Phylogenomic footprinting is an approach for ab initio identification of genome-wide regulatory elements in bacterial species based on sequence conservation. The statistical power of the phylogenomic approach depends on the degree of sequence conservation, the length of regulatory elements, and the level of phylogenetic divergence among genomes. Building on an earlier model, we propose a binomial model that uses synonymous tree lengths as neutral expectations for determining the statistical significance of conserved intergenic spacer (IGS) sequences. Simulations show that the binomial model is robust to variations in the value of evolutionary parameters, including base frequencies and the transition-to-transversion ratio. We used the model to search for regulatory sequences in the Lyme disease species group (Borrelia burgdorferi sensu lato) using 23 genomes. The model indicates that the currently available set of Borrelia genomes would not yield regulatory sequences shorter than five bases, suggesting that genome sequences of additional B. burgdorferi sensu lato species are needed. Nevertheless, we show that previously known regulatory elements are indeed strongly conserved in sequence or structure across these Borrelia species. Further, we predict with sufficient confidence two new RpoS binding sites, 39 promoters, 19 transcription terminators, 28 noncoding RNAs, and four sets of coregulated genes. These putative cis- and trans-regulatory elements suggest novel, Borrelia-specific mechanisms regulating the transition between the tick and host environments, a key adaptation and virulence mechanism of B. burgdorferi. Alignments of IGS sequences are available on BorreliaBase.org, an online database of orthologous open reading frame (ORF) and IGS sequences in Borrelia.
Martin, Che I.; Sukarna, Tika Y.; Akther, Saymon; Ramrattan, Girish; Pagan, Pedro; Di, Lia; Mongodin, Emmanuel F.; Fraser, Claire M.; Schutzer, Steven E.; Luft, Benjamin J.; Casjens, Sherwood R.; and Qiu, Wei-Gang, "Phylogenomic Identification of Regulatory Sequences in Bacteria: an Analysis of Statistical Power and an Application to Borrelia burgdorferi Sensu Lato" (2015). CUNY Academic Works.