A binding site for the cyclic adenosine 3',5'-monophosphate-response element-binding protein as a regulatory element in the grp78 promoter.

The 78-kDa glucose-regulated protein (GRP78) is ubiquitously expressed in many cell types. Its promoter contains multiple protein-binding sites and functional elements. In this study we examined a high affinity protein-binding site spanning bp -198 to -180 of the rat grp78 promoter, using nuclear extracts from both B-lymphoid and HeLa cells. This region contains a sequence TGACGTGA which, with the exception of one base, is identical to the cAMP-response element (CRE). Site-directed mutagenesis reveals that this sequence functions as a major basal level regulatory element in hamster fibroblast cells and is also necessary to maintain high promoter activity under stress-induced conditions. By gel mobility shift analysis, we detect two specific protein complexes. The major specific complex I, while immunologically distinct from the 42-kDa CRE-binding protein (CREB), binds most strongly to the grp site, but also exhibits affinity for the CRE consensus sequence. As such, complex I may consist of other members of the CREB/activating transcription factor protein family. The minor specific complex II consists of CREB or a protein antigenically related to it. A nonspecific complex III consists of the Ku autoantigen, an abundant 70- to 80-kDa protein complex in HeLa nuclear extracts. By cotransfection experiments, we demonstrate that in F9 teratocarcinoma cells, the grp78 promoter can be transactivated by the phosphorylated CREB or when the CREB-transfected cells are treated with the calcium ionophore A23187. The differential regulation of the grp78 gene by cAMP in specific cell types and tissues is discussed.

Inhibition of cannabinoid CB1 receptor upregulates Slc2a4 expression via nuclear factor-kappa B and sterol regulatory element-binding protein-1 in adipocytes

Evidences have suggested that the endocannabinoid system is overactive in obesity, resulting in enhanced endocannabinoid levels in both circulation and visceral adipose tissue. The blockade of cannabinoid receptor type 1 (CB1) has been proposed for the treatment of obesity. Besides loss of body weight, CB1 antagonism improves insulin sensitivity, in which the glucose transporter type 4 (GLUT4) plays a key role. The aim of this study was to investigate the modulation of GLUT4-encoded gene (Slc2a4 gene) expression by CB1 receptor. For this, 3T3-L1 adipocytes were incubated in the presence of a highly selective CB1 receptor agonist (1 mu M arachidonyl-2'-chloroethylamide) and/or a CB1 receptor antagonist/inverse agonist (0.1, 0.5, or 1 mu M AM251, 1-(2,4-dichlorophenyl)-5-(4-iodophenyl)-4-methyl-N-1-piperidinyl-1H-pyrazole-3-carboxamide). After acute (2 and 4 h) and chronic (24 h) treatments, cells were harvested to evaluate: i) Slc2a4, Cnr1 (CB1 receptor-encoded gene), and Srebf1 type a (SREBP-1a type-encoded gene) mRNAs (real-time PCR); ii) GLUT4 protein (western blotting); and iii) binding activity of nuclear factor (NF)-kappa B and sterol regulatory element-binding protein (SREBP)-1 specifically in the promoter of Slc2a4 gene (electrophoretic mobility shift assay). Results revealed that both acute and chronic CB1 receptor antagonism greatly increased (similar to 2.5-fold) Slc2a4 mRNA and protein content. Additionally, CB1-induced upregulation of Slc2a4 was accompanied by decreased binding activity of NF-kappa B at 2 and 24 h, and by increased binding activity of the SREBP-1 at 24 h. In conclusion, these findings reveal that the blockade of CB1 receptor markedly increases Slc2a4/GLUT4 expression in adipocytes, a feature that involves NF-kappa B and SREBP-1 transcriptional regulation. Journal of Molecular Endocrinology (2012) 49, 97-106

Two sterol regulatory element-like sequences mediate up-regulation of caveolin gene transcription in response to low density lipoprotein free cholesterol

Caveolae form the terminus for a major pathway of intracellular free cholesterol (FC) transport. Caveolin mRNA levels in confluent human skin fibroblasts were up-regulated following increased uptake of low density lipoprotein (LDL) FC. The increase induced by FC was not associated with detectable change in mRNA stability, indicating that caveolin mRNA levels were mediated at the level of gene transcription. A total of 924 bp of 5′ flanking region of the caveolin gene were cloned and sequenced. The promoter sequence included three G+C-rich potential sterol regulatory elements (SREs), a CAAT sequence and a Sp1 consensus sequence. Deletional mutagenesis of individual SRE-like sequences indicated that of these two (at −646 and −395 bp) were essential for the increased transcription rates mediated by LDL-FC, whereas the third was inconsequential. Gel shift analysis of protein binding from nuclear extracts to these caveolin promoter DNA sequences, together with DNase I footprinting, confirmed nucleoprotein binding to the SRE-like elements as part of the transcriptional response to LDL-FC. A supershift obtained with antibody to SRE-binding protein 1 (SPEBP-1) indicated that this protein binds at −395 bp. There was no reaction at −395 bp with anti-Sp1 antibody nor with either antibody at −646 bp. The cysteine protease inhibitor N-acetyl-leu-leu-norleucinal (ALLN), which inhibits SREBP catabolism, superinhibited caveolin mRNA levels regardless of LDL-FC. This finding suggests that SREBP inhibits caveolin gene transcription in contrast to its stimulating effect on other promoters. The findings of this study are consistent with the postulated role for caveolin as a regulator of cellular FC homeostasis in quiescent peripheral cells, and the coordinate regulation by SREBP of FC influx and efflux.

Smad proteins function as co-modulators for MEF2 transcriptional regulatory proteins

An emerging theme in transforming growth factor-β (TGF-β) signalling is the association of the Smad proteins with diverse groups of transcriptional regulatory proteins. Several Smad cofactors have been identified to date but the diversity of TGF-β effects on gene transcription suggests that interactions with other co-regulators must occur. In these studies we addressed the possible interaction of Smad proteins with the myocyte enhancer-binding factor 2 (MEF2) transcriptional regulators. Our studies indicate that Smad2 and 4 (Smad2/4) complexes cooperate with MEF2 regulatory proteins in a GAL4-based one-hybrid reporter gene assay. We have also observed in vivo interactions between Smad2 and MEF2A using co-immunoprecipitation assays. This interaction is confirmed by glutathione S-transferase pull-down analysis. Immunofluorescence studies in C2C12 myotubes show that Smad2 and MEF2A co-localise in the nucleus of multinuclear myotubes during differentiation. Interestingly, phospho-acceptor site mutations of MEF2 that render it unresponsive to p38 MAP kinase signalling abrogate the cooperativity with the Smads suggesting that p38 MAP Kinase-catalysed phosphorylation of MEF2 is a prerequisite for the Smad–MEF2 interaction. Thus, the association between Smad2 and MEF2A may subserve a physical link between TGF-β signalling and a diverse array of genes controlled by the MEF2 cis element.

Mutational analysis of NM23-H2/NDP kinase identifies the structural domains critical to recognition of a c-myc regulatory element.

NM23-H2, a presumed regulator of tumor metastasis in humans, is a hexameric protein with both enzymatic (NDP kinase) and regulatory (transcriptional activation) activity. While the structure and catalytic mechanisms have been well characterized, the mode of DNA binding is not known. We examined this latter function in a site-directed mutational study and identified residues and domains essential for the recognition of a c-myc regulatory sequence. Three amino acids, Arg-34, Asn-69, and Lys-135, were found among 30 possibilities to be critical for DNA binding. Two of these, Asn-69 and Lys-135, are not conserved between NM23 variants differing in DNA-binding potential, suggesting that DNA recognition resides partly in nonconserved amino acids. All three DNA-binding defective mutant proteins are active enzymatically and appear to be stable hexamers, suggesting that they perform at the level of DNA recognition and that separate functional domains exist for enzyme catalysis and DNA binding. In the context of the known crystal structure of NM23-H2, the DNA-binding residues are located within distinct structural motifs in the monomer, which are exposed to the surface near the 2-fold axis of adjacent subunits in the hexamer. These findings are explained by a model in which NM23-H2 binds DNA with a combinatorial surface consisting of the "outer" face of the dimer. Chemical crosslinking data support a dimeric DNA-binding mode by NM23-H2.

Domains of transcription factor Sp1 required for synergistic activation with sterol regulatory element binding protein 1 of low density lipoprotein receptor promoter.

Feedback regulation of transcription from the low density lipoprotein (LDL) receptor gene is fundamentally important in the maintenance of intracellular sterol balance. The region of the LDL receptor promoter responsible for normal sterol regulation contains adjacent binding sites for the ubiquitous transcription factor Sp1 and the cholesterol-sensitive sterol regulatory element-binding proteins (SREBPs). Interestingly, both are essential for normal sterolmediated regulation of the promoter. The cooperation by Sp1 and SREBP-1 occurs at two steps in the activation process. SREBP-1 stimulates the binding of Sp1 to its adjacent recognition site in the promoter followed by enhanced stimulation of transcription after both proteins are bound to DNA. In the present report, we have defined the protein domains of Sp1 that are required for both synergistic DNA binding and transcriptional activation. The major activation domains of Sp1 that have previously been shown to be essential to activation of promoters containing multiple Sp1 sites are required for activation of the LDL receptor promoter. Additionally, the C domain is also crucial. This slightly acidic approximately 120-amino acid region is not required for efficient synergistic activation by multiple Sp1 sites or in combination with other recently characterized transcriptional regulators. We also show that Sp1 domain C is essential for full, enhanced DNA binding by SREBP-1. Taken together with other recent studies on the role of Sp1 in promoter activation, the current experiments suggest a unique combinatorial mechanism for promoter activation by two distinct transcription factors that are both essential to intracellular cholesterol homeostasis.

The RGBarrier assay: the parallel study of gene regulatory element performance at defined chromosomal locations

Vertebrate genomes are organised into a variety of nuclear environments and chromatin states that have profound effects on the regulation of gene transcription. This variation presents a major challenge to the expression of transgenes for experimental research, genetic therapies and the production of biopharmaceuticals. The majority of transgenes succumb to transcriptional silencing by their chromosomal environment when they are randomly integrated into the genome, a phenomenon known as chromosomal position effect (CPE). It is not always feasible to target transgene integration to transcriptionally permissive “safe harbour” loci that favour transgene expression, so there remains an unmet need to identify gene regulatory elements that can be added to transgenes which protect them against CPE. Dominant regulatory elements (DREs) with chromatin barrier (or boundary) activity have been shown to protect transgenes from CPE. The HS4 element from the chicken beta-globin locus and the A2UCOE element from a human housekeeping gene locus have been shown to function as DRE barriers in a wide variety of cell types and species. Despite rapid advances in the profiling of transcription factor binding, chromatin states and chromosomal looping interactions, progress towards functionally validating the many candidate barrier elements in vertebrates has been very slow. This is largely due to the lack of a tractable and efficient assay for chromatin barrier activity. In this study, I have developed the RGBarrier assay system to test the chromatin barrier activity of candidate DREs at pre-defined isogenic loci in human cells. The RGBarrier assay consists in a Flp-based RMCE reaction for the integration of an expression construct, carrying candidate DREs, in a pre-characterised chromosomal location. The RGBarrier system involves the tracking of red, green and blue fluorescent proteins by flow cytometry to monitor on-target versus off-target integration and transgene expression. The analysis of the reporter (GFP) expression for several weeks gives a measure of the protective ability of each candidate elements from chromosomal silencing. This assay can be scaled up to test tens of new putative barrier elements in the same chromosomal context in parallel. The defined chromosomal contexts of the RGBarrier assays will allow for detailed mechanistic studies of chromosomal silencing and DRE barrier element action. Understanding these mechanisms will be of paramount importance for the design of specific solutions for overcoming chromosomal silencing in specific transgenic applications.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Identification of a cis-regulatory element by transient analysis of co-ordinately regulated genes

Background Transcription factors (TFs) co-ordinately regulate target genes that are dispersed throughout the genome. This co-ordinate regulation is achieved, in part, through the interaction of transcription factors with conserved cis-regulatory motifs that are in close proximity to the target genes. While much is known about the families of transcription factors that regulate gene expression in plants, there are few well characterised cis-regulatory motifs. In Arabidopsis, over-expression of the MYB transcription factor PAP1 (PRODUCTION OF ANTHOCYANIN PIGMENT 1) leads to transgenic plants with elevated anthocyanin levels due to the co-ordinated up-regulation of genes in the anthocyanin biosynthetic pathway. In addition to the anthocyanin biosynthetic genes, there are a number of un-associated genes that also change in expression level. This may be a direct or indirect consequence of the over-expression of PAP1. Results Oligo array analysis of PAP1 over-expression Arabidopsis plants identified genes co-ordinately up-regulated in response to the elevated expression of this transcription factor. Transient assays on the promoter regions of 33 of these up-regulated genes identified eight promoter fragments that were transactivated by PAP1. Bioinformatic analysis on these promoters revealed a common cis-regulatory motif that we showed is required for PAP1 dependent transactivation. Conclusion Co-ordinated gene regulation by individual transcription factors is a complex collection of both direct and indirect effects. Transient transactivation assays provide a rapid method to identify direct target genes from indirect target genes. Bioinformatic analysis of the promoters of these direct target genes is able to locate motifs that are common to this sub-set of promoters, which is impossible to identify with the larger set of direct and indirect target genes. While this type of analysis does not prove a direct interaction between protein and DNA, it does provide a tool to characterise cis-regulatory sequences that are necessary for transcription activation in a complex list of co-ordinately regulated genes.

Analysis of the hierarchical structure of the B. subtilis transcriptional regulatory network

The transcriptional regulation of gene expression is orchestrated by complex networks of interacting genes. Increasing evidence indicates that these `transcriptional regulatory networks' (TRNs) in bacteria have an inherently hierarchical architecture, although the design principles and the specific advantages offered by this type of organization have not yet been fully elucidated. In this study, we focussed on the hierarchical structure of the TRN of the gram-positive bacterium Bacillus subtilis and performed a comparative analysis with the TRN of the gram-negative bacterium Escherichia coli. Using a graph-theoretic approach, we organized the transcription factors (TFs) and sigma-factors in the TRNs of B. subtilis and E. coli into three hierarchical levels (Top, Middle and Bottom) and studied several structural and functional properties across them. In addition to many similarities, we found also specific differences, explaining the majority of them with variations in the distribution of s-factors across the hierarchical levels in the two organisms. We then investigated the control of target metabolic genes by transcriptional regulators to characterize the differential regulation of three distinct metabolic subsystems (catabolism, anabolism and central energy metabolism). These results suggest that the hierarchical architecture that we observed in B. subtilis represents an effective organization of its TRN to achieve flexibility in response to a wide range of diverse stimuli.

Information processing in the transcriptional regulatory network of yeast: Functional robustness

Background: Gene networks are considered to represent various aspects of molecular biological systems meaningfully because they naturally provide a systems perspective of molecular interactions. In this respect, the functional understanding of the transcriptional regulatory network is considered as key to elucidate the functional organization of an organism.

A mathematical model of the sterol regulatory element binding protein 2 cholesterol biosynthesis pathway

Cholesterol is one of the key constituents for maintaining the cellular membrane and thus the integrity of the cell itself. In contrast high levels of cholesterol in the blood are known to be a major risk factor in the development of cardiovascular disease. We formulate a deterministic nonlinear ordinary differential equation model of the sterol regulatory element binding protein 2 (SREBP-2) cholesterol genetic regulatory pathway in an hepatocyte. The mathematical model includes a description of genetic transcription by SREBP-2 which is subsequently translated to mRNA leading to the formation of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), a main precursor of cholesterol synthesis. Cholesterol synthesis subsequently leads to the regulation of SREBP-2 via a negative feedback formulation. Parameterised with data from the literature, the model is used to understand how SREBP-2 transcription and regulation affects cellular cholesterol concentration. Model stability analysis shows that the only positive steady-state of the system exhibits purely oscillatory, damped oscillatory or monotic behaviour under certain parameter conditions. In light of our findings we postulate how cholesterol homestasis is maintained within the cell and the advantages of our model formulation are discussed with respect to other models of genetic regulation within the literature.

Global and threshold-free transcriptional regulatory networks reconstruction through integrating ChIP-Chip and expression data

Inferring transcriptional regulatory networks from high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed TReNGO (Transcriptional Regulatory Networks reconstruction based on Global Optimization), a global and threshold-free algorithm with simulated annealing for inferring regulatory networks by the integration of ChIP-chip and expression data. Superior to existing methods, TReNGO was expected to find the optimal structure of transcriptional regulatory networks without any arbitrary thresholds or predetermined number of transcriptional modules (TMs). TReNGO was applied to both synthetic data and real yeast data in the rapamycin response. In these applications, we demonstrated an improved functional coherence of TMs and TF (transcription factor)- target predictions by TReNGO when compared to GRAM, COGRIM or to analyzing ChIP-chip data alone. We also demonstrated the ability of TReNGO to discover unexpected biological processes that TFs may be involved in and to also identify interesting novel combinations of TFs.