A novel method for prokaryotic promoter prediction based on DNA stability

Kanhere, Aditi; Bansal, Manju

doi:10.1186/1471-2105-6-1

Cited by 416 publications

(165 citation statements)

References 36 publications

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“…Thus, the failure of Francisella promoters to function in E. coli is perplexing. As promoters can be recognized generally as areas of low GϩC content (37), and the consensus Ϫ10 and Ϫ35 promoter elements are made up of A or T nucleotides in 10 of 12 sites, it seems that an organism with a chromosome with a low GϩC content, such as Francisella, will have a genome highly enriched for promoter-like sequences and may need an extra identifying feature to reduce nonspecific transcription initiation. Alignment at the Ϫ10 box of the 17 Francisella promoters for which the transcription start sites have been identified revealed a "TGn" motif directly upstream of the Ϫ10 box in 8 of the 17 promoters (47%).…”

Section: Discussionmentioning

confidence: 99%

Synthetic Promoters Functional in Francisella novicida and Escherichia coli

McWhinnie

Nano

2014

Appl Environ Microbiol

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In this work, we describe the identification of synthetic, controllable promoters that function in the bacterial pathogen Francisella novicida, a model facultative intracellular pathogen. Synthetic DNA fragments consisting of the tetracycline operator (tetO) flanked by a random nucleotide sequence were inserted into a Francisella-Escherichia coli shuttle plasmid upstream of a promoterless artificial operon containing the reporter genes cat and lacZ. Fragments able to promote transcription were selected for based on their ability to drive expression of the cat gene, conferring chloramphenicol resistance. Promoters of various strengths were found, many of which were repressed in the presence of the tetracycline repressor (TetR) and promoted transcription only in the presence of the TetR inducer anhydrotetracycline. A subset of both constitutive and inducible synthetic promoters were characterized to find their induction ratios and to identify their transcription start sites. In cases where tetO was located between or downstream of the ؊10 and ؊35 regions of the promoter, control by TetR was observed. If the tetO region was upstream of the ؊35 region by more than 9 bp, it did not confer TetR control. We found that three of three promoters isolated in F. novicida functioned at a comparable level in E. coli; however, none of the 10 promoters isolated in E. coli functioned at a significant level in F. novicida. Our results allowed us to isolate minimal F. novicida promoters of 47 and 48 bp in length.

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Section: Discussionmentioning

confidence: 99%

Synthetic Promoters Functional in Francisella novicida and Escherichia coli

McWhinnie

Nano

2014

Appl Environ Microbiol

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“…When the time-course data of a system are available, using dynamic system modeling is more appropriate than using statistical approach because it can model the dynamic behavior of the system (Tegner et al 2003; Chen et al 2004; Chen et al 2005; Chang et al 2005; Chang et al 2006). Therefore, in this paper we use a dynamic system model of gene regulation to describe the mechanism of how TFs may control a gene’s expression.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing a Network of Stress-Response Regulators via Dynamic System Modeling of Gene Regulation

Chen

2008

Gene�Regul Syst Bio

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Unicellular organisms such as yeasts have evolved mechanisms to respond to environmental stresses by rapidly reorganizing the gene expression program. Although many stress-response genes in yeast have been discovered by DNA microarrays, the stress-response transcription factors (TFs) that regulate these stress-response genes remain to be investigated. In this study, we use a dynamic system model of gene regulation to describe the mechanism of how TFs may control a gene’s expression. Then, based on the dynamic system model, we develop the Stress Regulator Identification Algorithm (SRIA) to identify stress-response TFs for six kinds of stresses. We identified some general stress-response TFs that respond to various stresses and some specific stress-response TFs that respond to one specific stress. The biological significance of our findings is validated by the literature. We found that a small number of TFs is probably sufficient to control a wide variety of expression patterns in yeast under different stresses. Two implications can be inferred from this observation. First, the response mechanisms to different stresses may have a bow-tie structure. Second, there may be regulatory cross-talks among different stress responses. In conclusion, this study proposes a network of stress-response regulators and the details of their actions.

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“…The definition of DNA geometric descriptors. differences in the stability of DNA, provide much better criteria than sequences alone for distinguishing promoter regions from non-promoter regions [1,8,9,13].…”

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confidence: 99%

“…So the sequence-dependent flexibility of the DNA three-dimensional (3D) structure is important for transcriptional regulation in the single binding sites and core promoter regions. Regulatory sequences such as core promoter regions not only contain specific sequence elements that serve as targets for interacting proteins, but also usually exhibit distinct structural properties [7][8][9][10][11]. The comprehensive analysis of promoter regions in all the investigated prokaryotic genomes shows that promoter DNA is more easy curved, less flexible, and less stable than DNA in coding regions and in intergenic DNA without promoters [8,12].…”

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confidence: 99%

The hidden physical codes for modulating the prokaryotic transcription initiation

Zuo

2010

Physica A: Statistical Mechanics and its Applications

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A novel method for prokaryotic promoter prediction based on DNA stability

Cited by 416 publications

References 36 publications

Synthetic Promoters Functional in Francisella novicida and Escherichia coli

Synthetic Promoters Functional in Francisella novicida and Escherichia coli

Reconstructing a Network of Stress-Response Regulators via Dynamic System Modeling of Gene Regulation

The hidden physical codes for modulating the prokaryotic transcription initiation

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