Use of 16S-rRNA to investigate microbial population dynamics during biodegradation of toluene and phenol by a binary culture

Abstract

Interspecies interactions and changes in the rate and extent of biodegradation in mixed culture-mixed substrate studies were investigated. A binary mixed culture of Pseudomonas putida F1 and Burkholderia sp. JS150 degraded toluene, phenol, and their mixture. Both toluene and phenol can serve as sole sources of carbon and energy for both P. putida F1 and strain JS150. To investigate the population dynamics of this system, a fluorescent in-situ hybridization method was chosen because of its ability to produce quantitative data, its low standard error, and the ease of use of this method. When the binary mixed culture was grown on toluene or phenol alone, significant interactions between the species were observed. These interactions could not be explained by a pure-and-simple competition model and were substrate dependent. Strain JS150 growth was slightly inhibited when grown with P. putida F1 on phenol, and P. putida F1 grew more rapidly than expected. Conversely, when the two species were grown together on toluene alone, P. putida F1 was inhibited while strain JS150 was unaffected. During growth of the mixed culture on a combination of toluene and phenol, the interactions were similar to that observed during growth on phenol alone; P. putida F1 growth was enhanced while strain JS150 was unaffected. Because of the observed interspecies interactions, monoculture kinetic parameters were not sufficient to describe the mixed culture kinetics in any experiment. This is one of the first reports of microbial population dynamics in which molecular microbial ecology and mathematical modeling have been combined. The use of the 16S-rRNA-based method allowed for observation and understanding of interspecies interactions that were not observable with standard culture-based methods. These results suggest the need for more investigations that account for both substrate and microbial interactions when predicting the fate of organic pollutants in real systems.