Microbial community structure is affected by both natural processes and human activities. In coastal area, anthropegenetic activity can usually lead to the discharge of the effluent from wastewater treatment plant (WWTP) to sea, and thus the water quality chronically turns worse and marine ecosystem becomes unhealthy. Microorganisms play key roles in pollutants degradation and ecological restoration; however, there are few studies about how the WWTP effluent disposal influences coastal microbial communities. In this study, sediment samples were collected from two WWTP effluent-receiving areas (abbreviated as JX and SY) in Hangzhou Bay. First, based on the high-throughput sequencing of 16S rRNA gene, microbial community structure was analyzed. Secondly, several statistical analyses were conducted to reveal the microbial community characteristics in response to the effluent disposal. Using PCoA, the significant difference of in microbial community structure was determined between JX and SY; using RDA, water COD and temperature, and sediment available phosphate and ammonia nitrogen were identified as the key environmental factors for the community difference; using LDA effect size analysis, the most distinctive microbes were found and their correlations with environmental factors were investigated; and according to detrended beta-nearest-taxon-index, the sediment microbial communities were found to follow “niche theory”. An interesting and important finding was that in SY that received more and toxic COD, many distinctive microbes were related to the groups that were capable of degrading toxic organic pollutants. This study provides a clear illustration of eco-environmental deterioration under the long-term human pressure from the view of microbial ecology.
Current technologies could identify the abundance and functions of specific microbes, and evaluate their individual effects on microbial ecology. However, these microbes interact with each other, as well as environmental factors, in the form of complex network. Determination of their combined ecological influences remains a challenge. In this study, we developed a tripartite microbial-environment network (TMEN) analysis method that integrates microbial abundance, metabolic function, and environmental data as a tripartite network to investigate the combined ecological effects of microbes. Applying TMEN to analyzing the microbial-environment community structure in the sediments of Hangzhou Bay, one of the most seriously polluted coastal areas in China, we found that microbes were well-organized into 4 bacterial communities and 9 archaeal communities. The total organic carbon, sulfate, chemical oxygen demand, salinity, and nitrogen-related indexes were detected as crucial environmental factors in the microbial-environmental network. With close interactions with these environmental factors, Nitrospirales and Methanimicrococcu were identified as hub microbes with connection advantage. Our TMEN method could close the gap between lack of efficient statistical and computational approaches and the booming of large-scale microbial genomic and environmental data. Based on TMEN, we discovered a potential microbial ecological mechanism that crucial species with significant influence on the microbial community ecology would possess one or two of the community advantages for enhancing their ecological status and essentiality, including abundance advantage and connection advantage.
The coastal area of the East China Sea has experienced rapid urbanization and industrialization in China since 1980s, resulting in severe pollution of its environments. Antibiotic resistance genes (ARGs) are regarded as a kind of emerging pollutant with potential high risk. The sediment samples were collected from Hangzhou Bay (HB), Xiangshan Bay (XB), and Taizhou Bay (TB) to investigate the spatial occurrence and distribution of 27 ARGs and class I integron–integrase gene (intI1) in the coastal area of the East China Sea. The PCR results showed the frequent presence of 11 ARGs and intI1 in the sediments of the three bays. The qPCR results further showed that sulfonamide resistance was the most prevalent ARG type and antibiotic target replacement and protection were the most important resistance mechanisms in the sediments. Regarding the subtype of ARGs, sulI, tetW, and dfrA13 were the most abundant ARGs, in which sulI was higher in TB (based on both the absolute and relative abundances) and dfrA13 was higher in HB (based on the relative abundances). The network analysis revealed that intI1 had significant correlations with tetC, sulI, sulII, and blaPSE-1. Oil was the key connected factor, which had positive connections with sulI, sulII, and blaPSE-1. In addition, the joint effect of heavy metals and nutrients & organic pollutants might be crucial for the fate of ARGs in the coastal sediments.