Superfund Research Program
Modeling the Biodegradation of Contaminant Mixtures
Many Superfund sites are contaminated with mixtures of hazardous substances and designing strategies to remediate them has proven to be a major challenge. To a great extent, the development of remediation processes has focused not on mixtures, but on single contaminants. In order to apply our knowledge to mixtures, we need tools to understand, describe, and predict the interactions of contaminants during remediation processes.
Engineers at Colorado State University (CSU) are investigating the bacterial degradation of mixtures of aromatic hydrocarbons. Their goal is to use data from simple experimental systems to develop mathematical models that can predict the complex kinetics of biodegradation of chemical mixtures by multiple species of bacteria. This information is fundamental to both the design of bioreactors for remediation and the prediction of the fate of pollutants in the environment.
The CSU researchers measured the biodegradation rates of single and mixed chemicals by the bacterium Pseudomonans putida F1, which can use many organic compounds as a source of carbon. As the first step, the bacteria were grown in the presence of either benzene, toluene, or phenol. Next, the bacteria were grown in mixtures of the chemicals. The researchers found that compared to predictions of the standard biodegradation kinetics models, the biodegradation of both benzene and phenol were significantly inhibited by the presence of toluene; benzene slowed the biodegradation of phenol but not toluene; and phenol had little effect on the biodegradation of either benzene or toluene. Similar results were found with Burkholderia sp. JS 150.
Building on the existing models, the CSU team developed the sum kinetics with interaction parameters (SKIP) model to describe bacterial growth on mixtures of chemicals that serve the same metabolic function (such as carbon source). The SKIP model includes interaction parameters that reflect the degree of inhibition of one mixture chemical on the biodegradation of another. Because the parameters from these experiments with single chemicals and mixtures of two chemicals were able to predict the rates of degradation of three-chemical mixtures, the SKIP model should be applicable to mixtures with any number of substrates. The CSU engineers have successfully extended the SKIP model to mixtures of up to 13 chemicals. To do this, they grouped the chemicals together and modeled 4 "pseudochemicals" rather than the 13 individual chemicals.
The researchers also investigated how different species of bacteria interact when grown together in the presence of single chemicals and mixtures. They observed mixed cultures of Pseudomonas putida F1 and Burkholderia sp. JS150 grown on toluene or phenol, and then on a mixture of the two chemicals. The CSU scientists used a molecular microbiological technique, in situ hybridization with fluorescently labeled DNA probes, to measure changes in the composition of the microbial populations during biodegradation. This tool enabled them to detect and count individual species in a mixed culture, which was not possible with traditional methods. This is one of the first studies of microbial population dynamics in which molecular microbial ecology and mathematical modeling have been combined.
To predict the biodegradation kinetics of a single pollutant by a mixture of bacterial species, researchers often apply a pure-and-simple competition model. This model assumes that there is no interaction between the species. The CSU studies demonstrate that the pure-and-simple competition model does not accurately predict the biodegradation kinetics trends of mixed culture degradation of a single pollutant. In phenol alone, P. putida F1 grew more rapidly than predicted by the model, and B. sp. JS150 was slightly inhibited. The opposite was observed when the two species were grown on toluene: P. putida F1 was inhibited and B. sp. JS150 was unaffected. These results indicate that the species are interacting, and that the type of interaction is dependent on the chemicals in their environment. The CSU researchers discovered that B. sp. JS150 produced either a beneficial metabolite or a growth-inhibiting substance, thereby affecting the growth of the other bacterium.
On a mixture of toluene and phenol, the interactions of the two species were similar to those observed on phenol alone. It is interesting to note that the mixed bacterial culture biodegraded the toluene and phenol mixture more rapidly than either of the individual species. This provides additional evidence to support hypotheses that microbial consortia can be more efficient at degrading organic contaminants than pure species.
The results of these experiments show that single chemical, single species kinetic parameters alone cannot be used to describe the interactions that occur in mixtures of chemicals and/or mixtures of bacterial species. The researchers at CSU have made significant progress towards the development of mathematical models to assist in the design of improved bioreactors and prediction of in situ bioremediation. This unique combination of molecular microbial ecology and mathematical modeling serves to improve our understanding of microbial degradation of pollutant mixtures in soil, water, and bioreactor environments.
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To learn more about this research, please refer to the following sources:
- Bull Rogers JD, DuTeau NM, Reardon KF. 2000. Use of 16S-rRNA to investigate microbial population dynamics during biodegradation of toluene and phenol by binary culture. Biotechnol Bioeng 70(4):436-445. PMID:11005926
- Bull Rogers JD, Reardon KF. 2000. Modeling substrate interactions during the biodegradation of mixtures of toluene and phenol by Burkholderia species JS150. Biotechnol Bioeng 70(4):429-435. PMID:11005925
- DuTeau NM, Bull Rogers JD, Bartholomay CT, Reardon KF. 1998. Species-specific oligonucleotides for enumeration of Pseudomonas putida F1, Burkholderia sp strain JS150, and Bacillus subtilis ATCC 7003 in biodegradation experiments. Appl Environ Microbiol 64(12):4994-4999. PMID:9835594 PMCID:PMC90954
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