. Artificial recruitment of a secondary transcription factor was able to restore transcription of an otherwise NF-κB–dependent target gene in the absence of p65, showing that the control of promoter occupancy constitutes a second, independent mode of transcriptional activation by p65. This mode enables a subset of promoters to utilize a wide choice of transcription factors, with the potential to regulate their expression accordingly, whilst remaining dependent for their activation on NF-κB. Transcriptional activation by the NF-κB family of transcription factors is crucial for the expression of multiple genes involved in cell survival, proliferation, differentiation, and inflammation. The activation domain of the p65 subunit of NF-κB has been extensively studied in vitro and on artificial reporter plasmids, but the molecular basis by which it drives expression of natural target genes in vivo is still not well understood. Moreover, it is unclear how any single activation mechanism could allow different target genes to fine tune their timing and expression according to their biological requirements. To address this, we experimentally blocked the interaction of p65 with the Mediator complex—a key factor for transcription by most, if not all, activators. While this prevented expression of many NF-κB–dependent genes, others were unaffected, revealing that p65 is able to drive their expression by an independent mode, which does not depend on direct contact with Mediator. Further experiments indicated that p65 accomplishes this by controlling the recruitment of other, secondary transcription factors to its target promoters. This may enable NF-κB to retain overall control over activation of its target genes, but at the same time allow secondary transcription factors to specify appropriate expression levels according to the cell-type and stimulus. The p65 subunit of NF-κB drives expression of target genes not only as a classical activator, via direct interactions with the basic transcriptional machinery, but also independently by controlling the recruitment of secondary transcription factors to target promoters. The goal of understanding transcriptional activation encompasses the description of an unbroken chain of events leading from the binding of a transcription factor to its natural target promoters in an intact cell, until the initiation of mRNA synthesis by RNA polymerase II (pol-II). In the case of the NF-κB family of transcription factors, this is a challenging task, since the tremendous functional diversity of its target genes makes it difficult to imagine a single activation mechanism able to satisfy the needs of all of them.Transcription factors belonging to the NF-κB family are found in metazoan organisms ranging from insects to mammals, and are essential in regulating the activation of hundreds of genes in response to various extracellular stimuli and developmental cues . In mostDuring the last two decades, experiments using reconstituted cell-free systems have succeeded in defining the minimal apparatus needed to drive activated transcription. An essential set of general transcription factors (GTFs) is sufficient to direct the binding of, and initiation of basal transcription by pol-II at the core regions of most promoters . In ordeThe capacity of p65 to activate transcription has also been the subject of numerous studies using synthetic reporter plasmids in transfected cell lines. In this context, the carboxy terminus of p65 (like those of c-Rel and RelB) is able to drive transcription in isolation when fused to a heterologous DNA-binding domain, leading to its definition as a transcriptional activation domain TAD ,9). Sinc. Sinc9])We found that contact with Mediator is indeed essential for p65 to drive the expression of many NF-κB target genes. Unexpectedly, though, many others were still expressed normally even when this contact was disrupted. Further experiments revealed that p65 has a second, independent mode of transcriptional activation, which acts by regulating promoter occupancy by secondary transcription factors.Drosophila NF-κB homologue Dif has been shown to interact with Med17 . Althousubunits , this stAn over-expressed, tagged form of Trap-80 could be co-immunoprecipitated with p65 from nuclear extracts of transfected HEK-293 cells B, confirSrb4 gene are nonviable, and in cells carrying a temperature-sensitive allele, most mRNA synthesis ceases at the restrictive temperature ), and mRNA was measured as in (B).(C) Reconstitution of Trap-80 in knock-down cells. Fibroblasts expressing an shRNA targeting Trap-80 were transduced with a retrovirus driving expression of an shRNA-resistant form of Trap-80 Click here for additional data file.Figure S5Mip-2, Nfkbia, Ip-10, and Il-6, in normal and p65-knockout fibroblasts stimulated with TNF-α. The results presented here are representative of more than ten experiments.mRNA expression of (1.69 MB TIF)Click here for additional data file.Figure S6Cumulative percentage of transcripts whose expression after 1 h TNF-α stimulation exceeds that in unstimulated fibroblasts by the indicated level, expressed both as the Δ log signal of the microarray probes  and the calculated fold induction using a standard curve generated by real-time PCR of a subset of 36 genes . The red line represents the top 100 Trap-80–dependent transcripts; the black line represents 5000 Trap-80–independent transcripts.(1.29 MB TIF)Click here for additional data file.Figure S7mRNA expression of a subset of Trap-80–dependent (A) and Trap-80–independent (B) genes, in wild-type and Trap-80 knock-down fibroblasts.(509 KB TIF)Click here for additional data file.Figure S8ChIP using antibodies against the HA epitope, from fibroblasts retrovirally over-expressing HA-Trap-80, or from control untransduced cells.(1.88 MB TIF)Click here for additional data file.Figure S9Mip-2 and Ip-10 mRNA was compared to wild-type and p65 knockout fibroblasts. The result is representative of three experiments.Wild-type 3T3 fibroblasts were transduced with a retrovirus driving expression of the p65 DBD (red triangles), and expression of (943 KB TIF)Click here for additional data file.Figure S10Mip-2 (left) and Ip-10 (right) mRNA was compared to that of wild-type cells. Note that the level of expression of p65 mutants in transduced cells exceeds that of endogenous p65 in wild-type cells by several-fold (not shown), which may account for the heightened expression of Mip-2. The results presented here are representative of three experiments.p65 knockout fibroblasts transduced with retroviruses expressing the indicated p65 mutants were stimulated with TNF-α for 1 h, and their expression of (1.61 MB TIF)Click here for additional data file.Figure S11n = 36) and –independent  TNF-α–induced genes. Promoters analysed are those of the top 30 Trap-80–dependent and 30 Trap-80–independent genes, from amongst the 200 most induced by TNF-α.Logos of position weight matrices of NF-κB binding motifs conserved between mouse and human in Trap-80–dependent Click here for additional data file.Figure S12p-values. No motifs were statistically over-represented in Trap-80–independent promoters compared with Trap-80–dependent promoters.Over-represented motifs detected in promoters of TNF-α–induced genes. See (1.08 MB TIF)Click here for additional data file.Figure S13MeCP2 (A) or Tk (B), and adjacent control regions located upstream or downstream at the indicated positions. Error bars indicate the standard errors of independent immunoprecipitations (from both unstimulated and TNF-α–stimulated cells).ChIP using antibodies against Sp1 in wild-type and p65-knockout fibroblasts. Primers were chosen to amplify the promoter regions of (1.79 MB TIF)Click here for additional data file.Figure S14Vcam-1 and Ip-10 in normal and p65-knockout DCs stimulated with LPS.(A) p65-dependent gene transcription in dendritic cells. mRNA expression of (B) ChIP using antibodies against c-Jun.(2.51 MB TIF)Click here for additional data file.Figure S15p65 knockout 3T3 fibroblasts were transduced with a retrovirus driving expression of the p65 DBD-Sp1, and ChIP was performed using antibodies against the p65 N terminus.(969 KB TIF)Click here for additional data file.Figure S16II250 in wild-type fibroblasts.ChIP using antibodies against TAF(815 KB TIF)Click here for additional data file.Table S1(177 KB DOC)Click here for additional data file.Table S2(113 KB DOC)Click here for additional data file.