Project description:Freshwater environments such as rivers receive effluent discharges from wastewater treatment plants, representing a potential hotspot for antibiotic resistance genes (ARGs). These effluents also contain low levels of different antimicrobials including biocides and antibiotics such as sulfonamides that can be frequently detected in rivers. The impact of such exposure on ARG prevalence and microbial diversity of riverine environment is unknown, so the aim of this study was to investigate the release of a sub-lethal concentration (<4 g L-1) of the sulfonamide compound sulfamethoxazole (SMX) on the river bacterial microbiome using a microflume system. This system was a semi-natural in-vitro microflume using river water (30 L) and sediment, with circulation to mimic river flow. A combination of ‘omics’ approaches were conducted to study the impact of SMX exposure on the microbiomes within the microflumes. Metaproteomics did not show differences in ARGs expression with SMX exposure in water.
Project description:Nitrate-reducing iron(II)-oxidizing bacteria are widespread in the environment contribute to nitrate removal and influence the fate of the greenhouse gases nitrous oxide and carbon dioxide. The autotrophic growth of nitrate-reducing iron(II)-oxidizing bacteria is rarely investigated and poorly understood. The most prominent model system for this type of studies is enrichment culture KS, which originates from a freshwater sediment in Bremen, Germany. To gain insights in the metabolism of nitrate reduction coupled to iron(II) oxidation under in the absence of organic carbon and oxygen limited conditions, we performed metagenomic, metatranscriptomic and metaproteomic analyses of culture KS. Raw sequencing data of 16S rRNA amplicon sequencing, shotgun metagenomics (short reads: Illumina; long reads: Oxford Nanopore Technologies), metagenome assembly, raw sequencing data of shotgun metatranscriptomes (2 conditions, triplicates) can be found at SRA in https://www.ncbi.nlm.nih.gov/bioproject/PRJNA682552. This dataset contains proteomics data for 2 conditions (heterotrophic and autotrophic growth conditions) in triplicates.