Streptococcus canis Are a Single Population Infecting Multiple Animal Hosts Despite the Diversity of the Universally Present M-Like Protein SCM

Streptococcus canis is an animal pathogen which occasionally causes infections in humans. The S. canis M-like protein (SCM) encoded by the scm gene, is its best characterized virulence factor but previous studies suggested it could be absent in a substantial fraction of isolates. We studied the distribution and variability of the scm gene in 188 S. canis isolates recovered from companion animals (n = 152), wild animal species (n = 20), and humans (n = 14). Multilocus sequence typing, including the first characterization of wildlife isolates, showed that the same lineages are present in all animal hosts, raising the possibility of extensive circulation between species. Whole-genome analysis revealed that emm-like genes found previously in S. canis correspond to divergent scm genes, indicating that what was previously believed to correspond to two genes is in fact the same scm locus. We designed primers allowing for the first time the successful amplification of the scm gene in all isolates. Analysis of the scm sequences identified 12 distinct types, which could be divided into two clusters: group I (76%, n = 142) and group II (24%, n = 46) sharing little sequence similarity. The predicted group I SCM showed extensive similarity with each other outside of the N-terminal hypervariable region and a conserved IgG binding domain. This domain was absent from group II SCM variants found in isolates previously thought to lack the scm gene, which also showed greater amino acid variability. Further studies are necessary to elucidate the possible host interacting partners of the group II SCM variants and their role in virulence.Additional co-authors listed: Portuguese Group for the Study of Streptococcal Infection

Coverage and amplitude of 95% confidence intervals for Wallace coefficient obtained from simulated classifications.

<p>Each dot represents a simulation with a particular set of parameters. The colors indicate the number of elements <i>n</i> of the simulated contingency tables as indicated in the figure legend. All simulated tables in this plot had 10×10 dimensions and the distribution of row cluster sizes followed a Zipfian distribution with exponent <i>a</i> = 1.</p

Wallace coefficients and respective 95% confidence intervals for the methods used to characterize 116 MRSA in (3).

*<p>Expected Wallace Coefficient if the classification of the method in the column is independent of the classifications of the methods in the rows.</p

Wallace coefficients and respective 95% confidence intervals for the methods used to characterize 325 macrolide-resistant GAS in (1).

*<p>Expected Wallace Coefficient if the classification of the method in the column is independent of the classifications of the methods in the rows.</p

Coverage and amplitude of 95% confidence intervals for Wallace coefficient obtained from simulated classifications.

<p>Each dot represents a simulation with a particular set of parameters. The colors indicate exponent <i>a</i> of the Zipfian distribution determining the distribution of row cluster sizes of the simulated contingency tables as indicated in the figure legend. All simulated tables in this plot had <i>n</i> = 300 elements and 10×10 dimensions.</p

Wallace coefficients and respective 95% confidence intervals for the methods used to characterize 82 MSSA in (3).

*<p>Expected Wallace Coefficient if the classification of the method in the column is independent of the classifications of the methods in the rows.</p

Wallace coefficients and respective 95% confidence intervals for the methods used to characterize 37 invasive GAS in (4) including MLST.

*<p>Expected Wallace Coefficient if the classification of the method in the column is independent of the classifications of the methods in the rows.</p

Coverage and amplitude of 95% confidence intervals for Wallace coefficient obtained from simulated classifications.

<p>Each dot represents a simulation with a particular set of parameters. The colors indicate the dimensions of the simulated contingency tables as indicated in the figure legend, which correspond to the number of clusters in each of the two classifications. All simulated tables in this plot had <i>n</i> = 300 elements and the distribution of row cluster sizes followed a Zipfian distribution with exponent <i>a</i> = 1.</p

Each circle represents the area under the ROC curve (AUC) for one array

Labels in the x axis identify the hybridization: RT – R6 (test) with TIGR4 control, GT – G54 with TIGR4 control, RM – R6 with mix control and GM - G54 with mix control. Panel A: Open circles represent the values obtained by analyzing together all the spots in the array. Panel B: For RT and GT hybridizations, open circles represent the AUC values for the set of T, TR and TG spots, filled circles represent the AUC values for the set of R, G and RG spots; for RM and GM hybridizations, open circles represent AUC values for the T, R and G spots, filled circles represent the AUC values for TR, TG and RG spots.<p><b>Copyright information:</b></p><p>Taken from "Optimal control and analysis of two-color genomotyping experiments using bacterial multistrain arrays"</p><p>http://www.biomedcentral.com/1471-2164/9/230</p><p>BMC Genomics 2008;9():230-230.</p><p>Published online 19 May 2008</p><p>PMCID:PMC2410139.</p><p></p

Distribution of pherotype-characterized strains according to the ability to form <i>in vitro</i> biofilms.

<p>A total of 90 strains with pherotype CSP1 (n = 67) or CSP2 (n = 23) were screened for their ability to form biofilms in 96-well plates without the addition of synthetic CSP. After 24h of incubation, the resulting biofilm was measured by crystal violet staining. Each plotted value is an average of 9 replicates normalized by the average biofilm mass measured for TIGR4 strain (OD_595nm = 0.147). Horizontal lines represent the relative biofilm formation of R36A, TIGR4 and their isogenic switched-pherotype mutant strains.</p