Inferring bacteriophage infection strategies from genome sequence: analysis of bacteriophage 7-11 and related phages.

Jelena Guzina,Marko Djordjevic

doi:10.1186/1471-2148-15-s1-s1

Abstract

BackgroundAnalyzing 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.ResultsWe 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.ConclusionsWhile 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.

Highlights

Bacteriophages represent a group of viruses that is dominant in the microbial world, which to a large degree outnumber the other life forms in the Biosphere [1,2]
A notable feature of the phage 7 - 11 genome is the sharp grouping of genes into the “+” oriented cluster, which contains the structural and DNA packaging genes, and the “-” oriented cluster, composed of the functional genes (Figure 1); such genome organization is common in bacteriophages encoding their own s factor/RNA polymerase (RNAP) [6,7]
The main goal was investigating if the main determinants of phage transcription regulation, and the phage infection strategy, can be inferred directly from the phage genome sequence

Summary

Introduction

Bacteriophages represent a group of viruses that is dominant in the microbial world, which to a large degree outnumber the other life forms in the Biosphere [1,2]. A large number of genes (almost 80%) in a novel phage does not code for proteins of known functions [1]. Bacteriophages share a lot of similar features, like gene expression strategies. Genes of a large number of bacteriophages can be designated as the “early”, “middle” and “late”, based on the temporal pattern of their expression during infection [3]. 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

Methods

Results

Discussion

Conclusion