Prevalence of season-specific Escherichia coli strains in the Yeongsan River Basin of South Korea
Article first published online: 30 AUG 2011
© 2011 Society for Applied Microbiology and Blackwell Publishing Ltd
Thematic Issue: Human Microbiome
Volume 13, Issue 12, pages 3103–3113, December 2011
How to Cite
Jang, J., Unno, T., Lee, S. W., Cho, K. H., Sadowsky, M. J., Ko, G., Kim, J. H. and Hur, H.-G. (2011), Prevalence of season-specific Escherichia coli strains in the Yeongsan River Basin of South Korea. Environmental Microbiology, 13: 3103–3113. doi: 10.1111/j.1462-2920.2011.02541.x
- Issue published online: 30 NOV 2011
- Article first published online: 30 AUG 2011
- Received 25 January, 2011; accepted 2 June, 2011.
Seasonal and spatial variation in the genotypic richness of 3480 Escherichia coli isolates obtained from the Yeongsan River basin in South Korea was investigated by using the horizontal fluorophore-enhanced rep-PCR (HFERP) DNA fingerprinting technique. The relationship between 60 E. coli isolates from each of 58 freshwater samples was determined by using multidimensional scaling (MDS) analysis and self-organized maps (SOMs). The MDS analysis, done based on HFERP DNA fingerprints, showed that E. coli isolates obtained in October through December clustered tightly, while those obtained in other sampling periods were more genetically diverse. However, site-specific E. coli genotypes were not observed. SOMs analysis, done using the 10 most frequently isolated E. coli genotypes, showed the occurrence of season-specific E. coli genotypes and the main SOMs clusters were most influenced by temperature, strain diversity and biochemical oxygen demand. Diversity among E. coli genotypes tended to decrease as water temperature decreased, and the numbers of E. coli genotypes observed in urban area were greater, more diverse and less dependent on water temperature than those obtained from agricultural areas. Taken together, our findings indicate that that an ecological approach needs to be considered in order to obtain a better understanding of E. coli community dynamics in the environment and that SOMs analysis is useful to visualize the multidimensional dependent variables that are influencing the types and dynamics of specific E. coli genotypes in the environment.