Frequency of the Gene Ontology (GO) terms found in transcriptome sequences of <i>Podosphaera plantaginis</i>, compared to the genomic data of three other fungal species: <i>Magnaporthae oryzae</i>, <i>Sclerotinia sclerotiorum</i> and <i>Tuber melanosporum</i>.

<p>Frequency of the Gene Ontology (GO) terms found in transcriptome sequences of <i>Podosphaera plantaginis</i>, compared to the genomic data of three other fungal species: <i>Magnaporthae oryzae</i>, <i>Sclerotinia sclerotiorum</i> and <i>Tuber melanosporum</i>.</p

Characteristics of the 33 SNP loci discovered in <i>Po. plantaginis populations</i> from the Åland archipelago: contig number, functional description of the gene (inferred from PANZER) and minor allele frequency in the dataset genotyped (the latter being available only for the 27 loci included in the SNP genotyping set).

<p>Characteristics of the 33 SNP loci discovered in <i>Po. plantaginis populations</i> from the Åland archipelago: contig number, functional description of the gene (inferred from PANZER) and minor allele frequency in the dataset genotyped (the latter being available only for the 27 loci included in the SNP genotyping set).</p

Revised Phylogeny and Novel Horizontally Acquired Virulence Determinants of the Model Soft Rot Phytopathogen <em>Pectobacterium wasabiae</em> SCC3193

<div><p>Soft rot disease is economically one of the most devastating bacterial diseases affecting plants worldwide. In this study, we present novel insights into the phylogeny and virulence of the soft rot model <em>Pectobacterium</em> sp. SCC3193, which was isolated from a diseased potato stem in Finland in the early 1980s. Genomic approaches, including proteome and genome comparisons of all sequenced soft rot bacteria, revealed that SCC3193, previously included in the species <em>Pectobacterium carotovorum</em>, can now be more accurately classified as <em>Pectobacterium wasabiae</em>. Together with the recently revised phylogeny of a few <em>P. carotovorum</em> strains and an increasing number of studies on <em>P. wasabiae</em>, our work indicates that <em>P. wasabiae</em> has been unnoticed but present in potato fields worldwide. A combination of genomic approaches and in planta experiments identified features that separate SCC3193 and other <em>P. wasabiae</em> strains from the rest of soft rot bacteria, such as the absence of a type III secretion system that contributes to virulence of other soft rot species. Experimentally established virulence determinants include the putative transcriptional regulator SirB, two partially redundant type VI secretion systems and two horizontally acquired clusters (Vic1 and Vic2), which contain predicted virulence genes. Genome comparison also revealed other interesting traits that may be related to life in planta or other specific environmental conditions. These traits include a predicted benzoic acid/salicylic acid carboxyl methyltransferase of eukaryotic origin. The novelties found in this work indicate that soft rot bacteria have a reservoir of unknown traits that may be utilized in the poorly understood latent stage in planta. The genomic approaches and the comparison of the model strain SCC3193 to other sequenced <em>Pectobacterium</em> strains, including the type strain of <em>P. wasabiae</em>, provides a solid basis for further investigation of the virulence, distribution and phylogeny of soft rot bacteria and, potentially, other bacteria as well.</p> </div

Virulence cluster 2 locus comparison.

<p>Visualization and a comparison of the Vic2 locus was completed in <i>Pectobacterium wasabiae</i> SCC3193, which contains a putative lipoprotein transporting system and a HopL1-like protein. A comparison of the gene cluster was conducted using blastn and blastp against a nucleotide collection and against non-redundant protein sequences to obtain strains with similar loci. ORFs are indicated using colored and scaled arrows, except in case of <i>P. wasabiae</i> CFPB 3304T for which operons were not predicted.</p

Synteny of <i>Pectobacterium wasabiae</i> and <i>Pectobacterium atrosepticum</i> genomes.

<p>Pairwise alignments of genomes were generated using Mauve. <i>P. wasabiae</i> CFBP 3304^T contigs were aligned according to <i>P. wasabiae</i> SCC3193. The sequence similarity in the pairwise alignment of <i>P. wasabiae</i> SCC3193 and CFBP 3304^T was 80.6%. The similarity between SCC3193 and WPP163 was 92.6% and between WPP163 and CFBP 3304^T was 80.9%. The sequence similarity compared to <i>P. atrosepticum</i> was 71.3% in the case of SCC3193, 69.6% for CFBP 3304^T and 72.2% for WPP163.</p

Comparison of proteomes of <i>Pectobacterium</i> and <i>Dickeya</i> strains.

<p>The strain numbers correspond to the following species: <i>Yersinia pestis</i> CO92 (outgroup), <i>Dickeya dadantii</i> Ech703, <i>Dickeya dadantii</i> Ech586, <i>Dickeya dadantii</i> 3937, <i>Dickeya zeae</i> Ech1591, <i>Pectobacterium carotovorum</i> subsp. <i>brasiliensis</i> PBR1692, <i>Pectobacterium carotovorum</i> WPP14, <i>Pectobacterium carotovorum</i> subsp. <i>carotovorum</i> PC1, <i>Pectobacterium atrosepticum</i> SCRI1043, <i>Pectobacterium wasabiae</i> WPP163, <i>Pectobacterium wasabiae</i> SCC3193 and <i>Pectobacterium wasabiae</i> CFBP 3304^T. (A) OrthoMCL clusters were converted into an orthologs vs. species matrix and visualized as a heat map. The core genome is visualized in the middle of the figure, and species and strain-specific protein clusters can be found above and below the core. (B) The correlations between proteomes were calculated and visualized to indicate their phylogenetic relationships.</p

Circular representation of the chromosome of <i>Pectobacterium wasabiae</i> SCC3193.

<p>The circles from outer to inner represent open reading frames on both strands, tRNAs (green) and rRNAs (orange), manually curated genomic islands (red), SIGI-HMM predicted islands (orange), IslandPath-DIMOB predicted islands (blue), IslandPick predicted islands (green) and GC percentage (gray).</p

sample information

Information on the samples used in the study, including sample origin, sex, phenotypic measurements (pupal and adult weight, flight metabolic rate), treatments, and GEO accession numbers for two libraries used in RNA-seq analysis

The four study populations in northern Europe and results on key life-history traits.

<p>(a) Map of the northern Baltic region with the locations of the four regional study populations: the Åland Islands (ÅL) in Finland, the Uppland coastal region (UP) in Sweden, the Saaremaa island (SA) in Estonia, and the Öland island (ÖL) in Sweden. (b) to (e), Peak flight metabolic rate (CO₂ production, ml/h, corrected for variation in body weight) during active flight in males (b) and females (c), and the weight of the 5^th larval instar following winter diapause (mg) in males (d) and females (e). In panels (b) to (e), populations from fragmented landscapes are shown with gray shading, populations from continuous landscapes with open box. In ANOVA of pooled data for the two sexes, the effects of sex, population nested within the landscape, and family nested within population were not significant for either flight metabolic rate nor larval weight, but the effect of the landscape type was highly significant: <i>P</i> = 3.9e-05 for flight metabolic rate and <i>P</i> = 8.6e-12 for larval weight. For more detailed analyses of these and other life-history traits see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101467#pone.0101467-Duplouy1" target="_blank">[19]</a>.</p

Genealogy of the four study populations.

<p>Branch lengths (also given as numbers: expectation and 95% posterior probability interval) represent divergence times in years. The branch lengths are measured on the scale <i>t/2N_e</i>, where <i>t</i> is the number of generations (here one per year) and <i>N_e</i> is the effective population size. <i>N_e</i> = 10⁴ was assumed for all populations (Materials and Methods).</p