Spatiotemporal Variations in Growth Rate and Virulence Plasmid Copy Number during Yersinia pseudotuberculosis Infection

Abstract

AbstractPathogenic Yersinia spp. depend on the activity of a potent virulence plasmid-encoded ysc/yop type 3 secretion system (T3SS) to colonize hosts and cause disease. It was recently shown that Y. pseudotuberculosis up-regulates the virulence plasmid copy number (PCN) during infection and the resulting elevated gene dose of plasmid-encoded T3SS genes is essential for virulence. When and how this novel regulatory mechanism is deployed and regulates the replication of the virulence plasmid during infection is unknown. In the current study, we applied droplet digital PCR (ddPCR) to investigate the dynamics of Y. pseudotuberculosis virulence PCN variations and growth rates in infected mouse organs. We demonstrated that both PCN and growth varied in different tissues and over time throughout the course of infection, indicating that the bacteria adapted to discrete microenvironments during infection. The PCN was highest in Peyer’s Patches and caecum during the clonal invasive phase of the infection, while the fastest growth rates were found in the draining mesenteric lymph nodes. In deeper, systemic organs, the PCN was lower and more modest growth rates were recorded. Our study indicates that increased gene dosage of the plasmid-encoded T3SS genes is most important early in the infection during invasion of the host. The described ddPCR approach will greatly simplify analyses of PCN, growth dynamics, and bacterial loads in infected tissues, and will be readily applicable to other infection models.ImportanceStudying pathogenic bacteria proliferating inside infected hosts is challenging using traditional methods, especially the transit and reversible genetic events. The bacteria are effectively diluted by the overwhelming number of host cells present in infected tissues. Using an innovative droplet digital PCR (ddPCR) approach, we have determined the virulence plasmid copy number (PCN) variations and growth rates of Yersinia during the course of infection in a mouse model. Here, we show that both the virulence plasmid copy number and bacterial growth rates display spatiotemporal variations in mice during infection. We demonstrate that the peak-to-trough ratio can be used as a proxy for determining the growth rate of invasive bacterial pathogen during infection, and ddPCR as the method of choice for quantifying DNA in host-pathogen interaction context. This proof-of-concept ddPCR approach can be easily applied for any bacterial pathogens and any infection models, for analysis of PCN, growth dynamics and bacterial loads.