Quantifying biofilm propagation on chemically modified surfaces.

Conditions affecting biofilm formation differ among bacterial species and this presents a challenge to studying biofilms in the lab. This work leverages functionalized silanes to control surface chemistry in the study of early biofilm propagation, quantified with a semi-automated image processing algorithm. These methods support the study of Pantoea sp. YR343, a gram-negative bacterium isolated from the poplar rhizosphere. We found that Pantoea sp. YR343 does not readily attach to hydrophilic surfaces but will form biofilms with a "honeycomb" morphology on hydrophobic surfaces. Our image processing algorithm described here quantified the evolution of the honeycomb morphology over time, and found the propagation to display a logarithmic behavior. This methodology was repeated with a flagella-deficient fliR mutant of Pantoea sp. YR343 which resulted in reduced surface attachment. Quantifiable differences between Pantoea WT and ΔfliR biofilm morphologies were captured by the image processing algorithm, further demonstrating the insight gained from these methods.

Application of electrochemical surface plasmon resonance (ESPR) to the study of electroactive microbial biofilms.

Electrochemical surface plasmon resonance (ESPR) monitors faradaic processes optically by the change in refractive index that occurs with a change in redox state at the electrode surface. Here we apply ESPR to investigate the anode-grown Geobacter sulfurreducens biofilm (GSB), a model system used to study electroactive microbial biofilms (EABFs) which perform electrochemical reactions using electrodes as metabolic electron acceptors or donors. A substantial body of evidence indicates that electron transfer reactions among hemes of c-type cytochromes (c-Cyt) play major roles in the extracellular electron transfer (EET) pathways that connect intracellular metabolic processes of cells in an EABF to the electrode surface. The results reported here reveal that when the potential of the electrode is changed from relatively oxidizing (0.40 V vs. SHE) to reducing (-0.55 V vs. SHE) and then back to oxidizing, 70% of c-Cyt residing closest to the biofilm/electrode (within hundreds of nm from the electrode surface) appear to remain trapped in the reduced state, requiring as long as 12 hours to be re-oxidized. c-Cyt storing electrons cannot contribute to EET, yet turnover current resulting from cellular oxidation of acetate coupled with EET to the electrode surface is unaffected. This suggests that a relatively small fraction of c-Cyt residing closest to the biofilm/electrode interface is involved in EET while the majority store electrons. The results also reveal that biomass density at the biofilm/electrode interface increases rapidly during lag phase, reaching its maximum value at the onset of exponential biofilm growth when turnover current begins to rapidly increase.

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions.

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P. aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density and local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models.

Assembly and Tracking of Microbial Community Development within a Microwell Array Platform.

The development of microbial communities depends on a combination of complex deterministic and stochastic factors that can dramatically alter the spatial distribution and activities of community members. We have developed a microwell array platform that can be used to rapidly assemble and track thousands of bacterial communities in parallel. This protocol highlights the utility of the platform and describes its use for optically monitoring the development of simple, two-member communities within an ensemble of arrays within the platform. This demonstration uses two mutants of Pseudomonas aeruginosa, part of a series of mutants developed to study Type VI secretion pathogenicity. Chromosomal inserts of either mCherry or GFP genes facilitate the constitutive expression of fluorescent proteins with distinct emission wavelengths that can be used to monitor community member abundance and location within each microwell. This protocol describes a detailed method for assembling mixtures of bacteria into the wells of the array and using time-lapse fluorescence imaging and quantitative image analysis to measure the relative growth of each member population over time. The seeding and assembly of the microwell platform, the imaging procedures necessary for the quantitative analysis of microbial communities within the array, and the methods that can be used to reveal interactions between microbial species area all discussed.