Transcriptional Regulatory Network Model Research Articles

Statistics of protein-DNA binding and the total number of binding sites for a transcription factor in the mammalian genome

BackgroundTranscription factor (TF)-DNA binding loci are explored by analyzing massive datasets generated with application of Chromatin Immuno-Precipitation (ChIP)-based high-throughput sequencing technologies. These datasets suffer from a bias in the information about binding loci availability, sample incompleteness and diverse sources of technical and biological noises. Therefore adequate mathematical models of ChIP-based high-throughput assay(s) and statistical tools are required for a robust identification of specific and reliable TF binding sites (TFBS), a precise characterization of TFBS avidity distribution and a plausible estimation the total number of specific TFBS for a given TF in the genome for a given cell type.ResultsWe developed an exploratory mixture probabilistic model for a specific and non-specific transcription factor-DNA (TF-DNA) binding. Within ChiP-seq data sets, the statistics of specific and non-specific DNA-protein binding is defined by a mixture of sample size-dependent skewed functions described by Kolmogorov-Waring (K-W) function (Kuznetsov, 2003) and exponential function, respectively. Using available Chip-seq data for eleven TFs, essential for self-maintenance and differentiation of mouse embryonic stem cells (SC) (Nanog, Oct4, sox2, KLf4, STAT3, E2F1, Tcfcp211, ZFX, n-Myc, c-Myc and Essrb) reported in Chen et al (2008), we estimated (i) the specificity and the sensitivity of the ChiP-seq binding assays and (ii) the number of specific but not identified in the current experiments binding sites (BSs) in the genome of mouse embryonic stem cells. Motif finding analysis applied to the identified c-Myc TFBSs supports our results and allowed us to predict many novel c-Myc target genes.ConclusionWe provide a novel methodology of estimating the specificity and the sensitivity of TF-DNA binding in massively paralleled ChIP sequencing (ChIP-seq) binding assay. Goodness-of fit analysis of K-W functions suggests that a large fraction of low- and moderate- avidity TFBSs cannot be identified by the ChIP-based methods. Thus the task to identify the binding sensitivity of a TF cannot be technically resolved yet by current ChIP-seq, compared to former experimental techniques. Considering our improvement in measuring the sensitivity and the specificity of the TFs obtained from the ChIP-seq data, the models of transcriptional regulatory networks in embryonic cells and other cell types derived from the given ChIp-seq data should be carefully revised.

The GlxR regulon of the amino acid producer Corynebacteriumglutamicum: Detection of the corynebacterial core regulon and integration into the transcriptional regulatory network model

Corynebacterium glutamicum is an industrially important producer of amino acids and an emerging model system for the Corynebacterineae. The glxR gene of C. glutamicum ATCC 13032 encodes a DNA binding transcription factor of the Crp-Fnr protein family. Available data indicated a prominent role of GlxR in the transcriptional regulatory network of C. glutamicum. We have used recently published whole-genome annotations of several corynebacteria to derive further evidence for GlxR-mediated transcriptional regulation from a comparative genomics approach. A bioinformatic strategy detected 22 genes belonging to a proposed GlxR core regulon in corynebacteria. Binding of purified GlxR protein to 40-mer oligonucleotides representing predicted GlxR binding sites in the upstream region of core genes from C. glutamicum ATCC 13032 was verified in vitro by electrophoretic mobility shift assays. Based on current data, a reliable genome-scale prediction of GlxR binding sites was performed, indicating that about 14% of the annotated C. glutamicum genes might be under direct transcriptional control by GlxR. Integration of GlxR-mediated regulatory interactions with the data stored in CoryneRegNet enabled the reconstruction of a refined transcriptional regulatory network model for C. glutamicum. This network model exhibited a hierarchical and modular structure and is characterized by the presence of master regulators in each functional module, with GlxR serving as the dominating regulatory hub.

Regulation by transcription factors in bacteria: beyond description

Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema.

Assessing TF regulatory relationships of divergently transcribed genes

Ambiguously located transcription factor (TF) binding sites may introduce a large number of potentially erroneous regulatory associations into models of transcriptional regulatory networks. We have used a two-step expression similarity strategy to distinguish between likely and unlikely regulatory associations for TFs located between divergently transcribed genes in the yeast genome. Most regulatory associations of divergently transcribed genes could be assigned to either high-confidence (HC) or low-confidence (LC) groups. In support of our result, we found that most of the previously characterized regulatory associations reported in the literature fell into the HC group rather than the LC group. Moreover, genomic distance analysis showed that TF binding sites tend to be located in relative proximity to the gene that is most likely to be regulated by this TF. Finally, removal of low-confidence (i.e., most probably erroneous) regulatory associations from the transcriptional regulatory network barely affected its basic architecture.

The GlxR regulon of the amino acid producer Corynebacterium glutamicum: In silico and in vitro detection of DNA binding sites of a global transcription regulator

The glxR ( cg0350) gene of Corynebacterium glutamicum ATCC 13032 encodes a DNA-binding trancription regulator of the CRP/FNR protein family. Five genomic DNA regions known to be bound by GlxR provided the seed information for DNA binding site discovery by expectation maximization and Gibbs sampling approaches. The detection of additional motifs in the genome sequence of C. glutamicum was performed with a position weight matrix and a profile hidden Markov model, both deduced from the initial motif discovery. A combined iterative search for GlxR binding sites revealed 201 potential operator sequences. The interaction of purified GlxR protein with 51 selected binding sites was demonstrated in vitro by performing electrophoretic mobility shift assays with double-stranded 40-mer oligonucleotides. Considering potential operon structures and the genomic organization of C. glutamicum, the expression of 53 transcription units comprising 96 genes may be controlled directly by GlxR. The DNA binding site of GlxR is apparently specified by the consensus sequence TGTGANNTANNTCACA. Integration of the data into the transcriptional regulatory network model of C. glutamicum revealed a high connectivity of the deduced regulatory interactions and suggested that GlxR controls at least (i) sugar uptake, glycolysis, and gluconeogenesis, (ii) acetate, lactate, gluconate, and ethanol metabolism, (iii) aromatic compound degradation, (iv) aerobic and anaerobic respiration, (v) glutamate uptake and nitrogen assimilation, (vi) fatty acid biosynthesis, (vii) deoxyribonucleotide biosynthesis, (viii) the cellular stress response, and (ix) resuscitation.

Genetic and genomic analysis modeling of germline c-MYC overexpression and cancer susceptibility

BackgroundGermline genetic variation is associated with the differential expression of many human genes. The phenotypic effects of this type of variation may be important when considering susceptibility to common genetic diseases. Three regions at 8q24 have recently been identified to independently confer risk of prostate cancer. Variation at 8q24 has also recently been associated with risk of breast and colorectal cancer. However, none of the risk variants map at or relatively close to known genes, with c-MYC mapping a few hundred kilobases distally.ResultsThis study identifies cis-regulators of germline c-MYC expression in immortalized lymphocytes of HapMap individuals. Quantitative analysis of c-MYC expression in normal prostate tissues suggests an association between overexpression and variants in Region 1 of prostate cancer risk. Somatic c-MYC overexpression correlates with prostate cancer progression and more aggressive tumor forms, which was also a pathological variable associated with Region 1. Expression profiling analysis and modeling of transcriptional regulatory networks predicts a functional association between MYC and the prostate tumor suppressor KLF6. Analysis of MYC/Myc-driven cell transformation and tumorigenesis substantiates a model in which MYC overexpression promotes transformation by down-regulating KLF6. In this model, a feedback loop through E-cadherin down-regulation causes further transactivation of c-MYC.ConclusionThis study proposes that variation at putative 8q24 cis-regulator(s) of transcription can significantly alter germline c-MYC expression levels and, thus, contribute to prostate cancer susceptibility by down-regulating the prostate tumor suppressor KLF6 gene.

Putting numbers on the network connections

DNA-protein interactions are fundamental to many biological processes, including the regulation of gene expression. Determining the binding affinities of transcription factors (TFs) to different DNA sequences allows the quantitative modeling of transcriptional regulatory networks and has been a significant technical challenge in molecular biology for many years. A recent paper by Maerkl and Quake1 demonstrated the use of microfluidic technology for the analysis of DNA-protein interactions. An array of short DNA sequences was spotted onto a glass slide, which was then covered with a microfluidic device allowing each spot to be within a chamber into which the flow of materials was controlled by valves. By trapping the DNA-protein complexes on the surface and measuring their concentrations microscopically, they could determine the binding affinity to a large number of DNA sequences that were varied systematically. They studied four TFs from the basic helix-loop-helix family of proteins, all of which bind to E-box sites with the consensus CAnnTG (where "n" can be any base), and showed that variations in affinity for different sites allows each TF to regulate different genes.

Identification and analysis of DNA-binding transcription factors in Bacillus subtilis and other Firmicutes- a genomic approach

BackgroundBacillus subtilis is one of the best-characterized organisms in Gram-positive bacteria. It represents a paradigm of gene regulation in bacteria due its complex life style (which could involve a transition between stages as diverse as vegetative cell and spore formation). In order to gain insight into the organization and evolution of the B. subtilis regulatory network and to provide an alternative framework for further studies in bacteria, we identified and analyzed its repertoire of DNA-binding transcription factors in terms of their abundance, family distribution and regulated genes.ResultsA collection of 237 DNA-binding Transcription Factors (TFs) was identified in B. subtilis, half of them with experimental evidence. 59% of them were predicted to be repressors, 17% activators, 17% were putatively identified as dual regulatory proteins and the remaining 6.3% could not be associated with a regulatory role. From this collection 56 TFs were found to be autoregulated, most of them negatively, though a significant proportion of positive feedback circuits were also identified. TFs were clustered into 51 regulatory protein families and then traced on 58 genomes from Firmicutes to detect their presence. From this analysis three families were found conserved in all the Firmicutes; fifteen families were distributed in all Firmicutes except in the phyla Mollicutes; two were constrained to Bacillales and finally two families were found to be specific to B. subtilis, due to their specie specific distribution. Repression seems to be the most common regulatory mechanism in Firmicutes due to the high proportion of repressors in the detected collection in these genomes. In addition, six global regulators were defined in B. subtilis based on the number and function of their regulated genes.ConclusionIn this work we identified and described the characteristics associated to the repertoire of DNA-binding TFs in B. subtilis. We also quantified their abundance, family distribution, and regulatory roles in the context of Firmicutes. This work should not only contribute to our understanding of the regulation of gene expression in bacteria from the perspective of B. subtilis but also provide us the basis for comprehensive modeling of transcriptional regulatory networks in Firmicutes.

The individual and common repertoire of DNA-binding transcriptional regulators of Corynebacterium glutamicum, Corynebacterium efficiens, Corynebacterium diphtheriae and Corynebacterium jeikeium deduced from the complete genome sequences

BackgroundThe genus Corynebacterium includes Gram-positive microorganisms of great biotechnologically importance, such as Corynebacterium glutamicum and Corynebacterium efficiens, as well as serious human pathogens, such as Corynebacterium diphtheriae and Corynebacterium jeikeium. Although genome sequences of the respective species have been determined recently, the knowledge about the repertoire of transcriptional regulators and the architecture of global regulatory networks is scarce. Here, we apply a combination of bioinformatic tools and a comparative genomic approach to identify and characterize a set of conserved DNA-binding transcriptional regulators in the four corynebacterial genomes.ResultsA collection of 127 DNA-binding transcriptional regulators was identified in the C. glutamicum ATCC 13032 genome, whereas 103 regulators were detected in C. efficiens YS-314, 63 in C. diphtheriae NCTC 13129 and 55 in C. jeikeium K411. According to amino acid sequence similarities and protein structure predictions, the DNA-binding transcriptional regulators were grouped into 25 regulatory protein families. The common set of DNA-binding transcriptional regulators present in the four corynebacterial genomes consists of 28 proteins that are apparently involved in the regulation of cell division and septation, SOS and stress response, carbohydrate metabolism and macroelement and metal homeostasis.ConclusionThis work describes characteristic features of a set of conserved DNA-binding transcriptional regulators present within the corynebacterial core genome. The knowledge on the physiological function of these proteins should not only contribute to our understanding of the regulation of gene expression but will also provide the basis for comprehensive modeling of transcriptional regulatory networks of these species.