title: Inferring bacteriophage infection strategies from genome sequence: analysis of bacteriophage 7-11 and related phages.
abstract: BACKGROUND
  Analyzing regulation of bacteriophage gene expression historically lead to establishing major paradigms of molecular biology, and may provide important medical applications in the future. Temporal regulation of bacteriophage transcription is commonly analyzed through a labor-intensive combination of biochemical and bioinformatic approaches and macroarray measurements. We here investigate to what extent one can understand gene expression strategies of lytic phages, by directly analyzing their genomes through bioinformatic methods. We address this question on a recently sequenced lytic bacteriophage 7 - 11 that infects bacterium Salmonella enterica.
  
  
  RESULTS
  We identify novel promoters for the bacteriophage-encoded σ factor, and test the predictions through homology with another bacteriophage (phiEco32) that has been experimentally characterized in detail. Interestingly, standard approach based on multiple local sequence alignment (MLSA) fails to correctly identify the promoters, but a simpler procedure that is based on pairwise alignment of intergenic regions identifies the desired motifs; we argue that such search strategy is more effective for promoters of bacteriophage-encoded σ factors that are typically well conserved but appear in low copy numbers, which we also verify on two additional bacteriophage genomes. Identifying promoters for bacteriophage encoded σ factors together with a more straightforward identification of promoters for bacterial encoded σ factor, allows clustering the genes in putative early, middle and late class, and consequently predicting the temporal regulation of bacteriophage gene expression, which we demonstrate on phage 7-11.
  
  
  CONCLUSIONS
  While MLSA algorithms proved highly useful in computational analysis of transcription regulation, we here established that a simpler procedure is more successful for identifying promoters that are recognized by bacteriophage encoded σ factor/RNA polymerase. We here used this approach for predicting sequence specificity of a novel (bacteriophage encoded) σ factor, and consequently inferring phage 7-11 transcription strategy. Therefore, direct analysis of bacteriophage genome sequences is a plausible first-line approach for efficiently inferring phage transcription strategies, and may provide a wealth of information on transcription initiation by diverse σ factors/RNA polymerases.
authors.forename: Jelena
authors.initials: J
authors.lastname: Guzina
authors.forename: Marko
authors.initials: M
authors.lastname: Djordjevic
mesh_terms: D012321:DNA-Directed RNA Polymerases / Q000378:metabolism; D015967:Gene Expression Regulation, Viral; D011401:Promoter Regions, Genetic; D012483:Salmonella Phages / Q000201:enzymology / Q000235:genetics* / Q000378:metabolism; D012808:Sigma Factor / Q000378:metabolism*; D014764:Viral Proteins / Q000032:analysis* / Q000378:metabolism
chemical_list: D012808:Sigma Factor; D014764:Viral Proteins; D012321:DNA-Directed RNA Polymerases