Microevolution of group A streptococci in vivo: capturing regulatory networks engaged in sociomicrobiology, niche adaptation, and hypervirulence

Abstract

The onset of infection and the switch from primary to secondary niches are dramatic environmental changes that not only alter bacterial transcriptional programs, but also perturb their sociomicrobiology, often driving minor subpopulations with mutant phenotypes to prevail in specific niches. Having previously reported that M1T1 Streptococcus pyogenes become hypervirulent in mice due to selection of mutants in the covRS regulatory genes, we set out to dissect the impact of these mutations in vitro and in vivo from the impact of other adaptive events. Using a murine subcutaneous chamber model to sample the bacteria prior to selection or expansion of mutants, we compared gene expression dynamics of wild type (WT) and previously isolated animal-passaged (AP) covS mutant bacteria both in vitro and in vivo, and we found extensive transcriptional alterations of pathoadaptive and metabolic gene sets associated with invasion, immune evasion, tissue-dissemination, and metabolic reprogramming. In contrast to the virulence-associated differences between WT and AP bacteria, Phenotype Microarray analysis showed minor in vitro phenotypic differences between the two isogenic variants. Additionally, our results reflect that WT bacteria's rapid host-adaptive transcriptional reprogramming was not sufficient for their survival, and they were outnumbered by hypervirulent covS mutants with SpeB(-)/Sda(high) phenotype, which survived up to 14 days in mice chambers. Our findings demonstrate the engagement of unique regulatory modules in niche adaptation, implicate a critical role for bacterial genetic heterogeneity that surpasses transcriptional in vivo adaptation, and portray the dynamics underlying the selection of hypervirulent covS mutants over their parental WT cells.

Figure 1. Hybridization scheme.

Diagram showing the cyclic hybridization scheme followed in the microarray experiments.

Figure 2. Pathovivogram of expression microarrays.

Heat maps of clustered normalized expression values from biological replicates showing ten major coexpression clusters (CCs). Shades of red: upregulation; shades of blue: downregulation; black: expression value below threshold. CC1-CC3 are clusters that differentiate bacteria grown in vitro from those grown in vivo (vivogram) and represent the adaptational transcriptional program. CC4-CC10 are clusters that differentiate WT from AP bacteria (pathogram), all of which but CC7 represent transcriptional differences driven largely by the AP mutation. CC7 represents transcriptional differences driven both by mutation and by in vivo adaptation. The right column displays the subsystems (SS) and neighbor clusters (NC) to which these genes belong. A higher resolution version of this figure is provided online as Figure S2. Detailed annotations are provided in Table S1.

Figure 3. Neighbor clustering of significantly differentially expressed genes mapped to M1 SF370 genome.

Fold-change ratios of significantly differentially expressed genes (P<0.05) are mapped to ORFs of the M1 SF370 genome (the M1 strain used as core for the microarray). SF370 prophages are shown, including those absent in M1T1. The graph shows sets of contiguous genes with similar coexpression patterns. A higher resolution version of the figure is provided online as Figure S4.

Figure 4. Examples of clusters of biological interest.

The correlation between different methods of analysis of five clusters is shown. The left panel includes the different clusters detected by the NC method. The middle panels display heat maps of five coexpression clusters representing different patterns (CC4, SpeB operon; CC6, SLS operon and Trx locus; CC8, Mga locus; CC9, Has operon). The corresponding NC graphs for genes within these clusters are shown in the right panels.

Figure 5. Genomic subsystems represented by the significantly differentially regulated genes.

Annotations and subsystem classification are based on NMPDR annotations as of October 2009. Subsystem classification has been manually verified and amended when necessary.