| Literature DB >> 31734394 |
Yi Li1, Chen Xu1, Wenlong Zhang2, Li Lin3, Longfei Wang1, Lihua Niu1, Huanjun Zhang1, Peifang Wang1, Chao Wang1.
Abstract
River confluences result in mixture and transformation of dissolved organic matter (DOM), influencing the phylogeny of microbial community, furthermore, the integrity and function of river systems. The relationship between the microbial community and DOM is complex, especially in the confluence zone. Previous reports focused on shifts in the different bacterial community in response to exposure to the same terrestrial DOM. However, the transformation of bacterial community induced by convergent DOM remains unknown. This study showed the shifts of DOM components at the junction via excitation-emission matrices parallel factor analysis. Metabolic differences were also determined via phylogenetic investigation of communities by reconstruction of unobserved states. The results demonstrated a direct link between the microbial metabolism and DOM biodegradation during the heterotrophic process. In response to diverse DOM conditions, the taxonomic composition and metabolic function of the microbial community presented significant differences. Different taxa may be involved in metabolizing various DOM components. As indicative bacteria that are closely associated with DOM components, Proteobacteria (Sphingomonas) are significant for microbial utilization and were important during the DOM-degrading process. Compared with other conditions, the abundance of carbon metabolism was higher in convergences where urban rivers joined with estuary or source water. Furthermore, humic-like DOM, converging in the confluence zone, induced a more active lipid metabolism. This study applied techniques that capture the diversity and complexity of bacterial communities and DOM, and provides new insight on the basis of the interaction between bacterial communities and DOM in confluence processes of biogeochemical significance.Entities:
Keywords: Co-occurrence; Dissolved organic matter; Functional prediction; Maximal information coefficient analysis; Microbial community; River confluence
Year: 2019 PMID: 31734394 DOI: 10.1016/j.watres.2019.115293
Source DB: PubMed Journal: Water Res ISSN: 0043-1354 Impact factor: 11.236