Two bacterial strains, Pseudomonas aeruginosa MJK1 and Escherichia coli MJK2, were constructed that both express green fluorescent protein (GFP) and carry out ureolysis. These two novel model organisms are useful for studying bacterial carbonate mineral precipitation processes and specifically ureolysis-driven microbially induced calcium carbonate precipitation (MICP). The strains were constructed by adding plasmid-borne urease genes (ureABC, ureD and ureFG) to the strains P. aeruginosa AH298 and E. coli AF504gfp, both of which already carried unstable GFP derivatives. The ureolytic activities of the two new strains were compared to the common, non-GFP expressing, model organism Sporosarcina pasteurii in planktonic culture under standard laboratory growth conditions. It was found that the engineered strains exhibited a lower ureolysis rate per cell but were able to grow faster and to a higher population density under the conditions of this study. Both engineered strains were successfully grown as biofilms in capillary flow cell reactors and ureolysis-induced calcium carbonate mineral precipitation was observed microscopically. The undisturbed spatiotemporal distribution of biomass and calcium carbonate minerals were successfully resolved in 3D using confocal laser scanning microscopy. Observations of this nature were not possible previously because no obligate urease producer that expresses GFP had been available. Future observations using these organisms will allow researchers to further improve engineered application of MICP as well as study natural mineralization processes in model systems.

Background/Objectives:Biofilms and specifically urea-hydrolysing biofilms are of interest to the medical community (for example, urinary tract infections), scientists and engineers (for example, microbially induced carbonate precipitation). To appropriately model these systems, biofilm-specific reaction rates are required. A simple method for determining biofilm-specific reaction rates is described and applied to a urea-hydrolysing biofilm.Methods:Biofilms were grown in small silicon tubes and influent and effluent urea concentrations were determined. Immediately after sampling, the tubes were thin sectioned to estimate the biofilm thickness profile along the length of the tube. Urea concentration and biofilm thickness data were used to construct an inverse model for the estimation of the urea hydrolysis rate.Results/Conclusions:It was found that urea hydrolysis in Escherichia coli MJK2 biofilms is well approximated by first-order kinetics between urea concentrations of 0.003 and 0.221 mol/l (0.186 and 13.3 g/l). The first-order rate coefficient (k1) was estimated to be 23.2±6.2 h−1. It was also determined that advection dominated the experimental system rather than diffusion, and that urea hydrolysis within the biofilms was not limited by diffusive transport. Beyond the specific urea-hydrolysing biofilm discussed in this work, the method has the potential for wide application in cases where biofilm-specific rates must be determined.

Knowledge of taxis (directed swimming) in the Archaea is currently expanding through identification of novel receptors, effectors, and proteins involved in signal transduction to the flagellar motor. Although the ability for biological cells to sense and swim toward hydrogen gas has been hypothesized for many years, this capacity has yet to be observed and demonstrated. Here we show that the average swimming velocity increases in the direction of a source of hydrogen gas for the methanogen, Methanococcus maripaludis using a capillary assay with anoxic gas-phase control and time-lapse microscopy. The results indicate that a methanogen couples motility to hydrogen concentration sensing and is the first direct observation of hydrogenotaxis in any domain of life. Hydrogenotaxis represents a strategy that would impart a competitive advantage to motile microorganisms that compete for hydrogen gas and would impact the C, S and N cycles. H ydrogen gas (H 2 ) is a crucial substrate for methanogens as well as a common source of energy for other organisms in both anaerobic and aerobic environments, including acetogens, sulfate-and sulfur-reducers, and hydrogen-oxidizers [1][2][3][4] . Biological methane (CH 4 ) production from H 2 and carbon dioxide (CO 2 ) contributes to greenhouse gas emissions and is possibly one of the oldest microbial metabolisms 5,6 . Understanding the ecological strategies of methanogens is not only important for our knowledge of early earth processes and present-day anaerobic environments, but also for determining potential roles in human health conditions (e.g., colon cancer and periodontal disease), where positive correlations have been made with incidence of disease and occurrence of methanogens 7,8 . Methanococcus maripaludis is an anaerobic archaeum that can use H 2 or formate as electron donor to reduce CO 2 to CH 4 and is considered a model mesophilic methanogen. Recently, the swimming behavior of M. maripaludis was described 9 , but chemotactic responses have not been shown. Chemotaxis has been demonstrated for Archaea, including methanogens 10,11 , but taxis to hydrogen has not been shown for any domain of life. The chemotaxis signal transduction system in Archaea is similar to the well-studied system in Bacteria; however, the flagellar switch is different and none of the archaeal flagellar proteins have homologs to bacterial flagellar proteins [12][13][14][15] . Chemotaxis has been the subject of many mathematical models and the majority have concentrated on reproducing the population-level observation of migrating bands of high cell concentration in swarm plates and capillary experiments 16 . Pioneering work in modeling chemotaxis behavior by Keller and Segel in 1971 has been the basis of the most common mathematical models 17 . In one dimension, with x being the spatial variable, the Keller-Segel model can be described as a flux, J, such thatwhere m is the cell diffusion coefficient that takes random, non-directed, movement of cells into account. b is the microbial population density, s...

Urea-hydrolysing biofilms are crucial to applications in medicine, engineering, and science. Quantitative information about ureolysis rates in biofilms is required to model these applications. We formulate a novel model of urea consumption in a biofilm that allows different kinetics, for example either first order or Michaelis-Menten. The model is fit it to synthetic data to validate and compare two approaches: Bayesian and nonlinear least squares (NLS), commonly used by biofilm practitioners. The shortcomings of NLS motivate the Bayesian approach where a simple Markov Chain Monte Carlo (MCMC) sampler is applied. The model is then fit to real data of influent and effluent urea concentrations from experiments on biofilms of Escherichia coli. Results from synthetic data aid in interpreting results from real data, where first order and Michaelis-Menten kinetic models are compared. The method shows potential for general applications requiring biofilm kinetic information.

This chapter describes how urinary tract infections can lead to stone formation. The most frequent type of infection stone is struvite (MgNH 4 PO 4 • 6H 2 O), although it is common that struvite stones and infections are associated with other stone types, often forming large staghorn calculi. A complete understanding of struvite stone formation requires knowledge of the pathogen biology, including metabolic activity and motility, as well as a basic understanding of how minerals form.The pathogens responsible for struvite stones are those that break down urea into ammonium (NH 4 + ) and inorganic carbon. This reaction, known as ureolysis, increases the pH of urine and the concentration of NH 4 + , thus increasing the saturation index of struvite. If supersaturation is reached, i.e. the ion activity product (IAP) is greater than the ion activity product at equilibrium (K sp ), struvite stone formation is possible.