Frederik P. J. Vandecasteele

Plasmid Donor Affects Host Range of Promiscuous IncP-1β Plasmid pB10 in an Activated-Sludge Microbial Community

ABSTRACT Horizontal transfer of multiresistance plasmids in the environment contributes to the growing problem of drug-resistant pathogens. Even though the plasmid host cell is the primary environment in which the plasmid functions, possible effects of the plasmid donor on the range of bacteria to which plasmids spread in microbial communities have not been investigated. In this study we show that the host range of a broad-host-range plasmid within an activated-sludge microbial community was influenced by the donor strain and that various mating conditions and isolation strategies increased the diversity of transconjugants detected. To detect transconjugants, the plasmid pB10 was marked with lacp-rfp, while rfp expression was repressed in the donors by chromosomal lacIq. The phylogeny of 306 transconjugants obtained was determined by analysis of partial 16S rRNA gene sequences. The transconjugants belonged to 15 genera of the α- and γ-Proteobacteria. The phylogenetic diversity of transconjugants obtained in separate matings with donors Pseudomonas putida SM1443, Ralstonia eutropha JMP228, and Sinorhizobium meliloti RM1021 was significantly different. For example, the transconjugants obtained after matings in sludge with S. meliloti RM1021 included eight genera that were not represented among the transconjugants obtained with the other two donors. Our results indicate that the spectrum of hosts to which a promiscuous plasmid transfers in a microbial community can be strongly influenced by the donor from which it transfers.

Use of Stochastic Models To Assess the Effect of Environmental Factors on Microbial Growth

ABSTRACT We present a novel application of a stochastic ecological model to the study and analysis of microbial growth dynamics as influenced by environmental conditions in an extensive experimental data set. The model proved to be useful in bridging the gap between theoretical ideas in ecology and an applied problem in microbiology. The data consisted of recorded growth curves of Escherichia coli grown in triplicate in a base medium with all 32 possible combinations of five supplements: glucose, NH4Cl, HCl, EDTA, and NaCl. The potential complexity of 25 experimental treatments and their effects was reduced to 22 as just the metal chelator EDTA, the presumed osmotic pressure imposed by NaCl, and the interaction between these two factors were enough to explain the variability seen in the data. The statistical analysis showed that the positive and negative effects of the five chemical supplements and their combinations were directly translated into an increase or decrease in time required to attain stationary phase and the population size at which the stationary phase started. The stochastic ecological model proved to be useful, as it effectively explained and summarized the uncertainty seen in the recorded growth curves. Our findings have broad implications for both basic and applied research and illustrate how stochastic mathematical modeling coupled with rigorous statistical methods can be of great assistance in understanding basic processes in microbial ecology.

Constructing Microbial Consortia with Minimal Growth Using a Genetic Algorithm

The processes occurring in microbial ecosystems are typically governed by the actions of many different microorganisms that can all interact with each other in a highly nonlinear way. Historically, most work in microbiology has been focused on pure cultures of single organisms, while the study of groups of organisms (consortia) still forms a major challenge. Although genetic algorithms are capable of optimising noisy and nonlinear systems and they should therefore be very well suited for studying microbial ecology, they have only rarely been used in this field. In this work, a genetic algorithm was successfully used to construct microbial consortia exhibiting minimal growth from separate isolated fast growing strains. The technique developed here may open the way to new ecological insights.

Using a genetic algorithm to drive a microbial ecosystem in a desirable direction

The functioning of natural microbial ecosystems is influenced by various biotic and abiotic conditions. The careful experimental manipulation of environmental conditions can drive microbial ecosystems toward exhibiting desirable types of functionality. Such manipulations can be systematically approached by viewing them as a combinatorial optimization problem, in which the optimal configuration of environmental conditions is sought. Such an effort requires a sound optimization technique. Genetic algorithms are a class of optimization methods that should be suitable for such a task because they can deal with multiple interacting variables and with experimental noise and because they do not require an intricate understanding or modelling of the ecosystem of interest. We propose the use of genetic algorithms to drive undefined microbial ecosystems in desirable directions by combinatorially optimizing sets of environmental conditions. We tested this approach in a model system where the microbial ecosystem of a human saliva sample was manipulated in successive steps to display increasing amounts of azo dye decoloration. The results of our experiments indicated that a genetic algorithm was capable of optimizing ecosystem function by manipulating the presence or absence of a set of 10 chemical supplements. Genetic algorithms hold promise for use as a tool in environmental microbiology for the efficient control of the functioning of natural and undefined microbial ecosystems.

Demonstrating the suitability of genetic algorithms for driving microbial ecosystems in desirable directions.

The functioning of natural microbial ecosystems is determined by biotic interactions, which are in turn influenced by abiotic environmental conditions. Direct experimental manipulation of such conditions can be used to purposefully drive ecosystems toward exhibiting desirable functions. When a set of environmental conditions can be manipulated to be present at a discrete number of levels, finding the right combination of conditions to obtain the optimal desired effect becomes a typical combinatorial optimisation problem. Genetic algorithms are a class of robust and flexible search and optimisation techniques from the field of computer science that may be very suitable for such a task. To verify this idea, datasets containing growth levels of the total microbial community of four different natural microbial ecosystems in response to all possible combinations of a set of five chemical supplements were obtained. Subsequently, the ability of a genetic algorithm to search this parameter space for combinations of supplements driving the microbial communities to high levels of growth was compared to that of a random search, a local search, and a hill-climbing algorithm, three intuitive alternative optimisation approaches. The results indicate that a genetic algorithm is very suitable for driving microbial ecosystems in desirable directions, which opens opportunities for both fundamental ecological research and industrial applications.

Genetic algorithms are suitable for driving microbial ecosystems in desirable directions

The behavior of natural, biological ecosystems is for a large part determined by environmental conditions. It should therefore be possible to experimentally manipulate such conditions to drive ecosystems in desirable directions. When a set of environmental conditions can be manipulated to be either present or absent, such an exercise becomes a typical combinatorial optimization problem, and one for which a genetic algorithm should be very suitable. In this work, four exhaustive experimental datasets were assembled, containing growth levels of different natural microbial ecosystem as influenced by all possible combinations of a set of five chemical supplements. The ability of a genetic algorithm to search these datasets for combinations of supplements driving the ecosystems to high levels of growth was compared to that of a random search, a local search, and a hill-climbing algorithm. The results indicate that a genetic algorithm is very suitable for driving microbial ecosystems in desirable directions, which opens opportunities for both applied and fundamental ecology.