Localized Gene Expression in Pseudomonas aeruginosa Biofilms

Gene expression in biofilms is dependent on bacterial responses to the local environmental conditions. Most techniques for studying bacterial gene expression in biofilms characterize average values across the entire population. Here, we describe the use of laser capture microdissection microscopy (LCMM) combined with multiplex quantitative real-time reverse transcriptase PCR (qRT-PCR) to isolate and quantify RNA transcripts from small groups of cells at spatially resolved sites within biofilms. The approach was first tested and analytical parameters were determined for Pseudomonas aeruginosa containing an isopropyl-beta-D-thiogalactopyranoside-inducible gene for the green fluorescent protein (gfp). The results show that the amounts of gfp mRNA were greatest in the top zones of the biofilms, and that gfp mRNA levels correlated with the zone of active green fluorescent protein fluorescence. The method then was used to quantify transcripts from wild-type P. aeruginosa biofilms for a housekeeping gene, acpP; the 16S rRNA; and two genes regulated by quorum sensing, phzA1 and aprA. The results demonstrated that the amount of acpP mRNA was greatest in the top 30 microm of the biofilm, with little or no mRNA for this gene at the base of the biofilms. In contrast, 16S rRNA amounts were relatively uniform throughout biofilm strata. Using this strategy, the RNA amounts of individual genes were determined, and therefore the results are dependent on both gene expression and the half-life of the transcripts. Therefore, the uniform amount of rRNA throughout the biofilms likely is due to the stability of the rRNA within ribosomes. The levels of aprA mRNA showed stratification, with the largest amounts in the upper 30-microm zone of these biofilms. The results demonstrate that mRNA levels for individual genes are not uniformly distributed throughout biofilms but may vary by orders of magnitude over small distances. The LCMM/qRT-PCR technique can be used to resolve and quantify this RNA variability at high spatial resolution.