Selection against Spurious Promoter Motifs Correlates with Translational Efficiency across Bacteria

Because binding of RNAP to misplaced sites could compromise the efficiency of transcription, natural selection for the optimization of gene expression should regulate the distribution of DNA motifs capable of RNAP-binding across the genome. Here we analyze the distribution of the −10 promoter motifs that bind the σ70 subunit of RNAP in 42 bacterial genomes. We show that selection on these motifs operates across the genome, maintaining an over-representation of −10 motifs in regulatory sequences while eliminating them from the nonfunctional and, in most cases, from the protein coding regions. In some genomes, however, −10 sites are over-represented in the coding sequences; these sites could induce pauses effecting regulatory roles throughout the length of a transcriptional unit. For nonfunctional sequences, the extent of motif under-representation varies across genomes in a manner that broadly correlates with the number of tRNA genes, a good indicator of translational speed and growth rate. This suggests that minimizing the time invested in gene transcription is an important selective pressure against spurious binding. However, selection against spurious binding is detectable in the reduced genomes of host-restricted bacteria that grow at slow rates, indicating that components of efficiency other than speed may also be important. Minimizing the number of RNAP molecules per cell required for transcription, and the corresponding energetic expense, may be most relevant in slow growers. These results indicate that genome-level properties affecting the efficiency of transcription and translation can respond in an integrated manner to optimize gene expression. The detection of selection against promoter motifs in nonfunctional regions also confirms previous results indicating that no sequence may evolve free of selective constraints, at least in the relatively small and unstructured genomes of bacteria

Genome-Wide Identification of Transcription Start Sites, Promoters and Transcription Factor Binding Sites in E. coli

Despite almost 40 years of molecular genetics research in Escherichia coli a major fraction of its Transcription Start Sites (TSSs) are still unknown, limiting therefore our understanding of the regulatory circuits that control gene expression in this model organism. RegulonDB (http://regulondb.ccg.unam.mx/) is aimed at integrating the genetic regulatory network of E. coli K12 as an entirely bioinformatic project up till now. In this work, we extended its aims by generating experimental data at a genome scale on TSSs, promoters and regulatory regions. We implemented a modified 5′ RACE protocol and an unbiased High Throughput Pyrosequencing Strategy (HTPS) that allowed us to map more than 1700 TSSs with high precision. From this collection, about 230 corresponded to previously reported TSSs, which helped us to benchmark both our methodologies and the accuracy of the previous mapping experiments. The other ca 1500 TSSs mapped belong to about 1000 different genes, many of them with no assigned function. We identified promoter sequences and type of σ factors that control the expression of about 80% of these genes. As expected, the housekeeping σ70 was the most common type of promoter, followed by σ38. The majority of the putative TSSs were located between 20 to 40 nucleotides from the translational start site. Putative regulatory binding sites for transcription factors were detected upstream of many TSSs. For a few transcripts, riboswitches and small RNAs were found. Several genes also had additional TSSs within the coding region. Unexpectedly, the HTPS experiments revealed extensive antisense transcription, probably for regulatory functions. The new information in RegulonDB, now with more than 2400 experimentally determined TSSs, strengthens the accuracy of promoter prediction, operon structure, and regulatory networks and provides valuable new information that will facilitate the understanding from a global perspective the complex and intricate regulatory network that operates in E. coli