Thermal Gradient Gel Electrophoresis Analysis of Bioprotection from Pollutant Shocks in the Activated Sludge Microbial Community

Abstract

ABSTRACT We used a culture-independent approach, namely, thermal gradient gel electrophoresis (TGGE) analysis of ribosomal sequences amplified directly from community DNA, to determine changes in the structure of the microbial community following phenol shocks in the highly complex activated sludge ecosystem. Parallel experimental model sewage plants were given shock loads of chlorinated and methylated phenols and simultaneously were inoculated (i) with a genetically engineered microorganism (GEM) able to degrade the added substituted phenols or (ii) with the nonengineered parental strain. The sludge community DNA was extracted, and 16S rDNA was amplified and analyzed by TGGE. To allow quantitative analysis of TGGE banding patterns, they were normalized to an external standard. The samples were then compared with each other for similarity by using the coefficient of Dice. The Shannon index of diversity, H , was calculated for each sludge sample, which made it possible to determine changes in community diversity. We observed a breakdown in community structure following shock loads of phenols by a decrease in the Shannon index of diversity from 1.13 to 0.22 in the noninoculated system. Inoculation with the GEM ( Pseudomonas sp. strain B13 SN45RE) effectively protected the microbial community, as indicated by the maintenance of a high diversity throughout the shock load experiment ( H decreased from 1.03 to only 0.82). Inoculation with the nonengineered parental strain, Pseudomonas sp. strain B13, did not protect the microbial community from being severely disturbed; H decreased from 1.22 to 0.46 for a 3-chlorophenol–4-methylphenol shock and from 1.03 to 0.70 for a 4-chlorophenol–4-methylphenol shock. The catabolic trait present in the GEM allowed for bioprotection of the activated sludge community from breakdown caused by toxic shock loading. In-depth TGGE analysis with similarity and diversity algorithms proved to be a very sensitive tool to monitor changes in the structure of the activated sludge microbial community, ranging from subtle shifts during adaptation to laboratory conditions to complete collapse following pollutant shocks.