Comparative survey of bacterial and archaeal communities in high arsenic shallow aquifers using 454 pyrosequencing and traditional methods

Ping Li, Dawei Jiang, Bing Li, Xinyue Dai, Yanhong Wang, Zhou Jiang, Yanxin Wang

A survey of bacterial and archaeal community structure was carried out in 10 shallow tube wells in a high arsenic groundwater system located in Hetao Basin, Inner Mongolia by 16S rRNA gene based two-step nested PCR-DGGE, clone libraries and 454 pyrosequencing. 12 bacterial and 18 archaeal DGGE bands and 26-136 species-level OTUs were detected for all the samples. 299 bacterial and 283 archaeal 16S rRNA gene clones for two typical samples were identified by phylogenetic analysis. Most of the results from these different methods were consistent with the dominant bacterial populations. But the proportions of the microbial populations were mostly different and the bacterial communities in most of these samples from pyrosequencing were both more abundant and more diverse than those from the traditional methods. Even after quality filtering, pyrosequencing revealed some populations including Alishewanella, Sulfuricurvum, Arthrobacter, Sporosarcina and Algoriphagus which were not detected with traditional techniques. The most dominant bacterial populations in these samples identified as some arsenic, iron, nitrogen and sulfur reducing and oxidizing related populations including Acinetobacter, Pseudomonas, Flavobacterium, Brevundimonas, Massilia, Planococcus, and Aquabacterium and archaeal communities Nitrosophaera and Methanosaeta. Acinetobacter and Pseudomonas were distinctly abundant in most of these samples. Methanogens were found as the dominant archeal population with three methods. From the results of traditional methods, the dominant archaeal populations apparently changed from phylum Thaumarchaeota to Euryarchaeota with the arsenic concentrations increasing. But this structure dynamic change was not revealed with pyrosequencing. Our results imply that an integrated approach combining the traditional methods and next generation sequencing approaches to characterize the microbial communities in high arsenic groundwater is recommended.