Inoculum Concentration Influences Pseudomonas Aeruginosa Phenotype and Biofilm Architecture

In infections, bacterial cells are often found as relatively small multicellular aggregates characterized by a heterogeneous distribution of phenotype, genotype, and growth rates depending on their surrounding microenvironment. Many laboratory models fail to mimic these characteristics, and experiments are often initiated from planktonic bacteria given optimal conditions for rapid growth without concerns about the microenvironmental characteristics during biofilm maturation. Therefore, we investigated how the initial bacterial concentration (henceforth termed the inoculum) influences the microenvironment during initial growth and how this affects the sizes and distribution of developed aggregates in an embedded biofilm model-the alginate bead biofilm model. Following 24 h of incubation, the viable biomass was independent of starting inoculum but with a radically different microenvironment which led to differences in metabolic activity depending on the inoculum. The inoculum also affected the number of cells surviving treatment with the antibiotic tobramycin, where the highest inoculum showed higher survival rates than the lowest inoculum. The change in antibiotic tolerance was correlated with cell-specific RNA content and O consumption rates, suggesting a direct role of metabolic activity. Thus, the starting number of bacteria results in different phenotypic trajectories governed by different microenvironmental characteristics, and we demonstrate some of the possible implications of such physiological gradients on the outcome of experiments. Biofilm aggregates grown in the alginate bead biofilm model bear resemblance to features of biofilms. Here, we show that changing the initial concentration of bacteria in the biofilm model leads to widely different behavior of the bacteria following an incubation period. This difference is influenced by the local conditions experienced by the bacteria during growth, which impact their response to antibiotic treatment. Our study provides a framework for manipulating aggregate sizes in biofilm models. It underlines the importance of how experiments are initiated, which can profoundly impact the outcomes and interpretation of microbiological experiments.