Defining the plasticity of transcription factor binding sites by Deconstructing DNA consensus sequences: the PhoP-binding sites among gamma/enterobacteria.
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 Mg(2+) 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.
Harari
Oscar
O
Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain.
Park
Sun-Yang
SY
Huang
Henry
H
Groisman
Eduardo A
EA
Zwir
Igor
I
D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000235:genetics; D001483:Base Sequence; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D016384:Consensus Sequence / Q000235:genetics*; D004269:DNA, Bacterial; D004755:Enterobacteriaceae / Q000235:genetics*; D019143:Evolution, Molecular; D020869:Gene Expression Profiling; D016680:Genome, Bacterial; D008957:Models, Genetic; D008969:Molecular Sequence Data; D009690:Nucleic Acid Conformation; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated; D016415:Sequence Alignment; D014157:Transcription Factors / Q000235:genetics*
D001426:Bacterial Proteins; D004269:DNA, Bacterial; D014157:Transcription Factors; C061064:PhoP protein, Bacteria