In Escherichia coli, the two-component Cpx system comprising the CpxA sensor kinase and the CpxR response regulator modulates gene expression in response to a variety of stresses including membrane-protein damage, starvation, and high osmolarity. To date, the few known CpxR-P target operons were mostly identified by genetic screens. To facilitate the discovery of all target operons, we derived a 15-bp weighted matrix for CpxR-P recognition that takes into account the relative base frequency at each nucleotide position. This matrix essentially consists of two tandem 5-GTAAA-3 motifs separated by a 5-bp linker. All of the 15-bp stretches on both strands of the E. coli MG1655 genome were then scored for their degree of matching with the matrix and classified in statistical deviation groups. The effectiveness of this screening is indicated by the identification of eight new target operons (ung, ompC, psd, mviA, aroK, rpoErseABC, secA, and aer) among eleven candidates tested. Moreover, the matrix score correlates with the likelihood that a site is a true target and with the relative site affinity for CpxR-P in vitro. Our data indicate that some 100 operons are under direct CpxR-P control and that the signal transduction pathway interacts with several other control circuits in manners hitherto unanticipated.

Cross-kingdom alternative splicing

A comprehensive survey of alternate splicing across 42 eukaryotes so as to gain insight into how spliceosomal introns are recognized. Abstract Background: Variations in transcript splicing can reveal how eukaryotes recognize intronic splice sites. Retained introns (RIs) commonly appear when the intron definition (ID) mechanism of splice site recognition inconsistently identifies intron-exon boundaries, and cassette exons (CEs) are often caused by variable recognition of splice junctions by the exon definition (ED) mechanism. We have performed a comprehensive survey of alternative splicing across 42 eukaryotes to gain insight into how spliceosomal introns are recognized.

Various computational approaches have been developed for predicting cis-regulatory DNA elements in prokaryotic genomes. We describe a novel method for predicting transcription-factor-binding sites in Escherichia coli. Our method takes advantage of the principle that transcription factors frequently coregulate gene expression, but without requiring prior knowledge of which groups of genes are coregulated. Using position weight matrices for 49 known transcription factors, we examined spacings between pairs of matrix hits. These pairs were assigned probabilities according to the overrepresentation of their separation distance.

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