Exploring microbial community dynamics: Positive selection for gain of RpoS function in Escherichia coli & microbial profiling of the Niagara Region

Abstract

The effect of changing environmental conditions on microbial population structure can be observed at both the species and community level. Within the Escherichia coli species, null mutations in the RpoS stationary phase regulator are commonly selected by growth on poor carbon sources. In contrast, mutations which restore RpoS function may provide a selective advantage for cells exposed to environmental stress. The loss and subsequent restoration of RpoS form a population-level switch for adaptation within poor carbon and high stress environments. To investigate selection for RpoS reversion, we exposed rpoS-deficient E. coli to high salt concentrations and assessed the phenotype of presumptive mutants. 3-9% of salt-resistant mutants contained reversion mutations within rpoS, while in 91-97% the loss of RpoS function was maintained and mutations at alternative gene loci were identified. These results show that RpoS function can be restored in deficient E. coli under selective pressure. At the community level, the application of next-generation sequencing (NGS) technology to characterize environmental microbial diversity can potentially augment traditional water quality monitoring methods. To investigate the use of NGS in identifying microbial taxa within the Niagara Region, we collected water samples from Lake Erie, Lake Ontario, and nearby areas and examined the metagenome of microbial communities. A QIIME (Quantitative Insights Into Microbial Ecology) analysis of sequence data identified significant differences in relative microbial abundance with respect to sample metadata (e.g. location and subtype), significant correlations between relative abundance and quantitative parameters (e.g. Escherichia coli counts and fecal DNA markers), and detected pathogen-containing taxa at a relative abundance of 0.1-1.5%. These results show that sequence-based analyses can be used in conjunction with traditional identification methods to profile the metagenomic community of environmental samples and predict water quality. Both within-species and community-wide analyses thus offer insight into how microbial populations respond and adapt to environmental fluctuations.