A Fluorescence-based Method to Study Bacterial Gene Regulation in Infected Tissues

Bacterial virulence genes are often regulated at the transcriptional level by multiple factors that respond to different environmental signals. Some factors act directly on virulence genes; others control pathogenesis by adjusting the expression of downstream regulators or the accumulation of signals that affect regulator activity. While regulation has been studied extensively during in vitro growth, relatively little is known about how gene expression is adjusted during infection. Such information is important when a particular gene product is a candidate for therapeutic intervention. Transcriptional approaches like quantitative, real-time RT-PCR and RNA-Seq are powerful ways to examine gene expression on a global level but suffer from many technical challenges including low abundance of bacterial RNA compared to host RNA, and sample degradation by RNases. Evaluating regulation using fluorescent reporters is relatively easy and can be multiplexed with fluorescent proteins with unique spectral properties. The method allows for single-cell, spatiotemporal analysis of gene expression in tissues that exhibit complex three-dimensional architecture and physiochemical gradients that affect bacterial regulatory networks. Such information is lost when data are averaged over the bulk population. Herein, we describe a method for quantifying gene expression in bacterial pathogens in situ. The method is based on simple tissue processing and direct observation of fluorescence from reporter proteins. We demonstrate the utility of this system by examining the expression of Staphylococcus aureus thermonuclease (nuc), whose gene product is required for immune evasion and full virulence ex vivo and in vivo. We show that nuc-gfp is strongly expressed in renal abscesses and reveal heterogeneous gene expression due in part to apparent spatial regulation of nuc promoter activity in abscesses fully engaged with the immune response. The method can be applied to any bacterium with a manipulatable genetic system and any infection model, providing valuable information for preclinical studies and drug development.