Diversification of the <em>Salmonella</em> Fimbriae: A Model of Macro- and Microevolution

<div><p>Bacteria of the genus <em>Salmonella</em> comprise a large and evolutionary related population of zoonotic pathogens that can infect mammals, including humans and domestic animals, birds, reptiles and amphibians. <em>Salmonella</em> carries a plethora of virulence genes, including fimbrial adhesins, some of them known to participate in mammalian or avian host colonization. Each type of fimbria has its structural subunit and biogenesis genes encoded by one fimbrial gene cluster (FGC). The accumulation of new genomic information offered a timely opportunity to better evaluate the number and types of FGCs in the <em>Salmonella</em> pangenome, to test the use of current classifications based on phylogeny, and to infer potential correlations between FGC evolution in various <em>Salmonella</em> serovars and host niches. This study focused on the FGCs of the currently deciphered 90 genomes and 60 plasmids of <em>Salmonella.</em> The analysis highlighted a fimbriome consisting of 35 different FGCs, of which 16 were new, each strain carrying between 5 and 14 FGCs. The <em>Salmonella</em> fimbriome was extremely diverse with FGC representatives in 8 out of 9 previously categorized fimbrial clades and subclades. Phylogenetic analysis of <em>Salmonella</em> suggested macroevolutionary shifts detectable by extensive FGC deletion and acquisition. In addition, microevolutionary drifts were best depicted by the high level of allelic variation in predicted or known adhesins, such as the type 1 fimbrial adhesin FimH for which 67 different natural alleles were identified in <em>S. enterica</em> subsp. I. Together with strain-specific collections of FGCs, allelic variation among adhesins attested to the pathoadaptive evolution of <em>Salmonella</em> towards specific hosts and tissues, potentially modulating host range, strain virulence, disease progression, and transmission efficiency. Further understanding of how each <em>Salmonella</em> strain utilizes its panel of FGCs and specific adhesin alleles for survival and infection will support the development of new approaches for the control of Salmonellosis.</p> </div

<i>Salmonella</i> and FGCs co-evolution model.

<p>Proposed tree that includes <i>E. coli</i> and the two <i>Salmonella</i> species, <i>S. bongori and</i> S. <i>enterica,</i> the latter being divided into seven subspecies (monophasic IIIa, IV, VII and diphasic I, VI, II, and IIIb; subsp. V is now <i>S. bongori</i>). FGCs shown in red are suggested to have been acquired by HGT. FGCs shown in blue have diverged from orthologous <i>E. coli</i> or other <i>Salmonella</i> FGCs. In purple are FGCs that were lost. In green are FGCs that were duplicated. A dotted line separates the subspecies based on the presence of one or two flagellin genes. A dotted frame includes all the <i>S. enterica</i> subsp. I. The 5 clades correspond to the ones shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038596#pone.0038596.s003" target="_blank">Figure S3</a>. The asterisks indicate that <i>sdi</i> and <i>sbb</i> were found in the integrative and conjugative element ICESe3 region of <i>Salmonella enterica</i> subsp. VII strain SARC16, suggesting independent acquisitions of these FGCs.</p

Predicted structural model of the <i>Salmonella</i> FimH fimbrial adhesin.

<p>The structure of the <i>Salmonella</i> FimH protein was based on the template structure 1klf (Protein Data Bank) from the <i>E. coli</i> FimH adhesin <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038596#pone.0038596-Hung1" target="_blank">[119]</a>. On the left, ribbon model of the predicted structure of <i>Salmonella</i> FimH with its lectin and pilin domains, each with one disulfide bond. ˜β -barrel are shown in yellow and α-helices are shown in pink. On the right, the variable amino acid positions are shown in a tube-rendering model of the FimH backbone structure, with a color gradation from blue (most conserved residues) to red (most variable positions). None of the natural variable positions were located in the predicted binding pocket, shown as green circles on both models.</p

Chaperone-usher fimbrial gene clusters (FGCs) of <i>Salmonella</i>.

<p>A phylogenetic tree was built for 35 types of FGCs by using the amino acid sequences of the combined 950 usher proteins from 90 genomes (MEGA 5.0, as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038596#s3" target="_blank">method</a>). The FGCs were divided into five clades. The scale indicates the number of substitutions per amino acid. The bottom box lists different protein domain families. The asterisk indicates that some subunits were not picked by CDD or InterPro Scan, but (i) showed sequence similarity with other subunit(s) in the same gene clusters and (ii) were typically β-sheet-rich, as are all fimbrial subunits. Framed arrows are either known or predicted adhesins (as described in the text). C, V, VV, VVV were used to define the level of amino sequence variability for each subunit. C indicates subunits for which there was only one sequence available, or subunits lacking variants; V, VV or VVV indicated respectively ≤1, 1–10 or >10 detected variations per 100 amino acids.</p

Correlation of <i>Salmonella</i> phylogenomic groups with specific collections of FGCs.

<p>On the left, phylogenomic tree of 90 <i>Salmonella</i> and two <i>E. coli</i> control strains, based on 45 highly conserved house-keeping genes totaling ∼43 Kb. Clade 1 to 4 correspond to the clades shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038596#pone-0038596-g002" target="_blank">Figure 2</a>, and clade 5 includes the few sequenced genomes from strains that were not <i>S. enterica,</i> subsp. I. The scale indicates the number of substitutions per nucleotide. On the top and heat map, hierarchical clustering support tree for the FGCs (MeV, complete linkage method with an euclidean distance threshold of 9.525, <a href="http://www.tm4.org" target="_blank">http://www.tm4.org</a>). FGCs with or without pseudogenes were shown as green or red rectangles, respectively. On the right, <i>Salmonella</i> serovars (somatic O and flagellar H antigens).</p

Phenotypic profiling data for elucidating genomic gaps

<p>Dataset 1. Raw OD600 growth curves (raw_od_curves.csv).</p> <p>MAPs optical density measurements from the plate reader for 96 wells. Numbered headers indicate the time (hrs) and the column contents indicate the OD600 measurement.</p> <p>Dataset 2. Parameters for logistic curves (curve_logistic_parameters.csv).</p> <p>Lag, maximum growth rate, and carrying capacity parameters for the 96 wells. Sum-squared error and growth level are included.</p> <p>Dataset 3. C.sedlakii KBase phenotypes (c.sedlakii_phenotypes.csv).</p> <p>Phenotype csv file required for KBase phenotype simulations. This file specifies media data object name, the KBase workspace, and growth. The gene knockout and additional compound columns were not used and set to none.</p

Coral Viromes

Coral viromes extracted from Porites rus, Acropora sp., Acropora yongei, Pocillopora verrucos

Phenotypic profiling data for elucidating genomic gaps

<p>Dataset 1. Raw OD600 growth curves (raw_od_curves.csv).</p><p>MAPs optical density measurements from the plate reader for 96 wells. Numbered headers indicate the time (hrs) and the column contents indicate the OD600 measurement.</p><p>Dataset 2. Parameters for logistic curves (curve_logistic_parameters.csv).</p><p>Lag, maximum growth rate, and carrying capacity parameters for the 96 wells. Sum-squared error and growth level are included.</p><p>Dataset 3. C.sedlakii KBase phenotypes (c.sedlakii_phenotypes.csv).</p><p>Phenotype csv file required for KBase phenotype simulations. This file specifies media data object name, the KBase workspace, and growth. The gene knockout and additional compound columns were not used and set to none.</p><p>Dataset 4. (C. sedlakii_nogapfill.sbml)</p><p>The initial metabolic model of Citrobacter sedlakii built solely from the functional annotations.</p><p>Dataset 5.  (C.sedlakii_ArgonneLB_gapfill.sbml)</p><p>The initial metabolic model of Citrobacter sedlakii with reactions identified by the gap-fill algorithm on the LB media condition.</p><p>Dataset 6. (C.sedlakii_MAP_gapfill.sbml)</p><p>The LB-gap-filled model with reactions identified by the gap-fill algorithm on the MAPs media conditions.</p

SEED Servers: High-Performance Access to the SEED Genomes, Annotations, and Metabolic Models

<div><p>The remarkable advance in sequencing technology and the rising interest in medical and environmental microbiology, biotechnology, and synthetic biology resulted in a deluge of published microbial genomes. Yet, genome annotation, comparison, and modeling remain a major bottleneck to the translation of sequence information into biological knowledge, hence computational analysis tools are continuously being developed for rapid genome annotation and interpretation. Among the earliest, most comprehensive resources for prokaryotic genome analysis, the SEED project, initiated in 2003 as an integration of genomic data and analysis tools, now contains >5,000 complete genomes, a constantly updated set of curated annotations embodied in a large and growing collection of encoded subsystems, a derived set of protein families, and hundreds of genome-scale metabolic models. Until recently, however, maintaining current copies of the SEED code and data at remote locations has been a pressing issue. To allow high-performance remote access to the SEED database, we developed the SEED Servers (<a href="http://www.theseed.org/servers">http://www.theseed.org/servers</a>): four network-based servers intended to expose the data in the underlying relational database, support basic annotation services, offer programmatic access to the capabilities of the RAST annotation server, and provide access to a growing collection of metabolic models that support flux balance analysis. The SEED servers offer open access to regularly updated data, the ability to annotate prokaryotic genomes, the ability to create metabolic reconstructions and detailed models of metabolism, and access to hundreds of existing metabolic models. This work offers and supports a framework upon which other groups can build independent research efforts. Large integrations of genomic data represent one of the major intellectual resources driving research in biology, and programmatic access to the SEED data will provide significant utility to a broad collection of potential users.</p> </div

Raw_sequence_reads

Fragment libraries were constructed for eight species of the family Halobacteriacea, three from the genus Haloarcula (Har. californiae, Har. sinaiiensis, Har. vallismortis) and five from the genus Haloferax (Hfx. denitrificans, Hfx. mediterranei, Hfx. mucosum, Hfx. sulfurifontis, and Hfx. volcanii), and sequenced on a single GS FLX Titanium run following standard protocols (454 Life Sciences - http://454.com/). Hfx. volcanii was included as a sequencing control, as its genome had been completed previously [19]. Additionally, for Har. sinaiiensis and Hfx. mediterranei, 8 Kb pair-end libraries were constructed and the terminal 100 bp of each end was sequenced, according to standard protocols. The paired-end information and any trimming information are specified using annotation strings on the description line of the reads. Reads were assembled using the Genome Sequencer De Novo assembler (454 Life Sciences - http://www.my454.com/)