SUBSTRATE SPECIFICITY OF AROMATIC HYDROCARBON DEGRADERS
|N. Gulensoy and P.J.J. Alvarez, Civil and Environmental Engineering, The University of Iowa, Iowa City, IA 52242||
When considering bioremediation, there are many unanswered questions regarding the catabolic capabilities of indigenous microorganisms towards aromatic contaminants (e.g., Which aromatic hydrocarbons are more frequently degraded? Which ones are the most recalcitrant? Are microbes that degrade a given compound also capable of degrading homologous contaminants? Are microbes that degrade polyaromatic hydrocarbons also capable of degrading simpler monoaromatic hydrocarbons, but not vice-versa?).
Answers to such questions are needed to understand microbial degradation capabilities and limitations and to explore the evolution and prevalence of different catabolic pathways. We hypothesize that the relaxed substrate specificity of many oxygenase enzymes allows for some correlations on ability or inability of indigenous microorganisms to degrade different aromatic hydrocarbons. To test this hypothesis, we determined the substrate specificity of 20 aromatic hydrocarbon degraders isolated from gasoline-contaminated sites and analyzed these results statistically together with published data from 39 additional isolates surveyed by Ridgway et al. (1990).
Kappa Statistics were used to determine how strongly connected the presence or absence of any two arbitrary biodegradation capabilities is in any given microbial strain. The two attributes compared were the ability and inability to degrade one compound with the ability and inability to degrade another one (positive and negative agreement). The Kappa values of such dichotomous data indicate whether the agreement in biodegradation capabilities of different isolates is higher than predicted by chance. Thus, broad substrate specificity is conducive to higher Kappa values. SPSS software was used to determine Kappa values and their statistical significance.
Most of the data analyzed were obtained from pseudomonad species. For these isolates, toluene was the most commonly degraded aromatic hydrocarbon, followed by p-xylene, ethylbenzene, naphthalene, benzene, and o-xylene. Coincidentally, the least frequently degraded, benzene and o-xylene, cannot be degraded by the enzymes coded in the TOL plasmid, which suggests that this plasmid might have played a major role in BTEX degradation by the tested isolates.
This is corroborated by the fact that the TOL plasmid codes for the ability to degrade toluene andp-xylene, and 80% of the toluene degraders tested were also capable of degrading pxylene. Kappa values also suggests a strong connection between the ability or inability to degrade benzene and o-xylene, toluene and ethylbenzene, and p-xylene and naphthalene. To further explore the nature of Kappa values, the percentage of times that an agreement was based on a simultaneous ability to degrade a given combination of compounds was determined separately from the percentage of times that an agreement was based on the inability to degrade such compounds.
An interesting finding of this analysis was that 100% of the ethylbenzene degraders were also capable of degrading toluene. In addition, a high percentage (70-90%) of the few microorganisms that degraded o-xylene were also capable of degrading most of the other 6 aromatic hydrocarbons. Relatively broad substrate specificity was also observed for microbes capable of degrading benzene and naphthalene, which were the next "less-frequently degraded" aromatic hydrocarbons.
Thus, microbes capable of degrading the more recalcitrant compounds seem to have broader substrate specificity. Other analyses involving multiple substrate combinations yielded smaller Kappa values, probably because there are very few organisms which can degrade all 7 compounds tested.
In conclusion, analysis of microbial substrate specificity using Kappa statistics can be used to develop heuristic relationships to infer about biodegradation capabilities based on a limited knowledge of known degradative abilities. This could be useful for neural network bioremediation models and to learn about the prevalence and backward evolution of known catabolic pathways.
Key words: bioremediation, BTEX, kappa statistics, naphthalene, TOL plasmid
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