A comprehensive analysis of the microbial diversity in natural and engineered ecosystems based on high-throughput sequencing of 16S rRNA gene

Abstract

Understanding microbial diversity across different ecosystems is important for discerning the mechanisms shaping the microbial community assembly. However, knowledge on differentiations of microbial diversity in various habitats especially engineered systems remains very limited. A comprehensive comparison of microbial diversity across natural and engineered environments, namely heavy metal polluted farm soil, forest, wastewater treatment plants (WWTPs), anaerobic digesters (ADs) and the Caspian Sea sediments, was made base on a collection of 115 samples from seven published 16S rRNA gene datasets generated by high-throughput sequencing. In each sample, 16,590 and 19,771 qualified sequences were detected, and they were classified into 615 to 3,162 operational taxonomic units (OTUs). The microbial communities in the engineered systems especially the AD showed relatively low alpha taxonomic and phylogenetic diversity. A total of 49 genera were present in more than 80% of the samples, accounting for 6.0% in population number and 25.6% in relative abundance (RA). The average abundance-weighted rRNA operon copy number of the communities from ADs and a food wastewater treatment plant (FW) was higher than that of others, suggesting that high loading rate of pollutants in ADs and FW resulted in relatively high abundance of r-strategists. Among 104 typical human bacterial pathogens (HBPs), 2 to 25 HBPs were detected in each sample, accounting for 0.05%–21.34% in RA. Mycobacterium phlei was widely present in 111 samples with an average RA of 0.12%. The number and RA of all the HBPs in each sample did not show an obvious ecosystem type related pattern. Results of detrended correspondence analysis (DCA) and cluster analysis showed that the microbial communities clustered based on habitat types. The communities of WWTPs and soil were very similar, falling into one cluster. For any two communities from the datasets, 75.6% of their beta nearest taxon index (βNTI) values were higher than +2 or lower than −2, suggesting that environmental conditions played an important role in shaping the microbial community assembly. Temperature and geographic locations were effective factors among all factors considered in this study.