While biofilms are ubiquitous in nature, the mechanism by which they form is still poorly understood. This study investigated the process by which bacteria deposit and, shortly after, attach irreversibly to surfaces by reorienting to create a stronger interaction, which leads to biofilm formation. A model for attachment of Pseudomonas aeruginosa was developed using a quartz crystal microbalance with dissipation monitoring (QCM-D) technology, along with a fluorescent microscope and camera to monitor kinetics of adherence of the cells over time. In this model, the interaction differs depending on the force that dominates between the viscous, inertial, and elastic loads. P. aeruginosa, grown to the midexponential growth phase (hydrophilic) and stationary phase (hydrophobic) and two different surfaces, silica (SiO2) and polyvinylidene fluoride (PVDF), which are hydrophilic and hydrophobic, respectively, were used to test the model. The bacteria deposited on both of the sensor surfaces, though on the silica surface the cells reached a steady state where there was no net increase in deposition of bacteria, while the quantity of cells depositing on the PVDF surface continued to increase until the end of the experiments. The change in frequency and dissipation per cell were both positive for each overtone (n), except when the cells and surface are both hydrophilic. In the model three factors, specifically, viscous, inertial, and elastic loads, contribute to the change in frequency and dissipation at each overtone when a cell deposits on a sensor. On the basis of the model, hydrophobic cells were shown to form an elastic connection to either surface, with an increase of elasticity at higher overtones. At lower overtones, hydrophilic cells depositing on the hydrophobic surface were shown to also be elastic, but as the overtone increases the connection between the cells and sensor becomes more viscoelastic. In the case of hydrophilic cells interacting with the hydrophilic surface, the connection is viscous at each overtone measured. It could be inferred that the transformation of the viscoelasticity of the cell–surface connection is due to changes in the orientation of the cells to the surface, which allow the bacteria to attach irreversibly and begin biofilm formation.

In this study we investigate how growth stage and depositional environment affect variability of cell properties and transport behavior of eight porcine E. coli isolates. We compared the surface properties for cells harvested during exponential and stationary growth phase and their transport behavior through columns packed with either uncoated or Fe-coated quartz sand. We then investigated correlations between measured cell properties and fitted bacterial attachment efficiencies. For both growth stages we found that bacterial attachment efficiencies in the uncoated quartz sand varied among the eight different isolates by over an order of magnitude whereas attachment efficiencies in the Fe-coated sands varied by a factor of less than two. With the exception of one isolate, growth condition had minimal impact on attachment efficiencies to the uncoated sands. A strong and statistically significant inverse relationship was observed between bacterial attachment efficiencies in the uncoated quartz sand columns and log-transformed zeta potential, whereas a mild yet statistically significant relationship between bacterial attachment efficiencies in the Fe-coated sands and cell width was observed. For the experimental conditions used in our study, we found that variability in E. coli transport was more dependent on the depositional environment than on growth conditions.

Genetic and environmental factors are well-studied influences on phenotype; however, time is a variable that is rarely considered when studying changes in cellular phenotype. Time-resolved microarray data revealed genome-wide transcriptional oscillation in a yeast continuous culture system with ~2 and ~4 h periods. We mapped the global patterns of transcriptional oscillations into a 3D map to represent different cellular phenotypes of redox cycles. This map shows the dynamic nature of gene expression in that transcripts are ordered and coupled to each other through time and concentration space. Although cells differed in oscillation periods, transcripts involved in certain processes were conserved in a deterministic way. When oscillation period lengthened, the peak to trough ratio of transcripts increased and the fraction of cells in the unbudded (G0/G1) phase of the cell division cycle increased. Decreasing the glucose level in the culture media was one way to increase the redox cycle, possibly from changes in metabolic flux. The period may be responding to lower glucose levels by increasing the fraction of cells in G1 and reducing S-phase gating so that cells can spend more time in catabolic processes. Our results support that gene transcripts are coordinated with metabolic functions and the cell division cycle.