Temporal disturbance of a model stream ecosystem by high microbial diversity from treated wastewater

Abstract

Microbial communities in freshwater streams play an essential role in ecosystem functioning via biogeochemical cycling. Yet, the impacts of treated wastewater influx into stream ecosystems on microbial strain diversity remain mostly unexplored. Here, we coupled full‐length 16S ribosomal RNA gene Nanopore sequencing and strain‐resolved metagenomics to investigate the impact of treated wastewater on a mesocosm system (AquaFlow) run with restored river water. Over 10 days, community Bray–Curtis dissimilarities between treated and control mesocosm decreased (0.57 ± 0.058 to 0.26 ± 0.046) based on ribosomal protein S3 gene clustering, finally converging to nearly identical communities. Similarly, strain‐resolved metagenomics revealed a high diversity of bacteria and viruses after the introduction of treated wastewater; these microbes also decreased over time resulting in the same strain clusters in control and treatment at the end of the experiment. Specifically, 39.2% of viral strains detected in all samples were present after the introduction of treated wastewater only. Although bacteria present at low abundance in the treated wastewater introduced additional antibiotic resistance genes, signals of naturally occurring ARG‐encoding organisms resembled the resistome at the endpoint. Our results suggest that the previously stressed freshwater stream and its microbial community are resilient to a substantial introduction of treated wastewater.

Complex compositional and cellular response of river sediment microbiomes to multiple anthropogenic stressors

“…This was accompanied with a uniform increase of diversity compared to all other mesocosm setups, including both controls and other stressor combinations (Shannon: p=0.0009, Simpson: p=0.008; Figure S2 ). Amplicon-based microbiome analysis is well known to be affected by primer-bias and PCR-bias which may lead to an unsuitable measure for accurate river bacteriome analyses (Stach et al, 2023). Microbiome analysis based on rpS3 gene sequences extracted from de novo assembled metagenomes aims at overcoming these issues by being PCR-free, and covering eukaryotic gene signatures and a higher diversity of uncultured prokaryotic microorganisms (Sharon et al, 2015).…”

“…Binning contigs into MAGs followed the approach described in Stach et al, 2023. In brief, MetaBAT2 (Kang et al, 2019), ABAWACA (Brown et al, 2016), and MaxBin2 (Wu et al, 2016) were used as binning tools.…”

“…The copyright holder for this preprint this version posted June 9, 2024. ; https://doi.org/10.1101/2024.06.07.597903 doi: bioRxiv preprint known to be affected by primer-bias and PCR-bias which may lead to an unsuitable measure for accurate river bacteriome analyses (Stach et al, 2023). Microbiome analysis based on rpS3 gene sequences extracted from de novo assembled metagenomes aims at overcoming these issues by being PCR-free, and covering eukaryotic gene signatures and a higher diversity of uncultured prokaryotic microorganisms (Sharon et al, 2015).…”

Rapid Identification of Bacterial Composition in Wastewater by Combining Reverse Purification Nucleic Acid Extraction and Nanopore Sequencing

“…17 Compared to other sequencing techniques, Oxford Nanopore is much faster and delivers results within 2 h. 13 The procedure was already used for various targets in wastewater research such as the identification of epidemiological causes, pathogen variations, or correlations between antibiotic usage and AMR abundance. 14,18,19 The bottleneck of sequencing is the correct preparation of the sample as the results can vary widely depending on the used nucleic acid extraction method. 20−22 A recent report showed that the detection of bacterial DNA in wastewater can be influenced by the extraction procedure used.…”

“…Compared to other sequencing techniques, Oxford Nanopore is much faster and delivers results within 2 h . The procedure was already used for various targets in wastewater research such as the identification of epidemiological causes, pathogen variations, or correlations between antibiotic usage and AMR abundance. ,, …”