Abstract
Transcriptional regulators recognize specific DNA sequences. Because these sequences are embedded in the background of genomic DNA, it is hard to identify the key cis-regulatory elements that determine disparate patterns of gene expression. The detection of the intra- and inter-species differences among these sequences is crucial for understanding the molecular basis of both differential gene expression and evolution. Here, we address this problem by investigating the target promoters controlled by the DNA-binding PhoP protein, which governs virulence and Mg2+ homeostasis in several bacterial species. PhoP is particularly interesting; it is highly conserved in different gamma/enterobacteria, regulating not only ancestral genes but also governing the expression of dozens of horizontally acquired genes that differ from species to species. Our approach consists of decomposing the DNA binding site sequences for a given regulator into families of motifs (i.e., termed submotifs) using a machine learning method inspired by the “Divide & Conquer” strategy. By partitioning a motif into sub-patterns, computational advantages for classification were produced, resulting in the discovery of new members of a regulon, and alleviating the problem of distinguishing functional sites in chromatin immunoprecipitation and DNA microarray genome-wide analysis. Moreover, we found that certain partitions were useful in revealing biological properties of binding site sequences, including modular gains and losses of PhoP binding sites through evolutionary turnover events, as well as conservation in distant species. The high conservation of PhoP submotifs within gamma/enterobacteria, as well as the regulatory protein that recognizes them, suggests that the major cause of divergence between related species is not due to the binding sites, as was previously suggested for other regulators. Instead, the divergence may be attributed to the fast evolution of orthologous target genes and/or the promoter architectures resulting from the interaction of those binding sites with the RNA polymerase.
Highlights
Whole genome sequences, as well as microarray and chromatin inmunoprecipitation with array hybridization (ChIP-chip) data provide the raw material for the characterization and understanding of the underlying regulatory systems
To discern the sequence elements relevant to differential gene expression, such as those corresponding to the binding sites (BSs) of transcriptional factors (TFs) and RNA polymerase (RNAP), when they are embedded in the background of genomic DNA sequences that do not play a role in gene expression [1]
These changes often occur in cis-elements relevant to transcriptional regulation that are difficult to discern, as they are short, and are embedded in a genomic background that does not play a direct role in gene expression, or that consists of disparate sequences such as those from horizontally acquired genes
Summary
As well as microarray and chromatin inmunoprecipitation with array hybridization (ChIP-chip) data provide the raw material for the characterization and understanding of the underlying regulatory systems. It is still challenging, to discern the sequence elements relevant to differential gene expression, such as those corresponding to the binding sites (BSs) of transcriptional factors (TFs) and RNA polymerase (RNAP), when they are embedded in the background of genomic DNA sequences that do not play a role in gene expression [1]. How does a regulator evolve given that there appears to be a non-monotonic co-evolution of regulators and targets [2,3,4]? This raises the question: how does a single regulator distinguish promoter sequences when affinity is a major determinant of differential expression? how does a regulator evolve given that there appears to be a non-monotonic co-evolution of regulators and targets [2,3,4]?
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