The impact of physico-chemical water quality parameters on bacterial diversity in the Vaal River, South Africa

Abstract

This study aimed to identify bacterial community structures in the Vaal River using PCR-DGGE (polymerase chain reaction denaturing gradient gel electrophoresis) and high-throughput sequencing. The impact of physico-chemical characteristics on bacterial structures was investigated through multivariate analysis. Samples were collected from 4 sampling stations along the Upper Vaal River during winter (June 2009) and summer (December 2010). Physico-chemical analysis was conducted on-site. Additional physico-chemical data were obtained from statutory bodies. DNA was directly isolated from water samples and PCR amplified using universal bacterial primer pairs. PCR products were subjected to DGGE fingerprinting and high-throughput sequencing, followed by Shannon-Weaver diversity calculations, cluster analysis and multivariate analysis. Physico-chemical parameters did not exceed the prescribed South African water quality standards for domestic use, aquatic ecosystems, livestock watering and irrigation. DGGE banding patterns revealed similar bacterial community structures for 3 of the 4 sampling stations. PCA and RDA indicated that pH, water temperature and inorganic nutrient concentrations could be used to explain changes in bacterial community structures. High-throughput sequencing data showed that bacterial assemblages were dominated by common freshwater groups: Cyanobacteria, Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Bacteroidetes and Actinobacteria. Other freshwater phyla such as Deltaproteobacteria, Epsilonbacteria, Acidobacteria, Verrucomicrobia, Firmicutes, Fusobacteria, Flavobacteria and Fibrobacteres were found in low proportions. This study provides an overview of the dominant bacterial groups in the Upper Vaal River and the impact of environmental changes on bacterial diversity.

Keywords: Vaal River, bacterial community structures, 16S rDNA PCR-DGGE, high-throughput sequencing, multivariate analysis