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Dissection of recognition determinants of Escherichia coli sigma32 suggests a composite -10 region with an 'extended -10' motif and a core -10 element.

ABSTRACT: Sigma32 controls expression of heat shock genes in Escherichia coli and is widely distributed in proteobacteria. The distinguishing feature of sigma32 promoters is a long -10 region (CCCCATNT) whose tetra-C motif is important for promoter activity. Using alanine-scanning mutagenesis of sigma32 and in vivo and in vitro assays, we identified promoter recognition determinants of this motif. The most downstream C (-13) is part of the -10 motif; our work confirms and extends recognition determinants of -13C. Most importantly, our work suggests that the two upstream Cs (-16, -15) constitute an 'extended -10' recognition motif that is recognized by K130, a residue universally conserved in beta- and gamma-proteobacteria. This residue is located in the alpha-helix of sigmaDomain 3 that mediates recognition of the extended -10 promoter motif in other sigmas. K130 is not conserved in alpha- and delta-/epsilon-proteobacteria and we found that sigma32 from the alpha-proteobacterium Caulobacter crescentus does not need the extended -10 motif for high promoter activity. This result supports the idea that K130 mediates extended -10 recognition. Sigma32 is the first Group 3 sigma shown to use the 'extended -10' recognition motif.

SUBMITTER: Koo BM

PROVIDER: S-EPMC4412615 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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Dissection of recognition determinants of Escherichia coli sigma32 suggests a composite -10 region with an 'extended -10' motif and a core -10 element.

Koo Byoung-Mo BM Rhodius Virgil A VA Campbell Elizabeth A EA Gross Carol A CA

Molecular microbiology 20090414 4

Sigma32 controls expression of heat shock genes in Escherichia coli and is widely distributed in proteobacteria. The distinguishing feature of sigma32 promoters is a long -10 region (CCCCATNT) whose tetra-C motif is important for promoter activity. Using alanine-scanning mutagenesis of sigma32 and in vivo and in vitro assays, we identified promoter recognition determinants of this motif. The most downstream C (-13) is part of the -10 motif; our work confirms and extends recognition determinants ...[more]

PMID: 19400791

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Project description:BackgroundComputational discovery of regulatory elements is an important area of bioinformatics research and more than a hundred motif discovery methods have been published. Traditionally, most of these methods have addressed the problem of single motif discovery - discovering binding motifs for individual transcription factors. In higher organisms, however, transcription factors usually act in combination with nearby bound factors to induce specific regulatory behaviours. Hence, recent focus has shifted from single motifs to the discovery of sets of motifs bound by multiple cooperating transcription factors, so called composite motifs or cis-regulatory modules. Given the large number and diversity of methods available, independent assessment of methods becomes important. Although there have been several benchmark studies of single motif discovery, no similar studies have previously been conducted concerning composite motif discovery.ResultsWe have developed a benchmarking framework for composite motif discovery and used it to evaluate the performance of eight published module discovery tools. Benchmark datasets were constructed based on real genomic sequences containing experimentally verified regulatory modules, and the module discovery programs were asked to predict both the locations of these modules and to specify the single motifs involved. To aid the programs in their search, we provided position weight matrices corresponding to the binding motifs of the transcription factors involved. In addition, selections of decoy matrices were mixed with the genuine matrices on one dataset to test the response of programs to varying levels of noise.ConclusionAlthough some of the methods tested tended to score somewhat better than others overall, there were still large variations between individual datasets and no single method performed consistently better than the rest in all situations. The variation in performance on individual datasets also shows that the new benchmark datasets represents a suitable variety of challenges to most methods for module discovery.

Dataset Information

Dissection of recognition determinants of Escherichia coli sigma32 suggests a composite -10 region with an 'extended -10' motif and a core -10 element.

Publications

Dissection of recognition determinants of Escherichia coli sigma32 suggests a composite -10 region with an 'extended -10' motif and a core -10 element.

Similar Datasets

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